Printable list of all miscellaneous SAQs

College Answer

Few studies have addressed the potential benefits of clinical examination in the critically ill.  Those that have addressed estimation of filling pressures have been disappointing.  In general benefits of clinical examination are only supported by lower levels of evidence (including extrapolation from other patient populations).

In the critically ill, as history may be difficult to obtain, especially in an emergency, clinical signs alone are used to guide treatment and investigation until more definitive information is available. Candidates should discuss potential risks & benefits (eg. early detection guiding treatment vs lack of sensitivity [missing disease states] and sensitivity [wrongly excluding differential diagnoses].

Types of information that are available and may influence management (either in an emergency or otherwise) include: assessment of airway and breathing (eg. position of ETT cuff, chest movement, breath sounds), circulation (eg. presence of pulses: peripheral/central and estimate of peripheral perfusion); neurological assessment (AVPU/GCS/pupils, localising signs, tone & reflexes, sensation);  presence  of  skin  lesions  (rash:  purpura,  erythematous,  papular;  spider  naevi  etc); localised tenderness (eg. limb, abdominal quadrant etc); presence of abnormal masses (eg. lymph nodes,  hepatosplenomegaly);  fundoscopic  assessment  (eg.  subhyaloid  haemorrhages, papilloedema); assessment of invasive devices/dressings/drains etc.

Discussion

Rationale:

• History and physical examination is the mainstay of diagnosis in non-ICU environments
• ICU patients are frequently unable to offer a history
• Physical examination may be able to reveal new pathology, which would otherwise have not been suspected from routine bloods and radiography.
• Clinical features are more reliable than other methods in the diagnosis of certain conditions (eg. delirium, weakness, etc)

Advantages:

• Cheap
• Non-invasive (mostly)
• Sequential
• May detect deterioration early
• Better than imaging for neurological assessment
• Assesses function as well as structure
• Many ICU devices enhance physical examination technique (eg. CVP waveform supercedes the examination of the JVP)

Disadvantages:

• Poor sensitivity and specificity
• New pathology may be missed
• Interpreter-dependent
• Poor reproduceability of findings
• Many barriers to traditional techniques in the ICU (eg. the patient is uncooperative, dressings and lines obscure physical signs)

Evidence:

• Little evidence in support of this widespread practice
• Benefits of clinical examination are extrapolated from outpatient population.
• A survey of ICU physicians from California has revealed that 59% think physical examination has limited utility, and 94% uncluded non-classical components (such as assessment of arterial and ventilator waveforms). Everybody was in agreement that percussion was the least useful physical sign.

Rudiger, A. "[The clinical examination of the critically ill patient in the intensive care unit]." Therapeutische Umschau. Revue therapeutique 63.7 (2006): 479-484.

Sackett, David L. "A primer on the precision and accuracy of the clinical examination." Jama 267.19 (1992): 2638-2644.

Dobb, G. J., and L. J. Coombs. "Clinical examination of patients in the intensive care unit." British journal of hospital medicine 38.2 (1987): 102-4.

Hillman, K., G. Bishop, and A. Flabouris. "Patient examination in the intensive care unit." Intensive Care Medicine. Springer New York, 2002. 942-950.

Guillamet, R. Vazquez, et al. "Physicians Perceptions Of The Utility Of Physical Exam In The Intensive Care Unit. A Qualitative Study." Am J Respir Crit Care Med 185 (2012): A1661.

College Answer

Standardised Mortality Ratio is defined as the observed mortality rate/expected mortality rate. Need to estimate expected mortality rate using a scoring system (eg. APACHE II or III, SAPS II or MPM). Better than comparison of non-adjusted mortality data.

The potential limitations of the system are multiple including: inconsistencies and inaccuracies associated with collection of data and scoring (eg. GCS, recording of parameters); problems of missing data limiting inclusion of all patients; problems of patient mix not adequately accounted for by the original population used for calculation of formulae (eg. transferred patients or delays before admission); small numbers of patients (increasing the error of the SMR estimate); accuracy of the prediction model; relying on mortality as a surrogate marker for quality of care; cost of use of proprietary system; etc.

Discussion

This question closely resembles Question 30 from the second paper of 2006.

Definition of the SMR

• This is the ratio of the observed hospital mortality vs. predicted hospital mortality for a specified time period.
• One can use this to compare hospitals and ICUs
• One needs to first calculate the predicted hospital mortality using an illness severity scoring system.
• An SMR of 1 means the mortality is as expected.
• An SMR of < 1 is better than expected, and >1 is worse than expected.

Limitations of the SMR

• Acceptable deviations from the SMR are not defined
• Suffers from inaccuracies associated with data collection
• SMR may be influenced by ICU admission and discharge practices (eg. discharging patients who are palliated, or admitting patients who are inevitably going to die).
• Accuracy of the SMR as a quality assessment tool may be influenced by patients who have been predominantly cared for at another ICU, and who have been received as a transfer.
• Mortality is not a surrogate for quality of care
• The populations used to calculate the predicted hospital mortality are potentially non-representative (i.e. the population may also contains a number of dying critically ill patients, or it may contain an unusually large proportion of people in robust health).

Limitations of comparing ICUs with the SMR:

• The SMR assumes all pre-ICU care is identical
• Ignores differences in case mix
• Sample sizes need to be large enough to obey the laws of logistic regression
• Data is assumed to be flawless and complete

Liddell, F. D. "Simple exact analysis of the standardised mortality ratio."Journal of Epidemiology and Community Health 38.1 (1984): 85-88.

Wolfe, Robert A. "The standardized mortality ratio revisited: improvements, innovations, and limitations." American Journal of Kidney Diseases 24.2 (1994): 290-297.

Gaffey, William R. "A critique of the standardized mortality ratio." Journal of Occupational and Environmental Medicine 18.3 (1976): 157-160.

Jones, Michael E., and Anthony J. Swerdlow. "Bias in the standardized mortality ratio when using general population rates to estimate expected number of deaths." American journal of epidemiology 148.10 (1998): 1012-1017.

van Gestel, Yvette RBM, et al. "The hospital standardized mortality ratio fallacy: a narrative review." Medical care 50.8 (2012): 662-667.

Combes, Alain, et al. "Adverse effect on a referral intensive care unit's performance of accepting patients transferred from another intensive care unit*."Critical care medicine 33.4 (2005): 705-710.

College Answer

Scoring systems stratify groups of critically ill patients by severity, compare groups of patients in research trials, compare ICUs, and predict mortality for individuals and groups. Most measure physiological variables, some measure interventions. Derived by logistic regression from large demographic data sets of critically ill patients. Commonly used systems include:
•    APACHE II. Commonly used in Australia to measure patient severity. Uses 12 physiological variables and previous health estimate. Requires measure of worst values in first 24 hours in ICU, so affected if ICU admission delayed. Not reliable for predicting outcome in individuals. Limited by derivation from an historical data set.
•    APACHE III. Better outcome predictions by using additional variables and a more recent data set for comparisons. Outcome predictions for Australian patients more accurate.
•    GCS. Used to quantify severity of coma. Scale from 3-15. Eye (1-4), Verbal (1-5), and Motor (1-6) components. Key score for outcome prediction after head injury. Affected by alcohol and sedation. Should be scored in non-sedated non-paralysed patients.  Important component of other scoring systems eg APACHE 11.
•    TISS. System to score patient severity by counting procedures done. Less widely used.
Physician dependent, so less useful to compare ICUs.
•    SOFA. Organ dysfunction scores. Often a secondary endpoint in research trials.

Discussion

Here is a link to the LITFL article on ICU scoring systems.

Here are some links to the seminal articles which describe these systems:

• APACHE II
• SOFA
• TISS
• SAPS II
• MPM II
• POSSUM

So.

How are these scoring systems useful?

• They (try to) predict outcome and length of stay
• They can be used to compare predicted and observed outcome
• They stratify patients for clinical trials, according to disease severity
• They assess ICU performance
• They allow resources to be allocated to ICUs according to the illness severity of their patients
• They allow a comparison of ICUs

Some examples:

APACHE

APACHE stands for Acute Physiology, Age and Chronic Health Evaluation (I-IV).

• APACHE II is the most commonly used one
• 12 variables are measured
• Scores range from 0 to 71
• The risk of hospital death is computed by combining APACHE II score with Knaus' weighted coefficient for different types of disease entities. A score of 25 represents a predicted mortality of 50% and a score of over 35 represents a predicted mortality of 80%.
• Derived from histrical data set

SOFA

SOFA stands for Sequential Organ Failure Assessment .

• 6 organ systems are scored according to their function
• The degree of organ support is taken into account
• Used to analyse secondary endpoints in clinical trials

There is a defined score of 1-4 for each organ system, which is collected daily. This not a predictive model- there are no mortality algorithms here. A higher SOFA score can be said to relate to increased mortality, but there is no mathematical model to help us figure out exactly how the total score relates to survival.

TISS: Therapeutic Interventions Scoring System

• 76 variables (interventions and treatments)
• Collected daily
• Indicates nursing and medical workload
• Does not indicate severity of illness
• Most useful for accountants

SAPS: Simplified Acute Physiology Score

• SAPS 1 only looked at physiology, and was used by French ICUs
• SAPS 2 added chronic health conditions, and was used in Europe and North America
• SAPS 3 had 20 variables and was used worldwide

MPM: Mortality Prediction Models

• MPM measures variables at admission and in the first 24 hours
• It calcuates the risk of in-hospital death on the basis of these variables, using a logistic regression model.
• MPM II was based on the same historical data set as SAPS 2 and predicts mortality at 24, 48 and 7 hours.

POSSUM: = Physiological and Operative Severity Score for the enumeration of Mortality and Morbidity

• 12 physiological parameters for surgeons
• Used by surgeons as a risk adjustment tool
• Different subspecialties have their own: V-POSSUM is for vacular surgeons, Cr-POSSUM is for colorectal, etc

Gunning, Kevin, and Kathy Rowan. "Outcome data and scoring systems." Bmj319.7204 (1999): 241-244.

Liddell, F. D. "Simple exact analysis of the standardised mortality ratio."Journal of Epidemiology and Community Health 38.1 (1984): 85-88.

Wolfe, Robert A. "The standardized mortality ratio revisited: improvements, innovations, and limitations." American Journal of Kidney Diseases 24.2 (1994): 290-297.

Gaffey, William R. "A critique of the standardized mortality ratio." Journal of Occupational and Environmental Medicine 18.3 (1976): 157-160.

Balci, C., et al. "[APACHE II, APACHE III, SOFA scoring systems, platelet counts and mortality in septic and nonseptic patients]." Ulusal travma ve acil cerrahi dergisi= Turkish journal of trauma & emergency surgery: TJTES 11.1 (2005): 29-34.

Halim, Dino Adrian, Tri Wahyu Murni, and Ike Sri Redjeki. "Comparison of Apache II, SOFA, and Modified SOFA scores in predicting mortality of surgical patients in intensive care unit at Dr. Hasan Sadikin General Hospital." Critical Care & Shock 12 (2009): 157-169.

Knaus, William A., et al. "APACHE II: a severity of disease classification system." Critical care medicine 13.10 (1985): 818-829.

Vincent, J-L., et al. "The SOFA (Sepsis-related Organ Failure Assessment) score to describe organ dysfunction/failure." Intensive care medicine 22.7 (1996): 707-710.

Ferreira, Flavio Lopes, et al. "Serial evaluation of the SOFA score to predict outcome in critically ill patients." Jama 286.14 (2001): 1754-1758.

Vincent, Jean-Louis, and Rui Moreno. "Clinical review: scoring systems in the critically ill." Crit Care 14.2 (2010): 207.

Livingston, Brian M., et al. "Assessment of the performance of five intensive care scoring models within a large Scottish database." Critical care medicine28.6 (2000): 1820-1827.

Wong, David T., et al. "Evaluation of predictive ability of APACHE II system and hospital outcome in Canadian intensive care unit patients." Critical care medicine 23.7 (1995): 1177-1183.

Cullen, David J., et al. "Therapeutic intervention scoring system: a method for quantitative comparison of patient care." Critical care medicine 2.2 (1974): 57-60.

Le Gall, Jean-Roger, Stanley Lemeshow, and Fabienne Saulnier. "A new simplified acute physiology score (SAPS II) based on a European/North American multicenter study." Jama 270.24 (1993): 2957-2963.

Lemeshow, Stanley, et al. "Mortality Probability Models (MPM II) based on an international cohort of intensive care unit patients." Jama 270.20 (1993): 2478-2486.

Neary, W. D., B. P. Heather, and J. J. Earnshaw. "The Physiological and Operative Severity Score for the enUmeration of Mortality and morbidity (POSSUM)." British journal of surgery 90.2 (2003): 157-165.

College Answer

Many problems are encountered after hospital discharge. The important problems include:
•    Patients have usually had a tracheostomy (and/or prolonged endotracheal intubation) - complications associated with these include laryngeal pathology [eg. polyps, ulcers], aspiration, difficulty with swallowing etc.
•    Limitation of mobility for some time – muscle tone, joint stiffness, Chronic Inflammatory
Polyneuropathy
•    Skin – hair loss, itching

•    Sexual dysfunction
•    Psychological problems – loss of memory, stress, nightmares, Post Traumatic Stress
Disorder, depression, chronic fatigue syndrome
•    Infectious: colonisation with resistant organisms (eg. MRSA)
•    Miscellaneous (loss of taste, loss of appetite, ocular trauma, scarring near region of tape fixing for ETT)
Tools to assess quality include: Quality Adjusted Life Years (objective measure), HAD – Hospital Anxiety & Depression, SF 36, PQOL (perceived quality of life), EuroQOL – European tool Simpler measures include Glasgow Outcome Scale. Some hospitals utilise follow up clinics. Eighteen out of twenty-six candidates passed this question.

Discussion

This question closely resembles Question 30 from the first paper of 2009.

College Answer

Standardised Mortality Ratio is defined as the observed mortality rate/expected mortality rate. Need to estimate expected mortality rate using a scoring system (eg. APACHE II or III, SAPS II or MPM). Better than comparison of non-adjusted mortality data.

The potential limitations of the system are multiple including: inconsistencies and inaccuracies associated with collection of data and scoring (eg. GCS, recording of parameters); problems of missing  data  limiting  inclusion  of  all  patients;  problems  of  patient  mix  not  adequately accounted for by the original population used for calculation of formulae (eg. transferred patients or delays before admission); small numbers of patients (increasing the error of the SMR estimate); accuracy of the prediction model; relying on mortality as a surrogate marker for quality of care; cost of use of proprietary system; etc.

Discussion

Definition of the SMR

• This is the ratio of the observed hospital mortality vs. predicted hospital mortality for a specified time period.
• One can use this to compare hospitals and ICUs
• One needs to first calculate the predicted hospital mortality using an illness severity scoring system.
• An SMR of 1 means the mortality is as expected.
• An SMR of < 1 is better than expected, and >1 is worse than expected.

Limitations of the SMR

• Acceptable deviations from the SMR are not defined
• Suffers from inaccuracies associated with data collection
• SMR may be influenced by ICU admission and discharge practices (eg. discharging patients who are palliated, or admitting patients who are inevitably going to die).
• Accuracy of the SMR as a quality assessment tool may be influenced by patients who have been predominantly cared for at another ICU, and who have been received as a transfer.
• Mortality is not a surrogate for quality of care
• The populations used to calculate the predicted hospital mortality are potentially non-representative (i.e. the population may also contains a number of dying critically ill patients, or it may contain an unusually large proportion of people in robust health).

Limitations of comparing ICUs with the SMR:

• The SMR assumes all pre-ICU care is identical
• Ignores differences in case mix
• Sample sizes need to be large enough to obey the laws of logistic regression
• Data is assumed to be flawless and complete

Liddell, F. D. "Simple exact analysis of the standardised mortality ratio."Journal of Epidemiology and Community Health 38.1 (1984): 85-88.

Wolfe, Robert A. "The standardized mortality ratio revisited: improvements, innovations, and limitations." American Journal of Kidney Diseases 24.2 (1994): 290-297.

Gaffey, William R. "A critique of the standardized mortality ratio." Journal of Occupational and Environmental Medicine 18.3 (1976): 157-160.

Jones, Michael E., and Anthony J. Swerdlow. "Bias in the standardized mortality ratio when using general population rates to estimate expected number of deaths." American journal of epidemiology 148.10 (1998): 1012-1017.

van Gestel, Yvette RBM, et al. "The hospital standardized mortality ratio fallacy: a narrative review." Medical care 50.8 (2012): 662-667.

Combes, Alain, et al. "Adverse effect on a referral intensive care unit's performance of accepting patients transferred from another intensive care unit*."Critical care medicine 33.4 (2005): 705-710.

College Answer

SMR is the ratio of the observed hospital mortality and the actual hospital mortality. A ratio of > 1 implies a mortality higher than expected. Potential explanations:

a)  Ensure data entry is correct and accurate and consistent with prior practice (ie comparable)
b)  Issues like quantifying GCS accurately will have an impact on APACHE scores and consequently SMR. Quantification of GCS is a major source of inaccuracy. Also source of admission and diagnosis
c)  SMR reflects system wide performance rather than ICU performance alone, because based upon hospital mortality, not ICU mortality. Look at pre ICU and post ICU facilities in the hospital

d)  SMR affected by case-mix, so changes in case mix may account for increase in SMR and increased other hospital admissions

e)  One needs to examine if there has been a deviation from clinical protocols in the ICU
f)   Lead time bias (pre ICU care) has been shown to impact on SMR and this neds to be factored into.
g)  Are there new inexperienced staff in ICU who might need training?

Discussion

LITFL have a point-form summary. In this summary, there is an excellent final paragraph, which discusses the reasons as to why the SMR might be changing.

The SMR is the ratio of the observed hospital mortality vs. predicted hospital mortality for a specified time period. An SMR of 1 means the mortality is as expected, and an SMR of >1 is worse than expected. The massive jump in SMR as described in the college question is indeed a disturbing development, one which has prompted LITFL authors to blame influenza pandemics and terrorist attacks.

So. Why might the SMR be on the rise?

It would for two possible reasons.

Either the observed mortality rate is increasing, or the predicted mortality rate is decreasing.

Increase in the observed mortality rate

• Change in protocols
• Change in admission practices -i.e. more patients being admitted who have little chance for survival
• Change in discharge practices - i.e. more patients remaining in hospital to be palliated instead of being discharged with community services (this is what happens when you cut funding to community palliative care nurses).
• Change in staff (inundation by incompetent staff?)
• Unstable transfer patients received from other hospitals
• Hospital performance as a whole may be affected by system-wide policy or staff changes (i.e. did all the senior nursing staff suddenly go on annual leave?)

Decrease in the predicted mortality rate

• Change in illness severity scale encoding - i.e. the encoding has been omitting factors which might otherwise have increased the APACHE and SOFA scores.
• "Lead time bias" - treatment received prior to ICU admission may result in artifically normalised acute physiology scores
• "Healthy worker effect" - a change towards selective ICU admission practices may be favouring patients who score low on illness severity scales, eg. young elective surgical patients.

Fletcher, John. "Standardised mortality ratios." BMJ 338 (2009).

Liddell, F. D. "Simple exact analysis of the standardised mortality ratio."Journal of Epidemiology and Community Health 38.1 (1984): 85-88.

Wolfe, Robert A. "The standardized mortality ratio revisited: improvements, innovations, and limitations." American Journal of Kidney Diseases 24.2 (1994): 290-297.

Gaffey, William R. "A critique of the standardized mortality ratio." Journal of Occupational and Environmental Medicine 18.3 (1976): 157-160.

McMichael, Anthony J. "Standardized mortality ratios and the'healthy worker effect': scratching beneath the surface." Journal of Occupational and Environmental Medicine 18.3 (1976): 165-168.

Tunnell, R. D., B. W. Millar, and G. B. Smith. "The effect of lead time bias on severity of illness scoring, mortality prediction and standardised mortality ratio in intensive care—a pilot study." Anaesthesia 53.11 (1998): 1045-1053.

Rosenberg, Andrew L., et al. "Accepting critically ill transfer patients: adverse effect on a referral center's outcome and benchmark measures." Annals of internal medicine 138.11 (2003): 882-890.

College Answer

SMR is the ratio of the observed hospital mortality and the actual hospital mortality. A ratio of > 1 implies a mortality higher than expected. Potential explanations:

a)  Ensure data entry is correct and accurate and consistent with prior practice (ie comparable)
b)  Issues like quantifying GCS accurately will have an impact on APACHE scores and consequently SMR. Quantification of GCS is a major source of inaccuracy. Also source of admission and diagnosis
c)  SMR reflects system wide performance rather than ICU performance alone, because based upon hospital mortality, not ICU mortality. Look at pre ICU and post ICU facilities in the hospital

d)  SMR affected by case-mix, so changes in case mix may account for increase in SMR and increased other hospital admissions

e)  One needs to examine if there has been a deviation from clinical protocols in the ICU
f)   Lead time bias (pre ICU care) has been shown to impact on SMR and this neds to be factored into.
g)  Are there new inexperienced staff in ICU who might need training?

Discussion

LITFL have a point-form summary. In this summary, there is an excellent final paragraph, which discusses the reasons as to why the SMR might be changing.

The SMR is the ratio of the observed hospital mortality vs. predicted hospital mortality for a specified time period. An SMR of 1 means the mortality is as expected, and an SMR of >1 is worse than expected. The massive jump in SMR as described in the college question is indeed a disturbing development, one which has prompted LITFL authors to blame influenza pandemics and terrorist attacks.

So. Why might the SMR be on the rise?

It would for two possible reasons.

Either the observed mortality rate is increasing, or the predicted mortality rate is decreasing.

Increase in the observed mortality rate

• Change in protocols
• Change in admission practices -i.e. more patients being admitted who have little chance for survival
• Change in discharge practices - i.e. more patients remaining in hospital to be palliated instead of being discharged with community services (this is what happens when you cut funding to community palliative care nurses).
• Change in staff (inundation by incompetent staff?)
• Unstable transfer patients received from other hospitals
• Hospital performance as a whole may be affected by system-wide policy or staff changes (i.e. did all the senior nursing staff suddenly go on annual leave?)

Decrease in the predicted mortality rate

• Change in illness severity scale encoding - i.e. the encoding has been omitting factors which might otherwise have increased the APACHE and SOFA scores.
• "Lead time bias" - treatment received prior to ICU admission may result in artifically normalised acute physiology scores
• "Healthy worker effect" - a change towards selective ICU admission practices may be favouring patients who score low on illness severity scales, eg. young elective surgical patients.

Fletcher, John. "Standardised mortality ratios." BMJ 338 (2009).

Liddell, F. D. "Simple exact analysis of the standardised mortality ratio."Journal of Epidemiology and Community Health 38.1 (1984): 85-88.

Wolfe, Robert A. "The standardized mortality ratio revisited: improvements, innovations, and limitations." American Journal of Kidney Diseases 24.2 (1994): 290-297.

Gaffey, William R. "A critique of the standardized mortality ratio." Journal of Occupational and Environmental Medicine 18.3 (1976): 157-160.

McMichael, Anthony J. "Standardized mortality ratios and the'healthy worker effect': scratching beneath the surface." Journal of Occupational and Environmental Medicine 18.3 (1976): 165-168.

Tunnell, R. D., B. W. Millar, and G. B. Smith. "The effect of lead time bias on severity of illness scoring, mortality prediction and standardised mortality ratio in intensive care—a pilot study." Anaesthesia 53.11 (1998): 1045-1053.

Rosenberg, Andrew L., et al. "Accepting critically ill transfer patients: adverse effect on a referral center's outcome and benchmark measures." Annals of internal medicine 138.11 (2003): 882-890.

College Answer

Presence of a notch – spleen
Spleen moves inferomedially on inspiration
Not ballotable or bimanually palpable
Usually no band of resonance over s splenic mass
Spleen has no palpable upper border
Dullness over ribs 9,10, 11

Discussion

This is another question which reaches into a deep dark recess of Talley and O'Connor.

The differences between spleens and kidneys are mobility, ballotability, and edge palpation.

• Thus, the spleen is mobile with respiration, whereas the kidneys is not.
• The kidney is "ballotable" whereas the spleen is not.
• The spleen has a notch on the anterior surface, and the kidney does not
• The spleen should be dull to percussion, where the kidney can be resonant due to overlying gas
• The spleen enlarges diagonally, towards the umbilicus and the RLQ, whereas the kidney enlarges inferiorly, to the ipsilateral pelvis.
• There is no palpable upper border to the spleen, whereas the kidney should have one

The reference for the above wisdom, shamefully, is Wikiversity.

College Answer

The ideal scoring system would have the following characteristics:

1.   Scores calculated on the basis of easily/routinely recordable variables
2.   Well calibrated
3.   A high level of discrimination
4.   Applicable to all patient populations in ICU
5.   Can be used in different countries
6.   The ability to predict mortality,functional status or quality of life after ICU discharge

Compare

Discussion

The various illness severity scoring systems are summarised elsewhere.

LITFL gives the a list of qualities for the "ideal" ICU scoring system. In his 2010 review of scoring systems, Jean-Louis Vincent also gives this list of "ideal" features.

I have incorporated these opinions into one master list of ideal features.

• Simple and inexpensive
• Routinely available in all ICUs
• Scores calculated on the basis of easily / routinely recordable variables
• Reliable (intra and inter-observer)
• Objective (that is, observer independent)
• Specific to the function of the organ in question
• Well calibrated and validated
• A high level of discrimination
• Therapy independent
• Sequential (available at ICU admission or shortly thereafter and then at fixed periods of time)
• Not affected by transient, reversible abnormalities associated with therapeutic or practical interventions 
• Reflect acute dysfunction of the organ in question but not chronic dysfunction
• Applicable to all patient populations in ICU
• Reproducible in large, heterogeneous groups of ICU patients
• Allows the comparison of groups in clinical trials
• Reproducible in several types of ICUs from different regions of the globe
• Abnormal in one direction only
• Using continuous rather than dichotomous variables
• Able to predict mortality, functional status or quality of life after ICU discharge

A comparison of SOFA and APACHE as a table is discussed in detail elsewhere; I will merely reproduce the comparsion table in the space below.

Balci, C., et al. "[APACHE II, APACHE III, SOFA scoring systems, platelet counts and mortality in septic and nonseptic patients]." Ulusal travma ve acil cerrahi dergisi= Turkish journal of trauma & emergency surgery: TJTES 11.1 (2005): 29-34.

Halim, Dino Adrian, Tri Wahyu Murni, and Ike Sri Redjeki. "Comparison of Apache II, SOFA, and Modified SOFA scores in predicting mortality of surgical patients in intensive care unit at Dr. Hasan Sadikin General Hospital." Critical Care & Shock 12 (2009): 157-169.

Knaus, William A., et al. "APACHE II: a severity of disease classification system." Critical care medicine 13.10 (1985): 818-829.

Vincent, J-L., et al. "The SOFA (Sepsis-related Organ Failure Assessment) score to describe organ dysfunction/failure." Intensive care medicine 22.7 (1996): 707-710.

Ferreira, Flavio Lopes, et al. "Serial evaluation of the SOFA score to predict outcome in critically ill patients." Jama 286.14 (2001): 1754-1758.

Vincent, Jean-Louis, and Rui Moreno. "Clinical review: scoring systems in the critically ill." Crit Care 14.2 (2010): 207.

Livingston, Brian M., et al. "Assessment of the performance of five intensive care scoring models within a large Scottish database." Critical care medicine28.6 (2000): 1820-1827.

Wong, David T., et al. "Evaluation of predictive ability of APACHE II system and hospital outcome in Canadian intensive care unit patients." Critical care medicine 23.7 (1995): 1177-1183.

College Answer

°     Inflammatory bowel disease

°     Thyrotoxicosis

°     Idiopathic

°     Familial

°     Cirrhosis

°     Celiac

°     Pregnancy

Discussion

There are numerous causes of clubbing.

Here is an unreasonably long list:

Causes of bilateral clubbing in both hands and feet

Cardiac

• Congenital heart disease, particularly cyanotic defects
• Congestive cardiac failure
• Aortic aneurysm
• Subacute bacterial endocarditis
• Arteriovenous fistula (and it would have to be a major one)

Respiratory

• Chronic lung disease of any sort, really;
  • Bronchiectasis, which makes you think of cystic fibrosis
  • COPD, asbestosis
  • Pulmonary fibrosis of any aetiology
  • Empyema which has been going on for a while
• Lung carcinoma (according T and O’C, usually not the small-cell variety)

Gastrointestinal

• Cirrhosis; for some reason, particularly primary biliary cirrhosis
• Coeliac disease
• Inflammatory bowel disease
• Malignancy of the colon

Uncommon causes of clubbing

• Thyrotoxicosis
• Pregnancy
• Syringomyelia
• Hyperparathyroidism
• Pregnancy
• Inherited familial (some people are just born that lucky – it’s a Mendelian dominant trait)

Causes of clubbing in feet only

• Coarctation of aorta
• Abdominal aortic aneurysm

Causes of unilateral clubbing 

• Any sort of arterial aneurysm leading into the clubbed limb
• Apical lung tumour
• Hemiplegia

Clinical Examination of the Critically Ill Patient, 3rd edition by L.I.G. Worthley - which can be ordered from our college here.

Clinical Examination: whatever edition, by Talley and O'Connor. Can be acquired any damn where. Get your own.

Rutherford, John D. "Digital Clubbing." Circulation 127.19 (2013): 1997-1999.

Velur, Prasuna, and Giridhar P. Kalamangalam. "Teaching NeuroImages: Unilateral clubbing in hemiplegia." Neurology 78.19 (2012): e122-e122.

Stoller, James K., et al. "Reduction of intrapulmonary shunt and resolution of digital clubbing associated with primary biliary cirrhosis after liver transplantation." Hepatology 11.1 (1990): 54-58.

Dickinson, C. J., and J. F. Martin. "Megakaryocytes and platelet clumps as the cause of finger clubbing." The Lancet 330.8573 (1987): 1434-1435.

College Answer

Many problems are encountered after hospital discharge. The important problems include Patients have usually had a tracheostomy (and/or prolonged endotracheal intubation) - complications associated with these include laryngeal pathology [eg. polyps, ulcers], aspiration, difficulty with swallowing etc.

°     Limitation of mobility for some time – muscle tone, joint stiffness, Chronic Inflammatory
Polyneuropathy 
°     Skin – hair loss, itching
°     Sexual dysfunction
°     Psychological problems – loss of memory, stress, nightmares, Post Traumatic Stress Disorder, depression, chronic fatigue syndrome
°     Infectious: colonisation with resistant organisms (eg. MRSA)
°     Miscellaneous (loss of taste, loss of appetite, ocular trauma, scarring near region of tape fixing for ETT)
°     Decreased visual acuity in patients who are profoundly hypotensive.
°     Unnecessary medication – Frequently medication commenced in ICU is commenced post discharge.

Tools to asses quality include:

Quality Adjusted Life Years (objective measure),

HAD – Hospital Anxiety & Depression,

SF 36,

PQOL (perceived quality of life),

EuroQOL – European tool
Simpler measures include Glasgow Outcome Scale.

Some hospitals utilise follow up clinics.

Discussion

This question is heavily based on Oh's Manual Chapter 8 (Common  problems  after  ICU).

A systematic response would resemble the following:

• Tracheostomy:
  • Tracheal stenosis
  • Tethering of skin
• Mobility:
  • Muscle wasting, neuromyopathy
  • Slow recovery of normal function (up to 1 year)
• Skin
  • Hair loss
  • Nail ridging
  • MRSA colonisation
  • Facial scraring due to pressure areas from NGT and ETT
• Sexual dysfunction
  • a 39% incidence
• Psychological problems
  • PTSD, in about 15% (27.5% for ARDS survivors)

The question about tools to assess the quality of life is derived from Oh's Manual as well, specifically Box 8.1 on page 62 of the new edition ("Quality of life tool examples").

The contents of this box:

• QALY(Quality Adjusted Life Years - the only objective tool)
• HAD (Hospital Anxiety and Depression)
• PQOL (Percieved Quality of Life)
• EuroQol
• SF 36 (36 item short-form survey)

Oh's Intensive Care manual: Chapter 8 (pp.61)  Common  problems  after  ICU   by Carl  S  Waldmann  and  Evelyn  Corner

College Answer

(a)        What does this image show?

A     creamy     supernatant      in     blood     tubes     (serum     and     plasma)     due     to     severe hypertriglyceridaemia (lipaemic serum).

(b)        List three (3) causes for this appearance in blood samples from critically ill patients.

Familial hyperlipedemia
Propofol infusion
TPN use
Pancreatitis  from hyperlipedemia

(c)        If  the  condition  causing  this  appearance  in  the  blood  tubes  were  to  be  long standing, what clinical signs specific to this condition may be found in this patient?

Eyes

• Lipaemia retinalis
• Corneal arcus senilis
• Xanthelasma

Skin

• Xanthomata
• Tendon
• Eruptive

Discussion

Of the ICU trainees, I am sure very few would have seen such a thing as this.

The specific "chicken fat" supernatant demonstrated in the college photograph is characteristic - that "cream" is all chylomicrons. In fact, this finding had in the olden days formed part of the classification of hyperlipidaemias- they used to observe "standing serum" to see if a supernatant would form. In the most severe forms of hyperlipidaemia, this fatty impurity can cause the blood to look milky and turbid.

There is little one can add to the college answer.

FREDRICKSON, DONALD S. "An international classification of hyperlipidemias and hyperlipoproteinemias." Annals of internal medicine 75.3 (1971): 471-472.

Aviram, Michael, Yael Sechter, and J. Gerald Brook. "Chylomicron-like particles in severe hypertriglyceridemia." Lipids 20.4 (1985): 211-215.

College Answer

List 2 causes (apart from cardiovascular or respiratory) of cyanosis.

•     Severe methemoglobinemia
•      Sulfhemoglobinemia
•     Hemoglobin mutation
•      Polycythaemia
•     Hypothermia / cold
•     High altitude

Discussion

The college presents us with a long and inventive list.

To this list, one can still add a few (obscure) differentials.

Here we go:

• Methylene blue or indocyanine gree dye injection
• Hemosiderosis
• Excess consumption of colloidal silver
• Chronic massive doses of amiodarone
• Chlorpromazine

And, the causes suggested by the college:

• Severe methemoglobinemia (though the blood in these cases is supposed to be chocolate brown)
• Sulfhemoglobinemia
• Hemoglobin mutation (eg. Haemoglobin Beth Israel, etc)
• Polycythaemia
• Hypothermia
• High altitude

Nagel, Ronald L., et al. "Hemoglobin Beth Israel: A mutant causing clinically apparent cyanosis." New England Journal of Medicine 295.3 (1976): 125-130.

Walker, H. Kenneth, et al. "Cyanosis." in Clinical Methods: The History, Physical, and Laboratory Examinations. 3rd edition (1990).

College Answer

• Factors predictive of poor outcome:
  • Severe TBI in an elderly patient
  • SDH increased risk of poor outcome
  • Warfarin therapy
  • Pre-existing co-morbidities – renal disease, diabetes, neurological dysfunction

Age-related changes:

• Cardiovascular
  • Increased incidence of coronary artery disease
  • Systolic and diastolic dysfunction with CCF
  • Conduction disorders (SSS, AF, BBB)
  • Valvular disease
  • Decreased response to sympathetic stimulation
• Respiratory
  • Decreased respiratory muscle strength
  • Decreased respiratory centre sensitivity to hypoxia and hypercarbia
  • Reduced elastic recoil of lung
  • Increased chest wall stiffness
  • Reduced vital capacity and FEV1.

Response to medications

• Age has been shown in multiple studies to be an independent risk factor for adverse drug reactions.
• Age related physiological changes affect absorption, distribution, metabolism and elimination of drugs.
• Poly-pharmacy is common, thus increased risk of adverse drug reaction.
• Cognitive impairment and drug errors – overdose, non-adherence, failure to disclose full medication list to often multiple medical practitioners involved in care.
• Reducing renal blood flow and GFR with age alters drug elimination potentially leading to drug accumulation.

Discussion

A more detailed all-systems look at age-related changes in the response to critical illness can be found in Question 8 from the second paper of 2007:"What are the age related factors which adversely affect outcome in the elderly (>65 years) critically ill patient?"

How did the college arrive at this answer?

What are the predictors of poor outcome in traumatic brain injury?

• Everyone seems to agree that age is a predictor of poor outcome.
•  Steyerberg et al found that age was associated with poor neurological outcomes and decreased survical. 
• Amacher et al also found that it doesnt matter how good your GCS is on admission, your old age will still play a role.
• Being over 60 is a poor prognostic indicator.
• Steyerberg et al found that a traumatic subarachnoid (rather than subdural) was a determinant of poor prognosis.
• Mortality in elderly patients with subdural haematoma is very high if they present with a GCS 3-5
• For these people,  craniotomy may not be justified, because of the extremely poor outcomes overall.

As far as age-related physiological changes go...

From this article on geriatric cardiology, I quote the following physiological changes associated with age:

Cardiovascular changes

• A decrease in elasticity and an increase in stiffness of the arterial system. Thus:
  • Increased afterload on the left ventricle
  • left ventricular hypertroph
  • Increase in systolic blood pressurey,
  • Changes in the left ventricular wall that prolong relaxation of the left ventricle in diastole; thus diastolic dysfunction and the propensity towards pulmonary oedema
  • Aortic valve calcification
• Dropout of atrial pacemaker cells resulting in a decrease in intrinsic heart rate.
• With fibrosis of the cardiac skeleton there is calcification at the base of the aortic valve and damage to the His bundle as it perforates the right fibrous trigone.
• Decreased responsiveness to β-adrenergic receptor stimulation
• Decreased reactivity to baroreceptors and chemoreceptors,
• Increase in the levels of circulating catecholamines.

Respiratory changes

• Decrease in exchange surface area ("senile emphysema"):
  • Dilatation of alveoli
  • Enlargement of airspaces
  • loss of supporting tissue for peripheral airways
  • Carbon monoxide transfer decreases with age, reflecting mainly a loss of surface area.
• Decreased static elastic recoil of the lung, thus
  • Increased residual volume
  • Increased functional residual capacity.
• Decreased expiratory flow rates (especially small airways)
  • The ventilation/perfusion ratio heterogeneity increases, with low V/Q zones appearing as a result of premature closing of dependent airways.
• Decreased compliance of the chest wall, thus increased work of breathing
• Decreased respiratory muscle strength (though this depends on the heart, and on nutrition)
• Decreased sensitivity of respiratory centres to hypoxia and hypercapnia

Pharmacokinetic changes:

• Reduction in first-pass metabolism, thus increased oral bioavailability of a few drugs.
• Body fat increases, body water decreases; thus:
  • Hydrophilic drugs have a smaller volume of distribution
  • lipophilic drugs have an increased volume of distribution and a longer half-life
• Drugs with a high hepatic extraction ratio decrease in systemic clearance
• Activities of cytochrome P450 enzymes are preserved in normal ageing
• Renal clearance may be decreased due to age-related changes in renal function

Langlois, Jean A., Wesley Rutland-Brown, and Marlena M. Wald. "The epidemiology and impact of traumatic brain injury: a brief overview." The Journal of head trauma rehabilitation 21.5 (2006): 375-378.

Steyerberg, Ewout W., et al. "Predicting outcome after traumatic brain injury: development and international validation of prognostic scores based on admission characteristics." PLoS medicine 5.8 (2008): e165.

Stocchetti, Nino, et al. "Traumatic brain injury in an aging population." Journal of neurotrauma 29.6 (2012): 1119-1125.

Amacher, Loren A., and David E. Bybee. "Toleration of head injury by the elderly." Neurosurgery 20.6 (1987): 954-958.

Jamjoom, Abdulhakim, et al. "Outcome following surgical evacuation of traumatic intracranial haematomas in the elderly." British journal of neurosurgery 6.1 (1992): 27-32.

Cheitlin, Melvin D. "Cardiovascular physiology—changes with aging." The American journal of geriatric cardiology 12.1 (2003): 9-13.

Shi, Shaojun, and Ulrich Klotz. "Age-related changes in pharmacokinetics."Current drug metabolism 12.7 (2011): 601-610.

Corsonello, A., C. Pedone, and R. Antonelli Incalzi. "Age-related pharmacokinetic and pharmacodynamic changes and related risk of adverse drug reactions." Current medicinal chemistry 17.6 (2010): 571-584.

Carbonin, P., et al. "Is age an independent risk factor of adverse drug reactions in hospitalized medical patients?." Journal of the American Geriatrics Society39.11 (1991): 1093-1099.

Timiras, Paola S., ed. Physiological basis of aging and geriatrics. CRC Press, 2013.

Janssens, J. P., J. C. Pache, and L. P. Nicod. "Physiological changes in respiratory function associated with ageing." European Respiratory Journal 13.1 (1999): 197-205.

College Answer

a)

ICU mortality

Advantages:

• simple, single metric
• concrete endpoint which is already available in hospital databases
• death is an important endpoint
• aggregation of a large number of diagnoses with a small number in each increases power to detect variation
• variation over time may reflect institutional and organisational events or characteristics- budget cuts, bed pressure etc. and be able to detect true quality deficiencies 
  · May be useful when combined as part of an overall quality program

Disadvantages:

• Definition of ICU is very hospital specific which can influence mortality (e.g. non-ICU stepdown areas in some hospitals)
• As a consequence can be ‘gamed’ e.g. transfers out to die in the ward or other units
• Poor correlation between mortality and quality of care in some diagnoses - alternatives available e.g. diagnosis specific risk models e.g. EuroSCORE for CABG, APACHE SMR, trauma scores. Can mask problems in low volume diagnostic groups
• Difficult to draw hospital comparisons and or allow league table construction
• False conclusions can be drawn unless robust statistical methods used

Hospital Mortality

Compared to ICU mortality, avoids many problems of censoring at ICU discharge. Hospital mortality can often be 50% higher than ICU mortality, and is a reasonable surrogate (90%) for 90 days mortality.

Advantages – gets over differences in definition of ICU and ICU discharge thresholds. Still a simple and robust endpoint which is easy to obtain from exisiting hospital databases.

Disadvantages – can confound intensive care outcomes with deficiencies in ward or other post ICU care. Does not address in any way functional outcomes [so discharge from hospital to a nursing home in a vegetative state is counted as a positive outcome]

90 day mortality 
Simple robust endpoint which addresses the issue of ongoing mortality after hospital discharge (though this difference is about 10% relative in recent large trials).

Advantages – simple robust endpoint; data may be available by linkage with external registries (e.g. Births, Deaths and Marriages)

Disadvantages – still an arbitrary time point [while 28 days is clearly inadequate, 90 days may still be insufficient to accurately measure the attributable mortality from an episode of critical illness]. Problems with loss to follow up after ICU discharge. Ethical implications of contacting patients after discharge (especially for research studies).

1 year functional outcome

Advantages – a ‘POEM’ – (patient oriented endpoint that matters). Takes into account disability and true long-term consequences of critical illness.

Disadvantages – no ideal soring tool available – existing tools all have problems; some measure particular functional domains well; problems with face validity. All functional outcome measures are time consuming to apply. Problems with loss to follow up. Time consuming, labour intensive, costly face to face vs. phone vs. mail follow up. Depending on disease may reflect more the natural history of the disease rather than the ICU care per se.

Discussion

The above answer is used as a backbone of the LITFL article on this topic. A good discussion of "at what point do we measure mortality" can be found at the AIHW statement on Measuring and reporting mortality in hospital patients (page 6, ch. 2.4.2).

As a good guideline for other ICU outcome measures which should be used to assess the outcomes of Phase II trials, ANZICS has published this statement in 2012 (the 2013 update is for some reason not available for free).

A good discussion of ICU outcome measures and some of their limitations can also be found in the1999 workshop publication of the American Thoracic Society.

A question like this probably lends itself better to a tabulated answer.

Caron, Guy, et al. "Submental endotracheal intubation: an alternative to tracheotomy in patients with midfacial and panfacial fractures." Journal of Trauma and Acute Care Surgery 48.2 (2000): 235-240.

Young, Paul, et al. "End points for phase II trials in intensive care: Recommendations from the Australian and New Zealand clinical trials group consensus panel meeting." Critical Care and Resuscitation 15.3 (2013): 211. - this one is not available for free, but the 2012 version still is:

Young, Paul, et al. "End points for phase II trials in intensive care: recommendations from the Australian and New Zealand Clinical Trials Group consensus panel meeting." Critical Care and Resuscitation 14.3 (2012): 211.

Suter, P., et al. "Predicting outcome in ICU patients." Intensive Care Medicine20.5 (1994): 390-397.

Martinez, Elizabeth A., et al. "Identifying Meaningful Outcome Measures for the Intensive Care Unit." American Journal of Medical Quality (2013): 1062860613491823.

Tipping, Claire J., et al. "A systematic review of measurements of physical function in critically ill adults." Critical Care and Resuscitation 14.4 (2012): 302.

Gunning, Kevin, and Kathy Rowan. "Outcome data and scoring systems." Bmj319.7204 (1999): 241-244.

Woodman, Richard, et al. Measuring and reporting mortality in hospital patients. Australian Institute of Health and Welfare, 2009.

Vincent, J-L. "Is Mortality the Only Outcome Measure in ICU Patients?."Anaesthesia, Pain, Intensive Care and Emergency Medicine—APICE. Springer Milan, 1999. 113-117.

Rosenberg, Andrew L., et al. "Accepting critically ill transfer patients: adverse effect on a referral center's outcome and benchmark measures." Annals of internal medicine 138.11 (2003): 882-890.

Burack, Joshua H., et al. "Public reporting of surgical mortality: a survey of New York State cardiothoracic surgeons." The Annals of thoracic surgery 68.4 (1999): 1195-1200.

Hayes, J. A., et al. "Outcome measures for adult critical care: a systematic review." Health technology assessment (Winchester, England) 4.24 (1999): 1-111.

RUBENFELD, GORDON D., et al. "Outcomes research in critical care: results of the American Thoracic Society critical care assembly workshop on outcomes research." American journal of respiratory and critical care medicine 160.1 (1999): 358-367.

College Answer

• Loss of comet tails and “marching ants” appearance
• Ribs and pleura move together
• “Lung point” – motionless horizontal lines are replaced by normal lung appearance moving from non-dependent to dependent region and also seen with inspiration and the probe held stationary.
• Loss of “waves on the beach” appearance in M-mode

Discussion

This is one of those things which is better explained with pictures.

In dry boring words, there is a list of features which one can easily memorise:

1. Absence of "Seashore sign" on M-mode
2. Absence of "B-lines" or "comet-tails" moving in synchrony with the pleura
3. Absence of sliding lung
4. Lung point sign - the point where the pneumothorax transitions into normal lung

Husain, Lubna F., et al. "Sonographic diagnosis of pneumothorax." Journal of Emergencies, Trauma & Shock 5.1 (2012).

College Answer

a)

The SMR of ICU A is above the upper 99% CI indicating the SMR is significantly higher than similar hospitals. Your ICU has significantly more deaths than expected compared to similar hospitals.

The overall SMR for the group is less than 1 and the SMR for ICU A is less than 1

b)

SMR = O/E    O= observed number of deaths, E = expected number of deaths

E is derived from the average of the sample/ population.

Usually a risk adjustment model is used to calculate and account for severity of illness.

c)

Can be “apparent” or “real”.

Data quality

Incomplete or errors in data submission causing underestimated expected risk Widely different casemix of this ICU compared to others.

Statistical model (risk adjustment) may no longer well calibrated True increase in mortality which can be due to

i.    Factors internal to ICU: very high occupancy, poor processes,, inadequate staffing,

ii.    Factors external to ICU; problems in services that are high users of ICU e.g. surgery, system issues

Additional Examiners’ Comments:

Many candidates showed a significant knowledge gap relating to this commonly used quality indicator with insufficient details and structure in their answers.

Discussion

Again, the college did not include their images here. The SMR funnel plot above has been ripped off from the ANZICS own database for 2013-2014, via the propaganda materials from The Alfred. The red dot in the bottom right of the funnel plot is that abovementioned centre of excellence, soaring ever higher into "good outlier" territory.

Generally, the college loves SMRs. Specifically, they like to discuss which it might be abnormally high, and what are its limitations as a measure of the quality of care.

• Question 24 from the second paper of 2015 asked to explain causes of a raised SMR, as well as to interpret a funnel plot of SMR data and to explain how it is calculated.
• Question 22 from the first paper of 2007 asked why an SMR might be raised suddenly
• Question 22 from the second paper of 2007 was identical to Question 22 from the first paper of 2007, i.e. the same question used twice within the same year.
• Question 13 from the first paper of 2003 wanted you to discuss the limitations of the SMR
• Question 30 from the second paper of 2006 was identical to Question 13 from the first paper of 2003

a)  ICU "A" is clearly having some sort of crisis. However, the overall SMR is still less than 1, which means that mortality has not exceeded the average mortality predicted by APACHE data. Which is good, because some might say APACHE data is crap for predicting mortality: these day mortality is probably lower for any given APACHE score than the outdated system might predict.

b)

• SMR is the ratio of the observed  mortality vs. predicted mortality for a specified time period.
• One can use this to compare hospitals and ICUs
• One needs to first calculate the predicted hospital mortality using an illness severity scoring system.
• An SMR of 1 means the mortality is as expected.
• An SMR of < 1 is better than expected, and >1 is worse than expected.

The above definition is probably enough to satisfy the minimum requirements for the college. 
In short,

SMR = observed number of deaths / expected number of deaths

In order to calculate this, one requires three main variables:

• A time interval (you decide - three months, one year, etc.)
• A measurement of the observed number of deaths (ICU mortality numbers should be available widely)
• An estimate of the predicted mortality (this can be achieved using a scoring system).

c)

Reasons for a spuriously elevated SMR

• Unfair mortality measurements should not be a problem, as there is no serious situation in which mortality might get over-reported (i.e. some patients walking around after ICU discharge who are formally dead according to the official record). An under-reporting of mortality is much more likely, and might occur in resource-poor environments where public record-keeping is rudimentary. Such dodgy records might still list the person as alive, whereas they died shortly after ICU discharge and nobody bothered to file the paperwork.
• Unfair comparison group mortality / APACHE mortality estimate is usually the culprit.
  • Poor data entry, eg. constant overestimation of GCS or failure to tick the chronic illness boxes, which makes the patients appear healthier than they actually are.
  • Missing data is (in the author's experience) usually rich in APACHE points, eg. the missed urine output entry obscuring anuria, or the failure to document biochemistry results which conceals the potassium level of 8.0mmol/L.
  • "Lead time bias" - treatment received prior to ICU admission may result in artifically normalised acute physiology scores
  • "Healthy worker effect" - a change towards selective ICU admission practices may be favouring patients who score low on illness severity scales, eg. young elective surgical patients

Reasons for a truly elevated SMR

• Issues external to the ICU
  • A population with greater pre-ICU morbidity is suddenly available (eg. to borrow an example from LITFL, you have suddenly decided to become a destination for the state's ECMO retrieval service).
  • Pre-ICU care has changed its practice (for the worse)
  • Parameters which govern ICU admission have changed (eg. administrative pressure is being placed on the ICU to rapidly admit ED patients who have had little management or workup)
  • Discharge arrangements have changed (eg. a local palliative care ward had shut down, and you keep dying patients in the ICU because it would be insensitive to transfer them to the next nearby palliative care unit)
• Issues internal to the ICU (i.e. genuine under-performance)
  • This could be a long list...
  • A new staffing model is in place (inexperienced staff)
  • Understaffing has impaired patient care
  • Junior people are not following unfamiliar protocols, or the new protocols are crap
  • New equipment you bought is useless

Young, Paul, et al. "End points for phase II trials in intensive care: Recommendations from the Australian and New Zealand clinical trials group consensus panel meeting." Critical Care and Resuscitation 14.3 (2012): 2111.

Suter, P., et al. "Predicting outcome in ICU patients." Intensive Care Medicine20.5 (1994): 390-397.

Martinez, Elizabeth A., et al. "Identifying Meaningful Outcome Measures for the Intensive Care Unit." American Journal of Medical Quality (2013): 1062860613491823.

Tipping, Claire J., et al. "A systematic review of measurements of physical function in critically ill adults." Critical Care and Resuscitation 14.4 (2012): 302.

Gunning, Kevin, and Kathy Rowan. "Outcome data and scoring systems." Bmj319.7204 (1999): 241-244.

Woodman, Richard, et al. Measuring and reporting mortality in hospital patients. Australian Institute of Health and Welfare, 2009.

Vincent, J-L. "Is Mortality the Only Outcome Measure in ICU Patients?."Anaesthesia, Pain, Intensive Care and Emergency Medicine—APICE. Springer Milan, 1999. 113-117.

Rosenberg, Andrew L., et al. "Accepting critically ill transfer patients: adverse effect on a referral center's outcome and benchmark measures." Annals of internal medicine 138.11 (2003): 882-890.

Burack, Joshua H., et al. "Public reporting of surgical mortality: a survey of New York State cardiothoracic surgeons." The Annals of thoracic surgery 68.4 (1999): 1195-1200.

Hayes, J. A., et al. "Outcome measures for adult critical care: a systematic review." Health technology assessment (Winchester, England) 4.24 (1999): 1-111.

RUBENFELD, GORDON D., et al. "Outcomes research in critical care: results of the American Thoracic Society critical care assembly workshop on outcomes research." American journal of respiratory and critical care medicine 160.1 (1999): 358-367.

Turnbull, Alison E., et al. "Outcome Measurement in ICU Survivorship Research From 1970 to 2013: A Scoping Review of 425 Publications." Critical care medicine (2016).

Solomon, Patricia J., Jessica Kasza, and John L. Moran. "Identifying unusual performance in Australian and New Zealand intensive care units from 2000 to 2010." BMC medical research methodology 14.1 (2014): 1.

Liddell, F. D. "Simple exact analysis of the standardised mortality ratio." Journal of Epidemiology and Community Health 38.1 (1984): 85-88.

Ben-Tovim, David, et al. "Measuring and reporting mortality in hospital patients." Canberra: Australian Institute of Health and Welfare (2009).

McMichael, Anthony J. "Standardized Mortality Ratios and the'Healthy Worker Effect': Scratching Beneath the Surface." Journal of Occupational and Environmental Medicine 18.3 (1976): 165-168.

Wolfe, Robert A. "The standardized mortality ratio revisited: improvements, innovations, and limitations." American Journal of Kidney Diseases 24.2 (1994): 290-297.

Kramer, Andrew A., Thomas L. Higgins, and Jack E. Zimmerman. "Comparing observed and predicted mortality among ICUs using different prognostic systems: why do performance assessments differ?." Critical care medicine 43.2 (2015): 261-269.

Spiegelhalter, David J. "Funnel plots for comparing institutional performance." Statistics in medicine 24.8 (2005): 1185-1202.

Teres, Daniel. "The value and limits of severity adjusted mortality for ICU patients." Journal of critical care 19.4 (2004): 257-263.

College answer

a)

Acromegaly

b)

Hyperglycaemia (Diabetes mellitus)
Hypercalcaemia
Hypercalciuria
Low cortisol
Hypernatraemia (diabetes insipidus)

Discussion

Now, I don't want to get into any sort of legal trouble by reproducing the images which the college decided to withhold as a part of their "official" papers, but reliable sources report that the images used in this question were ripped straight from Wikipedia. Instead of re-using these images (which would arguably be completely legitimate, as they are covered by essentially the same Creative Commons license as the rest of this site) I have sourced others. In case one requires the college originals, they can be viewed in Chanson et al (2008). 

The images I have used came from this weird alterna-health website where annoying popups tried to warn me about "Never Eat These Three Fatigue-Causing Foods" etc. Clearly these authors are not the original source of the photographic progression of male faces, which demonstrate the gradual changes associated with acromegaly. The hand pictures came from another severely ad-polluted site (Medindia.com). I hope these are unequivocal. No other answer should be possible for a).

b) affords more variation. Acromegaly is associated with a whole host of clinical features, of which biochemical abnormalities are only one part. Molitch (1992) lists a whole lot of these, but the article is paywalled. Instead one can get Melmed Shlomo's 2006 NEJM piece from tripod.com. It's just as good. In fact there is a table (Table 1, Clinical Features orf Acromegaly) which lists everything you could ever think of which might be associated witht his disease, from the local effects of pituitary adenoma to things like narcolepsy and galactorrhoea. The whole table is reproduced here.

College answer

a)    Infections:  
Viral e.g. Influenza, Coxsackie, Mumps 
Bacterial e.g. GAS, Diphtheria, Mycoplasma 
 
Drugs: 
Sulfonamides 
Beta-lactams 
Anti-convulsants 
NSAIDs 
Allopurinol 
Paracetamol 
 
Malignancy 
 
b)    General: 
Multi-disciplinary approach with dermatology, plastics, ophthalmology. Best managed in specialised burns unit 
Stop precipitating agents e.g. NSAID / allopurinol General Haemodynamic and respiratory support. 
Reverse-Isolation in single room with room temperature increased to 30-320C. 
Awareness of potentially high fluid loss: may require aggressive replacement 
Wound care: Cover the denuded skin with anti-septic soaked dressings, vigilance for secondary skin infections. No role for prophylactic antibiotics. 
Analgesia for painful skin lesions and for dressing change. 
Eye care: look for conjunctival hyperemia, epithelial defect & pseudomembrane formation. Treat with topical lubricants, topical steroids and topical antibiotic, as guided by ophthalmology. Attempt to place lines through normal skin if possible 
 
Specific: 
Cyclosporin: Early administration at the dose of 3-5mg/kg is beneficial and is recommended.
Steroids: The use of systemic corticosteroids has not been evaluated in clinical trials & remains controversial. Early observational studies indicated higher frequency of complications & death; but recent meta-analysis found that steroid treatment was associated with reduced risk of death. The dose, route, duration & timing of steroids remain uncertain.  
Plasmapheresis: Reported to be beneficial in small series and case reports, but role still not well defined. 
Anti-TNFα monoclonal antibodies e.g. infliximab has been used successfully in small series of patients, but not recommended. 
 

Discussion

It is remarkable that this SAQ on a relatively rare condition had a 58% pass rate. The last time toxic epidermal necrolysis came up (in Question 10 from the first paper of 2005), only 13% of the candidates passed. Presumably, of that 34-candidate cohort from 2005 (of whom 19 were successful), thirteen years later some proportion were senior college fellows and Part II examiners, and now they think this question is a really good idea from an assessment value standpoint.

The best literature for the management of TEN is unfortunately paywalled (Fromowitz et al, 2007). That particular article shines brightest because they incorporate a long (28-point) list of management recommendations from the University of Florida protocol. Fortunately, Schneider & Cohen have an even better article, which is more recent (2017). Additionally, the 2016 UK guidelines are available as a free PDF (Creamer et al, 2016).  These and other resources have been remixed and recut into the summary below.

• Supportive management:
  • A- Intubation is almost inevitable because of the sedation and analgesic requirements
  • B- Wherever mechanical ventilation can be avoided, humidified oxygen is preferred to regular wall oxygen because of the mucosal injuries
  • C- Expect a hyperdynamic vasodilated circulation with hypovolemia:
    • Replace lost fluid (see F below)
    • Vasopressors to maintain MAP targets
    • Use PICC access (anticipating long term IV access requirements with few normal patches of skin available for PIVCs and CVCs)
    • Beware of line dressings. Where possible, avoid adhesive dressings.
  • D - Expect complex pain needs:
    • The patient will likely require a multimodal approach to analgesia with some combination of IV opiate and opiate-sparing agents like ketamine. For dressing changes, expect to need either general anaesthetic, ketamine sedation or methoxyflurane.
    • Psychological support will be required to the patient and family (disfiguring illness, prolonged ICU stay, extreme pain - all the recipes for PTSD and depression)
  • E - Expect electrolytes to be deranged:
    • Hypernatremia due to water loss
    • Hypophosphataemia due to large-scale tissue regrowth
  • F - Not quite a burns-like fluid management strategy: fluid requirements are usually about 30% lower than for burns of a similar extent (Schenider & Cohen, 2017)
    • Replace large volumes of crystalloid, using a balanced crystalloid
    • Haemoconcentration is a guide to replacement adequacy
    • Albumin replacement will be required due to ongoing protein loss through wounds
  • G - Nutrition needs will be complex:
    • High caloric requirements, like sepsis (probably 125% of predicted) but lower than burns of a similar extent
    • High protein requirements (2.0-2.5g/kg/day) to account for losses and hypercatabolic state
    • Expect oral diet to be impossible owing to mucosal injuries; expect these injuries to frustrate NG placement. Early placement of a feeding NG tube is vitally important.
    • Anticipate constipation due to high dose opiates
    • Ensure vigorous ulcer prophylaxis (high dose steroids will be used)
    • High risk for C.difficile infection (likely, broad spectrum antibiotics will be used)
  • H - Expect the patient to be at high risk of VTE:
    • Chemical thromboprophylaxis needs to be fastidious, as there is usually nowhere to place TEDs and calf compressors
  • I - Infectious diseases specialists need to be included in decisionmaking. Broadly:
    • Antibiotics are not indicated unless there is a clinically evident infection
    • Topical antibiotics for conjunctiva are indicated (chloramphenicol)
    • Antibiotic-coated lines are indicated (especially if placed through affected skin)
    • If somebody started steroids, stop them.
• Specific management:
  • The following therapies are all largely based on case series or expert opinion. Representative articles are included for reference.
    • Cyclosporin (Arevalo et al, 2000)
    • Corticosteroids (thought to be useless - Schneck et al, 2008)
    • IV immunoglobulin decreases mortality (Barron et al, 2015)
    • Plasmapheresis (some positive results from Han et al, 2017)
    • Anti-TNFα monoclonal antibodies (case reports, eg. Hunger et al, 2005)
    • Thalidomide (potentially detrimental - Wolkenstein et al, 1998)

College answer

a)    Overview of SMR (20% marks)

SMR is one of the quality indicators that reflect the performance of an ICU.
Definition of SMR = ratio of observed deaths in the study group to expected deaths in the general population based on APACHE or other severity of illness
SMR values of 1 indicate expected performance, whereas values below 1 and above 1 indicate respectively better and worse performances than expected
 

b)    Causes for increase (80% marks)

Lower than expected predicted mortality
Errors in predicted/expected mortality due to gaps in data, changes in case-mix etc

Change in data collection systems or personnel – e.g., change in the way the expected mortality is estimated

Lead-time bias (pre-ICU care) – patients transferred from other facilities may have become more stable after receiving appropriate management at the original hospital.

Increases in observed mortality

Based on hospital mortality, not ICU mortality – therefore, influenced by pre-ICU and post ICU care in the hospital

Change in case-mix, so changes in case mix may account for increase in SMR and increased other hospital admissions

One-off events such as mass disasters, epidemics etc

Variations in practice, changes in clinical protocols either in the hospital or in the ICU Changes in personnel – e.g., new intensivist, new surgeon etc

Changes in staffing levels and training

New services introduced such as ECMO etc.

Examiner’s Comments:

The candidates rarely considered the denominator. Often wrote "admitted sicker patients" without considering these likely to also have higher predicted mortality. Rarely any structure.

Young, Paul, et al. "End points for phase II trials in intensive care: Recommendations from the Australian and New Zealand clinical trials group consensus panel meeting." Critical Care and Resuscitation 15.3 (2013): 211. - this one is not available for free, but the 2012 version still is:

Young, Paul, et al. "End points for phase II trials in intensive care: recommendations from the Australian and New Zealand Clinical Trials Group consensus panel meeting." Critical Care and Resuscitation 14.3 (2012): 211.

Suter, P., et al. "Predicting outcome in ICU patients." Intensive Care Medicine20.5 (1994): 390-397.

Martinez, Elizabeth A., et al. "Identifying Meaningful Outcome Measures for the Intensive Care Unit." American Journal of Medical Quality (2013): 1062860613491823.

Tipping, Claire J., et al. "A systematic review of measurements of physical function in critically ill adults." Critical Care and Resuscitation 14.4 (2012): 302.

Gunning, Kevin, and Kathy Rowan. "Outcome data and scoring systems." Bmj319.7204 (1999): 241-244.

Woodman, Richard, et al. Measuring and reporting mortality in hospital patients. Australian Institute of Health and Welfare, 2009.

Vincent, J-L. "Is Mortality the Only Outcome Measure in ICU Patients?."Anaesthesia, Pain, Intensive Care and Emergency Medicine—APICE. Springer Milan, 1999. 113-117.

Rosenberg, Andrew L., et al. "Accepting critically ill transfer patients: adverse effect on a referral center's outcome and benchmark measures." Annals of internal medicine 138.11 (2003): 882-890.

Burack, Joshua H., et al. "Public reporting of surgical mortality: a survey of New York State cardiothoracic surgeons." The Annals of thoracic surgery 68.4 (1999): 1195-1200.

Hayes, J. A., et al. "Outcome measures for adult critical care: a systematic review." Health technology assessment (Winchester, England) 4.24 (1999): 1-111.

RUBENFELD, GORDON D., et al. "Outcomes research in critical care: results of the American Thoracic Society critical care assembly workshop on outcomes research." American journal of respiratory and critical care medicine 160.1 (1999): 358-367.

Turnbull, Alison E., et al. "Outcome Measurement in ICU Survivorship Research From 1970 to 2013: A Scoping Review of 425 Publications." Critical care medicine (2016).

Solomon, Patricia J., Jessica Kasza, and John L. Moran. "Identifying unusual performance in Australian and New Zealand intensive care units from 2000 to 2010." BMC medical research methodology 14.1 (2014): 1.

Liddell, F. D. "Simple exact analysis of the standardised mortality ratio." Journal of Epidemiology and Community Health 38.1 (1984): 85-88.

Ben-Tovim, David, et al. "Measuring and reporting mortality in hospital patients." Canberra: Australian Institute of Health and Welfare (2009).

McMichael, Anthony J. "Standardized Mortality Ratios and the'Healthy Worker Effect': Scratching Beneath the Surface." Journal of Occupational and Environmental Medicine 18.3 (1976): 165-168.

Wolfe, Robert A. "The standardized mortality ratio revisited: improvements, innovations, and limitations." American Journal of Kidney Diseases 24.2 (1994): 290-297.

Kramer, Andrew A., Thomas L. Higgins, and Jack E. Zimmerman. "Comparing observed and predicted mortality among ICUs using different prognostic systems: why do performance assessments differ?." Critical care medicine 43.2 (2015): 261-269.

Spiegelhalter, David J. "Funnel plots for comparing institutional performance." Statistics in medicine 24.8 (2005): 1185-1202.

Teres, Daniel. "The value and limits of severity adjusted mortality for ICU patients." Journal of critical care 19.4 (2004): 257-263.

College answer

Freeman, William D., et al. "ICU management of patients with Parkinson's disease or Parkinsonism." Current Anaesthesia & Critical Care 18.5-6 (2007): 227-236.

Vincken, Walter G., et al. "Involvement of upper-airway muscles in extrapyramidal disorders: a cause of airflow limitation." New England Journal of Medicine 311.7 (1984): 438-442.

Katus, Linn, and Alexander Shtilbans. "Perioperative management of patients with Parkinson's disease." The American journal of medicine 127.4 (2014): 275-280.

College answer

Not available.

Discussion

a) Question 30.1 from the first paper of 2017 also asked for six clinical features of myotonic dystrophy. Fourtunately, there's plenty to choose from:

b)

One might expect that features suggestive of severity in chronic AR would be mainly features related to the effect of AR on cardiac function, not just generic features of AR

• LV dilatation (displaced apex, diffuse hyperdynamic impulse)
• Congestive cardiac failure (low blood pressure, peripheral oedema)
• Poor exercise tolerance
• Signs of widened pulse pressure (see below)
• An S3, suggestive of poor LV function

Generic features of AR are as follows:

• Chacteristic auscultatory findings:
  • Soft S1
  • Soft A2
  • An S3 if LV function is severely depressed
  • A systolic ejection sound due to abrupt aortic distension
• Signs of widened pulse pressure:
  • Corrigans sign
  • de Musset's sign 
  • Quincke's sign
  • Duroziez's sign 
  • Traube's sign
  • Mueller's sign
  • Becker's sign 
  • Hill's sign
  • Mayne's sign
  • Rosenbach's sign
  • Gerhard's sign

From the wording of the question, it is not clear that the trainees were expected to know what these signs were; i.e. to list the eponyms would have been enough to pass. Their meaning is discussed elsewhere, in case anybody is interested

c) Bilateral miosis with coma:

• Bilateral pontine lesions
• Bilateral thalamic lesions
• Metabolic encephalopathy
• Cholinergic drugs
  • Organophosphates
  • Myasthenia gravis drugs (the 'stigmines, eg. pyridostigmine)
  • Alzheimers nootropics (the 'pezils, eg. donepezil)
  • Sarin gas
• Non-cholinergic drugs:
  • Opiates
  • Barbiturates
  • GHB
  • Clonidine
  • GHB
  • Chloral hydrate
  • Valproate
  • Atypical antipsychotics
  • Phenothiazines

d) The latery medullural syndrome (Wallenberg syndrome) consists of the following classical findings:

• On the side of the lesion:
  • Facial sensory loss
  • Nystagmus
  • Horner's syndrome
  • Loss of gag reflex
  • Ipsilateral ataxia with a tendency to fall to the ipsilateral side
• On the contralateral side:
  • Pain and temperature sensory loss in the extremities
• Generally:
  • Vertigo
  • Nausea
  • Dysphagia

College answer

Not available.

Discussion

a) Assessment of frailty:

• Multidimensional assessment, consisting of:
  • History, eg. self-reported exhaustion, fatigue, mood, social participation
  • Proxy functional assessment, eg. use of mobility equipment, reliance on services, hospital admissions, frequency of falls
  • Functional disability assessment, eg. hand grip, gait, balance, cognitive assessment
  • Biometric data, eg. weight loss, muscle mass
  • Physical examination findings, for example:
    • Sarcopenia
    • Clinical features of malnutrition
    • Evidence of decreased energy expenditure
    • Features of multiple health deficits or comorbidities
  • Objective biological findings eg. echo findings, chronic organ system dysfunction, low serum prealbumin, etc.
• Use of a validated frailty scoring scale is recommended:
  • Numerous scales are available (see De Vries et al, 2011)
  • The CFS (Clinical Frailty Scale) is the one most commonly used in critically ill patients (Falvey & Ferrante, 2019)

b) Limitations of this assessment at the time of ICU admission:

• Assessment of frailty in general:
  • There may only be limited information regarding baseline function
  • Clinical features of frailty may be exaggerated by critical illness
  • Frailty may be absent on admission, but can then develop during the ICU stay
• Assessment using a fraily scoring scale:
  • There may be no time to use a comprehensive scoring scale
  • The use of such scales requires training
  • They are validated only among stable community outpatients
  • Their reliability is poorly studied (De Vries et al, 2011)

c) Utility of a frailty score in the management of critically ill patients:

• Prevention of non-beneficial therapies:
  • Frail patients have poorer ICU outcomes. 
  • High frailty scores are associated with discharge into residential care facilities,  medium to long term disability, and poor quality of life
  • Screening for frailty can be used as a prognostic indicator to inform decisionmaking around the goals of care and limitations of therapy
• Triage of critical care services
  • Under scenarios of system stress (eg. a pandemic response), frailty scoring may be a valid mechanism of allocating access to scarce critical care resources
• Redirection of clinical priorities: increased clinician attention to management strategies designed to maximise the preservation of function and the prevention of further enfeeblement, such as:
  • Nutrition (high caloric and high protein supplementation)
  • Micronutrient and vitamin replacement
  • Early extubation
  • Early mobility and physiotherapy
  • Delirium screening
  • Referral to services such as social work, psychology, occupational therapy and specialist geriatrician services

College answer

Aim: To allow the candidate to demonstrate expertise in data interpretation.

Key sources include: Common clinical practice with urinalysis, and paired urine and serum samples. CanMEDS Medical Expert.
Discussion:

This question asked for three causes of reddish urine with a clinical history of trauma and chemical exposure and half the candidates were able to do that. Correct examples included three of the following four causes: haemoglobinuria, haematuria, myoglobinuria or hydroxocobalamin. G6PD deficiency was not given marks as it is much less likely in the given scenario than the stated four causes.

Most answers were incomplete with candidates not able to describe the mechanisms or differentiation of their stated causes. This may be due to inadequate reading of the question or a knowledge deficit. The better candidate was able to address the clinical history in the stem and detail the mechanism and the differential process between the most likely diagnoses

Discussion

Ok, one does not put "explosion in a chemical warehouse" into an exam stem without expecting some kind of a reaction. The range of possibilities can be divided into "those that fit the stem" and "those that have minimal relationship with the stem but which are otherwise technically also possible". The latter would have scored minimal marks here, but are listed for the reader's amusement. Thus:

• Haematuria:
  • The patient may have had an injury to the kidneys or the renal tract (for example, as the result of being thrown by the blast)
  • This is revealed by finding red cells in the urine microscopy
• Haemoglobinuria:
  • The patient may have developed haemolysis as the result of severe thermal damage or blast injury
  • The presence of free haemoglobin in the blood, or a dipstick positive for blood in the absence of any red cells on urine microscopy, would be diagnostic
• Myoglobinuria:​​​​​​​
  • Blast injury can present with rhabdomyolysis due to widespread muscle damage
  • The resulting myoglobinuria can be detected by a urinary myoglobin level, and would also yield a a dipstick positive for blood in the absence of any red cells on urine microscopy (because the dipstick tests for haem, and myoglobin contains haem)
• Hydroxocobalamin:
  • "​​​​​​​Explosion in a chemical factory" is the college's way of making the trainees think about cyanide toxicity, which calls for a large dose of hydroxocobalamin. 
  • If for whatever reason this was not self-evident from the medication chart, one may be able to confirm the presence of a red non-haem dye in the urine by testing it for blood with a urine dipstick, which would of course be negative.

Other exciting possibilities where the explosion is irrelevant include:

• Not haematuria, but blood in urine:
  • Menstruation
  • Genital injury
• Other causes of intravascular haemolysis:
  • A mechanical valve
  • ECMO
  • G6PD deficiency
  • Sickle cell anaemia
  • Transfusion reaction
• Drugs:
  • Rifampicin, which, to be fair, is more orange
  • Isoniazid
  • Warfarin
  • Riboflavin
• Errors of metabolism
  • Porphyria
• Beetroot consumption, which causes harmless "beeturia"

Singh, Akhilesh Kumar, et al. "Differentials of abnormal urine color: a review." Ann Appl Biosci 1 (2014): R21-R25.

Wüthrich, R. P., and A. Serra. "The red urine." Therapeutische Umschau. Revue Therapeutique 63.9 (2006): 595-600.

Viswanathan, Stalin. "Urine bag as a modern day matula." International Scholarly Research Notices 2013 (2013).

Boutwell, Joseph H. "More causes of red urine." JAMA 238.14 (1977): 1501-1501.

Watts, A. R., et al. "Beeturia and the biological fate of beetroot pigments." Pharmacogenetics and Genomics 3.6 (1993): 302-311.

College answer

Aim. To explore the candidate knowledge of APACHE, SAPS, MOPS and use in ICU practice.

Key sources include: Paper 2009.1 Q11 - comparing APACHE with SOFA. 2005.2 SQ 4 - principles of Illness severity scoring systems in the critically ill pt. Systems in use daily in ICUs outcomes comparisons. CanMEDS scholar.

Discussion: Scoring systems used to predict mortality and other outcomes are universal in Australian and NZ ICUs. Trainees/SIMGs should be encouraged to have a working knowledge of those commonly used in the Binational ICU registry and assessment of risk severity in research. A demonstrated knowledge of any of the ICU prediction scoring systems gave an expert pass.

Given the wording of the question, other prediction scoring systems used in the ICU such as Child-Pugh scores would have answered the stated question. Examiners gave credit as the answers corresponded to the question asked.

The GCS is not in itself a predictive scoring system and was marked incorrect as it did not address the question. Other ways to improve the answer detail required included exploration of scoring systems derivation as part of the explanation of the components.

Discussion

Whereas the  Question 11 from the first paper of 2009 asked the candidates to compare APACHE with SOFA, this question asked for any predictive scoring system, which theoretically could mean that the candidates could have used a range of familiar systems. This is more similar to  Question 4 from the second paper of 2005 which wanted to know about the general principles of illness severity scoring systems (where, interestingly, the GCS was an acceptable response, and in fact formed a part of the college answer). The bottom line is, there are multiple options (there's several versions of the APACHE system, for example), and so it would be better to pick one system here, and dissect it using the provided headings. Arbitrarily, the ancient 1991 APACHE III-j* system is selected here, for no reason other than the fact that (at the time of writing, on a freezing July day in 2023) it is used by ANZICS CORE**, which makes it easier to answer the last part of the question - because what would a CICM trainee be able to say about the TISS, SAPS II, MPM II or POSSUM systems under that heading?

Thus:

Components of APACHE III-j

• Major medical and surgical disease categories
• Acute physiologic abnormalities in the first 24 hours (17 variables)
• Age
• Preexisting functional limitations
• Major comorbidities
• Treatment location immediately prior to ICU admission

Advantages of APACHE III-j

• Of APACHE-III-j specifically:
  • Easy to collect data (computerised information systems can calculate the score automatically without much human input)
  • Well-validated and internationally familiar
  • Makes comparison between health services easier
  • Is an improvement on the APACHE-II score, as some of the physiological variables have been re-weighed
• Of such scoring systems in general:
  • Can be used to standardise quality assurance studies and research
  • Can help perform comparisons between health services
  • Usually user-friendly and dependent on variables which are already being collected for patient care purposes

Disadvantages of APACHE III-j

• Of APACHE III-j specifically:
  • Does not incorporate frailty (but APACHE-IV does!)
  • Older, and drifting in calibration in terms of predictive value (last validated in 2003!)
  • Not a sequential score (thus, cannot be used to track response to therapy)
• Of these systems in general:
  • Generally these are poor predictors of individual patient outcome
  • They are susceptible to coding errors, particularly where one variable is subjective (eg. the "diagnosis" category in the APACHE score)
  • There is a variation in recording of data - not everyone is equally accurate at filling out the forms, and computerised systems can record spurious readings uncritically
  • There are differences in patient groups which influence "illness severity" which are not measured by the scoring system
  • Some data goes missing
  • Delay to ICU admission affects the initial score 
  • Outcomes may not be related to ICU alone - the whole hospital is involved

Use of APACHE III-j in the ICU

• Used in ICUs around the world to predict mortality for critically ill patients
• Permits benchmarking worldwide, including the analysis of endpoints in trials
• Allows the standardisation of illness severity scoring, permitting comparison between patient populations in trials
• Allows administrators to assess illness acuity in health services and to use this information to determine the allocation of resources and staff

A lot of these advantages and disadvantages were extracted from the excellent 2008 paper by Shann et al.

* The "j" in APACHE III-j is the 10th iteration of the APACHE-III model. 

** The ANZICS CORE now uses ANZROD (since 2014) as the predictive model for mortality, but they still collect APACHE-III data.

Knaus, William A., et al. "The APACHE III prognostic system: risk prediction of hospital mortality for critically III hospitalized adults." Chest 100.6 (1991): 1619-1636.

Shann, F., et al. "Critical care outcome prediction equation (COPE) for adult intensive care." Critical Care and Resuscitation 10.1 (2008): 35-41.

College Answer

Syllabus topic/section:

2.1.16    Populations requiring special considerations in Intensive Care.

Aim:

To explore the common sequalae of the long term critically ill patient.

Discussion:
The question was well answered, and most candidates had a good understanding of Post Intensive Care Syndrome. Some candidates could improve the structure of their answer. It’s important to remember to be specific when describing risk factors. E.g. "length of ICU stay" does not describe the risk, it needs to be quantified for example "increased length of ICU stay".
 

Discussion

a) Definition by the SCCM (Needham et al, 2012):

"new or worsening impairments in physical, cognitive, or mental health status arising after
critical illness and persisting beyond acute care hospitalization"

For this one-mark answer, this would have surely been enough.

b) Clinical manifestations:

• Physical:
  • Weakness, disuse atrophy
  • Fatigue, reduced exercise tolerance
  • Impairment in activities of daily living
  • Impaired respiratory function, reduced lung volumes and diffusion capacity
• Cognitive:
  • Impairments in memory, attention, executive function, mental processing speed, visuo-spatial ability
• Mental:
  • Depression, PTSD, anxiety, sleep disturbance, sexual dysfunction

c) Risk factors:

• Risk factors for post-ICU physical impairment:
  • Prolonged mechanical ventilation (> 7 days)
  • Sepsis
  • Multisystem organ failure
  • Hyperglycaemia
  • Systemic corticosteroids
  • Age
  • Pre-existing functional impairment
• Risk factors for post-ICU cognitive impairment:
  • Sedation
  • Hypoxia
  • ICU delirium
  • Pre-exisitng cognitive impairment
• Risk factors for post-ICU mental illness:
  • Sedation
  • Delirium and agitation
  • Use of physical restraints

d) Prevention:

• Elimination or correction of causative factors
  • Early extubation
  • Early mobilisation
• Monitoring for delirium and use of nonpharmacological strategies for its management
  • Using light or minimal sedation
  • Avoidance of physical restraints
• Reduction or elimination of sources of environmental stress, including
  • Minimising alarms
  • Minimising interruptions to sleep
• Frequent patient and family communication.

College answer

Syllabus topic/section:

2.1.2    Decision Making: Severity scoring and outcome prediction: L1

Discussion: 

This question focused on the core topic of ICU scoring systems. We recommend that candidates have good knowledge of the principles and details of common ICU severity scoring systems. The question was generally answered well if candidates took a broad approach.
Part a) required a list of the key components (acute physiological scores, age and chronic health conditions) rather than an extensive list of the individual acute physiological components. Many candidates missed simple marks by not mentioning the 24-hour timeframe for the data acquisition of the physiological scores or the impact of relevant chronic conditions.

Part b) was answered well if candidates provided a detailed outline of several applications rather than a superficial list. Candidates also did well if they used broad headings (safety and quality, clinical or research for example) and then added detail with subheadings to ensure they included a breadth of applications. Please note the glossary terms outline vs list.

Part c) required a simple list, and candidates scored well if they could provide several limitations. The marking rubric is included to aid the candidate’s future study.

Discussion

We have been here before. Question 11 from the first paper of 2009 asked the candidates to compare APACHE with SOFA, and Question 4 from the second paper of 2005 asked about the principles of scoring systems in general. And then, when examiners asked about scoring systems in Question 18 from the cursed first paper of 2023, whereas the intention was clearly to get answers about APACHE and SOFA, some candidates wrote about the GCS and the Child-Pugh score, which actually answered the question, and had to be marked. The specific choices offered to the candidates in this sitting appear to be a deliberate effort to guide them towards a specific answer.

What follows is a repurposed answer from Question 18. APACHE-IIIj is used here mostly because it is very similar to APACHE IV (only a couple of extra things were added, and the disease-specific coefficients have been updated to change the calculated risk). ANZROD is also basically APACHE IIIj with some adjustments to the weights, to reflect that the American critical care mortality is higher than Australian.

Components of APACHE III-j

• Major medical and surgical disease categories
• Acute physiologic abnormalities in the first 24 hours (17 variables)
• Age
• Preexisting functional limitations
• Major comorbidities
• Treatment location immediately prior to ICU admission

Advantages of APACHE III-j

• Of APACHE-III-j specifically:
  • Easy to collect data (computerised information systems can calculate the score automatically without much human input)
  • Well-validated and internationally familiar
  • Makes comparison between health services easier
  • Is an improvement on the APACHE-II score, as some of the physiological variables have been re-weighed
• Of such scoring systems in general:
  • Can be used to standardise quality assurance studies and research
  • Can help perform comparisons between health services
  • Usually user-friendly and dependent on variables which are already being collected for patient care purposes

Applications of APACHE III-j in the ICU

• Used in ICUs around the world to predict mortality for critically ill patients
• Permits comparison and benchmarking of critical care services worldwide, including the analysis of endpoints in trials
• Allows the standardisation of illness severity scoring, permitting comparison between patient populations in research
•  
  • Easy to collect data (computerised information systems can calculate the score automatically without much human input)
  • Well-validated and internationally familiar
  • Makes comparison between health services easier
  • Is an improvement on the APACHE-II score, as some of the physiological variables have been re-weighed
• Allows administrators to assess illness acuity in health services and to use this information to determine the allocation of resources and staff
• Predicts mortality, which may have implications for decisionmaking
•  

Limitations of APACHE III-j

• Of APACHE III-j specifically:
  • Does not incorporate frailty (but APACHE-IV does!)
  • Older, and drifting in calibration in terms of predictive value (last validated in 2003!)
  • Not a sequential score (thus, cannot be used to track response to therapy)
• Of these systems in general:
  • Generally these are poor predictors of individual patient outcome
  • They are susceptible to coding errors, particularly where one variable is subjective (eg. the "diagnosis" category in the APACHE score)
  • There is a variation in recording of data - not everyone is equally accurate at filling out the forms, and computerised systems can record spurious readings uncritically
  • There are differences in patient groups which influence "illness severity" which are not measured by the scoring system
  • Some data goes missing
  • Delay to ICU admission affects the initial score 
  • Outcomes may not be related to ICU alone - the whole hospital is involved

A lot of these advantages and disadvantages were extracted from the excellent 2008 paper by Shann et al.