Reviewed and revised 20 July 2016
Scoring systems commonly used in ICU
- GCS = Glasgow Coma Score in Coma
- TISS = Therapeutic Interventions Scoring System
- APACHE = Acute Physiology, Age and Chronic Health Evaluation Systems
- SAPS = Simplified Acute Physiology Score
- MPM = Mortality Prediction Models
- POSSUM = Physiological and Operative Severity Score for the enumeration of Mortality and Morbidity
- SOFA = Sequential Organ Failure Assessment
- also disease/ patient group specific scoring systems such as EuroSCORE (CABG), MELD score (liver failure), etc
- compare observed and predicted outcomes for patients
- stratification of patients for clinical trials
- assess ICU performance, relative to other ICUs and changes over time
- predict mortality, prognosis and length of stay for individuals and groups
- relating resource allocation to severity at presentation
- most measure physiological variables
- some measure interventions
- derived from logistic regression from large demographic data sets
IDEAL SCORING SYSTEM
The ideal scoring system should have the following characteristics :
- Scores calculated on the basis of easily / routinely recordable variables
- Well calibrated and validated
- A high level of discrimination
- Applicable to all patient populations in ICU
- Can be used in different countries, health systems or patient cohorts
- The ability to predict mortality, functional status or quality of life after ICU discharge
- considers co-morbidities
- considers organisational aspects
- provides a common language for discussion
- method to evaluate critical care practice and process
- allows ability to compare groups in clinical trials
APPLICATION OF SCORING SYSTEMS
- is the comparison of predicted with observed mortality rates
- need to be considered in the context of case mix and calibration
Description of Case Mix
- stratification of patients for clinical trials
- comparison of predicted and observed outcomes
- allows prediction of resources to manage severity of illness at presentation
- predict length of stay
Problems of Using Severity of Illness Scores to compare outcomes between ICUs
- often done
- problems revolve around: quality of data, appropriateness of model and disease categories
- specific problems:
- Variation in recording of data (timing, criteria) -> ideally should be audited and reviewed regularly by trained personnel.
- Differences in patient populations not accounting for by the diagnostic groups.
- Multiple organ involvement leads to difficult categorisation of disease.
- Corrupted data, missed data – lack of trained data collectors.
- Bias or fraud due competition for funding.
- Historical variations, comparing data across time.
- Outcomes may be dependent on not only ICU management, but the whole hospital package, surgeon, radiology…
EXAMPLES OF SCORING SYSTEMS
Glasgow Coma Scale (GCS)
- EVM scores
- provides systematic way of assessing the head injured
- directs investigation and therapy
- affected by alcohol and sedation
- used in APACHE II
- useful in prognostication
Therapeutic Intervention Scoring System (TISS)
- developed to: estimate severity of illness and quantify burden of work for ICU staff
- daily collection of 76 items (interventions and treatments)
- good indicator of nursing and medical work
- poor measure of severity of illness
- less widely used
- can be used as an allocation of resources (accountancy) tool
Acute Physiology, Age, and Chronic Health Evaluation Systems (APACHE I-IV)
- I – 1987
- II – 1985 (most widely used in the world, 12 variables, score of 0-71, worse values in first 24 hours in ICU, limited by derivation from an historical data set)
- III – 1991 (score of 0-299, 16 variables, improved prognostication, improved discrimination and calibration)
- IV – 2006 (large data set, more variables included, more accurate, only used in US)
Simplified Acute Physiology Score (SAPS 1-3)
- SAPS 1 (French ICU’s, solely looked at physiology)
- SAPS 2 (1993, European and North American, added chronic health conditions, greater calibration and discrimination)
- SAPS 3 (2005, around the world, 20 variables – prior to admission, at admission, acute physiological derangement)
Mortality Prediction Model I (MPM I)
- variables at admission and during first 24 hours
- computes a hospital risk of death from the absence or presence of factors in a logistic regression equation
Mortality Prediction Model II (MPM II)
- based on the same data set as SAPS II
- outcome prediction at 24, 48 and 72 hrs
- 12 acute physiological parameters (surgery and severity of surgery)
- useful tool for surgeons who needed a risk adjustment tool
- meant to predict death but was found to over predict.
- P-POSSUM – Portsmouth: predicts hospital mortality more accurately
- V-POSSUM – vascular surgery
- Cr-POSSUM – colon cancer resection
Sequential Organ Failure Assessment (SOFA)
- 6 organs and grades organ function
- simple and take into account supportive treatments
- good way of tracking patient morbidity
- often used to analyse secondary endpoints in research trials
References and Links
- Breslow MJ, Badawi O. Severity scoring in the critically ill: part 1–interpretation and accuracy of outcome prediction scoring systems. Chest. 2012 Jan;141(1):245-52. PMID: 22215834.
- Breslow MJ, Badawi O. Severity scoring in the critically ill: part 2: maximizing value from outcome prediction scoring systems. Chest. 2012 Feb;141(2):518-27. PMID: 22315120.
- Vincent JL, Moreno R. Clinical review: scoring systems in the critically ill. Critical care (London, England). 14(2):207. 2010. PMC2887099
Chris is an Intensivist and ECMO specialist at the Alfred ICU in Melbourne. He is also a Clinical Adjunct Associate Professor at Monash University. He is a co-founder of the Australia and New Zealand Clinician Educator Network (ANZCEN) and is the Lead for the ANZCEN Clinician Educator Incubator programme. He is on the Board of Directors for the Intensive Care Foundation and is a First Part Examiner for the College of Intensive Care Medicine. He is an internationally recognised Clinician Educator with a passion for helping clinicians learn and for improving the clinical performance of individuals and collectives.
After finishing his medical degree at the University of Auckland, he continued post-graduate training in New Zealand as well as Australia’s Northern Territory, Perth and Melbourne. He has completed fellowship training in both intensive care medicine and emergency medicine, as well as post-graduate training in biochemistry, clinical toxicology, clinical epidemiology, and health professional education.
He is actively involved in in using translational simulation to improve patient care and the design of processes and systems at Alfred Health. He coordinates the Alfred ICU’s education and simulation programmes and runs the unit’s education website, INTENSIVE. He created the ‘Critically Ill Airway’ course and teaches on numerous courses around the world. He is one of the founders of the FOAM movement (Free Open-Access Medical education) and is co-creator of litfl.com, the RAGE podcast, the Resuscitology course, and the SMACC conference.
His one great achievement is being the father of three amazing children.
On Twitter, he is @precordialthump.