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Table 4 Comparison of risk prediction scoring systems

From: Clinical review: Can we predict which patients are at risk of complications following surgery?

Risk prediction system

Description

Advantages

Disadvantages

American Society of Anesthetists

Numerical scale (1 to 5) based on severity of co-morbidities

Simple, easily applied, well known

Subjective, not individual or procedure specific, poor sensitivity and specificity

Charlson Comorbidity Score

Additive score based on weighting of preoperative diseases

Simple, better predictor than American Society of Anesthetists, good at estimating population risk

Subjective, does not look at procedure, mainly used as a research tool

Revised Cardiac Risk Index

Scoring system based on presence of one of six major co-morbidities and the severity of operation

Simple, well validated and good for predicting cardiac risk

Single-organ risk, broad categories, assessment of severity of operation is subjective

Acute Physiology and Chronic Health Evaluation

12 to 17 variables, measured over 24 hours

Individualised predictor of risk of mortality and morbidity, better predictor of outcome than American Society of Anesthetists, well known

Multiple variables over 24 hours of critical care, can be difficult to score before emergent surgery, not designed for use perioperatively

Simplified Acute Physiology Score

17 variables measured over 24 hours

Well validated for predictive mortality

Multiple variables over 24 hours of critical care, can be difficult to score before emergent surgery, not designed for use perioperatively

Physiological and Operative Severity Score for the Enumeration of Mortality and Morbidity

Scoring of 12 physiological and six operative variables, which are then entered into two mathematical equations to predict mortality and morbidity

Best validated and known/used scores for perioperative prediction various surgery-specific variations for specific areas

May overestimate or underestimate mortality and morbidity in specific populations due to use of logarithmic regression