Chest
Postgraduate Education CornerContemporary Reviews In Critical Care MedicineSeverity Scoring in the Critically Ill: Part 1—Interpretation and Accuracy of Outcome Prediction Scoring Systems
Section snippets
Comparison of ICU Scoring Systems
The three most commonly used ICU scoring systems are APACHE, MPM0 (where the 0 indicates from time of admission), and SAPS.3, 4, 5, 6, 7 Although the initial versions were introduced many years ago, each has undergone multiple revisions over the ensuing years.8, 9, 10 In addition to the regularly updated models introduced by the developers, other investigators have created alternate prognostic models using APACHE, SAPS, and MPM data elements that are customized to fit a specific patient
Evaluating Scoring Systems
Prior to discussing the performance of the different systems, several concepts bear brief review. First, it is important to understand how risk scoring systems are developed and evaluated. To generate a predictive model, most researchers divide their data set into two pools. The first is used to develop the model, generally through multivariable regression methods, whereas the second is reserved for validation after the final model is developed. Ideally, the performance in the development and
Data Rules and Data Quality
Data completeness rules and data quality can have major effects on outcome predictions and deserve major focus by all users of ICU scoring systems. Most ICU scoring systems generate predictions based on the data at hand, although APACHE will not generate predictions in the absence of certain data elements. Each system treats missing data as normal data. Afessa et al25 evaluated the impact of missing data on APACHE predictions and found worse outcomes in patients with missing data, suggesting
The Impact of ICU Structure and Care Processes on Score Interpretation
Variations in duration of care provided prior to admission to the ICU can introduce lead-time bias, which has been shown to affect severity-adjusted mortality rates.2, 27, 28, 29 Some EDs may define their primary mission as accurate triage and prompt transfer to the appropriate care locale, whereas others focus on ensuring optimal early treatment. The former approach results in patients possibly coming to the ICU with less treatment and, thus, more abnormal vital signs (eg, higher heart rates)
Barriers to Implementation
Currently, only 10% to 15% of US ICUs (by patient volume) use ICU scoring systems, which is clearly at odds with standard recommendations by quality experts in other fields for frequent measurement and close scrutiny of quality data.33 Commonly articulated obstacles to use include cost and concerns about accuracy and applicability to the patient population. Although annual licensing fees for APACHE dissuaded adoption by some hospitals in the past, open access to hospital mortality and ICU LOS
Conclusions
APACHE, MPM, and SAPS have evolved over the past 25 years in an effort to improve predictive accuracy and keep pace with evolving critical care practices. Each provides accurate hospital mortality predictions; APACHE (and potentially MPM) also generate LOS performance data. Given the dominant role of LOS in explaining ICU cost,35 hospital executives and ICU leaders will require data on this key financial and operational metric. Manual data collection burden is lower with MPM and SAPS. However,
Acknowledgments
Financial/nonfinancial disclosures: The authors have reported to CHEST the following conflicts of interest: Drs Breslow and Badawi are employees of Philips Healthcare Inc, where this work was performed.
Other contributions: We thank David Stone, MD, for his assistance in preparing this manuscript.
References (37)
- et al.
Mortality probability model III and simplified acute physiology score II: assessing their value in predicting length of stay and comparison to APACHE IV
Chest
(2009) - et al.
Variation in ICU risk-adjusted mortality: impact of methods of assessment and potential confounders
Chest
(2008) - et al.
Benchmark data from more than 240,000 adults that reflect the current practice of critical care in the United States
Chest
(2011) - et al.
Transferring critically ill patients out of hospital improves the standardized mortality ratio: a simulation study
Chest
(2007) - et al.
Transparency in health care: an issue throughout US history
Chest
(2008) - et al.
Clinical review: scoring systems in the critically ill
Crit Care
(2010) - et al.
Severity of illness scoring systems in the intensive care unit
Crit Care Med
(2011) - et al.
Acute Physiology and Chronic Health Evaluation (APACHE) IV: hospital mortality assessment for today's critically ill patients
Crit Care Med
(2006) - et al.
Intensive care unit length of stay: benchmarking based on Acute Physiology and Chronic Health Evaluation (APACHE) IV
Crit Care Med
(2006) - et al.
SAPS 3—from evaluation of the patient to evaluation of the intensive care unit. Part 1: objectives, methods and cohort description
Intensive Care Med
(2005)
SAPS 3—from evaluation of the patient to evaluation of the intensive care unit. Part 2: development of a prognostic model for hospital mortality at ICU admission
Intensive Care Med
Assessing contemporary intensive care unit outcome: an updated Mortality Probability Admission Model (MPM0-III)
Crit Care Med
APACHE-acute physiology and chronic health evaluation: a physiologically based classification system
Crit Care Med
A simplified acute physiology score for ICU patients
Crit Care Med
A method for predicting survival and mortality of ICU patients using objectively derived weights
Crit Care Med
A new risk prediction model for critical care: the Intensive Care National Audit & Research Centre (ICNARC) model
Crit Care Med
Severity of illness and outcome in ICU patients in the Netherlands: results from the NICE registry 2006–2007
Neth J Crit Care
Intensive Care Society's APACHE II study in Britain and Ireland—II: outcome comparisons of intensive care units after adjustment for case mix by the American APACHE II method
BMJ
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