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An R-Based Landscape Validation of a Competing Risk Model

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Caution when using prognostic models: a prospective comparison of 3 recent prognostic models.

Antonio Paulo Nassar¹, Amilcar Oshiro Mocelin, André Luiz Baptiston Nunes

¹Adult Intensive Care Units—Hospital e Maternidade São Camilo—São Paulo, Brazil. paulo_nassar@yahoo.com.br

Journal of Critical Care

|October 29, 2011

Summary

This study evaluated the performance of three general prognostic models in Brazilian intensive care units. While discrimination was good, all models showed poor calibration, highlighting the need for caution when using them for benchmarking.

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Area of Science:

Critical Care Medicine
Health Services Research
Biostatistics

Background:

Prognostic models are crucial for estimating mortality and comparing outcomes across intensive care units (ICUs).
Validation of these models in diverse populations is essential before widespread clinical application.
General prognostic models like APACHE IV, SAPS 3, and MPM(0)-III require local validation.

Purpose of the Study:

To assess the performance of three recently developed general prognostic models: APACHE IV, SAPS 3, and MPM(0)-III.
To validate these models in a population admitted to Brazilian medical-surgical intensive care units.
To evaluate the calibration and discrimination capabilities of the selected models in a specific healthcare setting.

Main Methods:

A cohort of 5780 patients admitted to three Brazilian ICUs between July 2008 and December 2009 was analyzed.
Model performance was assessed using standardized mortality ratios, the Hosmer-Lemeshow goodness-of-fit test for calibration, and the area under the receiver operator curve (AUC) for discrimination.
Statistical comparisons were made to determine significant differences in model performance.

Main Results:

In-hospital mortality was 9.1% in the study population.
All models demonstrated very good discrimination, with AUC values ranging from 0.840 (MPM(0)-III) to 0.883 (APACHE IV).
APACHE IV exhibited superior discrimination compared to SAPS 3 and MPM(0)-III (P < .001). However, all models showed poor calibration, significantly overestimating hospital mortality (P < .001 for all).

Conclusions:

Despite very good discrimination, all evaluated prognostic models exhibited poor calibration in the Brazilian ICU population.
These findings are consistent with previous validation studies, suggesting a common limitation of general prognostic models.
Caution is advised when utilizing these prognostic models for benchmarking purposes due to their calibration deficiencies.