The effect of casemix adjustment on mortality as predicted by APACHE II

D R Goldhill1, P S Withington

  • 1Anaesthetics Unit, Royal London Hospital, Whitechapel, UK.

Abstract

Insights

The APACHE II scoring system did not accurately predict mortality across diverse patient groups in intensive care units. This suggests that using mortality ratios to compare ICUs can be misleading without accounting for patient case mix.

Area of Science:

  • Critical Care Medicine
  • Health Services Research

Background:

  • The APACHE II scoring system is widely used to predict mortality in intensive care units (ICUs).
  • Accurate adjustment for patient case mix is crucial for comparing ICU performance.

Purpose of the Study:

  • To evaluate the effectiveness of APACHE II scoring in adjusting for patient case mix and predicting mortality.
  • To determine if mortality ratios derived from APACHE II scores are reliable for ICU comparisons.

Main Methods:

  • Retrospective analysis of 6258 ICU patients admitted between 1992 and 1994.
  • APACHE II scores were calculated within 24 hours of admission.
  • Observed hospital deaths were compared to predicted deaths using the APACHE II equation.

Main Results:

  • APACHE II predictions showed significant discrepancies with observed mortality across various patient subgroups.
  • Observed deaths exceeded predicted deaths for patients with lower predicted mortality, specific APACHE II score ranges, older age, and certain clinical categories.
  • Mortality ratios were markedly less than 1.0 only for non-operative cardiovascular patients.

Conclusions:

  • APACHE II scoring did not adequately account for case mix variations in this ICU database.
  • Relying on mortality ratios for ICU comparisons without considering case mix differences can lead to inaccurate and misleading conclusions.

Related Concept Videos

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast, controlled...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
Actuarial Approach01:20

Actuarial Approach

The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and Cox...