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Using severity measures to predict the likelihood of death for pneumonia inpatients
L I Iezzoni1, M Shwartz, A S Ash
1Department of Medicine, Harvard Medical School, Beth Israel Hospital, Boston, MA 02215, USA.
Objective:
To see whether predictions of patients, likelihood of dying in-hospital differed among severity methods.
Design:
Retrospective cohort.
Patients:
18,016 persons 18 years of age and older managed medically for pneumonia; 1,732 (9.6%) in-hospital deaths.
Methods:
Probability of death was calculated for each patient using logistic regression with age, age squared, sex, and each of five severity measures as the independent variables: 1) admission MedisGroups probability of death scores; 2) scores based on 17 admission physiologic variables; 3) Disease Staging's probability of mortality model; the Severity Score of Patient Management Categories (PMCs); 4) and the All Patient Refined Diagnosis-Related Groups (APR-DRGs). Patients were ranked by calculated probability of death; 5) rankings were compared across severity methods. Frequencies of 14 clinical findings considered poor prognostic indicators in pneumonia were examined for patients ranked differently by different methods.
Results:
MedisGroups and the physiology score predicted a similar likelihood of death for 89.2% of patients. In contrast, the three code-based severity methods rated over 25% of patients differently by predicted likelihood of death when compared with the rankings of the two clinical data-based methods [MedisGroups and the physiology score]. MedisGroups and the physiology score demonstrated better clinical credibility than the three severity methods based on discharge abstract data.
Conclusions:
Some pairs of severity measures ranked over 25% of patients very differently by predicted probability of death. Results of outcomes studies may vary depending on which severity method is used for risk adjustment.