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.
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.
Authors: M J Fine; B H Hanusa; J R Lave; D E Singer; R A Stone; L A Weissfeld; C M Coley; T J Marrie; W N Kapoor Journal: J Gen Intern Med Date: 1995-07 Impact factor: 5.128
Authors: M F Shapiro; M L Berk; S H Berry; C A Emmons; L A Athey; D C Hsia; A A Leibowitz; C A Maida; M Marcus; J F Perlman; C L Schur; M A Schuster; J W Senterfitt; S A Bozzette Journal: Health Serv Res Date: 1999-12 Impact factor: 3.402
Authors: Andrew D Auerbach; Judith Maselli; Jonathan Carter; Penelope S Pekow; Peter K Lindenauer Journal: J Am Coll Surg Date: 2010-09-15 Impact factor: 6.113