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Affiliation
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1 Bureau d'information et d'études en santé des populations, Institut national de santé publique du Québec 2 Département de médecine sociale et préventive, Faculté de médecine, Université Laval 3 Axe Santé des Populations et pratiques Optimales en Santé (Population Health and Opitmal Health Practices Research Unit), Traumatologie - Urgence - Soins intensifs (Trauma - Emergency - Critical Care Medicine), Centre de Recherche du Centre Hospitalier Universitaire (CHU) de Québec (Hôpital de l'Enfant-Jésus), Université Laval, Québec (Qc), Canada 4 Département de réadaptation, Faculté de médecine, Université Laval. |
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Abstract
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BACKGROUND: The International Classification of Diseases (ICD) is the main classification system used for population-based injury surveillance activities but does not contain information on injury severity. ICD-based injury severity measures can be empirically-derived or mapped, but no single approach has been formally recommended.
OBJECTIVE: To compare performance of ICD-based injury severity measures to predict in-hospital mortality among injury-related admissions.
METHODS: A systematic review and a meta-analysis were conducted. MEDLINE, EMBASE and Global Health databases were searched from their inception through September 2014. Observational studies that assessed the performance of ICD-based injury severity measures to predict in-hospital mortality and reported discriminative ability using the area under a receiver operating characteristic curve (AUC) were included. Metrics of model performance were extracted. Pooled AUC were estimated under random-effects models.
RESULTS: 22 eligible studies reported 72 assessments of discrimination on ICD-based injury severity measures. Reported AUC ranged from 0.681 to 0.958. Forty-six of the 72 assessments showed excellent (0.80≤AUC<0.90) and six outstanding (AUC≥0.90) discriminative ability. Pooled AUC for ICD-Injury Severity Score (ICISS) based on the product of traditional survival proportions was significantly higher than measures based on ICD mapped to AIS scores (0.863 versus 0.825 for ICDMAP-ISS (p=0.005) and ICDMAP-NISS (p=0.016). Similar results were observed when studies were stratified by the type of data used (trauma registry or hospital discharge) or the provenance of survival proportions (internally or externally-derived). However, among studies published after 2003 the Trauma Mortality Prediction Model based on ICD-9 codes (TMPM-9) demonstrated superior discriminative ability than ICISS using the product of traditional survival proportions (0.850 versus 0.802, p=0.002). Models generally showed poor calibration.
CONCLUSIONS: ICISS using the product of traditional survival proportions and TMPM-9 predict mortality more accurately than those mapped to AIS codes and should be preferred for describing injury severity when ICD is used to record injury diagnoses. LEVEL OF EVIDENCE: level VI.
Language: en |