Mathieu Gagné1, Lynne Moore, Marie-Josée Sirois, Marc Simard, Claudia Beaudoin, Brice Lionel Batomen Kuimi. 1. From the Bureau d'information et d'études en santé des populations, Institut national de santé publique du Québec, Québec City, Québec, Canada (M.G., M.S., C.B.); Département de médecine sociale et préventive, Faculté de médecine, Université Laval, Québec City, Québec, Canada (M.G., L.M., C.B., B.L.B.K.); Axe Santé des Populations et pratiques Optimales en Santé (Population Health and Optimal Health Practices Research Unit, and Traumatologie-Urgence-Soins intensifs (Trauma-Emergency-Critical Care Medicine), Centre de Recherche du Centre Hospitalier Universitaire (CHU) de Québec (Hôpital de l'Enfant-Jésus), Québec City, Québec, Canada (L.M., B.L.B.K.); Centre d'Excellence sur le Vieillissement de Québec; and Centre de Recherche du Centre Hospitalier Universitaire (CHU) de Québec (Hépital de l'Enfant-Jésus); and the Département de réadaptation, Faculté de médecine, Université Laval, Québec City, Québec, Canada (M.-J.S.).
Abstract
BACKGROUND: The International Classification of Diseases (ICD) is the main classification system used for population-based traumatic brain injury (TBI) surveillance activities but does not contain direct information on injury severity. International Classification of Diseases-based injury severity measures can be empirically derived or mapped to the Abbreviated Injury Scale, but no single approach has been formally recommended for TBI. OBJECTIVE: The aim of this study was to compare the accuracy of different ICD-based injury severity measures for predicting in-hospital mortality and intensive care unit (ICU) admission in TBI patients. METHODS: We conducted a population-based retrospective cohort study. We identified all patients 16 years or older with a TBI diagnosis who received acute care between April 1, 2006, and March 31, 2013, from the Quebec Hospital Discharge Database. The accuracy of five ICD-based injury severity measures for predicting mortality and ICU admission was compared using measures of discrimination (area under the receiver operating characteristic curve [AUC]) and calibration (calibration plot and the Hosmer-Lemeshow goodness-of-fit statistic). RESULTS: Of 31,087 traumatic brain-injured patients in the study population, 9.0% died in hospital, and 34.4% were admitted to the ICU. Among ICD-based severity measures that were assessed, the multiplied derivative of ICD-based Injury Severity Score (ICISS-Multiplicative) demonstrated the best discriminative ability for predicting in-hospital mortality (AUC, 0.858; 95% confidence interval, 0.852-0.864) and ICU admissions (AUC, 0.813; 95% confidence interval, 0.808-0.818). Calibration assessments showed good agreement between observed and predicted in-hospital mortality for ICISS measures. All severity measures presented high agreement between observed and expected probabilities of ICU admission for all deciles of risk. CONCLUSIONS: The ICD-based injury severity measures can be used to accurately predict in-hospital mortality and ICU admission in TBI patients. The ICISS-Multiplicative generally outperformed other ICD-based injury severity measures and should be preferred to control for differences in baseline characteristics between TBI patients in surveillance activities or injury research when only ICD codes are available. LEVEL OF EVIDENCE: Prognostic study, level III.
BACKGROUND: The International Classification of Diseases (ICD) is the main classification system used for population-based traumatic brain injury (TBI) surveillance activities but does not contain direct information on injury severity. International Classification of Diseases-based injury severity measures can be empirically derived or mapped to the Abbreviated Injury Scale, but no single approach has been formally recommended for TBI. OBJECTIVE: The aim of this study was to compare the accuracy of different ICD-based injury severity measures for predicting in-hospital mortality and intensive care unit (ICU) admission in TBIpatients. METHODS: We conducted a population-based retrospective cohort study. We identified all patients 16 years or older with a TBI diagnosis who received acute care between April 1, 2006, and March 31, 2013, from the Quebec Hospital Discharge Database. The accuracy of five ICD-based injury severity measures for predicting mortality and ICU admission was compared using measures of discrimination (area under the receiver operating characteristic curve [AUC]) and calibration (calibration plot and the Hosmer-Lemeshow goodness-of-fit statistic). RESULTS: Of 31,087 traumatic brain-injuredpatients in the study population, 9.0% died in hospital, and 34.4% were admitted to the ICU. Among ICD-based severity measures that were assessed, the multiplied derivative of ICD-based Injury Severity Score (ICISS-Multiplicative) demonstrated the best discriminative ability for predicting in-hospital mortality (AUC, 0.858; 95% confidence interval, 0.852-0.864) and ICU admissions (AUC, 0.813; 95% confidence interval, 0.808-0.818). Calibration assessments showed good agreement between observed and predicted in-hospital mortality for ICISS measures. All severity measures presented high agreement between observed and expected probabilities of ICU admission for all deciles of risk. CONCLUSIONS: The ICD-based injury severity measures can be used to accurately predict in-hospital mortality and ICU admission in TBIpatients. The ICISS-Multiplicative generally outperformed other ICD-based injury severity measures and should be preferred to control for differences in baseline characteristics between TBIpatients in surveillance activities or injury research when only ICD codes are available. LEVEL OF EVIDENCE: Prognostic study, level III.
Authors: Benjamin J Kuo; Silvia D Vaca; Joao Ricardo Nickenig Vissoci; Catherine A Staton; Linda Xu; Michael Muhumuza; Hussein Ssenyonjo; John Mukasa; Joel Kiryabwire; Lydia Nanjula; Christine Muhumuza; Henry E Rice; Gerald A Grant; Michael M Haglund Journal: PLoS One Date: 2017-10-31 Impact factor: 3.240
Authors: Silvia D Vaca; Benjamin J Kuo; Joao Ricardo Nickenig Vissoci; Catherine A Staton; Linda W Xu; Michael Muhumuza; Hussein Ssenyonjo; John Mukasa; Joel Kiryabwire; Henry E Rice; Gerald A Grant; Michael M Haglund Journal: Neurosurgery Date: 2019-01-01 Impact factor: 4.654
Authors: S Ariane Christie; Amanda S Conroy; Rachael A Callcut; Alan E Hubbard; Mitchell J Cohen Journal: PLoS One Date: 2019-04-10 Impact factor: 3.240
Authors: Faith C Robertson; Richard Briones; Rania A Mekary; Ronnie E Baticulon; Miguel A Jimenez; Andrew J M Leather; Marike L D Broekman; Kee B Park; William B Gormley; Lynne L Lucena Journal: World Neurosurg X Date: 2019-09-09