Adam D Laytin1, Rochelle A Dicker1, Martin Gerdin2, Nobhojit Roy3, Bhakti Sarang4, Vineet Kumar5, Catherine Juillard6. 1. Department of Surgery, Center for Global Surgical Studies, University of California, San Francisco, California. 2. Department of Public Health Sciences, Health Systems and Policy, Karolinska Institutet, Stockholm, Sweden. 3. Department of Surgery, Center for Global Surgical Studies, University of California, San Francisco, California; Department of Public Health Sciences, Health Systems and Policy, Karolinska Institutet, Stockholm, Sweden; Department of Surgery, BARC Hospital (Govt of India), Mumbai, India. 4. Department of Surgery, BARC Hospital (Govt of India), Mumbai, India. 5. Department of Surgery, Lokmanya Tilak Municipal Medical College, Mumbai, India. 6. Department of Surgery, Center for Global Surgical Studies, University of California, San Francisco, California. Electronic address: catherine.juillard@ucsf.edu.
Abstract
BACKGROUND: In most low- and middle-income countries (LMICs), the resources to accurately quantify injury severity using traditional injury scoring systems are limited. Novel injury scoring systems appear to have adequate discrimination for mortality in LMIC contexts, but they have not been rigorously compared where traditional injury scores can be accurately calculated. To determine whether novel injury scoring systems perform as well as traditional ones in a HIC with complete and comprehensive data collection. METHODS: Data from an American level-I trauma registry collected 2008-2013 were used to compare three traditional injury scoring systems: Injury Severity Score (ISS); Revised Trauma Score (RTS); and Trauma Injury Severity Score (TRISS); and three novel injury scoring systems: Kampala Trauma Score (KTS); Mechanism, GCS, Age and Pressure (MGAP) score; and GCS, Age and Pressure (GAP) score. Logistic regression was used to assess the association between each scoring system and mortality. Standardized regression coefficients (β2), Akaike information criteria, area under the receiver operating characteristics curve, and the calibration line intercept and slope were used to evaluate the discrimination and calibration of each model. RESULTS: Among 18,746 patients, all six scores were associated with hospital mortality. GAP had the highest effect size, and KTS had the lowest median Akaike information criteria. Although TRISS discriminated best, the discrimination of KTS approached that of TRISS and outperformed GAP, MGAP, RTS, and ISS. MGAP was best calibrated, and KTS was better calibrated than RTS, GAP, ISS, or TRISS. CONCLUSIONS: The novel injury scoring systems (KTS, MGAP, and GAP), which are more feasible to calculate in low-resource settings, discriminated hospital mortality as well as traditional injury scoring systems (ISS and RTS) and approached the discrimination of a sophisticated, data-intensive injury scoring system (TRISS) in a high-resource setting. Two novel injury scoring systems (KTS and MGAP) surpassed the calibration of TRISS. These novel injury scoring systems should be considered when clinicians and researchers wish to accurately account for injury severity. Implementation of these resource-appropriate tools in LMICs can improve injury surveillance, guiding quality improvement efforts, and supporting advocacy for resource allocation commensurate with the volume and severity of trauma.
BACKGROUND: In most low- and middle-income countries (LMICs), the resources to accurately quantify injury severity using traditional injury scoring systems are limited. Novel injury scoring systems appear to have adequate discrimination for mortality in LMIC contexts, but they have not been rigorously compared where traditional injury scores can be accurately calculated. To determine whether novel injury scoring systems perform as well as traditional ones in a HIC with complete and comprehensive data collection. METHODS: Data from an American level-I trauma registry collected 2008-2013 were used to compare three traditional injury scoring systems: Injury Severity Score (ISS); Revised Trauma Score (RTS); and Trauma Injury Severity Score (TRISS); and three novel injury scoring systems: Kampala Trauma Score (KTS); Mechanism, GCS, Age and Pressure (MGAP) score; and GCS, Age and Pressure (GAP) score. Logistic regression was used to assess the association between each scoring system and mortality. Standardized regression coefficients (β2), Akaike information criteria, area under the receiver operating characteristics curve, and the calibration line intercept and slope were used to evaluate the discrimination and calibration of each model. RESULTS: Among 18,746 patients, all six scores were associated with hospital mortality. GAP had the highest effect size, and KTS had the lowest median Akaike information criteria. Although TRISS discriminated best, the discrimination of KTS approached that of TRISS and outperformed GAP, MGAP, RTS, and ISS. MGAP was best calibrated, and KTS was better calibrated than RTS, GAP, ISS, or TRISS. CONCLUSIONS: The novel injury scoring systems (KTS, MGAP, and GAP), which are more feasible to calculate in low-resource settings, discriminated hospital mortality as well as traditional injury scoring systems (ISS and RTS) and approached the discrimination of a sophisticated, data-intensive injury scoring system (TRISS) in a high-resource setting. Two novel injury scoring systems (KTS and MGAP) surpassed the calibration of TRISS. These novel injury scoring systems should be considered when clinicians and researchers wish to accurately account for injury severity. Implementation of these resource-appropriate tools in LMICs can improve injury surveillance, guiding quality improvement efforts, and supporting advocacy for resource allocation commensurate with the volume and severity of trauma.