Adam D Laytin1, Vineet Kumar2, Catherine J Juillard3, Bhakti Sarang4, Angela Lashoher5, Nobhojit Roy6, Rochelle A Dicker7. 1. Department of Surgery, University of California San Francisco, San Francisco, CA, USA. Electronic address: adam.laytin@ucsf.edu. 2. Department of Surgery, Lokmanya Tilak Municipal Medical College and General Hospital, Mumbai, India. Electronic address: drvineetkumar@gmail.com. 3. Department of Surgery, University of California San Francisco, San Francisco, CA, USA. Electronic address: catherine.juillard@ucsf.edu. 4. Department of Surgery, Bhabha Atomic Research Centre Hospital, Mumbai, India. Electronic address: sarangbhakti@gmail.com. 5. Department of Surgery, University of California San Francisco, San Francisco, CA, USA; Department of Surgery, Lokmanya Tilak Municipal Medical College and General Hospital, Mumbai, India; Department of Surgery, Bhabha Atomic Research Centre Hospital, Mumbai, India. Electronic address: alashoher@gmail.com. 6. Department of Surgery, Bhabha Atomic Research Centre Hospital, Mumbai, India. Electronic address: nroy@jhsph.edu. 7. Department of Surgery, University of California San Francisco, San Francisco, CA, USA. Electronic address: dickerr@sfghsurg.ucsf.edu.
Abstract
INTRODUCTION: Injury is a major cause of morbidity and mortality in low- and middle-income countries. Effective trauma surveillance is imperative to guide research and quality improvement interventions, so an accurate metric for quantifying injury severity is crucial. The objectives of this study are (1) to assess the feasibility of calculating five injury scoring systems--ISS (injury severity score), RTS (revised trauma score), KTS (Kampala trauma score), MGAP (mechanism, GCS (Glasgow coma score), age, pressure) and GAP (GCS, age, pressure)--with data from a trauma registry in a lower middle-income country and (2) to determine which of these scoring systems most accurately predicts in-hospital mortality in this setting. PATIENTS AND METHODS: This is a retrospective analysis of data from an institutional trauma registry in Mumbai, India. Values for each score were calculated when sufficient data were available. Logistic regression was used to compare the correlation between each score and in-hospital mortality. RESULTS: There were sufficient data recorded to calculate ISS in 73% of patients, RTS in 35%, KTS in 35%, MGAP in 88% and GAP in 92%. ISS was the weakest predictor of in-hospital mortality, while RTS, KTS, MGAP and GAP scores all correlated well with in-hospital mortality (area under ROC (receiver operating characteristic) curve 0.69 for ISS, 0.85 for RTS, 0.86 for KTS, 0.84 for MGAP, 0.85 for GAP). Respiratory rate measurements, missing in 63% of patients, were a major barrier to calculating RTS and KTS. CONCLUSIONS: Given the realities of medical practice in low- and middle-income countries, it is reasonable to modify the approach to characterising injury severity to favour simplified injury scoring systems that accurately predict in-hospital mortality despite limitations in trauma registry datasets.
INTRODUCTION:Injury is a major cause of morbidity and mortality in low- and middle-income countries. Effective trauma surveillance is imperative to guide research and quality improvement interventions, so an accurate metric for quantifying injury severity is crucial. The objectives of this study are (1) to assess the feasibility of calculating five injury scoring systems--ISS (injury severity score), RTS (revised trauma score), KTS (Kampala trauma score), MGAP (mechanism, GCS (Glasgow coma score), age, pressure) and GAP (GCS, age, pressure)--with data from a trauma registry in a lower middle-income country and (2) to determine which of these scoring systems most accurately predicts in-hospital mortality in this setting. PATIENTS AND METHODS: This is a retrospective analysis of data from an institutional trauma registry in Mumbai, India. Values for each score were calculated when sufficient data were available. Logistic regression was used to compare the correlation between each score and in-hospital mortality. RESULTS: There were sufficient data recorded to calculate ISS in 73% of patients, RTS in 35%, KTS in 35%, MGAP in 88% and GAP in 92%. ISS was the weakest predictor of in-hospital mortality, while RTS, KTS, MGAP and GAP scores all correlated well with in-hospital mortality (area under ROC (receiver operating characteristic) curve 0.69 for ISS, 0.85 for RTS, 0.86 for KTS, 0.84 for MGAP, 0.85 for GAP). Respiratory rate measurements, missing in 63% of patients, were a major barrier to calculating RTS and KTS. CONCLUSIONS: Given the realities of medical practice in low- and middle-income countries, it is reasonable to modify the approach to characterising injury severity to favour simplified injury scoring systems that accurately predict in-hospital mortality despite limitations in trauma registry datasets.
Authors: Rebecca G Maine; Chifundo Kajombo; Gift Mulima; Jennifer Kincaid; Laura Purcell; Jared R Gallaher; Trista D Reid; Anthony G Charles Journal: World J Surg Date: 2020-06 Impact factor: 3.352
Authors: Zeinab Mohammed; Yaseen Saleh; Eman Mohammed AbdelSalam; Norhan B B Mohammed; Emad El-Bana; Jon Mark Hirshon Journal: BMC Emerg Med Date: 2022-05-28
Authors: Adam D Laytin; Nebyou Seyoum; Aklilu Azazh; Ayalew Zewdie; Catherine J Juillard; Rochelle A Dicker Journal: Trauma Surg Acute Care Open Date: 2018-11-30