Is There a Difference in the Mortality Prediction Performance of Two ICISS Approaches for Trauma Patients Admitted to Hospitals in Urban India?

Overview

This study aims to compare the predictive performance of two different approaches of the international classification of disease injury severity score (ICISS) using data from four public university hospitals in urban India.

Research question Is there a difference in the mortality prediction performance of two ICISS approaches for trauma patients admitted to hospitals in urban India? Study design The investigators will conduct a retrospective registry based study. Setting The data that will be used is from a prospective cohort study named towards improved trauma care outcomes in India (TITCO). It was collected from four public university hospitals in India between October 2013 and January 2015. The hospitals are in Mumbai, Delhi and Kolkata. The two centers in Mumbai were King Edward Memorial Hospital and Lokmanya Tilak Municipal General Hospital. The one in Delhi was Jai Prakash Narayan Apex Trauma Center and the one in Kolkata was the Institute of Post-Graduate Medical Education and Research and Seth Sukhlal Karnani Memorial Hospital. The data was collected by one trained project officer at each hospital, working eight-hour shifts with a rotating schedule between day, evening and night shifts. Data from patients admitted outside of the shift hours was collected retrospectively within days of arrival to hospital. The patients were followed until discharge, death or to a maximum of 30 days. If discharged, the patients were considered to be alive at 30 days. There was no follow-up after patient discharge or after the 30 days. Source and method of participant selection Project officers included consecutive patients that presented to participating hospitals. Patients were included either by direct observation during the project officers' shifts or by retrospective data extraction from patient records. Data sources/ measurement Patient mortality data was extracted from patient records, as was data on all covariates. If covariate data was missing in records an attempt was made to retrieve this data from the patient or patient relatives. The injury data was extracted from patient records, including imaging reports and intraoperative findings. Protocols from post-mortem examinations were not available. Injuries will be coded using ICD-10. The SRR for an ICD-code will be calculated by dividing the number of fatal outcomes for each ICD-code by the total number of patients with that ICD-code. This results in a number from 0 to 1 that is interpreted as the patient survival ratio. For example, if 65 out of 100 patients with a given ICD-code survived the SRR for that code would be 0,65. That would mean 65% of the patients with that ICD code survived. In this study, the SRRs used for ICISS calculations were taken from a publicly available SRR-set calculated from the TITCO dataset (TO BE RELEASED). The ICISS for each patient will then be calculated using two different approaches. The cICISS will be calculated as the product of all of the patient's SRRs. The swiICISS will be equal to the patient's lowest SRR. Both ICISS methods result in a number that ranges from 0 to 1 that should be interpreted as the patient specific probability of survival. Bias The project officers were trained by project management. They were not involved in patient care and only acquired data by observing hospital staff, using patient records or from patient relatives. All project officers had at least a health science master's degree and were continuously supervised by project management. Injury coders will be blinded to patient demographics and mortality data during the conversion from free-text injuries to ICD-codes and will be trained prior to the ICD-10 coding using the World Health Organization (WHO) ICD-10 online training module. They will gain access to the injury dataset first after reaching 80% agreement in several samples of 50 injuries compared to an external coder. Study size The sample size calculation is based on published recommendations on effective sample sizes needed to validate prediction models. These recommendations are based on simulations of the sample sizes needed to detect statistically significant differences in predictive performance measures between two scores setting the power to 80% and the significance level to 5%. Hence, the required sample size was calculated to include the most recent 200 consecutive events, i.e. patients who died within 24 hours, and all non-events enrolled during the same time period. Mortality within 24 hours for was used for the sample size calculation as the investigators wanted the study to be powered for secondary outcomes also. Quantitative variables All quantitative variables will be analyzed as continuous. Statistical methods and analyses The investigators will use R for all statistical analyses. Predictive performance will be assessed in terms of discrimination and calibration. Discrimination will be assessed by calculating the area under the receiver operating characteristics curve (AUROCC) and calibration will be assessed by comparing observed and predicted outcomes visually in a calibration plot and statistically by calculating the calibration slope. Confidence intervals for predictive performance measures will be estimated using a bootstrap approach (15). Overlapping confidence intervals will be interpreted as evidence of lack of a statistically significant difference. Parametric and non-parametric exact tests will be used as appropriate, with 95% confidence intervals and a 5% significance level. The main analysis will be a complete case analysis, in which observations with missing values in any of the following variables will be excluded: time of arrival, age, sex, mechanism of injury, transfer status, and outcome variables. Observations with no injuries reported will be assigned ICISS scores of 1 and for each observation the final ICISS scores will be calculated based only on SRRs for ICD-codes that occur at least ten times in the published SRR-set used in this study. The published SRR-set includes SRRs based on both mortality within 30 days, henceforth referred to as SRR-30D, and SRRs based on mortality within 24 hours, henceforth referred to as SRR-24H. The investigators will use these SRRs to calculate cICISS and swiICISS for each patient, henceforth referred to as cICISS-30D, cICISS-24H, swiICISS-30D and swiICISS-24H. Finally, the investigators will assess and compare the performance of cICISS-30D and swiICISS-30D in predicting mortality within 30 days and within 24 hours, and repeat this analysis for cICISS-24H and swiICISS-24H. Sensitivity analyses Four sensitivity analyses will be conducted. In the first sensitivity analysis the investigators will only include observations with complete outcome data, however missing values in covariates were allowed. In the second sensitivity analysis the investigators will exclude observations without any reported injury. In the third sensitivity analysis the investigators calculated cICISS and swiICISS based on all available SRRs, regardless of how frequently the corresponding ICD-10 codes occurred in the dataset. Finally, the investigators calculated the two ICISS scores for each patient based only on unique ICD-10 codes. In other words, each ICD-10 code was only allowed to contribute with one SRR to the ICISS scores even if it occurred more than once in the same patient.

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