Comparison of ISS, NISS, and RTS score as predictor of mortality in pediatric fall.

BACKGROUND: Studies to identify an ideal trauma score tool representing prediction of outcomes of the pediatric fall patient remains elusive. Our study was undertaken to identify better predictor of mortality in the pediatric fall patients. METHODS: Data was retrieved from prospectively maintained trauma registry project at level 1 trauma center developed as part of Multicentric Project-Towards Improving Trauma Care Outcomes (TITCO) in India. Single center data retrieved from a prospectively maintained trauma registry at a level 1 trauma center, New Delhi, for a period ranging from 1 October 2013 to 17 February 2015 was evaluated. Standard anatomic scores Injury Severity Score (ISS) and New Injury Severity Score (NISS) were compared with physiologic score Revised Trauma Score (RTS) using receiver operating curve (ROC). RESULTS: Heart rate and RTS had a statistical difference among the survivors to nonsurvivors. ISS, NISS, and RTS were having 50, 50, and 86% of area under the curve on ROCs, and RTS was statistically significant among them. CONCLUSIONS: Physiologically based trauma score systems (RTS) are much better predictors of inhospital mortality in comparison to anatomical based scoring systems (ISS and NISS) for unintentional pediatric falls.

Traumatic transfers: calibration is adversely affected when prediction models are transferred between trauma care contexts in India and the United States.

OBJECTIVE: We evaluated the transferability of prediction models between trauma care contexts in India and the United States and explored updating methods to adjust such models for new contexts. STUDY DESIGN AND SETTINGS: Using a combination of prospective cohort and registry data from 3,728 patients of Towards Improved Trauma Care Outcomes in India (TITCO) and from 18,756 patients of the US National Trauma Data Bank (NTDB), we derived models in one context and validated them in the other, assessing them for discrimination and calibration using systolic blood pressure, heart rate, and Glasgow coma scale as candidate predictors. RESULTS: Early mortality was 8% in the TITCO and 1-2% in the NTDB samples. Both models discriminated well, but the TITCO model overestimated the risk of mortality in NTDB patients, and the NTDB model underestimated the risk in TITCO patients. CONCLUSION: Transferability was good in terms of discrimination but poor in terms of calibration. It was possible to improve this miscalibration by updating the models' intercept. This updating method could be used in samples with as few as 25 events.