Comparison of the Pediatric Resuscitation and Trauma Outcome (PRESTO) Model and Pediatric Trauma Scoring Systems in a Middle-Income Country
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Background: The pediatric resuscitation and trauma outcome (PRESTO) model was developed to aid comparisons of risk-adjusted mortality after injury in low- and middle-income countries (LMICs). We sought to validate PRESTO using data from a middle-income country (MIC) trauma registry and compare its performance to the Pediatric Trauma Score (PTS), Revised Trauma Score, and pediatric age-adjusted shock index (SIPA).
Methods: We included children (age < 15 years) admitted to a single trauma center in South Africa from December 2012 to January 2019. We excluded patients missing variables necessary for the PRESTO model-age, systolic blood pressure, pulse, oxygen saturation, neurologic status, and airway support. Trauma scores were assigned retrospectively. PRESTO's previously high-income country (HIC)-validated optimal threshold was compared to MIC-validated threshold using area under the receiver operating characteristic curves (AUROC). Prediction of in-hospital death using trauma scoring systems was compared using ROC analysis.
Results: Of 1160 injured children, 988 (85%) had complete data for calculation of PRESTO. Median age was 7 (IQR: 4, 11), and 67% were male. Mortality was 2% (n = 23). Mean predicted mortality was 0.5% (range 0-25.7%, AUROC 0.93). Using the HIC-validated threshold, PRESTO had a sensitivity of 26.1% and a specificity of 99.7%. The MIC threshold showed a sensitivity of 82.6% and specificity of 89.4%. The MIC threshold yielded superior discrimination (AUROC 0.86 [CI 0.78, 0.94]) compared to the previously established HIC threshold (0.63 [CI 0.54, 0.72], p < 0.0001). PRESTO showed superior prediction of in-hospital death compared to PTS and SIPA (all p < 0.01).
Conclusion: PRESTO can be applied in MIC settings and discriminates between children at risk for in-hospital death following trauma. Further research should clarify optimal decision thresholds for quality improvement and benchmarking in LMIC settings.
DOI: 10.1007/s00268-020-05512-3
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