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Validation of the Pediatric Resuscitation and Trauma Outcome (PRESTO) model in injury patients in Tanzania.

Authors :
Keating EM
Mitao M
Kozhumam A
Souza JV
Anthony CS
Costa DB
Staton CA
Mmbaga BT
Vissoci JRN
Source :
BMJ open [BMJ Open] 2023 Apr 05; Vol. 13 (4), pp. e070747. Date of Electronic Publication: 2023 Apr 05.
Publication Year :
2023

Abstract

Introduction: Sub-Saharan Africa has the highest rate of unintentional paediatric injury deaths. The Pediatric Resuscitation and Trauma Outcome (PRESTO) model predicts mortality using patient variables available in low-resource settings: age, systolic blood pressure (SBP), heart rate (HR), oxygen saturation, need for supplemental oxygen (SO) and neurologic status (Alert Verbal Painful Unresponsive (AVPU)). We sought to validate and assess the prognostic performance of PRESTO for paediatric injury patients at a tertiary referral hospital in Northern Tanzania.<br />Methods: This is a cross-sectional study from a prospective trauma registry from November 2020 to April 2022. We performed exploratory analysis of sociodemographic variables and developed a logistic regression model to predict mortality using R (V.4.1). The logistic regression model was evaluated using area under the receiver operating curve (AUC).<br />Results: 499 patients were enrolled with a median age of 7 years (IQR 3.41-11.18). 65% were boys, and in-hospital mortality was 7.1%. Most were classified as alert on AVPU Scale (n=326, 86%) and had normal SBP (n=351, 98%). Median HR was 107 (IQR 88.5-124). The logistic regression model based on the original PRESTO model revealed that AVPU, HR and SO were statistically significant to predict in-hospital mortality. The model fit to our population revealed AUC=0.81, sensitivity=0.71 and specificity=0.79.<br />Conclusion: This is the first validation of a model to predict mortality for paediatric injury patients in Tanzania. Despite the low number of participants, our results show good predictive potential. Further research with a larger injury population should be done to improve the model for our population, such as through calibration.<br />Competing Interests: Competing interests: None declared.<br /> (© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Details

Language :
English
ISSN :
2044-6055
Volume :
13
Issue :
4
Database :
MEDLINE
Journal :
BMJ open
Publication Type :
Academic Journal
Accession number :
37019480
Full Text :
https://doi.org/10.1136/bmjopen-2022-070747