Evidence map›Paper›PMID 32151404›Full record

ArticleJournal of pediatric surgery2020

Calibration and validation of the pediatric resuscitation and trauma outcome model among injured children in Rwanda.

Etienne St-Louis, Robin Petroze, Robert Baird, Tarek Razek, Dan Poenaru, J Forest Calland, Jean-Claude Byiringiro, Edmond Ntaganda

Registry-linked trialAbstract read
In one paragraph

Article in Journal of pediatric surgery, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06075108 (P-KIDs CARE), which is not on this map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT06075108 narecruitingnot on this mapstarted 2024, after this paper: background citation

P-KIDs CARE: An Intervention to Address Health Systems Delays to Care for Injured Children in Tanzania

TypeinterventionalSponsorUniversity of UtahRan2024 to 2028Enrolled284ConditionsAccidental Injuries, Global Child Health, Pediatric Emergency Medicine, Public Health System ResearchArmsP-KIDs CARE
3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. A cohort of pediatric injury patients from a hospital-based trauma registry in Northern Tanzania.African journal of emergency medicine : Revue africaine de la medecine d'urgence · 2022
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Etienne St-LouisCentre for Global Surgery, McGill University Health Centre; Division of Pediatric General and Thoracic Surgery, Montreal, Children's Hospital. Electronic address: etienne.st-louis@mail.mcgill.ca.
Robin PetrozeDivision of Pediatric Surgery, University of Florida.
Robert BairdDivision of Pediatric General Surgery, British Columbia Children's Hospital.
Tarek RazekCentre for Global Surgery, McGill University Health Centre.
Dan PoenaruCentre for Global Surgery, McGill University Health Centre; Division of Pediatric General and Thoracic Surgery, Montreal, Children's Hospital.
J Forest CallandGlobal Surgery Initiative, Department of Surgery, University of Virginia School of Medicine.
Jean-Claude ByiringiroDivision of Clinical Education and Research, Centre Hospitalier Universitaire de Kigali.
Edmond NtagandaPediatric General Surgery Unit, Centre Hospitalier Universitaire de Kigali.

Funding

Fogarty International Clinical Research Scholars Support Center @ Vanderbilt-AAMCR24TW007988 · FIC · VANDERBILT UNIVERSITY · PI VERMUND, STEN H. · 2007 to 2011
$27.6M
FIC NIH HHS R24 TW007988
6 · The paper itself

Abstract

backgroundTrauma is a leading cause of mortality in low- and middle-income countries. The Pediatric Resuscitation and Trauma Outcomes (PRESTO) model uses six low-tech variables available at point of care in resource-limited environments to predict in-hospital mortality of injured children. This model was never calibrated and validated in a low-income country. We aimed to calibrate the model's coefficients and compare its performance against the Revised Trauma Score (RTS) and Kampala Trauma Score (KTS) using data from a low-income country. STUDY

designData from 2011 to 2015 in the prospectively-maintained Rwanda Injury Registry were reviewed after ethical approval was obtained. Patients were included for analysis if they were referred or admitted for traumatic injury, were younger than 15 years and if hospital outcomes were recorded. The variables in the PRESTO model include age, hypotension, heart rate, neurological status, oxygen saturation and airway intervention. The outcome of interest was in-hospital death. After calibration, Receiver-Operating-Characteristic curves were constructed to compare the area-under-curve (AUC) of PRESTO, RTS, and KTS with imputation of missing data. Comparisons of the relative AUC's were performed using Delong's test after bootstrapping in the full cohort and in a subset of patients <5 years-old.

resultsThere were 113 in-hospital deaths out of 1695 included patients (6.7%). The AUC for the PRESTO model was 0.90 (95% CI [0.82-0.91]), higher than for RTS (0.77, 95% CI [0.80-0.97], p < 0.01) but not statistically different from KTS (0.89, 95% CI [0.72-0.82], p = 0.856). In the under-five cohort, the PRESTO model AUC was 0.84 (95% CI [0.75-0.92]), significantly higher than RTS (0.73 95% CI [0.64-0.81], p < 0.01) and KTS (0.58, 95% CI [0.50-0.66], p < 0.01).

conclusionPRESTO appears to be the superior benchmarking tool for pediatric patients in a low- and middle-income country context. The PRESTO score outperforms the KTS in children <5 years of age. Further validation of the PRESTO model is needed from other low- and middle-income settings. LEVEL OF EVIDENCE: Level III: case-control (prognostic) study.

Indexed as

Wounds and InjuriesCalibrationChildChild, PreschoolHospital MortalityHumansRwandaTrauma Severity IndicesUgandaBenchmarkingLow-resourcePediatricRisk-adjustmentTrauma

Identifiers

PMID32151404
PMCPMC10767723

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Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.