The modified south African triage scale system for mortality prediction in resource-constrained emergency surgical centers: a retrospective cohort study

• Published in: BMC health services research 2017-08, Vol.17 (1), p.594-594, Article 594
• Main Authors: Massaut, Jacques, Valles, Pola, Ghismonde, Arnold, Jacques, Claudinette Jn, Louis, Liseberth Pierre, Zakir, Abdulmutalib, Van den Bergh, Rafael, Santiague, Lunick, Massenat, Rose Berly, Edema, Nathalie
• Format: Article
• Language: English
• Subjects: Adolescent; Adult; Afghanistan; Aged; Aged, 80 and over; Analysis; Child; Child, Preschool; Cohort analysis; Decision Support Techniques; Emergency medical care; Emergency service; Emergency services; Female; Haiti; Health services; Hospital Mortality - trends; Hospitalization; Hospitals; Humans; Infant; Infant, Newborn; Intensive care; Logistic Models; Male; Medical prognosis; Middle Aged; Mortality; Odds Ratio; Palliative care; Patient outcomes; Patients; Physiology; Retrospective Studies; Risk Factors; ROC Curve; Surgery; Trauma; Trauma Centers; Triage - standards; Young Adult
• ISSN: 1472-6963; 1472-6963
• DOI: 10.1186/s12913-017-2541-4

The South African Triage Scale (SATS) was developed to facilitate patient triage in emergency departments (EDs) and is used by Médecins Sans Frontières (MSF) in low-resource environments. The aim was to determine if SATS data, reason for admission, and patient age can be used to develop and validate a model predicting the in-hospital risk of death in emergency surgical centers and to compare the model's discriminative power with that of the four SATS categories alone. We used data from a cohort hospitalized at the Nap Kenbe Surgical Hospital in Haiti from January 2013 to June 2015. We based our analysis on a multivariate logistic regression of the probability of death. Age cutoff, reason for admission categorized into nine groups according to MSF classifications, and SATS triage category (red, orange, yellow, and green) were used as candidate parameters for the analysis of factors associated with mortality. Stepwise backward elimination was performed for the selection of risk factors with retention of predictors with P  45 and < = 65 years (odds ratio, 2.04), age > 65 years (odds ratio, 5.15) and the red (odds ratio, 65.08), orange (odds ratio, 3.5), and non-trauma (odds ratio, 3.15) categories. The combined model had an area under the receiver operating characteristic curve (AUROC) of 0.8723 and an AUROC corrected for optimism of 0.8601. The AUROC of the model run on the external data-set was 0.8340. The likelihood ratio test was highly significant in favor of the combined model for both the original and external data-sets. SATS category, patient age, and reason for admission can be used to predict in-hospital mortality. This predictive model had good discriminative ability to identify ED patients at a high risk of death and performed better than the SATS alone.