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

BACKGROUND: 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...

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Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5569494/
https://www.ncbi.nlm.nih.gov/pubmed/28835247
http://dx.doi.org/10.1186/s12913-017-2541-4
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author 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
author_facet 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
author_sort Massaut, Jacques
collection PubMed
description BACKGROUND: 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. METHODS: 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 < 0.05, and bootstrapping was used for internal validation. The likelihood ratio test was used to compare the combined and restricted models. These models were also applied to data from a cohort of patients from the Kunduz Trauma Center, Afghanistan, to validate mortality prediction in an external trauma patients population. RESULTS: A total of 7618 consecutive hospitalized patients from the Nap Kenbe Hospital were analyzed. Variables independently associated with in-hospital mortality were age > 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. CONCLUSIONS: 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12913-017-2541-4) contains supplementary material, which is available to authorized users.
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spelling pubmed-55694942017-08-29 The modified south African triage scale system for mortality prediction in resource-constrained emergency surgical centers: a retrospective cohort study 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 BMC Health Serv Res Research Article BACKGROUND: 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. METHODS: 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 < 0.05, and bootstrapping was used for internal validation. The likelihood ratio test was used to compare the combined and restricted models. These models were also applied to data from a cohort of patients from the Kunduz Trauma Center, Afghanistan, to validate mortality prediction in an external trauma patients population. RESULTS: A total of 7618 consecutive hospitalized patients from the Nap Kenbe Hospital were analyzed. Variables independently associated with in-hospital mortality were age > 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. CONCLUSIONS: 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12913-017-2541-4) contains supplementary material, which is available to authorized users. BioMed Central 2017-08-23 /pmc/articles/PMC5569494/ /pubmed/28835247 http://dx.doi.org/10.1186/s12913-017-2541-4 Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
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
The modified south African triage scale system for mortality prediction in resource-constrained emergency surgical centers: a retrospective cohort study
title The modified south African triage scale system for mortality prediction in resource-constrained emergency surgical centers: a retrospective cohort study
title_full The modified south African triage scale system for mortality prediction in resource-constrained emergency surgical centers: a retrospective cohort study
title_fullStr The modified south African triage scale system for mortality prediction in resource-constrained emergency surgical centers: a retrospective cohort study
title_full_unstemmed The modified south African triage scale system for mortality prediction in resource-constrained emergency surgical centers: a retrospective cohort study
title_short The modified south African triage scale system for mortality prediction in resource-constrained emergency surgical centers: a retrospective cohort study
title_sort modified south african triage scale system for mortality prediction in resource-constrained emergency surgical centers: a retrospective cohort study
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5569494/
https://www.ncbi.nlm.nih.gov/pubmed/28835247
http://dx.doi.org/10.1186/s12913-017-2541-4
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