Two simple replacements for the Triage Early Warning Score to facilitate the South African Triage Scale in low resource settings

Background: The South African Triage Scale (SATS) requires the calculation of the Triage Early Warning Score (TEWS), which takes time and is prone to error. Aim: to derive and validate triage scores from a clinical database collected in a low-resource hospital in sub-Saharan Africa over four years a...

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Main Authors: Lucien Wasingya-Kasereka, Pauline Nabatanzi, Immaculate Nakitende, Joan Nabiryo, Teopista Namujwiga, John Kellett
Format: Article
Language:English
Published: Elsevier 2021-03-01
Series:African Journal of Emergency Medicine
Subjects:
Online Access:http://www.sciencedirect.com/science/article/pii/S2211419X20301440
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spelling doaj-0f331a395a77401a9939eb35909202132021-02-21T04:33:25ZengElsevierAfrican Journal of Emergency Medicine2211-419X2021-03-011115359Two simple replacements for the Triage Early Warning Score to facilitate the South African Triage Scale in low resource settingsLucien Wasingya-Kasereka0Pauline Nabatanzi1Immaculate Nakitende2Joan Nabiryo3Teopista Namujwiga4John Kellett5Kitovu Hospital, Masaka, UgandaKitovu Hospital, Masaka, UgandaDepartment of Medicine, Kitovu Hospital, Masaka, UgandaDepartment of Medicine, Kitovu Hospital, Masaka, UgandaDepartment of Medicine, Kitovu Hospital, Masaka, UgandaDepartment of Emergency Medicine, Hospital of South West Jutland, Esbjerg, Denmark; Corresponding author at: Ballinaclough, Nenagh, County Tipperary, Ireland.Background: The South African Triage Scale (SATS) requires the calculation of the Triage Early Warning Score (TEWS), which takes time and is prone to error. Aim: to derive and validate triage scores from a clinical database collected in a low-resource hospital in sub-Saharan Africa over four years and compare them with the ability of TEWS to triage patients. Methods: A retrospective observational study carried out in Kitovu Hospital, Masaka, Uganda as part of an ongoing quality improvement project. Data collected on 4482 patients was divided into two equal cohorts: one for the derivation of scores by logistic regression and the other for their validation. Results: Two scores identified the largest number of patients with the lowest in-hospital mortality. A score based on oxygen saturation, mental status and mobility had a c statistic for discrimination of 0.83 (95% CI 0.079–0.87) in the derivation, and 0.81 (95% CI 0.77–0.86) in the validation cohort. Another score based on respiratory rate, mental status and mobility had a c statistic of 0.82 (95% CI 0.078–0.87) in the derivation, and 0.81 (95% CI 0.77–0.86) in the validation cohort. The oxygen saturation-based score of zero points identified 51% of patients in the derivation cohort who had in-hospital mortality rate of 0.5%, and 49% of patients in the validation cohort who had in-hospital mortality of 1.0%. A respiratory rate-based score of zero points identified 45% in the derivation cohort who had in-hospital mortality rate of 0.5%, and 44% of patients in the validation cohort who had in-hospital mortality of 0.8%. Both scores had comparable performance to TEWS. Conclusion: Two easy to calculate scores have comparable performance to TEWS and, therefore, could replace it to facilitate the adoption of SATS in low-resource settings.http://www.sciencedirect.com/science/article/pii/S2211419X20301440TriageLow resource settingPredictive scoresEmergency department
collection DOAJ
language English
format Article
sources DOAJ
author Lucien Wasingya-Kasereka
Pauline Nabatanzi
Immaculate Nakitende
Joan Nabiryo
Teopista Namujwiga
John Kellett
spellingShingle Lucien Wasingya-Kasereka
Pauline Nabatanzi
Immaculate Nakitende
Joan Nabiryo
Teopista Namujwiga
John Kellett
Two simple replacements for the Triage Early Warning Score to facilitate the South African Triage Scale in low resource settings
African Journal of Emergency Medicine
Triage
Low resource setting
Predictive scores
Emergency department
author_facet Lucien Wasingya-Kasereka
Pauline Nabatanzi
Immaculate Nakitende
Joan Nabiryo
Teopista Namujwiga
John Kellett
author_sort Lucien Wasingya-Kasereka
title Two simple replacements for the Triage Early Warning Score to facilitate the South African Triage Scale in low resource settings
title_short Two simple replacements for the Triage Early Warning Score to facilitate the South African Triage Scale in low resource settings
title_full Two simple replacements for the Triage Early Warning Score to facilitate the South African Triage Scale in low resource settings
title_fullStr Two simple replacements for the Triage Early Warning Score to facilitate the South African Triage Scale in low resource settings
title_full_unstemmed Two simple replacements for the Triage Early Warning Score to facilitate the South African Triage Scale in low resource settings
title_sort two simple replacements for the triage early warning score to facilitate the south african triage scale in low resource settings
publisher Elsevier
series African Journal of Emergency Medicine
issn 2211-419X
publishDate 2021-03-01
description Background: The South African Triage Scale (SATS) requires the calculation of the Triage Early Warning Score (TEWS), which takes time and is prone to error. Aim: to derive and validate triage scores from a clinical database collected in a low-resource hospital in sub-Saharan Africa over four years and compare them with the ability of TEWS to triage patients. Methods: A retrospective observational study carried out in Kitovu Hospital, Masaka, Uganda as part of an ongoing quality improvement project. Data collected on 4482 patients was divided into two equal cohorts: one for the derivation of scores by logistic regression and the other for their validation. Results: Two scores identified the largest number of patients with the lowest in-hospital mortality. A score based on oxygen saturation, mental status and mobility had a c statistic for discrimination of 0.83 (95% CI 0.079–0.87) in the derivation, and 0.81 (95% CI 0.77–0.86) in the validation cohort. Another score based on respiratory rate, mental status and mobility had a c statistic of 0.82 (95% CI 0.078–0.87) in the derivation, and 0.81 (95% CI 0.77–0.86) in the validation cohort. The oxygen saturation-based score of zero points identified 51% of patients in the derivation cohort who had in-hospital mortality rate of 0.5%, and 49% of patients in the validation cohort who had in-hospital mortality of 1.0%. A respiratory rate-based score of zero points identified 45% in the derivation cohort who had in-hospital mortality rate of 0.5%, and 44% of patients in the validation cohort who had in-hospital mortality of 0.8%. Both scores had comparable performance to TEWS. Conclusion: Two easy to calculate scores have comparable performance to TEWS and, therefore, could replace it to facilitate the adoption of SATS in low-resource settings.
topic Triage
Low resource setting
Predictive scores
Emergency department
url http://www.sciencedirect.com/science/article/pii/S2211419X20301440
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