Paediatric Emergency Triage, Assessment and Treatment (ETAT) – preparedness for implementation at primary care facilities in Malawi

King, Carina; Dube, Albert; Zadutsa, Beatiwel; Banda, Lumbani; Langton, Josephine; Desmond, Nicola; Lufesi, Norman; Makwenda, Charles; Hildenwall, Helena

doi:10.1080/16549716.2021.1989807

Cited by 11 publications

(11 citation statements)

References 26 publications

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“…These issues were exacerbated by the COVID-19 pandemic and persisted throughout the study period. The best chance for success, therefore, relies on a holistic approach which not only addresses the clinical elements of the treatment guideline but also include supply chain management, maintenance policies and coevolution of health systems infrastructure with pediatric triage and sepsis guidelines (26, 46, 47).…”

Section: Discussionmentioning

confidence: 99%

Implementation of Smart Triage combined with a quality improvement program for children presenting to facilities in Kenya and Uganda: An interrupted time series analysis

Ansermino,

Pillay,

Tagoola

et al. 2024

Preprint

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Sepsis occurs predominantly in low-middle-income countries. Sub-optimal triage contributes to poor early case recognition and outcomes from sepsis. We evaluated the impact of Smart Triage using improved time to intravenous antimicrobial administration in a multisite interventional study.Smart Triage was implemented (with control sites) in Kenya (February 2021-December 2022) and Uganda (April 2020-April 2022). Children presenting to the outpatient departments with an acute illness were enrolled. A controlled interrupted time series was used to assess the effect on time from arrival at the facility to intravenous antimicrobial administration. Secondary analyses included antimicrobial use, admission rates and mortality (NCT04304235).During the baseline period, the time to antimicrobials decreased significantly in Kenya (132 and 58 minutes) at control and intervention sites, but less in Uganda (3 minutes) at the intervention site. Then, during the implementation period in Kenya, the time to IVA at the intervention site decreased by 98 min (57%, 95% CI 81-114) but increased by 49 min (21%, 95% CI: 23-76) at the control site. In Uganda, the time to IVA initially decreased but was not sustained, and there was no significant difference between intervention and control sites. At the intervention sites, there was a significant reduction in IVA utilization of 47% (Kenya) and 33% (Uganda), a reduction in admission rates of 47% (Kenya) and 33% (Uganda) and a 25% (Kenya) and 75% (Uganda) reduction in mortality rates compared to the baseline period.We showed significant improvements in time to intravenous antibiotics in Kenya but not Uganda, likely due to COVID-19, a short study period and resource constraints. The reduced antimicrobial use and admission and mortality rates are remarkable and welcome benefits but should be interpreted cautiously as these were secondary outcomes. This study underlines the difficulty of implementing technologies and sustaining quality improvement in health systems.Author SummaryImplementing the Smart Triage platform and quality improvement program for children in Kenya and Uganda resulted in inconsistent improvements in time to intravenous antimicrobial administration. The time to IVA decreased significantly in Kenya during baseline and reduced further during the intervention while increasing at the control site. In Uganda the time to treatment initially decreased but was not sustained. The treatment times were significantly influenced by the improvements during baseline data collection and multiple external health system factors such as drug shortages, the COVID -19 pandemic, staff shortages and strikes. The dramatic reduction in treatment, admission, and mortality rates should be further investigated.

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Section: Discussionmentioning

confidence: 99%

Implementation of Smart Triage combined with a quality improvement program for children presenting to facilities in Kenya and Uganda: An interrupted time series analysis

Ansermino,

Pillay,

Tagoola

et al. 2024

Preprint

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“…The models developed here could be deployed on a mobile phone/tablet with an attached pulse oximeter, and used to augment triage of patients in low income settings. The World Health Organization (WHO) recommends the use of the Emergency Triage and Treatment (ETAT) guidelines for triaging sick children, but challenges remain in its implementation due to staff shortages and adequate training ( King et al , 2021 ). Therefore, having a model that could objectively identify the need for admission while the triaging nurse is taking measurements of SpO 2 – which is part of routine clinical practice, could complement ETAT guidelines in identifying severely ill children.…”

Section: Discussionmentioning

confidence: 99%

Using self-supervised feature learning to improve the use of pulse oximeter signals to predict paediatric hospitalization

Mwaniki¹,

Kamanu²,

Akech³

et al. 2023

Wellcome Open Res

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Background: The success of many machine learning applications depends on knowledge about the relationship between the input data and the task of interest (output), hindering the application of machine learning to novel tasks. End-to-end deep learning, which does not require intermediate feature engineering, has been recommended to overcome this challenge but end-to-end deep learning models require large labelled training data sets often unavailable in many medical applications. In this study, we trained self-supervised learning (SSL) models for automatic feature extraction from raw photoplethysmography (PPG) obtained using a pulse oximeter, with the aim of predicting paediatric hospitalization. Methods: We compared logistic regression models fitted using features extracted using SSL with models trained using both clinical and SSL features. In addition, we compared end-to-end deep learning models initialized randomly or using weights from the SSL models. We also compared the performance of SSL models trained on labelled data alone (n=1,031) with SSL trained using both labelled and unlabelled signals (n=7,578). Results: Logistic regression models were more predictive of hospitalization when trained on features extracted using labelled PPG signals only compared to SSL models trained on both labelled and unlabelled signals (AUC 0.83 vs 0.80). However, features extracted using SSL model trained on both labelled and unlabelled PPG signals were more predictive of hospitalization when concatenated with clinical features (AUC 0.89 vs 0.87). The end-to-end deep learning model had an AUC of 0.80 when initialized using the SSL model trained on all PPG signals, 0.77 when initialized using SSL trained on labelled data only, and 0.73 when initialized randomly. Conclusions: This study shows that SSL can extract features from PPG signals that are predictive of hospitalization or initialize end-to-end deep learning models. Furthermore, SSL can leverage larger unlabelled data sets to improve performance of models fitted using small labelled data sets.

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“…36 In Malawi, the infrastructure of health centers did not align with the ETAT structure; rural referral centers did not have the supplies or medications to provide basic emergency care, which, if provided, would divert resources from district hospitals. 37 Additionally, the suggested algorithms and simulation-based trainings in ATLS and PALS fail to be relevant in the local environment in many LMICs when they require equipment, such as defi brillators and ventilators, and disposable materials, which many not be standard in resource-limited settings. 38 A signifi cant shortcoming of implementing standardized courses in resource-limited settings is their lack of local epidemiologic relevance.…”

Section: Standardized Training Programsmentioning

confidence: 99%

“…In one effort to increase capacity in Rwanda, the challenges to implementing ETAT stemmed from the mode of training delivery (such as, language barriers, intense training schedules), while others were more related to uptake of guidelines in district hospitals (for example, staff turnover, reluctance to change, limited resources, conflicting protocols) 36. In Malawi, the infrastructure of health centers did not align with the ETAT structure; rural referral centers did not have the supplies or medications to provide basic emergency care, which, if provided, would divert resources from district hospitals 37. Additionally, the suggested algorithms and simulation-based trainings in ATLS and PALS fail to be relevant in the local environment in many LMICs when they require equipment, such as defibrillators and ventilators, and disposable materials, which many not be standard in resource-limited settings 38…”

Section: Curriculum Developmentmentioning

confidence: 99%

Capacity building in low- and middle-income countries

Silvers

Watters

Meter

2022

The Nurse Practitioner

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Paediatric Emergency Triage, Assessment and Treatment (ETAT) – preparedness for implementation at primary care facilities in Malawi

Cited by 11 publications

References 26 publications

Implementation of Smart Triage combined with a quality improvement program for children presenting to facilities in Kenya and Uganda: An interrupted time series analysis

Implementation of Smart Triage combined with a quality improvement program for children presenting to facilities in Kenya and Uganda: An interrupted time series analysis

Using self-supervised feature learning to improve the use of pulse oximeter signals to predict paediatric hospitalization

Capacity building in low- and middle-income countries

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