Logo Lanfrica
  • Accueil
  • Atlas
  • Analyses
  • Documentation
  • Sign in

© 2026 Lanfrica. Tous droits réservés. Tous les droits d'auteur des ressources affichées sur le site Web Lanfrica appartiennent aux détenteurs de droits d'auteur d'origine, sauf indication contraire explicite.

The FAST‐M complex intervention for the detection and management of maternal sepsis in low‐resource settings: a multi‐site evaluation

Domaine:

healthcare

Type de record:

paper
Créateur:
J CL, L MC K
Éditeur:
WILEY
Hôte:
Objective To evaluate whether the implementation of the FAST‐M complex intervention was feasible and improved the recognition and management of maternal sepsis in a low‐resource setting. Design A before‐and‐after design. Setting Fifteen government healthcare facilities in Malawi. Population Women suspected of having maternal sepsis. Methods The FAST‐M complex intervention consisted of the following components: the FAST‐M maternal sepsis treatment bundle and the FAST‐M implementation programme. Performance of selected process outcomes was compared between a 2‐month baseline phase and 6‐month intervention phase with compliance used as a proxy measure of feasibility. Main outcome result Compliance with vital sign recording and use of the FAST‐M maternal sepsis bundle. Results Following implementation of the FAST‐M intervention, women were more likely to have a complete set of vital signs taken on admission to the wards (0/163 [0%] versus 169/252 [67.1%], P  < 0.001). Recognition of suspected maternal sepsis improved with more cases identified following the intervention (12/106 [11.3%] versus 107/166 [64.5%], P  < 0.001). Sepsis management improved, with women more likely to receive all components of the FAST‐M treatment bundle within 1 hour of recognition (0/12 [0%] versus 21/107 [19.6%], P  = 0.091). In particular, women were more likely to receive antibiotics (3/12 [25.0%] versus 72/107 [67.3%], P  = 0.004) within 1 hour of recognition of suspected sepsis. Conclusion Implementation of the FAST‐M complex intervention was feasible and led to the improved recognition and management of suspected maternal sepsis in a low‐resource setting such as Malawi. Tweetable Abstract Implementation of a sepsis care bundle for low‐resources improved recognition & management of maternal sepsis.

Visit

doi.org

Licenses

http://creativecommons.org/licenses/by-nc/4.0/

Similaires

A Multi-Task Benchmark for Abusive Language Detection in Low-Resource SettingsResource availability for the management of maternal sepsis in Malawi, other low‐income countries, and lower‐middle‐income countriesEducational Intervention for Effective Postoperative Pain Management in Low Resource Settings: Evidence from EthiopiaTowards an Explainable Machine Learning System for Early Detection of Pediatric Sepsis in Low-Resource Hospital Settings in Nigeria: Challenges and ApplicationsA Fast Extraction-Free Isothermal LAMP Assay for Detection of SARS-CoV-2 in Resource-Limited SettingsA Fast, Lightweight nnUNet-Based Brain Tumor Segmentation Model Optimized for Low-Resource African Settings

A Multi-Task Benchmark for Abusive Language Detection in Low-Resource Settings

Content moderation research has recently made significant advances, but remains limited in serving t

Resource availability for the management of maternal sepsis in Malawi, other low‐income countries, and lower‐middle‐income countries

Abstract Objective

Educational Intervention for Effective Postoperative Pain Management in Low Resource Settings: Evidence from Ethiopia

Abstract Background The annual number of surgical operations performed is increasing throu

Towards an Explainable Machine Learning System for Early Detection of Pediatric Sepsis in Low-Resource Hospital Settings in Nigeria: Challenges and Applications

Pediatric sepsis remains one of the most pressing threats to child survival in low- and middle-incom

A Fast Extraction-Free Isothermal LAMP Assay for Detection of SARS-CoV-2 in Resource-Limited Settings

Abstract Background To retain the spread of SARS-CoV-2, fast, sensitive and cost-effectiv

A Fast, Lightweight nnUNet-Based Brain Tumor Segmentation Model Optimized for Low-Resource African Settings