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Prognostic algorithms for post-discharge readmission and mortality among mother-infant dyads: an observational study protocol

Authors :
Matthew O. Wiens
Jessica Trawin
Yashodani Pillay
Vuong Nguyen
Clare Komugisha
Nathan Kenya-Mugisha
Angella Namala
Lisa M. Bebell
J. Mark Ansermino
Niranjan Kissoon
Beth A. Payne
Marianne Vidler
Astrid Christoffersen-Deb
Pascal M. Lavoie
Joseph Ngonzi
Source :
Frontiers in Epidemiology, Vol 3 (2023)
Publication Year :
2023
Publisher :
Frontiers Media S.A., 2023.

Abstract

IntroductionIn low-income country settings, the first six weeks after birth remain a critical period of vulnerability for both mother and newborn. Despite recommendations for routine follow-up after delivery and facility discharge, few mothers and newborns receive guideline recommended care during this period. Prediction modelling of post-delivery outcomes has the potential to improve outcomes for both mother and newborn by identifying high-risk dyads, improving risk communication, and informing a patient-centered approach to postnatal care interventions. This study aims to derive post-discharge risk prediction algorithms that identify mother-newborn dyads who are at risk of re-admission or death in the first six weeks after delivery at a health facility.MethodsThis prospective observational study will enroll 7,000 mother-newborn dyads from two regional referral hospitals in southwestern and eastern Uganda. Women and adolescent girls aged 12 and above delivering singletons and twins at the study hospitals will be eligible to participate. Candidate predictor variables will be collected prospectively by research nurses. Outcomes will be captured six weeks following delivery through a follow-up phone call, or an in-person visit if not reachable by phone. Two separate sets of prediction models will be built, one set of models for newborn outcomes and one set for maternal outcomes. Derivation of models will be based on optimization of the area under the receiver operator curve (AUROC) and specificity using an elastic net regression modelling approach. Internal validation will be conducted using 10-fold cross-validation. Our focus will be on the development of parsimonious models (5–10 predictor variables) with high sensitivity (>80%). AUROC, sensitivity, and specificity will be reported for each model, along with positive and negative predictive values.DiscussionThe current recommendations for routine postnatal care are largely absent of benefit to most mothers and newborns due to poor adherence. Data-driven improvements to postnatal care can facilitate a more patient-centered approach to such care. Increasing digitization of facility care across low-income settings can further facilitate the integration of prediction algorithms as decision support tools for routine care, leading to improved quality and efficiency. Such strategies are urgently required to improve newborn and maternal postnatal outcomes. Clinical trial registrationhttps://clinicaltrials.gov/, identifier (NCT05730387).

Details

Language :
English
ISSN :
26741199
Volume :
3
Database :
Directory of Open Access Journals
Journal :
Frontiers in Epidemiology
Publication Type :
Academic Journal
Accession number :
edsdoj.f0ddd0fee24b4d1499c59bf04b0aa261
Document Type :
article
Full Text :
https://doi.org/10.3389/fepid.2023.1233323