Evidence map›Paper›PMID 34042599›Full record

ArticleJMIR medical informatics2021

An Algorithm (LaD) for Monitoring Childbirth in Settings Where Tracking All Parameters in the World Health Organization Partograph Is Not Feasible: Design and Expert Validation.

Michael S Balikuddembe, Peter K Wakholi, Nazarius M Tumwesigye, Thorkild Tylleskar

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Article in JMIR medical informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Michael S BalikuddembeCenter for International Health, University of Bergen, Bergen, Norway.ORCID https://orcid.org/0000-0001-6391-203X
Peter K WakholiSchool of Computing and Information Technology, Makerere University Kampala, Kampala, Uganda.ORCID https://orcid.org/0000-0002-0770-7995
Nazarius M TumwesigyeDepartment of Epidemiology and Biostatistics, Makerere University School of Public Health, Kampala, Uganda.ORCID https://orcid.org/0000-0001-7085-8780
Thorkild TylleskarCenter for International Health, University of Bergen, Bergen, Norway.ORCID https://orcid.org/0000-0003-4801-4324

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAfter determining the key childbirth monitoring items from experts, we designed an algorithm (LaD) to represent the experts' suggestions and validated it. In this paper we describe an abridged algorithm for labor and delivery management and use theoretical case to compare its performance with human childbirth experts.

objectiveThe objective of this study was to describe the LaD algorithm, its development, and its validation. In addition, in the validation phase we wanted to assess if the algorithm was inferior, equivalent, or superior to human experts in recommending the necessary clinical actions during childbirth decision making.

methodsThe LaD algorithm encompasses the tracking of 6 of the 12 childbirth parameters monitored using the World Health Organization (WHO) partograph. It has recommendations on how to manage a patient when parameters are outside the normal ranges. We validated the algorithm with purposively selected experts selecting actions for a stratified sample of patient case scenarios. The experts' selections were compared to obtain pairwise sensitivity and false-positive rates (FPRs) between them and the algorithm.

resultsThe mean weighted pairwise sensitivity among experts was 68.2% (SD 6.95; 95% CI 59.6-76.8), whereas that between experts and the LaD algorithm was 69.4% (SD 17.95; 95% CI 47.1-91.7). The pairwise FPR among the experts ranged from 12% to 33% with a mean of 23.9% (SD 9.14; 95% CI 12.6-35.2), whereas that between experts and the algorithm ranged from 18% to 43% (mean 26.3%; SD 10.4; 95% CI 13.3-39.3). The was a correlation (mean 0.67 [SD 0.06]) in the actions selected by the expert pairs for the different patient cases with a reliability coefficient (α) of .91.

conclusionsThe LaD algorithm was more sensitive, but had a higher FPR than the childbirth experts, although the differences were not statistically significant. An electronic tool for childbirth monitoring with fewer WHO-recommended parameters may not be inferior to human experts in labor and delivery clinical decision support.

Indexed as

algorithmchildbirth monitoringsoftware validationWHO partograph

Identifiers

PMID34042599
PMCPMC8193471

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