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Computerized predictive models in hospital settings for detecting severe maternal complications: a systematic review

Graziele Telles Vieira1, Stefhanie Conceição de Jesus2, Fiona Ann Lynn3

  • 1Federal University of Santa Catarina (UFSC), Florianópolis, Brazil. graziele.telles@ufsc.br.

Systematic Reviews
|March 28, 2026
PubMed
Summary

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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Computerized predictive models can help identify maternal health risks early in hospitals. This review assesses their effectiveness in preventing maternal mortality complications.

Area of Science:

  • Public Health
  • Medical Informatics
  • Maternal Health

Background:

  • Maternal mortality is a significant public health issue, particularly in low- and middle-income countries.
  • Key preventable causes include hypertension, gestational diabetes, pre-eclampsia, postpartum hemorrhage, and infections.
  • Computerized predictive models offer potential for early risk identification and prevention of severe maternal complications in hospital settings.

Purpose of the Study:

  • To systematically review the evidence on the effectiveness of computerized predictive models in intra-hospital environments.
  • To assess the impact of these models on adverse outcomes related to maternal mortality.

Main Methods:

  • Systematic review following PRISMA-P guidelines.
  • Inclusion of randomized and non-randomized studies from nine major databases.
Keywords:
Maternal healthMaternal mortalityPredictive modelsProtocolSystematic review

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  • Searches conducted in Portuguese, English, and Spanish, with no publication date restrictions.

    • Main Results:

    • Data extraction and quality assessment performed by independent researchers using GRADE and ROBINS-I tools.
    • Narrative synthesis guided by SWiM guidelines, with potential for meta-analysis.
    • PROSPERO registration number: CRD42024573613.

    Conclusions:

    • Findings aim to enhance understanding of predictive technologies in maternal healthcare.
    • Results will support clinical decision-making, educational initiatives, and health service planning.
    • Evidence generated will contribute to reducing preventable maternal deaths in hospital settings.