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Predictive Accuracy of Infant Clinical Sign Algorithms for Mortality in Young Infants Aged 0 to 59 Days: A Systematic

Yasir Shafiq1,2,3,4, Alastair Fung5, Sophie Driker1

  • 1Global Advancement of Infants and Mothers (AIM), Department of Pediatric Newborn Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States.

Pediatrics
|August 1, 2024
PubMed

Insights

Clinical sign algorithms can help identify young infants at risk of mortality, especially in hospitals. However, evidence certainty is low due to algorithm variety and limited external validation.

Area of Science:

  • Neonatal Health
  • Pediatric Mortality Prediction
  • Clinical Decision Support

Background:

  • Clinical sign algorithms are crucial for identifying young infants susceptible to mortality.
  • Accurate identification is essential for timely intervention and improved outcomes.

Purpose of the Study:

  • To systematically review and synthesize evidence on the accuracy of clinical sign algorithms in predicting all-cause mortality in infants aged 0-59 days.
  • To assess the reliability and generalizability of existing algorithms.

Main Methods:

  • A comprehensive literature search was conducted across multiple databases (MEDLINE, Embase, CINAHL, Global Index Medicus, Cochrane CENTRAL).
  • Studies evaluating infant clinical sign algorithms for mortality prediction were included.
  • Cochrane methods were used for study screening, data extraction, and risk of bias assessment, with certainty of evidence determined by Grading of Recommendations Assessment, Development and Evaluation (GRADE).

Main Results:

  • Eleven studies involving 26 algorithms were analyzed. Algorithms included sign-based checklists and hospital-based regression models (weighted scores, formulas, nomogram).
  • A community-based checklist showed high sensitivity and specificity for sepsis-related deaths in India but poor external validation in Bangladesh for all-cause mortality.
  • Hospital-based prediction models demonstrated good performance with Area Under the Curve (AUC) ranging from 0.76-0.93, with one model (Score for Essential Neonatal Symptoms and Signs) achieving an AUC of 0.89 in derivation and 0.83 in external validation.

Conclusions:

  • The heterogeneity of algorithms and insufficient external validation limit the current evidence base.
  • While clinical sign algorithms show promise for identifying at-risk infants, particularly in hospital settings, the overall certainty of evidence is low.
  • Further research with robust external validation is needed to improve the reliability of these tools.
Abstract

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