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A nomogram for predicting neonatal apnea: a retrospective analysis based on the MIMIC database.

Huisi Huang1, Yanhong Shi1, Yinghui Hong1

  • 1Department of Paediatrics, The Affiliated TCM Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China.

Frontiers in Pediatrics
|September 20, 2024
PubMed
Summary

This study developed a predictive model for neonatal apnea using routine examination indicators. The logistic regression model, incorporating 7 key factors, demonstrated strong predictive performance, aiding clinical staff in early detection.

Keywords:
MIMIC databaselogistic regressionneonatal apneanomogramretrospective analysis

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Area of Science:

  • Neonatal Medicine
  • Biostatistics
  • Predictive Modeling

Background:

  • Neonatal apnea is a common concern requiring effective prediction methods.
  • Routine neonatal examinations offer potential indicators for early detection.

Purpose of the Study:

  • To develop and validate a predictive model for neonatal apnea using routine examination data.
  • To identify key indicators for predicting neonatal apnea in newborns.

Main Methods:

  • Retrospective analysis of 8024 newborns from the MIMIC IV database.
  • Development of logistic regression and decision tree models.
  • Variable selection using stepwise logistic regression and LASSO regression.

Main Results:

  • A 7-indicator model (gestational age, birth weight, ethnicity, gender, monocytes, lymphocytes, acetaminophen) was developed.
  • Logistic regression model achieved an AUC of 0.879 (training) and 0.865 (validation).
  • Decision tree model achieved an AUC of 0.861 (training) and 0.850 (validation), with logistic regression showing superior performance.

Conclusions:

  • Blood indicators are valuable and effective predictors of neonatal apnea.
  • The developed model provides effective predictive information for medical staff.
  • Logistic regression model demonstrates robust predictive capability for neonatal apnea.