A nomogram for predicting neonatal apnea: a retrospective analysis based on the MIMIC database
Year: 2024
Type: article
Abstract: Introduction: The objective of this study is to develop a model based on indicators in the routine examination of neonates to effectively predict neonatal apnea. Methods: We retrospectively analysed 8024 newborns from the MIMIC IV database, building logistic regression models and decision tree models. The performance of the model is examined by decision curves, calibration curves and ROC curves. Variables were screened by stepwise logistic regression analysis and LASSO regression. Results: A total of 7 indicators were ultimately included in the model: gestational age, birth weight, ethnicity, gender, monocytes, lymphocytes and acetaminophen. The mean AUC (the area under the ROC curve) of the 5-fold cross-validation of the logistic regression model in the training set and the AUC in the validation set are 0.879 and 0.865, respectively. The mean AUC (the area under the ROC curve) of the 5-fold cross-validation of the decision tree model in the training set and the AUC in the validation set are 0.861 and 0.850, respectively. The calibration and decision curves in the two cohorts also demonstrated satisfactory predictive performance of the model. However, the logistic regression model performs relatively well. Discussion: Our results proved that blood indicators were valuable and effective predictors of neonatal apnea, which could provide effective predictive information for medical staff.
Source: Frontiers in Pediatrics
Authors: Huisi Huang, Yanhong Shi, Yinghui Hong, Lizhen Zhu, Mengyao Li, Yue Zhang
Institution: Guangzhou Medical University
Language: English
Subfield: Endocrine and Autonomic Systems
Field: Neuroscience
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $3,295
OpenAlex ID: W4402285093