Statistical analysis
Statistical analyses of all data, which were represented by counting data, performed using R software (Version 3.6.0; https://www.R-project.org). The least absolute shrinkage and selection operator (LASSO) method12 was used to screen out the clinical characteristics that it was best to predict the risk factors of fetal distress and entering into NICU, and then multivariate logistic regression analysis was used to build two predicting models of the risk factors of fetal distress and entering into NICU, P<0.05 was considered statistically significant. Two predicting models of nomogram were formulated based on the results of logistic regression analysis and by using R software. Discrimination of the two predicting models of nomogram were assessed by the concordance index (C-index) and receiver operating characteristic (ROC) curve. Bootstrapping validation with 1,000 resample were used for calculating a relatively corrected C-index. Selecting 70% of the total sample size randomly was as internal validation. Internal validation was assessed using the bootstrapping validation. Calibration and clinical usability of the two predicting models were respectively adopted by calibration curves and decision curve analysis. Decision curve analysis is a novel method that is better than the traditional decision analytic techniques to evaluate prediction models 13. Using the ROC curve and calibration to execute the Internal validation.