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.