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. 2024 Jun 5;14(1):12884.
doi: 10.1038/s41598-024-63593-9.

Real-time predictive model of extrauterine growth retardation in preterm infants with gestational age less than 32 weeks

Liang Gao #  1 , Wei Shen #  1 , Fan Wu  2 , Jian Mao  3 , Ling Liu  4 , Yan-Mei Chang  5 , Rong Zhang  6 , Xiu-Zhen Ye  7 , Yin-Ping Qiu  8 , Li Ma  9 , Rui Cheng  10 , Hui Wu  11 , Dong-Mei Chen  12 , Ling Chen  13 , Ping Xu  14 , Hua Mei  15 , San-Nan Wang  16 , Fa-Lin Xu  17 , Rong Ju  18 , Zhi Zheng  1 , Xin-Zhu Lin  19 , Xiao-Mei Tong  20 ; Chinese Multicenter EUGR Collaborative Group
Collaborators, Affiliations

Real-time predictive model of extrauterine growth retardation in preterm infants with gestational age less than 32 weeks

Liang Gao et al. Sci Rep. .

Abstract

The aim of this study was to develop a real-time risk prediction model for extrauterine growth retardation (EUGR). A total of 2514 very preterm infants were allocated into a training set and an external validation set. The most appropriate independent variables were screened using univariate analysis and Lasso regression with tenfold cross-validation, while the prediction model was designed using binary multivariate logistic regression. A visualization of the risk variables was created using a nomogram, while the calibration plot and receiver operating characteristic (ROC) curves were used to calibrate the prediction model. Clinical efficacy was assessed using the decision curve analysis (DCA) curves. Eight optimal predictors that namely birth weight, small for gestation age (SGA), hypertensive disease complicating pregnancy (HDCP), gestational diabetes mellitus (GDM), multiple births, cumulative duration of fasting, growth velocity and postnatal corticosteroids were introduced into the logistic regression equation to construct the EUGR prediction model. The area under the ROC curve of the training set and the external verification set was 83.1% and 84.6%, respectively. The calibration curve indicate that the model fits well. The DCA curve shows that the risk threshold for clinical application is 0-95% in both set. Introducing Birth weight, SGA, HDCP, GDM, Multiple births, Cumulative duration of fasting, Growth velocity and Postnatal corticosteroids into the nomogram increased its usefulness for predicting EUGR risk in very preterm infants.

Keywords: Extrauterine growth retardation; Nomogram; Prediction; Very preterm infant.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Flow chart of study design. VPIs very preterm infants, GA gestation age. Select the predictors and fit the model using only the training set.
Figure 2
Figure 2
Variable selection by the LASSO regression analysis with k-fold cross-validation in the training set. (a) Forteen variables with nonzero coeffificients were selected by deriving the optimal lambda. (b) we plotted the AUC curve versus log(lambda) and drew dotted vertical lines based on 1 standard error criteria.
Figure 3
Figure 3
Risk factors of birth weight, SGA, HDCP, GDM, multiple births, cumulative duration of fasting, growth velocity and postnatal corticosteroids for nomogram prediction model. EUGR extrauterine growth retardation, SGA small for gestation age, HDCP hypertensive disease complicating pregnancy, GDM gestational diabetes mellitus.
Figure 4
Figure 4
Validation of the EUGR risk nomogram. Receiver operating characteristic curve validation of the EUGR risk nomogram in the training set (a) and validation set (b). Calibration curves of the predictive EUGR risk nomogram in the training set (c) and validation set (d). Decision curve analysis for the EUGR risk nomogram in the training set (e) and validation set (f). EUGR extrauterine growth retardation.
See this image and copyright information in PMC

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