Establishment and validation of apnea risk prediction models in preterm infants: a retrospective case control study

Abstract

Background: Apnea is common in preterm infants and can be accompanied with severe hypoxic damage. Early assessment of apnea risk can impact the prognosis of preterm infants. We constructed a prediction model to assess apnea risk in premature infants for identifying high-risk groups.

Methods: A total of 162 and 324 preterm infants with and without apnea who were admitted to the neonatal intensive care unit of Xiamen University between January 2018 and December 2021 were selected as the case and control groups, respectively. Demographic characteristics, laboratory indicators, complications of the patients, pregnancy-related factors, and perinatal risk factors of the mother were collected retrospectively. The participants were randomly divided into modeling (n = 388) and validation (n = 98) sets in an 8:2 ratio. Least Absolute Shrinkage and Selection Operator (LASSO) and multivariate logistic regression analyses were used to independently filter variables from the modeling set and build a model. A nomogram was used to visualize models. The calibration and clinical utility of the model was evaluated using consistency index, receiver operating characteristic (ROC) curve, calibration curve, and decision curve, and the model was verified using the validation set.

Results: Results of LASSO combined with multivariate logistic regression analysis showed that gestational age at birth, birth length, Apgar score, and neonatal respiratory distress syndrome were predictors of apnea development in preterm infants. The model was presented as a nomogram and the Hosmer-Lemeshow goodness of fit test showed a good model fit (χ²=5.192, df=8, P=0.737), with Nagelkerke R² of 0.410 and C-index of 0.831. The area under the ROC curve and 95% CI were 0.831 (0.787-0.874) and 0.829 (0.722-0.935), respectively. Delong's test comparing the AUC of the two data sets showed no significant difference (P=0.976). The calibration curve showed good agreement between the predicted and actual observations. The decision curve results showed that the threshold probability range of the model was 0.07-1.00, the net benefit was high, and the constructed clinical prediction model had clinical utility.

Conclusions: Our risk prediction model based on gestational age, birth length, Apgar score 10 min post-birth, and neonatal respiratory distress syndrome was validated in many aspects and had good predictive efficacy and clinical utility.

Keywords: Apnea; Influencing factors; Lasso regression; Nomogram; Predictive model; Preterm infant.

Fig. 1

Dynamic process diagram of screening variables by Least Absolute Shrinkage and Selection Operator (LASSO) regression. The corresponding lambda value of the LASSO model was 0.0815 for determining the minimum mean square error of one standard deviation (right vertical dashed line) based on the cross-validation method (10 ×)

Fig. 2

The selection process of the best parameter using lambda values of the Least Absolute Shrinkage and Selection Operator (LASSO) model. The LASSO regression coefficients for the 50 variables. Based on the log(lambda) sequence, the optimal lambda produced five features with non-zero coefficients

Fig. 3

A nomogram predicting the occurrence of apnea in premature infants

Fig. 4

Area under the receiver operating characteristic (ROC) curve of the modeling set

Fig. 5

Area under the receiver operating characteristic (ROC) curve of the validation set

Fig. 6

The calibration curve of patient prediction nomogram of apnea of prematurity (AOP) risk in the modeling set

Fig. 7

The calibration curve of patient prediction nomogram of apnea of prematurity (AOP) risk in the validation set

Fig. 8

Decision curve for the predicted apnea of prematurity (AOP) risk in the modeling set using the nomogram

Fig. 9

Decision curve for the predicted apnea of prematurity (AOP) risk in the validation set using the nomogram