Abstract:

Objective To construct a risk prediction model for complications of midline catheters（MC） in neonates using machine learning methods，verify and compare the performance of various models，and provide a reference for the prediction and early prevention of MC complications in neonates. Methods The clinical data of 243 neonates who received MC insertion in the neonatal intensive care unit of a provincial tertiary general hospital in Bengbu City，Anhui Province from August 2020 to December 2023 and met the inclusion and exclusion criteria were retrospectively collected，including general neonatal information，underlying diseases，catheterization-related information，etc. The research subjects were randomly divided into a training set of 170 cases and a testing set of 73 cases in a 7：3 ratio. Recursive feature elimination algorithm was used to initially screen the features，and five machine learning algorithms，including logistic regression，random forest，support vector machine，least absolute shrinkage and selection operator，and extreme gradient boosting，were used to construct the prediction models. Model parameters were optimized through cross-validation，and the performance of different models was compared to select the best model for internal validation and to construct a nomogram. Results Among the 243 neonates with MC insertion，37（15.23%） developed complications. In the training set，all five models had good fitting degrees，among which the RF model had the largest AUC（0.843），specificity（0.916），PPV（0.613），accuracy（0.882），and F1 value（0.655），outperforming the other models. In the test set，the RF model also outperformed the other models in terms of AUC（0.881），specificity（0.937），PPV（0.636），accuracy（0.904），and F1 value（0.666）. In the RF model，the top five most important clinical features were birth weight，catheter tip position，gestational age，smoothness of catheter insertion，and puncture site. Conclusion The RF model for predicting the risk of MC complications in neonates based on machine learning has certain accuracy and practicality. The nomogram is helpful for clinical medical staff to identify high-risk neonates in advance and take targeted preventive measures.

Key words: Newborn, Medium Catheter, Complications, Prediction Model, Machine Learning