Development of an early prediction model for vomiting during hemodialysis using LASSO regression and Boruta feature selection

Hemodialysis patients (HD) frequently experience nausea and vomiting as side effects, which can make the procedure uncomfortable for them and cause it to end too soon. There are no known predictors of vomiting. We aim to create a nomogram that can anticipate vomiting in hemodialysis patients. We conducted a retrospective screening of patients with end-stage renal disease (ESRD) who received regular hemodialysis at the First People's Hospital of Nantong from January 1, 2023, to October 31, 2024. The outcome of the nomogram indicated vomiting, which was evaluated using the Korttila scale. The least absolute shrinkage selection operator (LASSO) method and Boruta feature selection were employed for the optimal prediction of predictors. Multiple logistic regression was employed to construct predictive models presented as nomograms. The efficacy of nomograms was evaluated using receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA). The model underwent internal validation by assessing the validation cohort's performance. The study included 281 patients. Ninety-two patients, representing 32.74%, exhibited symptoms of vomiting. Participants were randomly assigned to training (n = 196) and validation (n = 85) groups. The nomogram incorporated predictors such as sex, height, heart rate, spKt.V, lymphocytes, and lactate dehydrogenase. The ROC curves for both the training and verification groups demonstrate strong recognition capability, while the calibration curves indicate that the correction outcomes for both groups are highly satisfactory. This nomogram assists clinicians in identifying high-risk populations and supports the formulation of effective preventive strategies.