Development and internal validation of a clinical prediction model for hemodialysis-related headache using LASSO and Boruta feature selection.

A common neurological symptom in dialysis patients is hemodialysis-related headache (HRH), which can result in premature hemodialysis termination and cause patients to decline hemodialysis. Predictors of headache have not been identified. One of our objectives is to create a nomogram that can accurately predict headaches in hemodialysis patients. Between 1 January 2023 and 31 October 2024, we retrospectively enrolled single-center patients with end-stage renal disease (ESRD, CKD-G5D) - diagnosed according to the 2012 KDIGO Clinical Practice Guideline for Chronic Kidney Disease-who received regular hemodialysis at the First People's Hospital of Nantong. Headache was the outcome of the nomogram. The 289 patients were randomly split 7:3 into training (n = 202) and validation (n = 87) cohorts. Predictors were selected using sequential LASSO and Boruta algorithms. Multivariable logistic regression was employed to develop predictive models, which were subsequently presented as nomograms. The efficacy of the nomograms was evaluated using receiver operating characteristic (ROC) curves, calibration graphs, and decision curve analysis (DCA). The performance of the validation cohort was calculated to validate the model internally. A total of 289 patients were included in the investigation. Headache symptoms were reported by 103 patients (35.64%). Model performance was internally validated using 500 bootstrap resamplings for optimism correction in the full cohort. The nomogram included the following predictors: sex, age, weight, difference in pulse pressure, serum sodium concentration, and serum phosphorus concentration. Bootstrap-corrected calibration and discrimination showed good performance verification. In contrast, the area under the curve (AUC) values for the training and validation cohorts were 0.909 (95% CI 0.8662-0.9522) and 0.873 (0.8002-0.946), respectively. This nomogram may assist clinicians in identifying patients at higher risk, but external validation in independent multicenter cohorts is needed.