Abstract: Objective  To investigate risk factors for hemodialysis-related headache (HRH) and develop and validate a predictive model using logistic-nomogram analysis. Methods  Clinical data of hemodialysis patients at the Second Hospital of Lanzhou University from March 2021 to March 2024 were retrospectively analyzed. Patients treated from March 2021 to May 2023 were assigned to the training set (n=229), and those from June 2023 to March 2024 were included in the validation set (n=98). The influencing factors of HRH were analyzed by Logistic regression models. A nomogram model was constructed using the rms package in R, with calibration evaluated via the calibrate function. Receiver operating characteristic (ROC) curves assessed the model’s predictive performance. Results  There was no significant difference in clinical data between the training set and the validation set (P＞0.05). Logistic regression analysis showed that elevated pre-dialysis systolic blood pressure (SBP, OR =1.124, 95% CI: 1.051～1.203, P=0.001), diastolic blood pressure (DBP, OR=1.128, 95% CI: 1.066～1.194, P=0.001), and serum sodium (OR=1.119, 95% CI:1.076～1.338, P＜0.001) were independent risk factors for HRH in hemodialysis patients, while higher platelet count (PLT) was a protective factor for HRH (OR=0.932, 95% CI:0.895～0.971, P=0.001). The prediction model of HRH risk in hemodialysis patients was constructed with SBP, DBP, serum sodium and PLT as variables. The nomogram model demonstrated strong predictive performance, Dxy=0.831, C-index=0.916, mean absolute error (MAE) =0.017 in the training set, and Dxy=0.804, C-index=0.902, MAE=0.029 in the validation set. Calibration curves closely aligned with ideal curves in both sets.  ROC curve analysis showed that the area under the curve (AUC) =0.916 (95% CI: 0.872～0.948), Youden index=0.720, sensitivity=81.13%, specificity=90.91% in the training set, and AUC=0.903 (95% CI: 0.826～0.954), Youden index=0.756, sensitivity=92%, specificity= 83.56% in the validation set.  Conclusion  Elevated pre-dialysis SBP, DBP, and serum sodium are independent risk factors for HRH, while higher PLT is protective. The nomogram model based on these variables provides robust predictive value for HRH risk in hemodialysis patients.

Key words: Hemodialysis-related headache, Influencing factors, Nomogram, Prediction model