Objective To develop an onset risk prediction nomogram for patients with homocysteine-type (H-type) hypertension (HTH) based on pulse diagram parameters to assist early clinical prediction and diagnosis of HTH.

Methods Patients diagnosed with essential hypertension and admitted to Shanghai Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai Hospital of Traditional Chinese Medicine, and Shanghai Hospital of Integrated Traditional Chinese and Western Medicine from July 6th 2020 to June 16th 2021, and from August 11th 2023 to January 22nd 2024, were enrolled in this retrospective research. The baselines and clinical biochemical indicators of patients were collected. The SMART-I TCM pulse instrument was applied to gather pulse diagram parameters. Multivariate logistic regression was adopted to analyze the risk factors for HTH. RStudio was employed to construct the nomogram model, receiver operating characteristic (ROC) curve, and calibration curve (bootstrap self-sampling 200 times), and clinical decision curve were drawn to evaluate the model’s discrimination and clinical effectiveness.

Results A total of 168 hospitalized patients with essential hypertension were selected and divided into non-HTH group (n = 29) and HTH group (n = 139). Compared with non-HTH group, HTH group had a lower body mass index (BMI), and higher proportions of male patients and drinkers (P < 0.05). The ventricular wall thickening (VWT) could not be determined. The proportions of left common carotid intima-media wall thickness (LCCIMWT) and serum creatinine (SCR) were higher in HTH group (P < 0.05). The pulse diagram parameter As was significantly higher, and H4/H1 and T1/T were lower in HTH group (P < 0.05). Gender, alcohol consumption, serum creatinine, and the pulse diagram parameter H4/H1 were identified as independent risk factors for HTH (P < 0.05). The nomogram’s area under the ROC curve (AUC) was 0.795 95% confidence interval (CI): (0.706 6, 0.882 8), with a specificity of 0.724 and sensitivity of 0.799. After 200 times repeated bootstrap self-samplings, the calibration curve showed that the simulated curve fits well with the actual curve (x² = 9.5002, P = 0.301 9). The clinical decision curve indicated that the nomogram’s applicability was optimal when the threshold for predicting HTH was between 0.38 and 1.00.

Conclusion The nomogram model could be valuable for predicting the onset risk of HTH and pulse diagram parameters can facilitate early screening and prevention of HTH.