Novel risk-factor analysis and risk-evaluation model of falls in patients receiving maintenance hemodialysis

This study investigated the prevalence of falls in maintenance hemodialysis (MHD) patients, and established a nomogram model for evaluating the fall risk of MHD patients. This study enrolled 303 MHD patients from the dialysis department of a tertiary hospital in July 2021. The general data of the participants, as well as the scores on the FRAIL scale, Sarcopenia Screening Questionnaire (SARC-F), Short Physical Performance Battery (SPPB) Scale, and of anxiety and depression, and the occurrence of falls were recorded. Using R language, data were assigned to the training set (n = 212) and test set (n = 91), and a logistic regression model was established. The regression model was verified by the receiver operating characteristic (ROC) curve, area under the curve (AUC), and the calibration curve. As a result, the prevalence of falls in MHD patients was 20.46%. Risk factors for falls in the optimal multivariate logistic regression model included hearing impairment, the depression score, and the SPPB score, of which a higher depression score (odds ratio (OR): 1.28, 95% confidence interval (CI): 1.09-1.49, p = 0.002) and SPPB <= 6 (OR (vs) (SPPB9-12): 3.69, 95% CI: 1.04-13.14, p = 0.043) conferred independent risk for falls. AUC of the nomogram in the training was 0.773, which in the test group was 0.663. The calibration and standard curves were fitted closely, indicated that the evaluation ability of the model was good. Thus, a higher depression score and SPPB <= 6 are independent risk factors for falls in MHD patients, and the nomogram with good accuracy and discrimination that was established in this study has clinical application value.

Automated summary

This study investigated the prevalence of falls in maintenance hemodialysis (MHD) patients and developed a nomogram model for assessing fall risk. A total of 303 MHD patients were enrolled, and their general data, as well as scores on various scales and occurrence of falls, were recorded. Logistic regression and receiver operating characteristic analysis were performed to validate the model. Higher depression scores and SPPB <= 6 were identified as independent risk factors for falls in MHD patients. The nomogram showed good accuracy and discrimination in predicting fall risk.