Development and validation of a nomogram prediction model for sleep disorders in elderly Parkinson disease patients based on multidimensional data: A retrospective case-control study.
Development and validation of a nomogram prediction model for sleep disorders in elderly Parkinson disease patients based on multidimensional data: A retrospective case-control study.
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The study site has not been established. Author addresses may differ from where the research occurred.
Nanjing, CN · Author affiliation
Department of Geriatric, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China.Location evidence
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Original abstract
Sleep disturbances are highly prevalent among elderly patients with Parkinson disease (PD), substantially impairing their quality of life. This study aimed to identify associated risk factors and to develop and validate a nomogram risk prediction model based on multidimensional data, providing a practical tool for early clinical identification and intervention. A retrospective case-control study was conducted on 340 elderly PD patients admitted to a tertiary hospital between June 2023 and June 2025. Multidimensional data were collected through self-designed questionnaires, standardized clinical scales, and electronic medical records. Patients were classified into a sleep disorder group and a non-sleep disorder group using the second version of the Parkinson Disease Sleep Scale (PDSS-2) with a cutoff score of ≥18. Univariate and multivariate logistic regression analyses were performed to identify independent risk factors, which were subsequently incorporated into the nomogram model. Model discrimination was assessed by the receiver operating characteristic (ROC) curve, while calibration was evaluated using bootstrap resampling and calibration plots. The prevalence of sleep disturbances in elderly hospitalized PD patients was 61.2%. Multivariate analysis identified advanced Hoehn-Yahr stage, higher UPDRS-II score, severe nocturnal pain, increased nocturia frequency, and elevated HADS-depression scores as independent predictors of sleep disturbances. The developed nomogram demonstrated good discriminative ability, with an area under the ROC curve (AUC) of 0.867, sensitivity of 0.884, and specificity of 0.765. The Hosmer-Lemeshow test indicated satisfactory model calibration. Sleep disturbances are common in elderly hospitalized PD patients and are influenced by multiple clinical factors. The proposed nomogram model exhibits favorable predictive performance and calibration, offering an effective clinical tool for risk stratification and facilitating early intervention.