Development and validation of a decision tree model for prediction of insomnia risk among ischemic stroke convalescence patients
Abstract Background Insomnia is a common complication in ischemic stroke convalescence (ISC) patients. While the interaction of clinical, psychological, and social factors remains unclear, developing a predictive model system is urgently needed. Currently, few studies have established insomnia risk...
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BMC
2025-08-01
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| Series: | BMC Public Health |
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| Online Access: | https://doi.org/10.1186/s12889-025-24025-z |
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| author | Xuefeng Sun Zilin Wang Yuqing Song Deyu Cong Shu Sun Xinye Zhang Ye Zhang Hongshi Zhang |
| author_facet | Xuefeng Sun Zilin Wang Yuqing Song Deyu Cong Shu Sun Xinye Zhang Ye Zhang Hongshi Zhang |
| author_sort | Xuefeng Sun |
| collection | DOAJ |
| description | Abstract Background Insomnia is a common complication in ischemic stroke convalescence (ISC) patients. While the interaction of clinical, psychological, and social factors remains unclear, developing a predictive model system is urgently needed. Currently, few studies have established insomnia risk prediction models. Objectives To construct a decision tree model for insomnia risk among ISC patients based on the classification and regression tree algorithm. Design Across-sectional study. Setting China. Participants The study enrolled 823 adult ISC patients between February 2023 and October 2024. Participants were recruited from stroke units in two tertiary hospitals in Jilin Province. Methods Following the TRIPOD+AI guidelines, we constructed a decision tree model utilizing data from the Pittsburgh Sleep Quality Index (PSQI), Fatigue Severity Scale (FSS), Social Support Scale (SSRS), and other assessment tools. Model validation encompassed 10-fold cross-validation, incorporating confusion matrix, ROC curves, calibration curve, and Brier scores. The model was trained on 623 patients and externally validated on an independent cohort of 200 cases. Results The study revealed an insomnia prevalence of 37.72%camong ISC patients. Univariate analysis identified BMI, SAS, SSRS, FSS, SDS, and NIHSS as significant factors. The decision tree model delineated 24 pathways (depth = 6), with predictive contributions ranked as follows: SAS > SSRS > FSS > SDS > BMI > NIHSS, which were integrated into a nomogram. Internal validation exhibited robust predictive accuracy (90.4%), with a sensitivity of 0.96, specificity of 0.84, Youden index of 0.80, and F1 score of 0.89. The AUC was 0.96 (95% CI: 0.93–0.98; p < 0.001), indicating well-calibrated predictions (χ² = 9.36, p = 0.404). Brier scores were 0.06 for the training set and 0.08 for the validation set. External validation demonstrated an accuracy of 82%. The decision curve analysis demonstrated acceptable clinical utility. Conclusion This model demonstrates promise in forecasting insomnia among ISC patients. Anxiety and social support emerged as the most influential predictors, with fatigue, depression, BMI, and stroke severity collectively offering a comprehensive outlook for anticipating post-stroke insomnia. These results have implications for informing future strategies in managing insomnia. The model's applicability is moderately robust, necessitating additional refinement to accurately pinpoint insomnia. |
| format | Article |
| id | doaj-art-b011451687fe4b48af74a888e710dd2e |
| institution | Kabale University |
| issn | 1471-2458 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Public Health |
| spelling | doaj-art-b011451687fe4b48af74a888e710dd2e2025-08-24T11:55:53ZengBMCBMC Public Health1471-24582025-08-0125111510.1186/s12889-025-24025-zDevelopment and validation of a decision tree model for prediction of insomnia risk among ischemic stroke convalescence patientsXuefeng Sun0Zilin Wang1Yuqing Song2Deyu Cong3Shu Sun4Xinye Zhang5Ye Zhang6Hongshi Zhang7Changchun University of Chinese MedicineChangchun University of Chinese MedicineChangchun University of Chinese MedicineAffiliated Hospital of Changchun University of Chinese MedicineThe Third Affiliated Hospital of Changchun University of Chinese MedicineChangchun University of Chinese MedicineChangchun University of Chinese MedicineChangchun University of Chinese MedicineAbstract Background Insomnia is a common complication in ischemic stroke convalescence (ISC) patients. While the interaction of clinical, psychological, and social factors remains unclear, developing a predictive model system is urgently needed. Currently, few studies have established insomnia risk prediction models. Objectives To construct a decision tree model for insomnia risk among ISC patients based on the classification and regression tree algorithm. Design Across-sectional study. Setting China. Participants The study enrolled 823 adult ISC patients between February 2023 and October 2024. Participants were recruited from stroke units in two tertiary hospitals in Jilin Province. Methods Following the TRIPOD+AI guidelines, we constructed a decision tree model utilizing data from the Pittsburgh Sleep Quality Index (PSQI), Fatigue Severity Scale (FSS), Social Support Scale (SSRS), and other assessment tools. Model validation encompassed 10-fold cross-validation, incorporating confusion matrix, ROC curves, calibration curve, and Brier scores. The model was trained on 623 patients and externally validated on an independent cohort of 200 cases. Results The study revealed an insomnia prevalence of 37.72%camong ISC patients. Univariate analysis identified BMI, SAS, SSRS, FSS, SDS, and NIHSS as significant factors. The decision tree model delineated 24 pathways (depth = 6), with predictive contributions ranked as follows: SAS > SSRS > FSS > SDS > BMI > NIHSS, which were integrated into a nomogram. Internal validation exhibited robust predictive accuracy (90.4%), with a sensitivity of 0.96, specificity of 0.84, Youden index of 0.80, and F1 score of 0.89. The AUC was 0.96 (95% CI: 0.93–0.98; p < 0.001), indicating well-calibrated predictions (χ² = 9.36, p = 0.404). Brier scores were 0.06 for the training set and 0.08 for the validation set. External validation demonstrated an accuracy of 82%. The decision curve analysis demonstrated acceptable clinical utility. Conclusion This model demonstrates promise in forecasting insomnia among ISC patients. Anxiety and social support emerged as the most influential predictors, with fatigue, depression, BMI, and stroke severity collectively offering a comprehensive outlook for anticipating post-stroke insomnia. These results have implications for informing future strategies in managing insomnia. The model's applicability is moderately robust, necessitating additional refinement to accurately pinpoint insomnia.https://doi.org/10.1186/s12889-025-24025-zInsomniaIschemic stroke convalescenceDecision treePredictive modelNomogram |
| spellingShingle | Xuefeng Sun Zilin Wang Yuqing Song Deyu Cong Shu Sun Xinye Zhang Ye Zhang Hongshi Zhang Development and validation of a decision tree model for prediction of insomnia risk among ischemic stroke convalescence patients BMC Public Health Insomnia Ischemic stroke convalescence Decision tree Predictive model Nomogram |
| title | Development and validation of a decision tree model for prediction of insomnia risk among ischemic stroke convalescence patients |
| title_full | Development and validation of a decision tree model for prediction of insomnia risk among ischemic stroke convalescence patients |
| title_fullStr | Development and validation of a decision tree model for prediction of insomnia risk among ischemic stroke convalescence patients |
| title_full_unstemmed | Development and validation of a decision tree model for prediction of insomnia risk among ischemic stroke convalescence patients |
| title_short | Development and validation of a decision tree model for prediction of insomnia risk among ischemic stroke convalescence patients |
| title_sort | development and validation of a decision tree model for prediction of insomnia risk among ischemic stroke convalescence patients |
| topic | Insomnia Ischemic stroke convalescence Decision tree Predictive model Nomogram |
| url | https://doi.org/10.1186/s12889-025-24025-z |
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