BMC public health · 2025
Development and validation of a decision tree model for prediction of insomnia risk among ischemic stroke convalescence patients.
СтатьяВалидационное исследованиеНазвание и аннотация: Перевод в очереди
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Sun Xuefeng, Wang Zilin, Song Yuqing, Cong Deyu, Sun Shu, Zhang Xinye, Zhang Ye, Zhang Hongshi
Аннотация
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.
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.