移居高原人群睡眠障碍的实用筛查工具:基于问卷的列线图模型构建与验证OA

Objective To explore the risk factors for sleep disorders in high-altitude migrants and to develop and validate an individualized risk prediction nomogram model based on easily accessible variables.Methods A cluster random sampling method was employed from July to August 2025 to conduct a questionnaire survey among eligible high-altitude migrants in regions of different elevations in Xizang Autonomous Region.Data were collected on demographic characteristics,living habits,high-altitude residence history,past medical history,sleep status,and fatigue levels.Valid questionnaire data were ultimately obtained from 1405 resettled individuals.Participants were randomly divided into training set(n=984)and validation set(n=421)at a ratio of 7:3.Univariate analysis,least absolute shrinkage and selection operator(LASSO)regression,and multivariate logistic regression were used to screen independent risk factors,and a nomogram model was constructed.Internal validation of the model was performed using the receiver operating characteristic(ROC)curves,calibration plots,Hosmer-Lemeshow test,and decision curve analysis(DCA).Results After preliminary screening of potential relevant variables via univariate analysis and LASSO regression,multivariate logistic regression analysis revealed that educational level,altitude of residence,duration of high-altitude residence,duration of hyperlipidemia,duration of hyperuricemia,duration of intervertebral disc herniation,and total score of the Multidimensional Fatigue Inventory-20(MFI-20)were 7 independent risk factors for high-altitude sleep disorders.A nomogram model was established based on the above risk factors and validated.The area under the ROC curve(AUC)of the nomogram model was 0.857 in the training set and 0.818 in validation set.The calibration curves showed good consistency between the predicted probabilities and the actual probabilities,and the P values of the Hosmer-Lemeshow test were 0.433 in the training set and 0.087 in validation set,indicating satisfactory goodness of fit.Decision curve analysis(DCA)demonstrated that the model could provide significant net clinical benefit when the predicted risk thresholds ranged from 0.1 to 0.9 in the training set and from 0.15 to 1.0 in the validation set.Conclusions Educational level,altitude of residence,duration of high-altitude residence,duration of hyperlipidemia,duration of hyperuricemia,duration of intervertebral disc herniation,and MFI-20 total score are influencing factors for sleep disorders in high-altitude migrants.The nomogram model developed in this study,based on easily accessible questionnaire information,demonstrates good predictive performance in assessing the overall risk of sleep disorders in this population.