Objective To explore the factors affecting sleep disorders in hospitalized elderly patients and to construct a nomogram for sleep disorders based on comprehensive geriatric assessment to guide clinical practice.Methods By retrospectively analyzing the clinical data of hospitalized elderly patients in our hospital,logistic regression analysis was performed using comprehensive geriatric assessment indicators to determine the factors affecting sleep disorders and to construct a predictive model.The effectiveness of the model was evaluated by comparing the Pittsburgh sleep quality index (PSQI) scores of patients in the observation group using the model with those in the control group without using the model.Results Multivariate logistic regression analysis showed that age,urinary incontinence(frequency of urination),pain score,anxiety score,and depression score are independent risk factors for sleep disorders(P<0.05).The receiver operating characteristic (ROC) curve area(AUC) of the constructed model for predicting sleep disorders in hospitalized elderly patients was 0.802(95％CI:0.755～0.850),indicating good predictive ability.After using the model for sleep disorder assessment and targeted intervention in the observation group,the PSQI score of patients was reduced to (6.30±3.326),which was statistically significantly different from the control group's score of (7.60±3.20)(P<0.05).Multivariate logistic regression analysis showed that age,urinary incontinence(frequency),pain score,anxiety score,and depression score were independent risk factors for sleep disorders(P<0.05).The area under the ROC curve(AUC)of the nomogram for predicting sleep disorders in elderly hospitalized patients was 0.802(95％CI:0.755-0.850),with the internal calibration curve close to the standard curve.After using the nomogram to assess sleep disorders in elderly hospitalized patients and implementing targeted interventions,the PSQI score of the intervention group was effectively reduced to 6.30±3.33,lower than the PSQI score of the control group(7.60±3.201),with statistical significance(P<0.05).Conclusion The sleep disorder prediction model has high discrimination and accuracy,which helps clinical medical staff to accurately assess sleep disorders in hospitalized elderly patients and to carry out effective interventions,thereby improving the sleep quality of hospitalized elderly patients.
万方数据
维普
