Construction of risk prediction model for orthostatic hypotension in patients with Parkinson's disease
Objective To explore the influencing factors of orthostatic hypotension in patients with Parkinson's disease,and to con-struct a risk prediction model.Methods A total of 288 patients with Parkinson's disease admitted to a hospital from April 2020 to August 2021 were selected as the research objects.According to whether orthostatic hypotension occurred,they were divided into orthostatic hypoten-sion group and non-orthostatic hypotension group.Logistic regression analysis was used to analyze the risk factors of orthostatic hypotension in patients with Parkinson's disease.Based on this,a nomogram risk prediction model was constructed and internal verification was performed.The Hosmer-Lemeshow goodness of fit test was used to analyze the consistency of the nomogram model,and the receiver operating characteristic curve was used to evaluate the predictive value of the nomogram model.Results There were 92 cases of orthostatic hypotension in 288 pa-tients with Parkinson's disease,and the incidence was 31.94%.Age,body mass index,history of hypertension,history of diabetes,Hoehn-Yahr stage,rapid eye movement sleep behavior disorder,long-term bed rest,constipation,and total levodopa equivalent daily dose were independent risk factors for Parkinson's disease patients with orthostatic hypotension(P＜0.05).The result of the Hosmer-Lemeshow goodness of fit test showed that the consistency of the nomogram model was in good agreement(?2=1.248,P=0.428).The area under the curve of ROC was 0.995(95%CI:0.990,0.999),with a sensitivity of 0.989 and a specificity of 0.934.Conclusion The incidence of orthostatic hypotension in Parkinson's disease is relatively high,a nomogram risk prediction model for Parkinson's disease patients with orthostatic hypotension based on independent risk factors can provide a certain reference for clinical diagnosis and treatment.
Parkinson's diseaseorthostatic hypotensioninfluencing factorsnomogram model
万方数据
维普
