Objective To develop a nomogram model for predicting the risk of medication deviation in ischemic stroke patients after hospital discharge,providing insights for clinical nursing strategies.Methods A convenience sampling method was used to select 440 ischemic stroke patients hospitalized in the Neurology Department of Nanyang Central Hospital from March 2022 to September 2023.Data on basic patient information and post-discharge medication deviation were collected using the medication deviation assessment tool eight weeks after discharge.Patients were categorized into a no medication deviation group and a medication deviation group.Univariate and binomial logistic regression analyses were conducted to identify independent risk factors for post-discharge medication deviation,and a nomogram prediction model was developed.Results Data were successfully collected from 424 patients,with a response rate of 96.36％(424/440).The incidence of medication deviation was 58.02％(246/424).Binomial logistic regression analysis identified the absence of a fixed caregiver(OR=2.113,P=0.001),large number of comorbid chronic diseases(OR=1.773,P＜0.001),medication non-adherence(OR=2.443,P=0.003),and negative coping(OR=2.255,P＜0.001)as independent risk factors for medication deviation.A nomogram prediction model based on these four risk factors was constructed.The Hosmer-Lemeshow goodness-of-fit test indicated good calibration(x 2=10.569,P=0.227),with calibration curve closely approximating the ideal line.The area under the receiver operating characteristic curve was 0.770(95％CI:0.584-0.866).Conclusions The absence of a fixed caregiver,medication non-adherence,negative coping,and large number of comorbid chronic diseases are independent risk factors for medication deviation in ischemic stroke patients.The nomogram model developed in this study demonstrates good discrimination and accuracy,providing a basis for formulating targeted nursing strategies.
维普
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