Construction and validation of a medication deviation prediction model for hospital-to-home transition period in coronary heart disease patients with initial treatment
- VernacularTitle:初治冠心病患者在医院-家庭过渡期的用药偏差预测模型构建与验证
- Author:
Yushuang LI
1
;
Shu LI
2
;
Qianying ZHANG
2
;
Yan HUANG
2
;
Kun LIU
2
;
Xiulin GU
3
;
Huanhuan JIANG
2
Author Information
1. Dept. of Pharmacy，the Affiliated Hospital of North China University of Science and Technology，Hebei Tangshan 063000，China;School of Pharmacy，North China University of Science and Technology，Hebei Tangshan 063210，China
2. Dept. of Pharmacy，the Affiliated Hospital of North China University of Science and Technology，Hebei Tangshan 063000，China
3. Dept. of Pharmacy，the Second Affiliated Hospital of Hebei Northern University，Hebei Zhangjiakou 075100，China
- Publication Type:Journal Article
- Keywords:
coronary heart disease;
medication deviation;
hospital-to-home transition period;
influencing factors;
predictive model
- From:
China Pharmacy
2026;37(4):491-496
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVE To develope a predictive model for medication deviation risks during the hospital-to-home transition period in coronary heart disease （CHD） patients with initial treatment， aiming to assist medical staff in rapidly identifying high-risk groups for medication deviation. METHODS A total of 462 CHD patients with initial treatment from the Affiliated Hospital of North China University of Science and Technology （hereinafter referred to as “our hospital”） between January and July 2024 were enrolled. The patients were randomly divided into a modeling group and an internal validation group. The modeling group was further categorized into a medication deviation group and a non-medication deviation group based on whether medication deviations occurred. Similarly， 57 CHD patients with initial treatment from the cardiology department of our hospital between June and September 2025 were collected as an external validation group. Univariate analysis was used to screen predictive factors， followed by multivariate Logistic regression to construct the predictive model. Internal validation methods were employed to evaluate model performance， while external validation methods were used to test the model’s generalizability. RESULTS The 462 patients were divided into a modeling group （319 cases） and an internal validation group （143 cases）. In the modeling group， the medication deviation group （192 cases， 60.19%） and the non-medication deviation group （127 cases， 39.81%） were identified. Multivariate Logistic regression analysis revealed that age， medication type， medication adherence， and self-efficacy in rational medication use were predictive factors for medication deviations in CHD patients with initial treatment （ P ＜0.05）. The predictive model equation was logit P ＝ln[ P /（1－ P ） ] ＝1.321+1.732×age+4.091×medication type －4.360×medication adherence －3.081×self-efficacy in rational medication use. The model demonstrated good discrimination， with a Hosmer-Lemeshow goodness-of-fit test P -value of 0.439， an area under the receiver operating characteristic curve （AUC） of 0.870， sensitivity of 0.970， and specificity of 0.607. A risk nomogram with a total score of 350 points and a cutoff value of 110 points was plotted. The internal validation group showed an AUC o f 0.787 and a prediction accuracy of 77.6%， while the external validation group exhibited an AUC of 0.802 and a prediction accuracy of 73.7%. CONCLUSIONS This study successfully developed a predictive model for medication deviation risks during the hospital-to-home transition period in CHD patients with initial treatment. The model demonstrates excellent discrimination and predictive accuracy， effectively identifying high-risk populations for medication deviations. Age （＞70 years）， number of drug types≥5， poor medication adherence， and poor self-efficacy in rational medication use are independent risk factors for medication deviations.
