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.

BACKGROUND: Nowadays complex bone defects have become a great challenge to orthopedists. A synergistic contribution of various growth factors and a crosstalk between their signaling pathways have been suggested as determinatives for the overall osteogenic outcome.OBJECTIVE: To develop calcium phosphate cement (CPC) incorporated with γ-polyglutamic acid/carboxymethyl chitosan (PGA/CMCS), and to evaluate its physical and chemical properties and sustained-release function. METHODS: The γ-PGA/CMCS polymer composites were prepared by graft copolymerization and spray freeze drying methods, and then loaded with recombinant human bone morphogenetic protein 2 (rhBMP-2) growth factor. CPC served as control group, and γ-PGA/CMCS-CPC containing different contents of rhBMP-2 as experimental groups. A γ-PGA/CMCS-CPC scaffold with regular blade-like crystalline structure was fabricated by injection compression molding. Before mixed with the liquid phase, the solid additives were properly mixed by wet method of CPC solid and the γ-PGA/CMCS carrier, then the pre-blended mix was freeze-dried. The setting time and compressive strength of bone cement in each group were detected, and the microstructure of the material surface was observed under scanning electron microscopy. In vitro release of rhBMP-2 was investigated. The effect of bone cement extracts on cell proliferation was determined through MTS assay.RESULTS AND CONCLUSION: γ-PGA/CMCS-CPC had the same physicochemical properties to the CPC. Initial and final setting time, compressive strength of bone cement had no significant differences among groups. The scanning electron microscope results showed that the γ-PGA/CMCS-CPC scaffold was covered by regular blade-like crystalline structure and the γ-PGA/CMCS particles were uniformly dispersed in the CPC crystals. A sustained release of rhBMP-2 was observed from the γ-PGA/CMCS-CPC. The cell experiments exhibited that the samples with regular blade-like crystalline structure had better cell response compared to CPC control groups with irregular crystalline structure. These findings indicate that γ-PGA/CMCS-CPC can maintain good physicochemical properties, and release growth factor or drug to promote bone formation.