Systematic review on medication risk prediction models for hospitalized adult patients
- VernacularTitle:成人住院患者用药风险预测模型的系统评价
- Author:
Yang YANG
1
;
Xuefeng SHAN
2
;
Haidong LI
3
;
Yaozheng LI
4
;
Qiwen ZHOU
4
;
Hongmei WANG
4
Author Information
1. Dept. of Health Management Center，the First Affiliated Hospital of Chongqing Medical University，Chongqing 400016，China
2. Dept. of Pharmacy，Bishan Hospital of Chongqing Medical University，Chongqing 402760，China
3. Dept. of Science and Technology Education and Foreign Affairs，the Affiliated Stomatological Hospital of Chongqing Medical University，Chongqing 401147，China
4. Dept. of Pharmacy，the First Affiliated Hospital of Chongqing Medical University，Chongqing 400016，China
- Publication Type:Journal Article
- Keywords:
hospitalized patients;
medication risk;
drug-
- From:
China Pharmacy
2025;36(10):1254-1259
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVE To systematically evaluate medication risk prediction models for hospitalized adult patients and provide references for their development and clinical application. METHODS Databases including PubMed， Embase， Web of Science， CNKI， Wanfang data， VIP and CBM were searched for studies on medication risk prediction models from their inception to May 2024. After screening the literature， extracting data， and evaluating the quality of the literature， descriptive analysis was performed on the results of the included studies. RESULTS A total of 13 studies were included， involving 12 models. Nine studies used Logistic regression algorithm for modeling， and the number of included predictive factors ranged from 3 to 11； the area under the receiver operating characteristic curve ranged from 0.65 to 0.865. The literature quality evaluation results showed that 10 studies had high risk of bias； 10 studies had high applicability risk. A total of 31 predictive factors were extracted， including 15 items of basic patient information， 3 test indicators， and 5 items of medication information， and 8 others. CONCLUSIONS The existing medication risk prediction models for hospitalized adult inpatients are mainly Logistic regression algorithm， with predictive factors mainly focusing on basic indicators such as demographics. The overall prediction performance of the models needs to be improved， and the overall risk of bias is relatively high.
