Systematic review on medication risk prediction models for hospitalized adult patients

YANG Yang; SHAN Xuefeng; LI Haidong; LI Yaozheng; ZHOU Qiwen; WANG Hongmei

doi:10.6039/j.issn.1001-0408.2025.10.18

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Systematic review on medication risk prediction models for hospitalized adult patients

更新时间：2025-09-23

- Systematic review on medication risk prediction models for hospitalized adult patients
- ZHONGGUO YAOFANG Vol. 36, Issue 10, Pages: 1254-1259(2025)
- 作者机构：
  
  1.重庆医科大学附属第一医院健康管理中心，重庆　400016
  2.重庆医科大学附属璧山医院药学部，重庆　402760
  3.重庆医科大学附属口腔医院科技教育外事科，重庆　401147
  4.重庆医科大学附属第一医院药学部，重庆　400016
- 作者简介：
- 基金信息：
- DOI：10.6039/j.issn.1001-0408.2025.10.18
  CLC： R952
- Received：17 December 2024，
  
  Revised：2025-04-25，
  
  Accepted：25 April 2025，
  
  Published：30 May 2025
- 稿件说明：
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杨洋,单雪峰,李海东,等.成人住院患者用药风险预测模型的系统评价 [J].中国药房,2025,36(10):1254-1259.

YANG Yang,SHAN Xuefeng,LI Haidong,et al.Systematic review on medication risk prediction models for hospitalized adult patients[J].ZHONGGUO YAOFANG,2025,36(10):1254-1259.
杨洋,单雪峰,李海东,等.成人住院患者用药风险预测模型的系统评价 [J].中国药房,2025,36(10):1254-1259. DOI： 10.6039/j.issn.1001-0408.2025.10.18.

YANG Yang,SHAN Xuefeng,LI Haidong,et al.Systematic review on medication risk prediction models for hospitalized adult patients[J].ZHONGGUO YAOFANG,2025,36(10):1254-1259. DOI： 10.6039/j.issn.1001-0408.2025.10.18.

摘要

目的

系统评价成人住院患者用药风险预测模型，为用药风险预测模型的开发和临床应用提供参考。

方法

检索PubMed、Embase、Web of Science、中国知网、万方数据、维普网、中国生物医学文献数据库，收集成人住院患者用药风险预测模型的文献，检索时限为建库至2024年5月。筛选文献、提取资料、评价文献质量后，对纳入研究的结果进行描述性分析。

结果

共纳入13项研究，涉及12个模型。9项研究采用Logistic回归算法建模，模型纳入预测因子数为3～11个；受试者工作特征曲线下面积为0.65～0.865。文献质量评价结果显示，10项研究为高偏倚风险，10项研究为高适用性风险。共得到31个预测因子，涉及患者基础信息15个、检验指标3个、用药信息5个、其他8个。

结论

现有成人住院患者用药风险预测模型以Logistic回归算法为主，预测因子多聚焦于人口学等基础指标，模型总体预测性能有待提高，研究的整体偏倚风险较高。

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.

关键词

Keywords

references

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