Wolff Jan, Hefner Gudrun, Normann Claus, Kaier Klaus, Binder Harald, Domschke Katharina, Hiemke Christoph, Marschollek Michael, Klimke Ansgar
Peter L. Reichertz Institute for Medical Informatics, Hannover Medical School, Hannover, Germany
Department of Psychiatry and Psychotherapy, Medical Center-University of Freiburg, Freiburg, Germany.
BMJ Open. 2021 Apr 9;11(4):e045276. doi: 10.1136/bmjopen-2020-045276.
The aim was to use routine data available at a patient's admission to the hospital to predict polypharmacy and drug-drug interactions (DDI) and to evaluate the prediction performance with regard to its usefulness to support the efficient management of benefits and risks of drug prescriptions.
Retrospective, longitudinal study.
We used data from a large multicentred pharmacovigilance project carried out in eight psychiatric hospitals in Hesse, Germany.
Inpatient episodes consecutively discharged between 1 October 2017 and 30 September 2018 (year 1) or 1 January 2019 and 31 December 2019 (year 2).
The proportion of rightly classified hospital episodes.
We used gradient boosting to predict respective outcomes. We tested the performance of our final models in unseen patients from another calendar year and separated the study sites used for training from the study sites used for performance testing.
A total of 53 909 episodes were included in the study. The models' performance, as measured by the area under the receiver operating characteristic, was 'excellent' (0.83) and 'acceptable' (0.72) compared with common benchmarks for the prediction of polypharmacy and DDI, respectively. Both models were substantially better than a naive prediction based solely on basic diagnostic grouping.
This study has shown that polypharmacy and DDI can be predicted from routine data at patient admission. These predictions could support an efficient management of benefits and risks of hospital prescriptions, for instance by including pharmaceutical supervision early after admission for patients at risk before pharmacological treatment is established.
旨在利用患者入院时可获取的常规数据来预测多重用药及药物相互作用（DDI），并评估预测性能对支持药物处方效益和风险的有效管理的有用性。
回顾性纵向研究。
我们使用了来自德国黑森州八家精神病医院开展的一项大型多中心药物警戒项目的数据。
2017年10月1日至2018年9月30日（第1年）或2019年1月1日至2019年12月31日（第2年）期间连续出院的住院病例。
正确分类的医院病例比例。
我们使用梯度提升来预测各自的结果。我们在另一个日历年的未见过的患者中测试了最终模型的性能，并将用于训练的研究地点与用于性能测试的研究地点分开。
该研究共纳入53909例病例。通过受试者工作特征曲线下面积衡量，模型对于多重用药和DDI预测的性能分别为“优秀”（0.83）和“可接受”（0.72），与常见基准相比。两个模型均明显优于仅基于基本诊断分组的简单预测。
本研究表明，多重用药和DDI可根据患者入院时的常规数据进行预测。这些预测可为医院处方效益和风险的有效管理提供支持，例如通过在确定药物治疗前对有风险的患者入院后尽早纳入药学监护。
