Nguyen Tri-Long, Leguelinel-Blache Géraldine, Kinowski Jean-Marie, Roux-Marson Clarisse, Rougier Marion, Spence Jessica, Le Manach Yannick, Landais Paul
Department of Pharmacy, Nîmes University Hospital, Nîmes, France.
Laboratory of Biostatistics, Epidemiology, Clinical Research and Health Economics, University Institute of Clinical Research, Montpellier University, Montpellier, France.
PLoS One. 2017 Feb 13;12(2):e0171995. doi: 10.1371/journal.pone.0171995. eCollection 2017.
Preventive strategies to reduce clinically significant medication errors (MEs), such as medication review, are often limited by human resources. Identifying high-risk patients to allow for appropriate resource allocation is of the utmost importance. To this end, we developed a predictive model to identify high-risk patients and assessed its impact on clinical decision-making.
From March 1st to April 31st 2014, we conducted a prospective cohort study on adult inpatients of a 1,644-bed University Hospital Centre. After a clinical evaluation of identified MEs, we fitted and internally validated a multivariate logistic model predicting their occurrence. Through 5,000 simulated randomized controlled trials, we compared two clinical decision pathways for intervention: one supported by our model and one based on the criterion of age.
Among 1,408 patients, 365 (25.9%) experienced at least one clinically significant ME. Eleven variables were identified using multivariable logistic regression and used to build a predictive model which demonstrated fair performance (c-statistic: 0.72). Major predictors were age and number of prescribed drugs. When compared with a decision to treat based on the criterion of age, our model enhanced the interception of potential adverse drug events by 17.5%, with a number needed to treat of 6 patients.
We developed and tested a model predicting the occurrence of clinically significant MEs. Preliminary results suggest that its implementation into clinical practice could be used to focus interventions on high-risk patients. This must be confirmed on an independent set of patients and evaluated through a real clinical impact study.
减少具有临床意义的用药错误（MEs）的预防策略，如用药审查，常常受到人力资源的限制。识别高危患者以便进行适当的资源分配至关重要。为此，我们开发了一个预测模型来识别高危患者，并评估其对临床决策的影响。
2014年3月1日至4月31日，我们对一家拥有1644张床位的大学医院中心的成年住院患者进行了一项前瞻性队列研究。在对已识别的用药错误进行临床评估后，我们拟合并内部验证了一个预测其发生的多变量逻辑模型。通过5000次模拟随机对照试验，我们比较了两种干预的临床决策途径：一种由我们的模型支持，另一种基于年龄标准。
在1408名患者中，365名（25.9%）经历了至少一次具有临床意义的用药错误。使用多变量逻辑回归确定了11个变量，并用于构建一个预测模型，该模型表现良好（c统计量：0.72）。主要预测因素是年龄和处方药数量。与基于年龄标准的治疗决策相比，我们的模型将潜在药物不良事件的拦截率提高了17.5%，治疗所需人数为6名患者。
我们开发并测试了一个预测具有临床意义的用药错误发生的模型。初步结果表明，将其应用于临床实践可用于将干预重点放在高危患者身上。这必须在一组独立的患者中得到证实，并通过实际临床影响研究进行评估。
