Open AccessImproving medication safety: Development and impact of a multivariate model-based strategy to target high-risk patients
Author(s) -
TriLong Nguyen,
Géraldine Leguelinel-Blache,
Jean-Marie Kinowski,
Clarisse Roux-Marson,
M.-B. Rougier,
Jessica Spence,
Yannick Le Manach,
Paul Landais
Publication year - 2017
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0171995
Subject(s) - medicine , logistic regression , multivariate statistics , framingham risk score , clinical trial , emergency medicine , psychological intervention , multivariate analysis , prospective cohort study , confounding , risk assessment , intensive care medicine , computer science , machine learning , disease , psychiatry , computer security
Background 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. Methods From March 1 st to April 31 st 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. Results 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. Conclusion 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.