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Improving medication safety: Development and impact of a multivariate model-based strategy to target high-risk patients

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Listed:
  • Tri-Long Nguyen
  • Géraldine Leguelinel-Blache
  • Jean-Marie Kinowski
  • Clarisse Roux-Marson
  • Marion Rougier
  • Jessica Spence
  • Yannick Le Manach
  • Paul Landais

Abstract

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 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. 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.

Suggested Citation

  • Tri-Long Nguyen & Géraldine Leguelinel-Blache & Jean-Marie Kinowski & Clarisse Roux-Marson & Marion Rougier & Jessica Spence & Yannick Le Manach & Paul Landais, 2017. "Improving medication safety: Development and impact of a multivariate model-based strategy to target high-risk patients," PLOS ONE, Public Library of Science, vol. 12(2), pages 1-13, February.
    • Handle: RePEc:plo:pone00:0171995
    DOI: 10.1371/journal.pone.0171995
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    References listed on IDEAS

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    1. Ewout W. Steyerberg & Marinus J. C. Eijkemans & Frank E. Harrell Jr & J. Dik F. Habbema, 2001. "Prognostic Modeling with Logistic Regression Analysis," Medical Decision Making, , vol. 21(1), pages 45-56, February.
    2. Balamurugan Tangiisuran & Greg Scutt & Jennifer Stevenson & Juliet Wright & G Onder & M Petrovic & T J van der Cammen & Chakravarthi Rajkumar & Graham Davies, 2014. "Development and Validation of a Risk Model for Predicting Adverse Drug Reactions in Older People during Hospital Stay: Brighton Adverse Drug Reactions Risk (BADRI) Model," PLOS ONE, Public Library of Science, vol. 9(10), pages 1-9, October.
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    1. Liesbeth B E Bosma & Nienke van Rein & Nicole G M Hunfeld & Ewout W Steyerberg & Piet H G J Melief & Patricia M L A van den Bemt, 2019. "Development of a multivariable prediction model for identification of patients at risk for medication transfer errors at ICU discharge," PLOS ONE, Public Library of Science, vol. 14(4), pages 1-13, April.

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