Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2017 Feb 13;12(2):e0171995.
doi: 10.1371/journal.pone.0171995. eCollection 2017.

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

Tri-Long Nguyen  1   2   3   4 , Géraldine Leguelinel-Blache  1   2 , Jean-Marie Kinowski  1   2 , Clarisse Roux-Marson  1   2 , Marion Rougier  5 , Jessica Spence  3   4 , Yannick Le Manach  3   4 , Paul Landais  2   6
Affiliations

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

Tri-Long Nguyen et al. PLoS One. .

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.

PubMed Disclaimer

Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Simulated randomized controlled trial comparing two decision-making strategies for intervention to reduce medication errors.
The analysis was reiterated 1,000 times within 5 scenarios, within each the intervention coverage k was fixed at 10%, 30%, 50%, 70% and 90%.
Fig 2
Fig 2. Distribution of clinically significant medication errors according to hospitalization day.
Fig 3
Fig 3. Comparison of two strategies to focus interventions on high-risk patients: decision-making supported by the predictive model versus decision-making based on age.
See this image and copyright information in PMC

References

    1. Ernst FR, Grizzle AJ. Drug-related morbidity and mortality: updating the cost-of-illness model. Journal of the American Pharmaceutical Association. 2001;41(2):192–9. - PubMed
    1. Pharmaceutical Care Network Europe Foundation. The PCNE Classification V 6.2. 2010.
    1. Tam VC, Knowles SR, Cornish PL, Fine N, Marchesano R, Etchells EE. Frequency, type and clinical importance of medication history errors at admission to hospital: a systematic review. CMAJ: Canadian Medical Association journal = journal de l'Association medicale canadienne. 2005;173(5):510–5. PubMed Central PMCID: PMC1188190. 10.1503/cmaj.045311 - DOI - PMC - PubMed
    1. Basey AJ, Krska J, Kennedy TD, Mackridge AJ. Prescribing errors on admission to hospital and their potential impact: a mixed-methods study. BMJ quality & safety. 2014;23(1):17–25. - PubMed
    1. Dean B, Schachter M, Vincent C, Barber N. Prescribing errors in hospital inpatients: their incidence and clinical significance. Quality & safety in health care. 2002;11(4):340–4. PubMed Central PMCID: PMC1758003. - PMC - PubMed