An Observational Exploration of Various Medication Risk Scores Calculated from Electronic Health Record Data in a General Population to Predict Medical Outcomes.
Approved Research ID: 61422
Approval date: January 25th 2021
With advanced age, providing medical care can present challenges as these patients are at risk for multiple chronic diseases . Inappropriate response to drugs and drugs-related adverse events (ADE) are an increasingly important problem in healthcare. The elderly are at an increased risk of ADE due to multiple chronic conditions leading to the intake of several drugs at the same time.. It is recognized that a high proportion of ADE in the elderly may be preventable. Increasing identification and prediction of ADEs have the potential to reduce the burden associated with ADE, will promote patient safety in the elderly and reduce medical costs.
The aim of this study is to use previously validated algorithms considering drug information to identify subsets of the population included in the UKBioBank at risk for ADEs. We hypothesized that the risk stratification based on medication claims data could help identify patients at high risk of medication related problems, as well as provide insights on interventions that could be performed by pharmacists or other health professionals to prevent these ADEs. In addition, we will assess the effect of using multiple medications that could predispose patients to dementia or pneumonia The calculated medication risk score (MRS) will be updated and validated in a population from UK Biobank and the predictive capacity will be compared to other currently used risk scores by the medical community. An MRS tool aiming to better predict ADE and identifying factors may have a significant impact in clinical practice from a clinical and economic perspective.
Research has shown that an estimated 20 - 50% of adults in the elderly population are prescribed at least one medication with properties that increase their risk of ADEs such as pneumonia or dementia. We hypothesized that a newly developed MRS that takes into account the dose of drug administered would be better to predict the risk of ADEs associated with these drugs Other side effects associated with some drugs that will be monitored by this MRS include constipation, dry mouth, and dry eyes. We also propose that the newly developed MRS could predict the frequency of visits to the emergency room or hospitalization. Our project will calculate the MRS each year for each patient, using prescribed drug information Results will be compared with the previous MRS.