Statin Medication Adverse Event Detection and Prediction to Help Optimize Cardiovascular Therapy
2022-08
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Statin Medication Adverse Event Detection and Prediction to Help Optimize Cardiovascular Therapy
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2022-08
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As lipid-lowering drugs, statin medications have been shown to be effective therapies, especially for cardiovascular patients. However, patients cannot take full advantage of the benefits statin drugs offer due to statin related adverse drug events (ADE) that often foster medication non-adherence and add to patients’ morbidity and mortality. On the other hand, the increasing availability of EHR data in electronic repositories along with the advancement in data management and integration represent substantial opportunities for scientists to practically investigate unanswered questions including those of statin related ADEs. In particular, we can ask ourselves: based on available electronic medical record data, how could we identify patients taking statin drugs who are at risk of developing symptomatic adverse events?The current thesis set forth methodologies and scientific artifacts to help with clinical decision support for both general adverse drug events and statin related adverse drug events in particular. We defined methods for clinical data cohort quality assessment and improvement, compiled a list of drugs with the potential of statin to drug interactions, identified common co-morbidities that affect statin users, elaborated on advanced data science methods for clinical data cohort mining and machine leaning, built predictive models for statin related adverse events detection and surveillance, and defined contexts in which these tools can be used for health care decision support.
This contribution is significant because it has the advantage of not only reducing the occurrence of statin related ADEs but also propagating a feasible and replicable EHR solution for general ADE reporting. Healthcare organizations can rely on an easy to implement EHR solution to significantly reduce statin related ADEs, save on health care resources, and improve patient management. Local and state governments could reduce their healthcare expenditures. Healthcare quality indices such as number of hospitalizations, length of stay in hospital, re-admission, medication non-adherence, and CVD mortality could be improved. A greater capacity for broader ADE mitigation efforts could also be set forth, which could have a beneficial impact on human health.
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University of Minnesota Ph.D. dissertation. 2022. Major: Health Informatics. Advisor: Terrence Adam. 1 computer file (PDF); 99 pages.
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KIOGOU, SEBASTIEN. (2022). Statin Medication Adverse Event Detection and Prediction to Help Optimize Cardiovascular Therapy. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/250048.
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