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Predict drug adverse effects with Artificial Intelligence

Predict drug adverse effects with Artificial Intelligence, increasing patient health and satisfaction.

Problem

Every year, adverse drug reactions (ADRs) cause significant harm in healthcare settings, resulting in more than 750,000 inpatient injuries or deaths, affecting nearly two million hospital stays, and leading to over one million emergency department visits and 125,000 hospitalizations (1)(2). Older adults are particularly vulnerable, experiencing double the incidence of ADRs compared to younger populations and facing a threefold higher risk of mortality from these events. Additionally, between 20% to 60% of older adults use potentially inappropriate medications (PIMs), highlighting the pervasive risks associated with medication management in this demographic (3)(4).

Why it matters

  • Adverse drug reactions (ADRs) annually result in more than 750,000 inpatient injuries or deaths.
  • Older adults experience double the incidence of ADRs compared to younger populations and are three times as likely to die from them.
  • Between 20% to 60% of older adults use potentially inappropriate medications (PIMs), posing significant health risks.

Solution

A predictive AI model, "SafeMed AI", has been developed to specifically assess the risk level of ADRs in patients, using a synthetic data set that takes into account patient demographics and health history to measure the low, medium or high risk ADR probability, with the ultimate goal of improving patient safety and treatment outcomes.

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Datasources

The model dataset was structured to reflect clinical realities, based on research on ADRs in older adults by Nair et al. (5), analysis of spontaneous reports by Dubrall et al. (6), impact studies of medication continuation by Weir et al. (7), Pharmacogenetic risk factor piloting by Finkelstein et al. (8), medication appropriateness reviews by Fick (9), and intervention meta-analyses by Gray et al. (10). Using variables such as age, liver and kidney function, and number of medications, the model simulates patient profiles to help healthcare providers personalize treatment to reduce ADR risks.

Citations

  1. Sarah P,, Slight, et al. “The national cost of adverse drug events resulting from inappropriate medication-related alert overrides in the United States.” Journal of the American Medical Informatics Association, Volume 25, Issue 9, Sep. 2018, pp. 1183-1188.
  2. “Adverse Drug Events.” Department of Health and Human Services: Healthcare Quality, 2 Feb. 2020. Health.gov. Accessed 23 Jun. 2021.
  3. Beijer, H J M, and C J de Blaey. “Hospitalisations caused by adverse drug reactions (ADR): a meta-analysis of observational studies” Pharmacy World 8: Science, vol. 24, no. 2, 24 Apr. 2002, pp. 46-54. doi:10.1023/a:1015570104121.
  4. Jennings, Emma, et al. “Detection and Prevention of Adverse Drug Reactions in Multi-Morbid Older Patients” Age and Ageing, vol. 48, no.1, 12 Sep. 2018, pp. 10-13, academic.oup.com/ageing/article/48/1/10/5123812, 10.1093/ageing/afy157. Accessed 23 Jun 2021.
  5. Parameswaran, Nair N, et al. “Hospitalization in older patients due to adverse drug reactions -the need for a prediction tool.” Clin Interv Aging. 2016,11:497-505. 2 May 2016. doi:10.2147/C1A.599097.
  6. Dubrall, Diana et al. “Adverse drug reactions in older adults: a retrospective comparative analysis of spontaneous reports to the German Federal Institute for Drugs and Medical Devices.” BMC pharmacology € toxicology vol. 21, no. 25, 23 Mar. 2020, doi:10.1186/540360-020-0392-9.
  7. Weir, Daniala L., et al. “Both New and Chronic Potentially Inappropriate Medications Continued at Hospital Discharge Are Associated With Increased Risk of Adverse Events.” Journal of the American Geriatrics Society, vol. 68, no. 6, 31 Mar. 2020, pp. 1184-1192, pubmed.ncbi.nim.nih.gov/32232988/, 10.1111/j9516413. Accessed 23 Jun 2021.
  8. Finkelstein, Joseph et al. “Pharmacogenetic polymorphism as an independent risk factor for frequent hospitalizations in older adults with polypharmacy: a pilot study” Pharmacogenomics and personalized medicine vol. 9, 14 Oct. 2016, pp. 107-116. doi:10.2147/PGPM.S117014.
  9. Fick, Donna M. “Less Really Is More in Inappropriate Medication Use in Older Adults: How Can We Improve Prescribing and Deprescribing in Older Adults?" Journal of the American Geriatrics Society, vol. 68, no. 6, 4 May 2020, pp. 1175-1176, onlinelibrarywiley.com/doi/full/10.1111/jgs:16485, 10.1111/j95.16485. Accessed 23 Jun 2021.
  10. Gray, Shelly L., et al. “Meta-Analysis of Interventions to Reduce Adverse Drug Reactions in Older Adults." Journal of the American Geriatrics Society, vol. 66, no. 2, 19 Dec. 2017, pp. 282-288, pubmed.ncbi.nlm.nih.gov/29265170/, 10.1111/jg515195. Accessed 23 Jun 2021.

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