Applications of Immunopharmacogenomics: Predicting, Preventing, and Understanding Immune-Mediated Adverse Drug Reactions.
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ABSTRACT:
SUBMITTER: Karnes JH
PROVIDER: S-EPMC6409210 | biostudies-literature | 2019 Jan
REPOSITORIES: biostudies-literature
Publications
Applications of Immunopharmacogenomics: Predicting, Preventing, and Understanding Immune-Mediated Adverse Drug Reactions.
Karnes Jason H JH Miller Matthew A MA White Katie D KD Konvinse Katherine C KC Pavlos Rebecca K RK Redwood Alec J AJ Peter Jonathan G JG Lehloenya Rannakoe R Mallal Simon A SA Phillips Elizabeth J EJ
Annual review of pharmacology and toxicology 20180822
Adverse drug reactions (ADRs) are a significant health care burden. Immune-mediated adverse drug reactions (IM-ADRs) are responsible for one-fifth of ADRs but contribute a disproportionately high amount of that burden due to their severity. Variation in human leukocyte antigen ( HLA) genes has emerged as a potential preprescription screening strategy for the prevention of previously unpredictable IM-ADRs. Immunopharmacogenomics combines the disciplines of immunogenomics and pharmacogenomics and ...[more]
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Project description:BackgroundOlder patients are at an increased risk of developing adverse drug reactions (ADR). Of particular concern are the oldest old, which constitute an increasingly growing population. Having a validated clinical tool to identify those older patients at risk of developing an ADR during hospital stay would enable healthcare staff to put measures in place to reduce the risk of such an event developing. The current study aimed to (1) develop and (2) validate an ADR risk prediction model.MethodsWe used a combination of univariate analysis and multivariate binary logistic regression to identify clinical risk factors for developing an ADR in a population of older people from a UK teaching hospital. The final ADR risk model was then validated in a European population (European dataset).ResultsSix-hundred-ninety patients (median age 85 years) were enrolled in the development stage of the study. Ninety-five reports of ADR were confirmed by independent review in these patients. Five clinical variables were identified through multivariate analysis and included in our final model; each variable was attributed a score of 1. Internal validation produced an AUROC of 0.74, a sensitivity of 80%, and specificity of 55%. During the external validation stage the AUROC was 0.73, with sensitivity and specificity values of 84% and 43% respectively.ConclusionsWe have developed and successfully validated a simple model to use ADR risk score in a population of patients with a median age of 85, i.e. the oldest old. The model is based on 5 clinical variables (≥8 drugs, hyperlipidaemia, raised white cell count, use of anti-diabetic agents, length of stay ≥12 days), some of which have not been previously reported.
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