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Precision medicine: does ethnicity information complement genotype-based prescribing decisions?

ABSTRACT: Inter-ethnic differences in drug response are all too well known. These are underpinned by a number of factors, including pharmacogenetic differences across various ethnic populations. Precision medicine relies on genotype-based prescribing decisions with the aim of maximizing efficacy and mitigating the risks. When there is no access to genotyping tests, ethnicity is frequently regarded as a proxy of the patient's probable genotype on the basis of overall population-based frequency of genetic variations in the ethnic group the patient belongs to, with some variations being ethnicity-specific. However, ever-increasing transcontinental migration of populations and the resulting admixing of populations have undermined the utility of self-identified ethnicity in predicting the genetic ancestry, and therefore the genotype, of the patient. An example of the relevance of genetic ancestry of a patient is the inadequate performance of European-derived pharmacogenetic dosing algorithms of warfarin in African Americans, Brazilians and Caribbean Hispanics. Consequently, genotyping a patient potentially requires testing for all known clinically actionable variants that the patient may harbour, and new variants that are likely to be identified using state-of the art next-generation sequencing-based methods. Furthermore, self-identified ethnicity is associated with a number of ethnicity-related attributes and non-genetic factors that potentially influence the risk of phenoconversion (genotype-phenotype discordance), which may adversely impact the success of genotype-based prescribing decisions. Therefore, while genotype-based prescribing decisions are important in implementing precision medicine, ethnicity should not be disregarded.

SUBMITTER: Shah RR

PROVIDER: S-EPMC5753996 | biostudies-other | 2018 Jan

REPOSITORIES: biostudies-other

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Precision medicine: does ethnicity information complement genotype-based prescribing decisions?

Shah Rashmi R RR Gaedigk Andrea A

Therapeutic advances in drug safety 20171201 1

Inter-ethnic differences in drug response are all too well known. These are underpinned by a number of factors, including pharmacogenetic differences across various ethnic populations. Precision medicine relies on genotype-based prescribing decisions with the aim of maximizing efficacy and mitigating the risks. When there is no access to genotyping tests, ethnicity is frequently regarded as a proxy of the patient's probable genotype on the basis of overall population-based frequency of genetic v ...[more]

PMID: 29318005

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Project description:PurposeCoronavirus disease-2019 (COVID-19) continues to be a global threat and remains a significant cause of hospitalizations. Recent clinical guidelines have supported the use of corticosteroids or remdesivir in the treatment of COVID-19. However, uncertainty remains about which patients are most likely to benefit from treatment with either drug; such knowledge is crucial for avoiding preventable adverse effects, minimizing costs, and effectively allocating resources. This study presents a machine-learning system with the capacity to identify patients in whom treatment with a corticosteroid or remdesivir is associated with improved survival time.MethodsGradient-boosted decision-tree models used for predicting treatment benefit were trained and tested on data from electronic health records dated between December 18, 2019, and October 18, 2020, from adult patients (age ≥18 years) with COVID-19 in 10 US hospitals. Models were evaluated for performance in identifying patients with longer survival times when treated with a corticosteroid versus remdesivir. Fine and Gray proportional-hazards models were used for identifying significant findings in treated and nontreated patients, in a subset of patients who received supplemental oxygen, and in patients identified by the algorithm. Inverse probability-of-treatment weights were used to adjust for confounding. Models were trained and tested separately for each treatment.FindingsData from 2364 patients were included, with men comprising slightly more than 50% of the sample; 893 patients were treated with remdesivir, and 1471 were treated with a corticosteroid. After adjustment for confounding, neither corticosteroids nor remdesivir use was associated with increased survival time in the overall population or in the subpopulation that received supplemental oxygen. However, in the populations identified by the algorithms, both corticosteroids and remdesivir were significantly associated with an increase in survival time, with hazard ratios of 0.56 and 0.40, respectively (both, P = 0.04).ImplicationsMachine-learning methods have the capacity to identify hospitalized patients with COVID-19 in whom treatment with a corticosteroid or remdesivir is associated with an increase in survival time. These methods may help to improve patient outcomes and allocate resources during the COVID-19 crisis.

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Precision medicine: does ethnicity information complement genotype-based prescribing decisions?

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Precision medicine: does ethnicity information complement genotype-based prescribing decisions?

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