Takahashi Paul Y, Ryu Euijung, Bielinski Suzette J, Hathcock Matthew, Jenkins Gregory D, Cerhan James R, Olson Janet E
Division of Community Internal Medicine, Department of Internal Medicine, Mayo Clinic, Rochester, MN, USA.
Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
Pharmgenomics Pers Med. 2021 Feb 11;14:229-237. doi: 10.2147/PGPM.S281645. eCollection 2021.
The use of pharmacogenomics data is increasing in clinical practice. However, it is unknown if pharmacogenomics data can be used more broadly to predict outcomes like hospitalization or emergency department (ED) visit. We aim to determine the association between selected pharmacogenomics phenotypes and hospital utilization outcomes (hospitalization and ED visits).
This cohort study utilized 10,078 patients from the Mayo Clinic Biobank in the RIGHT protocol with sequence and interpreted phenotypes for 10 selected pharmacogenes including , and . The primary outcome was hospitalization with ED visits as a secondary outcome. We used Cox proportional hazards model to test the association between each pharmacogenomics phenotype and the risk of the outcomes.
During the follow-up period (median [in years] = 7.3), 13% (n=1354) and 8% (n=813) of the subjects experienced hospitalization and ED visits, respectively. Compared to subjects who did not experience hospitalization, hospitalized patients were older (median age [in years]: 67 vs 65), poorer self-rated health (15% vs 4.7% for fair/poor), and higher disease burden (median number of chronic conditions: 7 vs 4) at baseline. There was no association of hospitalization with any of the pharmacogenomics phenotypes. The pharmacogenomics phenotypes were not associated with disease burden, a well-established risk factor for hospital utilization outcomes. Similar findings were observed for patients with ED visits during the follow-up period.
We found no association of 10 well-established pharmacogenomics phenotypes with either hospitalization or ED visits in this relatively large biobank population and outside the context of specific drug use related to these genes. Traditional risk factors for hospitalization like age and self-rated health were much more likely to predict hospitalization and/or ED visits than this pharmacogenomics information.
药物基因组学数据在临床实践中的应用正在增加。然而，尚不清楚药物基因组学数据是否可更广泛地用于预测诸如住院或急诊就诊等结果。我们旨在确定选定的药物基因组学表型与医院利用结果（住院和急诊就诊）之间的关联。
这项队列研究利用了梅奥诊所生物样本库中10078名符合RIGHT方案的患者，对包括 、 和 在内的10个选定药物基因进行了测序并解读了表型。主要结局是住院，急诊就诊作为次要结局。我们使用Cox比例风险模型来检验每种药物基因组学表型与结局风险之间的关联。
在随访期间（中位时间[以年计]=7.3），分别有13%（n = 1354）和8%（n = 813）的受试者经历了住院和急诊就诊。与未住院的受试者相比，住院患者在基线时年龄更大（中位年龄[以年计]：67岁对65岁），自我健康评分更低（一般/差的比例为15%对4.7%），疾病负担更高（慢性病中位数：7种对4种）。住院与任何药物基因组学表型均无关联。药物基因组学表型与疾病负担无关，而疾病负担是医院利用结果的一个公认风险因素。在随访期间急诊就诊的患者中也观察到了类似的结果。
在这个相对较大的生物样本库人群中，且在与这些基因相关的特定药物使用背景之外，我们发现10种公认的药物基因组学表型与住院或急诊就诊均无关联。与年龄和自我健康评分等传统住院风险因素相比，这种药物基因组学信息预测住院和/或急诊就诊的可能性要小得多。
