Ross, Jason

Abstract Case reports of patients infected with COVID-19 and influenza virus (“flurona”) have raised questions around the prevalence and severity of coinfection. Using data from HHS Protect Public Data Hub, NCBI Virus, and CDC FluView, we analyzed trends in SARS-CoV-2 and influenza hospitalized coinfection cases and strain prevalences. We also characterized coinfection cases across the Mayo Clinic Enterprise from January 2020 to April 2022. We compared expected and observed coinfection case counts across different waves of the pandemic and assessed symptoms and outcomes of coinfection and COVID-19 monoinfection cases after propensity score matching on clinically relevant baseline characteristics. From both the Mayo Clinic and nationwide datasets, the observed coinfection rate for SARS-CoV-2 and influenza has been higher during the Omicron era (2021 December 14 to 2022 April 2) compared to previous waves, but no higher than expected assuming infection rates are independent. At the Mayo Clinic, only 120 coinfection cases were observed among 197,364 SARS-CoV-2 cases. Coinfected patients were relatively young (mean age: 26.7 years) and had fewer serious comorbidities compared to monoinfected patients. While there were no significant differences in 30-day hospitalization, ICU admission, or mortality rates between coinfected and matched COVID-19 monoinfection cases, coinfection cases reported higher rates of symptoms including congestion, cough, fever/chills, headache, myalgia/arthralgia, pharyngitis, and rhinitis. While most coinfection cases observed at the Mayo Clinic occurred among relatively healthy individuals, further observation is needed to assess outcomes among subpopulations with risk factors for severe COVID-19 such as older age, obesity, and immunocompromised status.

e24114 Background: Pharmacogenomics (PGx) testing is used increasingly in cancer supportive care for drug and dose selection, and side effects prediction with antidepressants, antiemetics, and pain medications. Genetic variants in genes that encode for drug metabolizing enzymes (CYPs), transporters (SLC6A4) and drug receptors (HTR2A, OPRM1, etc) are well known for these medications. They contribute to inter-individual differences in medication safety and efficacy. We report on their frequency and effect on supportive care drugs in patients with solid tumors. Methods: Between 06/06/19 and 12/18/19, solid tumor cancer patients at Mayo Clinic Arizona were enrolled in a PGx prospective study. They were genotyped using a multi-gene panel, OneOme RightMed. The panel assessed 27 genes, including CYP2C9, CYP2C19, CYP2D6, CYP3A4, COMT, OPRM1, GRIK4, HTR2A, SLC6A4, associated with pain medications, antidepressants, and antiemetics. PGx pharmacist provided medication recommendations and dose adjustments based on PGx test results and concurrent medications. Results: 200 patients were swabbed and 196 patients were genotyped. The results were available within 5 days. All patients were also assessed for pheno-conversion (drug-drug-gene interactions) by a pharmacist. Recommendations were made based on predicted phenotype. Various solid tumor types were represented, including prostate (19.9%), colorectal (17.9%), melanoma (14.8%), and others (47.4%). Median age was 65 with 59.2% patients being male and 40.85% female. All had at least one actionable polymorphism related to these medications. The most significant findings were related to CYP2C19 and CYP2D6 genes. Of 196 patients, a total of 132 (67.3%) had other than normal CYP2D6 metabolizer phenotype and 112 (57.1%) had other than normal CYP2C19 metabolizer phenotype. Based on these genotypes, alternatives to pain medications were recommended for 37 patients, to ondansetron in 9 patients, and to anti-depressants in 51 patients. Conclusions: Genetic variants were commonly found that affect supportive care medications used in oncology practice and contribute to inter-individual differences associated with increased risk of adverse drug effects and reduced efficacy. PGx guided recommendation may help physicians in individualizing medication treatment outcomes for patients with different cancer types and with polypharmacy. PGx trained pharmacist can serve as a valuable asset in optimizing personalized medication management.

The concept of optimizing health care by understanding and generating knowledge from previous evidence, ie, the Learning Health-care System (LHS), has gained momentum and now has national prominence. Meanwhile, the rapid adoption of electronic health records (EHRs) enables the data collection required to form the basis for facilitating LHS. A prerequisite for using EHR data within the LHS is an infrastructure that enables access to EHR data longitudinally for health-care analytics and real time for knowledge delivery. Additionally, significant clinical information is embedded in the free text, making natural language processing (NLP) an essential component in implementing an LHS. Herein, we share our institutional implementation of a big data-empowered clinical NLP infrastructure, which not only enables health-care analytics but also has real-time NLP processing capability. The infrastructure has been utilized for multiple institutional projects including the MayoExpertAdvisor, an individualized care recommendation solution for clinical care. We compared the advantages of big data over two other environments. Big data infrastructure significantly outperformed other infrastructure in terms of computing speed, demonstrating its value in making the LHS a possibility in the near future.