Wehbe, Firas

LBA110 Background: There are limited data on COVID-19 in patients with cancer. We characterize the outcomes of patients with cancer and COVID-19 and identify potential prognostic factors. Methods: The COVID-19 and Cancer Consortium (CCC19) cohort study includes patients with active or prior hematologic or invasive solid malignancies reported across academic and community sites. Results: We included 1,018 cases accrued March-April 2020. Median age was 66 years (range, 18-90). Breast (20%) and prostate (16%) cancers were most prevalent; 43% of patients were on active anti-cancer treatment. At time of data analysis, 106 patients (10.4%) have died and 26% met the composite outcome of death, severe illness requiring hospitalization, and/or mechanical ventilation. In multivariable logistic regression analysis, independent factors associated with increased 30-day mortality were age, male sex, former smoking, ECOG performance status (2 versus 0/1: partially adjusted odds ratio (pAOR) 2.74, 95% CI 1.31-5.7; 3/4 versus 0/1: pAOR 5.34, 95% CI 2.44-11.69), active malignancy (stable/responding, pAOR 1.93, 95% CI 1.06-3.5; progressing, pAOR 3.79, 95% CI 1.78-8.08), and receipt of azithromycin and hydroxychloroquine. Tumor type, race/ethnicity, obesity, number of comorbidities, recent surgery, and type of active cancer therapy were not significant factors for mortality. Conclusions: All-cause 30-day mortality and severe illness in this cohort were significantly higher than previously reported for the general population and were associated with general risk factors as well as those unique to patients with cancer. Cancer type and treatment were not independently associated with increased 30-day mortality. Longer follow-up is needed to better understand the impact of COVID-19 on outcomes in patients with cancer, including the ability to continue specific cancer treatments.

AbstractThe eMERGE Network is establishing methods for electronic transmittal of patient genetic test results from laboratories to healthcare providers across organizational boundaries. We surveyed the capabilities and needs of different network participants, established a common transfer format, and implemented transfer mechanisms based on this format. The interfaces we created are examples of the connectivity that must be instantiated before electronic genetic and genomic clinical decision support can be effectively built at the point of care. This work serves as a case example for both standards bodies and other organizations working to build the infrastructure required to provide better electronic clinical decision support for clinicians.

AbstractBackground Electronic health records (EHRs) are increasingly used for clinical and translational research through the creation of phenotype algorithms. Currently, phenotype algorithms are most commonly represented as noncomputable descriptive documents and knowledge artifacts that detail the protocols for querying diagnoses, symptoms, procedures, medications, and/or text-driven medical concepts, and are primarily meant for human comprehension. We present desiderata for developing a computable phenotype representation model (PheRM).Methods A team of clinicians and informaticians reviewed common features for multisite phenotype algorithms published in PheKB.org and existing phenotype representation platforms. We also evaluated well-known diagnostic criteria and clinical decision-making guidelines to encompass a broader category of algorithms.Results We propose 10 desired characteristics for a flexible, computable PheRM: (1) structure clinical data into queryable forms; (2) recommend use of a common data model, but also support customization for the variability and availability of EHR data among sites; (3) support both human-readable and computable representations of phenotype algorithms; (4) implement set operations and relational algebra for modeling phenotype algorithms; (5) represent phenotype criteria with structured rules; (6) support defining temporal relations between events; (7) use standardized terminologies and ontologies, and facilitate reuse of value sets; (8) define representations for text searching and natural language processing; (9) provide interfaces for external software algorithms; and (10) maintain backward compatibility.Conclusion A computable PheRM is needed for true phenotype portability and reliability across different EHR products and healthcare systems. These desiderata are a guide to inform the establishment and evolution of EHR phenotype algorithm authoring platforms and languages.