Privacy-protecting, reliable response data discovery using COVID-19 patient observations.

OBJECTIVE: To utilize, in an individual and institutional privacy-preserving manner, electronic health record (EHR) data from 202 hospitals by analyzing answers to COVID-19-related questions and posting these answers online. MATERIALS AND METHODS: We developed a distributed, federated network of 12 health systems that harmonized their EHRs and submitted aggregate answers to consortia questions posted at https://www.covid19questions.org. Our consortium developed processes and implemented distributed algorithms to produce answers to a variety of questions. We were able to generate counts, descriptive statistics, and build a multivariate, iterative regression model without centralizing individual-level data. RESULTS: Our public website contains answers to various clinical questions, a web form for users to ask questions in natural language, and a list of items that are currently pending responses. The results show, for example, that patients who were taking angiotensin-converting enzyme inhibitors and angiotensin II receptor blockers, within the year before admission, had lower unadjusted in-hospital mortality rates. We also showed that, when adjusted for, age, sex, and ethnicity were not significantly associated with mortality. We demonstrated that it is possible to answer questions about COVID-19 using EHR data from systems that have different policies and must follow various regulations, without moving data out of their health systems. DISCUSSION AND CONCLUSIONS: We present an alternative or a complement to centralized COVID-19 registries of EHR data. We can use multivariate distributed logistic regression on observations recorded in the process of care to generate results without transferring individual-level data outside the health systems.

Privacy-Protecting, Reliable Response Data Discovery Using COVID-19 Patient Observations.

There is an urgent need to answer questions related to COVID-19's clinical course and associations with underlying conditions and health outcomes. Multi-center data are necessary to generate reliable answers, but centralizing data in a single repository is not always possible. Using a privacy-protecting strategy, we launched a public Questions & Answers web portal (https://covid19questions.org) with analyses of comorbidities, medications and laboratory tests using data from 202 hospitals (59,074 COVID-19 patients) in the USA and Germany. We find, for example, that 8.6% of hospitalizations in which the patient was not admitted to the ICU resulted in the patient returning to the hospital within seven days from discharge and that, when adjusted for age, mortality for hospitalized patients was not significantly different by gender or ethnicity.

Textural Parameters of Tumor Heterogeneity in ¹8F-FDG PET/CT for Therapy Response Assessment and Prognosis in Patients with Locally Advanced Rectal Cancer.

UNLABELLED: (18)F-FDG PET/CT is effective in the assessment of therapy response. Changes in glucose uptake or tumor size are used as a measure. Tumor heterogeneity was found to be a promising predictive and prognostic factor. We investigated textural parameters for their predictive and prognostic capability in patients with rectal cancer using histopathology as the gold standard. In addition, a comparison to clinical outcome was performed. METHODS: Twenty-seven patients with rectal cancer underwent (18)F-FDG PET/CT before, 2 wk after the start, and 4 wk after the completion of neoadjuvant chemoradiotherapy. In all PET/CT scans, conventional parameters (tumor volume, diameter, maximum and mean standardized uptake values, and total lesion glycolysis [TLG]) and textural parameters (coefficient of variation [COV], skewness, and kurtosis) were determined to assess tumor heterogeneity. Values on pretherapeutic PET/CT as well as changes early in the course of therapy and after therapy were compared with histopathologic response. In addition, the prognostic value was assessed by correlation with time to progression and survival time. RESULTS: The COV showed a statistically significant capability to assess histopathologic response early in therapy (sensitivity, 68%; specificity, 88%) and after therapy (79% and 88%, respectively). Thereby, the COV had a higher area under the curve in receiver-operating-characteristic analysis than did any analyzed conventional parameter for early and late response assessment. The COV showed a statistically significant capability to evaluate disease progression and to predict survival, although the latter was not statistically significant. CONCLUSION: Tumor heterogeneity assessed by the COV, being superior to the investigated conventional parameters, is an important predictive factor in patients with rectal cancer. Furthermore, it can provide prognostic information. Therefore, its application is an important step for personalized treatment of rectal cancer.