https://onlinelibrary.wiley.com/doi/pdf/10.1002/wps.20806

Increased risk of COVID-19 infection and mortality in people with mental disorders: analysis from electronic health records in the United States

The global pandemic of COVID-19 is colliding with the epidemic of opioid use disorders (OUD) and other substance use disorders (SUD) in the United States (US). Currently, there is limited data on risks, disparity, and outcomes for COVID-19 in individuals suffering from SUD. This is a retrospective case-control study of electronic health records (EHRs) data of 73,099,850 unique patients, of whom 12,030 had a diagnosis of COVID-19. Patients with a recent diagnosis of SUD (within past year) were at significantly increased risk for COVID-19 (adjusted odds ratio or AOR = 8.699 [8.411-8.997], P < 10-30), an effect that was strongest for individuals with OUD (AOR = 10.244 [9.107-11.524], P < 10-30), followed by individuals with tobacco use disorder (TUD) (AOR = 8.222 ([7.925-8.530], P < 10-30). Compared to patients without SUD, patients with SUD had significantly higher prevalence of chronic kidney, liver, lung diseases, cardiovascular diseases, type 2 diabetes, obesity and cancer. Among patients with recent diagnosis of SUD, African Americans had significantly higher risk of COVID-19 than Caucasians (AOR = 2.173 [2.01-2.349], P < 10-30), with strongest effect for OUD (AOR = 4.162 [3.13-5.533], P < 10-25). COVID-19 patients with SUD had significantly worse outcomes (death: 9.6%, hospitalization: 41.0%) than general COVID-19 patients (death: 6.6%, hospitalization: 30.1%) and African Americans with COVID-19 and SUD had worse outcomes (death: 13.0%, hospitalization: 50.7%) than Caucasians (death: 8.6%, hospitalization: 35.2%). These findings identify individuals with SUD, especially individuals with OUD and African Americans, as having increased risk for COVID-19 and its adverse outcomes, highlighting the need to screen and treat individuals with SUD as part of the strategy to control the pandemic while ensuring no disparities in access to healthcare support.

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COVID-19 risk and outcomes in patients with substance use disorders: analyses from electronic health records in the United States.

Importance Patients with specific cancers may be at higher risk than those without cancer for coronavirus disease 2019 (COVID-19) and its severe outcomes. At present, limited data are available on the risk, racial disparity, and outcomes for COVID-19 illness in patients with cancer. Objectives To investigate how patients with specific types of cancer are at risk for COVID-19 infection and its adverse outcomes and whether there are cancer-specific race disparities for COVID-19 infection. Design, Setting, and Participants This retrospective case-control analysis of patient electronic health records included 73.4 million patients from 360 hospitals and 317 000 clinicians across 50 US states to August 14, 2020. The odds of COVID-19 infections for 13 common cancer types and adverse outcomes were assessed. Exposures The exposure groups were patients diagnosed with a specific cancer, whereas the unexposed groups were patients without the specific cancer. Main Outcomes and Measures The adjusted odds ratio (aOR) and 95% CI were estimated using the Cochran-Mantel-Haenszel test for the risk of COVID-19 infection. Results Among the 73.4 million patients included in the analysis (53.6% female), 2 523 920 had at least 1 of the 13 common cancers diagnosed (all cancer diagnosed within or before the last year), and 273 140 had recent cancer (cancer diagnosed within the last year). Among 16 570 patients diagnosed with COVID-19, 1200 had a cancer diagnosis and 690 had a recent cancer diagnosis of at least 1 of the 13 common cancers. Those with recent cancer diagnosis were at significantly increased risk for COVID-19 infection (aOR, 7.14 [95% CI, 6.91-7.39];P  Conclusions and Relevance In this case-control study, patients with cancer were at significantly increased risk for COVID-19 infection and worse outcomes, which was further exacerbated among African Americans. These findings highlight the need to protect and monitor patients with cancer as part of the strategy to control the pandemic.

/pdf/analyses-of-risk-racial-disparity-and-outcomes-among-us-8vrbmrh8ig.pdf

Analyses of Risk, Racial Disparity, and Outcomes Among US Patients With Cancer and COVID-19 Infection.

In recent years, there is increasing enthusiasm in the healthcare research community for artificial intelligence to provide big data analytics and augment decision making. One of the prime reasons for this is the enormous impact of deep learning for utilization of complex healthcare big data. Although deep learning is a powerful analytic tool for the complex data contained in electronic health records (EHRs), there are also limitations which can make the choice of deep learning inferior in some healthcare applications. In this paper, we give a brief overview of the limitations of deep learning illustrated through case studies done over the years aiming to promote the consideration of alternative analytic strategies for healthcare.

/pdf/deep-learning-and-alternative-learning-strategies-for-2xslop2et7.pdf

Deep learning and alternative learning strategies for retrospective real-world clinical data

Importance Although randomized clinical trials are considered to be the criterion standard for generating clinical evidence, the use of real-world evidence to evaluate the efficacy and safety of medical interventions is gaining interest. Whether observational data can be used to address the same clinical questions being answered by traditional clinical trials is still unclear. Objective To identify the number of clinical trials published in high-impact journals in 2017 that could be feasibly replicated using observational data from insurance claims and/or electronic health records (EHRs). Design, Setting, and Participants In this cross-sectional analysis, PubMed was searched to identify all US-based clinical trials, regardless of randomization, published between January 1, 2017, and December 31, 2017, in the top 7 highest-impact general medical journals of 2017. Trials were excluded if they did not involve human participants, did not use end points that represented clinical outcomes among patients, were not characterized as clinical trials, and had no recruitment sites in the United States. Main Outcomes and Measures The primary outcomes were the number and percentage of trials for which the intervention, indication, trial inclusion and exclusion criteria, and primary end points could be ascertained from insurance claims and/or EHR data. Results Of the 220 US-based trials analyzed, 33 (15.0%) could be replicated using observational data because their intervention, indication, inclusion and exclusion criteria, and primary end points could be routinely ascertained from insurance claims and/or EHR data. Of the 220 trials, 86 (39.1%) had an intervention that could be ascertained from insurance claims and/or EHR data. Among the 86 trials, 62 (72.1%) had an indication that could be ascertained. Forty-five (72.6%) of 62 trials had at least 80% of inclusion and exclusion criteria data that could be ascertained. Of these 45 studies, 33 (73.3%) had at least 1 primary end point that could be ascertained. Conclusions and Relevance This study found that only 15% of the US-based clinical trials published in high-impact journals in 2017 could be feasibly replicated through analysis of administrative claims or EHR data. This finding suggests the potential for real-world evidence to complement clinical trials, both by examining the concordance between randomized experiments and observational studies and by comparing the generalizability of the trial population with the real-world population of interest.

/pdf/feasibility-of-using-real-world-data-to-replicate-clinical-5cuhh1v59u.pdf

Feasibility of Using Real-World Data to Replicate Clinical Trial Evidence.

Electronic health records (EHRs) provide opportunities to enhance patient care, embed performance measures in clinical practice, and facilitate clinical research. Concerns have been raised about the increasing recruitment challenges in trials, burdensome and obtrusive data collection, and uncertain generalizability of the results. Leveraging electronic health records to counterbalance these trends is an area of intense interest. The initial applications of electronic health records, as the primary data source is envisioned for observational studies, embedded pragmatic or post-marketing registry-based randomized studies, or comparative effectiveness studies. Advancing this approach to randomized clinical trials, electronic health records may potentially be used to assess study feasibility, to facilitate patient recruitment, and streamline data collection at baseline and follow-up. Ensuring data security and privacy, overcoming the challenges associated with linking diverse systems and maintaining infrastructure for repeat use of high quality data, are some of the challenges associated with using electronic health records in clinical research. Collaboration between academia, industry, regulatory bodies, policy makers, patients, and electronic health record vendors is critical for the greater use of electronic health records in clinical research. This manuscript identifies the key steps required to advance the role of electronic health records in cardiovascular clinical research.

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Mark Leenay

Papers

Electronic health records to facilitate clinical research.