Accuracy of Computable Phenotyping Approaches for SARS-CoV-2 Infection and COVID-19 Hospitalizations from the Electronic Health Record

ObjectiveReal-world data have been critical for rapid-knowledge generation throughout the COVID-19 pandemic. To ensure high-quality results are delivered to guide clinical decision making and the public health response, as well as characterize the response to interventions, it is essential to establish the accuracy of COVID-19 case definitions derived from administrative data to identify infections and hospitalizations. MethodsElectronic Health Record (EHR) data were obtained from the clinical data warehouse of the Yale New Haven Health System (Yale, primary site) and 3 hospital systems of the Mayo Clinic (validation site). Detailed characteristics on demographics, diagnoses, and laboratory results were obtained for all patients with either a positive SARS-CoV-2 PCR or antigen test or ICD-10 diagnosis of COVID-19 (U07.1) between April 1, 2020 and March 1, 2021. Various computable phenotype definitions were evaluated for their accuracy to identify SARS-CoV-2 infection and COVID-19 hospitalizations. ResultsOf the 69,423 individuals with either a diagnosis code or a laboratory diagnosis of a SARS-CoV-2 infection at Yale, 61,023 had a principal or a secondary diagnosis code for COVID-19 and 50,355 had a positive SARS-CoV-2 test. Among those with a positive laboratory test, 38,506 (76.5%) and 3449 (6.8%) had a principal and secondary diagnosis code of COVID-19, respectively, while 8400 (16.7%) had no COVID-19 diagnosis. Moreover, of the 61,023 patients with a COVID-19 diagnosis code, 19,068 (31.2%) did not have a positive laboratory test for SARS-CoV-2 in the EHR. Of the 20 cases randomly sampled from this latter group for manual review, all had a COVID-19 diagnosis code related to asymptomatic testing with negative subsequent test results. The positive predictive value (precision) and sensitivity (recall) of a COVID-19 diagnosis in the medical record for a documented positive SARS-CoV-2 test were 68.8% and 83.3%, respectively. Among 5,109 patients who were hospitalized with a principal diagnosis of COVID-19, 4843 (94.8%) had a positive SARS-CoV-2 test within the 2 weeks preceding hospital admission or during hospitalization. In addition, 789 hospitalizations had a secondary diagnosis of COVID-19, of which 446 (56.5%) had a principal diagnosis consistent with severe clinical manifestation of COVID-19 (e.g., sepsis or respiratory failure). Compared with the cohort that had a principal diagnosis of COVID-19, those with a secondary diagnosis had a more than 2-fold higher in-hospital mortality rate (13.2% vs 28.0%, P<0.001). In the validation sample at Mayo Clinic, diagnosis codes more consistently identified SARS-CoV-2 infection (precision of 95%) but had lower recall (63.5%) with substantial variation across the 3 Mayo Clinic sites. Similar to Yale, diagnosis codes consistently identified COVID-19 hospitalizations at Mayo, with hospitalizations defined by secondary diagnosis code with 2-fold higher in-hospital mortality compared to those with a primary diagnosis of COVID-19. ConclusionsCOVID-19 diagnosis codes misclassified the SARS-CoV-2 infection status of many people, with implications for clinical research and epidemiological surveillance. Moreover, the codes had different performance across two academic health systems and identified groups with different risks of mortality. Real-world data from the EHR can be used to in conjunction with diagnosis codes to improve the identification of people infected with SARS-CoV-2.

Association of Angiotensin-Converting Enzyme Inhibitors and Angiotensin Receptor Blockers with the Risk of Hospitalization and Death in Hypertensive Patients with Coronavirus Disease-19

BackgroundWhether angiotensin-converting enzyme (ACE) Inhibitors and angiotensin receptor blockers (ARBs) mitigate or exacerbate SARS-CoV-2 infection remains uncertain. In a national study, we evaluated the association of ACE inhibitors and ARB with coronavirus disease-19 (COVID-19) hospitalization and mortality among individuals with hypertension. MethodsAmong Medicare Advantage and commercially insured individuals, we identified 2,263 people with hypertension, receiving [≥]1 antihypertensive agents, and who had a positive outpatient SARS-CoV-2 test (outpatient cohort). In a propensity score-matched analysis, we determined the association of ACE inhibitors and ARBs with the risk of hospitalization for COVID-19. In a second study of 7,933 individuals with hypertension who were hospitalized with COVID-19 (inpatient cohort), we tested the association of these medications with in-hospital mortality. We stratified all our assessments by insurance groups. ResultsAmong individuals in the outpatient and inpatient cohorts, 31.9% and 29.8%, respectively, used ACE inhibitors and 32.3% and 28.1% used ARBs. In the outpatient study, over a median 30.0 (19.0 - 40.0) days after testing positive, 12.7% were hospitalized for COVID-19. In propensity score-matched analyses, neither ACE inhibitors (HR, 0.77 [0.53, 1.13], P = 0.18), nor ARBs (HR, 0.88 [0.61, 1.26], P = 0.48), were significantly associated with risk of hospitalization. In analyses stratified by insurance group, ACE inhibitors, but not ARBs, were associated with a significant lower risk of hospitalization in the Medicare group (HR, 0.61 [0.41, 0.93], P = 0.02), but not the commercially insured group (HR: 2.14 [0.82, 5.60], P = 0.12; P-interaction 0.09). In the inpatient study, 14.2% died, 59.5% survived to discharge, and 26.3% had an ongoing hospitalization. In propensity score-matched analyses, neither use of ACE inhibitor (0.97 [0.81, 1.16]; P = 0.74) nor ARB (1.15 [0.95, 1.38]; P = 0.15) was associated with risk of in-hospital mortality, in total or in the stratified analyses. ConclusionsThe use of ACE inhibitors and ARBs was not associated with the risk of hospitalization or mortality among those infected with SARS-CoV-2. However, there was a nearly 40% lower risk of hospitalization with the use of ACE inhibitors in the Medicare population. This finding merits a clinical trial to evaluate the potential role of ACE inhibitors in reducing the risk of hospitalization among older individuals, who are at an elevated risk of adverse outcomes with the infection.

Evaluation of the Anticipated Burden of COVID-19 on Hospital-Based Healthcare Services Across the United States

BackgroundCoronavirus disease-19 (COVID-19) is a global pandemic, with the potential to infect nearly 60% of the population. The anticipated spread of the virus requires an urgent appraisal of the capacity of US healthcare services and the identification of states most vulnerable to exceeding their capacity MethodsIn the American Hospital Association survey for 2018, a database of US community hospitals, we identified total inpatient beds, adult intensive care unit (ICU) beds, and airborne isolation rooms across all hospitals in each state of continental US. The burden of COVID-19 hospitalizations was estimated based on a median hospitalization duration of 12 days and was evaluated for a 30-day reporting period. ResultsAt 5155 US community hospitals across 48 states in the contiguous US and Washington DC, there were a total of 788,032 inpatient beds, 68,280 adult ICU beds, and 44,222 isolation rooms. The median daily bed occupancy was 62.8% (IQR 58.1%, 66.6%) across states. Nationally, for every 10,000 individuals, there are 24.2 inpatient beds, 2.8 adult ICU beds, and 1.4 isolation beds. There is a 3-fold variation in the number of inpatient beds available across the US, ranging from 16.4 per 10,000 in Oregon to 47 per 10,000 in South Dakota. There was also a similar 3-fold variation in available or non-occupied beds, ranging from 4.7 per 10,000 in Connecticut through 18.3 per 10,000 in North Dakota. The availability of ICU beds is low nationally, ranging from 1.4 per 10,000 in Nevada to 4.7 per 10000 in Washington DC. Hospitalizations for COVID-19 in a median 0.2% (IQR 0.2 %, 0.3%) of state population, or 1.4% of states older adults (1.0%, 1.9%) will require all non-occupied beds. Further, a median 0.6% (0.5%, 0.8%) of state population, or 3.9% (3.1%, 4.6%) of older individuals would require 100% of inpatient beds. ConclusionThe COVID-19 pandemic is likely to overwhelm the limited number of inpatient and ICU beds for the US population. Hospitals in half of US states would exceed capacity if less than 0.2% of the state population requires hospitalization in any given month.

An Evaluation of the Vulnerable Physician Workforce in the United States During the Coronavirus Disease-19 Pandemic

BackgroundThe coronavirus disease-19 (COVID-19) pandemic threatens to overwhelm the healthcare resources of the country, but also poses a personal hazard to healthcare workers, including physicians. To address the potential impact of excluding physicians with a high risk of adverse outcomes based on age, we evaluated the current patterns of age of licensed physicians across the United States. MethodsWe compiled information from the 2018 database of actively licensed physicians in the Federation of State Medical Boards (FSMB) across the US. Both at a national- and the state-level, we assessed the number and proportion of physicians who would be at an elevated risk due to age over 60 years. ResultsOf the 985,026 licensed physicians in the US, 235857 or 23.9% were aged 25-40 years, 447052 or 45.4% are 40-60 years, 191794 or 19.5% were 60-70 years, and 106121 or 10.8% were 70 years or older. Age was not reported in 4202 or 0.4% of physicians. Overall, 297915 or 30.2% of physicians were 60 years of age or older, 246167 (25.0%) 65 years and older, and 106121 (10.8%) 70 years or older. States in the US reported that a median 5470 licensed physicians (interquartile range [IQR], 2394 to 10108) were 60 years of age or older. Notably, states of North Dakota (n=1180) and Vermont (n = 1215) had the lowest and California (n=50786) and New York (n=31582) the highest number of physicians over the age of 60 years (Figure 1). Across states, the median proportion of physicians aged 60 years and older was 28.9% (IQR, 27.2%, 31.4%), and ranged between 25.9% for Nebraska to 32.6% for New Mexico (Figure 2). DiscussionOlder physicians represent a large proportion of the US physician workforce, particularly in states with the worst COVID-19 outbreak. Therefore, their exclusion from patient care will be impractical. Optimizing care practices by limiting direct patient contact of physicians vulnerable to adverse outcomes from COVID-19, potentially by expanding their participation in telehealth may be a strategy to protect them.