Healthcare Utilization Among Patients Diagnosed with COVID-19 in a Large Integrated Health System.

BACKGROUND: The demands for healthcare resources following a COVID-19 diagnosis are substantial, but not currently quantified. OBJECTIVE: To describe trends in healthcare utilization within 180 days for patients diagnosed with COVID-19 and identify patient factors associated with increased healthcare use. DESIGN: Observational cohort study. PATIENTS: A total of 64,011 patients with a test-confirmed COVID-19 diagnosis from March to September 2020 in a large integrated healthcare system in Southern California. MAIN MEASURES: Overall healthcare utilization during the 180 days following COVID-19 diagnosis, as well as encounter types and reasons for visits during the first 30 days. Poisson regression was used to identify patient factors associated with higher utilization. Analyses were performed separately for patients who were and were not hospitalized for COVID-19. KEY RESULTS: Healthcare utilization was about twice as high for hospitalized patients compared to non-hospitalized patients in all time periods. The average number of visits was highest in the first 30 days (hospitalized: 12.3 visits/30 person-days; non-hospitalized: 6.6) and gradually decreased over time. In the first 30 days, the majority of healthcare visits were telehealth encounters (hospitalized: 9.0 visits; non-hospitalized: 5.6 visits), and the most prevalent reasons for visits were COVID-related diagnoses, COVID-related symptoms, and respiratory-related conditions. For hospitalized patients, older age (≥65: RR 1.27, 95% CI 1.15-1.41), female gender (RR 1.07, 95% CI 1.05-1.09), and higher BMI (≥40: RR 1.07, 95% CI 1.03-1.10) were associated with higher total utilization. For non-hospitalized patients, older age, female gender, higher BMI, non-white race/ethnicity, former smoking, and greater number of pre-existing comorbidities were all associated with increased utilization. CONCLUSIONS: Patients with COVID-19 seek healthcare frequently within 30 days of diagnosis, placing high demands on health systems. Identifying ways to support patients diagnosed with COVID-19 while adequately providing the usual recommended care to our communities will be important as we recover from the pandemic.

COVID-19 morbidity and mortality associated with angiotensin-converting enzyme inhibitors or angiotensin receptor blockers use among 14,129 patients with hypertension from a US integrated healthcare system.

OBJECTIVE: Although recent evidence suggests no increased risk of severe COVID-19 outcomes associated with angiotensin-converting enzyme inhibitors (ACEIs) or angiotensin receptor blockers (ARBs) use, the relationship is less clear among patients with hypertension and diverse racial/ethnic groups. This study evaluates the risk of hospitalization and mortality among patients with hypertension and COVID-19 in a large US integrated healthcare system. METHODS: Patients with hypertension and COVID-19 (between March 1- September 1, 2020) on ACEIs or ARBs were compared with patients on other frequently used antihypertensive medications. RESULTS: Among 14,129 patients with hypertension and COVID-19 infection (mean age 60 years, 48% men, 58% Hispanic), 21% were admitted to the hospital within 30 days of COVID-19 infection. Of the hospitalized patients, 24% were admitted to intensive care units, 17% required mechanical ventilation, and 10% died within 30 days of COVID-19 infection. Exposure to ACEIs or ARBs prior to COVID-19 infection was not associated with an increased risk of hospitalization or all-cause mortality (rate ratios for ACEIs vs other antihypertensive medications â€‹= â€‹0.98, 95% CI: 0.88, 1.08; ARBs vs others â€‹= â€‹1.00, 95% CI: 0.90, 1.11) after applying inverse probability of treatment weights. These associations were consistent across racial/ethnic groups. Use of ACEIs or ARBs during hospitalization was associated with a lower risk of all-cause mortality (odds ratios for ACEIs or ARBs vs others â€‹= â€‹0.50, 95% CI: 0.34, 0.72). CONCLUSION: Our study findings support continuation of ACEI or ARB use for patients with hypertension during the COVID-19 pandemic and after COVID-19 infection.

Natural Language Processing to Identify Pulmonary Nodules and Extract Nodule Characteristics From Radiology Reports.

BACKGROUND: There is an urgent need for population-based studies on managing patients with pulmonary nodules. RESEARCH QUESTION: Is it possible to identify pulmonary nodules and associated characteristics using an automated method? STUDY DESIGN AND METHODS: We revised and refined an existing natural language processing (NLP) algorithm to identify radiology transcripts with pulmonary nodules and greatly expanded its functionality to identify the characteristics of the largest nodule, when present, including size, lobe, laterality, attenuation, calcification, and edge. We compared NLP results with a reference standard of manual transcript review in a random test sample of 200 radiology transcripts. We applied the final automated method to a larger cohort of patients who underwent chest CT scan in an integrated health care system from 2006 to 2016, and described their demographic and clinical characteristics. RESULTS: In the test sample, the NLP algorithm had very high sensitivity (98.6%; 95% CI, 95.0%-99.8%) and specificity (100%; 95% CI, 93.9%-100%) for identifying pulmonary nodules. For attenuation, edge, and calcification, the NLP algorithm achieved similar accuracies, and it correctly identified the diameter of the largest nodule in 135 of 141 cases (95.7%; 95% CI, 91.0%-98.4%). In the larger cohort, the NLP found 217,771 reports with nodules among 717,304 chest CT reports (30.4%). From 2006 to 2016, the number of reports with nodules increased by 150%, and the mean size of the largest nodule gradually decreased from 11 to 8.9 mm. Radiologists documented the laterality and lobe (90%-95%) more often than the attenuation, calcification, and edge characteristics (11%-14%). INTERPRETATION: The NLP algorithm identified pulmonary nodules and associated characteristics with high accuracy. In our community practice settings, the documentation of nodule characteristics is incomplete. Our results call for better documentation of nodule findings. The NLP algorithm can be used in population-based studies to identify pulmonary nodules, avoiding labor-intensive chart review.

Angiotensin-Converting Enzyme Inhibitors or Angiotensin Receptor Blockers Use and COVID-19 Infection Among 824 650 Patients With Hypertension From a US Integrated Healthcare System.

Background Previous reports suggest that the use of angiotensin-converting enzyme inhibitors (ACEIs) or angiotensin receptor blockers (ARBs) may upregulate angiotensin-converting enzyme 2 receptors and increase severe acute respiratory syndrome coronavirus 2 infectivity. We evaluated the association between ACEI or ARB use and coronavirus disease 2019 (COVID-19) infection among patients with hypertension. Methods and Results We identified patients with hypertension as of March 1, 2020 (index date) from Kaiser Permanente Southern California. Patients who received ACEIs, ARBs, calcium channel blockers, beta blockers, thiazide diuretics (TD), or no therapy were identified using outpatient pharmacy data covering the index date. Outcome of interest was a positive reverse transcription polymerase chain reaction test for COVID-19 between March 1 and May 6, 2020. Patient sociodemographic and clinical characteristics were identified within 1 year preindex date. Among 824 650 patients with hypertension, 16 898 (2.0%) were tested for COVID-19. Of those tested, 1794 (10.6%) had a positive result. Overall, exposure to ACEIs or ARBs was not statistically significantly associated with COVID-19 infection after propensity score adjustment (odds ratio [OR], 1.06; 95% CI, 0.90-1.25) for ACEIs versus calcium channel blockers/beta blockers/TD; OR, 1.10; 95% CI, 0.91-1.31 for ARBs versus calcium channel blockers/beta blockers/TD). The associations between ACEI use and COVID-19 infection varied in different age groups (P-interaction=0.03). ACEI use was associated with lower odds of COVID-19 among those aged ≥85 years (OR, 0.30; 95% CI, 0.12-0.77). Use of no antihypertensive medication was significantly associated with increased odds of COVID-19 infection compared with calcium channel blockers/beta blockers/TD (OR, 1.32; 95% CI, 1.11-1.56). Conclusions Neither ACEI nor ARB use was associated with increased likelihood of COVID-19 infection. Decreased odds of COVID-19 infection among adults ≥85 years using ACEIs warrants further investigation.