COVID-19 severity scale for claims data research

Huespe I., Carboni Bisso I., Di Stefano S., Terrasa S., Gemelli N.A., Las Heras M.

Pei S., Yamana T.K., Kandula S., Galanti M., Shaman J.

The COVID-19 pandemic disrupted health systems and economies throughout the world during 2020 and was particularly devastating for the United States, which experienced the highest numbers of reported cases and deaths during 20201–3. Many of the epidemiological features responsible for observed rates of morbidity and mortality have been reported4–8; however, the overall burden and characteristics of COVID-19 in the United States have not been comprehensively quantified. Here we use a data-driven model-inference approach to simulate the pandemic at county-scale in the United States during 2020 and estimate critical, time-varying epidemiological properties underpinning the dynamics of the virus. The pandemic in the United States during 2020 was characterized by national ascertainment rates that increased from 11.3% (95% credible interval (CI): 8.3–15.9%) in March to 24.5% (18.6–32.3%) during December. Population susceptibility at the end of the year was 69.0% (63.6–75.4%), indicating that about one third of the US population had been infected. Community infectious rates, the percentage of people harbouring a contagious infection, increased above 0.8% (0.6–1.0%) before the end of the year, and were as high as 2.4% in some major metropolitan areas. By contrast, the infection fatality rate fell to 0.3% by year’s end. Data-driven modelling including numbers of cases and population movements is used to simulate the COVID-19 pandemic in the United States in 2020, providing insights into the transmission of the disease.

Miethke-Morais A., Cassenote A., Piva H., Tokunaga E., Cobello V., Rodrigues Gonçalves F.A., dos Santos Lobo R., Trindade E., Carneiro D`Albuquerque L.A., Haddad L.

A bstract Although patients’ clinical conditions have been shown to be associated with coronavirus disease (COVID-19) severity and outcome, their impact on hospital costs are not known. This economic evaluation of COVID-19 admissions aimed to assess direct and fixed hospital costs and describe their particularities in different clinical and demographic conditions and outcomes in the largest public hospital in Latin America, located in São Paulo, Brazil, where a whole institute was exclusively dedicated to COVID-19 patients in response to the pandemic. This is a partial economic evaluation performed from the hospital´s perspective and is a prospective, observational cohort study to assess hospitalization costs of suspected and confirmed COVID-19 patients admitted between March 30 and June 30, 2020, to Hospital das Clínicas of the University of São Paulo Medical School (HCFMUSP) and followed until discharge, death, or external transfer. Micro- and macro-costing methodologies were used to describe and analyze the total cost associated with each patient's underlying medical conditions, itinerary and outcomes as well as the cost components of different hospital sectors. The average cost of the 3254 admissions (51.7% of which involved intensive care unit stays) was US$12,637.42. The overhead cost was its main component. Sex, age and underlying hypertension (US$14,746.77), diabetes (US$15,002.12), obesity (US$18,941.55), chronic renal failure (US$15,377.84), and rheumatic (US$17,764.61), hematologic (US$15,908.25) and neurologic (US$15,257.95) diseases were associated with higher costs. Age strata >69 years, reverse transcription polymerase chain reaction (RT-PCR)-confirmed COVID-19, comorbidities, use of mechanical ventilation or dialysis, surgery and outcomes remained associated with higher costs. Knowledge of COVID-19 hospital costs can aid in the development of a comprehensive approach for decision-making and planning for future risk management.

Tsai Y., Vogt T.M., Zhou F.

This study used data from Medicare fee-for-service claims to examine characteristics and hospitalization risks among patients with COVID-19 who were aged 65 years or older and to estimate the costs of hospitalizations and outpatient visits associated with the disease.

Altschul D.J., Unda S.R., Benton J., de la Garza Ramos R., Cezayirli P., Mehler M., Eskandar E.N.

COVID-19 is commonly mild and self-limiting, but in a considerable portion of patients the disease is severe and fatal. Determining which patients are at high risk of severe illness or mortality is essential for appropriate clinical decision making. We propose a novel severity score specifically for COVID-19 to help predict disease severity and mortality. 4711 patients with confirmed SARS-CoV-2 infection were included. We derived a risk model using the first half of the cohort (n = 2355 patients) by logistic regression and bootstrapping methods. The discriminative power of the risk model was assessed by calculating the area under the receiver operating characteristic curves (AUC). The severity score was validated in a second half of 2356 patients. Mortality incidence was 26.4% in the derivation cohort and 22.4% in the validation cohort. A COVID-19 severity score ranging from 0 to 10, consisting of age, oxygen saturation, mean arterial pressure, blood urea nitrogen, C-Reactive protein, and the international normalized ratio was developed. A ROC curve analysis was performed in the derivation cohort achieved an AUC of 0.824 (95% CI 0.814–0.851) and an AUC of 0.798 (95% CI 0.789–0.818) in the validation cohort. Furthermore, based on the risk categorization the probability of mortality was 11.8%, 39% and 78% for patient with low (0–3), moderate (4–6) and high (7–10) COVID-19 severity score. This developed and validated novel COVID-19 severity score will aid physicians in predicting mortality during surge periods.

Haimovich A.D., Ravindra N.G., Stoytchev S., Young H.P., Wilson F.P., van Dijk D., Schulz W.L., Taylor R.A.

Study objective The goal of this study is to create a predictive, interpretable model of early hospital respiratory failure among emergency department (ED) patients admitted with coronavirus disease 2019 (COVID-19). Methods This was an observational, retrospective, cohort study from a 9-ED health system of admitted adult patients with severe acute respiratory syndrome coronavirus 2 (COVID-19) and an oxygen requirement less than or equal to 6 L/min. We sought to predict respiratory failure within 24 hours of admission as defined by oxygen requirement of greater than 10 L/min by low-flow device, high-flow device, noninvasive or invasive ventilation, or death. Predictive models were compared with the Elixhauser Comorbidity Index, quick Sequential [Sepsis-related] Organ Failure Assessment, and the CURB-65 pneumonia severity score. Results During the study period, from March 1 to April 27, 2020, 1,792 patients were admitted with COVID-19, 620 (35%) of whom had respiratory failure in the ED. Of the remaining 1,172 admitted patients, 144 (12.3%) met the composite endpoint within the first 24 hours of hospitalization. On the independent test cohort, both a novel bedside scoring system, the quick COVID-19 Severity Index (area under receiver operating characteristic curve mean 0.81 [95% confidence interval {CI} 0.73 to 0.89]), and a machine-learning model, the COVID-19 Severity Index (mean 0.76 [95% CI 0.65 to 0.86]), outperformed the Elixhauser mortality index (mean 0.61 [95% CI 0.51 to 0.70]), CURB-65 (0.50 [95% CI 0.40 to 0.60]), and quick Sequential [Sepsis-related] Organ Failure Assessment (0.59 [95% CI 0.50 to 0.68]). A low quick COVID-19 Severity Index score was associated with a less than 5% risk of respiratory decompensation in the validation cohort. Conclusion A significant proportion of admitted COVID-19 patients progress to respiratory failure within 24 hours of admission. These events are accurately predicted with bedside respiratory examination findings within a simple scoring system.

Bartsch S.M., Ferguson M.C., McKinnell J.A., O'Shea K.J., Wedlock P.T., Siegmund S.S., Lee B.Y.

With the coronavirus disease 2019 (COVID-19) pandemic, one of the major concerns is the direct medical cost and resource use burden imposed on the US health care system. We developed a Monte Carlo simulation model that represented the US population and what could happen to each person who got infected. We estimated resource use and direct medical costs per symptomatic infection and at the national level, with various “attack rates” (infection rates), to understand the potential economic benefits of reducing the burden of the disease. A single symptomatic COVID-19 case could incur a median direct medical cost of $3,045 during the course of the infection alone. If 80 percent of the US population were to get infected, the result could be a median of 44.6 million hospitalizations, 10.7 million intensive care unit (ICU) admissions, 6.5 million patients requiring a ventilator, 249.5 million hospital bed days, and $654.0 billion in direct medical costs over the course of the pandemic. If 20 percent of the US population were to get infected, there could be a median of 11.2 million hospitalizations, 2.7 million ICU admissions, 1.6 million patients requiring a ventilator, 62.3 million hospital bed days, and $163.4 billion in direct medical costs over the course of the pandemic.

Machado-Vieira R., Krause T.M., Jones G., Teixeira A.L., Shahani L.R., Lane S.D., Soares J.C., Truong C.N.

The coronavirus disease pandemic caused by the coronavirus SARS-CoV-2, which emerged in the United States in late 2019 to early 2020 and quickly escalated into a national public health crisis. Research has identified psychiatric conditions as possible risk factors associated with COVID-19 infection and symptom severity. This study aims to determine whether specific classes of psychiatric medications could reduce the likelihood of infection and alleviate the severity of the disease. The objective of this study is to investigate the relationship between neuropsychiatric medication usage and COVID-19 outcomes before the widespread utilization of COVID-19 vaccines. This cross-sectional study used Optum’s de-identified Clinformatics Data Mart Database to identify patients diagnosed with COVID-19 in 2020 and their psychiatric medication prescriptions in the United States. Ordered logistic regression was used to predict the likelihood of a higher COVID-19 severity level for long-term and new users. Results were adjusted for demographic characteristics and medical and psychiatric comorbidities. Most users were classified into the long-term user analysis group. Long-term users were 9% less likely to have a higher severity score (CI: 0.89–0.93, p-value < 0.001) than non-users. SSRI antidepressant users, both long-term (OR: 1.09; CI: 1.06–1.12) and short-term (OR: 1.17; CI: 1.07–1.27) were significantly more likely to have a lower severity score. However, the results varied across long-term and short-term users for all medication classes. Results of the current study suggest that psychopharmacological agents are associated with reduced COVID-19 severity levels and that antidepressant medications may have a protective role against COVID-19.

Mizuno T., Suzuki J., Takahashi S., Imai H., Itagaki H., Yoshida M., Endo S.

Systemic baricitinib and corticosteroids play important roles in treating severely and critically ill patients with coronavirus disease 2019 (COVID-19). However, the efficacy of the combination of baricitinib and corticosteroids compared to that of corticosteroid monotherapy in severely and critically ill hospitalized patients with COVID-19 remains unclear.