Prognostic Value of SARS-CoV-2 Anti-RBD IgG Antibody Quantitation on Clinical Outcomes in Hospitalized COVID-19 Patients

Background Antibody levels against SARS-CoV-2 can be used as an indicator of recent or past vaccination or infection. However, the prognostic value of antibodies targeting the receptor binding protein (anti-RBD) in hospitalized patients is not widely reported. Purpose Determine prognostic impact of SARS-CoV-2 antibody quantification at the time of admission on clinical outcomes in hospitalized COVID-19 patients. Methods We conducted a pilot observational study on patients hospitalized with SARS-CoV-2 infection to determine the prognostic impact of antibody quantitation within the first two days of admission. Anti-nucleocapsid IgG (anti-N) and Anti-RBD levels were measured. Anti-RBD level of 500 AU/mL was used as a cutoff to stratify patients. Spearman’s rank Coefficient (rs) was used to demonstrate association. Results Of the 26 patients included, those who were vaccinated more frequently tested positive for Anti-RBD (100% vs 46.2%, P = 0.005) with higher median titer level (623 vs 0, P = 0.011) compared to unvaccinated patients. Anti-N positivity was more frequently seen in unvaccinated patients (53.9% vs 7.7%, P = 0.03). Anti-RBD levels >500 were associated with lower overall hospital length of stay (LOS)(5 vs 10 days, P = 0.046). The analysis employing a Spearman Rank coefficient demonstrated a strong negative correlation between anti-S titer and LOS (rs=−.515, p = 0.007) and a moderate negative correlation with oxygen needs (rs =−.401, p = 0.042). Conclusion Anti-RBD IgG levels were associated with lower LOS and oxygen needs during hospitalization. Further studies are needed to determine if levels on admission can be used as a prognostic indicator.

High-dose intravenous vitamin C decreases rates of mechanical ventilation and cardiac arrest in severe COVID-19.

Intravenous vitamin C (IV-VitC) has been suggested as a treatment for severe sepsis and acute respiratory distress syndrome; however, there are limited studies evaluating its use in severe COVID-19. Efficacy and safety of high-dose IV-VitC (HDIVC) in patients with severe COVID-19 were evaluated. This observational cohort was conducted at a single-center, 530 bed, community teaching hospital and took place from March 2020 through July 2020. Inverse probability treatment weighting (IPTW) was utilized to compare outcomes in patients with severe COVID-19 treated with and without HDIVC. Patients were enrolled if they were older than 18 years of age and were hospitalized secondary to severe COVID-19 infection, indicated by an oxygenation index < 300. Primary study outcomes included mortality, mechanical ventilation, intensive care unit (ICU) admission, and cardiac arrest. From a total of 100 patients enrolled, 25 patients were in the HDIVC group and 75 patients in the control group. The average time to death was significantly longer for HDIVC patients (P = 0.0139), with an average of 22.9 days versus 13.7 days for control patients. Patients who received HDIVC also had significantly lower rates of mechanical ventilation (52.93% vs. 73.14%; ORIPTW = 0.27; P = 0.0499) and cardiac arrest (2.46% vs. 9.06%; ORIPTW = 0.23; P = 0.0439). HDIVC may be an effective treatment in decreasing the rates of mechanical ventilation and cardiac arrest in hospitalized patients with severe COVID-19. A longer hospital stay and prolonged time to death may suggest that HDIVC may protect against clinical deterioration in severe COVID-19.

Social Determinants of Health Correlating with Mechanical Ventilation of COVID-19 Patients: A Multi-Center Observational Study.

IMPORTANCE: Several studies have relayed the disproportionate impact of COVID-19 on marginalized communities; however, few have specifically examined the association between social determinants of health and mechanical ventilation (MV). OBJECTIVE: To determine which demographics impact MV rates among COVID-19 patients. DESIGN: This observational study included COVID-19 patient data from eight hospitals' electronic medical records (EMR) between February 25, 2020, to December 31, 2020. Associations between demographic data and MV rates were evaluated using uni- and multivariate analyses. SETTING: Multicenter (eight hospitals), largest health system in Southeast Michigan. PARTICIPANTS: Inpatients with a positive RT-PCR for SARS-CoV-2 on nasopharyngeal swab. Exclusion criteria were missing demographic data or non-permanent Michigan residents. EXPOSURE: Patients were divided into two groups: MV and non-MV. MAIN OUTCOME AND MEASURES: The primary outcome was MV rate per demographic. A multivariate model then predicted the odds of MV per demographic descriptor. Hypotheses were formulated prior to data collection. RESULTS: Among 11,304 COVID-19 inpatients investigated, 1621 (14.34%) were MV, and 49.96% were male with a mean age of 63.37 years (17.79). Significant social determinants for MV included Black race (40.19% MV vs 31.31% non-MV, p<0.01), poverty (14.60% vs. 13.21%, p<0.01), and disability (12.65% vs 9.14%; p<0.01). Black race (AOR 1.61 (CI 1.41-1.83; p<0.01)), median income (AOR 0.99 (CI 0.99-0.99; p<0.01)), disability (AOR 1.55 (CI 1.26, 1.90; p<0.01)), and non-English-speaking status (AOR 1.26 (CI 1.05, 1.53)) had significantly higher odds of MV. CONCLUSIONS AND RELEVANCE: Black race, low socioeconomic status, disability, and non-English-speaking status were significant risk factors for MV from COVID-19. An urgent need remains for a pandemic response program that strategizes care for marginalized communities.

The Impact of Disparities in Social Determinants of Health on Hospitalization Rates for Patients with COVID-19 in Michigan (USA).

IMPORTANCE: The COVID-19 pandemic continues to impact the health-care system in the United States and has brought further light on health disparities within it. However, only a few studies have examined hospitalization risk with regard to social determinants of health. OBJECTIVE: We aimed to identify how health disparities affect hospitalization rates among patients with COVID-19. DESIGN: This observational study included all individuals diagnosed with COVID-19 from February 25, 2020 to December 31, 2020. Uni- and multivariate analyses were utilized to evaluate associations between demographic data and inpatient versus outpatient status for patients with COVID-19. SETTING: Multicenter (8 hospitals), largest size health system in Southeast Michigan, a region highly impacted by the pandemic. PARTICIPANTS: All outpatients and inpatients with a positive RT-PCR for SARS-CoV-2 on nasopharyngeal swab were included. Exclusion criteria included missing demographic data or status as a non-permanent Michigan resident. EXPOSURE: Patients who met inclusion and exclusion criteria were divided in 2 groups: outpatients and inpatients. MAIN OUTCOME AND MEASURES: We described the comparative demographics and known disparities associated with hospitalization status. RESULTS: Of 30,292 individuals who tested positive for SARS-CoV-2, 34.01% were admitted to the hospital. White or Caucasian race was most prevalent (57.49%), and 23.35% were African-American. The most common ethnicity was non-Hispanic or Latino (70.48%). English was the primary language for the majority of patients (91.60%). Private insurance holders made up 71.11% of the sample. Within the hospitalized patients, lower socioeconomic status, African-American race and Hispanic and Latino ethnicity, non-English speaking status, and Medicare and Medicaid were more likely to be admitted to the hospital. CONCLUSIONS AND RELEVANCE: Several health disparities were associated with greater rates of hospitalization due to COVID-19. Addressing these inequalities from an individual to system level may improve health-care outcomes for those with health disparities and COVID-19.

Healthcare Disparities Correlated with In-Hospital Mortality in COVID-19 Patients.

INTRODUCTION: Increasing age, male gender, African American race, and medical comorbidities have been reported as risk factors for COVID-19 mortality. We aimed to identify health-care disparities associated with increased mortality in COVID-19 patients. METHODS: We performed an observational study of all hospitalized patients with SARS-CoV2 infection from within the largest multicenter healthcare system in Southeast Michigan, from February to December, 2020. RESULTS: From 11,304 hospitalized patients, 1295 died, representing an in-hospital mortality rate of 11.5%. The mean age of hospitalized patients was 63.77 years-old, with 49.96% being males. Older age (AOR = 1.05, p < 0.0001), male gender (AOR = 1.43, p < 0.0001), divorced status (AOR = 1.25, p = 0.0256), disabled status (AOR = 1.42, p = 0.0091), and homemakers (AOR = 1.96, p = 0.0216) were significantly associated with in-hospital mortality. CONCLUSION: Older age, male gender, divorced and disabled status and homemakers were significantly associated with in-hospital mortality if they developed COVID-19. Further research should aim to identify the underlying factors driving these disparities in COVID-19 in-hospital mortality.

Vestibular neuritis caused by severe acute respiratory syndrome coronavirus 2 infection diagnosed by serology: Case report.

Vestibular neuritis is a disorder selectively affecting the vestibular portion of the eighth cranial nerve generally considered to be inflammatory in nature. There have been no reports of severe acute respiratory syndrome coronavirus 2 causing vestibular neuritis. We present the case of a 42-year-old Caucasian male physician, providing care to COVID-19 patients, with no significant past medical history, who developed acute vestibular neuritis, 2 weeks following a mild respiratory illness, later diagnosed as COVID-19. Physicians should keep severe acute respiratory syndrome coronavirus 2 high on the list as a possible etiology when suspecting vestibular neuritis, given the extent and implications of the current pandemic and the high contagiousness potential.

Epidemiology, Clinical Characteristics, and Outcomes of a Large Cohort of COVID-19 Outpatients in Michigan.

BACKGROUND: Most outpatients with coronavirus disease 2019 (COVID-19) do not initially demonstrate severe features requiring hospitalization. Understanding this population's epidemiological and clinical characteristics to allow outcome anticipation is crucial in healthcare resource allocation. METHODS: Retrospective, multicenter (8 hospitals) study reporting on 821 patients diagnosed with COVID-19 by real-time reverse transcriptase-polymerase chain reaction assay of nasopharyngeal swabs and discharged home to self-isolate after evaluation in emergency departments (EDs) within Beaumont Health System in March, 2020. Outcomes were collected through April 14, 2020, with a minimum of 12 day follow-up and included subsequent ED visit, admission status, and mortality. RESULTS: Of the 821 patients, mean age was 49.3 years (SD 15.7), 46.8% were male and 55.1% were African-American. Cough was the most frequent symptom in 78.2% of patients with a median duration of 3 days (IQR 2-7), and other symptoms included fever 62.1%, rhinorrhea or nasal congestion 35.1% and dyspnea 31.2%. ACEI/ARBs usage was reported in 28.7% patients and 34.0% had diabetes mellitus. Return to the ED for re-evaluation was reported in 19.2% of patients from whom 54.4% were admitted. The patients eventually admitted to the hospital were older (mean age 54.4 vs 48.7 years, p=0.002), had higher BMI (35.4 kg/m2 vs 31.9 kg/m2, p=0.004), were more likely male (58.1% vs 45.4%, p=0.026), and more likely to have hypertension (52.3% vs 29.4%, p<0.001), diabetes mellitus (74.4% vs 29.3%, p<0.001) or prediabetes (25.6% vs 8.4%, p<0.001), COPD (39.5% vs 5.4%, p<0.001), and OSA (36% vs 19%, p<0.001). The overall mortality rate was 1.3%. CONCLUSION: We found that 80.8% of patients did not return to the ED for re-evaluation. Sending patients with COVID-19 home if they experience mild symptoms is a safe approach for most patients and might mitigate some of the financial and staffing pressures on healthcare systems.

Machine learning methods to predict mechanical ventilation and mortality in patients with COVID-19.

BACKGROUND: The Coronavirus disease 2019 (COVID-19) pandemic has affected millions of people across the globe. It is associated with a high mortality rate and has created a global crisis by straining medical resources worldwide. OBJECTIVES: To develop and validate machine-learning models for prediction of mechanical ventilation (MV) for patients presenting to emergency room and for prediction of in-hospital mortality once a patient is admitted. METHODS: Two cohorts were used for the two different aims. 1980 COVID-19 patients were enrolled for the aim of prediction ofMV. 1036 patients' data, including demographics, past smoking and drinking history, past medical history and vital signs at emergency room (ER), laboratory values, and treatments were collected for training and 674 patients were enrolled for validation using XGBoost algorithm. For the second aim to predict in-hospital mortality, 3491 hospitalized patients via ER were enrolled. CatBoost, a new gradient-boosting algorithm was applied for training and validation of the cohort. RESULTS: Older age, higher temperature, increased respiratory rate (RR) and a lower oxygen saturation (SpO2) from the first set of vital signs were associated with an increased risk of MV amongst the 1980 patients in the ER. The model had a high accuracy of 86.2% and a negative predictive value (NPV) of 87.8%. While, patients who required MV, had a higher RR, Body mass index (BMI) and longer length of stay in the hospital were the major features associated with in-hospital mortality. The second model had a high accuracy of 80% with NPV of 81.6%. CONCLUSION: Machine learning models using XGBoost and catBoost algorithms can predict need for mechanical ventilation and mortality with a very high accuracy in COVID-19 patients.

Emergency Center Curbside Screening During the COVID-19 Pandemic: Retrospective Cohort Study.

BACKGROUND: Coronavirus disease (COVID-19) is a global pandemic that has placed a significant burden on health care systems in the United States. Michigan has been one of the top states affected by COVID-19. OBJECTIVE: We describe the emergency center curbside testing procedure implemented at Beaumont Hospital, a large hospital in Royal Oak, MI, and aim to evaluate its safety and efficiency. METHODS: Anticipating a surge in patients requiring testing, Beaumont Health implemented curbside testing, operated by a multidisciplinary team of health care workers, including physicians, advanced practice providers, residents, nurses, technicians, and registration staff. We report on the following outcomes over a period of 26 days (March 12, 2020, to April 6, 2020): time to medical decision, time spent documenting electronic medical records, overall screening time, and emergency center return evaluations. RESULTS: In total, 2782 patients received curbside services. A nasopharyngeal swab was performed on 1176 patients (41%), out of whom 348 (29.6%) tested positive. The median time for the entire process (from registration to discharge) was 28 minutes (IQR 17-44). The median time to final medical decision was 15 minutes (IQR 8-27). The median time from medical decision to discharge was 9 minutes (IQR 5-16). Only 257 patients (9.2%) returned to the emergency center for an evaluation within 7 or more days, of whom 64 were admitted to the hospital, 11 remained admitted, and 4 expired. CONCLUSIONS: Our curbside testing model encourages the incorporation of this model at other high-volume facilities during an infectious disease pandemic.

External validation of a clinical risk score to predict hospital admission and in-hospital mortality in COVID-19 patients.

BACKGROUND: Identification of patients with novel coronavirus disease 2019 (COVID-19) requiring hospital admission or at high-risk of in-hospital mortality is essential to guide patient triage and to provide timely treatment for higher risk hospitalized patients. METHODS: A retrospective multi-centre (8 hospital) cohort at Beaumont Health, Michigan, USA, reporting on COVID-19 patients diagnosed between 1 March and 1 April 2020 was used for score validation. The COVID-19 Risk of Complications Score was automatically computed by the EHR. Multivariate logistic regression models were built to predict hospital admission and in-hospital mortality using individual variables constituting the score. Validation was performed using both discrimination and calibration. RESULTS: Compared to Green scores, Yellow Scores (OR: 5.72) and Red Scores (OR: 19.1) had significantly higher odds of admission (both p < .0001). Similarly, Yellow Scores (OR: 4.73) and Red Scores (OR: 13.3) had significantly higher odds of in-hospital mortality than Green Scores (both p < .0001). The cross-validated C-Statistics for the external validation cohort showed good discrimination for both hospital admission (C = 0.79 (95% CI: 0.77-0.81)) and in-hospital mortality (C = 0.75 (95% CI: 0.71-0.78)). CONCLUSIONS: The COVID-19 Risk of Complications Score predicts the need for hospital admission and in-hospital mortality patients with COVID-19. Key points: Can an electronic health record generated risk score predict the risk of hospital admission and in-hospital mortality in patients diagnosed with coronavirus disease 2019 (COVID-19)? In both validation cohorts of 2,025 and 1,290 COVID-19, the cross-validated C-Statistics showed good discrimination for both hospital admission (C = 0.79 (95% CI: 0.77-0.81)) and in-hospital mortality (C = 0.75 (95% CI: 0.71-0.78)), respectively. The COVID-19 Risk of Complications Score may help predict the need for hospital admission if a patient contracts SARS-CoV-2 infection and in-hospital mortality for a hospitalized patient with COVID-19.