Clinical and Virological Features of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Variants of Concern: A Retrospective Cohort Study Comparing B.1.1.7 (Alpha), B.1.351 (Beta), and B.1.617.2 (Delta).

BACKGROUND: The impact of SARS-CoV-2 variants of concern (VOCs) on disease severity is unclear. In this retrospective study, we compared the outcomes of patients infected with B.1.1.7, B.1.351, and B.1.617.2 with wild-type strains from early 2020. METHODS: National surveillance data from January to May 2021 were obtained and outcomes in relation to VOCs were explored. Detailed patient-level data from all patients with VOC infection admitted to our center between December 2020 and May 2021 were analyzed. Clinical outcomes were compared with a cohort of 846 patients admitted from January to April 2020. RESULTS: A total of 829 patients in Singapore in the study period were infected with these 3 VOCs. After adjusting for age and sex, B.1.617.2 was associated with higher odds of oxygen requirement, intensive care unit admission, or death (adjusted odds ratio [aOR], 4.90; 95% confidence interval [CI]: 1.43-30.78). Of these patients, 157 were admitted to our center. After adjusting for age, sex, comorbidities, and vaccination, the aOR for pneumonia with B.1.617.2 was 1.88 (95% CI: .95-3.76) compared with wild-type. These differences were not seen with B.1.1.7 and B.1.351. Vaccination status was associated with decreased severity. B.1.617.2 was associated with significantly lower polymerase chain reaction cycle threshold (Ct) values and longer duration of Ct value ≤30 (median duration 18 days for B.1.617.2, 13 days for wild-type). CONCLUSIONS: B.1.617.2 was associated with increased severity of illness, and with lower Ct values and longer viral shedding. These findings provide impetus for the rapid implementation of vaccination programs.

Major Coping-behavior and Risk-perception Factors for the COVID-19 Pandemic and their Psychobehavioral Characteristics (preprint)

Coronavirus disease 2019 (COVID-19) has dramatically changed people’s behavior, to prevent infection and overcome the general adversity caused by the implementation of infection-prevention measures. Here, we investigated the main coping-behavior and risk-perception factors, and the underlying psychological mechanisms (e.g., psychobehavioral characteristics) of coping behavior. We recruited 2,885 Japanese participants (1,524 women, ages 20–91 years). First, we identified four coping-behavior factors (two related to infection and two related to general adversity) and three risk-perception factors (one related to medical aspects and two related to society). Second, we demonstrated that infection prevention was promoted by female sex and etiquette in the Power to Live scale. General-adversity coping behavior was facilitated by shortages of daily necessities. Thus, we identified four parsimonious coping-behavior factors, as well as the risk-perception factors and demographic and psychobehavioral characteristics that influenced them. These results will benefit strategic approaches to optimize the social response to the pandemic.

Clinical and Virological Features of SARS-CoV-2 Variants of Concern: A Retrospective Cohort Study Comparing B.1.1.7 (Alpha), B.1.315 (Beta), and B.1.617.2 (Delta) (preprint)

Background: The impact of SARS-CoV-2 variants of concern (VOCs) on disease severity is unclear. In this retrospective cohort study, we compared outcomes of patients infected with B.1.1.7, B.1.351, and B.1.617.2 with those with wild-type strains from early 2020.Methods: National surveillance data from 1-January-2021 to 22-May-2021 were obtained from the Ministry of Health, and outcomes in relation to VOC were explored. Detailed patient level data from all SARS-CoV-2 patients with VOC infection admitted to our centre between 20-December-2020 and 12-May-2021 were analysed. Outcomes were compared with a cohort of 846 patients admitted January-April 2020.Findings: There were 838 VOC infections in Singapore in the study period. After adjusting for age and gender, B.1.617.2 infection was associated with higher odds of oxygen requirement, ICU admission, or death (adjusted odds ratio (aOR) 4·90, [95% CI 1·43-30·78]. 157 patients with VOCs were admitted to our centre. After adjusting for age, gender, comorbidities, and vaccination, aOR for pneumonia with B.1.617.2 was 1·88 [95% CI 0·95-3·76]) compared with wild-type. B.1.617.2 was associated with significantly lower PCR Ct values and significantly longer duration of Ct value ≤30 (estimated median duration 18 days for B.1.617.2, 13 days for wild-type). Vaccine breakthrough cases were less severe.Interpretation: There was a signal toward increased severity associated with B.1.617.2. The association of B.1.617.2 with lower Ct value and longer viral shedding provides a potential mechanism for increased transmissibility. These findings provide a strong impetus for the rapid implementation of vaccination programmes.Funding Information: National Medical Research Council grants COVID19RF-001 and COVID19RF-008.Declaration of Interests: BEY reports personal fees from Roche and Sanofi, outside the submitted work. All other authors declare no competing interests.Ethics Approval Statement: Informed consent for retrospective data collection was waived as approved by the institutional review board (NHG-DSRB reference number 2020/01122).

Maximum chest CT score is associated with progression to severe illness in patients with COVID-19: A retrospective study from Wuhan, China (preprint)

Background: The Coronavirus Disease 2019 (COVID-19) pandemic is a world-wide health crisis. Limited information is available regarding which patients will experience more severe disease symptoms. We evaluated hospitalized patients who were initially diagnosed with moderate COVID-19 for clinical parameters and radiological feature that showed an association with progression to severe/critical symptoms. Methods: : This study, a retrospective single-center study at the Central Hospital of Wuhan, enrolled 243 patients with confirmed COVID­19 pneumonia. Forty of these patients progressed from moderate to severe/critical symptoms during follow up. Demographic, clinical, laboratory, and radiological data were extracted from electronic medical records and compared between moderate- and severe/critical-type symptoms. Univariable and multivariable logistic regressions were used to identify the risk factors associated with symptom progression. Results: : Patients with severe/critical symptoms were older (p<0.001) and more often male (p=0.046). A combination of chronic obstructive pulmonary disease (COPD) and high maximum chest computed tomography (CT) score was associated with disease progression. Maximum CT score (>11) had the greatest predictive value for disease progression. The area under the receiver operating characteristic curve was 0.861 ( 95% confidence interval: 0.811-0.902). Conclusions: : Maximum CT score and COPD were associated with patient deterioration. Maximum CT score (>11) was associated with severe illness.

Prior diagnoses and medications as risk factors for COVID-19 in a Los Angeles Health System (preprint)

With the continuing coronavirus disease 2019 (COVID-19) pandemic coupled with phased reopening, it is critical to identify risk factors associated with susceptibility and severity of disease in a diverse population to help shape government policies, guide clinical decision making, and prioritize future COVID-19 research. In this retrospective case-control study, we used de-identified electronic health records (EHR) from the University of California Los Angeles (UCLA) Health System between March 9th, 2020 and June 14th, 2020 to identify risk factors for COVID-19 susceptibility (severe acute respiratory distress syndrome coronavirus 2 (SARS-CoV-2) PCR test positive), inpatient admission, and severe outcomes (treatment in an intensive care unit or intubation). Of the 26,602 individuals tested by PCR for SARS-CoV-2, 992 were COVID-19 positive (3.7% of Tested), 220 were admitted in the hospital (22% of COVID-19 positive), and 77 had a severe outcome (35% of Inpatient). Consistent with previous studies, males and individuals older than 65 years old had increased risk of inpatient admission. Notably, individuals self-identifying as Hispanic or Latino constituted an increasing percentage of COVID-19 patients as disease severity escalated, comprising 24% of those testing positive, but 40% of those with a severe outcome, a disparity that remained after correcting for medical co-morbidities. Cardiovascular disease, hypertension, and renal disease were premorbid risk factors present before SARS-CoV-2 PCR testing associated with COVID-19 susceptibility. Less well-established risk factors for COVID-19 susceptibility included pre-existing dementia (odds ratio (OR) 5.2 [3.2-8.3], p=2.6 x 10-10), mental health conditions (depression OR 2.1 [1.6-2.8], p=1.1 x 10-6) and vitamin D deficiency (OR 1.8 [1.4-2.2], p=5.7 x 10-6). Renal diseases including end-stage renal disease and anemia due to chronic renal disease were the predominant premorbid risk factors for COVID-19 inpatient admission. Other less established risk factors for COVID-19 inpatient admission included previous renal transplant (OR 9.7 [2.8-39], p=3.2x10-4) and disorders of the immune system (OR 6.0 [2.3, 16], p=2.7x10-4). Prior use of oral steroid medications was associated with decreased COVID-19 positive testing risk (OR 0.61 [0.45, 0.81], p=4.3x10-4), but increased inpatient admission risk (OR 4.5 [2.3, 8.9], p=1.8x10-5). We did not observe that prior use of angiotensin converting enzyme inhibitors or angiotensin receptor blockers increased the risk of testing positive for SARS-CoV-2, being admitted to the hospital, or having a severe outcome. This study involving direct EHR extraction identified known and less well-established demographics, and prior diagnoses and medications as risk factors for COVID-19 susceptibility and inpatient admission. Knowledge of these risk factors including marked ethnic disparities observed in disease severity should guide government policies, identify at-risk populations, inform clinical decision making, and prioritize future COVID-19 research.

Development of a Clinical Decision Support System for Severity Risk Prediction and Triage of COVID-19 Patients at Hospital Admission: an International Multicenter Study (preprint)

Key pointsO_ST_ABSQuestionC_ST_ABSHow do nomograms and machine-learning algorithms of severity risk prediction and triage of COVID-19 patients at hospital admission perform? FindingsThis model was prospectively validated on six test datasets comprising of 426 patients and yielded AUCs ranging from 0.816 to 0.976, accuracies ranging from 70.8% to 93.8%, sensitivities ranging from 83.7% to 100%, and specificities ranging from 41.0% to 95.7%. The cut-off probability values for low, medium, and high-risk groups were 0.072 and 0.244. MeaningThe findings of this study suggest that our models performs well for the diagnosis and prediction of progression to severe or critical illness of COVID-19 patients and could be used for triage of COVID-19 patients at hospital admission. IMPORTANCEThe outbreak of the coronavirus disease 2019 (COVID-19) has globally strained medical resources and caused significant mortality for severely and critically ill patients. However, the availability of validated nomograms and the machine-learning model to predict severity risk and triage of affected patients is limited. OBJECTIVETo develop and validate nomograms and machine-learning models for severity risk assessment and triage for COVID-19 patients at hospital admission. DESIGN, SETTING, AND PARTICIPANTSA retrospective cohort of 299 consecutively hospitalized COVID-19 patients at The Central Hospital of Wuhan, China, from December 23, 2019, to February 13, 2020, was used to train and validate the models. Six cohorts with 426 patients from eight centers in China, Italy, and Belgium, from February 20, 2020, to March 21, 2020, were used to prospectively validate the models. MAIN OUTCOME AND MEASURESThe main outcome was the onset of severe or critical illness during hospitalization. Model performances were quantified using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. RESULTSOf the 299 hospitalized COVID-19 patients in the retrospective cohort, the median age was 50 years ((interquartile range, 35.5-63.0; range, 20-94 years) and 137 (45.8%) were men. Of the 426 hospitalized COVID-19 patients in the prospective cohorts, the median age was 62.0 years ((interquartile range, 50.0-72.0; range, 19-94 years) and 236 (55.4%) were men. The model was prospectively validated on six cohorts yielding AUCs ranging from 0.816 to 0.976, with accuracies ranging from 70.8% to 93.8%, sensitivities ranging from 83.7% to 100%, and specificities ranging from 41.0% to 95.7%. The cut-off values of the low, medium, and high-risk probabilities were 0.072 and 0.244. The developed online calculators can be found at https://covid19risk.ai/. CONCLUSION AND RELEVANCEThe machine learning models, nomograms, and online calculators might be useful for the prediction of onset of severe and critical illness among COVID-19 patients and triage at hospital admission. Further prospective research and clinical feedback are necessary to evaluate the clinical usefulness of this model and to determine whether these models can help optimize medical resources and reduce mortality rates compared with current clinical practices.