Quality of life in patients treated for COVID-19-associated mucormycosis at a tertiary care hospital.

OBJECTIVES: Coronavirus disease 2019 (COVID-19)-associated mucormycosis (CAM) has emerged as a formidable infection in patients with COVID-19. The aggressive management of CAM affects quality of life (QOL); thus, this study was designed to assess the QOL in patients with CAM at a tertiary healthcare institution. METHODS: This cross-sectional study of 57 patients with CAM was conducted over 6 months using a semi-structured standard questionnaire (the abbreviated World Health Organization Quality of Life questionnaire [WHO-BREF]) and a self-rated improvement (SRI) scale ranging from 0 to 9. Cut-off values of ≤52 and <7 were considered to indicate poor QOL and poor improvement, respectively. The correlations of QOL and SRI scores were evaluated using Spearman rho values. RESULTS: In total, 27 patients (47.4%; 95% confidence interval [CI], 34.9%-60.1%) and 26 patients (45.6%; 95% CI, 33.4%-58.4%) had poor QOL and poor SRI scores, respectively. The overall median (interquartile range) QOL score was 52 (41-63). Headache (adjusted B, -12.3), localized facial puffiness (adjusted B , -16.4), facial discoloration (adjusted B, -23.4), loosening of teeth (adjusted B, -18.7), and facial palsy (adjusted B, -38.5) wer e significantly associated with the QOL score in patients with CAM. CONCLUSION: Approximately 1 in 2 patients with CAM had poor QOL and poor improvement. Various CAM symptoms were associated with QOL in these patients. Early recognition is the key to optimal treatment, improved outcomes, and improved QOL in patients with CAM.

Host Biomarkers Reflect Prognosis in Patients Presenting With Moderate Coronavirus Disease 2019: A Prospective Cohort Study.

Efficient resource allocation is essential for effective pandemic response. We measured host biomarkers in 420 patients presenting with moderate coronavirus disease 2019 and found that different biomarkers predict distinct clinical outcomes. Interleukin (IL)-1ra, IL-6, IL-10, and IL-8 exhibit dose-response relationships with subsequent disease progression and could potentially be useful for multiple use-cases.

Facilitating Safe Discharge Through Predicting Disease Progression in Moderate Coronavirus Disease 2019 (COVID-19): A Prospective Cohort Study to Develop and Validate a Clinical Prediction Model in Resource-Limited Settings.

BACKGROUND: In locations where few people have received coronavirus disease 2019 (COVID-19) vaccines, health systems remain vulnerable to surges in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. Tools to identify patients suitable for community-based management are urgently needed. METHODS: We prospectively recruited adults presenting to 2 hospitals in India with moderate symptoms of laboratory-confirmed COVID-19 to develop and validate a clinical prediction model to rule out progression to supplemental oxygen requirement. The primary outcome was defined as any of the following: SpO2 < 94%; respiratory rate > 30 BPM; SpO2/FiO2 < 400; or death. We specified a priori that each model would contain three clinical parameters (age, sex, and SpO2) and 1 of 7 shortlisted biochemical biomarkers measurable using commercially available rapid tests (C-reactive protein [CRP], D-dimer, interleukin 6 [IL-6], neutrophil-to-lymphocyte ratio [NLR], procalcitonin [PCT], soluble triggering receptor expressed on myeloid cell-1 [sTREM-1], or soluble urokinase plasminogen activator receptor [suPAR]), to ensure the models would be suitable for resource-limited settings. We evaluated discrimination, calibration, and clinical utility of the models in a held-out temporal external validation cohort. RESULTS: In total, 426 participants were recruited, of whom 89 (21.0%) met the primary outcome; 257 participants comprised the development cohort, and 166 comprised the validation cohort. The 3 models containing NLR, suPAR, or IL-6 demonstrated promising discrimination (c-statistics: 0.72-0.74) and calibration (calibration slopes: 1.01-1.05) in the validation cohort and provided greater utility than a model containing the clinical parameters alone. CONCLUSIONS: We present 3 clinical prediction models that could help clinicians identify patients with moderate COVID-19 suitable for community-based management. The models are readily implementable and of particular relevance for locations with limited resources.

Assessment of depression, anxiety and stress experienced by health care and allied workers involved in SARS-CoV2 pandemic.

Introduction: The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) causing coronavirus disease 2019 (COVID-19) has led to a global health crisis. Health workforce has been working vigorously in COVID-19 management. So, we have planned this study with an aim to assess the psychological profile of healthcare and allied workers involved in SARS-CoV2 pandemic and to look for associated factors. Methodology: A cross-sectional observational study was planned at All India Institute of Medical Sciences, Patna. Study population comprised of Health care workers and allied health care workers involved in COVID-19 management. Results: Data from 254 study participants have been included in the study. The prevalence of severe and extremely severe depression among study participants was 8.3 and 3.1 percent. Severe and extremely severe anxiety prevalence was found to be 9.4 and 13.8 percent. The prevalence of severe and extremely severe stress was 2.4 and 2.4 percent each. Education till post-graduation, unmarried, occupation of doctor, Comorbidity of headache and occurrence of influenza-like illness in last 3 months had a statistically significant association with high depression score. With high Anxiety score and high-stress score statistically significant association was seen in education till postgraduation, unmarried, occupation of doctor, duration of 1 COVID-19 duty of ≥8 hours. Conclusion: The study highlights high psychological comorbidities in the form of depression, anxiety and stress among health care workers and allied health care workers working in COVID-19 pandemic. Prevalence of psychological morbidity is higher among doctors compared to nurses and allied health workers. COVID-19 duty of ≥8 hours have been found to be hampering mental health.

Epidemiological, Clinical, and Laboratory Predictors of In-Hospital Mortality Among COVID-19 Patients Admitted in a Tertiary COVID Dedicated Hospital, Northern India: A Retrospective Observational Study.

INTRODUCTION: COVID-19 pandemic still poses a serious challenge to health system worldwide. This study was planned to determine exposure characteristics, in-hospital mortality, and predictors of in hospital mortality among COVID-19 patients. MATERIAL AND METHODS: We retrospectively investigated epidemiological, clinical, and laboratory profile of confirmed COVID-19 patients admitted from 25th March to 31st August 2020. COVID-19 patient profiles were collected from Medical Record Section of the hospital. RESULTS: In hospital mortality occurred in 159 (11%) cases. Increasing respiratory rate, higher temperature, higher total leukocyte count, and high blood urea levels were found to be independent risk factors for in hospital mortality whereas higher hemoglobin and higher oxygen saturation at the time of hospital admission were found to be protective against in hospital mortality. CONCLUSION: In hospital mortality among COVID-19 patients is almost 1 in 10 in tertiary care hospital. Patients with advancing age (AOR: 1.048; 95% CI: 1.021-1.076), higher respiratory rate (AOR: 1.248; 95% CI: 1.047-1.489), higher temperature (AOR: 1.758; 95% CI: 1.025-3.016), higher leukocyte count (AOR: 1.147; 95% CI: 1.035-1.270), and higher urea levels (AOR: 1.034; 95% CI: 1.005-1.064) at the time of admission are important predictors of COVID-19 in-hospital mortality.