A look back at the first wave of COVID-19 in China: A systematic review and meta-analysis of mortality and health care resource use among severe or critical patients

The short-term and long-term prognosis of discharged COVID-19 patients in Guangdong during the first wave of pandemic

COVID-19 survivors concerning about the rehabilitation and sustained sequelae of Coronavirus Disease 2019 (COVID-19) infection. We aimed to investigate the sequelae of patients' psychological and physical condition and its related factors in the early and late stages. This longitudinal study tracked 281 COVID-19 patients discharged from hospitals in Guangdong, China, for one year. Assessments occurred at 2,4,12,24 and 48 weeks post-discharge. We define 2 weeks, 4 weeks, and 12 weeks as early stage, and 24 weeks and 48 weeks as late stage. Psychological health was measured using the Patient Health Questionnaire-9 (PHQ-9), Generalized Anxiety Disorder-7 (GAD-7), Posttraumatic Stress Disorder Checklist for DSM-5 (PCL-5), and Pittsburgh Sleep Quality Index (PSQI) scales. Physical health was assessed through laboratory tests, chest computed tomography (CT) scans, and pulmonary function tests. Data were analyzed using multivariate regression models to evaluate the influence of demographic and clinical variables on health outcomes. COVID-19 survivors exhibited psychological and physical sequelae in both the early and late stages. Compared to the early stage, the proportions of patients with depression (early stage 14.6%, late stage 4.6%), anxiety (early stage 8.9%, late stage 5.3%), PTSD(early stage 3.6%, late stage 0.7%), abnormal liver function (early stage 24.6%, late stage 11.0%), abnormal cardiac function (early stage 10.0%, late stage 7.8%), abnormal renal function (early stage 20.6%, late stage 11.0%) and abnormal pulmonary function (early stage 40.9%, late stage 13.5%) were significantly reduced in the late stage. Factors such as gender, age, severity of COVID-19, hospitalization duration, and various comorbidities were significantly associated with these sequelae. We noticed that psychological and physical sequelae occurred to COVID-19 survivors in short and long stages, and these would gradually decrease as time went on. Male gender, age > 50 years old, severe clinical condition, longer hospitalization time and comorbidity history were related factors that significantly affected the rehabilitation of COVID-19 patients.

Real-Time On-Site Monitoring of Viruses in Wastewater Using Nanotrap® Particles and RICCA Technologies

Wastewater-based epidemiology (WBE) is an effective and efficient tool for the early detection of infectious disease outbreaks in a community. However, currently available methods are laborious, costly, and time-consuming due to the low concentration of viruses and the presence of matrix chemicals in wastewater that may interfere with molecular analyses. In the present study, we designed a highly sensitive "Quick Poop (wastewater with fecal waste) Sensor" (termed, QPsor) using a joint approach of Nanotrap microbiome particles and RICCA (RNA Isothermal Co-Assisted and Coupled Amplification). Using QPsor, the WBE study showed a strong correlation with standard PEG concentrations and the qPCR technique. Using a closed format for a paper-based lateral flow assay, we were able to demonstrate the potential of our assay as a real-time, point-of-care test by detecting the heat-inactivated SARS-CoV-2 virus in wastewater at concentrations of 100 copies/mL and within one hour. As a proof-of-concept demonstration, we analyzed the presence of viral RNA of the SARS-CoV-2 virus and PMMoV in raw wastewater samples from wastewater treatment plants on-site and within 60 min. The results show that the QPsor method can be an effective tool for disease outbreak detection by combining an AI-enabled case detection model with real-time on-site viral RNA extraction and amplification, especially in the absence of intensive clinical laboratory facilities. The lab-free, lab-quality test capabilities of QPsor for viral prevalence and transmission in the community can contribute to the efficient management of pandemic situations.

Predictors of Intensive Care Unit Admissions in Patients Presenting with Coronavirus Disease 2019

Background  Increased mortality rates among coronavirus disease 2019 (COVID-19) positive patients admitted to intensive care units (ICUs) highlight a compelling need to establish predictive criteria for ICU admissions. The aim of our study was to identify criteria for recognizing patients with COVID-19 at elevated risk for ICU admission. Methods  We identified patients who tested positive for COVID-19 and were hospitalized between March and May 2020. Patients' data were manually abstracted through review of electronic medical records. An ICU admission prediction model was derived from a random sample of half the patients using multivariable logistic regression. The model was validated with the remaining half of the patients using c-statistic. Results  We identified 1,094 patients; 204 (18.6%) were admitted to the ICU. Correlates of ICU admission were age, body mass index (BMI), quick Sequential Organ Failure Assessment (qSOFA) score, arterial oxygen saturation to fraction of inspired oxygen ratio, platelet count, and white blood cell count. The c-statistic in the derivation subset (0.798, 95% confidence interval [CI]: 0.748, 0.848) and the validation subset (0.764, 95% CI: 0.706, 0.822) showed excellent comparability. At 22% predicted probability for ICU admission, the derivation subset estimated sensitivity was 0.721, (95% CI: 0.637, 0.804) and specificity was 0.763, (95% CI: 0.722, 0.804). Our pilot predictive model identified the combination of age, BMI, qSOFA score, and oxygenation status as significant predictors for ICU admission. Conclusion  ICU admission among patients with COVID-19 can be predicted by age, BMI, level of hypoxia, and severity of illness.

Use of threshold PEP with an adult non-rebreather oxygen mask plus prone positioning in acute hypoxemic respiratory failure due to SARS-CoV-2 infection during the collapse of the health system in a low-income country

During the coronavirus disease 2019 pandemic, Ecuador reported a collapse of the healthcare system, in which intensive care unit beds were lacking. Therefore, we sought to determine whether the use of threshold expiratory positive pressure with an adult non-rebreather oxygen mask plus prone positioning is useful for improving oxygenation. Twelve patients were included. Eight patients (66.7%) survived, while four patients (33.3%) died. Baseline arterial oxygen saturation (%) prior placement median (interquartile range) 85.5% (80%-89%) and arterial oxygen saturation (%) post placement of the device was median (interquartile range) (93%-96%) (P = 0.0001). Respiratory rate before placement was median (interquartile range) 38 (36-42) and post placement of the device was median (interquartile range) 24 (22-30) (P = 0.0005). The use of an adapted device might be useful for the management of acute hypoxemic respiratory failure due to severe acute respiratory syndrome coronavirus 2 pneumonia, particularly when mechanical ventilators and high-flow oxygen systems are unavailable.

Risk Factors for One-Year Mortality in Hospitalized Adults with Severe COVID-19

As the body's immunity declines with age, elderly-hospitalized patients due to COVID-19 might be at higher mortality risk. Therefore, the aim of this prospective study was to examine the possible risk factors (demographic, social or comorbidities) most associated with mortality one-year after diagnosis of COVID-19. Routine data were collected from a cohort of hospitalized adults with severe COVID-19. The primary endpoint was mortality at one-year after diagnosis of COVID-19. We used a Cox proportional hazard model to estimate the hazard ratios (HRs) for both all-cause and specific cardiorespiratory mortality. A fully adjusted model included sex, socioeconomic status, institutionalization status, disability, smoking habit, and comorbidities as confounders. A total of 368 severe cases hospitalized on average 67.3 ± 15.9 years old were included. Participants aged ≥ 71 years had significantly higher HRs for all-cause mortality (adjusted HRs = 2.86, 95%CI: 2.01-4.07) and cardiorespiratory mortality (adjusted HRs = 2.86, 95%CI: 1.99-4.12). The association between age and mortality after diagnosis of COVID-19 due to both all-causes and cardiorespiratory mortality showed a consistent dose-response fashion. Institutionalization, disability, and socioeconomic status also showed a significant association with mortality. In conclusion, aging itself was the most important risk factor associated with mortality one year after diagnosis of COVID-19. People with disabilities, institutionalized or low socioeconomic status are significantly more likely to die after COVID-19.