Factors associated with mortality among moderate and severe patients with COVID-19 in India: a secondary analysis of a randomised controlled trial

Diabetes Mellitus and Other Comorbidities: Outcome among Covid-19 Patients in Kerala: A Retrospective Observational Study

Background

Covid-19 was declared by the WHO as a pandemic in 2020; India was also severely affected. Diabetes, a major lifestyle disorder, has seen its prevalence rate rising in developing countries. India is home to the world's second-largest population of diabetes. Several studies have reported greater severity and mortality of Covid-19 in diabetic patients.

Methodology

This was a hospital-based retrospective study done in a rural-based medical college in Kerala State. Data was collected using a semi-structured proforma and analysis was performed using Statistical Package for Social Sciences software version 25. The study was part of the STS research program of the Indian Council of Medical Research (ICMR).

Results

There were 567 patients in the study. Those with pre-existing diabetes had a worse outcome compared to those with newly detected diabetes. The presence of CKD was associated with a poor outcome. Patients admitted to the ICU, and those on assisted ventilation also had a lower survival rate; within the subgroup, those on non-invasive ventilation had a better outcome.

Conclusion

Mortality in Covid-19 is multifactorial. Those with diabetes have a poor outcome. Comorbidities have been reported to confer a high mortality rate in Covid-19 but this was not so in our study (except for CKD). Variability in outcome with respect to comorbidities and better outcomes in those who were non-invasively ventilated calls for more research to establish the relationship between pre-existing conditions and severity of disease. The use of non-invasive ventilation could also provide succor to resource-limited communities.

Impact of COVID-19 outbreak on healthcare workers in a Tertiary Healthcare Center in India: a cross sectional study

Numerous speculations have continually emerged, trying to explore the association between COVID-19 infection and a varied range of demographic and clinical factors. Frontline healthcare workers have been the primary group exposed to this infection, and there have been limited global research that examine this cohort. However, while there are a few large studies conducted on Indian healthcare professionals to investigate their potential risk and predisposing factors to COVID-19 infection, to our knowledge there are no studies evaluating the development of long COVID in this population. This cross-sectional study systematically utilized the demographic and clinical data of 3329 healthcare workers (HCW) from a tertiary hospital in India to gain significant insights into the associations between disease prevalence, severity of SARS-Cov-2 infection and long COVID. Most of the study population was found to be vaccinated (2,615, 78.5%), while 654 (19.65%) HCWs were found to be SARS-CoV-2 positive at least once. Of the infected HCWs, 75.1% (491) did not require hospitalization, whereas the rest were hospitalized for an average duration of 9 days. A total of 206 (6.19%) individuals were found to be suffering from long COVID. Persistent weakness/tiredness was the most experienced long-COVID symptom, while females (1.79, 1.25-2.57), individuals who consumed alcohol (1.85, 1.3-2.64) or had blood group B (1.9, 1.33-2.7) were at a significantly higher risk for developing long COVID.

Risk factors for COVID-19 mortality among telehealth patients in Bangladesh: A prospective cohort study

BACKGROUND AND OBJECTIVE

Estimating the contribution of risk factors of mortality due to COVID-19 is particularly important in settings with low vaccination coverage and limited public health and clinical resources. Very few studies of risk factors of COVID-19 mortality used high-quality data at an individual level from low- and middle-income countries (LMICs). We examined the contribution of demographic, socioeconomic and clinical risk factors of COVID-19 mortality in Bangladesh, a lower middle-income country in South Asia.

METHODS

We used data from 290,488 lab-confirmed COVID-19 patients who participated in a telehealth service in Bangladesh between May 2020 and June 2021, linked with COVID-19 death data from a national database to study the risk factors associated with mortality. Multivariable logistic regression models were used to estimate the association between risk factors and mortality. We used classification and regression trees to identify the risk factors that are the most important for clinical decision-making.

FINDINGS

This study is one of the largest prospective cohort studies of COVID-19 mortality in a LMIC, covering 36% of all lab-confirmed COVID-19 cases in the country during the study period. We found that being male, being very young or elderly, having low socioeconomic status, chronic kidney and liver disease, and being infected during the latter pandemic period were significantly associated with a higher risk of mortality from COVID-19. Males had 1.15 times higher odds (95% Confidence Interval, CI: 1.09, 1.22) of death compared to females. Compared to the reference age group (20-24 years olds), the odds ratio of mortality increased monotonically with age, ranging from an odds ratio of 1.35 (95% CI: 1.05, 1.73) for ages 30-34 to an odds ratio of 21.6 (95% CI: 17.08, 27.38) for ages 75-79 year group. For children 0-4 years old the odds of mortality were 3.93 (95% CI: 2.74, 5.64) times higher than 20-24 years olds. Other significant predictors were severe symptoms of COVID-19 such as breathing difficulty, fever, and diarrhea. Patients who were assessed by a physician as having a severe episode of COVID-19 based on the telehealth interview had 12.43 (95% CI: 11.04, 13.99) times higher odds of mortality compared to those assessed to have a mild episode. The finding that the telehealth doctors' assessment of disease severity was highly predictive of subsequent COVID-19 mortality, underscores the feasibility and value of the telehealth services.

CONCLUSIONS

Our findings confirm the universality of certain COVID-19 risk factors-such as gender and age-while highlighting other risk factors that appear to be more (or less) relevant in the context of Bangladesh. These findings on the demographic, socioeconomic, and clinical risk factors for COVID-19 mortality can help guide public health and clinical decision-making. Harnessing the benefits of the telehealth system and optimizing care for those most at risk of mortality, particularly in the context of a LMIC, are the key takeaways from this study.

Factors Associated with COVID-19 Death in a High-Altitude Peruvian Setting during the First 14 Months of the Pandemic: A Retrospective Multicenter Cohort Study in Hospitalized Patients

Risk factors for COVID-19 death in high-altitude populations have been scarcely described. This study aimed to describe risk factors for COVID-19 death in three referral hospitals located at 3399 m in Cusco, Peru, during the first 14 months of the pandemic. A retrospective multicenter cohort study was conducted. A random sample of ~50% (1225/2674) of adult hospitalized patients who died between 1 March 2020 and 30 June 2021 was identified. Of those, 977 individuals met the definition of death by COVID-19. Demographic characteristics, intensive care unit (ICU) admission, invasive respiratory support (IRS), disease severity, comorbidities, and clinical manifestation at hospital admission were assessed as risk factors using Cox proportional-hazard models. In multivariable models adjusted by age, sex, and pandemic periods, critical disease (vs. moderate) was associated with a greater risk of death (aHR: 1.27; 95%CI: 1.14–1.142), whereas ICU admission (aHR: 0.39; 95%CI: 0.27–0.56), IRS (aHR: 0.37; 95%CI: 0.26–0.54), the ratio of oxygen saturation (ROX) index ≥ 5.3 (aHR: 0.87; 95%CI: 0.80–0.94), and the ratio of SatO2/FiO2 ≥ 122.6 (aHR: 0.96; 95%CI: 0.93–0.98) were associated with a lower risk of death. The risk factors described here may be useful in assisting decision making and resource allocation.

Vaccination saves lives: a real-time study of patients with chronic diseases and severe COVID-19 infection

OBJECTIVES

This study aims to describe the demographic and clinical profile and ascertain the determinants of outcome among hospitalized coronavirus disease 2019 (COVID-19) adult patients enrolled in the National Clinical Registry for COVID-19 (NCRC).

METHODS

NCRC is an on-going data collection platform operational in 42 hospitals across India. Data of hospitalized COVID-19 patients enrolled in NCRC between 1st September 2020 to 26th October 2021 were examined.

RESULTS

Analysis of 29 509 hospitalized, adult COVID-19 patients [mean (SD) age: 51.1 (16.2) year; male: 18 752 (63.6%)] showed that 15 678 (53.1%) had at least one comorbidity. Among 25 715 (87.1%) symptomatic patients, fever was the commonest symptom (72.3%) followed by shortness of breath (48.9%) and dry cough (45.5%). In-hospital mortality was 14.5% (n = 3957). Adjusted odds of dying were significantly higher in age group ≥60 years, males, with diabetes, chronic kidney diseases, chronic liver disease, malignancy and tuberculosis, presenting with dyspnoea and neurological symptoms. WHO ordinal scale 4 or above at admission carried the highest odds of dying [5.6 (95% CI: 4.6-7.0)]. Patients receiving one [OR: 0.5 (95% CI: 0.4-0.7)] or two doses of anti-SARS CoV-2 vaccine [OR: 0.4 (95% CI: 0.3-0.7)] were protected from in-hospital mortality.

CONCLUSIONS

WHO ordinal scale at admission is the most important independent predictor for in-hospital death in COVID-19 patients. Anti-SARS-CoV2 vaccination provides significant protection against mortality.

Biomarkers and outcomes in hospitalised patients with COVID-19: a prospective registry

OBJECTIVES

To determine association of biomarkers-high-sensitivity C reactive protein (hsCRP), D-dimer, interleukin-6 (IL-6), lactic dehydrogenase (LDH), ferritin and neutrophil-lymphocyte ratio (NLR)-at hospitalisation with outcomes in COVID-19.

DESIGN AND SETTING

Tertiary-care hospital based prospective registry.

PARTICIPANTS

Successive virologically confirmed patients with COVID-19 hospitalised from April 2020 to July 2021 were prospectively recruited. Details of clinical presentation, investigations, management and outcomes were obtained.

PRIMARY AND SECONDARY OUTCOME MEASURES

All biomarkers were divided into tertiles to determine associations with clinical features and outcomes. Primary outcome was all-cause deaths and secondary outcome was oxygen requirement, non-invasive and invasive ventilation, dialysis, duration of stay in ICU and hospital. Numerical data are presented in median and interquartile range (IQR 25-75). Univariate and multivariate (age, sex, risk factors, comorbidities, treatments) ORs and 95% CIs were calculated.

RESULTS

3036 virologically confirmed patients with COVID-19 were detected and 1251 hospitalised. Men were 70.0%, aged >60 years 44.8%, hypertension 44.1%, diabetes 39.6% and cardiovascular disease 18.9%. Median symptom duration was 5 days (IQR 4-7) and oxygen saturation 95% (90%-97%). Total white cell count was 6.9×10⁹/L (5.0-9.8), neutrophils 79.2% (68.1%-88.2%), lymphocytes 15.8% (8.7%-25.5%) and creatinine 0.93 mg/dL (0.78-1.22). Median (IQR) for biomarkers were hsCRP 6.9 mg/dL (2.2-18.9), D-dimer 464 ng/dL (201-982), IL-6 20.1 ng/dL (6.5-60.4), LDH 284 mg/dL (220-396) and ferritin 351 mg/dL (159-676). Oxygen support at admission was in 38.6%, subsequent non-invasive or invasive ventilatory support in 11.0% and 11.6%, and haemodialysis in 38 (3.1%). 173 (13.9%) patients died and 15 (1.2%) transferred to hospice care. For each biomarker, compared with the first, those in the second and third tertiles had more clinical and laboratory abnormalities, and oxygen, ventilatory and dialysis support. Multivariate-adjusted ORs (95% CI) for deaths in second and third versus first tertiles, respectively, were hsCRP 2.24 (1.11 to 4.50) and 12.56 (6.76 to 23.35); D-dimer 3.44 (1.59 to 7.44) and 14.42 (7.09 to 29.30); IL-6 2.56 (1.13 to 5.10) and 10.85 (5.82 to 20.22); ferritin 2.88 (1.49 to 5.58) and 8.19 (4.41 to 15.20); LDH 1.75 (0.81 to 3.75) and 9.29 (4.75 to 18.14); and NLR 3.47 (1.68 to 7.14) and 17.71 (9.12 to 34.39) (p<0.001).

CONCLUSION

High levels of biomarkers-hsCRP, D-dimer, IL-6, LDH, ferritin and NLR-in COVID-19 are associated with more severe illness and higher in-hospital mortality. NLR, a widely available investigation, provides information similar to more expensive biomarkers.

Blood phenotype O and indirect bilirubin are associated with lower, early COVID-19-related mortality: A retrospective study

OBJECTIVES

To evaluate the ABO blood type and indirect bilirubin to predict early mortality in adults with severe COVID-19.

METHODS

This retrospective observational study was conducted on 268 adult patients with laboratory-confirmed COVID-19 who had attended the intensive care unit (ICU), Quena general hospital and Luxor International Hospital, and other hospitals or centers for the treatment of COVID-19, during the period from January 2021 till December 2021.

RESULTS

Relation between mortality and ABO group were highly significant, as we found non-O blood group with more risk of early mortality and intensive care unit admission ICU. There were significant differences between dead and alive cases as regards platelets, white blood cells WBCs (neutrophil, lymphocyte), albumin, liver enzymes aspartate transeferase (AST), alanine transferase (ALT), total direct and indirect bilirubin, creatinine, and urea.

CONCLUSION

There was a highly significant relation between dead cases and ABO blood group as between the O and non-O groups; also, group O was associated with less severe manifestations and or ventilation and less mortality in patients with severe COVID-19 infection. Direct bilirubin >0.5 was found to be the best predictor for mortality in cases with COVID-19 so indirect bilirubin may be considered a good protector against complications of the infection.

Establishing the First COVID-19 Convalescent Plasma Biobank in Jordan

Background: Antibodies with the specialized ability to fight infection can be found in the blood of individuals who have recovered from or have been vaccinated against COVID-19. As a result, plasma from these individuals could be used to treat critically ill patients. This treatment is known as convalescent plasma (CCP) therapy. Methods: Plasma units from 1555 consented healthy blood bank donors were collected from February to September 2021. Blood units were tested for the quantitative determination of Immunoglobulin G (IgG) antibodies to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus using one of the following assays based on the availability of the kits: The LIAISON^® SARS-CoV-2 TrimericS IgG assay or the Abbott SARS-CoV-2 IgG II Quant assay. Results: Among the tested donors, 1027 participants tested positive for neutralizing anti-SARS-CoV-2 IgG antibodies (66.04%). There were 484 donors whose plasma qualified to be used for CCP therapy (47.13%) and 214 CCP units were stored in the COVID-19 convalescent biobank. Conclusion: We were able to identify and store 214 fresh frozen plasma units qualified for CCP-plasma therapy for COVID-19 patients according to World Health Organization standards. Hence, we established the first COVID-19-convalescent plasma data and plasma biobank for treating COVID-19-infected cancer patients in Jordan and the region.

Clinical feasibility of remote intermittently scanned continuous glucose monitoring in coronavirus disease 2019 patients with and without diabetes during dexamethasone therapy

The clinical utility of intermittently scanned continuous glucose monitoring (isCGM) in patients with coronavirus disease 2019 (COVID-19) is unclear. Hence, we investigated the accuracy of isCGM in COVID-19 patients during dexamethasone therapy. We evaluated the accuracy of the FreeStyle Libre via smartphone isCGM device compared to point-of-care (POC) fingerstick glucose level monitoring in 16 patients with COVID-19 (10 with and 6 without diabetes, 13 men; HbA1c 6.9 ± 1.0%). Overall, isCGM correlated well with POC measurements (46.2% and 53.8% within areas A and B of the Parkes error grid, respectively). The overall mean absolute relative difference (MARD) for isCGM compared to POC measurements was 19.4%. The MARDs were 19.8% and 19.7% for POC blood glucose measurements ranging from 70 to 180 mg/dL and >180 mg/dL, respectively. When divided according to the presence and absence of diabetes, both groups of paired glucose measurements showed a good correlation (56.3% and 43.7%, and 27.1% and 72.9% within the A and B areas in patients with and without diabetes, respectively), but the MARD was not significant but higher in patients without diabetes (16.5% and 24.2% in patients with and without diabetes). In conclusion, although isCGM may not be as accurate as traditional blood glucose monitoring, it has good reliability in COVID-19 patients with and without diabetes during dexamethasone therapy.

Factors Associated with ICU Admission in Patients with COVID-19: The GOL2DS Score

Background and Objectives: The COVID-19 pandemic has been shaking lives around the world for nearly two years. The discovery of highly effective vaccines has not been able to stop the transmission of the virus. SARS-CoV-2 shows completely different clinical manifestations. A large percentage (about 40%) of admitted patients require treatment in an intensive care unit (ICU). This study investigates the factors associated with admission of COVID-19 patients to the ICU and whether it is possible to obtain a score that can help the emergency physician to select the hospital ward. Materials and Methods: We retrospectively recorded 313 consecutive patients who were presented to the emergency department (ED) of our hospital and had a diagnosis of COVID-19 confirmed by polymerase chain reaction (PCR) on an oropharyngeal swab. We used multiple logistic regression to evaluate demographic, clinical, and laboratory data statistically associated with ICU admission. These variables were used to create a prognostic score for ICU admission. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and receiver-operating characteristic curve (ROC) of the score for predicting ICU admission during hospitalization were calculated. Results: Of the variables evaluated, only blood type A (p = 0.003), PaO2/FiO2 (p = 0.002), LDH (p = 0.004), lactate (p = 0.03), dyspnea (p = 0.03) and SpO2 (p = 0.0228) were significantly associated with ICU admission after adjusting for sex, age and comorbidity using multiple logistic regression analysis. We used these variables to create a prognostic score called GOL2DS (group A, PaO2/FiO2, LDH, lactate and dyspnea, and SpO2), which had high accuracy in predicting ICU admission (AUROC 0.830 [95% CI, 0.791-0.892). Conclusions: In our single-center experience, the GOL2DS score could be useful in identifying patients at high risk for ICU admission.