Mental health outcome in hospitalized COVID-19 patients: An observational analysis from North Indian tertiary care hospital.

Aim: The study investigate the severity of perceived stress and wide domains of psychiatric symptoms reported on initial screening in hospitalized patients of COVID-19 with a second aim to determine the role of sociodemographic factors and coping styles in the hospitalized patients of COVID-19. Method: Total 224 patients of COVID-19 infection, hospitalized in various isolation facilities were assessed via web-based self-reported questionnaires on perceived stress scale, brief cope inventory, and DSM-5 crosscutting level-1 questionnaire. Results: Majority of the patients reported moderate level of stress followed by mild and severe. Depression and Anxiety symptoms were most common psychopathologies though the patients have reported greater severity in various domains of psychiatric symptoms. Coping styles explains most of variance (64.8%) of the perceived stress. Similarly total PSS scores, coping styles, COVID-19 status and sociodemographic factors contributed significantly to the variance of all psychiatric symptoms. Conclusion: Factors like female gender, being married, belonging to nuclear families, service class and urban domicile are the significant factors determining higher risk of stress and developing more psychopathologies. Furthermore, coping styles used by the patients have a greater moderating effect on mental health symptoms and their perceived stress which can be a major area for interventions to reduce the mental health morbidities.

A year of COVID-19 pandemic: an overview

The COVID-19 epidemic in India is declining while the West is reeling under the second or third wave. However, with the onset of winter, the approaching Christmas, New Year and Sankaranthi festivals, and the emergence of new mutant strains of the virus, the possibility of a second wave in India cannot be ruled out, and personal protection measures should be strictly adhered to. The economically weak and marginalized segment bore the brunt of the impact of the pandemic and the associated lockdown - economic and food security. A safety net of universal support, including an employment guarantee scheme subsidised food through the PDS and essential primary health care for all is necessary. Despite considerable improvement in patient management, a proven remedy for cure COVID-19 has eluded us so far. Research needs to be intensified on this front. Aided by the scientific and technical advances and encouraging political environment, vaccines have been developed and vaccinations have been initiated in some countries. India too is preparing to launch vaccination soon. However, we have to recognize that availability of vaccines may not translate into the end of the epidemic. Everyone needs to heed the warnings of public health experts that we have to continue with COVID-appropriate behaviour.

A case control investigation of COVID-19 associated mucormycosis in India.

BACKGROUND: Increased occurrence of mucormycosis during the second wave of COVID-19 pandemic in early 2021 in India prompted us to undertake a multi-site case-control investigation. The objectives were to examine the monthly trend of COVID-19 Associated Mucormycosis (CAM) cases among in-patients and to identify factors associated with development of CAM. METHODS: Eleven study sites were involved across India; archived records since 1st January 2021 till 30th September 2021 were used for trend analysis. The cases and controls were enrolled during 15th June 2021 to 30th September 2021. Data were collected using a semi-structured questionnaire. Among 1211 enrolled participants, 336 were CAM cases and 875 were COVID-19 positive non-mucormycosis controls. RESULTS: CAM-case admissions reached their peak in May 2021 like a satellite epidemic after a month of in-patient admission peak recorded due to COVID-19. The odds of developing CAM increased with the history of working in a dusty environment (adjusted odds ratio; aOR 3.24, 95% CI 1.34, 7.82), diabetes mellitus (aOR: 31.83, 95% CI 13.96, 72.63), longer duration of hospital stay (aOR: 1.06, 95% CI 1.02, 1.11) and use of methylprednisolone (aOR: 2.71, 95% CI 1.37, 5.37) following adjustment for age, gender, occupation, education, type of houses used for living, requirement of ventilatory support and route of steroid administration. Higher proportion of CAM cases required supplemental oxygen compared to the controls; use of non-rebreather mask (NRBM) was associated as a protective factor against mucormycosis compared to face masks (aOR: 0.18, 95% CI 0.08, 0.41). Genomic sequencing of archived respiratory samples revealed similar occurrences of Delta and Delta derivates of SARS-CoV-2 infection in both cases and controls. CONCLUSIONS: Appropriate management of hyperglycemia, judicious use of steroids and use of NRBM during oxygen supplementation among COVID-19 patients have the potential to reduce the risk of occurrence of mucormycosis. Avoiding exposure to dusty environment would add to such prevention efforts.

COVID-19 cases and their outcome among patients with uncommon co-existing illnesses: A lesson from Northern India

Introduction Newer coexisting conditions should be identified in order to modify newer risk factors. Aim was to identify patients with non-classical or less common coexisting conditions in patients infected of COVID 19. Method Single centred study from June 2020 to May 2021 at a tertiary centre in North India. A preformed questionnaire was used to record clinical and laboratory parameters and to identify cases which are in addition to CDC list and Indian data. Results 0.67% (46) cases out of 6832 patients were identified to have non-classical coexisting illness. It was divided into 2 groups-infections A (60.1%) and non-infections B (39.9%). Group A included-tuberculosis- pulmonary (14.3%) & extra pulmonary (32.9%), bacterial (25.0%) viral infections [dengue, hepatitis B & C] (14.3%), HIV disease (10.7%) and malaria (3.6%). Group B included- organ transplant (27.8%), autoimmune [myasthenia gravis, polymyositis, psoriasis] (22.6%), haematologic [Haemophilia, ITP, Aplastic anaemia, APML, CML] (27.8%), uncommon malignancies [disseminated sacral chordoma and GTN] (11.1%) and snakebite (11.1%). Serum Procalcitonin was not helpful for diagnosis of bacterial infection in COVID-19 disease. Group A had significantly longer duration of illness, hepatitis and elevated CRP. The mortality in group A & B were 32.1% and 43.8% respectively. Death in non-severe COVID cases was in tetanus and snakebite. 30.7% death among tuberculosis patients. More than 70% of deaths were attributable to COVID 19 in both the groups. Conclusion In Indian settings, comorbidities like tuberculosis and bacterial infections can precipitate severe COVID 19 unlike other parts of the world where tuberculosis is relatively uncommon.

Hematological Changes in the Second Wave of SARS-CoV-2 in North India.

Background COVID-19 is a rapidly spreading pandemic caused by SARS-CoV-2. India experienced a second wave peak in mid of April 2021, and it emerged as a medical crisis. This study was taken up to show if the hematological and peripheral blood changes can be used as a readily available tool to demarcate the patients needing ICU care so that the ICU can be utilized more prudently.  Material and method One hundred reverse transcription-polymerase chain reaction (RT-PCR) confirmed cases of COVID-19, 50 each from ICU and non-ICU wards, were included in this observational study. At the time of admission blood sample was collected for evaluation of hematological parameters. Results We noted that 74% of patients admitted in ICU were males and 28% were more than 60 years of age. In ICU patients, the absolute neutrophil count (ANC) was significantly raised when compared to non-ICU cases (p=0.023). The nadir absolute lymphocyte count (ALC) was 0.11x109/L in ICU patients and 0.95x109/L in non-ICU patients. There was a significant increase in neutrophil-lymphocyte ratio (NLR; p<0.001) in ICU patients with a proposed cut-off value of 7.73. Platelet-lymphocyte ratio (PLR) was also raised in ICU patients; however, this increase was not significant (p= 0.623). The proposed cut-off value of PLR is 126.73. A significant reduction in a lymphocyte-monocyte ratio (LMR) was observed in ICU patients when compared to non-ICU cases (p<0.001). Thrombocytopenia was more commonly seen in ICU patients; however, this was not statistically significant. Viral-induced cytopathic effects like plasmacytoid lymphocytes with cytoplasmic granules, the presence of toxic changes in neutrophils, and large-sized platelets were commonly observed in ICU patients. Conclusion Our results suggest that hematological parameters like ANC, absolute lymphocyte count (ALC), platelet count, NLR, PLR, and peripheral smear changes are simple assessment factors that can serve as indicators for the severity of COVID-19 and will demarcate the patients who need ICU-care. This will help in the judicious use of ICU facilities for patients who are actually in need.

SARS-CoV-2 IgG Surveillance in Asymptomatic Blood Donors and Health Workers.

MATERIALS AND METHODS: 2085 blood donors were allowed to donate blood only after fulfilling all the criteria laid down by the FDA of India with additional history of excluding COVID-19 suspects. IgG antibody testing was performed by chemiluminescence, and results were noted along with their reactive status. Their reactive status was analyzed with donor information to get an idea of the risk parameters for COVID-19. Medical healthcare workers in whom the study was carried out were 560, out of which 114 had worked in COVID-19 duties and 446 had worked in non-COVID-19 emergencies areas. COVID-19 area duties were further subdivided into triage, holding area, isolation, and COVID-19-related duties. The samples were run on architect i2000 and evaluated for their plasma immunoglobulin G. RESULTS: Amongst the asymptomatic blood donors, 1.9% was found to be COVID-19 IgG antibody positive. It was observed that maximum COVID-19 IgG positivity (57.1%) was seen in the age group 18-29 years followed by 26.2% in the age group 30-39 years. Donors in the age group 40-49 years showed antibody positivity of 16.7%, and no antibody-positive donors were found above 50 years of age. COVID-19 IgG positivity was maximum in replacement donors (61.9%) followed by family donors (28.6%) and least involuntary donors (0.6%) Blood donors who showed high IgG positivity were mainly of labor class. Antibody IgG testing on medical healthcare workers showed 2.3% positivity. The healthcare workers who were posted in COVID-19 duties showed 4.8% positivity in the holding area (waiting area with the treatment of patients till their RT PCR report comes) and 5.7% in other COVID-19 areas related to laboratory work. Healthcare workers doing duties in COVID-19 areas showed 2.7% positivity, while those doing duties in non-COVID-19 emergency areas showed a positivity of 2.2%. CONCLUSION: Our study shows that the prevalence of detectable antibodies was low in the general population in India and many patients were asymptomatic as seen in the blood donors, especially the labor class. Maximum exposure was present in young healthy males of labor class who remained asymptomatic. The healthcare workers were more exposed to COVID-19 as compared to the general population probably due to lack of precaution and awareness. Those doing non-COVID-19 duties were also exposed appreciably and needed to take all the precautions required for COVID-19 duties.

Hematological and serum biochemistry parameters as a prognostic indicator of severally ill versus mild Covid-19 patients: A study from tertiary hospital in North India.

BACKGROUND/OBJECTIVES: In, India coronavirus disease (COVID-19) cases are on the rise in terms of the total number of cases. Findings on clinical and hematological parameters alone carry no significance apart from telling patients present status and hence are diminutive. This study aims to assess the hematological and serum biochemistry parameters and correlate them with the presenting symptoms and severity of disease which can help predict the need for intensive care unit (ICU) care, help in triage, assess the severity of the disease which will help clinicians decide their future course of action and further improve patients clinical outcome. METHODS: A total of 200 COVID-19 positive patients were included. Hematological and serum biochemistry parameters were recorded for the patients at the time of admission and categorized as mild, moderate, and severely ill based on clinical status and then admitted into various wards. RESULTS: Total leucocyte count (TLC) was significantly different and higher in severely ill patients (13,200 ± 6,999.2) compared to cases presented with mild and moderate symptoms (12,100 ± 6,488.41& 8,788.20 ± 4,954.32, p = 0.001). The mean difference of TLC, Neutrophil% (N%), Lymphocyte% (L%) and Monocyte (M%) was significantly different between mild and moderate symptoms cases (p = 0.030, p = 0.002, p = 0.004 & p = 0.003). Between groups comparison of moderate vs. severely ill cases showed a significant difference in TLC (p = 0.000), N% (p = 0.000), L% (0.000), and L/N ratio (p = 0.002). The serum ionic calcium (Ca), random blood sugar (RBS), C-reactive protein (CRP), fibrinogen, prothrombin (PT), International Normalized Ratio (INR), ferritin, and Lactate Dehydrogenase (LDH) level also differed significantly between mild, moderate and severely ill cases (p = 0.001, p=<0.001, p = 0.002, p=<00.1, p = 0006, p = 0.005, p=<0.001 and p=<0.001) respectively. Comparison of the mild vs. severely ill cases showed a significant difference in urea, fibrinogen, and procalcitonin (PCT) level (p = 0.005, p = 0.000 & p = 0.048) respectively. CONCLUSION: The preliminary findings of this study suggest hematological and serum biochemistry parameters could be used as a screening tool to identify patients requiring intensive care and thus allowing clinical stratification and triage at the time of presentation.

A deep learning-based COVID-19 automatic diagnostic framework using chest X-ray images.

The lethal novel coronavirus disease 2019 (COVID-19) pandemic is affecting the health of the global population severely, and a huge number of people may have to be screened in the future. There is a need for effective and reliable systems that perform automatic detection and mass screening of COVID-19 as a quick alternative diagnostic option to control its spread. A robust deep learning-based system is proposed to detect the COVID-19 using chest X-ray images. Infected patient's chest X-ray images reveal numerous opacities (denser, confluent, and more profuse) in comparison to healthy lungs images which are used by a deep learning algorithm to generate a model to facilitate an accurate diagnostics for multi-class classification (COVID vs. normal vs. bacterial pneumonia vs. viral pneumonia) and binary classification (COVID-19 vs. non-COVID). COVID-19 positive images have been used for training and model performance assessment from several hospitals of India and also from countries like Australia, Belgium, Canada, China, Egypt, Germany, Iran, Israel, Italy, Korea, Spain, Taiwan, USA, and Vietnam. The data were divided into training, validation and test sets. The average test accuracy of 97.11 ± 2.71% was achieved for multi-class (COVID vs. normal vs. pneumonia) and 99.81% for binary classification (COVID-19 vs. non-COVID). The proposed model performs rapid disease detection in 0.137 s per image in a system equipped with a GPU and can reduce the workload of radiologists by classifying thousands of images on a single click to generate a probabilistic report in real-time.