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1.
Cytopathology ; 2022 Apr 18.
Article in English | MEDLINE | ID: covidwho-1794719

ABSTRACT

BACKGROUND: The coronavirus disease 2019 (COVID-19) infection caused by the novel severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) is associated with a wide range of disease patterns, ranging from mild to life-threatening pneumonia. COVID-19 can be associated with a suppressed immune response and/or hyperinflammatory state due to cytokine storm. Reduced immunity, combined with steroid usage to prevent cytokine storm along with various pre-existing co morbidities can prove to be a fertile ground for various secondary bacterial and fungal infection, including mucormycosis. Diagnosis of mucor is a challenging task given high negativity rate of various detection methods. While histopathology is considered the gold standard, the acquisition of necessary tissue biopsy specimens requires invasive procedures and is time consuming. METHOD: In this study various methods of mucor detection, like conventional cytopathology (CCP), liquid-based cytology (LBC, BD SurepathTM ), potassium hydroxide mount (KOH) preparation, culture and histopathology were analysed. Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were calculated for various methods. RESULTS: This study showed that LBC has sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 72.4%,100%,100% and 38.4% respectively. CONCLUSION: This study showed that, liquid-based cytology (LBC) can be a rapid and effective alternative to histopathology in mucor diagnosis.

2.
J Med Virol ; 93(7): 4553-4558, 2021 07.
Article in English | MEDLINE | ID: covidwho-1263100

ABSTRACT

A significant proportion of patients with coronavirus disease 2019 (COVID-19) require timely hospitalization to reduce the risk of complications and mortality. We describe the trends of the age and gender stratified outcomes among hospitalized COVID-19 patients with moderate to severe illness at the largest dedicated tertiary care COVID-19 government hospital in New Delhi, India. A retrospective cohort study through secondary data analysis from in-patient hospital data of patients admitted from April 1 to November 15, 2020 was conducted. The data of 10,314 laboratory-confirmed patients with COVID-19 was analyzed, of which 8899 (86.28%) were discharged after recovery, and 1415 (13.72%) died. The mean (SD) age of the hospitalized patients was 46.43 (18.74) years (n = 10,309) including 6031 (58.50%) male and 4278 (41.50%) female patients (n = 10,309). On bivariate analysis, increasing age was associated with significantly higher odds of mortality in both gender (p < .001). The mortality rate in female patients was lower (11.92%) compared with male patients (15.75%) (p = .675). However, elderly women had the highest odds of mortality (p < .001), indicating the possible role of delayed health seeking behavior, secondary to familial, and social neglect. Mortality in the patients with COVID-19 also occurred early after admission suggesting rapid deterioration, delayed reporting by patients, or their late referral from other health facilities. However, the overall statewide recovery rate showed steady improvement since the onset of the pandemic. In contrast, the recovery rate among the moderate-severe cases that were hospitalized at this tertiary care center during the same period reflected a lower nonspecific zigzag pattern indicating limited effectiveness of the COVID-19 treatment regimens.


Subject(s)
COVID-19/drug therapy , COVID-19/mortality , SARS-CoV-2/drug effects , Adult , Age Factors , Aged , Female , Hospitalization , Humans , India , Male , Middle Aged , Retrospective Studies , Sex Factors , Tertiary Care Centers , Treatment Outcome
4.
J Med Virol ; 93(7): 4553-4558, 2021 07.
Article in English | MEDLINE | ID: covidwho-1146976

ABSTRACT

A significant proportion of patients with coronavirus disease 2019 (COVID-19) require timely hospitalization to reduce the risk of complications and mortality. We describe the trends of the age and gender stratified outcomes among hospitalized COVID-19 patients with moderate to severe illness at the largest dedicated tertiary care COVID-19 government hospital in New Delhi, India. A retrospective cohort study through secondary data analysis from in-patient hospital data of patients admitted from April 1 to November 15, 2020 was conducted. The data of 10,314 laboratory-confirmed patients with COVID-19 was analyzed, of which 8899 (86.28%) were discharged after recovery, and 1415 (13.72%) died. The mean (SD) age of the hospitalized patients was 46.43 (18.74) years (n = 10,309) including 6031 (58.50%) male and 4278 (41.50%) female patients (n = 10,309). On bivariate analysis, increasing age was associated with significantly higher odds of mortality in both gender (p < .001). The mortality rate in female patients was lower (11.92%) compared with male patients (15.75%) (p = .675). However, elderly women had the highest odds of mortality (p < .001), indicating the possible role of delayed health seeking behavior, secondary to familial, and social neglect. Mortality in the patients with COVID-19 also occurred early after admission suggesting rapid deterioration, delayed reporting by patients, or their late referral from other health facilities. However, the overall statewide recovery rate showed steady improvement since the onset of the pandemic. In contrast, the recovery rate among the moderate-severe cases that were hospitalized at this tertiary care center during the same period reflected a lower nonspecific zigzag pattern indicating limited effectiveness of the COVID-19 treatment regimens.


Subject(s)
COVID-19/drug therapy , COVID-19/mortality , SARS-CoV-2/drug effects , Adult , Age Factors , Aged , Female , Hospitalization , Humans , India , Male , Middle Aged , Retrospective Studies , Sex Factors , Tertiary Care Centers , Treatment Outcome
6.
Indian Heart J ; 73(1): 109-113, 2021.
Article in English | MEDLINE | ID: covidwho-938960

ABSTRACT

BACKGROUND: There is no large contemporary data from India to see the prevalence of burnout in HCWs in covid era. Burnout and mental stress is associated with electrocardiographic changes detectable by artificial intelligence (AI). OBJECTIVE: The present study aims to estimate the prevalence of burnout in HCWs in COVID-19 era using Mini Z-scale and to develop predictive AI model to detect burnout in HCWs in COVID-19 era. METHODS: This is an observational and cross-sectional study to evaluate the presence of burnout in HCWs in academic tertiary care centres of North India in the COVID-19 era. At least 900 participants will be enrolled in this study from four leading premier government-funded/public-private centres of North India. Each study centre will be asked to recruit HCWs by approaching them through various listed ways for participation in the study. Interested participants after initial screening and meeting the eligibility criteria, will be asked to fill the questionnaire (having demographic and work related with Mini Z questionnaire) to assess burnout. The healthcare workers will include physicians at all levels of training, nursing staff and paramedical staff who are involved directly or indirectly in COVID-19 care. The analysis of the raw electrocardiogram (ECG) data and development of algorithm using convolutional neural networks (CNN) will be done by experts. CONCLUSIONS: In Summary, we propose that ECG data generated from the people with burnout can be utilized to develop AI-enabled model to predict the presence of stress and burnout in HCWs in COVID-19 era.


Subject(s)
Artificial Intelligence , Burnout, Professional/epidemiology , COVID-19/psychology , Electrocardiography , Health Personnel , COVID-19/epidemiology , Cross-Sectional Studies , Female , Humans , India/epidemiology , Male , Prevalence , Research Design , SARS-CoV-2
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