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1.
PLoS One ; 17(3): e0264785, 2022.
Article in English | MEDLINE | ID: covidwho-1745317

ABSTRACT

The variability of clinical course and prognosis of COVID-19 highlights the necessity of patient sub-group risk stratification based on clinical data. In this study, clinical data from a cohort of Indian COVID-19 hospitalized patients is used to develop risk stratification and mortality prediction models. We analyzed a set of 70 clinical parameters including physiological and hematological for developing machine learning models to identify biomarkers. We also compared the Indian and Wuhan cohort, and analyzed the role of steroids. A bootstrap averaged ensemble of Bayesian networks was also learned to construct an explainable model for discovering actionable influences on mortality and days to outcome. We discovered blood parameters, diabetes, co-morbidity and SpO2 levels as important risk stratification features, whereas mortality prediction is dependent only on blood parameters. XGboost and logistic regression model yielded the best performance on risk stratification and mortality prediction, respectively (AUC score 0.83, AUC score 0.92). Blood coagulation parameters (ferritin, D-Dimer and INR), immune and inflammation parameters IL6, LDH and Neutrophil (%) are common features for both risk and mortality prediction. Compared with Wuhan patients, Indian patients with extreme blood parameters indicated higher survival rate. Analyses of medications suggest that a higher proportion of survivors and mild patients who were administered steroids had extreme neutrophil and lymphocyte percentages. The ensemble averaged Bayesian network structure revealed serum ferritin to be the most important predictor for mortality and Vitamin D to influence severity independent of days to outcome. The findings are important for effective triage during strains on healthcare infrastructure.


Subject(s)
COVID-19/mortality , Hospitalization/statistics & numerical data , Adolescent , Adult , Aged , Aged, 80 and over , Bayes Theorem , COVID-19/epidemiology , COVID-19/etiology , Child , China/epidemiology , Female , Humans , India/epidemiology , Machine Learning , Male , Middle Aged , Models, Statistical , Risk Assessment/methods , Risk Factors , Young Adult
2.
Budhiraja, Sandeep, Tarai, Bansidhar, Jain, Dinesh, Aggarwal, Mona, Indrayan, Abhaya, Das, Poonam, Mishra, R. S.; Bali, Supriya, Mahajan, Monica, Kirtani, Jay, Tickoo, Rommel, Soni, Pankaj, Nangia, Vivek, Lall, Ajay, Kishore, Nevin, Jain, Ashish, Singh, Omender, Singh, Namrita, Kumar, Ashok, Saxena, Prashant, Dewan, Arun, Aggarwal, Ritesh, Mehra, Mukesh, Jain, Meenakshi, Nakra, Vimal, Sharma, B. D.; Pandey, Praveen Kumar, Singh, Y. P.; Arora, Vijay, Jain, Suchitra, Chhabra, Ranjana, Tuli, Preeti, Boobna, Vandana, Joshi, Alok, Aggarwal, Manoj, Gupta, Rajiv, Aneja, Pankaj, Dhall, Sanjay, Arora, Vineet, Chugh, Inder Mohan, Garg, Sandeep, Mittal, Vikas, Gupta, Ajay, Jyoti, Bikram, Sharma, Puneet, Bhasin, Pooja, Jain, Shakti, Singhal, R. K.; Bhasin, Atul, Vardani, Anil, Pal, Vivek, Pande, Deepak Gargi, Gulati, Tribhuvan, Nayar, Sandeep, Kalra, Sunny, Garg, Manish, Pande, Rajesh, Bag, Pradyut, Gupta, Arpit, Sharma, Jitin, Handoo, Anil, Burman, Purabi, Gupta, Ajay Kumar, Choudhary, Pankaj Nand, Gupta, Ashish, Gupta, Puneet, Joshi, Sharad, Tayal, Nitesh, Gupta, Manish, Khanna, Anita, Kishore, Sachin, Sahay, Shailesh, Dang, Rajiv, Mishra, Neelima, Sekhri, Sunil, Srivastava, Dr Rajneesh Chandra, Agrawal, Dr Mitali Bharat, Mathur, Mohit, Banwari, Akash, Khetarpal, Sumit, Pandove, Sachin, Bhasin, Deepak, Singh, Harpal, Midha, Devender, Bhutani, Anjali, Kaur, Manpreet, Singh, Amarjit, Sharma, Shalini, Singla, Komal, Gupta, Pooja, Sagar, Vinay, Dixit, Ambrish, Bajpai, Rashmi, Chachra, Vaibhav, Tyagi, Puneet, Saxena, Sanjay, Uniyal, Bhupesh, Belwal, Shantanu, Aier, Imliwati, Singhal, Mini, Khaduri, Ankit.
IJID Regions ; 2022.
Article in English | ScienceDirect | ID: covidwho-1708321

ABSTRACT

Objective : To get better insights into the extent of secondary bacterial and fungal infections in Indian hospitalized patients and to assess how these alter the course of COVID-19 so that the control measures can be suggested. Methods : This is a retrospective, multicentre study where data of all RT-PCR positive COVID-19 patients was accessed from Electronic Health Records (EHR) of a network of 10 hospitals across 5 North Indian states, admitted during the period from March 2020 to July 2021. Results : Of 19852 RT-PCR positive SARS-CO2 patients admitted during the study period, 1940 (9.8%) patients developed SIs. Patients with SIs were 8 years older on average (median age 62.6 years versus 54.3 years;P<0.001) than those without SIs. The risk of SIs was significantly (p < 0.001) associated with age, severity of disease at admission, diabetes, ICU admission, and ventilator use. The most common site of infection was urinary tract infection (UTI) (41.7%), followed by blood stream infection (BSI) (30.8%), sputum/BAL/ET fluid (24.8%), and the least was pus/wound discharge (2.6%). Gram negative bacilli (GNB) were the commonest organisms (63.2%), followed by Gram positive cocci (GPC) (19.6%) and fungus (17.3%). Most of the patients with SIs were on multiple antimicrobials – the most used were the BL-BLI for GNBs (76.9%) followed by carbapenems (57.7%), cephalosporins (53.9%) and antibiotics carbapenem resistant Enterobacteriaceae (47.1%). The usage of empirical antibiotics for GPCs was in 58.9% and of antifungals in 56.9% of cases, and substantially more than the results obtained by culture. The average stay in hospital for patients with SIs was almost twice than those without SIs (median 13 days versus 7 days). The overall mortality in the group with SIs (40.3%) was more than 8 times of that in those without SIs (4.6%). Only 1.2% of SI patients with mild COVID-19 at presentation died, while 17.5% of those with moderate disease and 58.5% of those with severe COVID-19 died (P< 0.001). The mortality was the highest in those with BSI (49.8%), closely followed by those with HAP (47.9%), and then UTI and SSTI (29.4% each). The mortality in diabetic patients with SIs was 45.2% while in non-diabetics it was 34.3% (p < 0.001). Conclusions : Secondary bacterial and fungal infections complicate the course of COVID-19 hospitalised patients. These patients tend to have a much longer stay in hospital, higher requirement for oxygen and ICU care, and significantly high mortality. The group most vulnerable to this complication are those with more severe COVID-19 illness, elderly, and diabetic patients. Judicious empiric use of combination antimicrobials in this set of vulnerable COVID-19 patients can save lives. It is desirable to have a region or country specific guidelines for appropriate use of antibiotics and antifungals to prevent their overuse.

3.
Front Microbiol ; 12: 653399, 2021.
Article in English | MEDLINE | ID: covidwho-1389208

ABSTRACT

Co-infection with ancillary pathogens is a significant modulator of morbidity and mortality in infectious diseases. There have been limited reports of co-infections accompanying SARS-CoV-2 infections, albeit lacking India specific study. The present study has made an effort toward elucidating the prevalence, diversity and characterization of co-infecting respiratory pathogens in the nasopharyngeal tract of SARS-CoV-2 positive patients. Two complementary metagenomics based sequencing approaches, Respiratory Virus Oligo Panel (RVOP) and Holo-seq, were utilized for unbiased detection of co-infecting viruses and bacteria. The limited SARS-CoV-2 clade diversity along with differential clinical phenotype seems to be partially explained by the observed spectrum of co-infections. We found a total of 43 bacteria and 29 viruses amongst the patients, with 18 viruses commonly captured by both the approaches. In addition to SARS-CoV-2, Human Mastadenovirus, known to cause respiratory distress, was present in a majority of the samples. We also found significant differences of bacterial reads based on clinical phenotype. Of all the bacterial species identified, ∼60% have been known to be involved in respiratory distress. Among the co-pathogens present in our sample cohort, anaerobic bacteria accounted for a preponderance of bacterial diversity with possible role in respiratory distress. Clostridium botulinum, Bacillus cereus and Halomonas sp. are anaerobes found abundantly across the samples. Our findings highlight the significance of metagenomics based diagnosis and detection of SARS-CoV-2 and other respiratory co-infections in the current pandemic to enable efficient treatment administration and better clinical management. To our knowledge this is the first study from India with a focus on the role of co-infections in SARS-CoV-2 clinical sub-phenotype.

4.
Lung India ; 38(Supplement): S105-S115, 2021 Mar.
Article in English | MEDLINE | ID: covidwho-1123963

ABSTRACT

During the times of the ongoing COVID pandemic, aerosol-generating procedures such as bronchoscopy have the potential of transmission of severe acute respiratory syndrome coronavirus 2 to the healthcare workers. The decision to perform bronchoscopy during the COVID pandemic should be taken judiciously. Over the years, the indications for bronchoscopy in the clinical practice have expanded. Experts at the Indian Association for Bronchology perceived the need to develop a concise statement that would assist a bronchoscopist in performing bronchoscopy during the COVID pandemic safely. The current Indian Association for Bronchology Consensus Statement provides specific guidelines including triaging, indications, bronchoscopy area, use of personal protective equipment, patient preparation, sedation and anesthesia, patient monitoring, bronchoscopy technique, sample collection and handling, bronchoscope disinfection, and environmental disinfection concerning the coronavirus disease-2019 situation. The suggestions provided herewith should be adopted in addition to the national bronchoscopy guidelines that were published recently. This statement summarizes the essential aspects to be considered for the performance of bronchoscopy in COVID pandemic, to ensure safety for both for patients and healthcare personnel.

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