Objectives The novel corona virus disease, which was initially reported in China in late 2019, has become a global pandemic affecting 330 million cases. COVID-19 affects predominantly the respiratory system, in addition to other organ systems, mainly the cardiovascular system. One of the hypotheses is that virus entering the target cells by binding to angiotensin converting enzyme 2 affecting hypothalamic pituitary axis could lead to dysautonomia which is measured by heart rate variability (HRV). HRV is a non-invasive measure of autonomic function that facilitates identification of COVID-19 patients at the risk of developing cardiovascular complications. So, we aimed to assess HRV in COVID patients and compare between COVID patients and normal controls. Methods In a case control design, we compared 63 COVID-19 infected patients with 43 healthy controls matched for age and gender. Along with clinical characterization, heart rate variability was evaluated using ambulatory 5 min ECG in lead II and expressed in frequency and time domain measures. Statistical analysis was performed using SPSS 17.0. Results Mean age of the study population was 49.1 ± 14.2 years and 71 (66.9%) were males. Frequency domain measures high (HF) and low (LF) frequency powers were significantly decreased in COVID-19 patients compared to controls. HF/LF and LF/HF ratios were not different between groups. Time domain measures rMSSD (root mean square of successive RR interval differences) and SDNN (standard deviation of NN intervals) were significantly increased among COVID-19 subjects. COVID-19 infection was associated with increased parasympathetic activity as defined by rMSSD>40 {adjusted odds ratio 7.609 (95% CI 1.61–35.94); p=0.01} and SDNN>60 {adjusted odds ratio 2.620 (95% CI 1.070–6.44); p=0.035} after adjusting for age, gender and comorbidities. Conclusions Our study results showed increased parasympathetic tone in COVID patients. Early diagnosis of autonomic imbalance in COVID patients is needed to plan management and limit progression of disease.

Cognitive performance was found to be impaired among shift working nurses, due to poor sleep quality and decreased alertness during wake state. Thus, shift work poses significant cognitive risks in work performance of nurses.

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Background In the ongoing COVID-19 pandemic, an increased incidence of ROCM was noted in India among those infected with COVID. We determined risk factors for rhino-orbito-cerebral mucormycosis (ROCM) post Coronavirus disease 2019 (COVID-19) among those never and ever hospitalized for COVID-19 separately through a multicentric, hospital-based, unmatched case-control study across India. Methods We defined cases and controls as those with and without post-COVID ROCM, respectively. We compared their socio-demographics, co-morbidities, steroid use, glycaemic status, and practices. We calculated crude and adjusted odds ratio (AOR) with 95% confidence intervals (CI) through logistic regression. The covariates with a p-value for crude OR of less than 0·20 were considered for the regression model. Results Among hospitalised, we recruited 267 cases and 256 controls and 116 cases and 231 controls among never hospitalised. Risk factors (AOR; 95% CI) for post-COVID ROCM among the hospitalised were age 45–59 years (2·1; 1·4 to 3·1), having diabetes mellitus (4·9; 3·4 to 7·1), elevated plasma glucose (6·4; 2·4 to 17·2), steroid use (3·2; 2 to 5·2) and frequent nasal washing (4·8; 1·4 to 17). Among those never hospitalised, age ≥ 60 years (6·6; 3·3 to 13·3), having diabetes mellitus (6·7; 3·8 to 11·6), elevated plasma glucose (13·7; 2·2 to 84), steroid use (9·8; 5·8 to 16·6), and cloth facemask use (2·6; 1·5 to 4·5) were associated with increased risk of post-COVID ROCM. Conclusions Hyperglycemia, irrespective of having diabetes mellitus and steroid use, was associated with an increased risk of ROCM independent of COVID-19 hospitalisation. Rational steroid usage and glucose monitoring may reduce the risk of post-COVID.

There was a progressive increase in antimicrobial resistance in isolates of E. coli, K. pneumoniae, P. aeruginosa and A. baumannii isolated from blood cultures. ESBL production was seen in the majority of isolates of E. coli and K. pneumoniae. Carbapenem resistance in K. pneumoniae and E. coli is increasing rapidly. Resistance to even tigecycline and polymyxin E, antibiotics of last resort, has begun to emerge. There is an urgent need for antimicrobial stewardship and other measures to limit worsening of Gram-negative resistance in India.

A 58-year-old male diabetic who was operated for carcinoma larynx 4 years back was admitted with exertional dyspnoea and bilateral leg swelling for the past 2 years. Over the last 2 months, there was a progressive worsening of symptoms. Echocardiography done 2 years back showed pericardial effusion. Echo done during the current admission also showed pericardial effusion with preserved left ventricular function; cytological examination of the pericardial fluid showed larvae of Strongyloides stercoralis. He was treated with antinematodal drugs. A follow-up echo done at discharge showed no pericardial effusion and the patient was completely asymptomatic. To our knowledge, this is the first reported case of Strongyloides pericardial effusion in a diabetic patient.

A bstract Background Coronavirus disease-2019 (COVID-19) infection is a multisystem disease not restricted to the lungs. It has a negative impact on the cardiovascular system by causing myocardial damage, vascular inflammation, plaque instability, and myocardial infarction. The presence of myocardial injury is a poor prognostic sign. Electrocardiogram (ECG), a simple bedside diagnostic test with high prognostic value, can be employed to assess early cardiovascular involvement in such patients. Various abnormalities in ECG like ST-T changes, arrhythmia, and conduction defects have been reported in COVID-19. We aimed to find out the ECG abnormalities of COVID-19 patients. Methods We performed a cross-sectional, hospital-based descriptive study among 315 COVID-19 in-patients who underwent ECG recording on admission. Patients’ clinical profiles were noted from their records, and the ECG abnormalities were studied. Results Among the abnormal ECGs 255 (81%), rhythm abnormalities were seen in 9 patients (2.9%), rate abnormalities in 115 patients (36.5%), and prolonged PR interval in 2.9%. Short QRS complex was seen in 8.3%. QT interval was prolonged in 8.3% of the patients. Significant changes in the ST and T segments (42.9%) were observed. In logistic regression analysis, ischemic changes in ECG were associated with systemic hypertension and respiratory failure. Conclusion In our study, COVID-19 patients had ischemic changes, rate, rhythm abnormalities, and conduction defects in their ECG. With this ongoing pandemic of COVID-19 and limited health resources, ECG—a simple bedside noninvasive tool is highly beneficial and helps in the early diagnosis and management of cardiac injury. How to cite this article Kaliyaperumal D, Bhargavi K, Ramaraju K, Nair KS, Ramalingam S, Alagesan M. Electrocardiographic Changes in COVID-19 Patients: A Hospital-based Descriptive Study. Indian J Crit Care Med 2022;26(1):43–48.

This paper presents the Coswara dataset, a dataset containing diverse set of respiratory sounds and rich meta-data, recorded between April-2020 and February-2022 from 2635 individuals (1819 SARS-CoV-2 negative, 674 positive, and 142 recovered subjects). The respiratory sounds contained nine sound categories associated with variants of breathing, cough and speech. The rich metadata contained demographic information associated with age, gender and geographic location, as well as the health information relating to the symptoms, pre-existing respiratory ailments, comorbidity and SARS-CoV-2 test status. Our study is the first of its kind to manually annotate the audio quality of the entire dataset (amounting to 65 hours) through manual listening. The paper summarizes the data collection procedure, demographic, symptoms and audio data information. A COVID-19 classifier based on bi-directional long short-term (BLSTM) architecture, is trained and evaluated on the different population sub-groups contained in the dataset to understand the bias/fairness of the model. This enabled the analysis of the impact of gender, geographic location, date of recording, and language proficiency on the COVID-19 detection performance.