Predictors of Mortality among Patients Hospitalized with COVID-19 during the First Wave in India: A Multisite Case-Control Study.

Severe acute respiratory syndrome coronavirus 2 disease (COVID-19) has caused more than 6 million deaths globally. Understanding predictors of mortality will help in prioritizing patient care and preventive approaches. This was a multicentric, unmatched, hospital-based case-control study conducted in nine teaching hospitals in India. Cases were microbiologically confirmed COVID-19 patients who died in the hospital during the period of study and controls were microbiologically confirmed COVID-19 patients who were discharged from the same hospital after recovery. Cases were recruited sequentially from March 2020 until December-March 2021. All information regarding cases and controls was extracted retrospectively from the medical records of patients by trained physicians. Univariable and multivariable logistic regression was done to assess the association between various predictor variables and deaths due to COVID-19. A total of 2,431 patients (1,137 cases and 1,294 controls) were included in the study. The mean age of patients was 52.8 years (SD: 16.5 years), and 32.1% were females. Breathlessness was the most common symptom at the time of admission (53.2%). Increasing age (adjusted odds ratio [aOR]: 46-59 years, 3.4 [95% CI: 1.5-7.7]; 60-74 years, 4.1 [95% CI: 1.7-9.5]; and ≥ 75 years, 11.0 [95% CI: 4.0-30.6]); preexisting diabetes mellitus (aOR: 1.9 [95% CI: 1.2-2.9]); malignancy (aOR: 3.1 [95% CI: 1.3-7.8]); pulmonary tuberculosis (aOR: 3.3 [95% CI: 1.2-8.8]); breathlessness at the time of admission (aOR: 2.2 [95% CI: 1.4-3.5]); high quick Sequential Organ Failure Assessment score at the time of admission (aOR: 5.6 [95% CI: 2.7-11.4]); and oxygen saturation < 94% at the time of admission (aOR: 2.5 [95% CI: 1.6-3.9]) were associated with mortality due to COVID-19. These results can be used to prioritize patients who are at increased risk of death and to rationalize therapy to reduce mortality due to COVID-19.

Restricting rural-urban connect to combat infectious disease epidemic as India fights COVID-19.

With declaration of 2019 novel coronavirus disease (COVID-19) as a pandemic on 11 March 2020 by World Health Organization, India came to alert for its being at next potential risk. It reached alert Level 2, i.e. local transmission for virus spread in early March 2020 and soon thereafter alert Level 3, i.e. community transmission. With on-going rise in COVID-19 cases in country, Government of India (GoI) has been taking multiple intense measures in coordination with the state governments, such as urban lockdown, active airport screening, quarantining, aggressive calls for 'work from home', public awareness, and active case detection with contact tracing in most places. Feedback from other countries exhibits COVID-19 transmission levels to have shown within country variations. With two-third of Indian population living in rural areas, present editorial hypothesizes that if India enters Level 3, rural hinterland would also be at risk importation (at least Level 1). Hence, we have to call for stringent containment on rural-urban and inter-state fringes. This along with other on-going measures can result in flattening curve and also in staggering 'lockdowns', and thus, helping sustain national economy.

Evaluation of Remote Monitoring Device for Monitoring Vital Parameters against Reference Standard: A Diagnostic Validation Study for COVID-19 Preparedness.

CONTEXT: Vital parameters including blood oxygen level, respiratory rate, pulse rate, and body temperature are crucial for triaging patients to appropriate medical care. Advances in remote health monitoring system and wearable health devices have created a new horizon for delivery of efficient health care from a distance. MATERIALS AND METHODS: This diagnostic validation study included patients attending the outpatient department of the institute. The accuracy of device under study was compared against the gold standard patient monitoring systems used in intensive care units. STATISTICAL ANALYSIS: The statistical analysis involved computation of intraclass correlation coefficient. Bland-Altman graphs with limits of agreement were plotted to assess agreement between methods. P <0.05 was considered statistically significant. RESULTS: A total of 200 patients, including 152 males and 48 females in the age range of 2-80 years, formed the study group. A strong correlation (intraclass correlation coefficient; r > 0.9) was noted between the two devices for all the investigated parameters with significant P value (<0.01). Bland-Altman plot drawn for each vital parameter revealed observations in agreement from both the devices. CONCLUSION: The wearable device can be reliably used for remote health monitoring. Its regulated use can help mitigate the scarcity of hospital beds and reduce exposure to health-care workers and demand of personal protection equipment.