Critical Care for Severe COVID-19: A Population-based Study from a Province with Low Case-fatality Rate in China

Abstract

Background Data regarding critical care for patients with severe COVID-19 are limited. We aimed to describe the clinical course, multi-strategy management, and respiratory support usage for the severe COVID-19 at the provincial level. Methods Using data from Sichuan Provincial Department of Health and the multicentre cohort study, all microbiologically confirmed COVID-19 patients in Sichuan who met the national severe criteria were included and followed-up from the day of inclusion (D1), until discharge, death, or the end of the study. Findings Out of 539 COVID-19 patients, 81 severe cases (15.0%) were identified. The median (IQR) age was 50 (39-65) years, 37% were female, and 53.1% had chronic comorbidities. All severe cases were identified before requiring mechanical ventilation and treated in the intensive care units (ICUs), among whom 51 (63.0%) were treated in provisional ICUs and 77 patients (95.1%) were admitted by D1. On D1, 76 (93.8%) were administered by respiratory support, including 55 (67.9%) by conventional oxygen therapy (COT), 8 (9.9%) by high-flow nasal cannula (HFNC) and 13 (16.0%) by non-invasive ventilation (NIV). By D28, 53 (65.4%) were discharged, three (3.7%) were deceased, and 25 (30.9%) were still hospitalized. COT, administered to 95.1% of the patients, was the most commonly used respiratory support and met 62.7% of the respiratory support needed, followed by HFNC (19.3%), NIV ventilation (9.4%) and IV 8.5%. Interpretation The multi-strategy management for severe COVID-19 patients including early identification and timely critical care may contribute to the low case-fatailty. Preparation of sufficient conventional oxygen equipment should be prioritized.

Neutrophil-to-lymphocyte ratio as a predictive biomarker for moderate-severe ARDS in severe COVID-19 patients

“…The maximum value of NLR, PLR, PCT, and CRP during the first 3 days after being diagnosed as severe COVID-19 was included in the analysis. Severe COVID-19 and ARDS were defined according to previous study [4] and Berlin definition [5], respectively. Multivariate logistic regression analysis and the area under the receiver operating characteristic (ROC) curve were used to analyze the ability of NLR in predicting ARDS.…”

Obesity-Driven Deficiencies of Specialized Pro-resolving Mediators May Drive Adverse Outcomes During SARS-CoV-2 Infection

“…Obesity is an independent risk factor for increased morbidity and mortality upon infection with the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) responsible for the current COVID-19 pandemic. Several studies underscore the notion that obesity, in addition to a range of other co-morbidities and dietary factors, may increase the risk for SARS-CoV-2 (1)(2)(3)(4)(5)(6)(7)(8)(9)(10). As an example, in a study from Mexico, the odds of having COVID-19 among obese patients with a BMI > 30 kg/m 2 was 61% higher than that of control non-obese patients (1).…”

“…As an example, in a study from Mexico, the odds of having COVID-19 among obese patients with a BMI > 30 kg/m 2 was 61% higher than that of control non-obese patients (1). Generally, amongst patients with symptoms, those with severe or critical conditions had much higher BMI and prevalence of obesity than the normal population or COVID-19 negative patients (2)(3)(4)(5)(6)(7)(8)(9)(10). One study used the UK Biobank data (n = 285,817) to show that obesity almost doubled the risk of infection, adjusted for age, sex, ethnicity and socioeconomic status (9).…”