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Critical Care for Patients with Severe Covid-2019 in Sichuan Province, China: Provincial Cohort Study
Preprint
in English
| medRxiv
| ID: ppmedrxiv-20041277
ABSTRACT
BackgroundData 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. MethodsUsing 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. FindingsOut 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%. InterpretationThe 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. Trial registration numberChiCTR2000029758.
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Full text:
Available
Collection:
Preprints
Database:
medRxiv
Type of study:
Cohort_studies
/
Observational study
/
Prognostic study
/
Rct
Language:
English
Year:
2020
Document type:
Preprint