Ling Liu1, Jianfeng Xie1, Wenjuan Wu2, Hui Chen3, Shusheng Li4, Hongli He5, Yuetian Yu6, Ming Hu7, Jinxiu Li8, Ruiqiang Zheng9, Xuyan Li10, Haibo Qiu1, Zhaohui Tong10, Bin Du11, Eddy Fan12, Yi Yang13, Arthur S Slutsky14. 1. Jiangsu Provincial Key Laboratory of Critical Care Medicine, Department of Critical Care Medicine, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China. 2. Department of Critical Care Medicine, Wuhan Jinyintan Hospital, Wuhan, China. 3. Department of Intensive Care Medicine, The First Affiliated Hospital of Soochow University, Suzhou, China. 4. Department of Critical Care Medicine, Tongji Hospital, Tongji Medical College Huazhong University of Science and Technology, Wuhan, China. 5. Department of Critical Care Medicine, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China. 6. Department of Critical Care Medicine, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China. 7. Department of Infectious Disease, Wuhan Pulmonary Hospital, Wuhan, China. 8. Department of Critical Care Medicine, Shenzhen Third People's Hospital, Shenzhen, China. 9. Department of Critical Care Medicine, Northern Jiangsu People's Hospital, Clinical Medical School, Yangzhou University, Yangzhou, China. 10. Department of Respiratory and Critical Care Medicine, Beijing Institute of Respiratory Medicine, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China. 11. Medical Intensive Care Unit, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China. 12. Interdepartmental Division of Critical Care Medicine, University of Toronto, Toronto, ON, Canada; Division of Respirology and Critical Care Medicine, Toronto General Hospital, Toronto, ON, Canada. 13. Jiangsu Provincial Key Laboratory of Critical Care Medicine, Department of Critical Care Medicine, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China. Electronic address: yiyiyang2004@163.com. 14. Interdepartmental Division of Critical Care Medicine, University of Toronto, Toronto, ON, Canada; Keenan Research Center, Li Ka Shing Knowledge Institute, St Michael's Hospital, Toronto, ON, Canada; Department of Medicine, Department of Surgery, and Department of Biomedical Engineering, University of Toronto, Toronto, ON, Canada.
Abstract
BACKGROUND: Non-invasive respiratory strategies (NIRS) including high-flow nasal cannula (HFNC) and non-invasive ventilation (NIV) have become widely used in patients with COVID-19 who develop acute respiratory failure. However, use of these therapies, if ineffective, might delay initiation of invasive mechanical ventilation (IMV) in some patients. We aimed to determine early predictors of NIRS failure and develop a simple nomogram and online calculator that can identify patients at risk of NIRS failure. METHODS: We did a retrospective, multicentre observational study in 23 hospitals designated for patients with COVID-19 in China. Adult patients (≥18 years) with severe acute respiratory syndrome coronavirus 2 infection and acute respiratory failure receiving NIRS were enrolled. A training cohort of 652 patients (21 hospitals) was used to identify early predictors of NIRS failure, defined as subsequent need for IMV or death within 28 days after intensive care unit admission. A nomogram was developed by multivariable logistic regression and concordance statistics (C-statistics) computed. C-statistics were validated internally by cross-validation in the training cohort, and externally in a validation cohort of 107 patients (two hospitals). FINDINGS: Patients were enrolled between Jan 1 and Feb 29, 2020. NIV failed in 211 (74%) of 286 patients and HFNC in 204 (56%) of 366 patients in the training cohort. NIV failed in 48 (81%) of 59 patients and HFNC in 26 (54%) of 48 patients in the external validation cohort. Age, number of comorbidities, respiratory rate-oxygenation index (ratio of pulse oximetry oxygen saturation/fraction of inspired oxygen to respiratory rate), Glasgow coma scale score, and use of vasopressors on the first day of NIRS in the training cohort were independent risk factors for NIRS failure. Based on the training dataset, the nomogram had a C-statistic of 0·80 (95% CI 0·74-0·85) for predicting NIV failure, and a C-statistic of 0·85 (0·82-0·89) for predicting HFNC failure. C-statistic values were stable in both internal validation (NIV group mean 0·79 [SD 0·10], HFNC group mean 0·85 [0·07]) and external validation (NIV group value 0·88 [95% CI 0·72-0·96], HFNC group value 0·86 [0·72-0·93]). INTERPRETATION: We have developed a nomogram and online calculator that can be used to identify patients with COVID-19 who are at risk of NIRS failure. These patients might benefit from early triage and more intensive monitoring. FUNDING: Ministry of Science and Technology of the People's Republic of China, Key Research and Development Plan of Jiangsu Province, Chinese Academy of Medical Sciences.
BACKGROUND: Non-invasive respiratory strategies (NIRS) including high-flow nasal cannula (HFNC) and non-invasive ventilation (NIV) have become widely used in patients with COVID-19 who develop acute respiratory failure. However, use of these therapies, if ineffective, might delay initiation of invasive mechanical ventilation (IMV) in some patients. We aimed to determine early predictors of NIRS failure and develop a simple nomogram and online calculator that can identify patients at risk of NIRS failure. METHODS: We did a retrospective, multicentre observational study in 23 hospitals designated for patients with COVID-19 in China. Adult patients (≥18 years) with severe acute respiratory syndrome coronavirus 2infection and acute respiratory failure receiving NIRS were enrolled. A training cohort of 652 patients (21 hospitals) was used to identify early predictors of NIRS failure, defined as subsequent need for IMV or death within 28 days after intensive care unit admission. A nomogram was developed by multivariable logistic regression and concordance statistics (C-statistics) computed. C-statistics were validated internally by cross-validation in the training cohort, and externally in a validation cohort of 107 patients (two hospitals). FINDINGS:Patients were enrolled between Jan 1 and Feb 29, 2020. NIV failed in 211 (74%) of 286 patients and HFNC in 204 (56%) of 366 patients in the training cohort. NIV failed in 48 (81%) of 59 patients and HFNC in 26 (54%) of 48 patients in the external validation cohort. Age, number of comorbidities, respiratory rate-oxygenation index (ratio of pulse oximetry oxygen saturation/fraction of inspired oxygen to respiratory rate), Glasgow coma scale score, and use of vasopressors on the first day of NIRS in the training cohort were independent risk factors for NIRS failure. Based on the training dataset, the nomogram had a C-statistic of 0·80 (95% CI 0·74-0·85) for predicting NIV failure, and a C-statistic of 0·85 (0·82-0·89) for predicting HFNC failure. C-statistic values were stable in both internal validation (NIV group mean 0·79 [SD 0·10], HFNC group mean 0·85 [0·07]) and external validation (NIV group value 0·88 [95% CI 0·72-0·96], HFNC group value 0·86 [0·72-0·93]). INTERPRETATION: We have developed a nomogram and online calculator that can be used to identify patients with COVID-19 who are at risk of NIRS failure. These patients might benefit from early triage and more intensive monitoring. FUNDING: Ministry of Science and Technology of the People's Republic of China, Key Research and Development Plan of Jiangsu Province, Chinese Academy of Medical Sciences.
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