A simple nomogram for predicting failure of non-invasive respiratory strategies in adults with COVID-19: a retrospective multicentre study

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.