Predictors of CPAP outcome in hospitalised COVID-19 patients

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Abstract

Introduction

Throughout March – April 2020, many patients with COVID-19 presented to Southend University Hospital with Acute Hypoxaemic Respiratory Failure (AHRF). Patients were managed in a Specialist Respiratory High Dependency Unit. We present our experience on the usage of continuous positive airway pressure (CPAP) therapy and possible indicators of its success in this patient group.

Methods

Data from patients (n=89) requiring mechanical ventilation during the months of March-April 2020, were retrospectively collected and analysed. 37 patients received IMV (Invasive Mechanical Ventilation) without a CPAP trial beforehand. 52 patients underwent a CPAP trial, of which 21 patients successfully avoided intubation and ITU admission.

Results

The 52 patients, prior to receiving CPAP had significant respiratory failure as evidenced by a low PaO2: FiO2 (PFR) (mean± SD 123 ± 60 mmHg) and mean SpO2:FiO2 (SFR) (mean ± SD: 140 ± 50). The main indicators of CPAP success were: higher SFR before and after CPAP, lower respiratory rate (RR), lower Neutrophil to Lymphocyte ratio (NLR) and higher PFR prior to CPAP.

Discussion

CPAP proved successful in 40% of COVID-19 patients presenting with AHRF. SFR, PFR, RR and NLR are predictors of such success. SFR can be used for effective real time monitoring of patients before and after CPAP to identify likelihood of success. Based on our results, we have suggested a modified CPAP management protocol in COVID-19. These findings can guide future studies and will allow improved triage of patients to either CPAP or IMV, in the event of a future COVID peak.

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  1. SciScore for 10.1101/2020.06.14.20130880: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power AnalysisPerformance of each classification tree was determined by calculating accuracy of model’s predicted probability with corresponding area under the curve. (11) We did not calculate sample size, however, power analysis for each of AUC for SFR before and after CPAP as well as NLR was at least more than 80%.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Statistical analysis was done using IBM SPSS statistics program (12), and R software with the following packages: “rpart”, “rpart.plot”, “pROC” and “ROCR”.
    SPSS
    suggested: (SPSS, RRID:SCR_00286…