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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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 Statement not detected. Randomization not detected. Blinding not detected. Power Analysis Performance 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 variable not detected. Table 2: Resources
Software and Algorithms Sentences Resources Statistical analysis was done using IBM SPSS statistics program (12), and R software with the following packages: “rpart”, “rpart.plot”, “pROC” and “ROCR”. SPSSsuggested: (SPSS, RRID:SCR_00286…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 Statement not detected. Randomization not detected. Blinding not detected. Power Analysis Performance 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 variable not detected. Table 2: Resources
Software and Algorithms Sentences Resources Statistical analysis was done using IBM SPSS statistics program (12), and R software with the following packages: “rpart”, “rpart.plot”, “pROC” and “ROCR”. SPSSsuggested: (SPSS, RRID:SCR_002865)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:(Figure 4) Limits: Our study is not without limitations. First, it is a retrospective observational study conducted on a small group of patients. Our results need confirmation in a larger prospective work. Second, many mechanisms can contribute to AHRF even in a single disease (e.g. Pneumonia, ARDS, but also pulmonary embolism which is not rare in COVID-19). Third, it remains to be determined if the CPAP trial affected the outcome of intubated patients in comparison to those promptly intubated from the start. Furthermore, we did not use High Flow Nasal Cannula (HFNC) which may have a role. A randomised controlled trial may be needed for full comparison between the 3 treatment modalities of HFNC, CPAP and early IMV. Conclusion: CPAP has a significant role in the management of COVID-19 patients presenting with AHRF. We have demonstrated that an experienced medical team following protocol driven management can successfully use CPAP to manage patients with COVID-19 and AHRF. In our group of patients, its success rate was 40%. SFR, PFR, NLR and RR are predictors of such success. SFR is a non-invasive, real-time measurement which can be used effectively to monitor these patients before and after CPAP to identify likelihood of success and reduce the need for frequent ABGs. The proposed modified CPAP management algorithm provides an initial step for better identifying those patients who will likely succeed on CPAP therapy. Thus, reducing the need for unnecessary IMV, and its associat...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
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- No protocol registration statement was detected.
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