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      Respiratory Non-Invasive Venous Waveform Analysis for Assessment of Respiratory Distress in Coronavirus Disease 2019 Patients: An Observational Study

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          Abstract

          Supplemental Digital Content is available in the text.

          Abstract

          OBJECTIVES:

          Due to the rapid rate of severe acute respiratory syndrome coronavirus 2 transmission and the heterogeneity of symptoms of coronavirus disease 2019, expeditious and effective triage is critical for early treatment and effective allocation of hospital resources.

          DESIGN:

          A post hoc analysis of respiratory data from non-invasive venous waveform analysis among patients enrolled in an observational study was performed.

          SETTING:

          Vanderbilt University Medical Center.

          PATIENTS:

          Peripheral venous waveforms were recorded from admission to discharge in enrolled coronavirus disease 2019–positive patients and healthy age-matched controls.

          INTERVENTIONS:

          Data were analyzed in LabChart 8 to transform venous waveforms to the frequency domain using fast Fourier transforms. The peak respiratory frequency was normalized to the peak cardiac frequency to generate a respiratory non-invasive venous waveform analysis respiratory index. Paired Fisher exact tests were used to compare each patient’s respiratory non-invasive venous waveform analysis respiratory index at admission and discharge. A nonparametric one-way analysis of variance was used for multiple comparisons between patients with coronavirus disease 2019 and healthy controls for respiratory non-invasive venous waveform analysis respiratory index.

          MEASUREMENTS AND MAIN RESULTS:

          Fifty coronavirus disease 2019–positive patients were enrolled between April 2020, and September 2020, and 45 were analyzed; 34 required supplemental oxygen and 11 did not. The respiratory non-invasive venous waveform analysis respiratory index was significantly higher for the 34 patients with coronavirus disease 2019 who received supplemental oxygen (median, 0.27; interquartile range, 0.11—1.28) compared with the 34 healthy controls (median, 0.06; interquartile range, 0.03–0.14) ( p < 0.01). For patients with coronavirus disease 2019 who received supplemental oxygen, respiratory non-invasive venous waveform analysis respiratory index was significantly lower at hospital discharge ( p = 0.02; 95% CI, 0.10–1.9) compared with hospital admission (median = 0.12; interquartile range, 0.05–0.56). For patients with coronavirus disease 2019, a respiratory non-invasive venous waveform analysis respiratory index of 0.64 demonstrated sensitivity of 92%, specificity of 47%, and positive predictive value of 93% for predicting requirement of supplemental oxygen during the hospitalization.

          CONCLUSIONS:

          Respiratory non-invasive venous waveform analysis respiratory index represents a novel physiologic respiratory measurement with a promising ability to triage early care and predict the need for oxygen support therapy in coronavirus disease 2019 patients.

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          Most cited references10

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          Mild or Moderate Covid-19

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            An Index Combining Respiratory Rate and Oxygenation to Predict Outcome of Nasal High-Flow Therapy

            Rationale: One important concern during high-flow nasal cannula (HFNC) therapy in patients with acute hypoxemic respiratory failure is to not delay intubation. Objectives: To validate the diagnostic accuracy of an index (termed ROX and defined as the ratio of oxygen saturation as measured by pulse oximetry/FiO2 to respiratory rate) for determining HFNC outcome (need or not for intubation). Methods: This was a 2-year multicenter prospective observational cohort study including patients with pneumonia treated with HFNC. Identification was through Cox proportional hazards modeling of ROX association with HFNC outcome. The most specific cutoff of the ROX index to predict HFNC failure and success was assessed. Measurements and Main Results: Among the 191 patients treated with HFNC in the validation cohort, 68 (35.6%) required intubation. The prediction accuracy of the ROX index increased over time (area under the receiver operating characteristic curve: 2 h, 0.679; 6 h, 0.703; 12 h, 0.759). ROX greater than or equal to 4.88 measured at 2 (hazard ratio, 0.434; 95% confidence interval, 0.264-0.715; P = 0.001), 6 (hazard ratio, 0.304; 95% confidence interval, 0.182-0.509; P < 0.001), or 12 hours (hazard ratio, 0.291; 95% confidence interval, 0.161-0.524; P < 0.001) after HFNC initiation was consistently associated with a lower risk for intubation. A ROX less than 2.85, less than 3.47, and less than 3.85 at 2, 6, and 12 hours of HFNC initiation, respectively, were predictors of HFNC failure. Patients who failed presented a lower increase in the values of the ROX index over the 12 hours. Among components of the index, oxygen saturation as measured by pulse oximetry/FiO2 had a greater weight than respiratory rate. Conclusions: In patients with pneumonia with acute respiratory failure treated with HFNC, ROX is an index that can help identify those patients with low and those with high risk for intubation. Clinical trial registered with www.clinicaltrials.gov (NCT02845128).
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              Early triage of critically ill COVID-19 patients using deep learning

              The sudden deterioration of patients with novel coronavirus disease 2019 (COVID-19) into critical illness is of major concern. It is imperative to identify these patients early. We show that a deep learning-based survival model can predict the risk of COVID-19 patients developing critical illness based on clinical characteristics at admission. We develop this model using a cohort of 1590 patients from 575 medical centers, with internal validation performance of concordance index 0.894 We further validate the model on three separate cohorts from Wuhan, Hubei and Guangdong provinces consisting of 1393 patients with concordance indexes of 0.890, 0.852 and 0.967 respectively. This model is used to create an online calculation tool designed for patient triage at admission to identify patients at risk of severe illness, ensuring that patients at greatest risk of severe illness receive appropriate care as early as possible and allow for effective allocation of health resources.
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                Author and article information

                Journal
                Crit Care Explor
                Crit Care Explor
                CC9
                Critical Care Explorations
                Lippincott Williams & Wilkins (Hagerstown, MD )
                2639-8028
                01 October 2021
                October 2021
                : 3
                : 10
                : e0539
                Affiliations
                [1 ] Department of Anesthesiology, Vanderbilt University Medical Center, Nashville, TN.
                [2 ] Department of Biomedical Engineering, Vanderbilt University, Nashville, TN.
                [3 ] VoluMetrix, LLC., Nashville, TN.
                [4 ] Department of Surgery, Vanderbilt University Medical Center, Nashville, TN.
                [5 ] Department of Medicine, Division of Cardiology, Heart Failure and Transplant, Vanderbilt University Medical Center, Nashville, TN.
                [6 ] Department of Medicine, Division of Cardiology, University of Pittsburgh Medical Center, Pittsburgh, PA.
                [7 ] Department of Medicine, Division of Allergy, Pulmonology, and Critical Care Medicine, Vanderbilt University Medical Center, Nashville, TN.
                Author notes
                For information regarding this article, E-mail: bret.d.alvis@ 123456vumc.org
                Article
                00006
                10.1097/CCE.0000000000000539
                8489896
                34617035
                67fba418-f2b5-41ed-98b9-9223d9194749
                Copyright © 2021 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of the Society of Critical Care Medicine.

                This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal.

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                coronavirus,coronavirus disease 2019,non-invasive venous waveform analysis,severe acute respiratory syndrome coronavirus 2

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