Identification of Exertional Hypoxia and Its Implications in SARS-CoV-2 Pneumonia

Hussain, Tanweer; Saman, Harman; Yousaf, Zohaib

doi:10.4269/ajtmh.20-1012

Cited by 4 publications

(3 citation statements)

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“…For instance, “silent” (asymptomatic) hypoxia in CAP is a commonly overlooked clinical entity ( 34 ). To permit timely CAP diagnosis, physicians should employ a 6-min walk test to diagnose exertional hypoxia ( 35 ) or conduct a meticulous clinical examination ( 34 ). If considered together, the functional estimates (e.g., arterial blood gases, pulse oximetry, etc.)…”

Section: Discussionmentioning

confidence: 99%

Deep Learning-Based Automatic Assessment of Lung Impairment in COVID-19 Pneumonia: Predicting Markers of Hypoxia With Computer Vision

et al. 2022

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BackgroundHypoxia is a potentially life-threatening condition that can be seen in pneumonia patients.ObjectiveWe aimed to develop and test an automatic assessment of lung impairment in COVID-19 associated pneumonia with machine learning regression models that predict markers of respiratory and cardiovascular functioning from radiograms and lung CT.Materials and MethodsWe enrolled a total of 605 COVID-19 cases admitted to Al Ain Hospital from 24 February to 1 July 2020 into the study. The inclusion criteria were as follows: age ≥ 18 years; inpatient admission; PCR positive for SARS-CoV-2; lung CT available at PACS. We designed a CNN-based regression model to predict systemic oxygenation markers from lung CT and 2D diagnostic images of the chest. The 2D images generated by averaging CT scans were analogous to the frontal and lateral view radiograms. The functional (heart and breath rate, blood pressure) and biochemical findings (SpO2, HCO3-, K+, Na+, anion gap, C-reactive protein) served as ground truth.ResultsRadiologic findings in the lungs of COVID-19 patients provide reliable assessments of functional status with clinical utility. If fed to ML models, the sagittal view radiograms reflect dyspnea more accurately than the coronal view radiograms due to the smaller size and the lower model complexity. Mean absolute error of the models trained on single-projection radiograms was approximately 11÷12% and it dropped by 0.5÷1% if both projections were used (11.97 ± 9.23 vs. 11.43 ± 7.51%; p = 0.70). Thus, the ML regression models based on 2D images acquired in multiple planes had slightly better performance. The data blending approach was as efficient as the voting regression technique: 10.90 ± 6.72 vs. 11.96 ± 8.30%, p = 0.94. The models trained on 3D images were more accurate than those on 2D: 8.27 ± 4.13 and 11.75 ± 8.26%, p = 0.14 before lung extraction; 10.66 ± 5.83 and 7.94 ± 4.13%, p = 0.18 after the extraction. The lung extraction boosts 3D model performance unsubstantially (from 8.27 ± 4.13 to 7.94 ± 4.13%; p = 0.82). However, none of the differences between 3D and 2D were statistically significant.ConclusionThe constructed ML algorithms can serve as models of structure-function association and pathophysiologic changes in COVID-19. The algorithms can improve risk evaluation and disease management especially after oxygen therapy that changes functional findings. Thus, the structural assessment of acute lung injury speaks of disease severity.

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Section: Discussionmentioning

confidence: 99%

Deep Learning-Based Automatic Assessment of Lung Impairment in COVID-19 Pneumonia: Predicting Markers of Hypoxia With Computer Vision

et al. 2022

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“…Additionally, other authors have advocated for exertional oximetry measurements to evaluate patients for discharge readiness after resolution of COVID-19 symptoms [16], a strategy already adopted in our hospital. A small study evaluated 47 patients with COVID-19 prior to hospital discharge with the 6-min walk test and found the test useful to screen for asymptomatic hypoxia [17].…”

Section: Discussionmentioning

confidence: 99%

Exertional hypoxia in patients without resting hypoxia is an early predictor of moderate to severe COVID-19

Bhasin

et al. 2021

Intern Emerg Med

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The importance of exertional hypoxia without resting hypoxia in COVID-19 is unknown and may help objectively identify high-risk patients. Interventions may be initiated earlier with sufficient lead-time between development of exertional hypoxia and other outcome measures. We performed a retrospective study of adult patients hospitalized with COVID-19 from March 1, 2020 to October 30, 2020 in an integrated academic medical system in the Chicagoland area. We analyzed patients who had daily exertional oximetry measurements taken. We defined exertional hypoxia as SpO2 < 90% with ambulation. We excluded patients who had first exertional oximetry measurements or first exertional hypoxia after the use of oxygen therapies. We determined the association of exertional hypoxia without resting hypoxia with the eventual need for nasal cannula or advanced oxygen therapies (defined as high flow nasal cannula, Bi-PAP, ventilator, or extracorporeal membrane oxygenation). We also calculated the time between development of exertional hypoxia and the need for oxygen therapies. Of 531 patients included, 132 (24.9%) had exertional hypoxia. Presence of exertional hypoxia was strongly associated with eventual use of nasal cannula (OR 4.8, 95% CI 2.8-8.4) and advanced oxygen therapy (IRR 7.7,). Exertional hypoxia preceded nasal cannula use by a median 12.5 h [IQR 3.25, 29.25] and advanced oxygenation by 54 h [IQR 25, 82]. Exertional hypoxia without resting hypoxia may serve as an early, non-invasive physiologic marker for the likelihood of developing moderate to severe COVID-19. It may help clinicians triage patients and initiate earlier interventions.

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“…10 To our knowledge, the presence of ambulatory hypoxia has never been shown to acutely predict cardiopulmonary disease progression. Recently, some authors have advocated for ambulatory oximetry measurements to evaluate patients for discharge readiness after resolution of COVID-19 symptoms, 11 a strategy already adopted in our hospital. We present the first study showing that ambulatory hypoxia predicts worsening pulmonary disease in patients with COVID-19.…”

Section: Discussionmentioning

confidence: 99%

The Presence of Ambulatory Hypoxia as an Early Predictor of Moderate to Severe COVID-19 Disease

Bhasin

et al. 2020

Preprint

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BackgroundThe importance of ambulatory hypoxia without resting hypoxia in COVID-19 is unknown. Ambulatory hypoxia without resting hypoxia may help objectively identify high-risk patients hospitalized with COVID-19. Interventions may be initiated earlier with sufficient lead-time between development of ambulatory hypoxia and other outcome measures.MethodsWe performed a retrospective study of adult patients hospitalized with COVID-19 from March 1, 2020 to October 30, 2020 in ten hospitals in an integrated academic medical system in the Chicagoland area. We analyzed patients who had daily ambulatory oximetry measurements, excluding patients who had first ambulatory oximetry measurements after the use of oxygen therapies (nasal cannula or advanced oxygen therapies). We determined the association of ambulatory hypoxia without resting hypoxia with the eventual need for nasal cannula or advanced oxygen therapies (defined as high flow nasal cannula, Bi-PAP, ventilator, or extracorporeal membrane oxygenation). We also calculated the time between development of ambulatory hypoxia and the need for oxygen therapies.ResultsOf 531 patients included in the study, 132 (24.9%) had ambulatory hypoxia. Presence of ambulatory hypoxia was strongly associated with subsequent use of nasal cannula (OR 4.8, 95% CI 2.8 – 8.4) and advanced oxygen therapy (IRR 7.7, 95% CI 3.4 – 17.5). Ambulatory hypoxia preceded nasal cannula use by a median 12.5 hours [IQR 3.25, 29.25] and advanced oxygenation therapies by 54 hours [IQR 25, 82].ConclusionAmbulatory hypoxia without resting hypoxia may serve as an early, non-invasive physiologic marker for the likelihood of developing moderate to severe COVID-19 and help clinicians triage patients and initiate earlier interventions.

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Identification of Exertional Hypoxia and Its Implications in SARS-CoV-2 Pneumonia

Abstract: This is an open-access article distributed under the terms of the Creative Commons Attribution (CC-BY) License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Cited by 4 publications

References 10 publications

Deep Learning-Based Automatic Assessment of Lung Impairment in COVID-19 Pneumonia: Predicting Markers of Hypoxia With Computer Vision

Deep Learning-Based Automatic Assessment of Lung Impairment in COVID-19 Pneumonia: Predicting Markers of Hypoxia With Computer Vision

Exertional hypoxia in patients without resting hypoxia is an early predictor of moderate to severe COVID-19

The Presence of Ambulatory Hypoxia as an Early Predictor of Moderate to Severe COVID-19 Disease

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