The Diagnostic Accuracy of Subjective Dyspnea in Detecting Hypoxemia Among Outpatients with COVID-19

Berezin, Linor; Zhabokritsky, Alice; Andany, Nisha; Chan, Adrienne K.; Gershon, Andrea S.; Lam, Philip W; Leis, Jerome A; MacPhee, Scott; Mubareka, Samira; Simor, Andrew E.; Daneman, Nick

doi:10.1101/2020.08.10.20172262

Cited by 7 publications

(5 citation statements)

References 23 publications

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“…A-DROP was found to be reliable in predicting in-hospital death in COVID-19 [47]. However, subjective dyspnea scores were found to be inadequate in assessing hypoxemia in COVID-19 [48,49]. Interestingly, high sRAGE discriminated the need for HFNO/MV and MV independently from a high SOFA score in our multivariate models.…”

Section: Discussionmentioning

confidence: 69%

Soluble receptor for advanced glycation end products (sRAGE) as a biomarker of COVID-19 disease severity and indicator of the need for mechanical ventilation, ARDS and mortality

Lim

Radujkovic

Weigand

et al. 2021

Ann. Intensive Care

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Background COVID-19 pneumonia and subsequent respiratory failure is causing an immense strain on intensive care units globally. Early prediction of severe disease enables clinicians to avoid acute respiratory distress syndrome (ARDS) development and improve management of critically ill patients. The soluble receptor of advanced glycation endproducts (sRAGE) is a biomarker shown to predict ARDS. Although sRAGE level varies depending on the type of disease, there is limited information available on changes in sRAGE levels in COVID-19. Therefore, sRAGE was measured in COVID-19 patients to determine sRAGE level variation in COVID-19 severity and to examine its ability to predict the need for mechanical ventilation (MV) and mortality in COVID-19. Methods In this single-centre observational cohort study in Germany, serum sRAGE during acute COVID-19, 20 weeks after the start of COVID-19 symptoms, as well as in control groups of non-COVID-19 pneumonia patients and healthy controls were measured using ELISA. The primary endpoint was severe disease (high-flow nasal oxygen therapy (HFNO)/MV and need of organ support). The secondary endpoints were respiratory failure with need of MV and 30-day mortality. The area under the curve (AUC), cut-off based on Youden’s index and odds ratio with 95% CI for sRAGE were calculated with regard to prediction of MV need and mortality. Results Serum sRAGE in 164 COVID-19 patients, 101 matched COVID-19 convalescent patients, 23 non-COVID-19 pneumonia patients and 15 healthy volunteers were measured. sRAGE level increased with COVID-19 severity, need for oxygen therapy, HFNO/MV, ARDS severity, need of dialysis and catecholamine support, 30-day mortality, sequential organ failure assessment (SOFA) and quick SOFA (qSOFA) score. sRAGE was found to be a good predictor of MV need in COVID-19 inpatients and mortality with an AUC of 0.871 (0.770–0.973) and 0.903 (0.817–0.990), respectively. When adjusted for male gender, age, comorbidity and SOFA score ≥ 3, sRAGE was independently associated with risk of need for HFNO/MV. When adjusted for SOFA score ≥ 3, sRAGE was independently associated with risk of need for MV. Conclusions Serum sRAGE concentrations are elevated in COVID-19 patients as disease severity increases. sRAGE should be considered as a biomarker for predicting the need for MV and mortality in COVID-19.

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Section: Discussionmentioning

confidence: 69%

Soluble receptor for advanced glycation end products (sRAGE) as a biomarker of COVID-19 disease severity and indicator of the need for mechanical ventilation, ARDS and mortality

Lim

Radujkovic

Weigand

et al. 2021

Ann. Intensive Care

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show abstract

“…Subjective dyspnea can be assessed in person or remotely [14] using patient interview, and augmented by surrogate measures such as the Medical Research Council (MRC) Dyspnea Scale [15] and Borg Scale [16]. Studies indicated that subjective dyspnea measures have inadequate accuracy in high-risk COVID patients, not only because the sensation is gradual and varied with time, but also the patients can become nervous after encountering positive test results [17], both of which can contribute to biases in the selfreport. The subjective dyspnea score can also vary for each person based on emotion and tolerance, and can be challenging to assess for those who refuse to cooperate or cannot communicate due to medical issues such as stroke, dementia, and sudden loss of speech.…”

Section: Introductionmentioning

confidence: 99%

Detection and Prediction of Sleep Disorders by Covert Bed-Integrated RF Sensors

Zhang

Conroy

Krieger

et al. 2023

IEEE Trans. Biomed. Eng.

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Objective: Dyspnea is one of the most common symptoms for many pulmonary diseases including COVID-19. Clinical assessment of dyspnea is mainly performed by subjective self-report, which has limited accuracy and is challenging for continuous monitoring. The objective of this research study is to determine if dyspnea progression in COVID patients can be assessed using a non-invasive wearable sensor and if the findings are comparable to a learning model of physiologically induced dyspnea on healthy subjects. Methods: Non-invasive wearable respiratory sensors were employed to retrieve continuous respiratory characteristics with user comfort and convenience. Overnight (~16h) respiratory waveforms were collected on 12 COVID-19 patients, and a benchmark on 13 healthy subjects with exertion-induced dyspnea were also performed for blind comparison. The learning model was built from the respiratory features with self report on 32 healthy subjects under exertion and airway blockage. Results: High similarity between dyspnea on COVID patients and physiologically induced dyspnea on healthy subjects was established. COVID patients have consistently high objective dyspnea scores in comparison with normal breathing of healthy subjects. We also exhibited continuous dyspnea scoring capability for 12-16 hours on patients. Conclusion: This paper validates the viability to use our objective dyspnea scoring for clinical dyspnea assessment on COVID patients. Significance: The proposed system can help the identification of dyspneic exacerbation in conditions such as COVID, leading to early intervention and possibly improving their outcome. This approach can be potentially applied to other pulmonary disorders such as asthma, emphysema, and pneumonia.

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“…Dyspnea scores can be assessed in person or remotely [ 16 ] using patient interviews, and augmented by subjective measures, such as the Medical Research Council (MRC) Dyspnea Scale [ 17 ] and Borg visual analog scale (VAS) [ 18 ]. Studies indicated that subjective dyspnea measures have inadequate accuracy in high-risk COVID-19 patients, not only because the sensation is gradual and varies with time but also because the patients can become nervous after knowing positive test results [ 19 ], both of which can contribute to biases in the self-report. The subjective dyspnea score can also vary for each person based on emotion and tolerance and can be challenging to assess for those who refuse to cooperate or cannot communicate due to medical issues, such as stroke, dementia, and loss of speech.…”

Section: Introductionmentioning

confidence: 99%

Deduced Respiratory Scores on COVID-19 Patients Learning from Exertion-Induced Dyspnea

Zhang

Zhou

Conroy

et al. 2023

Sensors

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Dyspnea is one of the most common symptoms of many respiratory diseases, including COVID-19. Clinical assessment of dyspnea relies mainly on self-reporting, which contains subjective biases and is problematic for frequent inquiries. This study aims to determine if a respiratory score in COVID-19 patients can be assessed using a wearable sensor and if this score can be deduced from a learning model based on physiologically induced dyspnea in healthy subjects. Noninvasive wearable respiratory sensors were employed to retrieve continuous respiratory characteristics with user comfort and convenience. Overnight respiratory waveforms were collected on 12 COVID-19 patients, and a benchmark on 13 healthy subjects with exertion-induced dyspnea was also performed for blind comparison. The learning model was built from the self-reported respiratory features of 32 healthy subjects under exertion and airway blockage. A high similarity between respiratory features in COVID-19 patients and physiologically induced dyspnea in healthy subjects was observed. Learning from our previous dyspnea model of healthy subjects, we deduced that COVID-19 patients have consistently highly correlated respiratory scores in comparison with normal breathing of healthy subjects. We also performed a continuous assessment of the patient’s respiratory scores for 12–16 h. This study offers a useful system for the symptomatic evaluation of patients with active or chronic respiratory disorders, especially the patient population that refuses to cooperate or cannot communicate due to deterioration or loss of cognitive functions. The proposed system can help identify dyspneic exacerbation, leading to early intervention and possible outcome improvement. Our approach can be potentially applied to other pulmonary disorders, such as asthma, emphysema, and other types of pneumonia.

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The Diagnostic Accuracy of Subjective Dyspnea in Detecting Hypoxemia Among Outpatients with COVID-19

Cited by 7 publications

References 23 publications

Soluble receptor for advanced glycation end products (sRAGE) as a biomarker of COVID-19 disease severity and indicator of the need for mechanical ventilation, ARDS and mortality

Soluble receptor for advanced glycation end products (sRAGE) as a biomarker of COVID-19 disease severity and indicator of the need for mechanical ventilation, ARDS and mortality

Detection and Prediction of Sleep Disorders by Covert Bed-Integrated RF Sensors

Deduced Respiratory Scores on COVID-19 Patients Learning from Exertion-Induced Dyspnea

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