A Nomogram-Based Model to Predict Respiratory Dysfunction at 6 Months in Non-Critical COVID-19 Survivors
To assess the prevalence of respiratory sequelae of Coronavirus disease 2019 (COVID-19) survivors at 6 months after hospital discharge and develop a model to identify at-risk patients. In this prospective cohort study, hospitalized, non-critical COVID-19 patients evaluated at 6-month follow-up betwe...
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Published in | Frontiers in medicine Vol. 9; p. 781410 |
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Main Authors | , , , , , , , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
Switzerland
Frontiers Media S.A
23.02.2022
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Subjects | |
Online Access | Get full text |
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Summary: | To assess the prevalence of respiratory sequelae of Coronavirus disease 2019 (COVID-19) survivors at 6 months after hospital discharge and develop a model to identify at-risk patients.
In this prospective cohort study, hospitalized, non-critical COVID-19 patients evaluated at 6-month follow-up between 26 August, 2020 and 16 December, 2020 were included. Primary outcome was respiratory dysfunction at 6 months, defined as at least one among tachypnea at rest, percent predicted 6-min walking distance at 6-min walking test (6MWT) ≤ 70%, pre-post 6MWT difference in Borg score ≥ 1 or a difference between pre- and post-6MWT oxygen saturation ≥ 5%. A nomogram-based multivariable logistic regression model was built to predict primary outcome. Validation relied on 2000-resample bootstrap. The model was compared to one based uniquely on degree of hypoxemia at admission.
Overall, 316 patients were included, of whom 118 (37.3%) showed respiratory dysfunction at 6 months. The nomogram relied on sex, obesity, chronic obstructive pulmonary disease, degree of hypoxemia at admission, and non-invasive ventilation. It was 73.0% (95% confidence interval 67.3-78.4%) accurate in predicting primary outcome and exhibited minimal departure from ideal prediction. Compared to the model including only hypoxemia at admission, the nomogram showed higher accuracy (73.0 vs 59.1%,
< 0.001) and greater net-benefit in decision curve analyses. When the model included also respiratory data at 1 month, it yielded better accuracy (78.2 vs. 73.2%) and more favorable net-benefit than the original model.
The newly developed nomograms accurately identify patients at risk of persistent respiratory dysfunction and may help inform clinical priorities. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Edited by: Farid Rahimi, Australian National University, Australia Reviewed by: Ashwin Sawant, Icahn School of Medicine at Mount Sinai, United States; Erwin Chiquete, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán (INCMNSZ), Mexico This article was submitted to Infectious Diseases – Surveillance, Prevention and Treatment, a section of the journal Frontiers in Medicine |
ISSN: | 2296-858X 2296-858X |
DOI: | 10.3389/fmed.2022.781410 |