ObjectiveTo 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. Patients and MethodsIn 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. ResultsOverall, 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%, P < 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. ConclusionThe newly developed nomograms accurately identify patients at risk of persistent respiratory dysfunction and may help inform clinical priorities.

A Nomogram-Based Model to Predict Respiratory Dysfunction at 6 Months in Non-Critical COVID-19 Survivors / De Lorenzo, Rebecca; Magnaghi, Cristiano; Cinel, Elena; Vitali, Giordano; Martinenghi, Sabina; Mazza, Mario G; Nocera, Luigi; Cilla, Marta; Damanti, Sarah; Compagnone, Nicola; Ferrante, Marica; Conte, Caterina; Benedetti, Francesco; Ciceri, Fabio; Rovere-Querini, Patrizia. - In: FRONTIERS IN MEDICINE. - ISSN 2296-858X. - 9:(2022), p. 781410. [10.3389/fmed.2022.781410]

A Nomogram-Based Model to Predict Respiratory Dysfunction at 6 Months in Non-Critical COVID-19 Survivors

De Lorenzo, Rebecca;Cinel, Elena;Mazza, Mario G;Nocera, Luigi;Cilla, Marta;Damanti, Sarah;Compagnone, Nicola;Ferrante, Marica;Conte, Caterina;Benedetti, Francesco;Ciceri, Fabio;Rovere-Querini, Patrizia
2022-01-01

Abstract

ObjectiveTo 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. Patients and MethodsIn 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. ResultsOverall, 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%, P < 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. ConclusionThe newly developed nomograms accurately identify patients at risk of persistent respiratory dysfunction and may help inform clinical priorities.
2022
COVID-19
follow-up
long-term
prediction algorithm
respiratory sequelae
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11768/126699
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