1. A Nomogram-Based Model to Predict Respiratory Dysfunction at 6 Months in Non-Critical COVID-19 Survivors
- Author
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Rebecca De Lorenzo, Cristiano Magnaghi, Elena Cinel, Giordano Vitali, Sabina Martinenghi, Mario G. Mazza, Luigi Nocera, Marta Cilla, Sarah Damanti, Nicola Compagnone, Marica Ferrante, Caterina Conte, Francesco Benedetti, Fabio Ciceri, and Patrizia Rovere-Querini
- Subjects
COVID-19 ,long-term ,respiratory sequelae ,follow-up ,prediction algorithm ,Medicine (General) ,R5-920 - 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.
- Published
- 2022
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