AUTHOR=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 

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

JOURNAL=Frontiers in Medicine

VOLUME=Volume 9 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2022.781410

DOI=10.3389/fmed.2022.781410

ISSN=2296-858X

ABSTRACT=Objective: To assess the prevalence of respiratory sequelae of Coronavirus disease 2019 (COVID-19) survivors at six months after hospital discharge and develop a model to identify at-risk patients.
Patients and Methods: In this prospective cohort study, hospitalized, non-critical COVID-19 patients evaluated at six-month follow-up between 26 August, 2020 and 16 December, 2020 were included. Primary outcome was respiratory dysfunction at six months, defined as at least one among tachypnea at rest, percent predicted 6-minute walking distance at 6-minute walking test (6MWT) ≤70%, or pre-post 6MWT difference in Borg score ≥1. 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. 
Results: Overall, 316 patients were included, of whom 118 (37.3%) showed respiratory dysfunction at six 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 (P<.001) and greater net-benefit in decision curve analyses. When the model included also respiratory data at one month, it yielded better accuracy (78.2 vs 73.2%) and more favorable net-benefit than the original model.
Conclusion: The newly developed nomograms accurately identify patients at risk of persistent respiratory dysfunction and may help inform clinical priorities.