A Machine Learning Approach to Predict the Rehabilitation Outcome in Convalescent COVID-19 Patients.

Sarah Adamo,Marco Mosella,Pasquale Ambrosino,Mauro Maniscalco,Giovanni D’Addio,Carlo Ricciardi,Mario Cesarelli,Mariasofia Accardo

doi:10.3390/jpm12030328

Abstract

Background: After the acute disease, convalescent coronavirus disease 2019 (COVID-19) patients may experience several persistent manifestations that require multidisciplinary pulmonary rehabilitation (PR). By using a machine learning (ML) approach, we aimed to evaluate the clinical characteristics predicting the effectiveness of PR, expressed by an improved performance at the 6-min walking test (6MWT). Methods: Convalescent COVID-19 patients referring to a Pulmonary Rehabilitation Unit were consecutively screened. The 6MWT performance was partitioned into three classes, corresponding to different degrees of improvement (low, medium, and high) following PR. A multiclass supervised classification learning was performed with random forest (RF), adaptive boosting (ADA-B), and gradient boosting (GB), as well as tree-based and k-nearest neighbors (KNN) as instance-based algorithms. Results: To train and validate our model, we included 189 convalescent COVID-19 patients (74.1% males, mean age 59.7 years). RF obtained the best results in terms of accuracy (83.7%), sensitivity (84.0%), and area under the ROC curve (94.5%), while ADA-B reached the highest specificity (92.7%). Conclusions: Our model enables a good performance in predicting the rehabilitation outcome in convalescent COVID-19 patients.

Highlights

The coronavirus disease 2019 (COVID-19) is a syndrome with a number of clinical manifestations, ranging from mild symptoms to severe complications necessitating intensive care unit (ICU) admittance [1]
Using the clinical characteristics of convalescent COVID-19 patients hospitalized for pulmonary rehabilitation (PR), the aim of our study was to develop a model predicting the effectiveness of multidisciplinary rehabilitation in terms of improved performance at the 6-min walking test (6MWT)
Among 197 patients screened for eligibility, three (1.5%) were ineligible for protocol adherence issues

Summary

Introduction

The coronavirus disease 2019 (COVID-19) is a syndrome with a number of clinical manifestations, ranging from mild symptoms to severe complications necessitating intensive care unit (ICU) admittance [1]. Convalescent COVID-19 patients may experience several persistent symptoms, such as fatigue and muscular weakness [2], with a residual pulmonary impairment potentially lasting for months after a negative swab test [3]. Given the high proportion of patients with such persistent manifestations, the new paradigm of a “post-acute COVID-19 syndrome” has been introduced [3]. Among the functional outcome measures of pulmonary rehabilitation (PR), the 6-min walking test (6MWT) is widely accepted as an accurate and cost-effective method [9]. Patients may experience several persistent manifestations that require multidisciplinary pulmonary rehabilitation (PR). By using a machine learning (ML) approach, we aimed to evaluate the clinical characteristics predicting the effectiveness of PR, expressed by an improved performance at the. Results: To train and validate our model, we included 189 convalescent

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