Optimizing cardiopulmonary rehabilitation duration for long COVID patients: an exercise physiology monitoring approach

Abstract

The presence of prolonged symptoms after COVID infection worsens the workability and quality of life. 200 adults with long COVID syndrome were enrolled after medical, physical, and mental screening, and were divided into two groups based on their performance. The intervention group (n = 100) received supervised rehabilitation at Department of Pulmonology, Semmelweis University with the registration number 160/2021 between 01/APR/2021-31/DEC/2022, while an age-matched control group (n = 100) received a single check-up. To evaluate the long-term effects of the rehabilitation, the intervention group was involved in a 2- and 3-month follow-up, carrying out cardiopulmonary exercise test. Our study contributes understanding long COVID rehabilitation, emphasizing the potential benefits of structured cardiopulmonary rehabilitation in enhancing patient outcomes and well-being. Significant difference was found between intervention group and control group at baseline visit in pulmonary parameters, as forced vital capacity, forced expiratory volume, forced expiratory volume, transfer factor for carbon monoxide, transfer coefficient for carbon monoxide, and oxygen saturation (all p < 0.05). Our follow-up study proved that a 2-week long, patient-centered pulmonary rehabilitation program has a positive long-term effect on people with symptomatic long COVID syndrome. Our data showed significant improvement between two and three months in maximal oxygen consumption (p < 0.05). Multidisciplinary, individualized approach may be a key element of a successful cardiopulmonary rehabilitation in long COVID conditions, which improves workload, quality of life, respiratory function, and status of patients with long COVID syndrome.

Keywords: Functional parameters; Long COVID; Post-COVID; Pulmonary rehabilitation; Quality of life.

Fig. 1

Study flowchart

Fig. 2

The eligibility criteria and exclusion criteria for long COVID rehabilitation

Fig. 3

The timeline for study design

Fig. 4

Random Forest, a Machine learning determined an optimal classification model to find reliable predictor for rehabilitation according to participants’ performance

Fig. 5

Main symptoms that were characteristic during the enrollment period, comparing the two group: out-patient clinic (n = 100) vs. participants enrolled in pulmonary rehabilitation (n = 100)