Cardiopulmonary recovery after COVID-19 – an observational prospective multi-center trial

Thomas Sonnweber, Sabina Sahanic, Alex Pizzini, Anna Luger, Christoph Schwabl, Bettina Sonnweber, Katharina Kurz, Sabine Koppelstätter, David Haschka, Verena Petzer, Anna Boehm, Magdalena Aichner, Piotr Tymoszuk, Daniela Lener, Markus Theurl, Almut Lorsbach-Köhler, Amra Tancevski, Anna Schapfl, Marc Schaber, Richard Hilbe, Manfred Nairz, Bernhard Puchner, Doris Hüttenberger, Christoph Tschurtschenthaler, Malte Aßhoff, Andreas Peer, Frank Hartig, Romuald Bellmann, Michael Joannidis, Can Gollmann-Tepeköylü, Johannes Holfeld, Gudrun Feuchtner, Alexander Egger, Gregor Hoermann, Andrea Schroll, Gernot Fritsche, Sophie Wildner, Rosa Bellmann-Weiler, Rudolf Kirchmair, Raimund Helbok, Helmut Prosch, Dietmar Rieder, Zlatko Trajanoski, Florian Kronenberg, Ewald Wöll, Günter Weiss, Gerlig Widmann, Judith Löffler-Ragg, Ivan Tancevski
European Respiratory Journal 2020; DOI: 10.1183/13993003.03481-2020

Abstract

Background After the 2002/2003 SARS outbreak, 30% of survivors exhibited persisting structural pulmonary abnormalities. The long-term pulmonary sequelae of coronavirus disease 2019 (COVID-19) are yet unknown, and comprehensive clinical follow-up data are lacking.

Methods In this prospective, multicentre, observational study, we systematically evaluated the cardiopulmonary damage in subjects recovering from COVID-19 at 60 and 100 days after confirmed diagnosis. We conducted a detailed questionnaire, clinical examination, laboratory testing, lung function analysis, echocardiography, and thoracic low-dose computed tomography (CT).

Results Data from 145 COVID-19 patients were evaluated, and 41% of all subjects exhibited persistent symptoms 100 days after COVID-19 onset, with dyspnea being most frequent (36%). Accordingly, patients still displayed an impaired lung function, with a reduced diffusing capacity in 21% of the cohort being the most prominent finding. Cardiac impairment, including a reduced left ventricular function or signs of pulmonary hypertension, was only present in a minority of subjects. CT scans unveiled persisting lung pathologies in 63% of patients, mainly consisting of bilateral ground-glass opacities and/or reticulation in the lower lung lobes, without radiological signs of pulmonary fibrosis. Sequential follow-up evaluations at 60 and 100 days after COVID-19 onset demonstrated a vast improvement of both, symptoms and CT abnormalities over time.

Conclusion A relevant percentage of post-COVID-19 patients presented with persisting symptoms and lung function impairment along with pulmonary abnormalities more than 100 days after the diagnosis of COVID-19. However, our results indicate a significant improvement in symptoms and cardiopulmonary status over time.

Abstract

100 days after COVID-19 onset, a high portion of patients exhibit persisting symptoms and cardiopulmonary impairment. Still, a marked improvement of symptoms, pulmonary function, and CT pathologies was found at follow-up.

Introduction

COVID-19 shares clinical and mechanistic characteristics with the severe acute respiratory syndrome (SARS) in 2002/2003 including ACE2-dependent cellular entry and interleukin-6 (IL6) driven hyper-inflammation, potentially leading to imbalanced immune responses and acute respiratory distress syndrome (ARDS) [1–13]. In different studies evaluating SARS survivors months after infection, fibrotic features, including abnormal scoring of airspace opacity and reticular shadowing, were observed in up to 36% of the patients [2, 14, 15]. A one-year follow-up study on 97 recovering SARS patients in Hong Kong showed that 27.8% of survivors presented with decreased lung function and signs of pulmonary fibrosis compared to a normal population, which was confirmed by another follow-up study [2, 14, 16]. Moreover, the phylogenetically related Middle East respiratory syndrome-coronavirus (MERS-CoV) was shown to induce pulmonary fibrosis in up to 33% of patients [17]. As SARS-CoV-2 shares high homology with SARS-CoV-1, and to a lesser extent with MERS-CoV, it is thus conceivable that survivors of COVID-19 may also develop pulmonary fibrosis [7]. With worldwide over 27 million confirmed cases by today, and on average 20% of patients with a moderate-to-severe course of the infection often needing hospitalisation, the development of fibrosing lung disease after clearance of infection could become an enormous health concern [1].

We thus designed a prospective, multicentre, observational study, the Development of interstitial lung disease (ILD) in patients with severe SARS-CoV-2 infection (CovILD) study, aimed at systematically evaluating the persisting cardiopulmonary damage of COVID-19 patients 60 days and 100 days after COVID-19 onset.

Methods

Study design

Enrollment of the CovILD study began on April 29, 2020 (Fig S1). The trial site is located at the Department of Internal Medicine II, Medical University of Innsbruck (Austria), with two additional participating medical centers in Zams and Münster (Austria), which are tertiary care centers (Innsbruck, Zams) and an acute rehabilitation facility (Münster) all located in Tyrol, the first major COVID-19 hotspot in Austria. Diagnosis of COVID-19 was confirmed if a typical clinical presentation (according to current WHO guidelines) along with a positive RT-PCR SARS-CoV-2 test obtained from a nasopharyngeal or oropharyngeal swab were present [18]. Eligibility included the necessity of hospitalisation (either on a normal ward or intensive care unit (ICU)) or outpatient care with persisting symptoms. A total of 190 patients were screened for study participation and 145 individuals were included in the final study. Reasons for non-participation were mainly logistic (e.g. tourists who left the country, and individuals who lived too far away from the study center in Innsbruck to attend regular follow-up, N=27) or rejection of study participation (N=18).

We herein present a follow-up evaluation performed 60 days (63±23 (mean±sd); visit 1) and 100 days (103±21); visit 2) after diagnosis of COVID-19. Of note, in Tyrol the healthcare system was never overloaded at the local peak of the pandemic, thus, all patients received supportive care according to the standard of care at the trial site hospitals and no selection bias due to triage methods was apparent. The trial protocol was approved by the institutional review board at Innsbruck Medical University (EK Nr: 1103/2020) and was registered at ClinicalTrials.gov (registration number: NCT04416100). Informed consent was obtained from each patient.

Procedures

During the follow-up visits, the following examinations were performed: clinical examination, medical history, a questionnaire about typical COVID-19 symptoms including cardiorespiratory, gastrointestinal, and neurological symptoms during the disease and at follow-up, the Modified British Medical Research Council (mMRC) dyspnea score, a standardised performance status score, lung function testing including spirometry, body plethysmography, diffusion capacity for carbon monoxide (DLCO) adjusted for hemoglobin, capillary blood gas analysis, trans-thoracic echocardiography, standard laboratory examinations and a low-dose computed tomography (CT) scan of the chest.

All serological markers were determined on fully automated random access instruments: C-reactive protein (CRP), interleukin-6 (IL-6), procalcitonin (PCT), N-terminal pro natriuretic peptide (NT-proBNP), and serum ferritin were determined on a Roche Cobas 8000 analyser and D-dimer on a Siemens BCS-XP instrument using the Siemens D-Dimer Innovance® reagent.

CT scans were acquired without ECG gating on a 128 slice multidetector CT with a 128×0.6 mm collimation and spiral pitch factor of 1.1 (SOMATOM Definition Flash, Siemens Healthineers, Erlangen, Germany). Scans were obtained in craniocaudal direction without iodine contrast agent and in low-dose setting (100 kVp tube potential). If patients had a clinical suspicion for pulmonary embolism (PE), additional contrast CTs were conducted. Axial reconstructions were performed with a slice thickness of 1 mm. CT images were evaluated for the presence of ground-glass opacities (GGO), consolidations, bronchiectasis, and reticulations as defined by the glossary of terms of the Fleischner society [19]. When present, the distribution of the findings was graded according to their distribution (unilateral/bilateral, involved lobes). Overall, pulmonary findings were graded for every lobe using the following CT severity score: 0-none, 1-minimal (subtle GGO), 2-mild (several GGO, subtle reticulation), 3-moderate (multiple GGO, reticulation, small consolidation), 4-severe (extensive GGO, consolidation, reticulation with distortion), and 5-massive (massive findings, parenchymal destructions). The maximum score was 25 (i.e. maximum score 5 per lobe). This score was used because the BSTI (British Society of Thoracic Imaging) COVID-19 classifies only the extent of abnormality <25%, 26–50%, 51–75%, and >75%, and the BSTI Post-COVID-19 CT Report Codes do not discriminate between “Improving” (PCVCT1 - No significant fibrosis or concerning features) from “fibrosis” (PCVCT3 - fibrosis±inflammatory change present (inflammation>fibrosis/fibrosis>inflammation/fibrosis without inflammation)). In addition, we did not have a pre-COVID-19 baseline and an acute phase COVID-19 CT scan in most patients.

Syngo.via CT Pneumonia Analysis (Siemens Healthineers, Erlangen, Germany) research prototype for the detection and quantification of abnormalities consistent with pneumonia was used to calculate the percentage of opacity and percentage of high opacity, indicating percentages of GGO and consolidation, respectively.

Statistical analysis

Statistical analyses were performed with statistical analysis software package (IBM SPSS Statistics version 24·0, IBM, USA). According to descriptive statistical analysis including tests for homoscedasticity and data distribution (Levene test, Kolmogorov-Smirnov test, Shapiro-Wilk test, and density blot/histogram analysis) two-sided parametric or non-parametric tests were applied as appropriate. For group comparisons of continuous data, the Mann-Whitney-U test, Kruskal-Wallis, or Wilcoxon singed-rank test were applied. Binary and categorical data were analysed with Fisher's exact test, Chi-Square test, or Nemar test. Multiple testing was adjusted by the Sidak formula. Correlations were assessed with Spearman rank (non-parametric data) or Pearson's (parametric data) test. To identify demographic and clinical factors impacting on the persistence of symptoms and radiological lung findings at follow-up, a series of fixed-effect ordinary and generalised linear models were created using R programming suite version 3.6.3. Details on the statistical analysis and the used software packages are reported in the Supplementary methods section. Independently of the testing technique, effects were termed significant for p<0.05.

Results

Characteristics of the cohort

In total, 145 patients with a confirmed diagnosis of COVID-19 participated in the CovILD study, and 133 subjects were available also for the second follow-up. The mean time from the diagnosis of COVID-19, as defined by positive SARS-COV2 PCR testing, to follow-up was 63 days (sd±23) for visit 1 and 103 days (sd±21) for visit 2. The study cohort consisted of 55% male individuals, aged 50 to 70 years (table 1). Sixty-one-percent of COVID-19 patients were overweight or obese (BMI>25 or >30, respectively). Most individuals had preexisting comorbidities (77%) with cardiovascular and metabolic diseases being the most frequent (table 1). The majority of study participants (75%) were hospitalised during the acute phase of COVID-19, half of the hospitalised patients required oxygen supply, and 22% of all subjects were admitted to the ICU due to the necessity of non-invasive or invasive mechanical ventilation as determined by the treating physicians. Patients who were admitted to the ICU had more co-morbidities such as cardiovascular disease, arterial hypertension, diabetes mellitus, hypercholesterolemia, chronic kidney disease, or immune-deficiency as compared to subjects without the necessity of ICU treatment (Table S1). Additionally, higher age and male gender were related to a more severe course of acute COVID-19.

View this table:
TABLE 1

Demographics and clinical characteristics of patients enrolled in CovILD

Clinical evaluation at follow-up

At the second follow-up visit, a relevant number of patients still reported an impaired performance status and persisting symptoms including dyspnea (36%), night sweat (24%), sleep disorders (22%), or hyposmia/anosmia (19%), but with decreasing frequency compared to the acute phase of COVID-19 and the first follow-up visit (fig. 1). Notably, severe symptoms, such as a severely impaired performance status (as assessed by a standardised questionnaire) or severe dyspnea (mMRC 3–4) were only found in 2% and 4% of all study participants at second follow-up, respectively. Overall, a marked and continuous improvement of all assessed symptoms (namely impairment of performance status, dyspnea, cough, fever, diarrhea or vomiting, night sweat, hyposmia/anosmia, and sleep disorders) from disease onset to follow-up at visits 1 and 2 was observed (fig. 1).

FIGURE 1
FIGURE 1

Symptom burden in the CovILD study cohort during acute COVID-19 and at follow-up. a) Using a standardised questionnaire, performance status and overall burden of symptoms were assessed for the time-point of disease onset, 60 days (V1), and 100 days (V2) after diagnosis of COVID-19. b) Symptom burden was assessed with a standardised questionnaire at COVID-19 onset and at 100 days post COVID-19 diagnosis. All symptoms significantly improved over time (p<0.001 for all read-outs). Nacute=145, Nfollow-up=135.

Cardiopulmonary evaluation at follow-up

At follow-up visit 1 and visit 2, trans-thoracic echocardiography unveiled a high rate of diastolic dysfunction (60% and 55% of all subjects, respectively). Signs of pulmonary hypertension as well as a pericardial effusion were detected only in a smaller portion of the cohort (Table S2). Only four participants presented with a reduced left ventricular ejection fraction (LVEF). Whereas the frequency of diastolic dysfunction, signs of pulmonary hypertension, and LVEF impairment did not significantly change from follow-up visit 1 to 2, the number of patients with pericardial effusion diminished over time (p=0.039).

An impaired lung function, reflected by reduced static and/or dynamic lung volumes, or impaired DLCO, was present in 42% and 36% of individuals at visit 1 and visit 2, respectively (table 2). In detail, 100 days after diagnosis of COVID-19 a reduction in FVC and/or FEV1 was found in 22%, a reduced TLC in 11%, and an impaired DLCO in 21% of all individuals. Additionally, hypoxia, as reflected by a reduced pO2 assessed with capillary blood gas analysis (pO2 <75 mmHg), was still present in 37% of all subjects, and these patients demonstrated mild (80%, pO2 <75–65 mmHg) or moderate (20%, pO2 <65–55 mmHg) hypoxia at rest. Notably, dynamic lung volumes and DLCO significantly increased over time. As compared to follow-up visit 1 there was a moderate but significant improvement of most of these parameters over time (table 2).

View this table:
TABLE 2

Pulmonary function of COVID-19 patients at follow-up

Serological markers

At visit 2, mild elevations in inflammatory markers such as CRP (12%), IL-6 (6%), or PCT (9%) were still present in a smaller portion of the cohort (also refer to Table S2). Accordingly, biomarkers associated with COVID-19 disease severity, such as D-dimer, NT-proBNP, and serum ferritin were still elevated in 27%, 23%, and 17% of COVID-19 patients at second follow-up, respectively.

Pulmonary imaging

CT imaging revealed radiological lung abnormalities typical for COVID-19 in 77% of patients at visit 1 and in 63% of individuals at visit 2 (fig. 2a). The main findings were GGOs, consolidation, and reticulation. Bronchial dilation was found in a small portion of the cohort (representative CT scans are depicted in fig. 3). In 75% of patients, pulmonary involvement was found bilateral, with the lower lobes most prominently affected (Table S4). Notably, by the time of visit 2, consolidations and bronchial dilations almost completely resolved, and the mean extent of GGOs significantly decreased. Contrary, reticulations only gradually improved from visit 1 to 2 (fig. 2a). In eight patients, who presented with a clinical suspicion of an incidental PE (e.g. deteriorating dyspnea despite resolution of radiological lung findings, tachycardia, significant hypoxia, or lack of D-dimer decrease/D-dimer increase), additional contrast agent CT was performed, and one incidental PE was detected.

FIGURE 2
FIGURE 2

Chest computed tomography lung analysis at COVID-19 onset and follow-up. a) The pattern of pathological findings assessed with computed tomography at 60 and 100 days after diagnosis of COVID-19. b) Automated analysis of lung opacities assessed on CT scans from the acute disease phase, 60 days, and 100 days after COVID-19 diagnosis employing Syngo.via CT Pneumonia Analysis software. c) CT severity scoring by radiologists at COVID-19 onset, 60 days, and 100 days after COVID-19 diagnosis. The severity score was calculated via CT evaluation by two independent radiologists who qualitatively graded lung impairment for each lobe separately (grade 0–5, with 0 for no involvement and 5 for massive involvement). A total score was achieved by summation of grades for all five lobes (maximum: 25 points). b-c) bars indicate mean; whiskers depict one standard error (se). Nacute=23, NV1=145, NV2=135.

FIGURE 3
FIGURE 3

Representative CT scans of COVID-19 patients with minimal (a), moderate (b), and severe (c) radiological findings at first follow-up. Percentage of opacity/high opacity (a) 0.07/0.00; (b) 10.29/0.69; (c) 56.87/5.92.

Radiological assessment of the severity of COVID-19 related lung pathologies was performed on CT scans using both an automated software-based analysis with Syngo.via CT Pneumonia Analysis Software as well as an evaluation by two independent radiologists. Importantly, the software-based pneumonia quantification and the severity scoring by two radiologists demonstrated a high correlation (correlation coefficient ρ=0.893, p<0.001). The pulmonary CT severity score unveiled a moderate structural involvement in most patients at the first follow-up, which significantly improved over time (figs. 2b, c and 4)). The majority of participants (81%) demonstrated an improvement in the CT severity score. Interestingly, we found only weak to moderate correlations of FVC (ρ=0.322, p<0.001) and FEV1 (ρ=0.237, p<0.001) measurements and moderate correlations of TLC (ρ=0.455, p<0.001) and DLCO (ρ=0.543, p<0.001) assessment with the CT severity score. The CT severity score (ρ=0.200, p<0.001) and lung function parameters including FVC (ρ=0.206, p<0.001), FEV1 (ρ=0.212, p<0.001), TLC (ρ=0.145, p<0.05), and DLCO (ρ=0.204, p<0.001) demonstrated a weak correlation to the severity of dyspnea.

Finally, we analysed the impact of demographic and clinical factors on the persistence of symptoms, patient performance status, and CT findings at follow-up with a series of fixed-effect ordinary and generalised linear models (Figure S2, S3, and S4). These analyses revealed that the severity of acute COVID-19 (as reflected by the need for medical treatment), age, gender, and preexisting diseases such as cardiovascular diseases, pulmonary diseases, diabetes mellitus type 2, and malignancy were related to patient recovery. Despite that, a striking improvement of the CT severity score was found in patients of all COVID-19 severity groups, including subjects who needed mechanical ventilation at an ICU (fig. 5).

FIGURE 4
FIGURE 4

Representative sequential CT scans of a 56-year old male COVID-19 patient during acute disease and follow-up. Pulmonary 3D modelling assessed with CT during (a) acute COVID-19, at (b) 60 days follow-up and at (c) 100 days follow-up is shown. Pulmonary opacities, mainly reflecting ground-glass opacities and/or consolidation, were quantified with Syngo.via CT Pneumonia Analysis software. Areas with increased opacity are marked with red colour, whereas normal lung areas are indicated in green.

FIGURE 5
FIGURE 5

Changes of pulmonary impairment according to computed tomography analysis in patients of different acute COVID-19 disease severities. a) Time-dependent changes of CT severity score in patients with mild to very severe acute COVID-19. Disease severity was graded by the need for acute medical treatment: mild=outpatient care, moderate=hospitalisation without respiratory support, severe=hospitalisation with the need for oxygen supply, critical=patients treated at the ICU with the need for non-invasive or invasive ventilation. Except for patients with mild COVID-19, who demonstrated only minor pulmonary CT abnormalities, all other patient groups demonstrated a significant improvement of lung abnormalities in CT scans (p=0.042 to p<0.001 for time-dependent changes according to Friedman's or Wilcoxon signed-rank test). CT severity scoring ranges from 0 to 25 and was applied as detailed in the methods section. Visit1 (V1) and visit 2 (V2) were performed 60 and 100 days after the diagnosis of COVID-19, respectively. Dots indicate mean, whiskers depict one standard error (se).

Discussion

The ongoing pandemic considerably increases the demand for healthcare and raises concerns on physiological and radiological disease outcome in COVID-19 patients needing hospitalisation or even treatment in the ICU [20]. Accordingly, early lung function analysis of patients with COVID-19 at the time of discharge from hospital revealed a high rate of abnormalities indicative of potential interstitial lung disease. Moreover, a retrospective study 30 days after discharge had shown a reduced diffusion capacity as the central finding in 26% of post-acute COVID-19 patients [21, 22]. To our knowledge, this prospective study represents the first three-month cardiopulmonary follow-up analysis of patients with confirmed COVID-19 including critical cases. The majority of patients enrolled in the CovILD study were previously hospitalised, male and displayed co-morbidities, which have been identified as risk factors for a severe course of COVID-19 [23–26]. Importantly, 21% of patients were admitted to the ICU, comparable to the ICU admission rates of 26% to 33% previously published for COVID-19 patients [1, 25].

During post-acute care, patients reported a high rate of persisting dyspnea, and one-third of COVID-19 patients displayed an impaired lung function, with a reduced diffusing capacity being the most prominent finding even more than 100 days after COVID-19 diagnosis. This is in accordance with observations in 110 SARS survivors three and 6 months after infection [14]. In line, long-term SARS studies revealed disturbed gas exchange as the most relevant residual finding ranging from 15.5% to 57.2% of patients [2, 14, 27]. Furthermore, several studies on ARDS survivors have shown that their pulmonary function generally returns to normal or near to normal within 6 to 12 months, and predominantly DLCO may remain abnormal in patients at 1 year after recovery [28, 29]. Although, a relevant portion of our cohort presented with persisting cardiopulmonary impairment at follow-up, as evidenced by echocardiography or lung function testing, we observed a gradual improvement over time.

The major CT findings of patients affected by COVID-19 were previously shown to include GGO and consolidation with bilateral involvement and peripheral and diffuse distribution [30]. Importantly, this study reveals that more than 100 days after disease onset, two-thirds of patients had residual radiological changes, predominantly including residual GGO combined with reticulation. Since most patients had no CT scan before or during hospitalisation, pre-existing lung injury cannot be completely ruled out for the sub-group of patients with moderate to severe findings present at follow-up.

In line with our findings, CT abnormalities with low mean severity were also detected in 75.4% of SARS survivors 6 months after admission to the hospital [15]. From a clinical perspective, it is important to note that the high prevalence of persisting radiological abnormalities in our cohort was not well reflected by pulmonary function testing. Data on the inferiority of pulmonary function testing in the screening for ILD in a population at high risk for progressive disease support these findings [31]. To date, it remains unclear, if such subclinical structural changes justify the routine repetitive use of CT imaging in follow-up care after COVID-19 pneumonia. For example, follow up-studies in ARDS survivors including chest CT have shown that persistent radiographic abnormalities were of little clinical relevance [32]. In addition, a 15-years follow-up study of 71 patients with moderate to severe SARS showed that the rate of interstitial abnormalities remarkably declined within the first 2 years of recovery to only 4.6% of patients [27]. In line, our imaging findings demonstrate high reversibility of COVID-19 associated cardiopulmonary damage, and no clear signs of progressive ILD have been identified so far.

Finally, we have to acknowledge limitations of the presented study. First, the CovILD trial was initiated during the onset of the COVID-19 epidemic in Austria, thus pre-existing co-morbidities were assessed retrospectively, and cardiopulmonary evaluation prior COVID-19 was only available in a small portion of the cohort. Thus, we cannot fully evaluate the impact of potentially pre-existing cardiopulmonary impairment on the findings presented herein. Secondly, according to the ethics approval we used low dose CT without contrast agent to assess radiological pulmonary impairment. Whereas this strategy reduces the cumulative radiation dose and precludes contrast agent related complications, it is also associated with limitations. Although the image quality of low-dose CT was found sufficient, some very subtle fibrotic changes may be recognised as (non-fibrotic) reticulations due to the lower image quality. However, even in regular dose CT, clear discrimination between residual inflammation and early fibrosis is extremely difficult, especially in patients with a clear overall improvement. Additionally, as we did not routinely use contrast agent, we did not screen the cohort for occult PE, and additional contrast CTs were only indicated in case of clinical suspicion of PE.

Conclusion

In summary, we describe significant residual clinical, functional, and CT-morphological changes in the majority of COVID-19 patients, which dramatically improved within the observation period of 3 months. We conclude that follow-up care should be considered for patients with persisting clinical symptoms after COVID-19 which may include serial measures of lung function, echocardiography, and CT scans of the chest.

Acknowledgments

We acknowledge the dedication, commitment, and sacrifice of the staff, providers, and personnel in our institutions through the COVID-19 crisis and the suffering and loss of our patients as well as in their families and our community.

Footnotes

  • Author Contribution: T.S., J.LR., S.S. and I.T. designed the study. T.S., S.S., A.P., G.W., A.L., C.S., K.K., S.K., D.H., V.P., B.S., A.B., M.A., D.L., M.T., A.LK., A.T., A.S., M.S., R.H., M.N., B.P., D.Hu., C.T., M.A., A.Pe., F.H., R.B., M.J., C.GT., J.H., G.F., A.E., G.H., A.Sc., G.F., S.W., R.BW., R.K., R.He., E.W., G.W., J.LR., I.T. performed the clinical investigations and collected the data. T.S., G.W., A.L., C.S., P.T., G.F., H.P., D.R., Z.T., and F.K. performed data analysis. T.S., S.S., A.P., G.W., A.L., C.S., P.T., D.R., Z.T., F.K., G.We., J.LR. and I.T interpreted data. T.S., G.W., H.P., F.K., R.K., G.W., J.LR., and I.T. wrote the manuscript. All authors critically reviewed the final version of the manuscript.

  • This article has supplementary material available from erj.ersjournals.com

  • ClinicalTrials.gov number: NCT04416100

  • Data availability: Study protocol will be available immediately following publication for anyone who wishes to access. Individual data will not be made publically available.

  • Support statement: This study was supported by the Austrian National Bank Fund (Project 17271, J.LR.), the “Verein zur Förderung von Forschung und Weiterbildung in Infektiologie und Immunologie, Innsbruck“ (G.W.). Additionally, I.T. was awarded an Investigator-Initiated Study (IIS) grant by Boehringer Ingelheim (IIS 1199-0424). Boehringer Ingelheim; DOI: http://dx.doi.org/10.13039/100001003; Grant: IIS 1199-0424 to I.T.; Oesterreichische Nationalbank; DOI: http://dx.doi.org/10.13039/501100004061; Grant: Project 17271, J.LR.

  • Conflict of interest: Dr. Sonnweber has nothing to disclose.

  • Conflict of interest: Dr. Sahanic has nothing to disclose.

  • Conflict of interest: Dr. Pizzini has nothing to disclose.

  • Conflict of interest: Dr. Luger has nothing to disclose.

  • Conflict of interest: Dr. Schwabl has nothing to disclose.

  • Conflict of interest: Dr. Sonnweber has nothing to disclose.

  • Conflict of interest: Dr. Kurz has nothing to disclose.

  • Conflict of interest: Dr. Koppelstätter has nothing to disclose.

  • Conflict of interest: Dr. Haschka has nothing to disclose.

  • Conflict of interest: Dr. Petzer has nothing to disclose.

  • Conflict of interest: Dr. Boehm has nothing to disclose.

  • Conflict of interest: Dr. Aichner has nothing to disclose.

  • Conflict of interest: Dr. Tymoszuk has nothing to disclose.

  • Conflict of interest: Dr. Lehner has nothing to disclose.

  • Conflict of interest: Dr. Theurl has nothing to disclose.

  • Conflict of interest: Dr. Lorsbach-Koehler has nothing to disclose.

  • Conflict of interest: Dr. Tancevski has nothing to disclose.

  • Conflict of interest: Dr. Schaffl has nothing to disclose.

  • Conflict of interest: Dr. Schaber has nothing to disclose.

  • Conflict of interest: Dr. Hilbe has nothing to disclose.

  • Conflict of interest: Dr. Nairz has nothing to disclose.

  • Conflict of interest: Dr. Puchner has nothing to disclose.

  • Conflict of interest: Ms. Huettenberger has nothing to disclose.

  • Conflict of interest: Dr. Tschurtschenthaler has nothing to disclose.

  • Conflict of interest: Dr. Aßhoff has nothing to disclose.

  • Conflict of interest: Dr. Peer has nothing to disclose.

  • Conflict of interest: Dr. Hartig has nothing to disclose.

  • Conflict of interest: Prof. Bellmann has nothing to disclose.

  • Conflict of interest: Dr. Joannidis has nothing to disclose.

  • Conflict of interest: Dr. Gollmann-Tepeköylü has nothing to disclose.

  • Conflict of interest: Dr. Holfeld has nothing to disclose.

  • Conflict of interest: Dr. Feuchtner has nothing to disclose.

  • Conflict of interest: Dr. Egger has nothing to disclose.

  • Conflict of interest: Dr. Hörmann has nothing to disclose.

  • Conflict of interest: Dr. Schroll has nothing to disclose.

  • Conflict of interest: Dr. Fritsche has nothing to disclose.

  • Conflict of interest: Dr. Wildner has nothing to disclose.

  • Conflict of interest: Dr. Bellmann-Weiler has nothing to disclose.

  • Conflict of interest: Prof. Kirchmair has nothing to disclose.

  • Conflict of interest: Prof. Helbok has nothing to disclose.

  • Conflict of interest: Prof. Prosch has nothing to disclose.

  • Conflict of interest: Dr. Rieder has nothing to disclose.

  • Conflict of interest: Dr. Trajanoski has nothing to disclose.

  • Conflict of interest: Prof. Kronenberg has nothing to disclose.

  • Conflict of interest: Dr. Wöll has nothing to disclose.

  • Conflict of interest: Prof. Weiss has nothing to disclose.

  • Conflict of interest: Dr. Widmann has nothing to disclose.

  • Conflict of interest: Prof. Loeffler-Ragg has nothing to disclose.

  • Conflict of interest: Dr. Tancevski reports grants from Investigator Initiated Study (IIS) grant by Boehringer Ingelheim (IIS 1199-0424), outside the submitted work.

  • Received September 11, 2020.
  • Accepted November 17, 2020.
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References

  1. ↵
    1. Huang C,
    2. Wang Y,
    3. Li X, et al.
    Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 2020; 395: 497–506. doi:10.1016/S0140-6736(20)30183-5
    OpenUrlCrossRefPubMed
  2. ↵
    1. Hui DS,
    2. Joynt GM,
    3. Wong KT, et al.
    Impact of severe acute respiratory syndrome (SARS) on pulmonary function, functional capacity and quality of life in a cohort of survivors. Thorax 2005; 60: 401–409. doi:10.1136/thx.2004.030205
    OpenUrlAbstract/FREE Full Text
    1. Blanco-Melo D,
    2. Nilsson-Payant BE,
    3. Liu WC, et al.
    Imbalanced Host Response to SARS-CoV-2 Drives Development of COVID-19. Cell 2020; 181: 1036–1045.e1039. doi:10.1016/j.cell.2020.04.026
    OpenUrlCrossRefPubMed
    1. Moore JB,
    2. June CH
    . Cytokine release syndrome in severe COVID-19. Science 2020; 368: 473–474. doi:10.1126/science.abb8925
    OpenUrlAbstract/FREE Full Text
    1. McGonagle D,
    2. Sharif K,
    3. O'Regan A, et al.
    The Role of Cytokines including Interleukin-6 in COVID-19 induced Pneumonia and Macrophage Activation Syndrome-Like Disease. Autoimmun Rev 2020; 19: 102537. doi:10.1016/j.autrev.2020.102537
    OpenUrlCrossRefPubMed
    1. Peiris JS,
    2. Chu CM,
    3. Cheng VC, et al.
    Clinical progression and viral load in a community outbreak of coronavirus-associated SARS pneumonia: a prospective study. Lancet 2003; 361: 1767–1772. doi:10.1016/S0140-6736(03)13412-5
    OpenUrlCrossRefPubMedWeb of Science
  7. ↵
    1. Walls AC,
    2. Park YJ,
    3. Tortorici MA, et al.
    Structure, Function, and Antigenicity of the SARS-CoV-2 Spike Glycoprotein. Cell 2020; 181: 281–292.e286. doi:10.1016/j.cell.2020.02.058
    OpenUrlCrossRefPubMed
    1. Jaimes JA,
    2. Andre NM,
    3. Chappie JS, et al.
    Phylogenetic Analysis and Structural Modeling of SARS-CoV-2 Spike Protein Reveals an Evolutionary Distinct and Proteolytically Sensitive Activation Loop. J Mol Biol 2020; 432: 3309–3325. doi:10.1016/j.jmb.2020.04.009
    OpenUrlCrossRefPubMed
    1. Monteil V,
    2. Kwon H,
    3. Prado P, et al.
    Inhibition of SARS-CoV-2 Infections in Engineered Human Tissues Using Clinical-Grade Soluble Human ACE2. Cell 2020; 181: 905–913.e907. doi:10.1016/j.cell.2020.04.004
    OpenUrl
    1. Imai Y,
    2. Kuba K,
    3. Penninger JM
    . The discovery of angiotensin-converting enzyme 2 and its role in acute lung injury in mice. Exp Physiol 2008; 93: 543–548. doi:10.1113/expphysiol.2007.040048
    OpenUrlCrossRefPubMed
    1. Imai Y,
    2. Kuba K,
    3. Rao S, et al.
    Angiotensin-converting enzyme 2 protects from severe acute lung failure. Nature 2005; 436: 112–116. doi:10.1038/nature03712
    OpenUrlCrossRefPubMedWeb of Science
    1. Fu Y,
    2. Cheng Y,
    3. Wu Y
    . Understanding SARS-CoV-2-Mediated Inflammatory Responses: From Mechanisms to Potential Therapeutic Tools. Virol Sin 2020; 35: 266–271. doi:10.1007/s12250-020-00207-4
    OpenUrl
  13. ↵
    1. Kuba K,
    2. Imai Y,
    3. Rao S, et al.
    A crucial role of angiotensin converting enzyme 2 (ACE2) in SARS coronavirus-induced lung injury. Nat Med 2005; 11: 875–879. doi:10.1038/nm1267
    OpenUrlCrossRefPubMedWeb of Science
  14. ↵
    1. Hui DS,
    2. Wong KT,
    3. Ko FW, et al.
    The 1-year impact of severe acute respiratory syndrome on pulmonary function, exercise capacity, and quality of life in a cohort of survivors. Chest 2005; 128: 2247–2261. doi:10.1378/chest.128.4.2247
    OpenUrlCrossRefPubMedWeb of Science
  15. ↵
    1. Ng CK,
    2. Chan JW,
    3. Kwan TL, et al.
    Six month radiological and physiological outcomes in severe acute respiratory syndrome (SARS) survivors. Thorax 2004; 59: 889–891. doi:10.1136/thx.2004.023762
    OpenUrlAbstract/FREE Full Text
  16. ↵
    1. Ngai JC,
    2. Ko FW,
    3. Ng SS, et al.
    The long-term impact of severe acute respiratory syndrome on pulmonary function, exercise capacity and health status. Respirology 2010; 15: 543–550. doi:10.1111/j.1440-1843.2010.01720.x
    OpenUrlCrossRefPubMed
  17. ↵
    1. Das KM,
    2. Lee EY,
    3. Singh R, et al.
    Follow-up chest radiographic findings in patients with MERS-CoV after recovery. Indian J Radiol Imaging 2017; 27: 342–349. doi:10.4103/ijri.IJRI_469_16
    OpenUrlCrossRefPubMed
  18. ↵
    (WHO) WHO. Coronavirus. 2020. www.who.int/health-topics/coronavirus#tab=tab_3. [cited 2020 27/10/2020].
  19. ↵
    1. Hansell DM,
    2. Bankier AA,
    3. MacMahon H, et al.
    Fleischner Society: glossary of terms for thoracic imaging. Radiology 2008; 246: 697–722. doi:10.1148/radiol.2462070712
    OpenUrlCrossRefPubMedWeb of Science
  20. ↵
    1. Rees EM,
    2. Nightingale ES,
    3. Jafari Y, et al.
    COVID-19 length of hospital stay: a systematic review and data synthesis. BMC Med 2020; 18: 270. doi:10.1186/s12916-020-01726-3
    OpenUrl
  21. ↵
    1. Mo X,
    2. Jian W,
    3. Su Z, et al.
    Abnormal pulmonary function in COVID-19 patients at time of hospital discharge. Eur Respir J 2020; 55: 2001217. doi:10.1183/13993003.01217-2020
    OpenUrlAbstract/FREE Full Text
  22. ↵
    1. Frija-Masson J,
    2. Debray MP,
    3. Gilbert M, et al.
    Functional characteristics of patients with SARS-CoV-2 pneumonia at 30 days post-infection. Eur Respir J 2020; 56: 2001754. doi:10.1183/13993003.01754-2020
    OpenUrlAbstract/FREE Full Text
  23. ↵
    1. Richardson S,
    2. Hirsch JS,
    3. Narasimhan M, et al.
    Presenting Characteristics, Comorbidities, and Outcomes Among 5700 Patients Hospitalized With COVID-19 in the New York City Area. JAMA 2020; 323: 2052–2059. doi:10.1001/jama.2020.6775
    OpenUrlPubMed
    1. Grasselli G,
    2. Zangrillo A,
    3. Zanella A, et al.
    Baseline Characteristics and Outcomes of 1591 Patients Infected With SARS-CoV-2 Admitted to ICUs of the Lombardy Region, Italy. JAMA 2020; 323: 1574–1581. doi:10.1001/jama.2020.5394
    OpenUrlPubMed
  25. ↵
    1. Zhou F,
    2. Yu T,
    3. Du R, et al.
    Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet 2020; 395: 1054–1062. doi:10.1016/S0140-6736(20)30566-3
    OpenUrlCrossRefPubMed
  26. ↵
    1. Klein SJ,
    2. Fries D,
    3. Kaser S, et al.
    Unrecognized diabetes in critically ill COVID-19 patients. Crit Care 2020; 24: 406. doi:10.1186/s13054-020-03139-3
    OpenUrl
  27. ↵
    1. Zhang P,
    2. Li J,
    3. Liu H, et al.
    Long-term bone and lung consequences associated with hospital-acquired severe acute respiratory syndrome: a 15-year follow-up from a prospective cohort study. Bone Res 2020; 8: 8. doi:10.1038/s41413-020-0084-5
    OpenUrlCrossRefPubMed
  28. ↵
    1. Herridge MS,
    2. Cheung AM,
    3. Tansey CM, et al.
    One-year outcomes in survivors of the acute respiratory distress syndrome. N Engl J Med 2003; 348: 683–693. doi:10.1056/NEJMoa022450
    OpenUrlCrossRefPubMedWeb of Science
  29. ↵
    1. Cheung AM,
    2. Tansey CM,
    3. Tomlinson G, et al.
    Two-year outcomes, health care use, and costs of survivors of acute respiratory distress syndrome. Am J Respir Crit Care Med 2006; 174: 538–544. doi:10.1164/rccm.200505-693OC
    OpenUrlCrossRefPubMedWeb of Science
  30. ↵
    1. Wang Y,
    2. Dong C,
    3. Hu Y, et al.
    Temporal Changes of CT Findings in 90 Patients with COVID-19 Pneumonia: A Longitudinal Study. Radiology 2020; 296: E55–E64. doi:10.1148/radiol.2020200843
    OpenUrlPubMed
  31. ↵
    1. Suliman YA,
    2. Dobrota R,
    3. Huscher D, et al.
    Brief Report: Pulmonary Function Tests: High Rate of False-Negative Results in the Early Detection and Screening of Scleroderma-Related Interstitial Lung Disease. Arthritis Rheumatol 2015; 67: 3256–3261. doi:10.1002/art.39405
    OpenUrl
  32. ↵
    1. Burnham EL,
    2. Janssen WJ,
    3. Riches DW, et al.
    The fibroproliferative response in acute respiratory distress syndrome: mechanisms and clinical significance. Eur Respir J 2014; 43: 276–285. doi:10.1183/09031936.00196412
    OpenUrlAbstract/FREE Full Text
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Cardiopulmonary recovery after COVID-19 – an observational prospective multi-center trial
Thomas Sonnweber, Sabina Sahanic, Alex Pizzini, Anna Luger, Christoph Schwabl, Bettina Sonnweber, Katharina Kurz, Sabine Koppelstätter, David Haschka, Verena Petzer, Anna Boehm, Magdalena Aichner, Piotr Tymoszuk, Daniela Lener, Markus Theurl, Almut Lorsbach-Köhler, Amra Tancevski, Anna Schapfl, Marc Schaber, Richard Hilbe, Manfred Nairz, Bernhard Puchner, Doris Hüttenberger, Christoph Tschurtschenthaler, Malte Aßhoff, Andreas Peer, Frank Hartig, Romuald Bellmann, Michael Joannidis, Can Gollmann-Tepeköylü, Johannes Holfeld, Gudrun Feuchtner, Alexander Egger, Gregor Hoermann, Andrea Schroll, Gernot Fritsche, Sophie Wildner, Rosa Bellmann-Weiler, Rudolf Kirchmair, Raimund Helbok, Helmut Prosch, Dietmar Rieder, Zlatko Trajanoski, Florian Kronenberg, Ewald Wöll, Günter Weiss, Gerlig Widmann, Judith Löffler-Ragg, Ivan Tancevski
European Respiratory Journal Jan 2020, 2003481; DOI: 10.1183/13993003.03481-2020
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