Post-Discharge Spirometry Evaluation in Patients Recovering from Moderate-to-Critical COVID-19: A Prospective Cohort Study

Post-Discharge Spirometry Evaluation in Patients Recovering from Moderate-to-Critical COVID-19: A Prospective Cohort Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Post-Discharge Spirometry Evaluation in Patients Recovering from Moderate-to-Critical COVID-19: A Prospective Cohort Study Chee-Shee Chai, Muhammad Amin Bin Ibrahim, Nur Amira Binti Azhar, and 13 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4165804/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 16 Jul, 2024 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract To determine the prevalence and types of spirometry abnormalities among post-COVID-19 patients in Malaysia, with secondary objective focusing on associated factors. Conducted at the COVID-19 Research Clinic, Faculty of Medicine, University Technology MARA, from March 2021 to December 2022, this study included patients three months post-discharge from hospitals following moderate-to-critical COVID-19. Of 408 patients studied, abnormal spirometry was found in 46.8%, with 28.4% exhibiting a restrictive pattern, 17.4% showing preserved ratio impaired spirometry (PRISm), and 1.0% displaying an obstructive pattern. Factors independently associated with abnormal spirometry included older age (OR: 1.0, 95% CI: 1.01–1.04, p = 0.003), underlying cardiovascular disease (OR: 3.5, 95% CI: 1.19–10.47, p = 0.023), history of acute respiratory distress syndrome (p < 0.001), shorter discharge-to-follow-up interval (OR: 0.9, 95% CI: 1.00–1.02, p = 0.035), oxygen desaturation during 6-minute walk test (OR: 1.9, 95% CI: 1.20–3.06, p = 0.007), and presence of consolidation (OR: 8.1, 95% CI: 1.75–37.42, p = 0.008) or ground-glass opacity (OR: 2.6, 95% CI: 1.52–4.30, p < 0.001) on chest X-ray. This study highlights patients recovering from moderate-to-critical COVID-19 often exhibit abnormal spirometry, notably a restrictive pattern and PRISm. Routine spirometry screening for high-risk patients is recommended. Health sciences/Diseases Health sciences/Risk factors COVID-19 spirometry restrictive obstructive PRISm radio-imaging Figures Figure 1 Figure 2 Introduction The Coronavirus 2019 (COVID-19) pandemic is the worst-ever global health emergency, resulting in substantial human casualties and economic downturn. As of 6th March 2024, global COVID-19 infections have reached 704 million, leading to over seven million fatalities. 1 Even though the World Health Organization (WHO) no longer considers COVID-19 a public health emergency of international concern, 2 the continual emergence of new virus variants poses a persistent threat to the end of the pandemic. Severe acute respiratory syndrome (SARS) coronavirus (CoV)-2 (SARS-CoV-2) that causes COVID-19 is a beta coronavirus with genomic similarity of 80% to the 2002 SARS-CoV-1 and 50% to the 2012 Middle East Respiratory Syndrome (MERS)-CoV. 3 SARS-CoV-2 enters the human body via binding to the angiotensin-converting enzyme-2 receptor, predominantly located in the respiratory system. 4 On-going mutations of the virus have given rise to concerning variants including the Alpha (B.1.1.7), Beta (B.1.351), Gamma (P.1), Delta (B.1.617.2), and Omicron (B.1.1.529). 5 SARS-CoV-1, MERS-CoV, and SARS-CoV-2 primarily target the lower respiratory tract, leading to acute lung injuries like pneumonia and acute respiratory distress syndrome (ARDS). 6 Survivors of SARS and MERS demonstrated abnormal lung function, reduced effort tolerance, and impaired quality of life months or even years after the illness. 7–9 Recent studies conducted in China highlighted that abnormal lung function was observed in 47.2% of hospitalized COVID-19 patients upon discharge, 10 75.4% after a month, 11 and 25.5% after three months. 12 Malaysia has reported 5.27 million COVID-19 cases to date, with a recovery rate of 98.9%. 1 Routine assessment of lung function in patients recovering from COVID-19, however, remains a major challenge here due to a few constraints. First, conducting widespread lung function tests is time-consuming, costly, and manpower intensive. Second, equipment (such as spirometer and body plethysmograph) and expertise required to perform lung function tests are only available in selected tertiary healthcare centres. Third, non-respiratory clinicians often have difficulty interpreting the results of lung function tests. Fourth, the management strategies for abnormal lung function following COVID-19 remain unclear, particularly lacking standardized guidelines. Thus, only a very small proportion of patients recovering from COVID-19 were offered lung function tests. Research looking into the prevalence of abnormal lung function and its associated factors among patients recovering from COVID-19 in Malaysia is essential to help healthcare authorities develop follow-up strategies to enhance post-COVID care. This study focuses on evaluating lung function in patients with COVID-19 at least three months after their hospital discharge, aiming to determine the prevalence and types of abnormal spirometry results as primary objectives and identifying its associated factors as secondary objectives. Methods Study design and patients This is a prospective cohort study of patients attending the COVID-19 Research Clinic at the Faculty of Medicine, University Technology MARA (UITM) in Malaysia, from March 2021 to December 2022. The inclusion criteria were Malaysians aged eighteen years and above, with confirmed COVID-19 via validated reverse transcription-polymerase chain reaction method, who had moderate-to-critical illness according to the WHO classification, 13 and were at least three months post-discharge from either the Sungai Buloh Hospital or the UITM Medical Centre. 14 Patients with pre-existing chronic lung diseases before COVID-19, including bronchial asthma, as well as individuals who were pregnant, completely immobilized, had uncontrolled psychiatric illness, or were contraindicated for spirometry were excluded. A minimum sample size of 386 subjects was determined using the formula for a cross-sectional study - sample size = Z 1-α 2 p(1 - p)/d 2 . 15 Z represented the confidence interval at 95%, d denoted the margin of error at 5%, and p referred to the proportion of abnormal lung function (52.7%) among SARS survivors in a previous study. 8 All patients provided written informed consent before participating in the study. The study received ethics approval from the Medical Research Ethics Committee of the Ministry of Health Malaysia ( NMRR-20-2011-56330 (IIR) and the respective hospitals, and it was conducted in adherence to the Declaration of Helsinki. Procedure and outcomes Eligible patients were consecutively identified from the COVID-19 registry of Sungai Buloh Hospital and UITM Medical Centre. Those meeting all inclusion criteria and having none of the exclusion criteria were scheduled for early physical appointments at the COVID-19 Research Clinic. 1. Demographic, clinical, and hospitalization data: Demographic, clinical, and hospitalization data were gathered through face-to-face interviews and the electronic records. Demographic information included age, gender, and ethnicity, while clinical details included smoking status, obesity, and the presence of underlying chronic diseases. Hospitalization data included the duration of illness before admission, length of hospital stays, COVID-19 severity at presentation, the most severe COVID-19 episode during hospitalization, pharmacotherapy administrated, respiratory support provided, the occurrence of respiratory complications, and details regarding intensive care unit (ICU) admission, including its length of stay. The severity of COVID-19 was defined according to the WHO classification as: asymptomatic, mild (symptomatic without pneumonia), moderate (pneumonia without hypoxia), severe (pneumonia with hypoxia requiring oxygen supplementation), and critical (critically ill, such as ARDS, sepsis, or septic shock). 13 Available treatments for COVID-19 during the study period included corticosteroids, hydroxychloroquine, immunomodulators (tocilizumab, interferon beta, and interferon alpha), and antivirals (favipiravir, lopinavir-ritonavir, ritonavir, and atazanavir). 16 Respiratory support was categorized into oxygen supplementation by nasal cannulae, venti-mask, or high-flow mask, non-invasive mechanical ventilation (NIV) or nasal high flow (NHF), and invasive mechanical ventilation (IMV). 17,18 Common respiratory complications of COVID-19 that were recorded included ARDS, pulmonary embolism, pneumothorax, and pleural effusion. 19,20 2. Patients reported outcomes (PROs): Patients were instructed to independently complete the modified Medical Research Council (mMRC) dyspnea scale and the post-COVID-19 Functional Status (PCFS) scale with minimal assistance from investigators. The mMRC and PCFS was interpreted as per the original validation of the questionnaire. 21,22 A higher score indicates a greater degree of symptom severity and impairment, respectively. 3. Lung function tests: Spirometry was conducted using SpiroUSB TM (Vyaire Medical, Chicago, IL) to obtain dynamic lung volumes, including the forced expiratory volume in one second (FEV 1 ) and forced vital capacity (FVC). The cut-off value of ≥ 80% of the predicted was deemed normal for both parameters. Spirometry results were categorized into four groups: normal spirometry – normal FEV 1 , normal FVC, and FEV 1 /FVC > 0.7; restrictive pattern – reduced or normal FEV 1 , reduced FVC, and FEV 1 /FVC > 0.7; obstructive pattern – reduced FEV 1 , reduced or normal FVC, and FEV 1 /FVC 0.7. 23,24 For patients with an obstructive pattern, post-bronchodilator spirometry was performed to identify reversible airflow obstruction. Those with a restrictive pattern were scheduled for static lung volumes and diffusion capacity measurement within two weeks using PFT Vyntus Bodybox TM (Vyaire Medical, Chicago, IL). The parameters measured included residual volume (RV), total lung capacity (TLC), diffusion capacity for carbon monoxide (DLCO), and carbon monoxide transfer coefficient (DLCO/Va). All lung function tests were conducted by certified respiratory technicians following the American Thoracic Society (ATS) and European Respiratory Society guidelines. 25,26 4. Cardiopulmonary functional tests: Patients underwent a 6-minute walk test (6MWT) under the guidance of a certified respiratory physiotherapist, following the ATS guideline. 27 Their pulses and oxygen saturation were continuously monitored using the Nonin® WristOx2 ™ 3150 Bluetooth Pulse Oximeter. A 1-minute sit-to-stand test (1MSTS) guided by the same respiratory physiotherapist followed and in accordance with the procedure outlined in a previous study. 28 Both assessments utilized a digital stopwatch for time measurement, and the Borg scale was employed to assess the severity of dyspnea and fatigue. 5. Radio-imaging: All patients underwent a standard posterior-anterior chest X-ray examination. Only those demonstrating a restrictive pattern in spirometry were scheduled for high-resolution computed tomography (HRCT) of the lungs within one month. Radiologists, blinded to patients' information, independently reviewed these images to identify consolidation, ground glass opacity (GGO), and lung parenchymal reticulation. Additionally, the HRCT also documented the presence of organizing pneumonia (OP) and other findings, such as lung nodules, atelectasis, pleural effusion or thickening, diaphragmatic elevation, cardiomegaly, and fractures, if any. Lung involvement severity was assessed using the CT-score method developed by Kunhua Li et al. 29 Each lobe received a score ranging from 0 to 5 based on its level of involvement: 0 (0%), 1 ( 75%). The total score, representing cumulative involvement across all lobes, ranged from 0 to 25 points. Statistical analyses Categorical variables are presented as percentages, while continuous variables are presented as mean ± standard deviation (SD). Patients were categorized into those with normal versus those with abnormal spirometry for two-group comparisons, as well as normal versus restrictive pattern or PRISm/obstructive pattern spirometry for three-group comparisons. Between-group differences were assessed using an independent t -test for continuous variables and a Chi-Square test for categorical variables. A two-sided p-value of less than 0.05 was considered statistically significant. For multivariate analyses, variables exhibiting significant two-sided p-values in the univariate analyses were included as covariates in binary logistic regression and multinomial logistic regression. The latter analysis excluded variables showing multicollinearity (variance inflation factor > 5). The analysis aimed to derive odds ratios (OR), 95% confidence intervals (95% CI), and two-sided p-values. Statistical analysis was conducted using the Statistical Package for the Social Sciences (SPSS for Windows version 25.0, SPSS Inc, Chicago, IL, USA). Results Sociodemographic and clinical characteristics A total of 408 patients were included in the study (Fig. 1 ). The sociodemographic and clinical characteristics of these patients are presented in Table 1 . The mean age of the patients was 51.6 ± 13.32 years. The majority were male (59.8%), of Malay ethnicity (71.8%), and had underlying chronic diseases (63.7%). The most common disease was hypertension (43.4%), followed by diabetes mellitus (30.6%), cardiovascular disease (4.9%), chronic kidney disease (1.5%), chronic liver disease (0.5%), and cerebrovascular disease (0.2%). Only 23.3% of the patients were current or ex-smokers, while 17.9% were classified as obese. Hospitalization and management of the patients The patients were admitted to the hospital after a mean duration of 8.7 ± 5.32 days from symptom onset and the mean hospitalization duration was 13.0 ± 10.62 days. Most of the patients had severe illness (61.3%) on admission (Table 2 ). Subsequently, 28.9%, 55.6%, and 15.5% developed critical, severe, and moderate illness during their hospital stay, respectively. Corticosteroids (83.1%) were the most frequently administrated medication, followed by antivirals (37.0%), immunomodulators (14.2%), and hydroxychloroquine (13.7%). Among patients requiring respiratory support (78.4%), 47.8% received supplemental oxygen only, 13.2% had NIV or HFNO, and 17.4% underwent IMV. Respiratory complications occurred in 26.5% of patients, primarily manifesting as pulmonary embolisms (24.3%). For the 43.4% of patients who were admitted to the ICU, the mean duration of ICU stay was 10.6 ± 16.32 days. PROs, cardiopulmonary functional tests, and chest X-ray findings at follow-up The patients were assessed at a mean duration of 162.6 ± 113.97 days post-hospital discharge (Table 3 ). They reported a mean mMRC score of 0.9 ± 0.95 and a mean PCFS score of 0.4 ± 0.74. Of 404 patients who completed the 6MWT, 31.4% experienced oxygen desaturation. Among the 402 patients who completed 1MSTS, 29.1% experienced oxygen desaturation. Chest X-ray revealed abnormalities in 33.6% of patients which included GGO (26.0%), lung parenchymal reticulation (10.1%), and consolidation (4.2%). Spirometry and factors associated with abnormal results Abnormal spirometry was detected in 46.8% of the patients, with 28.4% having a restrictive pattern, 17.4% having PRISm, and 1.0% having an obstructive pattern. The mean values of their FEV 1 , FVC, and FEV 1 /FVC are presented in Fig. 2 . Factors associated with abnormal spirometry results included patients’ age (p < 0.001), hypertension (p < 0.001), cardiovascular disease (p = 0.010), corticosteroids treatment (p = 0.006), IMV support (p = 0.042), ARDS (p = 0.035), pulmonary embolism (p = 0.007), ICU admission (p = 0.001), duration from discharge to follow-up (p = 0.001), oxygen desaturation with 6MWT (p = 0.002), oxygen desaturation with 1MSTS (p = 0.003), as well as the presence of consolidation (p < 0.001), GGO (p < 0.001), and parenchymal reticulation on chest X-ray (p = 0.004) (Table 4 ). Multivariate analyses using binary logistic regression showed that older patients (OR:1.0, 95% CI:1.01–1.04, p = 0.003), those with underlying cardiovascular disease (OR: 3.5, 95% CI: 1.19–10.47, p = 0.023), those who developed ARDS (p < 0.001), a shorter interval from discharge to follow-up (OR: 0.9, 95% CI: 1.00–1.02, p = 0.035), those with oxygen desaturation during 6MWT (OR: 1.9, 95% CI: 1.20–3.06, p = 0.007), and those with consolidation (OR: 8.1, 95% CI: 1.75–37.42, p = 0.008) or GGO appearance (OR: 2.6, 95% CI: 1.52–4.30, p < 0.001) on chest X-ray were significantly more likely to have abnormal spirometry. Restrictive pattern, obstructive pattern, and PRISm Multinomial logistic regression, using normal spirometry as a reference, showed that female patients (OR: 2.1, 95% CI: 1.13–3.94, p = 0.019), those treated with immunomodulators (OR: 2.4, 95% CI: 1.07–5.28, p = 0.034), those with consolidation (OR: 10.1, 95% CI: 1.88–54.83, p = 0.007) or GGO appearance (OR: 2.2, 95% CI: 1.16–4.26, p = 0.016) on chest X-ray were significantly more likely to show a restrictive pattern spirometry. Conversely, older patients (OR: 1.0, 95% CI: 1.01–1.07, p = 0.004), those treated with hydroxychloroquine (OR: 4.4, 95% CI: 1.15–16.97, p = 0.030), those who developed pulmonary embolism (OR: 2.3, 95% CI: 1.07–5.13, p = 0.033), those with oxygen desaturation in the 6MWT (OR: 2.4, 95% CI: 1.20–4.75, p = 0.014), those with consolidation (OR: 8.6, 95% CI: 1.41–52.57, p = 0.020) or GGO appearance (OR: 2.6, 95% CI: 1.3–5.3, p = 0.009) on chest X-ray were significantly more likely to show PRISm and obstructive pattern spirometry. Findings on body plethysmography, diffusion capacity, and HRCT of the lungs Eighty-nine patients underwent body plethysmography and diffusion capacity assessment, revealing a mean RV of 57.8 ± 39.08% predicted, a mean TLC of 65.1 ± 13.25% predicted, a mean DLCO of 62.5 ± 13.94% predicted, and a mean DLCO/Va of 103.6 ± 17.52 (Table 5 ). Of 80 patients who underwent HRCT of the lungs, 81.3% had GGO, 52.5% had OP, 85.0% had parenchymal reticulation, and 33.8% showed other findings. The mean CT score for these patients was 9.8 ± 5.96. Discussion The current study highlights that nearly half of the patients hospitalized for moderate-to-critical COVID-19 continue to show abnormal spirometry three months after discharge. Approximately one-third of them displayed a restrictive pattern, while another one-fifth surprisingly manifested PRISm. This study identifies chest X-ray as a reliable tool for predicting abnormal spirometry and its subtypes, particularly when consolidation and GGO are present. Furthermore, the 6MWT could be a valuable tool for predicting abnormal spirometry. Although certain demographic and clinical data were also found to be useful, the 1MSTS and PROs do not add additional value to the prediction of spirometry abnormalities. A meta-analysis of seven studies, primarily conducted in China, revealed that 22.9% of patients hospitalized for COVID-19 demonstrated abnormal spirometry within three months post-discharge. 30 Among these, 15.0% exhibited a restrictive pattern while 7.9% showed an obstructive pattern. 30 A separate study in Thailand reported abnormal spirometry in 17.2% of patients hospitalized for mild-to-severe COVID-19 at sixty days post-discharge, with 9.2% having an obstructive pattern and 8.0% having a restrictive pattern. 31 In Spain and Belgium, studies reported solely a restrictive pattern among hospitalized COVID-19 patients. In the Spanish study, 14.3% of patients requiring oxygen supplementation for pneumonia exhibited this pattern at two months, 9.3% at six months, and 6.7% at twelve months. 32 In the Belgian study, 55% of patients admitted to the ICU for ARDS demonstrated a restrictive pattern at three months. 33 Compared to these other studies, our study showed a high prevalence of abnormal spirometry potentially attributed to the predominance of severe and critical COVID-19 cases among our cohort. The observation that the restrictive pattern was the most common spirometry abnormality aligns with findings in China, 30 France, 34 Spain, 32 and Belgium. 33 The increased proportion of patients with an obstructive pattern in the Thailand study, however, could be due to the non-exclusion of individuals with pre-existing lung diseases, including bronchial asthma and chronic obstructive pulmonary disease (COPD). 31 The majority of existing studies have focused on investigating the lung function of patients recovering from COVID-19 based on severity of illness. These studies have consistently shown that individuals with more severe illness tend to exhibit significantly lower static lung volumes and diffusion capacity, while their dynamic lung volumes in spirometry often remain preserved. 11,34–37 To date, only a study in Thailand and China have respectively reported significantly lower dynamic lung volumes in patients with more severe illness, 31,38 while another study in the Netherlands found only FVC to be significantly lower in such cases. 39 Additional studies have shown that for individuals post-COVID-19, spirometry indices were not significantly different from the healthy population, 40 those with other viral upper respiratory tract infections, 41 or the same cohort of patients one year before the infection. 42 A review by Thomas et al further concluded that spirometry indices are often well-preserved in COVID-19, without being significantly affected by illness severity. 43 As far as we know, our study is the first to demonstrate no significant differences in spirometry patterns between patients with varying severity of COVID-19. Our study identifies several factors associated with abnormal spirometry in patients recovering from COVID-19, notably abnormal chest X-ray and 6MWT during follow-up, as well as older age, underlying cardiovascular disease, previous ARDS, and shorter discharge-to-follow-up interval. In Thailand, individuals with abnormal chest X-ray after COVID-19 had significantly lower dynamic lung volumes. 31 Additionally, chest CT abnormalities after COVID-19 were correlated with lower dynamic lung volumes and diffusion capacity among those in Austria, 44 Netherlands, 36 and China, 45 although not in France. 34 Oxygen desaturation during the 6MWT was associated with diffusion capacity impairment among post-COVID patients in the Netherlands, 36 but not in Thailand or Germany. 31,46 The association between mMRC scores and lung function in those post-COVID was seldom studied, 32,33,36,37,47,48 with only one study in Austria reporting a negative correlation. 44 Two other studies found concurrent abnormalities in lung function, radio-imaging, 6MWT, and mMRC in the same cohort of post-COVID patients, 11,33 suggesting a potential relationship between these factors. Other factors associated with impaired lung function in previous studies included older age, 32,42 female gender, 32 lower body mass index, 32 underlying chronic lung disease, 42 higher inflammatory markers at presentation, 32,45 previous ARDS, 33 and shorter discharge-to-follow-up interval. 44,47 Corticosteroid treatment was linked to better lung function recovery, 33,47 while this was not observed with other treatment modalities. 39 Overall, our study findings are consistent with most of other studies. Our study is the first to report PRISm in post-COVID patients. PRISm, previously known as pre-COPD, restrictive, or non-specific pattern, has a prevalence of 4.7–22.3% in the general population. 49 Recent studies indicate that it primarily affects the small airways and vessels while sparing lung parenchyma. 49,50 Two studies have shown that 25.1% and 32.6% of individuals with PRISm, respectively progress to spirometry-defined COPD in 5 years. 51,52 Conversely, improvement of spirometry from obstructive pattern to PRISm over time has also been observed. 53 Therefore, individuals with PRISm in this study could either indicate an improvement from airflow obstruction or an early sign of deterioration to COPD after COVID-19. Additionally, the possibility that this reflects population prevalence rather than being directly attributed to COVID-19 cannot be discounted. Future studies that prospectively following up on this patient cohort could provide a definitive answer. The high prevalence of the restrictive pattern among our patients can be explained by the aberrant wound healing typically following diffuse alveolar damage by SARS-CoV-2, leading to severe scarring and fibrosis. 54 Respiratory muscle weakness following SARS-CoV-2 infection could also be another possibility. 55 Fewer abnormal spirometry results were detected among patients who were followed up at longer intervals from discharge suggesting COVID-19-related lung damage may be reversible in the long run, or that there is a certain degree of resilience and adaptability in lung function. 56 The findings from this study have several clinical implications. First, spirometry should be routinely performed in patients post moderate-to-critical COVID-19 due to the high prevalence of abnormality. Second, when universal spirometry screening is not feasible among them, a targeted risk stratification approach considering chest X-ray, 6MWT, and specific demographic and clinical characteristics is recommended. Third, chest X-ray proves to be the most reliable screening tool for abnormal spirometry. Clinicians should focus on detecting consolidation and GGO features. Fourth, the 6MWT also emerges as a valuable screening tool for abnormal spirometry. Fifth, 1MSTS and PROs may not add significant value to the screening and should not be prioritized during follow-up. Sixth, this study suggests the potential development of a scoring system that combines these factors, providing a practical tool for clinicians to efficiently select patients for lung function tests. The large sample size of this study allows for the generalizability of the result. It is one of the few studies in the Southeast Asia, where outcomes may differ from other parts of the world due to variations in genetic, environmental, and lifestyle factors. The study focused on patients hospitalized with moderate-to-critical COVID-19 who were more susceptible to long-term lung injuries. The comprehensive study outcomes include objective assessments like lung function, radio-imaging, and cardiopulmonary functional evaluations, alongside subjective assessments such as PROs. However, this study was conducted during the peak of the pandemic. Travel restrictions, public reluctance to visit hospitals, and constrained healthcare resources could lead to several weaknesses. First, the convenience sampling method may introduce bias. Second, not every patient can undergo examination with body plethysmography, for diffusion capacity, and with HRCT. Third, some patients who were offered these investigations defaulted. Fourth, due to practical and logistic reasons the follow-up assessments could not be conducted at a fixed interval, such as three months, six months, or twelve months post-discharge. Fifth, factors associated with specific abnormal spirometry patterns should be interpreted with caution, as the sample size may not be powerful enough to accurately reflect these secondary outcomes. Sixth, while older age may demonstrate statistical significance, the OR of 1.0 suggests that it lacks clinical significance. Seventh, multidimensional assessment of PROs such as health-related quality of life was not performed. Lastly, lung function tests were not conducted as a follow-up after the study to observe potential changes in patterns. Conclusions Patients recovering from moderate-to-critical COVID-19 often demonstrated abnormal spirometry, particularly manifesting a restrictive pattern and PRISm. Therefore, spirometry should be routinely offered to those at higher risk of abnormalities, such as individuals with abnormal chest X-ray and 6MWT during follow-up, underlying cardiovascular disease, and previous ARDS. PRISm represents a novel finding among post-COVID patients, warranting further follow-up to elucidate the underlying mechanism of this lung function abnormality. Declarations Data Availability: The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing Interest: The authors declare no potential conflicts of interest in respect to the research, authorship, and publication of this article. Funding: This study was supported by research grants from University Malaysia Sarawak (F05/RISE/2089/2021), Selangor State Government (100–3/2/3JLD[ 25 ]), Lung Foundation of Malaysia (GI/F05/LFM/2021), Persatuan Pendidikan Kesihatan Paru-Paru (IRG/F05/PPKP/85316/2022), Compass Medical Sdn Bhd (GI/F05/CMSB/2021), and Aliran Pasifik (M) Sdn Bhd (IRG/F05/APMSB/85317/2022). The funding bodies only financially supported this study and did not take part in the design of the study; or collection, analysis, and interpretation of the data; or writing of the manuscript. The open access funding was provided by Universiti Malaysia Sarawak. Author Contribution All authors contributed equally to the conception and design of the study, data acquisition, data analysis and interpretation, drafting of the article, and critically revising it. All authors made final approval of the version to be published. All authors agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of the work are appropriately investigated and resolved. Acknowledgements: We would like to thank the Director General of Health Malaysia for his permission to publish this article. We want to express our gratitude to all the patients who participated in the study. References Worldometer. COVID - Coronavirus Statistics. https://www.worldometers.info/coronavirus/? (2024). Wise, J. Covid-19: WHO declares end of global health emergency. BMJ 381, p1041, doi: 10.1136/bmj.p1041 (2023). Lu, R. et al. Genomic characterisation and epidemiology of 2019 novel coronavirus: implications for virus origins and receptor binding. The Lancet 395, 565–574, doi: https://doi.org/10.1016/S0140-6736(20)30251-8 (2020). Gheblawi, M. et al. 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The effects of COVID-19 on respiratory muscle performance: making the case for respiratory muscle testing and training. Eur Respir Rev 31, doi: 10.1183/16000617.0006-2022 (2022). Suppini, N. et al. Longitudinal Analysis of Pulmonary Function Impairment One Year Post-COVID-19: A Single-Center Study. J Pers Med 13, doi: 10.3390/jpm13081190 (2023). Tables Table 1. Demographic and clinical data of the patients. In italic: only the number of patients with the respective underlying chronic diseases is presented; *, the p-value for differences between patients with normal and abnormal spirometry; #, the p-value for differences between patients with normal, restrictive pattern, and PRISm and obstructive pattern spirometry. Parameters All patients, n = 408 Result of Spirometry p-value* p-value # Normal, n = 217 Abnormal, n = 191 Restrictive, n = 116 PRISm and obstructive, n = 75 Age, Mean ( + SD), years 51.6 + 13.32 48.8 + 13.48 54.9 + 12.39 54.0 + 12.45 56.3 + 12.24 < 0.001 < 0.001 Gender, n (%) Male Female 244 (59.8) 164 (40.2) 127 (58.5) 90 (41.5) 117 (61.3) 74 38.7) 59 (50.9) 57 (49.1) 58 (77.3) 17 (22.7) 0.574 0.001 Ethnicity, n (%) Malay Chinese Indian Others 293 (71.8) 90 (22.1) 23 (5.6) 2 (0.5) 155 (71.4) 54 (24.9) 7 (3.2) 1 (0.5) 138 (72.3) 36 (18.8) 16 (8.4) 1 (0.5) 82 (70.7) 22 (19.0) 11 (9.5) 1 (0.9) 56 (74.7) 14 (18.7) 5 (6.7) 0 0.091 0.245 Smoking status, n (%) Never smoker Current and ex-smoker 313 (76.7) 95 (23.3) 172 (79.3) 45 (20.7) 141 (73.8) 50 (26.2) 89 (76.7) 27 (23.3) 52 (69.3) 23 (30.7) 0.194 0.215 Obesity, n (%) No Yes 335 (82.1) 73 (17.9) 175 (80.6) 42 (19.4) 160 (83.8) 31 (16.2) 95 (81.9) 21 (18.1) 65 (86.7) 10 (13.3) 0.411 0.501 Underlying chronic diseases, n (%) No Yes Diabetes mellitus Hypertension Cardiovascular disease Cerebrovascular disease Chronic liver disease Chronic kidney disease Others 148 (36.3) 260 (63.7) 125 (30.6) 177 (43.4) 20 (4.9) 1 (0.2) 2 (0.5) 6 (1.5) 39 (9.6) 91 (41.9) 126 (58.1) 59 (27.2) 75 (34.6) 5 (2.3) 1 (0.5) 0 2 (0.9) 17 (7.8) 57 (29.8) 134 (70.2) 66 (34.6) 102 (53.4) 15 (7.9) 0 2 (1.0) 4 (2.1) 22 (11.5) 28 (24.1) 88 (75.9) 47 (40.5) 68 (58.6) 9 (7.8) 0 2 (1.7) 2 (1.7) 12 (10.3) 29 (38.7) 46 (61.3) 19 (25.3) 34 (45.3) 6 (8.0) 0 0 2 (2.7) 10 (13.3) 0.011 0.107 < 0.001 0.010 0.348 0.131 0.326 0.207 0.005 0.023 < 0.001 0.035 0.643 0.080 0.537 0.356 Table 2. Hospitalization data of the patients. In italic: only the number of patients with the respective pharmacotherapy, respiratory support, and respiratory complications are presented; *, p-value for differences between patients with normal and abnormal spirometry; #, p-value for differences between patients with normal, restrictive pattern, and PRISm and obstructive pattern spirometry. Parameters All patients, n = 408 Result of Spirometry p-value* p-value # Normal, n = 217 Abnormal, n = 191 Restrictive, n = 116 PRISm and obstructive, n = 75 Duration of illness, Mean ( + SD), days 8.7 + 5.32 8.5 + 5.07 8.9 + 5.59 8.7 + 5.99 9.1 + 4.93 0.495 0.703 Length of hospital stay, Mean ( + SD), days 13.0 + 10.62 12.1 + 11.30 14.1 + 9.70 16.1 + 10.92 11.1 + 6.37 0.054 0.001 COVID-19 severity at presentation, n (%) Asymptomatic Mild Moderate Severe Critical 24 (5.9) 56 (13.7) 39 (9.6) 250 (61.3) 39 (9.6) 18 (8.3) 32 (14.7) 22 (10.1) 126 (58.1) 19 (8.8) 6 (3.1) 24 (12.6) 17 (8.9) 124 (64.9) 20 (10.5) 2 (1.7) 11 (9.5) 8 (6.9) 80 (69.0) 15 (12.9) 4 (5.3) 13 (17.3) 9 (12.0) 44 (58.7) 5 (6.7) 0.185 0.107 Most severe illness during hospitalization, n (%) Moderate Severe Critical 63 (15.5) 227 (55.6) 118 (28.9) 38 (17.5) 127 (58.5) 52 (24.0) 25 (13.1) 100 (52.4) 66 (34.5) 7 (6.0) 56 (48.3) 53 (45.7) 18 (24.0) 44 (58.7) 13 (17.3) 0.052 < 0.001 Pharmacotherapy, n (%) No Yes Corticosteroids Hydroxychloroquine Immunomodulators Antivirals 26 (6.4) 382 (93.6) 339 (83.1) 56 (13.7) 58 (14.2) 151 (37.0) 15 (6.9) 202 (93.1) 170 (78.3) 36 (16.6) 24 (11.1) 84 (38.7) 11(5.8) 180 (94.2) 169 (88.5) 20 (10.5) 34 (17.8) 67 (35.1) 7(6.0) 109 (94.0) 108 (93.1) 4 (3.4) 28 (24.1) 39 (33.6) 4 (5.3) 71 (94.7) 61 (81.3) 16 (21.3) 6 (8.0) 28 (37.3) 0.634 0.006 0.073 0.052 0.448 0.876 0.003 < 0.001 0.001 0.656 Respiratory support, n (%) None Yes Oxygen supplementation by nasal cannula/venti-mask/high flow mask NIV/NHF IMV 88 (21.6) 320 (78.4) 195 (47.8) 54 (13.2) 71 (17.4) 54 (24.9) 163 (75.1) 109 (50.2) 24 (11.1) 30 (13.8) 34 (17.8) 157 (82.2) 86 (45.0) 30 (15.7) 41 (21.5) 12 (10.3) 104 (89.7) 49 (42.2) 22 (19.0) 33 (28.4) 22 (29.3) 53 (70.7) 37 (49.3) 8 (10.7) 8 (10.7) 0.083 0.294 0.167 0.042 0.002 0.364 0.098 0.001 Respiratory complications, n (%) No Yes ARDS Pulmonary embolism Pneumothorax Pleural effusion 300 (73.5) 108 (26.5) 5 (1.2) 99 (24.3) 10 (2.5) 3 (0.7) 169 (77.9) 48 (22.1) 5 (2.3) 41 (18.9) 6 (2.8) 2 (0.9) 131 (68.6) 60 (31.4) 0 58 (30.4) 4 (2.1) 1 (0.5) 81 (69.8) 35 (30.2) 0 35 (30.2) 2 (1.7) 1 (0.5) 50 (66.7) 25 (33.3) 0 23 (30.7) 2 (2.7) 0 0.034 0.035 0.007 0.662 0.639 0.093 0.108 0.026 0.835 0.710 ICU admission, n (%) No Yes 231 (56.6) 177 (43.4) 140 (64.5) 77 (35.5) 91 (47.6) 100 (52.4) 45 (38.8) 71 (61.2) 46 (61.3) 29 (38.7) 0.001 < 0.001 Length of ICU stay, Mean ( + SD), days 10.6 + 16.32 9.6 + 12.59 11.3 + 18.69 11.7 + 19.62 10.5 + 16.55 0.522 0.774 Table 3. PROs, cardiopulmonary functional tests, and chest X-ray findings of the patients. ^, 404 patients performed 6MWT: 217 had normal spirometry, 187 had abnormal spirometry (114 restrictive, 73 PRISm and obstructive); +, 402 patients performed 1MSTS: 215 had normal spirometry, 187 had abnormal spirometry (114 restrictive, 73 PRISm and obstructive); *, p-value for differences between patients with normal and abnormal spirometry; #, p-value for differences between patients with normal, restrictive pattern, and PRISm and obstructive pattern spirometry. Parameters All patients, n = 408 Result of Spirometry p-value* p-value # Normal, n = 217 Abnormal, n = 191 Restrictive, n = 116 PRISm and obstructive, n = 75 Time from discharge to follow-up, Mean ( + SD), days 162.6 + 113.97 179.6 + 125.11 143.5 + 96.73 121.9 + 61.46 176.1 + 127.50 0.001 < 0.001 mMRC score, Mean ( + SD) 0.9 + 0.95 0.8 + 0.90 1.0 + 1.0 1.2 + 1.06 0.6 + 0.82 0.212 < 0.001 PCFS score, Mean ( + SD) 0.4 + 0.74 0.4 + 0.74 0.4 + 0.75 0.5 + 0.77 0.3 + 0.69 0.698 0.307 6MWT^ 6MWT distance, Mean ( + SD), meter Oxygen desaturation, n (%) No Yes Baseline oxygen saturation, Mean ( + SD). % Nadir oxygen saturation, Mean ( + SD), %Oxygen saturation on recovery, Mean ( + SD), % 389.9 + 78.18 277 (68.6) 127 (31.4) 96.6 + 1.76 93.8 + 2.95 96.5 + 1.95 396.2 + 71.70 163 (75.1) 54 (24.9) 96.8 + 1.70 94.3 + 2.54 96.9 + 1.56 382.5 + 84.69 114 (61.0) 73 (39.0) 96.5 + 1.81 93.2 + 3.28 96.0 + 2.23 366.8 + 73.10 69 (60.5) 45 (39.5) 96.2 + 1.80 92.7 + 3.26 95.7 + 2.38 407.0 + 95.60 45 (61.6) 28 (38.4) 96.9 + 1.75 94.0 + 3.18 96.5 + 1.90 0.078 0.002 0.087 < 0.001 < 0.001 0.001 0.009 0.006 < 0.001 < 0.001 1MSTS + Repetitions, n Mean ( + SD) Oxygen desaturation, n (%) No Yes Baseline oxygen saturation, % Mean ( + SD) Nadir oxygen saturation, Mean ( + SD), % Oxygen saturation on recovery, Mean ( + SD), % 20.6 + 5.44 285 (70.9) 117 (29.1) 97.0 + 1.30 94.1 + 2.83 96.6 + 1.52 20.9 + 5.17 166 (77.2) 49 (22.8) 97.2 + 1.23 94.6 + 2.38 96.8 + 1.37 20.2 + 5.73 119 (63.6) 68 (36.4) 96.9 + 1.36 93.5 + 3.16 96.4 + 1.63 19.6 + 6.10 70 (61.4) 44 (38.6) 96.7 + 1.42 93.1 + 3.31 96.2 + 1.66 21.2 + 5.00 49 (67.1) 24 (32.9) 97.1 + 1.24 94.0 + 2.83 96.6 + 1.57 0.204 0.003 0.025 < 0.001 0.002 0.063 0.008 0.012 < 0.001 0.002 Chest X-ray abnormalities, n (%) No Yes Consolidation GGO Reticulation 271 (66.4) 137 (33.6) 17 (4.2) 106 (26.0) 41 (10.1) 174 (80.2) 43 (19.8) 2 (0.9) 35 (16.1) 13 (6.0) 97 (50.8) 94 (49.2) 15 (7.9) 71 (37.2) 28 (14.7) 52 (44.8) 64 (55.2) 10 (8.6) 45 (38.8) 18 (18.1) 45 (60.0) 30 (40.0) 5 (6.7) 26 (34.7) 7 (9.3) < 0.001 < 0.001 < 0.001 0.004 < 0.001 0.002 < 0.001 0.002 Table 4. Binary and multinomial logistic regression analyses to determine factors associated with abnormal spirometry results. *, binary logistic regression analysis using patients with normal spirometry as reference; #, multinomial logistic regression analysis using patients with normal spirometry as reference; ^, continuous variables; !, Most severe illness during hospitalization was added as a covariate for binary logistic regression even though univariate p = 0.052. Binary logistic regression* Multinomial logistic regression # Parameters Abnormal spirometry, OR (95% CI), p-value Restrictive, OR (95% CI), p-value PRISm and obstructive, OR (95% CI), p-value Age^ 1.0 (1.01 – 1.04), 0.003 1.0 (0.98 – 1.03), 0.778 1.0 (1.01 – 1.07), 0.004 Gender, Male Female - Ref 2.1 (1.13 -3.94), 0.019 Ref 0.5 (0.23 – 1.04), 0.062 Diabetes mellitus, No Yes - Ref 0.8 (0.42 – 1.53), 0.501 Ref 0.64 (0.30 – 1.37), 0.249 Hypertension, No Yes Ref 1.5 (0.90 – 2.39), 0.123 Ref 1.8 (0.97 – 3.50), 0.061 Ref 1.1 (0.54 – 2.23), 0.800 Cardiovascular disease, No Yes Ref 3.5 (1.19 – 10.47), 0.023 Ref 3.1 (0.89 – 10.82), 0.075 Ref 3.1 (0.78 – 12.50), 0.108 Most severe illness during hospitalization, ! Moderate Severe Critical Ref 0.6 (0.27 – 1.28), 0.182 0.9 (0.41 – 2.22), 0.954 Ref 1.5 (0.37 – 6.50), 0.554 0.9 (0.42 – 2.08), 0.857 Ref 3.6 (0.79 – 16.23), 0.098 1.4 (0.47 – 3.98), 0.566 Received corticosteroids during hospitalization, No Yes Ref 1.3 (0.61 – 2.94), 0.475 Ref 1.3 (0.39 – 4.08), 0.703 Ref 1.5 (0.54 – 4.06), 0.439 Received hydroxychloroquine during hospitalization, No Yes - Ref 0.5 (0.08 – 2.65), 0.388 Ref 4.4 (1.15 – 16.97), 0.030 Received immunomodulators during hospitalization, No Yes - Ref 2.4 (1.07 – 5.28), 0.034 Ref 0.7 (0.23 - 2.36), 0.606 Required IMV, No Yes Ref 0.9 (0.44 – 1.95), 0.843 Ref 1.2 (0.53 – 2.85), 0.623 Ref 0.9 (0.29 – 3.11), 0.922 ARDS, No Yes Ref -, < 0.001 - - Pulmonary embolism, No Yes Ref 1.3 (0.73 – 2.22), 0.391 Ref 1.0 (0.53 – 2.00), 0.925 Ref 2.3 (1.07 – 5.13), 0.033 ICU admission, No Yes Ref 1.4 (0.80 – 2.46), 0.244 Ref 1.3 (0.62 – 2.62), 0.519 Ref 1.2 (0.51 – 2.85), 0.679 Length of hospital stay^ - 1.0 (0.99 – 1.04), 0.357 0.9 (0.92 – 1.00). 0.099 Time from discharge to follow-up^ 0.9 (1.00 – 1.02), 0.035 0.9 (0.99 – 1.00), 0.060 1.0 (0.99 – 1.00), 0.201 mMRC score^ - 1.2 (0.92 – 1.66), 0.164 0.7 (0.50 -1.12), 0.158 6MWT distance^ - 1.0 (0.99 – 1.00), 0.501 1.0 (0.99 – 1.00), 0.678 6MWT oxygen desaturation, No Yes Ref 1.9 (1.20 – 3.06), 0.007 Ref 1.4 (0.76 – 2.52), 0.290 Ref 2.4 (1.20 – 4.75), 0.014 1MSTS oxygen desaturation, No Yes Ref 1.4 (0.85 – 2.30). 0.184 Ref 1.5 (0.81 – 2.79). 0.194 Ref 1.4 (0.70 -2.91). 0.329 Consolidation on chest X-ray, No Yes Ref 8.1 (1.75 – 37.42). 0.008 Ref 10.1 (1.88 – 54.83), 0.007 Ref 8.6 (1.41 – 52.57), 0.020 GGO on chest X-ray, No Yes Ref 2.6 (1.52 – 4.30), < 0.001 Ref 2.2 (1.16 – 4.26), 0.016 Ref 2.6 (1.3 – 5.3), 0.009 Reticulation on chest X-ray, No Yes Ref 1.2 (0.55 – 2.68), 0.628 Ref 2.0 (0.81 – 4.81), 0.134 Ref 0.6 (0.19 – 1.87), 0.372 Table 5. Findings of body plethysmography, diffusion capacity, and HRCT of the lungs for patients with restrictive pattern spirometry. Parameters of body plethysmography, and diffusion capacity Total number of patients, n = 89 Findings of lungs HRCT Total number of patients, n = 80 FEV 1 , Mean ( + SD), % predicted FVC, Mean ( + SD), % predicted FEV 1 /FVC, Mean ( + SD), % RV, Mean ( + SD), % predicted TLC, Mean ( + SD), % predicted DLCO, Mean ( + SD), % predicted DLCO/Va Mean ( + SD), % predicted 76.8 + 13.97 75.6 + 14.00 0.86 + 0.12 57.8 + 39.08 65.1 + 13.25 62.5 + 13.94 103.6 + 17.52 HRCT abnormalities, n (%) No Yes GGO OP Reticulation Others 6 (7.5) 74 (92.5) 65 (81.3) 42 (52.5) 68 (85.0) 27 (33.8) CT-scores, Mean ( + SD), score 9.8 + 5.96 Additional Declarations No competing interests reported. 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Chai","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsklEQVRIiWNgGAWjYHACNgYGAxsGBmYStaSRrIXhMAnq+fsPP3vwo+B84nZ27sSPPxi2JTYQ0iJxI83csMfgduLOZt7N0jwMtwlrMZDgYZPgAWrZcJh3gzQDUVr4z7BJ/jE4B9Ky+ecPorQw5LBJ8xgcAGnZJkGUw4B+MZOWMUg2BmmxBrrQmKAWUIhJvvljJ7vh/NnNN39U3JYlqAXDnY6kamFgsCdZxygYBaNgFAx7AADYXjy5wLvS6wAAAABJRU5ErkJggg==","orcid":"","institution":"University Malaysia Sarawak","correspondingAuthor":true,"prefix":"","firstName":"Chee-Shee","middleName":"","lastName":"Chai","suffix":""},{"id":287255195,"identity":"11ef37cf-76a3-4a0e-a761-9be2c1981142","order_by":1,"name":"Muhammad Amin Bin Ibrahim","email":"","orcid":"","institution":"University Technology MARA","correspondingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"Amin Bin","lastName":"Ibrahim","suffix":""},{"id":287255197,"identity":"5bc8b817-0462-4708-80ef-3034e492715a","order_by":2,"name":"Nur Amira Binti Azhar","email":"","orcid":"","institution":"Ministry of Health Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Nur","middleName":"Amira Binti","lastName":"Azhar","suffix":""},{"id":287255199,"identity":"a3edfb7e-0c99-41ee-82bb-2365f316464b","order_by":3,"name":"Zulaika Binti Roslan","email":"","orcid":"","institution":"Ministry of Health Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Zulaika","middleName":"Binti","lastName":"Roslan","suffix":""},{"id":287255202,"identity":"4db8812a-a9bf-4e21-b6f9-97a043c530ea","order_by":4,"name":"Rozila Binti Harun","email":"","orcid":"","institution":"Ministry of Health Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Rozila","middleName":"Binti","lastName":"Harun","suffix":""},{"id":287255203,"identity":"d8d6a1ee-549b-4f82-b31b-418ecdbaad11","order_by":5,"name":"Swarna Lata Krishnabahawan","email":"","orcid":"","institution":"Ministry of Health Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Swarna","middleName":"Lata","lastName":"Krishnabahawan","suffix":""},{"id":287255204,"identity":"e3bcfa41-2044-4a26-bc0f-4063ef839753","order_by":6,"name":"Aruna A/P Karthigayan","email":"","orcid":"","institution":"Ministry of Health Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Aruna","middleName":"A/P","lastName":"Karthigayan","suffix":""},{"id":287255205,"identity":"dbe3a7f2-9594-4f28-81a4-3e3f6b330c0a","order_by":7,"name":"Roqiah Fatmawati Binti Abdul Kadir","email":"","orcid":"","institution":"University Technology MARA","correspondingAuthor":false,"prefix":"","firstName":"Roqiah","middleName":"Fatmawati Binti Abdul","lastName":"Kadir","suffix":""},{"id":287255206,"identity":"00dc265e-6521-491e-b312-687a1deb1d64","order_by":8,"name":"Busra Binti Johari","email":"","orcid":"","institution":"University Technology MARA","correspondingAuthor":false,"prefix":"","firstName":"Busra","middleName":"Binti","lastName":"Johari","suffix":""},{"id":287255207,"identity":"b9d0918a-0033-4af2-8393-413367042423","order_by":9,"name":"Diana-Leh-Ching Ng","email":"","orcid":"","institution":"University Malaysia Sarawak","correspondingAuthor":false,"prefix":"","firstName":"Diana-Leh-Ching","middleName":"","lastName":"Ng","suffix":""},{"id":287255208,"identity":"dddae3e3-eeee-48eb-9cf8-4a0f51683bd2","order_by":10,"name":"Benedict-Lim-Heng Sim","email":"","orcid":"","institution":"Ministry of Health Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Benedict-Lim-Heng","middleName":"","lastName":"Sim","suffix":""},{"id":287255209,"identity":"aed5f307-194a-429c-af85-92cb623aee79","order_by":11,"name":"Chong-Kin Liam","email":"","orcid":"","institution":"University of Malaya","correspondingAuthor":false,"prefix":"","firstName":"Chong-Kin","middleName":"","lastName":"Liam","suffix":""},{"id":287255211,"identity":"518658bc-d3d5-4725-80b3-e95f27caab15","order_by":12,"name":"Abdul Razak Bin Muttalif","email":"","orcid":"","institution":"MAHSA University Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Abdul","middleName":"Razak Bin","lastName":"Muttalif","suffix":""},{"id":287255213,"identity":"17bcbb0a-3c0d-48c2-8123-66b9a22397c3","order_by":13,"name":"Ahmad Hata Bin Rasit","email":"","orcid":"","institution":"University Malaysia Sarawak","correspondingAuthor":false,"prefix":"","firstName":"Ahmad","middleName":"Hata Bin","lastName":"Rasit","suffix":""},{"id":287255215,"identity":"c4fe392c-ab82-4b69-a53a-946aa357c9bb","order_by":14,"name":"Kalaiarasu M Peariasamy","email":"","orcid":"","institution":"National Institutes of Health, Ministry of Health Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Kalaiarasu","middleName":"M","lastName":"Peariasamy","suffix":""},{"id":287255218,"identity":"cde198fd-14ff-4b57-b55c-16f71156e859","order_by":15,"name":"Mohammed Fauzi Bin Abdul Rani","email":"","orcid":"","institution":"University Technology MARA","correspondingAuthor":false,"prefix":"","firstName":"Mohammed","middleName":"Fauzi Bin Abdul","lastName":"Rani","suffix":""}],"badges":[],"createdAt":"2024-03-25 22:44:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4165804/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4165804/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-67536-2","type":"published","date":"2024-07-16T15:57:32+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":54177422,"identity":"d07f005c-71ad-4d68-be72-bfb968708309","added_by":"auto","created_at":"2024-04-05 16:07:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":40835,"visible":true,"origin":"","legend":"\u003cp\u003eAlgorithm of patients’ recruitment into the study.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4165804/v1/637aa765e1bd811af9aae483.png"},{"id":54177423,"identity":"22808668-bb2a-4bdc-a587-a9bf0cdd297b","added_by":"auto","created_at":"2024-04-05 16:07:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":47477,"visible":true,"origin":"","legend":"\u003cp\u003eResults of spirometry for 408 patients.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4165804/v1/a364f9b58b98f177a87fe499.png"},{"id":61595289,"identity":"ad0b57b3-2369-460d-a7fd-e65f2f0d8507","added_by":"auto","created_at":"2024-08-01 17:21:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1302017,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4165804/v1/3e781cfe-1191-48ee-8b40-b6ea532a1e49.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Post-Discharge Spirometry Evaluation in Patients Recovering from Moderate-to-Critical COVID-19: A Prospective Cohort Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe Coronavirus 2019 (COVID-19) pandemic is the worst-ever global health emergency, resulting in substantial human casualties and economic downturn. As of 6th March 2024, global COVID-19 infections have reached 704\u0026nbsp;million, leading to over seven million fatalities.\u003csup\u003e1\u003c/sup\u003e Even though the World Health Organization (WHO) no longer considers COVID-19 a public health emergency of international concern,\u003csup\u003e2\u003c/sup\u003e the continual emergence of new virus variants poses a persistent threat to the end of the pandemic.\u003c/p\u003e \u003cp\u003eSevere acute respiratory syndrome (SARS) coronavirus (CoV)-2 (SARS-CoV-2) that causes COVID-19 is a beta coronavirus with genomic similarity of 80% to the 2002 SARS-CoV-1 and 50% to the 2012 Middle East Respiratory Syndrome (MERS)-CoV.\u003csup\u003e3\u003c/sup\u003e SARS-CoV-2 enters the human body via binding to the angiotensin-converting enzyme-2 receptor, predominantly located in the respiratory system.\u003csup\u003e4\u003c/sup\u003e On-going mutations of the virus have given rise to concerning variants including the Alpha (B.1.1.7), Beta (B.1.351), Gamma (P.1), Delta (B.1.617.2), and Omicron (B.1.1.529).\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eSARS-CoV-1, MERS-CoV, and SARS-CoV-2 primarily target the lower respiratory tract, leading to acute lung injuries like pneumonia and acute respiratory distress syndrome (ARDS).\u003csup\u003e6\u003c/sup\u003e Survivors of SARS and MERS demonstrated abnormal lung function, reduced effort tolerance, and impaired quality of life months or even years after the illness.\u003csup\u003e7\u0026ndash;9\u003c/sup\u003e Recent studies conducted in China highlighted that abnormal lung function was observed in 47.2% of hospitalized COVID-19 patients upon discharge,\u003csup\u003e10\u003c/sup\u003e 75.4% after a month,\u003csup\u003e11\u003c/sup\u003e and 25.5% after three months.\u003csup\u003e12\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eMalaysia has reported 5.27\u0026nbsp;million COVID-19 cases to date, with a recovery rate of 98.9%.\u003csup\u003e1\u003c/sup\u003e Routine assessment of lung function in patients recovering from COVID-19, however, remains a major challenge here due to a few constraints. First, conducting widespread lung function tests is time-consuming, costly, and manpower intensive. Second, equipment (such as spirometer and body plethysmograph) and expertise required to perform lung function tests are only available in selected tertiary healthcare centres. Third, non-respiratory clinicians often have difficulty interpreting the results of lung function tests. Fourth, the management strategies for abnormal lung function following COVID-19 remain unclear, particularly lacking standardized guidelines. Thus, only a very small proportion of patients recovering from COVID-19 were offered lung function tests.\u003c/p\u003e \u003cp\u003eResearch looking into the prevalence of abnormal lung function and its associated factors among patients recovering from COVID-19 in Malaysia is essential to help healthcare authorities develop follow-up strategies to enhance post-COVID care. This study focuses on evaluating lung function in patients with COVID-19 at least three months after their hospital discharge, aiming to determine the prevalence and types of abnormal spirometry results as primary objectives and identifying its associated factors as secondary objectives.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy design and patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis is a prospective cohort study of patients attending the COVID-19 Research Clinic at the Faculty of Medicine, University Technology MARA (UITM) in Malaysia, from March 2021 to December 2022. The inclusion criteria were Malaysians aged eighteen years and above, with confirmed COVID-19 via validated\u0026nbsp;reverse transcription-polymerase chain reaction method, who had moderate-to-critical illness according to the WHO classification,\u003csup\u003e13\u003c/sup\u003e and were at least three months post-discharge from either the Sungai Buloh Hospital or the UITM Medical Centre.\u003csup\u003e14\u003c/sup\u003e Patients with pre-existing chronic lung diseases before COVID-19, including bronchial asthma, as well as individuals who were pregnant, completely immobilized, had uncontrolled psychiatric illness, or were contraindicated for spirometry were excluded.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA minimum sample size of 386 subjects was determined using the formula for a cross-sectional study - sample size = Z\u003csub\u003e1-\u0026alpha;\u003c/sub\u003e \u003csup\u003e2\u003c/sup\u003ep(1 - p)/d\u003csup\u003e2\u003c/sup\u003e.\u003csup\u003e15\u003c/sup\u003e Z represented the confidence interval at 95%, d denoted the margin of error at 5%, and p referred to the proportion of abnormal lung function (52.7%) among SARS survivors in a previous study.\u003csup\u003e8\u003c/sup\u003e All patients provided \u0026nbsp;written informed consent before participating in the study. The study received ethics approval from the Medical Research Ethics Committee of the Ministry of Health Malaysia \u003cstrong\u003e(\u003c/strong\u003eNMRR-20-2011-56330 (IIR) and the respective hospitals, and it\u0026nbsp;was conducted in adherence to the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProcedure and outcomes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEligible patients were consecutively identified from the COVID-19 registry of Sungai Buloh Hospital and UITM Medical Centre. Those meeting all inclusion criteria and having none of the exclusion criteria were scheduled for early physical appointments at the COVID-19 Research Clinic.\u003c/p\u003e\n\u003cp\u003e1. \u0026nbsp; Demographic, clinical, and hospitalization data:\u003c/p\u003e\n\u003cp\u003eDemographic, clinical, and hospitalization data were gathered through face-to-face interviews and the electronic records. Demographic information included age, gender, and ethnicity, while clinical details included smoking status, obesity, and the presence of underlying chronic diseases. Hospitalization data included the duration of illness before admission, length of hospital stays, COVID-19 severity at presentation, the most severe COVID-19 episode during hospitalization, pharmacotherapy administrated, respiratory support provided, the occurrence of respiratory complications, and details regarding intensive care unit (ICU) admission, including its length of stay.\u003c/p\u003e\n\u003cp\u003eThe severity of COVID-19 was defined according to the WHO classification as: \u0026nbsp; asymptomatic, mild \u0026nbsp;(symptomatic without pneumonia), moderate (pneumonia without hypoxia), severe (pneumonia with hypoxia requiring oxygen supplementation), and critical (critically ill, such as ARDS, sepsis, or septic shock).\u003csup\u003e13\u003c/sup\u003e Available treatments for COVID-19 during the study period included corticosteroids, hydroxychloroquine, immunomodulators (tocilizumab, interferon beta, and interferon alpha), and antivirals (favipiravir, lopinavir-ritonavir, ritonavir, and atazanavir).\u003csup\u003e16\u003c/sup\u003e Respiratory support was categorized into oxygen supplementation by nasal cannulae, venti-mask, or high-flow mask, non-invasive mechanical ventilation (NIV) or nasal high flow (NHF), and invasive mechanical ventilation (IMV).\u003csup\u003e17,18\u003c/sup\u003e Common respiratory complications of COVID-19 that were recorded included ARDS, pulmonary embolism, pneumothorax, and pleural effusion.\u003csup\u003e19,20\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e2. \u0026nbsp; Patients reported outcomes (PROs):\u003c/p\u003e\n\u003cp\u003ePatients were instructed to independently complete the modified Medical Research Council (mMRC) dyspnea scale and the post-COVID-19 Functional Status (PCFS) scale\u0026nbsp;with minimal assistance from investigators. The mMRC and PCFS was interpreted as per the original validation of the questionnaire.\u003csup\u003e21,22\u003c/sup\u003e A higher score indicates a greater degree of symptom severity and impairment, respectively.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3. \u0026nbsp; Lung function tests:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSpirometry was conducted using SpiroUSB\u003csup\u003eTM\u003c/sup\u003e (Vyaire Medical, Chicago, IL) to obtain dynamic lung volumes, including the forced expiratory volume in one second (FEV\u003csub\u003e1\u003c/sub\u003e) and forced vital capacity (FVC). The cut-off value of \u0026ge; 80% of the predicted was deemed normal for both parameters. \u0026nbsp;Spirometry results were categorized into four groups: normal spirometry \u0026ndash; normal FEV\u003csub\u003e1\u003c/sub\u003e, normal FVC, and FEV\u003csub\u003e1\u003c/sub\u003e/FVC \u0026gt; 0.7; restrictive pattern \u0026ndash; reduced or normal FEV\u003csub\u003e1\u003c/sub\u003e, reduced FVC, and FEV\u003csub\u003e1\u003c/sub\u003e/FVC \u0026gt; 0.7; obstructive pattern \u0026ndash; reduced FEV\u003csub\u003e1\u003c/sub\u003e, reduced or normal FVC, and FEV\u003csub\u003e1\u003c/sub\u003e/FVC \u0026lt; 0.7; and preserved ratio impaired spirometry (PRISm) \u0026ndash; reduced FEV\u003csub\u003e1\u003c/sub\u003e, normal FVC\u0026cedil; and FEV\u003csub\u003e1\u003c/sub\u003e/FVC \u0026gt; 0.7.\u003csup\u003e23,24\u003c/sup\u003e For patients with an obstructive pattern, post-bronchodilator spirometry was performed to identify reversible airflow obstruction. Those with a restrictive pattern were scheduled for static lung volumes and diffusion capacity measurement within two weeks using PFT Vyntus Bodybox\u003csup\u003eTM\u003c/sup\u003e (Vyaire Medical, Chicago, IL). The parameters measured included residual volume (RV), total lung capacity (TLC), diffusion capacity for carbon monoxide (DLCO), and carbon monoxide transfer coefficient (DLCO/Va). All lung function tests were conducted by certified respiratory technicians following the American Thoracic Society (ATS) and European Respiratory Society guidelines.\u003csup\u003e25,26\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e4. \u0026nbsp; Cardiopulmonary functional tests:\u003c/p\u003e\n\u003cp\u003ePatients underwent a 6-minute walk test (6MWT) under the guidance of a certified respiratory physiotherapist, following the ATS guideline.\u003csup\u003e27\u003c/sup\u003e Their pulses and oxygen saturation were continuously monitored using the Nonin\u0026reg; WristOx2 \u0026trade; 3150 Bluetooth Pulse Oximeter. A 1-minute sit-to-stand test (1MSTS) guided by the same respiratory physiotherapist followed and in accordance with the procedure outlined in a previous study.\u003csup\u003e28\u003c/sup\u003e Both assessments utilized a digital stopwatch for time measurement, and the Borg scale was employed to assess the severity of dyspnea and fatigue.\u003c/p\u003e\n\u003cp\u003e5. \u0026nbsp; Radio-imaging:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll patients underwent a standard posterior-anterior chest X-ray examination. Only those demonstrating a restrictive pattern in spirometry were scheduled for high-resolution computed tomography (HRCT) of the lungs within one month. Radiologists, blinded to patients\u0026apos; information, independently reviewed these images to identify consolidation, ground glass opacity (GGO), and lung parenchymal reticulation. Additionally, the HRCT also documented the presence of organizing pneumonia (OP) and other findings, such as lung nodules, atelectasis, pleural effusion or thickening, diaphragmatic elevation, cardiomegaly, and fractures, if any. Lung involvement severity was assessed using the CT-score method developed by Kunhua Li et al.\u003csup\u003e29\u003c/sup\u003e Each lobe received a score ranging from 0 to 5 based on its level of involvement: 0 (0%), 1 (\u0026lt; 5%), 2 (5 \u0026ndash; 25%), 3 (26 \u0026ndash; 49%), 4 (50 \u0026ndash; 75%), and 5 (\u0026gt; 75%). The total score, representing cumulative involvement across all lobes, ranged from 0 to 25 points.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCategorical variables are presented as percentages, while continuous variables are presented as mean \u0026plusmn; standard deviation (SD). Patients were categorized into those with normal versus those with abnormal spirometry for two-group comparisons, as well as normal versus restrictive pattern or PRISm/obstructive pattern spirometry for three-group comparisons. Between-group differences were assessed using an independent \u003cem\u003et\u003c/em\u003e-test for continuous variables and a Chi-Square test for categorical variables. A two-sided p-value of less than 0.05 was considered statistically significant.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor multivariate analyses, variables exhibiting significant two-sided p-values in the univariate analyses were included as covariates in binary logistic regression and multinomial logistic regression. The latter analysis excluded variables showing multicollinearity (variance inflation factor \u0026gt; 5). The analysis aimed to derive odds ratios (OR), 95% confidence intervals (95% CI), and two-sided p-values. Statistical analysis was conducted using the Statistical Package for the Social Sciences (SPSS for Windows version 25.0, SPSS Inc, Chicago, IL, USA). \u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003eSociodemographic and clinical characteristics\u003c/h2\u003e\n \u003cp\u003eA total of 408 patients were included in the study (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The sociodemographic and clinical characteristics of these patients are presented in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. The mean age of the patients was 51.6\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;13.32 years. The majority were male (59.8%), of Malay ethnicity (71.8%), and had underlying chronic diseases (63.7%). The most common disease was hypertension (43.4%), followed by diabetes mellitus (30.6%), cardiovascular disease (4.9%), chronic kidney disease (1.5%), chronic liver disease (0.5%), and cerebrovascular disease (0.2%). Only 23.3% of the patients were current or ex-smokers, while 17.9% were classified as obese.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eHospitalization and management of the patients\u003c/h2\u003e\n \u003cp\u003eThe patients were admitted to the hospital after a mean duration of 8.7\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;5.32 days from symptom onset and the mean hospitalization duration was 13.0\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;10.62 days. Most of the patients had severe illness (61.3%) on admission (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Subsequently, 28.9%, 55.6%, and 15.5% developed critical, severe, and moderate illness during their hospital stay, respectively. Corticosteroids (83.1%) were the most frequently administrated medication, followed by antivirals (37.0%), immunomodulators (14.2%), and hydroxychloroquine (13.7%). Among patients requiring respiratory support (78.4%), 47.8% received supplemental oxygen only, 13.2% had NIV or HFNO, and 17.4% underwent IMV. Respiratory complications occurred in 26.5% of patients, primarily manifesting as pulmonary embolisms (24.3%). For the 43.4% of patients who were admitted to the ICU, the mean duration of ICU stay was 10.6\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;16.32 days.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003ePROs, cardiopulmonary functional tests, and chest X-ray findings at follow-up\u003c/h2\u003e\n \u003cp\u003eThe patients were assessed at a mean duration of 162.6\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;113.97 days post-hospital discharge (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). They reported a mean mMRC score of 0.9\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.95 and a mean PCFS score of 0.4\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.74. Of 404 patients who completed the 6MWT, 31.4% experienced oxygen desaturation. Among the 402 patients who completed 1MSTS, 29.1% experienced oxygen desaturation. Chest X-ray revealed abnormalities in 33.6% of patients which included GGO (26.0%), lung parenchymal reticulation (10.1%), and consolidation (4.2%).\u003c/p\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003eSpirometry and factors associated with abnormal results\u003c/h2\u003e\n \u003cp\u003eAbnormal spirometry was detected in 46.8% of the patients, with 28.4% having a restrictive pattern, 17.4% having PRISm, and 1.0% having an obstructive pattern. The mean values of their FEV\u003csub\u003e1\u003c/sub\u003e, FVC, and FEV\u003csub\u003e1\u003c/sub\u003e/FVC are presented in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eFactors associated with abnormal spirometry results included patients\u0026rsquo; age (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), hypertension (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), cardiovascular disease (p\u0026thinsp;=\u0026thinsp;0.010), corticosteroids treatment (p\u0026thinsp;=\u0026thinsp;0.006), IMV support (p\u0026thinsp;=\u0026thinsp;0.042), ARDS (p\u0026thinsp;=\u0026thinsp;0.035), pulmonary embolism (p\u0026thinsp;=\u0026thinsp;0.007), ICU admission (p\u0026thinsp;=\u0026thinsp;0.001), duration from discharge to follow-up (p\u0026thinsp;=\u0026thinsp;0.001), oxygen desaturation with 6MWT (p\u0026thinsp;=\u0026thinsp;0.002), oxygen desaturation with 1MSTS (p\u0026thinsp;=\u0026thinsp;0.003), as well as the presence of consolidation (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), GGO (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and parenchymal reticulation on chest X-ray (p\u0026thinsp;=\u0026thinsp;0.004) (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eMultivariate analyses using binary logistic regression showed that older patients (OR:1.0, 95% CI:1.01\u0026ndash;1.04, p\u0026thinsp;=\u0026thinsp;0.003), those with underlying cardiovascular disease (OR: 3.5, 95% CI: 1.19\u0026ndash;10.47, p\u0026thinsp;=\u0026thinsp;0.023), those who developed ARDS (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), a shorter interval from discharge to follow-up (OR: 0.9, 95% CI: 1.00\u0026ndash;1.02, p\u0026thinsp;=\u0026thinsp;0.035), those with oxygen desaturation during 6MWT (OR: 1.9, 95% CI: 1.20\u0026ndash;3.06, p\u0026thinsp;=\u0026thinsp;0.007), and those with consolidation (OR: 8.1, 95% CI: 1.75\u0026ndash;37.42, p\u0026thinsp;=\u0026thinsp;0.008) or GGO appearance (OR: 2.6, 95% CI: 1.52\u0026ndash;4.30, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) on chest X-ray were significantly more likely to have abnormal spirometry.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eRestrictive pattern, obstructive pattern, and PRISm\u003c/h2\u003e\n \u003cp\u003eMultinomial logistic regression, using normal spirometry as a reference, showed that female patients (OR: 2.1, 95% CI: 1.13\u0026ndash;3.94, p\u0026thinsp;=\u0026thinsp;0.019), those treated with immunomodulators (OR: 2.4, 95% CI: 1.07\u0026ndash;5.28, p\u0026thinsp;=\u0026thinsp;0.034), those with consolidation (OR: 10.1, 95% CI: 1.88\u0026ndash;54.83, p\u0026thinsp;=\u0026thinsp;0.007) or GGO appearance (OR: 2.2, 95% CI: 1.16\u0026ndash;4.26, p\u0026thinsp;=\u0026thinsp;0.016) on chest X-ray were significantly more likely to show a restrictive pattern spirometry. Conversely, older patients (OR: 1.0, 95% CI: 1.01\u0026ndash;1.07, p\u0026thinsp;=\u0026thinsp;0.004), those treated with hydroxychloroquine (OR: 4.4, 95% CI: 1.15\u0026ndash;16.97, p\u0026thinsp;=\u0026thinsp;0.030), those who developed pulmonary embolism (OR: 2.3, 95% CI: 1.07\u0026ndash;5.13, p\u0026thinsp;=\u0026thinsp;0.033), those with oxygen desaturation in the 6MWT (OR: 2.4, 95% CI: 1.20\u0026ndash;4.75, p\u0026thinsp;=\u0026thinsp;0.014), those with consolidation (OR: 8.6, 95% CI: 1.41\u0026ndash;52.57, p\u0026thinsp;=\u0026thinsp;0.020) or GGO appearance (OR: 2.6, 95% CI: 1.3\u0026ndash;5.3, p\u0026thinsp;=\u0026thinsp;0.009) on chest X-ray were significantly more likely to show PRISm and obstructive pattern spirometry.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eFindings on body plethysmography, diffusion capacity, and HRCT of the lungs\u003c/h2\u003e\n \u003cp\u003eEighty-nine patients underwent body plethysmography and diffusion capacity assessment, revealing a mean RV of 57.8\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;39.08% predicted, a mean TLC of 65.1\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;13.25% predicted, a mean DLCO of 62.5\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;13.94% predicted, and a mean DLCO/Va of 103.6\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;17.52 (Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). Of 80 patients who underwent HRCT of the lungs, 81.3% had GGO, 52.5% had OP, 85.0% had parenchymal reticulation, and 33.8% showed other findings. The mean CT score for these patients was 9.8\u0026thinsp;\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;5.96.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe current study highlights that nearly half of the patients hospitalized for moderate-to-critical COVID-19 continue to show abnormal spirometry three months after discharge. Approximately one-third of them displayed a restrictive pattern, while another one-fifth surprisingly manifested PRISm. This study identifies chest X-ray as a reliable tool for predicting abnormal spirometry and its subtypes, particularly when consolidation and GGO are present. Furthermore, the 6MWT could be a valuable tool for predicting abnormal spirometry. Although certain demographic and clinical data were also found to be useful, the 1MSTS and PROs do not add additional value to the prediction of spirometry abnormalities.\u003c/p\u003e \u003cp\u003eA meta-analysis of seven studies, primarily conducted in China, revealed that 22.9% of patients hospitalized for COVID-19 demonstrated abnormal spirometry within three months post-discharge.\u003csup\u003e30\u003c/sup\u003e Among these, 15.0% exhibited a restrictive pattern while 7.9% showed an obstructive pattern.\u003csup\u003e30\u003c/sup\u003e A separate study in Thailand reported abnormal spirometry in 17.2% of patients hospitalized for mild-to-severe COVID-19 at sixty days post-discharge, with 9.2% having an obstructive pattern and 8.0% having a restrictive pattern.\u003csup\u003e31\u003c/sup\u003e In Spain and Belgium, studies reported solely a restrictive pattern among hospitalized COVID-19 patients. In the Spanish study, 14.3% of patients requiring oxygen supplementation for pneumonia exhibited this pattern at two months, 9.3% at six months, and 6.7% at twelve months.\u003csup\u003e32\u003c/sup\u003e In the Belgian study, 55% of patients admitted to the ICU for ARDS demonstrated a restrictive pattern at three months.\u003csup\u003e33\u003c/sup\u003e Compared to these other studies, our study showed a high prevalence of abnormal spirometry potentially attributed to the predominance of severe and critical COVID-19 cases among our cohort. The observation that the restrictive pattern was the most common spirometry abnormality aligns with findings in China,\u003csup\u003e30\u003c/sup\u003e France,\u003csup\u003e34\u003c/sup\u003e Spain,\u003csup\u003e32\u003c/sup\u003e and Belgium.\u003csup\u003e33\u003c/sup\u003e The increased proportion of patients with an obstructive pattern in the Thailand study, however, could be due to the non-exclusion of individuals with pre-existing lung diseases, including bronchial asthma and chronic obstructive pulmonary disease (COPD).\u003csup\u003e31\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe majority of existing studies have focused on investigating the lung function of patients recovering from COVID-19 based on severity of illness. These studies have consistently shown that individuals with more severe illness tend to exhibit significantly lower static lung volumes and diffusion capacity, while their dynamic lung volumes in spirometry often remain preserved.\u003csup\u003e11,34\u0026ndash;37\u003c/sup\u003e To date, only a study in Thailand and China have respectively reported significantly lower dynamic lung volumes in patients with more severe illness,\u003csup\u003e31,38\u003c/sup\u003e while another study in the Netherlands found only FVC to be significantly lower in such cases.\u003csup\u003e39\u003c/sup\u003e Additional studies have shown that for individuals post-COVID-19, spirometry indices were not significantly different from the healthy population,\u003csup\u003e40\u003c/sup\u003e those with other viral upper respiratory tract infections,\u003csup\u003e41\u003c/sup\u003e or the same cohort of patients one year before the infection.\u003csup\u003e42\u003c/sup\u003e A review by Thomas et al further concluded that spirometry indices are often well-preserved in COVID-19, without being significantly affected by illness severity.\u003csup\u003e43\u003c/sup\u003e As far as we know, our study is the first to demonstrate no significant differences in spirometry patterns between patients with varying severity of COVID-19.\u003c/p\u003e \u003cp\u003eOur study identifies several factors associated with abnormal spirometry in patients recovering from COVID-19, notably abnormal chest X-ray and 6MWT during follow-up, as well as older age, underlying cardiovascular disease, previous ARDS, and shorter discharge-to-follow-up interval. In Thailand, individuals with abnormal chest X-ray after COVID-19 had significantly lower dynamic lung volumes.\u003csup\u003e31\u003c/sup\u003e Additionally, chest CT abnormalities after COVID-19 were correlated with lower dynamic lung volumes and diffusion capacity among those in Austria,\u003csup\u003e44\u003c/sup\u003e Netherlands,\u003csup\u003e36\u003c/sup\u003e and China,\u003csup\u003e45\u003c/sup\u003e although not in France.\u003csup\u003e34\u003c/sup\u003e Oxygen desaturation during the 6MWT was associated with diffusion capacity impairment among post-COVID patients in the Netherlands,\u003csup\u003e36\u003c/sup\u003e but not in Thailand or Germany.\u003csup\u003e31,46\u003c/sup\u003e The association between mMRC scores and lung function in those post-COVID was seldom studied,\u003csup\u003e32,33,36,37,47,48\u003c/sup\u003e with only one study in Austria reporting a negative correlation.\u003csup\u003e44\u003c/sup\u003e Two other studies found concurrent abnormalities in lung function, radio-imaging, 6MWT, and mMRC in the same cohort of post-COVID patients,\u003csup\u003e11,33\u003c/sup\u003e suggesting a potential relationship between these factors. Other factors associated with impaired lung function in previous studies included older age, \u003csup\u003e32,42\u003c/sup\u003e female gender,\u003csup\u003e32\u003c/sup\u003e lower body mass index,\u003csup\u003e32\u003c/sup\u003e underlying chronic lung disease,\u003csup\u003e42\u003c/sup\u003e higher inflammatory markers at presentation,\u003csup\u003e32,45\u003c/sup\u003e previous ARDS,\u003csup\u003e33\u003c/sup\u003e and shorter discharge-to-follow-up interval.\u003csup\u003e44,47\u003c/sup\u003e Corticosteroid treatment was linked to better lung function recovery,\u003csup\u003e33,47\u003c/sup\u003e while this was not observed with other treatment modalities.\u003csup\u003e39\u003c/sup\u003e Overall, our study findings are consistent with most of other studies.\u003c/p\u003e \u003cp\u003eOur study is the first to report PRISm in post-COVID patients. PRISm, previously known as pre-COPD, restrictive, or non-specific pattern, has a prevalence of 4.7\u0026ndash;22.3% in the general population.\u003csup\u003e49\u003c/sup\u003e Recent studies indicate that it primarily affects the small airways and vessels while sparing lung parenchyma.\u003csup\u003e49,50\u003c/sup\u003e Two studies have shown that 25.1% and 32.6% of individuals with PRISm, respectively progress to spirometry-defined COPD in 5 years.\u003csup\u003e51,52\u003c/sup\u003e Conversely, improvement of spirometry from obstructive pattern to PRISm over time has also been observed.\u003csup\u003e53\u003c/sup\u003e Therefore, individuals with PRISm in this study could either indicate an improvement from airflow obstruction or an early sign of deterioration to COPD after COVID-19. Additionally, the possibility that this reflects population prevalence rather than being directly attributed to COVID-19 cannot be discounted. Future studies that prospectively following up on this patient cohort could provide a definitive answer. The high prevalence of the restrictive pattern among our patients can be explained by the aberrant wound healing typically following diffuse alveolar damage by SARS-CoV-2, leading to severe scarring and fibrosis.\u003csup\u003e54\u003c/sup\u003e Respiratory muscle weakness following SARS-CoV-2 infection could also be another possibility.\u003csup\u003e55\u003c/sup\u003e Fewer abnormal spirometry results were detected among patients who were followed up at longer intervals from discharge suggesting COVID-19-related lung damage may be reversible in the long run, or that there is a certain degree of resilience and adaptability in lung function.\u003csup\u003e56\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe findings from this study have several clinical implications. First, spirometry should be routinely performed in patients post moderate-to-critical COVID-19 due to the high prevalence of abnormality. Second, when universal spirometry screening is not feasible among them, a targeted risk stratification approach considering chest X-ray, 6MWT, and specific demographic and clinical characteristics is recommended. Third, chest X-ray proves to be the most reliable screening tool for abnormal spirometry. Clinicians should focus on detecting consolidation and GGO features. Fourth, the 6MWT also emerges as a valuable screening tool for abnormal spirometry. Fifth, 1MSTS and PROs may not add significant value to the screening and should not be prioritized during follow-up. Sixth, this study suggests the potential development of a scoring system that combines these factors, providing a practical tool for clinicians to efficiently select patients for lung function tests.\u003c/p\u003e \u003cp\u003eThe large sample size of this study allows for the generalizability of the result. It is one of the few studies in the Southeast Asia, where outcomes may differ from other parts of the world due to variations in genetic, environmental, and lifestyle factors. The study focused on patients hospitalized with moderate-to-critical COVID-19 who were more susceptible to long-term lung injuries. The comprehensive study outcomes include objective assessments like lung function, radio-imaging, and cardiopulmonary functional evaluations, alongside subjective assessments such as PROs. However, this study was conducted during the peak of the pandemic. Travel restrictions, public reluctance to visit hospitals, and constrained healthcare resources could lead to several weaknesses. First, the convenience sampling method may introduce bias. Second, not every patient can undergo examination with body plethysmography, for diffusion capacity, and with HRCT. Third, some patients who were offered these investigations defaulted. Fourth, due to practical and logistic reasons the follow-up assessments could not be conducted at a fixed interval, such as three months, six months, or twelve months post-discharge. Fifth, factors associated with specific abnormal spirometry patterns should be interpreted with caution, as the sample size may not be powerful enough to accurately reflect these secondary outcomes. Sixth, while older age may demonstrate statistical significance, the OR of 1.0 suggests that it lacks clinical significance. Seventh, multidimensional assessment of PROs such as health-related quality of life was not performed. Lastly, lung function tests were not conducted as a follow-up after the study to observe potential changes in patterns.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003ePatients recovering from moderate-to-critical COVID-19 often demonstrated abnormal spirometry, particularly manifesting a restrictive pattern and PRISm. Therefore, spirometry should be routinely offered to those at higher risk of abnormalities, such as individuals with abnormal chest X-ray and 6MWT during follow-up, underlying cardiovascular disease, and previous ARDS. PRISm represents a novel finding among post-COVID patients, warranting further follow-up to elucidate the underlying mechanism of this lung function abnormality.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eData Availability:\u003c/h2\u003e \u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e \u003c/div\u003e\n\u003ch2\u003eCompeting Interest:\u003c/h2\u003e \u003cp\u003eThe authors declare no potential conflicts of interest in respect to the research, authorship, and publication of this article.\u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThis study was supported by research grants from University Malaysia Sarawak (F05/RISE/2089/2021), Selangor State Government (100\u0026ndash;3/2/3JLD[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]), Lung Foundation of Malaysia (GI/F05/LFM/2021), Persatuan Pendidikan Kesihatan Paru-Paru (IRG/F05/PPKP/85316/2022), Compass Medical Sdn Bhd (GI/F05/CMSB/2021), and Aliran Pasifik (M) Sdn Bhd (IRG/F05/APMSB/85317/2022). The funding bodies only financially supported this study and did not take part in the design of the study; or collection, analysis, and interpretation of the data; or writing of the manuscript. The open access funding was provided by Universiti Malaysia Sarawak.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors contributed equally to the conception and design of the study, data acquisition, data analysis and interpretation, drafting of the article, and critically revising it. All authors made final approval of the version to be published. All authors agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of the work are appropriately investigated and resolved.\u003c/p\u003e\u003ch2\u003eAcknowledgements:\u003c/h2\u003e \u003cp\u003e We would like to thank the Director General of Health Malaysia for his permission to publish this article. 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Eur Respir J 55, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1183/13993003.00607-2020\u003c/span\u003e\u003cspan address=\"10.1183/13993003.00607-2020\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeverin, R. \u003cem\u003eet al.\u003c/em\u003e The effects of COVID-19 on respiratory muscle performance: making the case for respiratory muscle testing and training. Eur Respir Rev 31, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1183/16000617.0006-2022\u003c/span\u003e\u003cspan address=\"10.1183/16000617.0006-2022\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSuppini, N. \u003cem\u003eet al.\u003c/em\u003e Longitudinal Analysis of Pulmonary Function Impairment One Year Post-COVID-19: A Single-Center Study. J Pers Med 13, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/jpm13081190\u003c/span\u003e\u003cspan address=\"10.3390/jpm13081190\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1. Demographic and clinical data of the patients.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn italic: only the number of patients with the respective underlying chronic diseases is presented; *, the p-value for differences between patients with normal and abnormal spirometry; #, the p-value for differences between patients with normal, restrictive pattern, and PRISm and obstructive pattern spirometry.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"907\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.18412348401323%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eParameters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.009922822491731%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eAll patients,\u003c/p\u003e\n \u003cp\u003en = 408\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"49.06284454244763%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eResult of Spirometry\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.371554575523705%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ep-value*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.371554575523705%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ep-value\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.719101123595507%\" valign=\"top\"\u003e\n \u003cp\u003eNormal,\u003c/p\u003e\n \u003cp\u003en = 217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.9438202247191%\" valign=\"top\"\u003e\n \u003cp\u003eAbnormal,\u003c/p\u003e\n \u003cp\u003en = 191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.9438202247191%\" valign=\"top\"\u003e\n \u003cp\u003eRestrictive,\u003c/p\u003e\n \u003cp\u003en = 116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.39325842696629%\" valign=\"top\"\u003e\n \u003cp\u003ePRISm and obstructive,\u003c/p\u003e\n \u003cp\u003en = 75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.18412348401323%\" valign=\"top\"\u003e\n \u003cp\u003eAge,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eMean (\u003cu\u003e+\u003c/u\u003e SD), years\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.009922822491731%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e51.6 \u003cu\u003e+\u003c/u\u003e 13.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.127894156560089%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e48.8 \u003cu\u003e+\u003c/u\u003e 13.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.238147739801544%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e54.9 \u003cu\u003e+\u003c/u\u003e 12.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.238147739801544%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e54.0 \u003cu\u003e+\u003c/u\u003e 12.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.458654906284455%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e56.3 \u003cu\u003e+\u003c/u\u003e 12.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.371554575523705%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.371554575523705%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.18412348401323%\" valign=\"top\"\u003e\n \u003cp\u003eGender, n (%)\u003c/p\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.009922822491731%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e244 (59.8)\u003c/p\u003e\n \u003cp\u003e164 (40.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.127894156560089%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e127 (58.5)\u003c/p\u003e\n \u003cp\u003e90 (41.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.238147739801544%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e117 (61.3)\u003c/p\u003e\n \u003cp\u003e74 38.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.238147739801544%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e59 (50.9)\u003c/p\u003e\n \u003cp\u003e57 (49.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.458654906284455%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e58 (77.3)\u003c/p\u003e\n \u003cp\u003e17 (22.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.371554575523705%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.574\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.371554575523705%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.18412348401323%\" valign=\"top\"\u003e\n \u003cp\u003eEthnicity, n (%)\u003c/p\u003e\n \u003cp\u003eMalay\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eChinese\u003c/p\u003e\n \u003cp\u003eIndian\u003c/p\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.009922822491731%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e293 (71.8)\u003c/p\u003e\n \u003cp\u003e90 (22.1)\u003c/p\u003e\n \u003cp\u003e23 (5.6)\u003c/p\u003e\n \u003cp\u003e2 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.127894156560089%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e155 (71.4)\u003c/p\u003e\n \u003cp\u003e54 (24.9)\u003c/p\u003e\n \u003cp\u003e7 (3.2)\u003c/p\u003e\n \u003cp\u003e1 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.238147739801544%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e138 (72.3)\u003c/p\u003e\n \u003cp\u003e36 (18.8)\u003c/p\u003e\n \u003cp\u003e16 (8.4)\u003c/p\u003e\n \u003cp\u003e1 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.238147739801544%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e82 (70.7)\u003c/p\u003e\n \u003cp\u003e22 (19.0)\u003c/p\u003e\n \u003cp\u003e11 (9.5)\u003c/p\u003e\n \u003cp\u003e1 (0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.458654906284455%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e56 (74.7)\u003c/p\u003e\n \u003cp\u003e14 (18.7)\u003c/p\u003e\n \u003cp\u003e5 (6.7)\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.371554575523705%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.371554575523705%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.245\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.18412348401323%\" valign=\"top\"\u003e\n \u003cp\u003eSmoking status, n (%)\u003c/p\u003e\n \u003cp\u003eNever smoker\u003c/p\u003e\n \u003cp\u003eCurrent and ex-smoker\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.009922822491731%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e313 (76.7)\u003c/p\u003e\n \u003cp\u003e95 (23.3)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.127894156560089%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e172 (79.3)\u003c/p\u003e\n \u003cp\u003e45 (20.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.238147739801544%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e141 (73.8)\u003c/p\u003e\n \u003cp\u003e50 (26.2)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.238147739801544%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e89 (76.7)\u003c/p\u003e\n \u003cp\u003e27 (23.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.458654906284455%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e52 (69.3)\u003c/p\u003e\n \u003cp\u003e23 (30.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.371554575523705%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.371554575523705%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.215\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.18412348401323%\" valign=\"top\"\u003e\n \u003cp\u003eObesity, n (%)\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.009922822491731%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e335 (82.1)\u003c/p\u003e\n \u003cp\u003e73 (17.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.127894156560089%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e175 (80.6)\u003c/p\u003e\n \u003cp\u003e42 (19.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.238147739801544%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e160 (83.8)\u003c/p\u003e\n \u003cp\u003e31 (16.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.238147739801544%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e95 (81.9)\u003c/p\u003e\n \u003cp\u003e21 (18.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.458654906284455%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e65 (86.7)\u003c/p\u003e\n \u003cp\u003e10 (13.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.371554575523705%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.411\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.371554575523705%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.501\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.18412348401323%\" valign=\"top\"\u003e\n \u003cp\u003eUnderlying chronic diseases, n (%)\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eDiabetes mellitus\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eHypertension\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eCardiovascular disease\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eCerebrovascular disease\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eChronic liver disease\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eChronic kidney disease\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eOthers\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.009922822491731%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e148 (36.3)\u003c/p\u003e\n \u003cp\u003e260 (63.7)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e125 (30.6)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e177 (43.4)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e20 (4.9)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e1 (0.2)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e2 (0.5)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e6 (1.5)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e39 (9.6)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.127894156560089%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e91 (41.9)\u003c/p\u003e\n \u003cp\u003e126 (58.1)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e59 (27.2)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e75 (34.6)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e5 (2.3)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e1 (0.5)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e2 (0.9)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e17 (7.8)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.238147739801544%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e57 (29.8)\u003c/p\u003e\n \u003cp\u003e134 (70.2)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e66 (34.6)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e102 (53.4)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e15 (7.9)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e2 (1.0)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e4 (2.1)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e22 (11.5)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.238147739801544%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e28 (24.1)\u003c/p\u003e\n \u003cp\u003e88 (75.9)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e47 (40.5)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e68 (58.6)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e9 (7.8)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e2 (1.7)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e2 (1.7)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e12 (10.3)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.458654906284455%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e29 (38.7)\u003c/p\u003e\n \u003cp\u003e46 (61.3)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e19 (25.3)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e34 (45.3)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e6 (8.0)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e2 (2.7)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e10 (13.3)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.371554575523705%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.107\u003c/p\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003cp\u003e0.348\u003c/p\u003e\n \u003cp\u003e0.131\u003c/p\u003e\n \u003cp\u003e0.326\u003c/p\u003e\n \u003cp\u003e0.207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.371554575523705%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003cp\u003e0.643\u003c/p\u003e\n \u003cp\u003e0.080\u003c/p\u003e\n \u003cp\u003e0.537\u003c/p\u003e\n \u003cp\u003e0.356\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTable 2. Hospitalization data of the patients.\u003c/p\u003e\n\u003cp\u003eIn italic: only the number of patients with the respective pharmacotherapy, respiratory support, and respiratory complications are presented; *, p-value for differences between patients with normal and abnormal spirometry; #, p-value for differences between patients with normal, restrictive pattern, and PRISm and obstructive pattern spirometry.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"907\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.757174392935983%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eParameters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.472406181015453%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eAll patients,\u003c/p\u003e\n \u003cp\u003en = 408\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"49.00662251655629%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eResult of Spirometry\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.381898454746137%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ep-value*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.381898454746137%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ep-value\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.830699774266364%\" valign=\"top\"\u003e\n \u003cp\u003eNormal,\u003c/p\u003e\n \u003cp\u003en = 217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.153498871331827%\" valign=\"top\"\u003e\n \u003cp\u003eAbnormal,\u003c/p\u003e\n \u003cp\u003en = 191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.507900677200904%\" valign=\"top\"\u003e\n \u003cp\u003eRestrictive,\u003c/p\u003e\n \u003cp\u003en = 116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.507900677200904%\" valign=\"top\"\u003e\n \u003cp\u003ePRISm and obstructive,\u003c/p\u003e\n \u003cp\u003en = 75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.77900552486188%\" valign=\"top\"\u003e\n \u003cp\u003eDuration of illness,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eMean (\u003cu\u003e+\u003c/u\u003e SD), days\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.486187845303867%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e8.7 \u003cu\u003e+\u003c/u\u003e 5.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.154696132596685%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e8.5 \u003cu\u003e+\u003c/u\u003e 5.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.823204419889503%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e8.9 \u003cu\u003e+\u003c/u\u003e 5.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.486187845303867%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e8.7 \u003cu\u003e+\u003c/u\u003e 5.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.486187845303867%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e9.1 \u003cu\u003e+\u003c/u\u003e 4.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.392265193370166%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.495\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.392265193370166%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.703\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.77900552486188%\" valign=\"top\"\u003e\n \u003cp\u003eLength of hospital stay,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eMean (\u003cu\u003e+\u003c/u\u003e SD), days\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.486187845303867%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e13.0 \u003cu\u003e+\u003c/u\u003e 10.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.154696132596685%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e12.1 \u003cu\u003e+\u003c/u\u003e 11.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.823204419889503%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e14.1\u003cu\u003e+\u003c/u\u003e 9.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.486187845303867%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e16.1\u003cu\u003e+\u003c/u\u003e 10.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.486187845303867%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e11.1\u003cu\u003e+\u003c/u\u003e 6.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.392265193370166%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.392265193370166%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.77900552486188%\" valign=\"top\"\u003e\n \u003cp\u003eCOVID-19 severity at presentation, n (%)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eAsymptomatic\u003c/p\u003e\n \u003cp\u003eMild\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003cp\u003eSevere\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eCritical\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.486187845303867%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e24 (5.9)\u003c/p\u003e\n \u003cp\u003e56 (13.7)\u003c/p\u003e\n \u003cp\u003e39 (9.6)\u003c/p\u003e\n \u003cp\u003e250 (61.3)\u003c/p\u003e\n \u003cp\u003e39 (9.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.154696132596685%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e18 (8.3)\u003c/p\u003e\n \u003cp\u003e32 (14.7)\u003c/p\u003e\n \u003cp\u003e22 (10.1)\u003c/p\u003e\n \u003cp\u003e126 (58.1)\u003c/p\u003e\n \u003cp\u003e19 (8.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.823204419889503%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e6 (3.1)\u003c/p\u003e\n \u003cp\u003e24 (12.6)\u003c/p\u003e\n \u003cp\u003e17 (8.9)\u003c/p\u003e\n \u003cp\u003e124 (64.9)\u003c/p\u003e\n \u003cp\u003e20 (10.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.486187845303867%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2 (1.7)\u003c/p\u003e\n \u003cp\u003e11 (9.5)\u003c/p\u003e\n \u003cp\u003e8 (6.9)\u003c/p\u003e\n \u003cp\u003e80 (69.0)\u003c/p\u003e\n \u003cp\u003e15 (12.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.486187845303867%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e4 (5.3)\u003c/p\u003e\n \u003cp\u003e13 (17.3)\u003c/p\u003e\n \u003cp\u003e9 (12.0)\u003c/p\u003e\n \u003cp\u003e44 (58.7)\u003c/p\u003e\n \u003cp\u003e5 (6.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.392265193370166%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.392265193370166%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.107\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.77900552486188%\" valign=\"top\"\u003e\n \u003cp\u003eMost severe illness during hospitalization, n (%)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003cp\u003eSevere\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eCritical\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.486187845303867%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e63 (15.5)\u003c/p\u003e\n \u003cp\u003e227 (55.6)\u003c/p\u003e\n \u003cp\u003e118 (28.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.154696132596685%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e38 (17.5)\u003c/p\u003e\n \u003cp\u003e127 (58.5)\u003c/p\u003e\n \u003cp\u003e52 (24.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.823204419889503%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e25 (13.1)\u003c/p\u003e\n \u003cp\u003e100 (52.4)\u003c/p\u003e\n \u003cp\u003e66 (34.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.486187845303867%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e7 (6.0)\u003c/p\u003e\n \u003cp\u003e56 (48.3)\u003c/p\u003e\n \u003cp\u003e53 (45.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.486187845303867%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e18 (24.0)\u003cbr\u003e\u0026nbsp;44 (58.7)\u003c/p\u003e\n \u003cp\u003e13 (17.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.392265193370166%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.392265193370166%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.77900552486188%\" valign=\"top\"\u003e\n \u003cp\u003ePharmacotherapy, n (%)\u003c/p\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eCorticosteroids\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eHydroxychloroquine\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eImmunomodulators\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eAntivirals\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.486187845303867%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e26 (6.4)\u003c/p\u003e\n \u003cp\u003e382 (93.6)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e339 (83.1)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e56 (13.7)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e58 (14.2)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;151 (37.0)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.154696132596685%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e15 (6.9)\u003c/p\u003e\n \u003cp\u003e202 (93.1)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e170 (78.3)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e36 (16.6)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e24 (11.1)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e84 (38.7)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.823204419889503%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e11(5.8)\u003c/p\u003e\n \u003cp\u003e180 (94.2)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e169 (88.5)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e20 (10.5)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e34 (17.8)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e67 (35.1)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.486187845303867%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e7(6.0)\u003c/p\u003e\n \u003cp\u003e109 (94.0)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e108 (93.1)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e4 (3.4)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e28 (24.1)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e39 (33.6)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.486187845303867%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e4 (5.3)\u003c/p\u003e\n \u003cp\u003e71 (94.7)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e61 (81.3)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e16 (21.3)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e6 (8.0)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e28 (37.3)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.392265193370166%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.634\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e0.006\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e0.073\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e0.052\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e0.448\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.392265193370166%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.876\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e0.003\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt; 0.001\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e0.001\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e0.656\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.77900552486188%\" valign=\"top\"\u003e\n \u003cp\u003eRespiratory support, n (%)\u003c/p\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eOxygen supplementation by nasal cannula/venti-mask/high flow mask\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eNIV/NHF\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eIMV\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.486187845303867%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e88 (21.6)\u003c/p\u003e\n \u003cp\u003e320 (78.4)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e195 (47.8)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e54 (13.2)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e71 (17.4)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.154696132596685%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e54 (24.9)\u003c/p\u003e\n \u003cp\u003e163 (75.1)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e109 (50.2)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e24 (11.1)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e30 (13.8)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.823204419889503%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e34 (17.8)\u003c/p\u003e\n \u003cp\u003e157 (82.2)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e86 (45.0)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e30 (15.7)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e41 (21.5)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.486187845303867%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e12 (10.3)\u003c/p\u003e\n \u003cp\u003e104 (89.7)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e49 (42.2)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e22 (19.0)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e33 (28.4)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.486187845303867%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e22 (29.3)\u003c/p\u003e\n \u003cp\u003e53 (70.7)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e37 (49.3)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e8 (10.7)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e8 (10.7)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.392265193370166%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.083\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e0.294\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e0.167\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e0.042\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.392265193370166%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e0.364\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e0.098\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.77900552486188%\" valign=\"top\"\u003e\n \u003cp\u003eRespiratory complications, n (%)\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eARDS\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003ePulmonary embolism\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003ePneumothorax\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003ePleural effusion\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.486187845303867%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e300 (73.5)\u003c/p\u003e\n \u003cp\u003e108 (26.5)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e5 (1.2)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e99 (24.3)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e10 (2.5)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e3 (0.7)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.154696132596685%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e169 (77.9)\u003c/p\u003e\n \u003cp\u003e48 (22.1)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e5 (2.3)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e41 (18.9)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e6 (2.8)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e2 (0.9)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.823204419889503%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e131 (68.6)\u003c/p\u003e\n \u003cp\u003e60 (31.4)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e58 (30.4)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e4 (2.1)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e1 (0.5)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.486187845303867%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e81 (69.8)\u003c/p\u003e\n \u003cp\u003e35 (30.2)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e35 (30.2)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e2 (1.7)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e1 (0.5)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.486187845303867%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e50 (66.7)\u003c/p\u003e\n \u003cp\u003e25 (33.3)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e23 (30.7)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e2 (2.7)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.392265193370166%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e0.035\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e0.007\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e0.662\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e0.639\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.392265193370166%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.093\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e0.108\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e0.026\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e0.835\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e0.710\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.77900552486188%\" valign=\"top\"\u003e\n \u003cp\u003eICU admission, n (%)\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.486187845303867%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e231 (56.6)\u003c/p\u003e\n \u003cp\u003e177 (43.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.154696132596685%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e140 (64.5)\u003c/p\u003e\n \u003cp\u003e77 (35.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.823204419889503%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e91 (47.6)\u003c/p\u003e\n \u003cp\u003e100 (52.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.486187845303867%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e45 (38.8)\u003c/p\u003e\n \u003cp\u003e71 (61.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.486187845303867%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e46 (61.3)\u003c/p\u003e\n \u003cp\u003e29 (38.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.392265193370166%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.392265193370166%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.77900552486188%\" valign=\"top\"\u003e\n \u003cp\u003eLength of ICU stay,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eMean (\u003cu\u003e+\u003c/u\u003e SD), days\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.486187845303867%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e10.6 \u003cu\u003e+\u003c/u\u003e 16.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.154696132596685%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e9.6 \u003cu\u003e+\u003c/u\u003e 12.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.823204419889503%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e11.3 \u003cu\u003e+\u003c/u\u003e 18.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.486187845303867%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e11.7 \u003cu\u003e+\u003c/u\u003e 19.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.486187845303867%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e10.5 \u003cu\u003e+\u003c/u\u003e 16.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.392265193370166%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.522\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.392265193370166%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.774\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTable 3. \u0026nbsp;PROs, cardiopulmonary functional tests, and chest X-ray findings of the patients.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e^, 404 patients performed 6MWT: 217 had normal spirometry, 187 had abnormal spirometry (114 restrictive, 73 PRISm and obstructive); +, 402 patients performed 1MSTS: 215 had normal spirometry, 187 had abnormal spirometry (114 restrictive, 73 PRISm and obstructive); *, p-value for differences between patients with normal and abnormal spirometry; #, p-value for differences between patients with normal, restrictive pattern, and PRISm and obstructive pattern spirometry.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"907\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.625137816979052%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eParameters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.458654906284455%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eAll patients, n = 408\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"49.17309812568909%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eResult of Spirometry\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.371554575523705%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ep-value*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.371554575523705%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ep-value\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.39325842696629%\" valign=\"top\"\u003e\n \u003cp\u003eNormal,\u003c/p\u003e\n \u003cp\u003en = 217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.820224719101123%\" valign=\"top\"\u003e\n \u003cp\u003eAbnormal,\u003c/p\u003e\n \u003cp\u003en = 191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.39325842696629%\" valign=\"top\"\u003e\n \u003cp\u003eRestrictive,\u003c/p\u003e\n \u003cp\u003en = 116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.39325842696629%\" valign=\"top\"\u003e\n \u003cp\u003ePRISm and obstructive,\u003c/p\u003e\n \u003cp\u003en = 75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.646799116997794%\" valign=\"top\"\u003e\n \u003cp\u003eTime from discharge to follow-up,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eMean (\u003cu\u003e+\u003c/u\u003e SD), days\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.472406181015453%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e162.6 \u003cu\u003e+\u003c/u\u003e 113.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.472406181015453%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e179.6 \u003cu\u003e+\u0026nbsp;\u003c/u\u003e125.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.699779249448124%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e143.5 \u003cu\u003e+\u003c/u\u003e 96.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.472406181015453%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e121.9 \u003cu\u003e+\u003c/u\u003e 61.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.472406181015453%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e176.1 \u003cu\u003e+\u003c/u\u003e 127.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.381898454746137%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.381898454746137%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.646799116997794%\" valign=\"top\"\u003e\n \u003cp\u003emMRC score,\u003c/p\u003e\n \u003cp\u003eMean (\u003cu\u003e+\u003c/u\u003e SD)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.472406181015453%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.9 \u003cu\u003e+\u003c/u\u003e 0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.472406181015453%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.8 \u003cu\u003e+\u003c/u\u003e 0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.699779249448124%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.0 \u003cu\u003e+\u003c/u\u003e 1.0\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.472406181015453%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.2 \u003cu\u003e+\u003c/u\u003e 1.06\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.472406181015453%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.6 \u003cu\u003e+\u003c/u\u003e 0.82\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.381898454746137%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.381898454746137%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.646799116997794%\" valign=\"top\"\u003e\n \u003cp\u003ePCFS score,\u003c/p\u003e\n \u003cp\u003eMean (\u003cu\u003e+\u003c/u\u003e SD)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.472406181015453%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.4 \u003cu\u003e+\u003c/u\u003e 0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.472406181015453%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.4 \u003cu\u003e+\u003c/u\u003e 0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.699779249448124%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.4 \u003cu\u003e+\u003c/u\u003e 0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.472406181015453%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.5 \u003cu\u003e+\u003c/u\u003e 0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.472406181015453%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.3 \u003cu\u003e+\u003c/u\u003e 0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.381898454746137%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.698\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.381898454746137%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.307\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.646799116997794%\" valign=\"top\"\u003e\n \u003cp\u003e6MWT^\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e6MWT distance,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eMean (\u003cu\u003e+\u003c/u\u003e SD), meter\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eOxygen desaturation, n (%)\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003eBaseline oxygen saturation,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eMean (\u003cu\u003e+\u003c/u\u003e SD). %\u003c/p\u003e\n \u003cp\u003eNadir oxygen saturation,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eMean (\u003cu\u003e+\u003c/u\u003e SD), %Oxygen saturation on recovery,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eMean (\u003cu\u003e+\u003c/u\u003e SD), %\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.472406181015453%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e389.9 \u003cu\u003e+\u003c/u\u003e 78.18\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e277 (68.6)\u003c/p\u003e\n \u003cp\u003e127 (31.4)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e96.6 \u003cu\u003e+\u003c/u\u003e 1.76\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e93.8 \u003cu\u003e+\u003c/u\u003e 2.95\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e96.5 \u003cu\u003e+\u003c/u\u003e 1.95\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.472406181015453%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e396.2 \u003cu\u003e+\u003c/u\u003e 71.70\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e163 (75.1)\u003c/p\u003e\n \u003cp\u003e54 (24.9)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e96.8 \u003cu\u003e+\u003c/u\u003e 1.70\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e94.3 \u003cu\u003e+\u003c/u\u003e 2.54\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e96.9 \u003cu\u003e+\u003c/u\u003e 1.56\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.699779249448124%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e382.5 \u003cu\u003e+\u003c/u\u003e 84.69\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e114 (61.0)\u003c/p\u003e\n \u003cp\u003e73 (39.0)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e96.5 \u003cu\u003e+\u003c/u\u003e 1.81\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e93.2 \u003cu\u003e+\u003c/u\u003e 3.28\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e96.0 \u003cu\u003e+\u003c/u\u003e 2.23\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.472406181015453%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e366.8 \u003cu\u003e+\u003c/u\u003e 73.10\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e69 (60.5)\u003c/p\u003e\n \u003cp\u003e45 (39.5)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e96.2 \u003cu\u003e+\u003c/u\u003e 1.80\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e92.7 \u003cu\u003e+\u003c/u\u003e 3.26\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e95.7 \u003cu\u003e+\u003c/u\u003e 2.38\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.472406181015453%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e407.0 \u003cu\u003e+\u003c/u\u003e 95.60\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e45 (61.6)\u003c/p\u003e\n \u003cp\u003e28 (38.4)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e96.9 \u003cu\u003e+\u003c/u\u003e 1.75\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e94.0 \u003cu\u003e+\u003c/u\u003e 3.18\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e96.5 \u003cu\u003e+\u003c/u\u003e 1.90\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.381898454746137%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.078\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.381898454746137%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.646799116997794%\" valign=\"top\"\u003e\n \u003cp\u003e1MSTS\u003csup\u003e\u0026nbsp;+\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRepetitions, n\u003c/p\u003e\n \u003cp\u003eMean (\u003cu\u003e+\u003c/u\u003e SD)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eOxygen desaturation, n (%)\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eBaseline oxygen saturation, %\u003c/p\u003e\n \u003cp\u003eMean (\u003cu\u003e+\u003c/u\u003e SD)\u003c/p\u003e\n \u003cp\u003eNadir oxygen saturation,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eMean (\u003cu\u003e+\u003c/u\u003e SD), %\u003c/p\u003e\n \u003cp\u003eOxygen saturation on recovery,\u0026nbsp;Mean (\u003cu\u003e+\u003c/u\u003e SD), %\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.472406181015453%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e20.6 \u003cu\u003e+\u003c/u\u003e 5.44\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e285 (70.9)\u003c/p\u003e\n \u003cp\u003e117 (29.1)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e97.0 \u003cu\u003e+\u003c/u\u003e 1.30\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e94.1 \u003cu\u003e+\u003c/u\u003e 2.83\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e96.6 \u003cu\u003e+\u003c/u\u003e 1.52\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.472406181015453%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e20.9 \u003cu\u003e+\u003c/u\u003e 5.17\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e166 (77.2)\u003c/p\u003e\n \u003cp\u003e49 (22.8)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e97.2 \u003cu\u003e+\u003c/u\u003e 1.23\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e94.6 \u003cu\u003e+\u003c/u\u003e 2.38\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e96.8 \u003cu\u003e+\u003c/u\u003e 1.37\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.699779249448124%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e20.2 \u003cu\u003e+\u003c/u\u003e 5.73\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e119 (63.6)\u003c/p\u003e\n \u003cp\u003e68 (36.4)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e96.9 \u003cu\u003e+\u003c/u\u003e 1.36\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e93.5 \u003cu\u003e+\u003c/u\u003e 3.16\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e96.4 \u003cu\u003e+\u003c/u\u003e 1.63\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.472406181015453%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e19.6 \u003cu\u003e+\u003c/u\u003e 6.10\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e70 (61.4)\u003c/p\u003e\n \u003cp\u003e44 (38.6)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e96.7 \u003cu\u003e+\u003c/u\u003e 1.42\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e93.1 \u003cu\u003e+\u003c/u\u003e 3.31\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e96.2 \u003cu\u003e+\u003c/u\u003e 1.66\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.472406181015453%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e21.2 \u003cu\u003e+\u003c/u\u003e 5.00\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e49 (67.1)\u003c/p\u003e\n \u003cp\u003e24 (32.9)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e97.1 \u003cu\u003e+\u003c/u\u003e 1.24\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e94.0 \u003cu\u003e+\u003c/u\u003e 2.83\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e96.6 \u003cu\u003e+\u003c/u\u003e 1.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.381898454746137%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.204\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.381898454746137%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.646799116997794%\" valign=\"top\"\u003e\n \u003cp\u003eChest X-ray abnormalities, n (%)\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eConsolidation\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eGGO\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eReticulation \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.472406181015453%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e271 (66.4)\u003c/p\u003e\n \u003cp\u003e137 (33.6)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e17 (4.2)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e106 (26.0)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e41 (10.1)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.472406181015453%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e174 (80.2)\u003c/p\u003e\n \u003cp\u003e43 (19.8)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e2 (0.9)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e35 (16.1)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e13 (6.0)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.699779249448124%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e97 (50.8)\u003c/p\u003e\n \u003cp\u003e94 (49.2)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e15 (7.9)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e71 (37.2)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e28 (14.7)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.472406181015453%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e52 (44.8)\u003c/p\u003e\n \u003cp\u003e64 (55.2)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e10 (8.6)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e45 (38.8)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e18 (18.1)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.472406181015453%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e45 (60.0)\u003c/p\u003e\n \u003cp\u003e30 (40.0)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e5 (6.7)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e26 (34.7)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e7 (9.3)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.381898454746137%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt; 0.001\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt; 0.001\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e0.004\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.381898454746137%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e0.002\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt; 0.001\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e0.002\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTable 4. \u0026nbsp;Binary and multinomial logistic regression analyses to determine factors associated with abnormal spirometry results.\u003c/p\u003e\n\u003cp\u003e*, binary logistic regression analysis using patients with normal spirometry as reference;\u003csup\u003e\u0026nbsp;\u003c/sup\u003e#,\u003csup\u003e\u0026nbsp;\u003c/sup\u003emultinomial logistic regression analysis using patients with normal spirometry as reference;\u003csup\u003e\u0026nbsp;\u003c/sup\u003e^, continuous variables;\u003csup\u003e\u0026nbsp;\u003c/sup\u003e!, Most severe illness during hospitalization was added as a covariate for binary logistic regression even though univariate p = 0.052.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"765\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.328981723237597%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.49869451697128%\" valign=\"top\"\u003e\n \u003cp\u003eBinary logistic regression*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"48.17232375979112%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eMultinomial logistic regression\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.328981723237597%\" valign=\"top\"\u003e\n \u003cp\u003eParameters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.49869451697128%\" valign=\"top\"\u003e\n \u003cp\u003eAbnormal spirometry, OR (95% CI), p-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.673629242819842%\" valign=\"top\"\u003e\n \u003cp\u003eRestrictive,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eOR (95% CI), p-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.49869451697128%\" valign=\"top\"\u003e\n \u003cp\u003ePRISm and obstructive,\u0026nbsp;OR (95% CI), p-value\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.328981723237597%\" valign=\"top\"\u003e\n \u003cp\u003eAge^\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.49869451697128%\" valign=\"top\"\u003e\n \u003cp\u003e1.0 (1.01 \u0026ndash; 1.04), 0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.673629242819842%\" valign=\"top\"\u003e\n \u003cp\u003e1.0 (0.98 \u0026ndash; 1.03), 0.778\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.49869451697128%\" valign=\"top\"\u003e\n \u003cp\u003e1.0 (1.01 \u0026ndash; 1.07), 0.004\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.328981723237597%\" valign=\"top\"\u003e\n \u003cp\u003eGender,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.49869451697128%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.673629242819842%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003cp\u003e2.1 (1.13 -3.94), 0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.49869451697128%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003cp\u003e0.5 (0.23 \u0026ndash; 1.04), 0.062\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.328981723237597%\" valign=\"top\"\u003e\n \u003cp\u003eDiabetes mellitus,\u003c/p\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.49869451697128%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.673629242819842%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003cp\u003e0.8 (0.42 \u0026ndash; 1.53), 0.501\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.49869451697128%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003cp\u003e0.64 (0.30 \u0026ndash; 1.37), 0.249\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.328981723237597%\" valign=\"top\"\u003e\n \u003cp\u003eHypertension,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.49869451697128%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003cp\u003e1.5 (0.90 \u0026ndash; 2.39), 0.123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.673629242819842%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003cp\u003e1.8 (0.97 \u0026ndash; 3.50), 0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.49869451697128%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003cp\u003e1.1 (0.54 \u0026ndash; 2.23), 0.800\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.328981723237597%\" valign=\"top\"\u003e\n \u003cp\u003eCardiovascular disease,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.49869451697128%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003cp\u003e3.5 (1.19 \u0026ndash; 10.47), 0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.673629242819842%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003cp\u003e3.1 (0.89 \u0026ndash; 10.82), 0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.49869451697128%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003cp\u003e3.1 (0.78 \u0026ndash; 12.50), 0.108\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.328981723237597%\" valign=\"top\"\u003e\n \u003cp\u003eMost severe illness during hospitalization,\u003csup\u003e!\u0026nbsp;\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003cp\u003eSevere\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eCritical \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.49869451697128%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003cp\u003e0.6 (0.27 \u0026ndash; 1.28), 0.182\u003c/p\u003e\n \u003cp\u003e0.9 (0.41 \u0026ndash; 2.22), 0.954\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.673629242819842%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003cp\u003e1.5 (0.37 \u0026ndash; 6.50), 0.554\u003c/p\u003e\n \u003cp\u003e0.9 (0.42 \u0026ndash; 2.08), 0.857\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.49869451697128%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003cp\u003e3.6 (0.79 \u0026ndash; 16.23), 0.098\u003c/p\u003e\n \u003cp\u003e1.4 (0.47 \u0026ndash; 3.98), 0.566\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.328981723237597%\" valign=\"top\"\u003e\n \u003cp\u003eReceived corticosteroids during hospitalization,\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.49869451697128%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003cp\u003e1.3 (0.61 \u0026ndash; 2.94), 0.475\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.673629242819842%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003cp\u003e1.3 (0.39 \u0026ndash; 4.08), 0.703\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.49869451697128%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003cp\u003e1.5 (0.54 \u0026ndash; 4.06), 0.439\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.328981723237597%\" valign=\"top\"\u003e\n \u003cp\u003eReceived hydroxychloroquine during hospitalization,\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.49869451697128%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.673629242819842%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003cp\u003e0.5 (0.08 \u0026ndash; 2.65), 0.388\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.49869451697128%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003cp\u003e4.4 (1.15 \u0026ndash; 16.97), 0.030\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.328981723237597%\" valign=\"top\"\u003e\n \u003cp\u003eReceived immunomodulators during hospitalization,\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.49869451697128%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.673629242819842%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003cp\u003e2.4 (1.07 \u0026ndash; 5.28), 0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.49869451697128%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003cp\u003e0.7 (0.23 - 2.36), 0.606\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.328981723237597%\" valign=\"top\"\u003e\n \u003cp\u003eRequired IMV,\u003c/p\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.49869451697128%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003cp\u003e0.9 (0.44 \u0026ndash; 1.95), 0.843\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.673629242819842%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003cp\u003e1.2 (0.53 \u0026ndash; 2.85), 0.623\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.49869451697128%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003cp\u003e0.9 (0.29 \u0026ndash; 3.11), 0.922\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.328981723237597%\" valign=\"top\"\u003e\n \u003cp\u003eARDS,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.49869451697128%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003cp\u003e-, \u0026lt; 0.001\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.673629242819842%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.49869451697128%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.328981723237597%\" valign=\"top\"\u003e\n \u003cp\u003ePulmonary embolism,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.49869451697128%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003cp\u003e1.3 (0.73 \u0026ndash; 2.22), 0.391\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.673629242819842%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003cp\u003e1.0 (0.53 \u0026ndash; 2.00), 0.925\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.49869451697128%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003cp\u003e2.3 (1.07 \u0026ndash; 5.13), 0.033\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.328981723237597%\" valign=\"top\"\u003e\n \u003cp\u003eICU admission,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.49869451697128%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003cp\u003e1.4 (0.80 \u0026ndash; 2.46), 0.244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.673629242819842%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003cp\u003e1.3 (0.62 \u0026ndash; 2.62), 0.519\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.49869451697128%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003cp\u003e1.2 (0.51 \u0026ndash; 2.85), 0.679\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.328981723237597%\" valign=\"top\"\u003e\n \u003cp\u003eLength of hospital stay^\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.49869451697128%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.673629242819842%\" valign=\"top\"\u003e\n \u003cp\u003e1.0 (0.99 \u0026ndash; 1.04), 0.357\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.49869451697128%\" valign=\"top\"\u003e\n \u003cp\u003e0.9 (0.92 \u0026ndash; 1.00). 0.099\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.328981723237597%\" valign=\"top\"\u003e\n \u003cp\u003eTime from discharge to follow-up^\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.49869451697128%\" valign=\"top\"\u003e\n \u003cp\u003e0.9 (1.00 \u0026ndash; 1.02), 0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.673629242819842%\" valign=\"top\"\u003e\n \u003cp\u003e0.9 (0.99 \u0026ndash; 1.00), 0.060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.49869451697128%\" valign=\"top\"\u003e\n \u003cp\u003e1.0 (0.99 \u0026ndash; 1.00), 0.201\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.328981723237597%\" valign=\"top\"\u003e\n \u003cp\u003emMRC score^\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.49869451697128%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.673629242819842%\" valign=\"top\"\u003e\n \u003cp\u003e1.2 (0.92 \u0026ndash; 1.66), 0.164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.49869451697128%\" valign=\"top\"\u003e\n \u003cp\u003e0.7 (0.50 -1.12), 0.158\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.328981723237597%\" valign=\"top\"\u003e\n \u003cp\u003e6MWT distance^\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.49869451697128%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.673629242819842%\" valign=\"top\"\u003e\n \u003cp\u003e1.0 (0.99 \u0026ndash; 1.00), 0.501\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.49869451697128%\" valign=\"top\"\u003e\n \u003cp\u003e1.0 (0.99 \u0026ndash; 1.00), 0.678\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.328981723237597%\" valign=\"top\"\u003e\n \u003cp\u003e6MWT oxygen desaturation,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.49869451697128%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003cp\u003e1.9 (1.20 \u0026ndash; 3.06), 0.007\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.673629242819842%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003cp\u003e1.4 (0.76 \u0026ndash; 2.52), 0.290\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.49869451697128%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003cp\u003e2.4 (1.20 \u0026ndash; 4.75), 0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.328981723237597%\" valign=\"top\"\u003e\n \u003cp\u003e1MSTS oxygen desaturation,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.49869451697128%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003cp\u003e1.4 (0.85 \u0026ndash; 2.30). 0.184\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.673629242819842%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003cp\u003e1.5 (0.81 \u0026ndash; 2.79). 0.194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.49869451697128%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003cp\u003e1.4 (0.70 -2.91). 0.329\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.328981723237597%\" valign=\"top\"\u003e\n \u003cp\u003eConsolidation on chest X-ray,\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003eYes \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.49869451697128%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003cp\u003e8.1 (1.75 \u0026ndash; 37.42). 0.008\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.673629242819842%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003cp\u003e10.1 (1.88 \u0026ndash; 54.83), 0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.49869451697128%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003cp\u003e8.6 (1.41 \u0026ndash; 52.57), 0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.328981723237597%\" valign=\"top\"\u003e\n \u003cp\u003eGGO on chest X-ray,\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.49869451697128%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003cp\u003e2.6 (1.52 \u0026ndash; 4.30), \u0026lt; 0.001\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.673629242819842%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003cp\u003e2.2 (1.16 \u0026ndash; 4.26), 0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.49869451697128%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003cp\u003e2.6 (1.3 \u0026ndash; 5.3), 0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.328981723237597%\" valign=\"top\"\u003e\n \u003cp\u003eReticulation on chest X-ray,\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.49869451697128%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003cp\u003e1.2 (0.55 \u0026ndash; 2.68), 0.628\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.673629242819842%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003cp\u003e2.0 (0.81 \u0026ndash; 4.81), 0.134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.49869451697128%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003cp\u003e0.6 (0.19 \u0026ndash; 1.87), 0.372\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTable 5. \u0026nbsp;Findings of body plethysmography, diffusion capacity, and HRCT of the lungs for patients with restrictive pattern spirometry.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"614\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.73170731707317%\" valign=\"top\"\u003e\n \u003cp\u003eParameters of body plethysmography, and diffusion capacity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.089430894308943%\" valign=\"top\"\u003e\n \u003cp\u003eTotal number of patients, n = 89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.178861788617887%\" valign=\"top\"\u003e\n \u003cp\u003eFindings of lungs HRCT\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eTotal number of patients, n = 80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.73170731707317%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eFEV\u003csub\u003e1\u003c/sub\u003e,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eMean (\u003cu\u003e+\u003c/u\u003e SD), % predicted\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eFVC,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eMean (\u003cu\u003e+\u003c/u\u003e SD), % predicted\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eFEV\u003csub\u003e1\u003c/sub\u003e/FVC,\u003c/p\u003e\n \u003cp\u003eMean (\u003cu\u003e+\u003c/u\u003e SD), %\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRV,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eMean (\u003cu\u003e+\u003c/u\u003e SD), % predicted\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eTLC,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eMean (\u003cu\u003e+\u003c/u\u003e SD), % predicted\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eDLCO,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eMean (\u003cu\u003e+\u003c/u\u003e SD), % predicted\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eDLCO/Va\u003c/p\u003e\n \u003cp\u003eMean (\u003cu\u003e+\u003c/u\u003e SD), % predicted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.089430894308943%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e76.8 \u003cu\u003e+\u003c/u\u003e 13.97\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e75.6 \u003cu\u003e+\u003c/u\u003e 14.00\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.86 \u003cu\u003e+\u003c/u\u003e 0.12\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e57.8 \u003cu\u003e+\u003c/u\u003e 39.08\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e65.1 \u003cu\u003e+\u003c/u\u003e 13.25\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e62.5 \u003cu\u003e+\u003c/u\u003e 13.94\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e103.6 \u003cu\u003e+\u003c/u\u003e 17.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.178861788617887%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eHRCT abnormalities, n (%)\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eGGO\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eOP\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eReticulation\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eOthers\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e6 (7.5)\u003c/p\u003e\n \u003cp\u003e74 (92.5)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e65 (81.3)\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e42 (52.5)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e68 (85.0)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e27 (33.8)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"56.690140845070424%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eCT-scores,\u003c/p\u003e\n \u003cp\u003eMean (\u003cu\u003e+\u003c/u\u003e SD), score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"43.309859154929576%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e9.8 \u003cem\u003e\u003cu\u003e+\u003c/u\u003e\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e5.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"COVID-19, spirometry, restrictive, obstructive, PRISm, radio-imaging","lastPublishedDoi":"10.21203/rs.3.rs-4165804/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4165804/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTo determine the prevalence and types of spirometry abnormalities among post-COVID-19 patients in Malaysia, with secondary objective focusing on associated factors. Conducted at the COVID-19 Research Clinic, Faculty of Medicine, University Technology MARA, from March 2021 to December 2022, this study included patients three months post-discharge from hospitals following moderate-to-critical COVID-19. Of 408 patients studied, abnormal spirometry was found in 46.8%, with 28.4% exhibiting a restrictive pattern, 17.4% showing preserved ratio impaired spirometry (PRISm), and 1.0% displaying an obstructive pattern. Factors independently associated with abnormal spirometry included older age (OR: 1.0, 95% CI: 1.01\u0026ndash;1.04, p\u0026thinsp;=\u0026thinsp;0.003), underlying cardiovascular disease (OR: 3.5, 95% CI: 1.19\u0026ndash;10.47, p\u0026thinsp;=\u0026thinsp;0.023), history of acute respiratory distress syndrome (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), shorter discharge-to-follow-up interval (OR: 0.9, 95% CI: 1.00\u0026ndash;1.02, p\u0026thinsp;=\u0026thinsp;0.035), oxygen desaturation during 6-minute walk test (OR: 1.9, 95% CI: 1.20\u0026ndash;3.06, p\u0026thinsp;=\u0026thinsp;0.007), and presence of consolidation (OR: 8.1, 95% CI: 1.75\u0026ndash;37.42, p\u0026thinsp;=\u0026thinsp;0.008) or ground-glass opacity (OR: 2.6, 95% CI: 1.52\u0026ndash;4.30, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) on chest X-ray. This study highlights patients recovering from moderate-to-critical COVID-19 often exhibit abnormal spirometry, notably a restrictive pattern and PRISm. Routine spirometry screening for high-risk patients is recommended.\u003c/p\u003e","manuscriptTitle":"Post-Discharge Spirometry Evaluation in Patients Recovering from Moderate-to-Critical COVID-19: A Prospective Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-05 16:07:46","doi":"10.21203/rs.3.rs-4165804/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-06-11T12:20:53+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-07T15:34:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"31151752742669198173985729554841459204","date":"2024-06-03T13:16:21+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-04-27T09:48:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"99d590d7-accc-45d3-81eb-c2e9b6027618_SNPRID","date":"2024-04-23T08:25:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"8f7726b2-71ef-49b1-ae2b-013a2e4ca351","date":"2024-04-21T16:14:47+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-04-16T10:13:03+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-16T10:07:57+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-04-03T10:41:35+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-04-02T11:45:11+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-03-25T22:33:22+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1d71e4b6-fc3e-4b04-9177-ef183370d849","owner":[],"postedDate":"April 5th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":30238371,"name":"Health sciences/Diseases"},{"id":30238372,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2024-08-01T16:19:11+00:00","versionOfRecord":{"articleIdentity":"rs-4165804","link":"https://doi.org/10.1038/s41598-024-67536-2","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2024-07-16 15:57:32","publishedOnDateReadable":"July 16th, 2024"},"versionCreatedAt":"2024-04-05 16:07:46","video":"","vorDoi":"10.1038/s41598-024-67536-2","vorDoiUrl":"https://doi.org/10.1038/s41598-024-67536-2","workflowStages":[]},"version":"v1","identity":"rs-4165804","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4165804","identity":"rs-4165804","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}