Detection of structural pulmonary changes with real-time and high-fidelity analysis of expiratory CO2

Detection of structural pulmonary changes with real-time and high-fidelity analysis of expiratory CO2 | 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 Research Article Detection of structural pulmonary changes with real-time and high-fidelity analysis of expiratory CO2 Teresa Sassmann, Gabor Kovacs, Philipp Douschan, Vasile Foris, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3894602/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background There is an unmet need for easily available sensitive markers of structural lung disease. Assessment of lung diffusion capacity with foreign gases is currently state-of-the-art, however, results are unspecific and the methods are technically demanding. We developed a fully-automatic algorithm to analyze high-fidelity expiratory CO 2 flows from resting ventilation and compared the derived readouts with the diffusing capacity for carbon monoxide (DLCO) regarding their diagnostic accuracy. Methods This pilot study enrolled clinically well characterized patients with chronic obstructive pulmonary disease (COPD), interstitial lung disease (ILD), pulmonary arterial hypertension (PAH) and controls without lung disease from a pulmonary hypertension clinic and investigated them by means of our newly developed algorithm. We evaluated dead-, mixed- and alveolar space volumes (DSV, MSV, ASV, respectively), their respective ventilatory equivalents for CO 2 (EqCO 2 ) and the fraction of expiratory CO 2 (FECO 2 ) over expired volume (VE) as primary readouts for diagnosis of structural lung disease and pulmonary hypertension. Results We enrolled 52 subjects, 11 COPD (7 men; median (IQR) age 64 (63–69) years), 10 ILD (7 men; 61 (54–77) years), 10 PAH patients (1 man; 64 (61–73) years) and 21 healthy controls (9 men; 56 (52–61) years; 11 non-smokers). Patients, compared to controls, showed higher MSV (221 (164–270) mL vs. 144 (131–167) mL, p < 0.001) and higher EqCO 2 of the whole exhalation (38 (34–42) vs. 30 (29–35), p < 0.001), respectively. While EqCO 2 was elevated in all diseased groups, MSV was only increased in COPD and ILD but not in PAH. MSV and maximum FECO 2 /VE slope were significantly correlated with DLCO ( ρ =-0.69 and ρ = 0.72, respectively; both p < 0.001). According to receiver operating characteristic (ROC) analysis, MSV distinguished diseased from healthy subjects with an area under the curve (AUC) of 0.81 (95% CI: 0.69–0.93) with an optimal cut-off at 191 mL (sensitivity 68%, specificity 90%), and the parenchymal diseases COPD and ILD from PAH with AUC 0.74 (95% CI: 0.55–0.92), optimal cut-off at 210 mL; sensitivity 71%, specificity 80%). Conclusions Fully-automatic high-fidelity expiratory CO 2 flow analysis is technically feasible, easy and safe to perform, and may represent a novel approach to detect structural changes of the lung parenchyma and/or pulmonary hypertension without need for foreign gas. Expiratory CO2 analysis pulmonary structural changes COPD interstitial lung disease pulmonary arterial hypertension lung function Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Lung function testing with spirometry or body plethysmography is a well-established non-invasive method to detect pulmonary functional and structural changes and to differentiate between obstructive and restrictive lung diseases [ 1 , 2 ]. However, due to the low sensitivity for detecting parenchymal and small-airway changes, early tissue remodeling due to chronic obstructive pulmonary disease (COPD) or interstitial lung disease (ILD) may be undetectable until a large number of the small airways have vanished [ 3 – 7 ]. Diffusion capacity for foreign gases is more sensitive for structural changes of the lung, however, technically demanding and expensive. In addition, results are not very specific for obstructive, restrictive or vascular lung diseases [ 3 , 8 – 10 ]. Furthermore, both spirometry and the diffusing capacity for carbon monoxide (DLCO) results are strongly dependent on patients’ cooperation and depend on well-trained technical personal. The same is true for cardiopulmonary exercise testing [ 11 – 14 ]. Pulmonary hypertension is diagnosed by right heart catheter investigation, while screening and diagnostic procedures include electrocardiography, echocardiogram, DLCO, high-resolution computed tomography, perfusion lung scan, magnetic resonance imaging, or single photon emission computed tomography using injected radioisotopes [ 11 ]. However, these tools are expensive and not justified in every patient with dyspnea [ 11 , 15 ]. Some adults are even not able to perform body plethysmography due to cognitive skills, body composition or communication problems. With real-time high-fidelity CO 2 analysis, we might be able to offer an easy technology that is operator-independent and needs very little co-operation by the patient. Breath gas analysis during resting ventilation may provide valuable information on alveolar blood gases and the flow and distribution of these gases during expiration. The EasyOne Pro device (ndd, Zürich, Switzerland) provides digital readouts of real-time high fidelity expiratory CO 2 partial pressure (pCO 2 ) and airflow to determine various readouts characterizing the patient’s lung. Similar approaches have been used to partition the dead space volume [ 16 ] and several studies showed promising results in asthma, COPD, chronic thromboembolic pulmonary hypertension (CTEPH) and idiopathic PAH (iPAH) as well as acute pulmonary embolism [ 17 – 21 ]. However, although the principles were first published nearly one century ago [ 22 ], capnography has not become part of the routine diagnostic algorithm [ 23 ]. This may be due to technical difficulties capturing the CO 2 concentration without any delays from the expiratory flow and the breath-to-breath variability during normal breathing. We hypothesized that our new automatic algorithm, analyzing the real-time high-fidelity CO 2 signals, may allow detection of parenchymal lung diseases like COPD and ILD and isolated vascular lung diseases like PAH. The aim of this pilot study was to investigate the diagnostic accuracy of different readouts of our fully-automatic algorithm. Methods Study design, patients and ethics In this pilot study, patients and controls were enrolled at our outpatient clinic at the University Hospital of Graz from 10/2020 until 07/2021. We included five groups of subjects: COPD patients, ILD patients, PAH patients and healthy controls with and without smoking. The diagnosis of COPD and the severity of airflow limitation were established according to the GOLD recommendations by two independent respiratory physicians and ILD diagnosis was established according to the ILD board or consensus between a pulmonologist and a rheumatologist involved in the treatment of this patient [ 3 , 4 , 24 ]. Diagnosis of PAH was established in our pulmonary hypertension clinic, according to recent guidelines [ 11 ]. Two groups of healthy controls were enrolled, comprising smokers and non-smokers. For inclusion and exclusion criteria see Table 1 . All subjects signed informed consent (IC) before participating in this study. The study protocol conformed to the Declaration of Helsinki and was approved by the Ethics Committee of the Medical University of Graz (EK 32–276 ex 19/20). Table 1. Inclusion and exclusion criteria of the enrolled groups. Inclusion criteria Exclusion criteria COPD FEV1/FVC < 70% FEV1 ≤ 65% recent RHC (<2years) or echocardiography Missing IC/not able to sign due to cognitive reasons Contraindication for lung function testing Within 6 weeks: myocardial infarction, pulmonary embolism, pneumonia, pneumothorax, abdominal, ophthalmic, brain or thoracic operation, hemoptysis. Uncontrolled hypertension or ventricular arrhythmia. Dissecting aortic aneurysm ILD FEV1/FVC ≥ 70% FVC < 70% CT typical signs (any time) recent echocardiography or RHC (25mmHg and PVR >3WU, PAWP<15mmHg recent echocardiography Non smokers no cardio-pulmonary comorbidities never smoker or ex smoker with <10py smokers no cardio-pulmonary diseases active smoking, or ex smoker with ≥10py Clinical workup All patients underwent pulmonary function test including spirometry for forced vital capacity (FVC) and forced expiratory volume in the first second of expiration (FEV1) as well as body plethysmography (MasterScreen Body Pro, Jaeger, Höchberg, Germany) for total lung capacity (TLC), single breath diffusing capacity for carbon monoxide (DLCOcSB) and diffusing capacity for carbon monoxide by alveolar volume (DLCOcVA = KCO) (MasterScreen-PFT, Jaeger, Höchberg, Germany), both corrected for hemoglobin. Blood gas analysis for partial oxygen pressure (pO 2 ), partial carbon dioxide (pCO 2 ) pressure and alveolar-arterial oxygen gradient (AaDO 2 ) were performed from hyperemized capillary ear blood (SBL 510 Radiometer, Copenhagen, Denmark). Further investigations like laboratory testing (e.g. hemoglobin, white blood count, platelets, creatinine and N-terminal pro-brain natriuretic peptide (Nt-proBNP)), thoracic imaging including thoracic computer tomography scans, right heart catheterization and cardio pulmonary exercise testing, were part of the clinical work-up. We performed transthoracic echocardiography (Vivid E9, GE HealthCare, Munich, Germany) to gain information about left ventricular systolic function (ejection fraction), left ventricular diastolic function (ratio of maximal early diastolic transmitral velocity (E) to maximal septal early diastolic myocardial velocity (E’)) and right heart function (systolic pulmonary arterial pressure (sPAP), tricuspid annular plane systolic excursion (TAPSE) and peak systolic right ventricular velocity (s’)). Exhalative CO 2 measurement and data processing The expiratory pCO 2 tracings and the flows were captured by means of an EasyOne Pro device (ndd, Zürich, Switzerland). The measurements were based on a non-dispersive infrared absorption sensor, which recorded pCO 2 with a temporal resolution of 5 ms. The flow sensor was located at the mouthpiece and the gas sensor in the device together with a second flow sensor used to ascertain proper timing of measurements. The time-difference between mouthpiece and sensor was assessed on a ms base and used to achieve simultaneous capturing of flows and concentrations. Patients were sitting upright, wearing a nose clip and instructed to breathe calmly. Seven consecutive breaths were captured in each subject. The whole procedure lasted less than 3 minutes. The digital breath recordings were analyzed with a fully-automatic in-house developed software (MATLAB version 9.7.0 (R2019b), MathWorks, Natick, MA, USA), which included five complete breaths in every subject. Respiratory cycles with incomplete exhalations or coughs were automatically excluded. In one patient, two breaths were manually excluded due to inconsistent breathing pattern. Average expiration time, average inspiration time, ventilation, expired CO 2 and breathing frequency were determined as general readouts. Because of the relatively large variation of in- and expiration time from breath to breath, exhaled air flow and CO 2 partial pressure were used to plot the fraction of exhaled CO 2 volume (FECO 2 ) over exhaled volume (VE), which resulted in robust characteristics within each subject (Figs. 1 , S1 and S2). These plots were fitted by a logistic function, yielding readouts for maximum FECO 2 , maximum FECO 2 /VE slope and coordinates of the maximum slope in the plots. The intersections of the maximum FECO 2 /VE slope with the x-axis and the maximum slope with a horizontal line at the level of the maximum FECO 2 were used to define the end of dead space and mixed space volumes, respectively (Fig. 1 ). Based on these values, dead space volume (DSV), alveolar space volume (ASV) and mixed space volume (MSV) were fully-automatically determined. The ventilatory equivalent for CO 2 (EqCO 2 ) was determined from the fits as complete exhaled volume over exhaled CO 2 for the whole exhalation (EqCO 2,total ), as well as the mixed space (EqCO 2,ms ) and the alveolar space (EqCO 2,as ) separately. Additionally, end-tidal pCO 2 (PETCO 2 ) was analyzed. Statistical analysis Data were analyzed using SPSS statistical package (version 28; SPSS Inc., Chicago, USA) and R (version 4.3.1, R Core Development Team, Vienna, Austria), using RStudio (version 2023.06.1, RStudio, Boston, USA). Continuous variables are presented as median and interquartile range. Differences between the groups were analyzed using non-parametric tests (Mann-Whitney-U test or Kruskal-Wallis test). Categorical data are presented as absolute and relative numbers and differences between the groups were analyzed using chi-squared tests. In case of comparisons with more than two groups, pairwise group comparisons were performed using post-hoc Bonferroni tests. Associations were analyzed by Spearman correlation. The discriminatory power between healthy controls and diseased patients as well as between the parenchymal diseases COPD and ILD and isolated pulmonary hypertension due to PAH were determined with receiver operating characteristic (ROC) analysis. Youden indices were used to determine optimal cut-off values for these distinctions. P-values < 0.05 were considered significant. Results Clinical and demographic characteristics Fifty-two subjects (11 with COPD, 10 with ILD, 10 with PAH and 21 controls, consisting of 10 smokers and 11 non-smokers) were enrolled. Patient characteristics are presented in Table 2 . Five COPD patients were in GOLD stage II and six in GOLD stage III. Nt-proBNP was significantly higher in all diseased groups vs. controls. As expected, smokers and COPD patients had significantly more packyears and the COPD and ILD groups performed worst at spirometry. PaO 2 and AaDO 2 were lower in all diseased groups compared to controls. Systolic pulmonary arterial pressure (sPAP) was significantly higher in COPD, ILD and PAH vs. controls and for those who received RHC (N = 9 of COPD, N = 5 of ILD and N = 10 PAH), there was no statistically significant difference in pulmonary hemodynamics between the groups (Supplement Table S1), suggesting that PH was present in all our patient groups. Left ventricular function was not significantly different between the groups. Table 2 Baseline characteristics. Abbreviations: BMI = Body mass index, NYHA = New York Heart Association, LTOT = long term oxygen therapy, FVC = forced vital capacity, FEV1 = forced expiratory volume in the first second of expiration, TLC = total lung capacity, DLCO = diffusing capacity of lung for carbon monoxide; SB = single-breath, KCO = DLCOcVA = diffusing capacity of lung for carbon monoxide by alveolar volume, corrected for hemoglobin, pO 2 = partial oxygen pressure, pCO 2 = partial carbon dioxide pressure, AaDO 2 = alveolar-arterial oxygen gradient. Hb = Hemoglobin, Nt-proBNP = N-terminal pro brain natriuretic peptide, LVEF = left ventricular ejection fraction, E’ = early diastolic myocardial velocity, E = early diastolic filling velocity, SPAP = systolic pulmonary arterial pressure, TAPSE = tricuspid annular plane systolic excursion, s’ = peak systolic right ventricular velocity. Continuous variables are expressed as median and interquartile range. Superscripted numbers show the intergroup contrasts in the post-hoc Bonferroni analysis. Variables COPD (1) N = 11 ILD (2) N = 10 PAH (3) N = 10 Smoking control (4) N = 10 Non-smoking control (5) N = 11 p-value Age, yr 65(62–74) 5,4 61(50–78) 64(60–74) 5 58(48–62) 1 55(52–64) 1,3 p = 0.027 BMI, kg/m² 23.5(21.3–30.7) 25.4(22.4–29.1) 26.8(21.6–36.5) 26.8(24.0-28.6) 24.1(20.2–26.8) n.s. Sex(female/male) 4/7 3/7 9/1 5/5 7/4 - NYHA I/II/III/IV 1/2/7/1 1/5/3/1 2/4/4/0 8/2/0/0 10/1/0/0 - Smoking status Never/quit/active 2/7/2 5/5/0 4/5/1 0/4/6 10/1/0 - LTOT treatment 7 1 2 0 0 - Pack years 40(15–60) 2,3,5 2.5(0–10) 1,4 8.8(0–21) 1,4 30(19–39) 2,3,5 0(0–0) 1,4 p < 0.001 FVC, % predicted 74(52–78) 3,4,5 66(61–71) 3,4,5 93(73–110) 2,1 98(73–108) 2,1 103(97–113) 2,1 p < 0.001 FEV1/FVC Ratio 49(43–57) 2,4,5 78(74–87) 1 72(70–77) 76(74–82) 1 80(76–82) 1 p < 0.001 FEV1, % predicted 47(31–54) 3,4,5 68(71–92) 5 82(71–92) 1,5 91(70–100) 1 102(91–117) 1,2,3 p < 0.001 TLC, % predicted 114(89–119) 2 66(57–80) 1,4,5 95(79–111) 106(84–115) 2 109(103–115) 2 p < 0.001 DLCO, % predicted 28(19–69) 4,5 45(34–68) 4,5 51(39–74) 5 82(70–87) 1,2 93(85–108) 1,2,3 p < 0.001 KCO, % predicted 36(25–62) 2,4,5 72(48–86) 1,5 70(53–78) 4,5 90(80–96) 1,2,3 96(87–120) 1,2,3 p < 0.001 pO 2 , mmHg N = 11/10/10/10/6 62(49–69) 2,4,5 75(63–84) 68(60–72) 1,4,5 79(74–85) 1,3 84(80–86) 1,3 p < 0.001 pCO 2 , mmHg N = 11/10/10/10/6 37(36–39) 38(34–43) 36(32–40) 38(35–39) 36(34–39) n.s. AaDO 2 , mmHg N = 11/10/10/10/6 64(41–110) 4,5 32(18–52) 5 37(26–58) 4,5 19(14–23) 1,3 16(13–19) 1,2,3 p < 0.001 Hb, g/dL N = 11/10/10/10/10 14.2(13.5–15.8) 13.9(13.4–15) 14.3(11.7–15) 13.7(12.5–15.3) 13.6(13.1–15.3) n.s. Creatinine,mg/dL N = 11/10/10/10/10 0.9(0.7–1.3) 0.9(0.8-1.0) 1.0(0.8–1.1) 0.8(0.7–0.9) 0.8(0.7–0.9) n.s. Nt-proBNP,pg/mL N = 11/10/10/5/6 761(86-1329) 4 208(56–636) 4 344(193–468) 4 33(30–46) 1,2,3 63(56–103) p = 0.004 LVEF, % N = 10/10/8/9/11 61(55–65) 61(54–65) 60(57–62) 66(60–67) 66(61–69) n.s. E/E’ N = 11/10/10/9/11 8.5(4.4–12.5) 9.1(5.6–10) 9.9(7.9–14.4) 8.1(7.4–10.3) 7(5.5–8.9) n.s. SPAP, mmHg N = 11/10/9/7/8 53(36–73) 4,5 36(27–54) 4,5 59(43–73) 4,5 23(21–24) 1,2,3 24(20–24) 1,2,3 p < 0.001 TAPSE, mm N = 11/9/10/10/11 19(12–27) 20(16–24) 22(20–24) 23(20–24) 26(21–27) n.s. s’, cm/s N = 8/8/8/9/11 12(10–13) 12(10–18) 12(10–15) 13(12–15) 12(11–15) n.s. Expiratory CO 2 analysis We found several differences between healthy controls and diseased as well as between the individual diseased groups (Fig. 2 ). Healthy controls, compared to diseased patients, reached higher maximum values of FECO 2 (0.041 (0.037–0.043) mL CO 2 /mL vs. 0.035 (0.032–0.039) mL CO 2 /mL, p = 0.002) with steeper maximum FECO 2 /VE slopes (0.27 (0.23–0.32) L CO 2 /L 2 vs. 0.16 (0.12–0.21) L CO 2 /L 2 , p < 0.001, Fig. 3 ). Differences between healthy controls and patients are presented in Table 3 . Healthy controls also showed smaller MSV (144 (131–167) mL vs. 221 (164–270) mL, p < 0.001, Fig. 3 ) and EqCO 2,total (30 (29–35) vs. 38 (34–42), p < 0.001) than the patients. In addition, their minute ventilation and breathing frequency was lower (p = 0.010 and p < 0.001) as compared to the patient groups. In healthy controls, PETCO 2 was higher than in the patients (p = 0.008), whereas CO 2 output over time (VCO 2 ) and DSV were not different. EqCO 2,total showed the best contrasts between patients and controls, as compared to the partitioned EqCO 2,as and EqCO 2,ms . Table 3 Results of CO 2 flow analysis by disease status. Data were extracted from the curve fits. Continuous variables are expressed as median and interquartile range. Kruskal Wallis rank sum test was used. Abbreviations: DSV = Dead space volume; MSV = Mixed space volume. Variables Diseased N = 31 Healthy N = 21 p-value Average inspiration time (s) 1.2 (1.1–1.4) 1.5 (1.3–1.7) p < 0.001 Average expiration time (s) 1.7 (1.5–1.9) 2.2 (1.6–2.5) p = 0.004 Ventilation (L/min) 15.7 (12.0-17.2) 12.3 (9.3–14.6) p = 0.011 CO 2 output (mL CO 2 /mL) 370 (327–459) 402 (300–437) n.s. Breathing frequency (1/min) 21 (19–23) 16 (15–19) p = 0.002 Maximum FECO 2 (mL CO 2 /mL) 0.035 (0.032–0.038) 0.041 (0.037–0.044) p = 0.002 Maximum FECO 2 /VE slope (L CO 2 /L²) 0.16 (0.12–0.22) 0.27 (0.23–0.32) p < 0.001 PETCO 2 (mmHg) 31 (30–36) 35 (32–39) p = 0.008 DSV (mL) 69 (62–91) 79 (68–91) n.s. MSV (mL) 221 (162–280) 144 (130–175) p < 0.001 EqCO 2,ms 57 (52–62) 49 (46–54) p = 0.002 EqCO 2,as 29 (26–32) 25 (23–27) p = 0.001 EqCO 2,total 38 (34–43) 30 (28–38) p < 0.001 In a next step we compared our readouts between the different groups (COPD vs. ILD vs. PAH vs. healthy smokers vs. healthy non-smokers) in a multivariate analysis. Average in- and expiration time, breathing frequency, maximum FECO 2 , maximum FECO 2 /VE slope and MSV were significantly different between the groups (Table 4 ). Likewise, EqCO 2 in the mixed space, the alveolar space and EqCO 2, total were significantly different between the groups. However, post-hoc tests only showed significant contrasts between distinct groups for expiration time, maximum FECO 2 /VE slope, MSV and EqCO 2,total . Of note, MSV was different between COPD and ILD but not PAH vs. controls and the maximum FECO 2 /VE slope was decreased in COPD vs. controls. There were no significant differences between healthy smokers and healthy non-smokers (Table 4 ). Table 4 Between-group comparison of values of CO 2 flow analysis and the single groups. Data extracted from curve fits. Continuous variables are expressed as median and interquartile range. p value < 0.05 from Dunn's rank sum test comparing healthy smokers vs. healthy non-smokers vs. COPD vs. ILD vs PAH. Superscripted numbers show the intergroup contrasts according to the post-hoc Bonferroni test. Variables COPD (1) N = 11 ILD (2) N = 10 PAH (3) N = 10 Healthy smokers (4) N = 10 Healthy non-smokers (5) N = 11 p-value Average inspiration time (s) 1.1 (1.0-1.6) 5 1.3 (1.1–1.4) 1.2 (1.1–1.3) 1.6 (1.3–1.8) 1.5 (1.2–1.7) 1 p = 0006 Average expiration time (s) 1.9 (1.8–2.3) 2 1.5 (1.2–1.7) 1,4 1.6 (1.3–1.7) 4 2.1 (1.8–2.6) 3 2.3 (1.4–2.4) p < 0.001 Ventilation (L/min) 15 (11–17) 15 (13–23) 16 (13–17) 12 (8–15) 13 (10–14) p = 0.127 Breathing frequency (1/min) 21 (15–21) 21 (19–25) 4 22 (20–24) 4 16 (14–18) 2,3 15 (15–23) p = 0.002 Maximum FECO 2 (mL CO 2 /mL) 0.035 (0.031–0.039) 0.036 (0.032–0.041) 0.034 (0.032–0.040) 0.042 (0.038–0.043) 0.041 (0.037–0.044) p = 0.039 Maximum FECO 2 /VE slope (L CO 2 /L²) 0.15 (0.10–0.22) 4,5 0.15 (0.12–0.21) 5 0.20 (0.15–0.24) 0.26 (0.19–0.32) 1 0.28 (0.23–0.32) 1,2 p < 0.001 MSV (mL) 247 (169–394) 5 248 (190–277) 5 179 (160–215) 164 (127–193) 142 (131–165) 1,2 p < 0.001 EqCO 2,ms 56 (52–65) 55 (49–63) 59 (51–62) 48 (46–57) 49 (45–54) p = 0.039 EqCO 2 , as 29 (26–33) 28 (25–32) 30 (26–32) 24 (23–27) 25 (23–27) p = 0.026 EqCO 2 , total 40 (33–44) 37 (34–44) 37 (33–40) 31 (28–37) 30 (28–35) p = 0.008 Correlations We performed correlation analysis for those CO 2 readouts with the strongest contrasts between diseased and healthy subjects (Table 5 ). Lung function parameters (FEV1/FVC, FEV1, FVC, DLCO, KCO), AaDO 2 , and packyears were significantly correlated with both MSV and maximum FECO 2 /VE slope, with correlation coefficients for maximum FECO 2 /VE slope being higher than those for MSV for nearly all lung function parameters. DLCO, a parameter reflecting structural damage of the lung parenchyma, was strongest correlated with maximum FECO 2 /VE slope and MSV ( ρ = 0.721 and ρ =-0.695, respectively; both p < 0.001) (Fig. 4 ). Markers of pulmonary hypertension (sPAP and Nt-proBNP) were also positively correlated to MSV and maximum FECO 2 /VE slope, while left ventricular ejection fraction was not. For most of the lung function and PH parameters, the correlations with EqCO 2,total , EqCO 2,as and EqCO 2,ms were in a similar range. However, EqCO 2,total was the gas flow ratio with the strongest correlations with DLCO and KCO ( ρ =-0.661 and ρ =-0.710, respectively; both p < 0.001). Further significant correlations, not listed in Table 5 , were observed between average inspiration time and FEV1 ( ρ = 0.509; p < 0.001), FVC ( ρ = 0.456; p < 0.001), DLCO ( ρ = 0.541; p < 0.001), KCO, ( ρ = 0.541; p < 0.001), AaDO 2 ( ρ =-0.468; p < 0.001) and sPAP ( ρ =-0.500; p < 0.001). Table 5 Correlations of lung function, blood gas and cardiac function parameters with values from CO 2 flow analysis. Data extracted from curve fits. Spearman (ρ) correlation was used. Highlighted readouts show a statistically significant correlation. Abbreviations: FVC = forced vital capacity, FEV1 = forced expiratory volume in the first record of expiration, TLC = total lung capacity, DLCO = diffusing capacity of lung for carbon monoxide corrected for hemoglobin, KCO = diffusing capacity of lung for carbon monoxide by alveolar volume corrected for hemoglobin (Krogh factor), pO 2 = partial oxygen pressure, pCO 2 = partial carbon dioxide pressure, AaDO 2 = alveolar-arterial oxygen gradient, LVEF = left ventricular ejection fraction, SPAP = systolic pulmonary arterial pressure, TPR = transpulmonary gradient, NT-proBNP = N-terminal pro natriuretic peptide. Variables MSV (mL) Maximum FECO 2 /VE slope EqCO 2ms EqCO 2as EqCO 2total Packyears N = 52 ρ = 0.318 p = 0.021 ρ = -0.288 p = 0.038 ρ = 0.182 p = 0.195 ρ = 0.191 p = 0.176 ρ = 0.191 p = 0.174 FEV1/FVC (% predicted) N = 52 ρ = 0.345 p = 0.012 ρ = 0.366 p = 0.008 ρ = 0.291 p = 0.036 ρ = 0.299 p = 0.031 ρ =-0.227 p = 0.106 FEV1 (% predicted) N = 52 ρ =-0.524 p < 0.001 ρ = 0.534 p < 0.001 ρ =-0.366 p = 0.008 ρ =-0.386 p = 0.005 ρ =-0.447 p = 0.001 FVC (% predicted) N = 52 ρ =-0.432 p = 0.001 ρ = 0.430 p < 0.001 ρ =-0.234 p = 0.096 ρ =-0.257 p = 0.065 ρ =-0.395 p = 0.004 TLC (% predicted) N = 52 ρ =-0.206 p = 0.142 ρ = 0.178 p = 0.206 ρ =-0.086 p = 0.542 ρ =-0.084 p = 0.552 ρ =-0.157 p = 0.265 DLCO (% predicted) N = 52 ρ =-0.695 p < 0.001 ρ = 0.721 p < 0.001 ρ =-0.520 p < 0.001 ρ =-0.547 p < 0.001 ρ =-0.661 p < 0.001 KCO (% predicted) N = 52 ρ =-0.667 p < 0.001 ρ = 0.736 p < 0.001 ρ =-0.633 p < 0.001 ρ =-0.655 p < 0.001 ρ =-0.710 p < 0.001 PO 2 (mmHg) N = 47 ρ =-0.476 p = 0.001 ρ = 0.534 p < 0.001 ρ =-0.463 p = 0.001 ρ =-0.471 p = 0.001 ρ =-0.413 p = 0.004 PCO 2 (mmHg) N = 47 ρ =-0.097 p = 0.517 ρ = 0.182 p = 0.232 ρ =-0.357 p = 0.014 ρ =-0.343 p = 0.018 ρ =-0.233 p = 0.116 AaDO 2 (mmHg) N = 47 ρ = 0.619 p < 0.001 ρ =-0.687 p < 0.001 ρ = 0.550 p < 0.001 ρ = 0.564 p < 0.001 ρ = 0.561 p < 0.001 LVEF (%) N = 49 ρ =-0.23 p = 0.112 ρ = 0.27 p = 0.088 ρ =-0.331 p = 0.02 ρ =-0.342 p = 0.016 ρ =-0.456 p = 0.001 SPAP (mmHg) N = 45 ρ = 0.636 p < 0.001 ρ = -0.701 p < 0.001 ρ = 0.651 p < 0.001 ρ = 0.663 p < 0.001 ρ = 0.641 p < 0.001 Nt-proBNP (pg/mL) N = 42 ρ = 0.522 p < 0.001 ρ = -0.630 p < 0.001 ρ = 0.567 p < 0.001 ρ = 0.584 p 0.001 Diagnostic accuracy Concerning the differentiation between healthy and diseased lungs, MSV, maximum FECO 2 /VE slope, and DLCO showed the strongest contrasts. Receiver operating characteristic (ROC) analysis to distinguish diseased from healthy subjects using MSV showed an area under the curve (AUC) of 0.81 (95% CI: 0.69–0.93) and identified an optimal cut-off value of 191 mL (sensitivity 68%, specificity 90%). ROC analysis for maximum FECO 2 /VE slope showed an AUC of 0.84 (95% CI: 0.73–0.95) and a cut-off value of 0.25 l CO 2 /l² (sensitivity 90%, specificity 62%). DLCO showed an AUC of 0.97 (95% CI: 0.93-1.00) and a DLCO < 70% predicted a diseased lung with a sensitivity of 87% and a specificity of 91% The best predictors to discriminate between healthy and COPD/ILD patients, were an MSV of 199 mL (AUC: 0.85 (95% CI: 0.73–0.97); sensitivity 76%, specificity 90%) (Fig. 4 )., a maximum FECO 2 /VE slope of 0.19 L CO 2 /L² (AUC: 0.86 (95% CI: 0.74–0.98); sensitivity 76%, specificity 90%) and a DLCO of 70% predicted (AUC 0.98 (95% CI: 0.95-1.00), sensitivity 95%, specificity 90%). Parameters providing the best differentiation between COPD and ILD vs. PAH were again MSV, maximum FECO 2 /VE slope and DLCO. The optimal cut-off values for MSV, maximum FECO 2 /VE slope, and DLCO were 210 mL (AUC: 0.74 (95% CI: 0.56–0.92); sensitivity 71.4%, specificity 80%), 0.19 l CO 2 /l² (AUC: 0.71 (95% CI: 0.52–0.89) sensitivity 76.2%, specificity 70%), and 49% predicted (AUC 0.72 (95% CI: 0.53–0.91), sensitivity 76%, specificity 60%) (Fig. 4 ). Discussion In this pilot study we analyzed the diagnostic and differential diagnostic features of high-fidelity expiratory CO 2 flow analysis for parenchymal and vascular lung diseases and found that it may provide a safe and easy diagnostic method without need for foreign gas. We found significantly higher EqCO 2 , MSV and significantly lower maximum FECO 2 /VE slope values in patients with chronic diseases of the lung parenchyma and/or pulmonary hypertension compared to healthy controls. Further, these readouts may discriminate between parenchymal lung diseases and isolated pulmonary arterial hypertension. This indicates that real-time high-fidelity CO 2 flow analysis might provide a sensitive diagnostic and differential diagnostic tool, suitable for screening for chronic lung diseases. Heterogeneity in CO 2 analysis Although expiratory CO 2 analysis was first described in 1891 and capnography has since been used to determine dead space volume and perfusion heterogeneity in the lungs, it has not become part of diagnostic routine [ 22 ]. Most studies explored just end-tidal carbon dioxide pressure (PETCO 2 ) or the course of FECO 2 over the expiratory time, but did not perform CO 2 flow analysis. PETCO 2 measurement has been used in the intensive-care or anesthesia setting for monitoring adequacy of ventilation [ 25 , 26 ] and for screening for acute pulmonary embolism [ 15 ]. In a study by Singh et al., 60 patients with asthma had significantly lower resting PETCO 2 values than healthy controls [ 27 ], suggesting that CO 2 assessment might be suitable for asthma screening. Hemnes et al. showed that a bedside test for PETCO 2 had a high negative predictive value for pulmonary embolism [ 21 ]. The same group found that patients with PAH had lower PETCO 2 values than non-PH patients and concluded, that PETCO 2 was a promising tool to differentiate these diseases, even indicating pulmonary hemodynamic improvement after PAH therapy [ 28 ]. Further, there may be a differential diagnostic value within pulmonary hypertension patients. A study by Scheidl et al. investigated capillary to end-tidal pCO 2 gradients at rest and during exercise in patients with chronic thromboembolic PH (CTEPH) and idiopathic pulmonary arterial hypertension (iPAH) in order differentiate CTEPH from IPAH and found markedly higher systemic capillary to end-tidal pCO 2 gradients in patients with CTEPH compared to iPAH [ 19 ]. Diagnostic value of EqCO 2 This is, to our knowledge, the first study to analyze EqCO 2 , an established marker of breathing efficacy, separately for alveolar space and mixed space as well as for the whole exhalation. Increased EqCO 2,total is a hallmark of pulmonary hypertension (PH) [ 11 , 29 ]. However, elevated EqCO 2,total is also found in COPD, ILD and chronic heart failure [ 30 , 31 ]. During incremental exercise tests, at the ventilatory threshold, EqCO 2,total is an independent predictor of mortality and clinical worsening in PAH and chronic heart failure patients [ 12 , 32 – 34 ]. EqCO 2,total is part of the diagnostic algorithm for patients with exertional dyspnea and the risk assessment in PAH [ 11 , 35 ]. In this study, we found significantly higher resting EqCO 2,total values in all diseased groups, compared to controls, indicating that resting EqCO 2,total might be valuable in screening for lung diseases including pulmonary hypertension. Our expectation was, that partitioning EqCO 2,total into the dead space, the mixed space and the alveolar space EqCO 2 would even improve the diagnostic or discriminative power, however, EqCO 2,total showed the strongest contrasts between groups. As EqCO 2,total is the ventilatory equivalent over the whole exhalation, it most likely cumulates the differences in EqCO 2 for the mixed space and the alveolar space between healthy subjects and patients providing the best diagnostic properties. The EqCO 2 of the alveolar space might be of interest in the diagnostics for IPAH vs. CTEPH, because the difference between alveolar and arterial CO 2 appears to be a most sensitive marker for the perfusion heterogeneity in CTEPH [ 19 ], but this was not in the scope of this study. Two new parameters: MSV and maximum FECO 2 /VE slope Capnovolumetry, i.e. plotting pCO 2 against expired volumes, provides better results as compared to plotting these fractions against time [ 36 – 38 ]. Kellerer et al. performed capnovolumetric molar mass measurements by ultrasound estimations of exhaled CO 2 concentrations in 1287 subjects and concluded that ventilatory inhomogeneities would result in a flattening of the slope in the mixed phase and a steepening of the slope in the alveolar phase [ 18 ]. Their optimal cutoff for the ratio between these two slopes was 0.08 g*mL/mol for detection of airway obstruction and they achieved an AUC of 0.68 (95% CI 0.65–0.71) with 59% sensitivity and 69% specificity [ 18 ]. Our AUC was considerably higher, probably because we analyzed the MSV and the maximum FECO 2 /VE slope and because we used cutting edge technology concerning source data generation and automatic trace analysis. Further, we found that MSV and maximum FECO 2 /VE slope were as good as DLCO to differentiate between parenchymal diseases and isolated pulmonary hypertension, while, unlike for DLCO, no foreign gas is required. Therefore, our method may be a valuable tool for screening of pulmonary diseases and may guide further differential diagnostic or even therapeutic decisions. Of course, validation of these results is warranted. Pathophysiological considerations The respiratory system can be divided into a dead space with no relevant gas exchange and the alveolar space, characterized by complete gas exchange with the blood. Under the assumption that the gas content of all alveoli would have the same travel time to the mouth, we would expect a steep increase of CO 2 concentration in the expired air, once the dead space is emptied. However, different travel times are caused by different bronchial lengths and different flow characteristics due to heterogeneities in airway resistance, parenchymal compliance, and external forces working on the lungs. Therefore, we find a “mixed space volume” (MSV) between the dead space and the alveolar volume. In a healthy lung, this mixed space volume is small, resulting in a steep increase of FECO 2 over VE. Any condition that increases the heterogeneity of airway resistance, pulmonary compliance, and external forces are expected to increase MSV and decrease the maximum FECO2/VE slope. This has been shown in our study and agrees with nearly all previous studies [ 11 , 16 , 36 , 37 , 39 , 40 ]. CO 2 analysis to determine dead space volume Originally, capnography has been described to assess dead space ventilation by means of the Bohr equation [ 22 ] and has been used to estimate the degree of perfusion mismatch. In our study, the dead space, excluding the mixed space volume, had no diagnostic value. Just the ILD patients showed slightly elevated volumes that were not statistically significant. This suggests that dead space volume per se is less sensitive to structural lung diseases as compared to mixed space volume. Similarly, Steiss et al. investigated 47 asthmatic children and found that expiratory CO 2 flow characteristics provided the clinically important information [ 41 ]. Limitations This was a small hypothesis generating study without a validation cohort. However, we developed a new automatic algorithm based on real-time high-fidelity CO 2 flow signals from an established and approved diagnostic device, providing clinically meaningful measures. We detected strong correlations between MSV, maximum FECO 2 /VE slope and DLCO, indicating that our automatic algorithm produces sensitive parameters without the need for foreign gas. We included healthy smokers and non-smokers but failed to detect any significant differences between them, either because our technology was not sensitive enough or because there were not enough differences. Based on the fact, that all COPD patients had a reduced DLCO, we categorized them together with patients with ILD into the “parenchymal disease group”. Our controls were compared to patient groups with quite advanced disease, including pulmonary hypertension. However, the matching was suboptimal, as controls were slightly but significantly younger. This may be acceptable because our primary aim was to compare groups with different characteristics to assess the accuracy of our readouts and not to assess the severity of the diseases. Still, it remains unknown how our automatic measures would detect early disease stages. Conclusion In this pilot study we show that fully-automatic high-fidelity expiratory CO 2 flow analysis may be a fast, easy and low-cost approach to detect structural changes of the lungs including vascular lung disease without the use of foreign gas. Validation in larger prospective studies and in less severely affected individuals is warranted. Abbreviations CO2: carbon dioxide; DLCO: diffusing capacity for carbon monoxide; COPD: chronic obstructive pulmonary disease; ILD: interstitial lung disease; PAH: pulmonary arterial hypertension; DSV: dead space volume; MSV: mixed space volume; ASV: alveolar space volume; EqCO2: ventilatory equivalents for carbon dioxide; FECO2: fraction of expiratory carbon dioxide; ROC: receiver operating characteristic; AUC: area under the curve; CTEPH: chronic thromboembolic pulmonary hypertension; iPAH: idiopathic pulmonary arterial hypertension; BMI: Body mass index; NYHA: New York Heart Association; LTOT: long term oxygen therapy; FVC: forced vital capacity; FEV1: forced expiratory volume in the first second of expiration; TLC = total lung capacity; DLCO = diffusing capacity of lung for carbon monoxide; SB = single-breath; DLCOcVA: diffusing capacity of lung for carbon monoxide by alveolar volume corrected for hemoglobin; pO2: partial oxygen pressure, pCO2: partial carbon dioxide pressure; AaDO2: alveolar-arterial oxygen gradient; Hb: Hemoglobin; Nt-proBNP: N-terminal pro brain natriuretic peptide; LVEF: left ventricular ejection fraction; E’: early diastolic myocardial velocity; E: early diastolic filling velocity; SPAP = systolic pulmonary arterial pressure; TAPSE: tricuspid annular plane systolic excursion; s’: peak systolic right ventricular velocity; FECO2: fraction of exhaled CO2; VE: volume over exhaled volume Declarations Ethics approval and consent to participate: The study protocol conformed to the Declaration of Helsinki and was approved by the Ethics Committee of the Medical University of Graz, Austria (EK 32-276 ex 19/20). Informed consent was signed by every subject before participating this study. Data were pseudonymized and stored safely at our study center on a special password saved server and only assigned study personal had access to the data. Consent for publication: Not applicable. Availability of data and material: Not applicable. Competing interests: The authors declare that they have no competing interests. Funding: None. Clinical trials registry: ClinicalTrials.gov Identifier: NCT05092035; Other Study ID Numbers: ID 7684; Date: October 25, 2021 Authors' contributions: TS performed the study, data collection and abstraction and wrote the manuscript. MP performed data processing, data analysis and designed figures. HO had the main lead of the project. HO and MP conceived the project and were responsible for final manuscript approval. NJ, MG, GK and PD had impact on data collection and management of the database. All authors contributed to the editing of the manuscript. 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Clin Physiol Funct Imaging 2008;28:332–6. https://doi.org/10.1111/j.1475-097X.2008.00815.x. Additional Declarations No competing interests reported. Supplementary Files Supplement.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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Kovacs","email":"","orcid":"","institution":"Medical University of Graz, Department of Internal Medicine, Division of Pulmonology","correspondingAuthor":false,"prefix":"","firstName":"Gabor","middleName":"","lastName":"Kovacs","suffix":""},{"id":270027666,"identity":"f7ba4a9f-68e5-4507-80dc-8313d6f2d5e9","order_by":2,"name":"Philipp Douschan","email":"","orcid":"","institution":"Medical University of Graz, Department of Internal Medicine, Division of Pulmonology","correspondingAuthor":false,"prefix":"","firstName":"Philipp","middleName":"","lastName":"Douschan","suffix":""},{"id":270027667,"identity":"7921f148-2411-434a-828b-2402135529c7","order_by":3,"name":"Vasile Foris","email":"","orcid":"","institution":"Medical University of Graz, Department of Internal Medicine, Division of Pulmonology","correspondingAuthor":false,"prefix":"","firstName":"Vasile","middleName":"","lastName":"Foris","suffix":""},{"id":270027668,"identity":"1e5c2aed-0de8-4d04-b8a2-c8914ebef471","order_by":4,"name":"Maximilian Gumpoldsberger","email":"","orcid":"","institution":"Medical University of Graz","correspondingAuthor":false,"prefix":"","firstName":"Maximilian","middleName":"","lastName":"Gumpoldsberger","suffix":""},{"id":270027669,"identity":"9d25206b-d64c-40c6-a61f-c4edde955a9b","order_by":5,"name":"Nikolaus John","email":"","orcid":"","institution":"Medical University of Graz, Department of Internal Medicine, Division of Pulmonology","correspondingAuthor":false,"prefix":"","firstName":"Nikolaus","middleName":"","lastName":"John","suffix":""},{"id":270027670,"identity":"e973ff30-45b5-48e2-a141-5b3c2e4b99d7","order_by":6,"name":"Katarina Zeder","email":"","orcid":"","institution":"Medical University of Graz, Department of Internal Medicine, Division of Pulmonology","correspondingAuthor":false,"prefix":"","firstName":"Katarina","middleName":"","lastName":"Zeder","suffix":""},{"id":270027671,"identity":"a08c1183-bb03-4923-9b53-f2a7ae3b4565","order_by":7,"name":"Andreas Zirlik","email":"","orcid":"","institution":"Medical University of Graz, Department of Internal Medicine, Division of Cardiology","correspondingAuthor":false,"prefix":"","firstName":"Andreas","middleName":"","lastName":"Zirlik","suffix":""},{"id":270027672,"identity":"62880388-2010-4112-af12-177c3c55aaa6","order_by":8,"name":"Horst Olschewski","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABL0lEQVRIie2RQUuEQBTH3yJ4GvH6QsmvYAhWJOtXMQamy1RXT+FJL3Vf2PocdXQR8jJdQ/CSLHQqMIIoMkgtlmKnYG8R/hhmhsf8+L+ZARgY+IPYab94JgDpNgHR2xn78ZuSAiMLZS36pgQ/KdlC6StfIiRKfjJ/rFvFSq5mc9Iw07nOZqV2sTU+T/IbeHleUnyRu9il2OKAOlrMiVsyuqMJpKciCEbHkpSCqX1jNhLX2I/CVuGuocVIEYIUiFRR6k6xJmTzlTchcaaHTx+KXkWjRqpA3xgUxFW4yoltcLVTxogUFFmKuFRR2Ky9C3eMt5gRLJmzfRZjgHgLmckkLxYrdRh661YiNh4mDfX1Ka2K+/jIR32vqu68JeVTlNR2I+h+bBX8lU4PDAwM/GfeAalsacFtnBDAAAAAAElFTkSuQmCC","orcid":"","institution":"Ludwig Boltzmann Institute for Lung Vascular Research","correspondingAuthor":true,"prefix":"","firstName":"Horst","middleName":"","lastName":"Olschewski","suffix":""},{"id":270027673,"identity":"f4e2ea18-c032-4988-a8ac-a0342e3d3dac","order_by":9,"name":"Michael Pienn","email":"","orcid":"","institution":"Ludwig Boltzmann Institute for Lung Vascular Research","correspondingAuthor":false,"prefix":"","firstName":"Michael","middleName":"","lastName":"Pienn","suffix":""}],"badges":[],"createdAt":"2024-01-24 15:59:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3894602/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3894602/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50447132,"identity":"715bc5d6-5a95-4e34-9315-54ac2efe363d","added_by":"auto","created_at":"2024-01-31 16:17:12","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":108659,"visible":true,"origin":"","legend":"\u003cp\u003eTracings of fraction of exhaled CO\u003csub\u003e2\u003c/sub\u003e volume (FECO\u003csub\u003e2\u003c/sub\u003e) over exhaled volume (VE), representing 5 complete expirations in a healthy smoker, and our automatic curve fit with derived measures. At the beginning of exhalation, the dead space volume (DSV) is expired, with no significant increase in CO\u003csub\u003e2\u003c/sub\u003e concentration. DSV was determined by the “A-Point” (intersection of slope at the inflexion point of the CO\u003csub\u003e2\u003c/sub\u003e curve C with the x-axis). In the next phase of expiration, an accelerating increase of FECO\u003csub\u003e2\u003c/sub\u003e is recognized that decelerates before the alveolar phase is reached. Mixed space volume (MSV) is defined by B – A. The alveolar space volume (ASV) is characterized by an approximately constant FECO\u003csub\u003e2\u003c/sub\u003e over expired volume. FECO\u003csub\u003e2\u003c/sub\u003e during ASV gives information related to alveolar FECO\u003csub\u003e2\u003c/sub\u003e.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-3894602/v1/ac4e9609d60fcfafd8b33280.png"},{"id":50447131,"identity":"f50f74fa-d029-4906-a6b8-6db735a335df","added_by":"auto","created_at":"2024-01-31 16:17:12","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":59032,"visible":true,"origin":"","legend":"\u003cp\u003eFive fraction of exhaled CO\u003csub\u003e2\u003c/sub\u003e volume (FECO\u003csub\u003e2\u003c/sub\u003e) over exhaled volume (VE) curves of representative patients for COPD, ILD and PAH patients as well as smokers and non-smokers.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-3894602/v1/4980ec7c1c77ba965f562dee.png"},{"id":50447133,"identity":"0ff39aec-5ef0-4ecf-b147-1ed2d49ffdbf","added_by":"auto","created_at":"2024-01-31 16:17:12","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":554628,"visible":true,"origin":"","legend":"\u003cp\u003eMaximum fraction of exhaled CO\u003csub\u003e2\u003c/sub\u003e volume (FECO\u003csub\u003e2\u003c/sub\u003e) over exhaled volume (VE) curves in diseased vs. healthy subjects (a) in the five groups (b), respectively. Mixed space volume (MSV) in diseased vs. healthy subjects (c) and MSV in the five groups (d), respectively. */**/***: p-value \u0026lt;0.05/0.01/0.001, ns: not significant.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-3894602/v1/1806dd55fea4fefe9ffdd008.png"},{"id":50447134,"identity":"2290eb5a-65f1-45ea-9724-82b44b9ebb39","added_by":"auto","created_at":"2024-01-31 16:17:12","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":184006,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between Diffusion Capacity for Carbon Dioxide (DLCO) and MSV (a) and EqCO\u003csub\u003e2,total\u003c/sub\u003e (b), respectively. Receiver operating characteristic curve of MSV\u003csub\u003e \u003c/sub\u003eto discriminate between healthy subjects and diseased patients (c) and to separate between patients with parenchymal lung disease (COPD or ILD) and isolated pulmonary arterial hypertension (d). ***: p-value \u0026lt;0.001\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-3894602/v1/aa4ca9e8e4207e236635bf85.png"},{"id":51455888,"identity":"046148d4-ea27-4813-b22b-df265c499ffa","added_by":"auto","created_at":"2024-02-22 01:39:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1337977,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3894602/v1/51f8817e-4e31-45e8-a568-4ea3e392fbd3.pdf"},{"id":50447135,"identity":"6b6f07db-c5d3-47cc-800b-ca3521e8babb","added_by":"auto","created_at":"2024-01-31 16:17:12","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":380715,"visible":true,"origin":"","legend":"","description":"","filename":"Supplement.docx","url":"https://assets-eu.researchsquare.com/files/rs-3894602/v1/f0255dff75a2b628d4b42839.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Detection of structural pulmonary changes with real-time and high-fidelity analysis of expiratory CO2","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLung function testing with spirometry or body plethysmography is a well-established non-invasive method to detect pulmonary functional and structural changes and to differentiate between obstructive and restrictive lung diseases [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. However, due to the low sensitivity for detecting parenchymal and small-airway changes, early tissue remodeling due to chronic obstructive pulmonary disease (COPD) or interstitial lung disease (ILD) may be undetectable until a large number of the small airways have vanished [\u003cspan additionalcitationids=\"CR4 CR5 CR6\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Diffusion capacity for foreign gases is more sensitive for structural changes of the lung, however, technically demanding and expensive. In addition, results are not very specific for obstructive, restrictive or vascular lung diseases [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Furthermore, both spirometry and the diffusing capacity for carbon monoxide (DLCO) results are strongly dependent on patients\u0026rsquo; cooperation and depend on well-trained technical personal. The same is true for cardiopulmonary exercise testing [\u003cspan additionalcitationids=\"CR12 CR13\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePulmonary hypertension is diagnosed by right heart catheter investigation, while screening and diagnostic procedures include electrocardiography, echocardiogram, DLCO, high-resolution computed tomography, perfusion lung scan, magnetic resonance imaging, or single photon emission computed tomography using injected radioisotopes [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, these tools are expensive and not justified in every patient with dyspnea [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Some adults are even not able to perform body plethysmography due to cognitive skills, body composition or communication problems. With real-time high-fidelity CO\u003csub\u003e2\u003c/sub\u003e analysis, we might be able to offer an easy technology that is operator-independent and needs very little co-operation by the patient.\u003c/p\u003e \u003cp\u003eBreath gas analysis during resting ventilation may provide valuable information on alveolar blood gases and the flow and distribution of these gases during expiration. The EasyOne Pro device (ndd, Z\u0026uuml;rich, Switzerland) provides digital readouts of real-time high fidelity expiratory CO\u003csub\u003e2\u003c/sub\u003e partial pressure (pCO\u003csub\u003e2\u003c/sub\u003e) and airflow to determine various readouts characterizing the patient\u0026rsquo;s lung. Similar approaches have been used to partition the dead space volume [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] and several studies showed promising results in asthma, COPD, chronic thromboembolic pulmonary hypertension (CTEPH) and idiopathic PAH (iPAH) as well as acute pulmonary embolism [\u003cspan additionalcitationids=\"CR18 CR19 CR20\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. However, although the principles were first published nearly one century ago [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], capnography has not become part of the routine diagnostic algorithm [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. This may be due to technical difficulties capturing the CO\u003csub\u003e2\u003c/sub\u003e concentration without any delays from the expiratory flow and the breath-to-breath variability during normal breathing.\u003c/p\u003e \u003cp\u003eWe hypothesized that our new automatic algorithm, analyzing the real-time high-fidelity CO\u003csub\u003e2\u003c/sub\u003e signals, may allow detection of parenchymal lung diseases like COPD and ILD and isolated vascular lung diseases like PAH. The aim of this pilot study was to investigate the diagnostic accuracy of different readouts of our fully-automatic algorithm.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eStudy design, patients and ethics\u003c/h2\u003e\n \u003cp\u003eIn this pilot study, patients and controls were enrolled at our outpatient clinic at the University Hospital of Graz from 10/2020 until 07/2021. We included five groups of subjects: COPD patients, ILD patients, PAH patients and healthy controls with and without smoking. The diagnosis of COPD and the severity of airflow limitation were established according to the GOLD recommendations by two independent respiratory physicians and ILD diagnosis was established according to the ILD board or consensus between a pulmonologist and a rheumatologist involved in the treatment of this patient [\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e]. Diagnosis of PAH was established in our pulmonary hypertension clinic, according to recent guidelines [\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e]. Two groups of healthy controls were enrolled, comprising smokers and non-smokers. For inclusion and exclusion criteria see Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. All subjects signed informed consent (IC) before participating in this study. The study protocol conformed to the Declaration of Helsinki and was approved by the Ethics Committee of the Medical University of Graz (EK 32\u0026ndash;276 ex 19/20).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\"\u003eTable 1. Inclusion and exclusion criteria of the enrolled groups.\u003c/div\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"642\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.641744548286605%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.638629283489095%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eInclusion criteria\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.7196261682243%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eExclusion criteria\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.641744548286605%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCOPD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.638629283489095%\" valign=\"top\"\u003e\n \u003cul\u003e\n \u003cli\u003eFEV1/FVC \u0026lt; 70%\u003c/li\u003e\n \u003cli\u003eFEV1 \u0026le; 65%\u003c/li\u003e\n \u003cli\u003erecent RHC (\u0026lt;2years) or echocardiography\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.7196261682243%\" rowspan=\"5\" valign=\"top\"\u003e\n \u003cul\u003e\n \u003cli\u003eMissing IC/not able to sign due to cognitive reasons\u003c/li\u003e\n \u003cli\u003eContraindication for lung function testing\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eWithin 6 weeks: myocardial infarction, pulmonary embolism, pneumonia, pneumothorax, abdominal, ophthalmic, brain or thoracic operation, hemoptysis.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eUncontrolled hypertension or ventricular arrhythmia.\u003c/li\u003e\n \u003cli\u003eDissecting aortic aneurysm\u003c/li\u003e\n \u003c/ul\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.289405684754524%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eILD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"75.71059431524547%\" valign=\"top\"\u003e\n \u003cul\u003e\n \u003cli\u003eFEV1/FVC \u0026ge; 70%\u003c/li\u003e\n \u003cli\u003eFVC \u0026lt; 70%\u003c/li\u003e\n \u003cli\u003eCT typical signs (any time)\u003c/li\u003e\n \u003cli\u003erecent echocardiography or RHC (\u0026lt;2years)\u0026nbsp;\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.289405684754524%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePAH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"75.71059431524547%\" valign=\"top\"\u003e\n \u003cul\u003e\n \u003cli\u003eRHC (any time), mPAP \u0026gt;25mmHg and PVR \u0026gt;3WU, PAWP\u0026lt;15mmHg\u003c/li\u003e\n \u003cli\u003erecent echocardiography\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.289405684754524%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon smokers\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"75.71059431524547%\" valign=\"top\"\u003e\n \u003cul\u003e\n \u003cli\u003eno cardio-pulmonary comorbidities\u003c/li\u003e\n \u003cli\u003enever smoker or ex smoker with \u0026lt;10py\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.289405684754524%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003esmokers\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"75.71059431524547%\" valign=\"top\"\u003e\n \u003cul\u003e\n \u003cli\u003eno cardio-pulmonary diseases\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eactive smoking, or ex smoker with \u0026ge;10py\u0026nbsp;\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003eClinical workup\u003c/h2\u003e\n \u003cp\u003eAll patients underwent pulmonary function test including spirometry for forced vital capacity (FVC) and forced expiratory volume in the first second of expiration (FEV1) as well as body plethysmography (MasterScreen Body Pro, Jaeger, H\u0026ouml;chberg, Germany) for total lung capacity (TLC), single breath diffusing capacity for carbon monoxide (DLCOcSB) and diffusing capacity for carbon monoxide by alveolar volume (DLCOcVA\u0026thinsp;=\u0026thinsp;KCO) (MasterScreen-PFT, Jaeger, H\u0026ouml;chberg, Germany), both corrected for hemoglobin. Blood gas analysis for partial oxygen pressure (pO\u003csub\u003e2\u003c/sub\u003e), partial carbon dioxide (pCO\u003csub\u003e2\u003c/sub\u003e) pressure and alveolar-arterial oxygen gradient (AaDO\u003csub\u003e2\u003c/sub\u003e) were performed from hyperemized capillary ear blood (SBL 510 Radiometer, Copenhagen, Denmark). Further investigations like laboratory testing (e.g. hemoglobin, white blood count, platelets, creatinine and N-terminal pro-brain natriuretic peptide (Nt-proBNP)), thoracic imaging including thoracic computer tomography scans, right heart catheterization and cardio pulmonary exercise testing, were part of the clinical work-up. We performed transthoracic echocardiography (Vivid E9, GE HealthCare, Munich, Germany) to gain information about left ventricular systolic function (ejection fraction), left ventricular diastolic function (ratio of maximal early diastolic transmitral velocity (E) to maximal septal early diastolic myocardial velocity (E\u0026rsquo;)) and right heart function (systolic pulmonary arterial pressure (sPAP), tricuspid annular plane systolic excursion (TAPSE) and peak systolic right ventricular velocity (s\u0026rsquo;)).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003eExhalative CO\u003csub\u003e2\u003c/sub\u003e measurement and data processing\u003c/h2\u003e\n \u003cp\u003eThe expiratory pCO\u003csub\u003e2\u003c/sub\u003e tracings and the flows were captured by means of an EasyOne Pro device (ndd, Z\u0026uuml;rich, Switzerland). The measurements were based on a non-dispersive infrared absorption sensor, which recorded pCO\u003csub\u003e2\u003c/sub\u003e with a temporal resolution of 5 ms. The flow sensor was located at the mouthpiece and the gas sensor in the device together with a second flow sensor used to ascertain proper timing of measurements. The time-difference between mouthpiece and sensor was assessed on a ms base and used to achieve simultaneous capturing of flows and concentrations. Patients were sitting upright, wearing a nose clip and instructed to breathe calmly. Seven consecutive breaths were captured in each subject. The whole procedure lasted less than 3 minutes. The digital breath recordings were analyzed with a fully-automatic in-house developed software (MATLAB version 9.7.0 (R2019b), MathWorks, Natick, MA, USA), which included five complete breaths in every subject. Respiratory cycles with incomplete exhalations or coughs were automatically excluded. In one patient, two breaths were manually excluded due to inconsistent breathing pattern. Average expiration time, average inspiration time, ventilation, expired CO\u003csub\u003e2\u003c/sub\u003e and breathing frequency were determined as general readouts. Because of the relatively large variation of in- and expiration time from breath to breath, exhaled air flow and CO\u003csub\u003e2\u003c/sub\u003e partial pressure were used to plot the fraction of exhaled CO\u003csub\u003e2\u003c/sub\u003e volume (FECO\u003csub\u003e2\u003c/sub\u003e) over exhaled volume (VE), which resulted in robust characteristics within each subject (Figs. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e and S2). These plots were fitted by a logistic function, yielding readouts for maximum FECO\u003csub\u003e2\u003c/sub\u003e, maximum FECO\u003csub\u003e2\u003c/sub\u003e/VE slope and coordinates of the maximum slope in the plots. The intersections of the maximum FECO\u003csub\u003e2\u003c/sub\u003e/VE slope with the x-axis and the maximum slope with a horizontal line at the level of the maximum FECO\u003csub\u003e2\u003c/sub\u003e were used to define the end of dead space and mixed space volumes, respectively (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Based on these values, dead space volume (DSV), alveolar space volume (ASV) and mixed space volume (MSV) were fully-automatically determined. The ventilatory equivalent for CO\u003csub\u003e2\u003c/sub\u003e (EqCO\u003csub\u003e2\u003c/sub\u003e) was determined from the fits as complete exhaled volume over exhaled CO\u003csub\u003e2\u003c/sub\u003e for the whole exhalation (EqCO\u003csub\u003e2,total\u003c/sub\u003e), as well as the mixed space (EqCO\u003csub\u003e2,ms\u003c/sub\u003e) and the alveolar space (EqCO\u003csub\u003e2,as\u003c/sub\u003e) separately. Additionally, end-tidal pCO\u003csub\u003e2\u003c/sub\u003e (PETCO\u003csub\u003e2\u003c/sub\u003e) was analyzed.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003eStatistical analysis\u003c/h2\u003e\n \u003cp\u003eData were analyzed using SPSS statistical package (version 28; SPSS Inc., Chicago, USA) and R (version 4.3.1, R Core Development Team, Vienna, Austria), using RStudio (version 2023.06.1, RStudio, Boston, USA). Continuous variables are presented as median and interquartile range. Differences between the groups were analyzed using non-parametric tests (Mann-Whitney-U test or Kruskal-Wallis test). Categorical data are presented as absolute and relative numbers and differences between the groups were analyzed using chi-squared tests. In case of comparisons with more than two groups, pairwise group comparisons were performed using post-hoc Bonferroni tests. Associations were analyzed by Spearman correlation. The discriminatory power between healthy controls and diseased patients as well as between the parenchymal diseases COPD and ILD and isolated pulmonary hypertension due to PAH were determined with receiver operating characteristic (ROC) analysis. Youden indices were used to determine optimal cut-off values for these distinctions. P-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered significant.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\"\u003e\n \u003ch2\u003eClinical and demographic characteristics\u003c/h2\u003e\n \u003cp\u003eFifty-two subjects (11 with COPD, 10 with ILD, 10 with PAH and 21 controls, consisting of 10 smokers and 11 non-smokers) were enrolled. Patient characteristics are presented in Table\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e. Five COPD patients were in GOLD stage II and six in GOLD stage III. Nt-proBNP was significantly higher in all diseased groups vs. controls. As expected, smokers and COPD patients had significantly more packyears and the COPD and ILD groups performed worst at spirometry. PaO\u003csub\u003e2\u003c/sub\u003e and AaDO\u003csub\u003e2\u003c/sub\u003e were lower in all diseased groups compared to controls. Systolic pulmonary arterial pressure (sPAP) was significantly higher in COPD, ILD and PAH vs. controls and for those who received RHC (N\u0026thinsp;=\u0026thinsp;9 of COPD, N\u0026thinsp;=\u0026thinsp;5 of ILD and N\u0026thinsp;=\u0026thinsp;10 PAH), there was no statistically significant difference in pulmonary hemodynamics between the groups (Supplement Table S1), suggesting that PH was present in all our patient groups. Left ventricular function was not significantly different between the groups.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eBaseline characteristics. Abbreviations: BMI\u0026thinsp;=\u0026thinsp;Body mass index, NYHA\u0026thinsp;=\u0026thinsp;New York Heart Association, LTOT\u0026thinsp;=\u0026thinsp;long term oxygen therapy, FVC\u0026thinsp;=\u0026thinsp;forced vital capacity, FEV1\u0026thinsp;=\u0026thinsp;forced expiratory volume in the first second of expiration, TLC\u0026thinsp;=\u0026thinsp;total lung capacity, DLCO\u0026thinsp;=\u0026thinsp;diffusing capacity of lung for carbon monoxide; SB\u0026thinsp;=\u0026thinsp;single-breath, KCO\u0026thinsp;=\u0026thinsp;DLCOcVA\u0026thinsp;=\u0026thinsp;diffusing capacity of lung for carbon monoxide by alveolar volume, corrected for hemoglobin, pO\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;partial oxygen pressure, pCO\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;partial carbon dioxide pressure, AaDO\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;alveolar-arterial oxygen gradient. Hb\u0026thinsp;=\u0026thinsp;Hemoglobin, Nt-proBNP\u0026thinsp;=\u0026thinsp;N-terminal pro brain natriuretic peptide, LVEF\u0026thinsp;=\u0026thinsp;left ventricular ejection fraction, E\u0026rsquo; = early diastolic myocardial velocity, E\u0026thinsp;=\u0026thinsp;early diastolic filling velocity, SPAP\u0026thinsp;=\u0026thinsp;systolic pulmonary arterial pressure, TAPSE\u0026thinsp;=\u0026thinsp;tricuspid annular plane systolic excursion, s\u0026rsquo; = peak systolic right ventricular velocity. Continuous variables are expressed as median and interquartile range. Superscripted numbers show the intergroup contrasts in the post-hoc Bonferroni analysis.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCOPD (1)\u003c/p\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;11\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eILD (2)\u003c/p\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;10\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePAH (3)\u003c/p\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;10\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSmoking control (4)\u003c/p\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;10\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNon-smoking control (5)\u003c/p\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;11\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge, yr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65(62\u0026ndash;74) \u003csup\u003e5,4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61(50\u0026ndash;78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64(60\u0026ndash;74) \u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58(48\u0026ndash;62) \u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55(52\u0026ndash;64) \u003csup\u003e1,3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI, kg/m\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.5(21.3\u0026ndash;30.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.4(22.4\u0026ndash;29.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.8(21.6\u0026ndash;36.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.8(24.0-28.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.1(20.2\u0026ndash;26.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en.s.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex(female/male)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4/7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3/7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9/1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5/5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7/4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNYHA I/II/III/IV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1/2/7/1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1/5/3/1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2/4/4/0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8/2/0/0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10/1/0/0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmoking status\u003c/p\u003e\n \u003cp\u003eNever/quit/active\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2/7/2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5/5/0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4/5/1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0/4/6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10/1/0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLTOT treatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePack years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40(15\u0026ndash;60) \u003csup\u003e2,3,5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.5(0\u0026ndash;10) \u003csup\u003e1,4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.8(0\u0026ndash;21) \u003csup\u003e1,4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30(19\u0026ndash;39) \u003csup\u003e2,3,5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0\u0026ndash;0) \u003csup\u003e1,4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFVC, % predicted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74(52\u0026ndash;78) \u003csup\u003e3,4,5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66(61\u0026ndash;71) \u003csup\u003e3,4,5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93(73\u0026ndash;110) \u003csup\u003e2,1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98(73\u0026ndash;108) \u003csup\u003e2,1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e103(97\u0026ndash;113) \u003csup\u003e2,1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFEV1/FVC Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49(43\u0026ndash;57) \u003csup\u003e2,4,5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78(74\u0026ndash;87) \u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72(70\u0026ndash;77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76(74\u0026ndash;82) \u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80(76\u0026ndash;82) \u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFEV1, % predicted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47(31\u0026ndash;54) \u003csup\u003e3,4,5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68(71\u0026ndash;92) \u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82(71\u0026ndash;92) \u003csup\u003e1,5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91(70\u0026ndash;100) \u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e102(91\u0026ndash;117) \u003csup\u003e1,2,3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTLC, % predicted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e114(89\u0026ndash;119) \u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66(57\u0026ndash;80) \u003csup\u003e1,4,5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95(79\u0026ndash;111)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e106(84\u0026ndash;115) \u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e109(103\u0026ndash;115) \u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDLCO, % predicted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28(19\u0026ndash;69) \u003csup\u003e4,5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45(34\u0026ndash;68) \u003csup\u003e4,5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51(39\u0026ndash;74) \u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82(70\u0026ndash;87) \u003csup\u003e1,2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93(85\u0026ndash;108) \u003csup\u003e1,2,3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKCO, % predicted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36(25\u0026ndash;62) \u003csup\u003e2,4,5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72(48\u0026ndash;86) \u003csup\u003e1,5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70(53\u0026ndash;78) \u003csup\u003e4,5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90(80\u0026ndash;96) \u003csup\u003e1,2,3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96(87\u0026ndash;120) \u003csup\u003e1,2,3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epO\u003csub\u003e2\u003c/sub\u003e, mmHg\u003c/p\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;11/10/10/10/6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62(49\u0026ndash;69) \u003csup\u003e2,4,5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75(63\u0026ndash;84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68(60\u0026ndash;72) \u003csup\u003e1,4,5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79(74\u0026ndash;85) \u003csup\u003e1,3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84(80\u0026ndash;86) \u003csup\u003e1,3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epCO\u003csub\u003e2\u003c/sub\u003e, mmHg\u003c/p\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;11/10/10/10/6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37(36\u0026ndash;39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38(34\u0026ndash;43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36(32\u0026ndash;40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38(35\u0026ndash;39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36(34\u0026ndash;39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en.s.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAaDO\u003csub\u003e2\u003c/sub\u003e, mmHg\u003c/p\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;11/10/10/10/6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64(41\u0026ndash;110) \u003csup\u003e4,5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32(18\u0026ndash;52) \u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37(26\u0026ndash;58) \u003csup\u003e4,5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19(14\u0026ndash;23) \u003csup\u003e1,3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16(13\u0026ndash;19) \u003csup\u003e1,2,3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHb, g/dL\u003c/p\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;11/10/10/10/10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.2(13.5\u0026ndash;15.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.9(13.4\u0026ndash;15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.3(11.7\u0026ndash;15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.7(12.5\u0026ndash;15.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.6(13.1\u0026ndash;15.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en.s.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCreatinine,mg/dL\u003c/p\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;11/10/10/10/10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9(0.7\u0026ndash;1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9(0.8-1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0(0.8\u0026ndash;1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8(0.7\u0026ndash;0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8(0.7\u0026ndash;0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en.s.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNt-proBNP,pg/mL\u003c/p\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;11/10/10/5/6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e761(86-1329) \u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e208(56\u0026ndash;636) \u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e344(193\u0026ndash;468) \u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33(30\u0026ndash;46) \u003csup\u003e1,2,3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63(56\u0026ndash;103)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLVEF, %\u003c/p\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;10/10/8/9/11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61(55\u0026ndash;65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61(54\u0026ndash;65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60(57\u0026ndash;62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66(60\u0026ndash;67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66(61\u0026ndash;69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en.s.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eE/E\u0026rsquo;\u003c/p\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;11/10/10/9/11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.5(4.4\u0026ndash;12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.1(5.6\u0026ndash;10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.9(7.9\u0026ndash;14.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.1(7.4\u0026ndash;10.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7(5.5\u0026ndash;8.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en.s.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSPAP, mmHg\u003c/p\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;11/10/9/7/8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53(36\u0026ndash;73) \u003csup\u003e4,5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36(27\u0026ndash;54) \u003csup\u003e4,5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59(43\u0026ndash;73) \u003csup\u003e4,5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23(21\u0026ndash;24) \u003csup\u003e1,2,3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24(20\u0026ndash;24) \u003csup\u003e1,2,3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTAPSE, mm\u003c/p\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;11/9/10/10/11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19(12\u0026ndash;27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20(16\u0026ndash;24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22(20\u0026ndash;24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23(20\u0026ndash;24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26(21\u0026ndash;27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en.s.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003es\u0026rsquo;, cm/s\u003c/p\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;8/8/8/9/11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12(10\u0026ndash;13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12(10\u0026ndash;18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12(10\u0026ndash;15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13(12\u0026ndash;15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12(11\u0026ndash;15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en.s.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\"\u003e\n \u003ch2\u003eExpiratory CO\u003csub\u003e2\u003c/sub\u003e analysis\u003c/h2\u003e\n \u003cp\u003eWe found several differences between healthy controls and diseased as well as between the individual diseased groups (Fig.\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e). Healthy controls, compared to diseased patients, reached higher maximum values of FECO\u003csub\u003e2\u003c/sub\u003e (0.041 (0.037\u0026ndash;0.043) mL CO\u003csub\u003e2\u003c/sub\u003e/mL vs. 0.035 (0.032\u0026ndash;0.039) mL CO\u003csub\u003e2\u003c/sub\u003e/mL, p\u0026thinsp;=\u0026thinsp;0.002) with steeper maximum FECO\u003csub\u003e2\u003c/sub\u003e/VE slopes (0.27 (0.23\u0026ndash;0.32) L CO\u003csub\u003e2\u003c/sub\u003e/L\u003csup\u003e2\u003c/sup\u003e vs. 0.16 (0.12\u0026ndash;0.21) L CO\u003csub\u003e2\u003c/sub\u003e/L\u003csup\u003e2\u003c/sup\u003e, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan\u003e3\u003c/span\u003e). Differences between healthy controls and patients are presented in Table\u0026nbsp;\u003cspan\u003e3\u003c/span\u003e. Healthy controls also showed smaller MSV (144 (131\u0026ndash;167) mL vs. 221 (164\u0026ndash;270) mL, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan\u003e3\u003c/span\u003e) and EqCO\u003csub\u003e2,total\u003c/sub\u003e (30 (29\u0026ndash;35) vs. 38 (34\u0026ndash;42), p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) than the patients. In addition, their minute ventilation and breathing frequency was lower (p\u0026thinsp;=\u0026thinsp;0.010 and p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) as compared to the patient groups. In healthy controls, PETCO\u003csub\u003e2\u003c/sub\u003e was higher than in the patients (p\u0026thinsp;=\u0026thinsp;0.008), whereas CO\u003csub\u003e2\u003c/sub\u003e output over time (VCO\u003csub\u003e2\u003c/sub\u003e) and DSV were not different. EqCO\u003csub\u003e2,total\u003c/sub\u003e showed the best contrasts between patients and controls, as compared to the partitioned EqCO\u003csub\u003e2,as\u003c/sub\u003e and EqCO\u003csub\u003e2,ms\u003c/sub\u003e.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eResults of CO\u003csub\u003e2\u003c/sub\u003e flow analysis by disease status. Data were extracted from the curve fits. Continuous variables are expressed as median and interquartile range. Kruskal Wallis rank sum test was used. Abbreviations: DSV\u0026thinsp;=\u0026thinsp;Dead space volume; MSV\u0026thinsp;=\u0026thinsp;Mixed space volume.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDiseased N\u0026thinsp;=\u0026thinsp;31\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHealthy N\u0026thinsp;=\u0026thinsp;21\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAverage inspiration time (s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2 (1.1\u0026ndash;1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.5 (1.3\u0026ndash;1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAverage expiration time (s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.7 (1.5\u0026ndash;1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.2 (1.6\u0026ndash;2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVentilation (L/min)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.7 (12.0-17.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.3 (9.3\u0026ndash;14.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e output (mL CO\u003csub\u003e2\u003c/sub\u003e/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e370 (327\u0026ndash;459)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e402 (300\u0026ndash;437)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en.s.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBreathing frequency (1/min)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21 (19\u0026ndash;23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16 (15\u0026ndash;19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMaximum FECO\u003csub\u003e2\u003c/sub\u003e (mL CO\u003csub\u003e2\u003c/sub\u003e/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.035 (0.032\u0026ndash;0.038)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.041 (0.037\u0026ndash;0.044)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMaximum FECO\u003csub\u003e2\u003c/sub\u003e/VE slope (L CO\u003csub\u003e2\u003c/sub\u003e/L\u0026sup2;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.16 (0.12\u0026ndash;0.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.27 (0.23\u0026ndash;0.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePETCO\u003csub\u003e2\u003c/sub\u003e (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31 (30\u0026ndash;36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35 (32\u0026ndash;39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDSV (mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69 (62\u0026ndash;91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79 (68\u0026ndash;91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en.s.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMSV (mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e221 (162\u0026ndash;280)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e144 (130\u0026ndash;175)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEqCO\u003csub\u003e2,ms\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57 (52\u0026ndash;62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49 (46\u0026ndash;54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEqCO\u003csub\u003e2,as\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29 (26\u0026ndash;32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25 (23\u0026ndash;27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEqCO\u003csub\u003e2,total\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38 (34\u0026ndash;43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30 (28\u0026ndash;38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eIn a next step we compared our readouts between the different groups (COPD vs. ILD vs. PAH vs. healthy smokers vs. healthy non-smokers) in a multivariate analysis. Average in- and expiration time, breathing frequency, maximum FECO\u003csub\u003e2\u003c/sub\u003e, maximum FECO\u003csub\u003e2\u003c/sub\u003e/VE slope and MSV were significantly different between the groups (Table\u0026nbsp;\u003cspan\u003e4\u003c/span\u003e). Likewise, EqCO\u003csub\u003e2\u003c/sub\u003e in the mixed space, the alveolar space and EqCO\u003csub\u003e2, total\u003c/sub\u003e were significantly different between the groups. However, post-hoc tests only showed significant contrasts between distinct groups for expiration time, maximum FECO\u003csub\u003e2\u003c/sub\u003e/VE slope, MSV and EqCO\u003csub\u003e2,total\u003c/sub\u003e. Of note, MSV was different between COPD and ILD but not PAH vs. controls and the maximum FECO\u003csub\u003e2\u003c/sub\u003e/VE slope was decreased in COPD vs. controls. There were no significant differences between healthy smokers and healthy non-smokers (Table\u0026nbsp;\u003cspan\u003e4\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 4\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eBetween-group comparison of values of CO\u003csub\u003e2\u003c/sub\u003e flow analysis and the single groups. Data extracted from curve fits. Continuous variables are expressed as median and interquartile range. p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 from Dunn\u0026apos;s rank sum test comparing healthy smokers vs. healthy non-smokers vs. COPD vs. ILD vs PAH. Superscripted numbers show the intergroup contrasts according to the post-hoc Bonferroni test.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCOPD (1)\u003c/p\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;11\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eILD (2)\u003c/p\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;10\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePAH (3)\u003c/p\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;10\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHealthy smokers (4)\u003c/p\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;10\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHealthy non-smokers (5)\u003c/p\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;11\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAverage inspiration time (s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.1 (1.0-1.6) \u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.3 (1.1\u0026ndash;1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2 (1.1\u0026ndash;1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.6 (1.3\u0026ndash;1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.5 (1.2\u0026ndash;1.7) \u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAverage expiration time (s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.9 (1.8\u0026ndash;2.3) \u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.5 (1.2\u0026ndash;1.7) \u003csup\u003e1,4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.6 (1.3\u0026ndash;1.7) \u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.1 (1.8\u0026ndash;2.6) \u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.3 (1.4\u0026ndash;2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVentilation (L/min)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15 (11\u0026ndash;17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15 (13\u0026ndash;23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16 (13\u0026ndash;17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 (8\u0026ndash;15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13 (10\u0026ndash;14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.127\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBreathing frequency (1/min)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21 (15\u0026ndash;21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21 (19\u0026ndash;25) \u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22 (20\u0026ndash;24) \u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16 (14\u0026ndash;18) \u003csup\u003e2,3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15 (15\u0026ndash;23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMaximum FECO\u003csub\u003e2\u003c/sub\u003e (mL CO\u003csub\u003e2\u003c/sub\u003e/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.035 (0.031\u0026ndash;0.039)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.036 (0.032\u0026ndash;0.041)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.034 (0.032\u0026ndash;0.040)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.042 (0.038\u0026ndash;0.043)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.041 (0.037\u0026ndash;0.044)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMaximum FECO\u003csub\u003e2\u003c/sub\u003e/VE slope\u003c/p\u003e\n \u003cp\u003e(L CO\u003csub\u003e2\u003c/sub\u003e/L\u0026sup2;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.15 (0.10\u0026ndash;0.22) \u003csup\u003e4,5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.15 (0.12\u0026ndash;0.21) \u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.20 (0.15\u0026ndash;0.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.26 (0.19\u0026ndash;0.32) \u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.28 (0.23\u0026ndash;0.32) \u003csup\u003e1,2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMSV (mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e247 (169\u0026ndash;394) \u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e248 (190\u0026ndash;277)\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e179 (160\u0026ndash;215)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e164 (127\u0026ndash;193)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e142 (131\u0026ndash;165) \u003csup\u003e1,2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEqCO\u003csub\u003e2,ms\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56 (52\u0026ndash;65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55 (49\u0026ndash;63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59 (51\u0026ndash;62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48 (46\u0026ndash;57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49 (45\u0026ndash;54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEqCO\u003csub\u003e2\u003c/sub\u003e,\u003csub\u003eas\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29 (26\u0026ndash;33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28 (25\u0026ndash;32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30 (26\u0026ndash;32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24 (23\u0026ndash;27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25 (23\u0026ndash;27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEqCO\u003csub\u003e2\u003c/sub\u003e,\u003csub\u003etotal\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40 (33\u0026ndash;44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37 (34\u0026ndash;44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37 (33\u0026ndash;40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31 (28\u0026ndash;37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30 (28\u0026ndash;35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\"\u003e\n \u003ch2\u003eCorrelations\u003c/h2\u003e\n \u003cp\u003eWe performed correlation analysis for those CO\u003csub\u003e2\u003c/sub\u003e readouts with the strongest contrasts between diseased and healthy subjects (Table\u0026nbsp;\u003cspan\u003e5\u003c/span\u003e). Lung function parameters (FEV1/FVC, FEV1, FVC, DLCO, KCO), AaDO\u003csub\u003e2\u003c/sub\u003e, and packyears were significantly correlated with both MSV and maximum FECO\u003csub\u003e2\u003c/sub\u003e/VE slope, with correlation coefficients for maximum FECO\u003csub\u003e2\u003c/sub\u003e/VE slope being higher than those for MSV for nearly all lung function parameters. DLCO, a parameter reflecting structural damage of the lung parenchyma, was strongest correlated with maximum FECO\u003csub\u003e2\u003c/sub\u003e/VE slope and MSV (\u003cem\u003e\u0026rho;\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.721 and \u003cem\u003e\u0026rho;\u003c/em\u003e=-0.695, respectively; both p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan\u003e4\u003c/span\u003e). Markers of pulmonary hypertension (sPAP and Nt-proBNP) were also positively correlated to MSV and maximum FECO\u003csub\u003e2\u003c/sub\u003e/VE slope, while left ventricular ejection fraction was not. For most of the lung function and PH parameters, the correlations with EqCO\u003csub\u003e2,total\u003c/sub\u003e, EqCO\u003csub\u003e2,as\u003c/sub\u003e and EqCO\u003csub\u003e2,ms\u003c/sub\u003e were in a similar range. However, EqCO\u003csub\u003e2,total\u003c/sub\u003e was the gas flow ratio with the strongest correlations with DLCO and KCO (\u003cem\u003e\u0026rho;\u003c/em\u003e=-0.661 and \u003cem\u003e\u0026rho;\u003c/em\u003e=-0.710, respectively; both p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Further significant correlations, not listed in Table\u0026nbsp;\u003cspan\u003e5\u003c/span\u003e, were observed between average inspiration time and FEV1 (\u003cem\u003e\u0026rho;\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.509; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), FVC (\u003cem\u003e\u0026rho;\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.456; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), DLCO (\u003cem\u003e\u0026rho;\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.541; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), KCO, (\u003cem\u003e\u0026rho;\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.541; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), AaDO\u003csub\u003e2\u003c/sub\u003e (\u003cem\u003e\u0026rho;\u003c/em\u003e=-0.468; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and sPAP (\u003cem\u003e\u0026rho;\u003c/em\u003e=-0.500; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\n \u003cdiv\u003e\n \u003c/div\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 5\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eCorrelations of lung function, blood gas and cardiac function parameters with values from CO\u003csub\u003e2\u003c/sub\u003e flow analysis. Data extracted from curve fits. Spearman (\u0026rho;) correlation was used. Highlighted readouts show a statistically significant correlation. Abbreviations: FVC\u0026thinsp;=\u0026thinsp;forced vital capacity, FEV1\u0026thinsp;=\u0026thinsp;forced expiratory volume in the first record of expiration, TLC\u0026thinsp;=\u0026thinsp;total lung capacity, DLCO\u0026thinsp;=\u0026thinsp;diffusing capacity of lung for carbon monoxide corrected for hemoglobin, KCO\u0026thinsp;=\u0026thinsp;diffusing capacity of lung for carbon monoxide by alveolar volume corrected for hemoglobin (Krogh factor), pO\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;partial oxygen pressure, pCO\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;partial carbon dioxide pressure, AaDO\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;alveolar-arterial oxygen gradient, LVEF\u0026thinsp;=\u0026thinsp;left ventricular ejection fraction, SPAP\u0026thinsp;=\u0026thinsp;systolic pulmonary arterial pressure, TPR\u0026thinsp;=\u0026thinsp;transpulmonary gradient, NT-proBNP\u0026thinsp;=\u0026thinsp;N-terminal pro natriuretic peptide.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMSV (mL)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMaximum FECO\u003csub\u003e2\u003c/sub\u003e/VE slope\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEqCO\u003csub\u003e2ms\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEqCO\u003csub\u003e2as\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEqCO\u003csub\u003e2total\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePackyears N\u0026thinsp;=\u0026thinsp;52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026rho;\u003c/strong\u003e\u0026thinsp;\u003cstrong\u003e=\u0026thinsp;0.318\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ep\u0026thinsp;=\u0026thinsp;0.021\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026rho;\u003c/strong\u003e\u003cstrong\u003e= -0.288\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ep\u0026thinsp;=\u0026thinsp;0.038\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026rho;\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.182\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026rho;\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.191\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026rho;\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.191\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.174\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFEV1/FVC (% predicted) N\u0026thinsp;=\u0026thinsp;52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026rho;\u003c/strong\u003e\u0026thinsp;\u003cstrong\u003e=\u0026thinsp;0.345\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ep\u0026thinsp;=\u0026thinsp;0.012\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026rho;\u003c/strong\u003e\u0026thinsp;\u003cstrong\u003e=\u0026thinsp;0.366\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ep\u0026thinsp;=\u0026thinsp;0.008\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026rho;\u003c/strong\u003e\u0026thinsp;\u003cstrong\u003e=\u0026thinsp;0.291\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ep\u0026thinsp;=\u0026thinsp;0.036\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026rho;\u003c/strong\u003e\u0026thinsp;\u003cstrong\u003e=\u0026thinsp;0.299\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ep\u0026thinsp;=\u0026thinsp;0.031\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026rho;\u003c/em\u003e=-0.227\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.106\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFEV1 (% predicted) N\u0026thinsp;=\u0026thinsp;52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026rho;\u003c/strong\u003e\u003cstrong\u003e=-0.524\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026rho;\u003c/strong\u003e\u0026thinsp;\u003cstrong\u003e=\u0026thinsp;0.534\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026rho;\u003c/strong\u003e\u003cstrong\u003e=-0.366\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ep\u0026thinsp;=\u0026thinsp;0.008\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026rho;\u003c/strong\u003e\u003cstrong\u003e=-0.386\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ep\u0026thinsp;=\u0026thinsp;0.005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026rho;\u003c/strong\u003e\u003cstrong\u003e=-0.447\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ep\u0026thinsp;=\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFVC (% predicted) N\u0026thinsp;=\u0026thinsp;52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026rho;\u003c/strong\u003e\u003cstrong\u003e=-0.432\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ep\u0026thinsp;=\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026rho;\u0026thinsp;=\u0026thinsp;0.430\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026rho;\u003c/em\u003e=-0.234\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026rho;\u003c/em\u003e=-0.257\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026rho;\u003c/strong\u003e\u003cstrong\u003e=-0.395\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ep\u0026thinsp;=\u0026thinsp;0.004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTLC (% predicted) N\u0026thinsp;=\u0026thinsp;52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026rho;\u003c/em\u003e=-0.206\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026rho;\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.178\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026rho;\u003c/em\u003e=-0.086\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.542\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026rho;\u003c/em\u003e=-0.084\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.552\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026rho;\u003c/em\u003e=-0.157\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.265\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDLCO (% predicted) N\u0026thinsp;=\u0026thinsp;52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026rho;\u003c/strong\u003e\u003cstrong\u003e=-0.695\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026rho;\u003c/strong\u003e\u0026thinsp;\u003cstrong\u003e=\u0026thinsp;0.721\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026rho;\u003c/strong\u003e\u003cstrong\u003e=-0.520\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026rho;\u003c/strong\u003e\u003cstrong\u003e=-0.547\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026rho;\u003c/strong\u003e\u003cstrong\u003e=-0.661\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKCO (% predicted) N\u0026thinsp;=\u0026thinsp;52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026rho;\u003c/strong\u003e\u003cstrong\u003e=-0.667\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026rho;\u003c/strong\u003e\u0026thinsp;\u003cstrong\u003e=\u0026thinsp;0.736\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026rho;\u003c/strong\u003e\u003cstrong\u003e=-0.633\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026rho;\u003c/strong\u003e\u003cstrong\u003e=-0.655\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026rho;\u003c/strong\u003e\u003cstrong\u003e=-0.710\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePO\u003csub\u003e2\u003c/sub\u003e (mmHg) N\u0026thinsp;=\u0026thinsp;47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026rho;\u003c/strong\u003e\u003cstrong\u003e=-0.476\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ep\u0026thinsp;=\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026rho;\u003c/strong\u003e\u0026thinsp;\u003cstrong\u003e=\u0026thinsp;0.534\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026rho;\u003c/strong\u003e\u003cstrong\u003e=-0.463\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ep\u0026thinsp;=\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026rho;\u003c/strong\u003e\u003cstrong\u003e=-0.471\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ep\u0026thinsp;=\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026rho;\u003c/strong\u003e\u003cstrong\u003e=-0.413\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ep\u0026thinsp;=\u0026thinsp;0.004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePCO\u003csub\u003e2\u003c/sub\u003e (mmHg) N\u0026thinsp;=\u0026thinsp;47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026rho;\u003c/em\u003e=-0.097\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.517\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026rho;\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.182\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026rho;\u003c/strong\u003e\u003cstrong\u003e=-0.357\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ep\u0026thinsp;=\u0026thinsp;0.014\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026rho;\u003c/strong\u003e\u003cstrong\u003e=-0.343\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ep\u0026thinsp;=\u0026thinsp;0.018\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026rho;\u003c/em\u003e=-0.233\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.116\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAaDO\u003csub\u003e2\u003c/sub\u003e (mmHg) N\u0026thinsp;=\u0026thinsp;47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026rho;\u003c/strong\u003e\u0026thinsp;\u003cstrong\u003e=\u0026thinsp;0.619\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026rho;\u003c/strong\u003e\u003cstrong\u003e=-0.687\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026rho;\u003c/strong\u003e\u0026thinsp;\u003cstrong\u003e=\u0026thinsp;0.550\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026rho;\u003c/strong\u003e\u0026thinsp;\u003cstrong\u003e=\u0026thinsp;0.564\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026rho;\u003c/strong\u003e\u0026thinsp;\u003cstrong\u003e=\u0026thinsp;0.561\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLVEF (%) N\u0026thinsp;=\u0026thinsp;49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026rho;\u003c/em\u003e=-0.23\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026rho;\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.27\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026rho;\u003c/strong\u003e\u003cstrong\u003e=-0.331\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ep\u0026thinsp;=\u0026thinsp;0.02\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026rho;\u003c/strong\u003e\u003cstrong\u003e=-0.342\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ep\u0026thinsp;=\u0026thinsp;0.016\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026rho;\u003c/strong\u003e\u003cstrong\u003e=-0.456\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ep\u0026thinsp;=\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSPAP (mmHg) N\u0026thinsp;=\u0026thinsp;45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026rho;\u003c/strong\u003e\u0026thinsp;\u003cstrong\u003e=\u0026thinsp;0.636\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026rho;\u003c/strong\u003e\u003cstrong\u003e= -0.701\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026rho;\u003c/strong\u003e\u0026thinsp;\u003cstrong\u003e=\u0026thinsp;0.651\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026rho;\u003c/strong\u003e\u0026thinsp;\u003cstrong\u003e=\u0026thinsp;0.663\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026rho;\u003c/strong\u003e\u0026thinsp;\u003cstrong\u003e=\u0026thinsp;0.641\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNt-proBNP (pg/mL) N\u0026thinsp;=\u0026thinsp;42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026rho;\u003c/strong\u003e\u0026thinsp;\u003cstrong\u003e=\u0026thinsp;0.522\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026rho;\u003c/strong\u003e\u003cstrong\u003e= -0.630\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026rho;\u003c/strong\u003e\u0026thinsp;\u003cstrong\u003e=\u0026thinsp;0.567\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026rho;\u003c/strong\u003e\u0026thinsp;\u003cstrong\u003e=\u0026thinsp;0.584\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026rho;\u003c/strong\u003e\u0026thinsp;\u003cstrong\u003e=\u0026thinsp;0.562\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\"\u003e\n \u003ch2\u003eDiagnostic accuracy\u003c/h2\u003e\n \u003cp\u003eConcerning the differentiation between healthy and diseased lungs, MSV, maximum FECO\u003csub\u003e2\u003c/sub\u003e/VE slope, and DLCO showed the strongest contrasts. Receiver operating characteristic (ROC) analysis to distinguish diseased from healthy subjects using MSV showed an area under the curve (AUC) of 0.81 (95% CI: 0.69\u0026ndash;0.93) and identified an optimal cut-off value of 191 mL (sensitivity 68%, specificity 90%). ROC analysis for maximum FECO\u003csub\u003e2\u003c/sub\u003e/VE slope showed an AUC of 0.84 (95% CI: 0.73\u0026ndash;0.95) and a cut-off value of 0.25 l CO\u003csub\u003e2\u003c/sub\u003e/l\u0026sup2; (sensitivity 90%, specificity 62%). DLCO showed an AUC of 0.97 (95% CI: 0.93-1.00) and a DLCO\u0026thinsp;\u0026lt;\u0026thinsp;70% predicted a diseased lung with a sensitivity of 87% and a specificity of 91% The best predictors to discriminate between healthy and COPD/ILD patients, were an MSV of 199 mL (AUC: 0.85 (95% CI: 0.73\u0026ndash;0.97); sensitivity 76%, specificity 90%) (Fig.\u0026nbsp;\u003cspan\u003e4\u003c/span\u003e)., a maximum FECO\u003csub\u003e2\u003c/sub\u003e/VE slope of 0.19 L CO\u003csub\u003e2\u003c/sub\u003e/L\u0026sup2; (AUC: 0.86 (95% CI: 0.74\u0026ndash;0.98); sensitivity 76%, specificity 90%) and a DLCO of 70% predicted (AUC 0.98 (95% CI: 0.95-1.00), sensitivity 95%, specificity 90%).\u003c/p\u003e\n \u003cp\u003eParameters providing the best differentiation between COPD and ILD vs. PAH were again MSV, maximum FECO\u003csub\u003e2\u003c/sub\u003e/VE slope and DLCO. The optimal cut-off values for MSV, maximum FECO\u003csub\u003e2\u003c/sub\u003e/VE slope, and DLCO were 210 mL (AUC: 0.74 (95% CI: 0.56\u0026ndash;0.92); sensitivity 71.4%, specificity 80%), 0.19 l CO\u003csub\u003e2\u003c/sub\u003e/l\u0026sup2; (AUC: 0.71 (95% CI: 0.52\u0026ndash;0.89) sensitivity 76.2%, specificity 70%), and 49% predicted (AUC 0.72 (95% CI: 0.53\u0026ndash;0.91), sensitivity 76%, specificity 60%) (Fig.\u0026nbsp;\u003cspan\u003e4\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this pilot study we analyzed the diagnostic and differential diagnostic features of high-fidelity expiratory CO\u003csub\u003e2\u003c/sub\u003e flow analysis for parenchymal and vascular lung diseases and found that it may provide a safe and easy diagnostic method without need for foreign gas. We found significantly higher EqCO\u003csub\u003e2\u003c/sub\u003e, MSV and significantly lower maximum FECO\u003csub\u003e2\u003c/sub\u003e/VE slope values in patients with chronic diseases of the lung parenchyma and/or pulmonary hypertension compared to healthy controls. Further, these readouts may discriminate between parenchymal lung diseases and isolated pulmonary arterial hypertension. This indicates that real-time high-fidelity CO\u003csub\u003e2\u003c/sub\u003e flow analysis might provide a sensitive diagnostic and differential diagnostic tool, suitable for screening for chronic lung diseases.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eHeterogeneity in CO\u003csub\u003e2\u003c/sub\u003e analysis\u003c/h2\u003e \u003cp\u003eAlthough expiratory CO\u003csub\u003e2\u003c/sub\u003e analysis was first described in 1891 and capnography has since been used to determine dead space volume and perfusion heterogeneity in the lungs, it has not become part of diagnostic routine [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMost studies explored just end-tidal carbon dioxide pressure (PETCO\u003csub\u003e2\u003c/sub\u003e) or the course of FECO\u003csub\u003e2\u003c/sub\u003e over the expiratory time, but did not perform CO\u003csub\u003e2\u003c/sub\u003e flow analysis. PETCO\u003csub\u003e2\u003c/sub\u003e measurement has been used in the intensive-care or anesthesia setting for monitoring adequacy of ventilation [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] and for screening for acute pulmonary embolism [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In a study by Singh et al., 60 patients with asthma had significantly lower resting PETCO\u003csub\u003e2\u003c/sub\u003e values than healthy controls [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], suggesting that CO\u003csub\u003e2\u003c/sub\u003e assessment might be suitable for asthma screening. Hemnes et al. showed that a bedside test for PETCO\u003csub\u003e2\u003c/sub\u003e had a high negative predictive value for pulmonary embolism [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The same group found that patients with PAH had lower PETCO\u003csub\u003e2\u003c/sub\u003e values than non-PH patients and concluded, that PETCO\u003csub\u003e2\u003c/sub\u003e was a promising tool to differentiate these diseases, even indicating pulmonary hemodynamic improvement after PAH therapy [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Further, there may be a differential diagnostic value within pulmonary hypertension patients. A study by Scheidl et al. investigated capillary to end-tidal pCO\u003csub\u003e2\u003c/sub\u003e gradients at rest and during exercise in patients with chronic thromboembolic PH (CTEPH) and idiopathic pulmonary arterial hypertension (iPAH) in order differentiate CTEPH from IPAH and found markedly higher systemic capillary to end-tidal pCO\u003csub\u003e2\u003c/sub\u003e gradients in patients with CTEPH compared to iPAH [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eDiagnostic value of EqCO\u003csub\u003e2\u003c/sub\u003e\u003c/h2\u003e \u003cp\u003eThis is, to our knowledge, the first study to analyze EqCO\u003csub\u003e2\u003c/sub\u003e, an established marker of breathing efficacy, separately for alveolar space and mixed space as well as for the whole exhalation. Increased EqCO\u003csub\u003e2,total\u003c/sub\u003e is a hallmark of pulmonary hypertension (PH) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. However, elevated EqCO\u003csub\u003e2,total\u003c/sub\u003e is also found in COPD, ILD and chronic heart failure [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. During incremental exercise tests, at the ventilatory threshold, EqCO\u003csub\u003e2,total\u003c/sub\u003e is an independent predictor of mortality and clinical worsening in PAH and chronic heart failure patients [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. EqCO\u003csub\u003e2,total\u003c/sub\u003e is part of the diagnostic algorithm for patients with exertional dyspnea and the risk assessment in PAH [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, we found significantly higher resting EqCO\u003csub\u003e2,total\u003c/sub\u003e values in all diseased groups, compared to controls, indicating that resting EqCO\u003csub\u003e2,total\u003c/sub\u003e might be valuable in screening for lung diseases including pulmonary hypertension. Our expectation was, that partitioning EqCO\u003csub\u003e2,total\u003c/sub\u003e into the dead space, the mixed space and the alveolar space EqCO\u003csub\u003e2\u003c/sub\u003e would even improve the diagnostic or discriminative power, however, EqCO\u003csub\u003e2,total\u003c/sub\u003e showed the strongest contrasts between groups. As EqCO\u003csub\u003e2,total\u003c/sub\u003e is the ventilatory equivalent over the whole exhalation, it most likely cumulates the differences in EqCO\u003csub\u003e2\u003c/sub\u003e for the mixed space and the alveolar space between healthy subjects and patients providing the best diagnostic properties. The EqCO\u003csub\u003e2\u003c/sub\u003e of the alveolar space might be of interest in the diagnostics for IPAH vs. CTEPH, because the difference between alveolar and arterial CO\u003csub\u003e2\u003c/sub\u003e appears to be a most sensitive marker for the perfusion heterogeneity in CTEPH [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], but this was not in the scope of this study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eTwo new parameters: MSV and maximum FECO\u003csub\u003e2\u003c/sub\u003e/VE slope\u003c/h2\u003e \u003cp\u003eCapnovolumetry, i.e. plotting pCO\u003csub\u003e2\u003c/sub\u003e against expired volumes, provides better results as compared to plotting these fractions against time [\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Kellerer et al. performed capnovolumetric molar mass measurements by ultrasound estimations of exhaled CO\u003csub\u003e2\u003c/sub\u003e concentrations in 1287 subjects and concluded that ventilatory inhomogeneities would result in a flattening of the slope in the mixed phase and a steepening of the slope in the alveolar phase [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Their optimal cutoff for the ratio between these two slopes was 0.08 g*mL/mol for detection of airway obstruction and they achieved an AUC of 0.68 (95% CI 0.65\u0026ndash;0.71) with 59% sensitivity and 69% specificity [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Our AUC was considerably higher, probably because we analyzed the MSV and the maximum FECO\u003csub\u003e2\u003c/sub\u003e/VE slope and because we used cutting edge technology concerning source data generation and automatic trace analysis. Further, we found that MSV and maximum FECO\u003csub\u003e2\u003c/sub\u003e/VE slope were as good as DLCO to differentiate between parenchymal diseases and isolated pulmonary hypertension, while, unlike for DLCO, no foreign gas is required. Therefore, our method may be a valuable tool for screening of pulmonary diseases and may guide further differential diagnostic or even therapeutic decisions. Of course, validation of these results is warranted.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003ePathophysiological considerations\u003c/h2\u003e \u003cp\u003eThe respiratory system can be divided into a dead space with no relevant gas exchange and the alveolar space, characterized by complete gas exchange with the blood. Under the assumption that the gas content of all alveoli would have the same travel time to the mouth, we would expect a steep increase of CO\u003csub\u003e2\u003c/sub\u003e concentration in the expired air, once the dead space is emptied. However, different travel times are caused by different bronchial lengths and different flow characteristics due to heterogeneities in airway resistance, parenchymal compliance, and external forces working on the lungs. Therefore, we find a \u0026ldquo;mixed space volume\u0026rdquo; (MSV) between the dead space and the alveolar volume. In a healthy lung, this mixed space volume is small, resulting in a steep increase of FECO\u003csub\u003e2\u003c/sub\u003e over VE. Any condition that increases the heterogeneity of airway resistance, pulmonary compliance, and external forces are expected to increase MSV and decrease the maximum FECO2/VE slope. This has been shown in our study and agrees with nearly all previous studies [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eCO\u003csub\u003e2\u003c/sub\u003e analysis to determine dead space volume\u003c/h2\u003e \u003cp\u003eOriginally, capnography has been described to assess dead space ventilation by means of the Bohr equation [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] and has been used to estimate the degree of perfusion mismatch. In our study, the dead space, excluding the mixed space volume, had no diagnostic value. Just the ILD patients showed slightly elevated volumes that were not statistically significant. This suggests that dead space volume \u003cem\u003eper se\u003c/em\u003e is less sensitive to structural lung diseases as compared to mixed space volume. Similarly, Steiss et al. investigated 47 asthmatic children and found that expiratory CO\u003csub\u003e2\u003c/sub\u003e flow characteristics provided the clinically important information [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThis was a small hypothesis generating study without a validation cohort. However, we developed a new automatic algorithm based on real-time high-fidelity CO\u003csub\u003e2\u003c/sub\u003e flow signals from an established and approved diagnostic device, providing clinically meaningful measures. We detected strong correlations between MSV, maximum FECO\u003csub\u003e2\u003c/sub\u003e/VE slope and DLCO, indicating that our automatic algorithm produces sensitive parameters without the need for foreign gas. We included healthy smokers and non-smokers but failed to detect any significant differences between them, either because our technology was not sensitive enough or because there were not enough differences. Based on the fact, that all COPD patients had a reduced DLCO, we categorized them together with patients with ILD into the \u0026ldquo;parenchymal disease group\u0026rdquo;. Our controls were compared to patient groups with quite advanced disease, including pulmonary hypertension. However, the matching was suboptimal, as controls were slightly but significantly younger. This may be acceptable because our primary aim was to compare groups with different characteristics to assess the accuracy of our readouts and not to assess the severity of the diseases. Still, it remains unknown how our automatic measures would detect early disease stages.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this pilot study we show that fully-automatic high-fidelity expiratory CO\u003csub\u003e2\u003c/sub\u003e flow analysis may be a fast, easy and low-cost approach to detect structural changes of the lungs including vascular lung disease without the use of foreign gas. Validation in larger prospective studies and in less severely affected individuals is warranted.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cem\u003eCO2: carbon dioxide; DLCO: diffusing capacity for carbon monoxide; COPD: chronic obstructive pulmonary disease; ILD: interstitial lung disease; PAH: pulmonary arterial hypertension; DSV: dead space volume; MSV: mixed space volume; ASV: alveolar space volume; EqCO2: ventilatory equivalents for carbon dioxide; FECO2: fraction of expiratory carbon dioxide; ROC: receiver operating characteristic; AUC: area under the curve; CTEPH: chronic thromboembolic pulmonary hypertension; iPAH: idiopathic pulmonary arterial hypertension; BMI: Body mass index; NYHA: New York Heart Association; LTOT: long term oxygen therapy; FVC: forced vital capacity; FEV1: forced expiratory volume in the first second of expiration; TLC = total lung capacity; DLCO = diffusing capacity of lung for carbon monoxide; SB = single-breath; DLCOcVA: diffusing capacity of lung for carbon monoxide by alveolar volume corrected for hemoglobin; pO2: partial oxygen pressure, pCO2: partial carbon dioxide pressure; AaDO2: alveolar-arterial oxygen gradient; Hb: Hemoglobin; Nt-proBNP: N-terminal pro brain natriuretic peptide; LVEF: left ventricular ejection fraction; E\u0026rsquo;: early diastolic myocardial velocity; E: early diastolic filling velocity; SPAP = systolic pulmonary arterial pressure; TAPSE: tricuspid annular plane systolic excursion; s\u0026rsquo;: peak systolic right ventricular velocity; FECO2: fraction of exhaled CO2; VE: volume over exhaled volume\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThe study protocol conformed to the Declaration of Helsinki and was approved by the Ethics Committee of the Medical University of Graz, Austria (EK 32-276 ex 19/20). Informed consent was signed by every subject before participating this study. Data were pseudonymized and stored safely at our study center on a special password saved server and only assigned study personal had access to the data.\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Consent for publication:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNot applicable.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Availability of data and material:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNot applicable.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Competing interests:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThe authors declare that they have no competing interests.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Funding:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNone.\u003c/em\u003e\u003cbr\u003e\u0026nbsp;Clinical trials registry:\u003c/p\u003e\n\u003cp\u003eClinicalTrials.gov Identifier: NCT05092035; Other Study ID Numbers: ID 7684; Date: October 25, 2021\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Authors\u0026apos; contributions:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTS performed the study, data collection and abstraction and wrote the manuscript. MP performed data processing, data analysis and designed figures. HO had the main lead of the project. HO and MP conceived the project and were responsible for final manuscript approval. NJ, MG, GK and PD had impact on data collection and management of the database. All authors contributed to the editing of the manuscript. All authors read and approved the final manuscript.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003eAuthor details:\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eMedical University of Graz, Austria, Department of Internal Medicine, Division of Pulmonology, \u003csup\u003e2\u003c/sup\u003eLudwig Boltzmann Institute for Lung Vascular Research, Graz, Austria\u003csup\u003e\u0026nbsp;3\u003c/sup\u003eMedical University of Graz, Austria, Department of Internal Medicine, Division of Cardiology\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMiller MR, Hankinson J, Brusasco V, Burgos F, Casaburi R, Coates A, et al. Standardisation of spirometry. Eur Respir J 2005;26:319\u0026ndash;38. https://doi.org/10.1183/09031936.05.00034805.\u003c/li\u003e\n\u003cli\u003eGraham BL, Steenbruggen I, Miller MR, Barjaktarevic IZ, Cooper BG, Hall GL, et al. Standardization of Spirometry 2019 Update. 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Respir Res 2019;20:92. https://doi.org/10.1186/s12931-019-1067-1.\u003c/li\u003e\n\u003cli\u003eScheidl SJ, Englisch C, Kovacs G, Reichenberger F, Schulz R, Breithecker A, et al. Diagnosis of CTEPH versus IPAH using capillary to end-tidal carbon dioxide gradients. Eur Respir J 2012;39:119\u0026ndash;24. https://doi.org/10.1183/09031936.00109710.\u003c/li\u003e\n\u003cli\u003eFabius TM, Eijsvogel MMM, Brusse-Keizer MGJ, Sanchez OM, Verschuren F, de Jongh FHC. Retrospective validation of a new volumetric capnography parameter for the exclusion of pulmonary embolism at the emergency department. ERJ Open Res 2018;4:00099\u0026ndash;2018. https://doi.org/10.1183/23120541.00099-2018.\u003c/li\u003e\n\u003cli\u003eHemnes AR, Newman AL, Rosenbaum B, Barrett TW, Zhou C, Rice TW, et al. Bedside end-tidal CO2 tension as a screening tool to exclude pulmonary embolism. European Respiratory Journal 2010;35:735\u0026ndash;41. https://doi.org/10.1183/09031936.00084709.\u003c/li\u003e\n\u003cli\u003eBohr C. Ueber die Lungenathmung. Skand Arch Physiol 2: 236\u0026ndash;268, doi:10.1111/j.1748-1716.1891.tb00581.x. 1891.\u003c/li\u003e\n\u003cli\u003eAitken RS, Clark-Kennedy AE. On the fluctuation in the composition of the alveolar air during the respiratory cycle in muscular exercise. J Physiol 1928;65:389\u0026ndash;411. https://doi.org/10.1113/jphysiol.1928.sp002485.\u003c/li\u003e\n\u003cli\u003eKreuter M, Behr J, Bonella F, Costabel U, Gerber A, Hamer OW, et al. S1 Leitlinie Interdisziplin\u0026auml;re Diagnostik interstitieller Lungenerkrankungen im Erwachsenenalter. AWMF Online 2023.\u003c/li\u003e\n\u003cli\u003eWhitaker DK. Time for capnography - everywhere. Anaesthesia 2011;66:544\u0026ndash;9. https://doi.org/10.1111/j.1365-2044.2011.06793.x.\u003c/li\u003e\n\u003cli\u003eNassar BS, Schmidt GA. Capnography During Critical Illness. Chest 2016;149:576\u0026ndash;85. https://doi.org/10.1378/chest.15-1369.\u003c/li\u003e\n\u003cli\u003eSingh OP, Ahmed IB, Malarvili MB. Assessment of newly developed real-time human respiration carbon dioxide measurement device for management of asthma outside of hospital. Technol Health Care 2018;26:785\u0026ndash;94. https://doi.org/10.3233/THC-181288.\u003c/li\u003e\n\u003cli\u003eHemnes AR, Pugh ME, Newman AL, Robbins IM, Tolle J, Austin ED, et al. End tidal CO(2) tension: pulmonary arterial hypertension vs pulmonary venous hypertension and response to treatment. Chest 2011;140:1267\u0026ndash;73. https://doi.org/10.1378/chest.11-0155.\u003c/li\u003e\n\u003cli\u003eWasserman K, editor. Principles of exercise testing \u0026amp; interpretation: including pathophysiology and clinical applications. 3rd ed. Philadelphia: Lippincott Williams \u0026amp; Wilkins; 1999.\u003c/li\u003e\n\u003cli\u003eNeder JA, Berton DC, Arbex FF, Alencar MC, Rocha A, Sperandio PA, et al. Physiological and clinical relevance of exercise ventilatory efficiency in COPD. Eur Respir J 2017;49:1602036. https://doi.org/10.1183/13993003.02036-2016.\u003c/li\u003e\n\u003cli\u003ePhillips DB, Collins S\u0026Eacute;, Stickland MK. Measurement and Interpretation of Exercise Ventilatory Efficiency. Front Physiol 2020;11:659. https://doi.org/10.3389/fphys.2020.00659.\u003c/li\u003e\n\u003cli\u003eSchwaiblmair M, Faul C, von Scheidt W, Berghaus TM. Ventilatory efficiency testing as prognostic value in patients with pulmonary hypertension. BMC Pulm Med 2012;12:23. https://doi.org/10.1186/1471-2466-12-23.\u003c/li\u003e\n\u003cli\u003eWensel R, Opitz CF, Anker SD, Winkler J, H\u0026ouml;ffken G, Kleber FX, et al. Assessment of survival in patients with primary pulmonary hypertension: importance of cardiopulmonary exercise testing. Circulation 2002;106:319\u0026ndash;24. https://doi.org/10.1161/01.cir.0000022687.18568.2a.\u003c/li\u003e\n\u003cli\u003ePonikowski P, Chua TP, Anker SD, Francis DP, Doehner W, Banasiak W, et al. Peripheral chemoreceptor hypersensitivity: an ominous sign in patients with chronic heart failure. Circulation 2001;104:544\u0026ndash;9. https://doi.org/10.1161/hc3101.093699.\u003c/li\u003e\n\u003cli\u003eDeboeck G, Scoditti C, Huez S, Vachi\u0026eacute;ry J-L, Lamotte M, Sharples L, et al. Exercise testing to predict outcome in idiopathic versus associated pulmonary arterial hypertension. Eur Respir J 2012;40:1410\u0026ndash;9. https://doi.org/10.1183/09031936.00217911.\u003c/li\u003e\n\u003cli\u003eBhavani-Shankar K, Moseley H, Kumar AY, Delph Y. Capnometry and anaesthesia. Can J Anaesth 1992;39:617\u0026ndash;32. https://doi.org/10.1007/BF03008330.\u003c/li\u003e\n\u003cli\u003eBhavani-Shankar K, Kumar AY, Moseley HS, Ahyee-Hallsworth R. Terminology and the current limitations of time capnography: a brief review. J Clin Monit 1995;11:175\u0026ndash;82. https://doi.org/10.1007/BF01617719.\u003c/li\u003e\n\u003cli\u003eFletcher R, Jonson B. Deadspace and the single breath test for carbon dioxide during anaesthesia and artificial ventilation. Effects of tidal volume and frequency of respiration. Br J Anaesth 1984;56:109\u0026ndash;19. https://doi.org/10.1093/bja/56.2.109.\u003c/li\u003e\n\u003cli\u003eKunkov S, Pinedo V, Silver EJ, Crain EF. Predicting the Need for Hospitalization in Acute Childhood Asthma Using End-tidal Capnography: Pediatric Emergency Care 2005;21:574\u0026ndash;7. https://doi.org/10.1097/01.pec.0000177197.83655.d8.\u003c/li\u003e\n\u003cli\u003eXu Q-F, Wang H-Y, Xiao Y, Ding H-J, Fan J, Zhang L. [Ventilatory response at maximal exercise in patients with chronic obstructive pulmonary disease]. Zhonghua Yi Xue Za Zhi 2008;88:2108\u0026ndash;11.\u003c/li\u003e\n\u003cli\u003eSteiss JO, Rudloff S, Landmann E, Zimmer KP, Lindemann H. Capnovolumetry: a new tool for lung function testing in children with asthma. Clin Physiol Funct Imaging 2008;28:332\u0026ndash;6. https://doi.org/10.1111/j.1475-097X.2008.00815.x.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Expiratory CO2 analysis, pulmonary structural changes, COPD, interstitial lung disease, pulmonary arterial hypertension, lung function","lastPublishedDoi":"10.21203/rs.3.rs-3894602/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3894602/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThere is an unmet need for easily available sensitive markers of structural lung disease. Assessment of lung diffusion capacity with foreign gases is currently state-of-the-art, however, results are unspecific and the methods are technically demanding. We developed a fully-automatic algorithm to analyze high-fidelity expiratory CO\u003csub\u003e2\u003c/sub\u003e flows from resting ventilation and compared the derived readouts with the diffusing capacity for carbon monoxide (DLCO) regarding their diagnostic accuracy.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis pilot study enrolled clinically well characterized patients with chronic obstructive pulmonary disease (COPD), interstitial lung disease (ILD), pulmonary arterial hypertension (PAH) and controls without lung disease from a pulmonary hypertension clinic and investigated them by means of our newly developed algorithm. We evaluated dead-, mixed- and alveolar space volumes (DSV, MSV, ASV, respectively), their respective ventilatory equivalents for CO\u003csub\u003e2\u003c/sub\u003e (EqCO\u003csub\u003e2\u003c/sub\u003e) and the fraction of expiratory CO\u003csub\u003e2\u003c/sub\u003e (FECO\u003csub\u003e2\u003c/sub\u003e) over expired volume (VE) as primary readouts for diagnosis of structural lung disease and pulmonary hypertension.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eWe enrolled 52 subjects, 11 COPD (7 men; median (IQR) age 64 (63\u0026ndash;69) years), 10 ILD (7 men; 61 (54\u0026ndash;77) years), 10 PAH patients (1 man; 64 (61\u0026ndash;73) years) and 21 healthy controls (9 men; 56 (52\u0026ndash;61) years; 11 non-smokers). Patients, compared to controls, showed higher MSV (221 (164\u0026ndash;270) mL vs. 144 (131\u0026ndash;167) mL, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and higher EqCO\u003csub\u003e2\u003c/sub\u003e of the whole exhalation (38 (34\u0026ndash;42) vs. 30 (29\u0026ndash;35), p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), respectively. While EqCO\u003csub\u003e2\u003c/sub\u003e was elevated in all diseased groups, MSV was only increased in COPD and ILD but not in PAH. MSV and maximum FECO\u003csub\u003e2\u003c/sub\u003e/VE slope were significantly correlated with DLCO (\u003cem\u003eρ\u003c/em\u003e=-0.69 and \u003cem\u003eρ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.72, respectively; both p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). According to receiver operating characteristic (ROC) analysis, MSV distinguished diseased from healthy subjects with an area under the curve (AUC) of 0.81 (95% CI: 0.69\u0026ndash;0.93) with an optimal cut-off at 191 mL (sensitivity 68%, specificity 90%), and the parenchymal diseases COPD and ILD from PAH with AUC 0.74 (95% CI: 0.55\u0026ndash;0.92), optimal cut-off at 210 mL; sensitivity 71%, specificity 80%).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eFully-automatic high-fidelity expiratory CO\u003csub\u003e2\u003c/sub\u003e flow analysis is technically feasible, easy and safe to perform, and may represent a novel approach to detect structural changes of the lung parenchyma and/or pulmonary hypertension without need for foreign gas.\u003c/p\u003e","manuscriptTitle":"Detection of structural pulmonary changes with real-time and high-fidelity analysis of expiratory CO2","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-31 16:17:07","doi":"10.21203/rs.3.rs-3894602/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"48c790f5-b175-4c89-ab73-0ac5d6944814","owner":[],"postedDate":"January 31st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-02-22T01:31:14+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-31 16:17:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3894602","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3894602","identity":"rs-3894602","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}