The role of hyperinflation in patients with heart failure (HF) remains poorly characterized. It may contribute to fluid overload (FO) and impaired cardiopulmonary function. We aimed to assess the relationship between hyperinflation and proxies of FO and functional capacity in patients with chronic HF.
MethodsIn this prospective study, 144 stable ambulatory HF patients underwent body plethysmography for assessment of static hyperinflation. FO was evaluated using a combination of clinical examination, ultrasound imaging, and circulating markers. Functional capacity was assessed by distance walked in 6min (6MWT). The relationships between hyperinflation assessed by the inspiratory capacity/total lung capacity (IC/TLC) ratio and the endpoints were analyzed using multivariable logistic and linear regression models.
ResultsThe mean age was 72.8±9.7 years, 108 (75%) were men, 125 (86.8%) had NYHA class II, 71 (49.3%) had preserved ejection fraction, and 46 (30.9%) had COPD (predominantly mild). The mean IC/TLC was 0.37±0.11. Lower IC/TLC was independently associated with several markers of systemic congestion, including jugular engorgement, venous renal congestion, and higher plasmatic levels of carbohydrate antigen 125, and shorter 6MWT. Risk of orthopnea, bendopnea, ultrasound pulmonary congestion, peripheral edema, and higher levels of NT-proBNP did not differ across IC/TLC. These associations remained significant after adjustment for COPD status and other confounders.
ConclusionsIn ambulatory HF patients, lower IC/TLC, a proxy of hyperinflation, was associated with some clinical, imaging, and circulating proxies of FO, independent of COPD diagnosis. Additionally, lower IC/TLC identified patients with lower 6MWT.
Hyperinflation is a key pathophysiological feature in patients with chronic obstructive pulmonary disease (COPD) [1–3]. It is characterized by increased lung volumes due to air trapping and impaired exhalation, resulting in elevated intrathoracic pressures and altered respiratory mechanics [1,2,4]. Although hyperinflation is primarily associated with deleterious pulmonary consequences, it may also play a role in the pathophysiology of heart failure (HF) [2,4]. Specifically, hyperinflation may exacerbate fluid overload (FO) and neurohormonal activation and aggravate functional impairment by decreasing cardiac preload, increasing right ventricular afterload, and impairing ventricular filling due to elevated intrathoracic pressures [4–6]. A lower inspiratory capacity (IC) and inspiratory-to-total lung capacity (IC/TLC) ratio is a well-known parameter of lung hyperinflation reflecting the end-expiratory lung volume [4,7]. In COPD, lower IC/TLC ratios reflecting static hyperinflation have been associated, particularly in emphysema-predominant phenotypes, with reduced intrathoracic blood volume and smaller cardiac chamber dimensions, as demonstrated in imaging-based studies using cardiac magnetic resonance and computed tomography [8,9]. These structural and hemodynamic alterations are thought to result from mechanical compression and reduced pulmonary vascular bed rather than being uniformly present across all obstructive phenotypes. However, its clinical implications have not yet been examined in patients with HF. Given the high prevalence of concomitant COPD and HF, hyperinflation may play an underrecognized role in influencing the severity of HF syndrome.
In this study, we sought to explore the association between hyperinflation and proxies of FO and functional capacity in patients with ambulatory HF, encompassing clinical indicators of pulmonary and systemic congestion, biomarkers reflecting intravascular amino-terminal pro-brain natriuretic peptide (NT-proBNP), and interstitial volume expansion and carbohydrate antigen 125 (CA125) [10]. Insights from this study may enhance our understanding of the cardiopulmonary interactions and potentially provide valuable guidance for developing targeted management strategies to improve patient outcomes.
MethodsStudy design and populationThis is a prospective, consecutive, and single-center study performed in a third-level teaching center in Spain. From November 2020 to February 2024, 161 patients with ambulatory symptomatic HF from the HF Unit of the institution accepted to participate. HF was defined as a clinical syndrome characterized by symptoms and signs resulting from an abnormality of cardiac structure or function [11,12]. All patients included were diagnosed by cardiologists, including measurement of natriuretic peptides and echocardiographic assessment revealing cardiac structural or functional abnormalities according to clinical guidelines [11,12]. Patients with worsening HF requiring parenteral administration of diuretics (n=5) or inability to perform plethysmography (n=5) were excluded. Additionally, 6 patients were excluded because of a nonreliable assessment of plethysmography, leaving a final study sample of 144 patients. Demographic, clinical, electrocardiographic, echocardiographic, laboratory data, and medical treatment were recorded in pre-established electronic questionnaires.
COPD diagnosis was previously established according to GOLD criteria, defined by postbronchodilator FEV1/FVC<0.70. COPD status and treatment were recorded at study entry. Among patients with COPD (n=46), 42 were receiving inhaled maintenance therapy (dual or triple inhaled therapy).
Patients were clinically stable and on optimized HF therapy for at least 4 weeks before study assessment. Loop diuretics (furosemide or torsemide) were recorded under “diuretics.” Sequential nephron blockade was defined as the combination of loop diuretics with thiazide or thiazide-like agents. SGLT2 inhibitors were analyzed separately. Short-acting bronchodilators were withheld before testing according to ERS/ATS recommendations. Long-acting inhaled therapies were not routinely discontinued.
The investigation conforms with the principles outlined in the Declaration of Helsinki. Informed consent was obtained from all participating subjects. The institution's ethics committee approved the study.
ProceduresPulmonary function testing and lung volume measurements were performed according to American Thoracic Society (ATS) and European Respiratory Society (ERS) standards by experienced personnel [13]. Predicted values were calculated using the Global Lung Function Initiative (GLI) reference equations. Two-dimensional echocardiographic assessment followed contemporary guideline recommendations.
SpirometrySpirometry confirmed the COPD diagnosis with a postbronchodilator forced expiratory volume in 1 second (FEV1) to forced vital capacity (FVC) ratio of less than 0.70. The severity of COPD was assessed according to the Global Initiative for Chronic Obstructive Lung Disease (GOLD) guidelines [14].
PlethysmographyIC was defined as the maximum volume of air that can be inhaled from the end of a normal tidal exhalation. It is calculated as the sum of tidal volume (TV) and inspiratory reserve volume (IRV). IC reflects the capacity of the lungs to accommodate additional air during deep inspiration and serves as a measure of lung compliance and respiratory reserve. IC/TLC ratio is the proportion of the lung total capacity available for inspiratory expansion. This ratio provides an indicator of lung hyperinflation and the degree of ventilatory restriction, particularly in patients with obstructive pulmonary diseases such as COPD. A lower IC/TLC ratio typically indicates increased lung hyperinflation, where the IC is reduced relative to the TLC, limiting the patient's ability to take deep breaths. This parameter is particularly valuable in assessing disease severity and its impact on functional capacity [15]. Diffusing capacity for carbon monoxide (DLCO) was measured according to ATS/ERS standards, and percent predicted values were derived from GLI reference equations.
EchocardiographyTransthoracic echocardiography was performed using a phased-array transducer (2–5MHz) to evaluate left and right ventricular function, valvular abnormalities, and overall cardiac structure. Left ventricular ejection fraction (LVEF) was measured using the Simpson biplane method, in accordance with contemporary echocardiographic recommendations [16]. Diastolic function was assessed by Doppler analysis of mitral inflow (E/A ratio), tissue Doppler imaging (e′ velocity), and estimation of left atrial pressure. Right ventricular function was evaluated using tricuspid annular plane systolic excursion (TAPSE). Additional parameters such as pulmonary artery systolic pressure (PASP) were also recorded when feasible. All measurements were performed by trained operators on the same day as pulmonary tests.
EndpointsProxies of fluid overload assessmentClinical examinationA thorough clinical examination was performed by trained cardiologists. The following parameters were assessed: (a) Orthopnea (yes/no), defined as dyspnea that occurs when lying flat, requiring the patient to sleep propped up in a more upright position; (b) Bendopnea (yes/no), defined as shortness of breath or difficulty breathing that occurs upon bending over or stooping [17]; (c) Jugular engorgement (yes/no), assessed by observation of elevated jugular venous pressure (JVP); and (d) Peripheral edema (yes/no), assessed by visual inspection and palpation for the presence of swelling in the lower extremities. Clinical examination was performed on the same day as pulmonary tests.
UltrasonographyA systematic echographic evaluation was performed to determine congestion parameters, including measurement of the inferior vena cava (IVC), pulmonary congestion, and systemic venous congestion on the same day as pulmonary tests.
Assessment of pulmonary congestionLung ultrasound was performed by scanning 8 pulmonary zones in B mode. Pulmonary congestion was defined by the presence of B-lines (>3 B-lines per intercostal space in at least 2 pulmonary zones), indicative of interstitial edema [18].
Measurement of the inferior vena cava (IVC)IVC assessment was performed using transthoracic echocardiography in the subxiphoid view. The maximum diastolic IVC diameter was measured in 2-dimensional (B-mode) imaging at 1–2cm from the junction with the right atrium.
Assessment of renal venous congestionRenal venous flow was evaluated using pulsed Doppler to assess the presence of continuous or discontinuous flow patterns in the renal veins. Renal venous congestion was defined by a discontinuous flow pattern [19]. Renal venous Doppler was assessed in 128 patients (88.9% of the sample) because of technical limitations.
Circulating biomarkersA blood sample was obtained 24–48h before the patient examination. Sample collection included analyses for blood cell count, hemoglobin, hematocrit, basic biochemistry, NT-proBNP, and CA125. All samples were processed and analyzed in certified laboratories using standardized techniques to ensure the accuracy and reliability of the results.
Six-minute walking test (6MWT)The 6MWT was conducted according to standard guidelines in a controlled environment and performed by trained personnel following plethysmography.
Statistical analysisContinuous variables were expressed as mean (standard deviation [SD]) for normally distributed data or median (interquartile range [IQR]) for nonnormally distributed data, as determined by the Kolmogorov–Smirnov test. Categorical variables were expressed as frequencies and percentages. Differences in baseline characteristics across IC/TLC quartiles were tested by calculating P values for trend. Multivariable logistic or linear regression analyses assessed the association among proxies of FO and distance walked in 6min (dependent variables) and the exposure (IC/TLC). IC/TLC was modeled both categorically and continuously to capture potential heterogeneity. Nonlinear associations were explored using fractional polynomial regression to avoid imposing linear assumptions. We also evaluated the relationship between these parameters considering the RV/TLC ratio as the exposure variable. Covariates tested in all multivariable models were based on biological knowledge (variables associated with more advanced HF disease and FO) and included age, sex, body mass index, New York Heart Association (NYHA) class, time from HF diagnosis, systolic blood pressure, heart rate, atrial fibrillation, estimated glomerular filtration rate (eGFR), hemoglobin, NT-proBNP, left ventricular ejection fraction, tricuspid annular plane systolic excursion (TAPSE), mitral regurgitation grade III/IV, tricuspid regurgitation grade III/IV, left atrial area, and diuretic and inhaled treatments. COPD status was included as a covariate in multivariable models to account for potential confounding.
The final covariates in each multivariable model are presented in the respective figure legend. All analyses were performed with STATA 18.1 (STATA Statistical Software, StataCorp LP, College Station, TX, USA).
ResultsBaseline characteristicsThe mean age of the sample was 72.8±9.7 years, 108 (75%) were men, and the median (p25%–p75%) time from HF diagnosis was 36 months (7–96). Most patients (86.8%) were in stable NYHA class II at enrollment. Ischemic etiology and atrial fibrillation were present in 35.4% and 58.3% of patients, respectively. Regarding LVEF, 49 (34%), 24 (16.7%), and 71 (49.3%) patients displayed HF with reduced, mildly reduced, and preserved ejection fraction, respectively. The median (p25%–p75%) eGFR and hemoglobin were 55.5mL/min/1.73m2 (40.5–68.1) and 14.6g/dL (13–15.6), respectively. Most patients were receiving loop diuretics. A total of 46 patients (30.9%) had COPD (10, 26, and 10 GOLD grades 1, 2, and 3, respectively), and 42 were receiving double or triple inhalation therapy.
Inspiratory capacity/total lung capacity ratio (IC/TLC) and proxies of fluid overloadIn the whole sample, the mean±SD of IC/TLC was 0.37±0.11, being lower in COPD patients (0.31±0.10 vs 0.39±0.10; P<.001).
A detailed description of baseline characteristics across IC/TLC quartiles is presented in Table 1 and Supplementary Table 1. Overall, subjects with lower IC/TLC showed epidemiological, clinical, echocardiographic, and laboratory findings of more advanced HF. Likewise, lower IC/TLC quartiles identified subjects with lower FEV1/FVC, FEV1, and diffusing capacity for carbon monoxide (DLCO) (Table 1).
Baseline characteristics across IC/TLC quartiles.
| IC/TLC Q1(N=36) | IC/TLC Q2(N=36) | IC/TLC Q3(N=36) | IC/TLC Q4(N=36) | Total(N=144) | p-Value for trend | |
|---|---|---|---|---|---|---|
| Demographics and past medical history | ||||||
| Age, years | 75.1±9.3 | 74.7±8.2 | 71.9±9.7 | 69.5±10.6 | 72.8±9.7 | .011 |
| Women, n (%) | 14 (38.9) | 8 (22.2) | 7 (19.4) | 7 (19.4) | 39 (26.2) | .059 |
| Weight, kg | 72.5±16.1 | 74.9±11.6 | 84.1±12.9 | 84.1±17.4 | 78.9±15.5 | <.001 |
| Height, m | 163±9 | 163±9 | 169±8 | 166±8 | 165±9 | .031 |
| BMI, kg/m2 | 27.1±5.0 | 28.3±4.9 | 29.6±4.4 | 30.7±6.9 | 28.9±5.5 | .006 |
| Hypertension, n (%) | 26 (72.2) | 21 (58.3) | 30 (83.3) | 27 (75.0) | 108 (72.5) | .320 |
| Dyslipidemia, n (%) | 16 (44.4) | 20 (55.6) | 25 (69.4) | 21 (58.3) | 83 (55.7) | .134 |
| Diabetes, n (%) | 22 (61.1) | 25 (69.4) | 20 (55.6) | 23 (63.9) | 92 (61.7) | .878 |
| Current smoker, n (%) | 0 | 6 (16.7) | 7 (19.4) | 6 (16.7) | 20 (13.4) | .037 |
| Past smoker, n (%) | 24 (66.7) | 22 (61.1) | 21 (58.3) | 20 (55.6) | 88 (59.1) | .324 |
| Smoking pack yearsa | 24.0 (0.0, 63.0) | 40.0 (10.9, 73.1) | 34.8 (4.9, 50.0) | 19.0 (0.0, 53.5) | 29.7 (0.0, 55.0) | .392 |
| CAD, n (%) | 12 (33.3) | 15 (41.7) | 15 (41.7) | 9 (25.0) | 53 (35.6) | .788 |
| COPD spirometry, n (%) | 16 (44.4) | 19 (52.8) | 6 (16.7) | 5 (13.9) | 46 (30.9) | <.001 |
| Asthma, n (%) | 2 (5.6) | 0 | 1 (2.8) | 2 (5.6) | 5 (3.4) | .839 |
| OSA, n (%) | 7 (19.4) | 5 (13.9) | 8 (22.2) | 7 (19.4) | 27 (18.1) | .775 |
| CKD, n (%) | 6 (16.7) | 9 (25.0) | 5 (13.9) | 7 (19.4) | 28 (18.8) | .924 |
| Prior stroke, n (%) | 2 (5.6) | 5 (13.9) | 1 (2.8) | 6 (16.7) | 14 (9.4) | .316 |
| Time since HF diagnosisa, months | 32.5 (6.5, 71.0) | 36.0 (9.5, 98.5) | 36.0 (5.0, 102.0) | 36.0 (8.0, 84.0) | 36.0 (7.0, 96.0) | .704 |
| Electrocardiogram and Echocardiography | ||||||
| Sinus rhythm, n (%) | 15 (41.7) | 15 (41.7) | 18 (50.0) | 12 (33.3) | 63 (42.3) | .651 |
| LBBB, n (%) | 8 (22.2) | 13 (36.1) | 11 (30.6) | 8 (22.2) | 40 (26.8) | .868 |
| LVEF, % | 50±16 | 48±14 | 49±14 | 47±17 | 48±15 | .407 |
| TAPSE, mm | 19.5±4.8 | 20.4±4.0 | 19.3±4.8 | 20.2±4.1 | 19.9±4.4 | .775 |
| Left atrium diameter, mm | 44.4±12.5 | 44.1±7.2 | 44.5±9.3 | 45.6±8.1 | 44.6±9.4 | .145 |
| Physical examination | ||||||
| SBP, mmHg | 132±27 | 127±25 | 130±22 | 132±23 | 130±24 | .676 |
| DBP, mmHg | 70±13 | 67±11 | 72±10 | 70±12 | 70±12 | .337 |
| HR, bpm | 77±19 | 72±14 | 74±12 | 83±21 | 76±17 | .927 |
| Laboratory data | ||||||
| Hemoglobin, g/dL | 14.4±3.1 | 14.5±1.6 | 14.7±1.8 | 14.6±2.0 | 14.5±2.2 | .405 |
| eGFR (MDRD)a, mL/min/1.73m2 | 56±19 | 58±22 | 61±18 | 63±25 | 60±21 | .211 |
| Serum sodium, mEq/L | 140±3 | 140±3 | 140±3 | 141±3 | 140±3 | .504 |
| Serum potassium, mEq/L | 4.3±0.6 | 4.4±0.4 | 4.4±0.5 | 4.5±0.5 | 4.4±0.5 | .017 |
| Spirometry and plethismography | ||||||
| FEV1/FVC | 0.69±0.15 | 0.70±0.12 | 0.75±0.08 | 0.77±0.07 | 0.73±0.11 | <.001 |
| FEV1, % of predicted value | 74.7±23.6 | 80.5±17.5 | 87.7±18.3 | 96.0±21.5 | 84.7±21.7 | 0.007 |
| Residual volume, % of predicted | 125.7±63.4 | 114.7±33.8 | 100.9±25.0 | 94.9±20.4 | 109.0±40.8 | 0.004 |
| Total IC, L | 1.1±0.4 | 1.8±0.5 | 2.3±0.5 | 2.7±0.6 | 2.0±0.8 | <0.001 |
| IC, % of predicted value | 68.5±90.5 | 80.6±17.4 | 89.7±15.1 | 104.9±20.7 | 85.8±49.3 | <0.001 |
| Total TLC, L | 5.0±2.0 | 5.4±1.3 | 5.6±1.1 | 5.3±1.2 | 5.3±1.5 | 0.047 |
| TLC, % of predicted | 89.9±24.9 | 93.8±16.6 | 89.9±11.5 | 89.1±11.9 | 90.7±17.0 | 0.906 |
| FRC, % of predicted | 125.3±44.5 | 112.6±21.6 | 100.2±13.2 | 84.9±15.2 | 106.1±29.8 | <0.001 |
| DLCO, % predicted | 52.5±21.3 | 62.4±22.7 | 66.7±16.9 | 69.1±17.5 | 62.7±20.5 | <0.001 |
BMI: body mass index; CAD: coronary artery disease; CKD: chronic kidney disease; COPD: chronic obstructive pulmonary disease; DBP: diastolic blood pressure; DLCO: diffusing capacity for carbon monoxide; eGFR: estimated glomerular filtration rate; FEV1: forced expiratory volume in 1 second; FRC: functional respiratory capacity; FVC: forced vital capacity; HF: heart failure; HR: heart rate; IC: inspiratory capacity; LBBB: left bundle branch block; MDRD: Modification of Diet in Renal Disease; OSA: obstructive sleep apnea; PASP: pulmonary artery systolic pressure; SBP: systolic blood pressure; RV: residual volume; TAPSE: tricuspid annular plane systolic excursion; TLC: total lung capacity.
Values are expressed as mean±standard deviation, unless otherwise specified.
The presence of peripheral edema was the most frequent proxy of FO finding (28.5%), followed by IVC diameter>21mm (26.4%), jugular engorgement (17.4%), renal venous congestion (16.4%), and orthopnea (16.0%). The median (p25–p75%) NT-proBNP and CA125 were 1272pg/mL (779–2322) and 17U/mL (11–33), respectively. A description of FO parameters across IC/TLC quartiles is presented in Table 2.
Fluid overload, natriuresis, and distance walked in 6min across IC/TLC quartiles.
| IC/TLC Q1(N=36) | IC/TLC Q2(N=36) | IC/TLC Q3(N=36) | IC/TLC Q4(N=36) | Total(N=144) | p-Value for trend | |
|---|---|---|---|---|---|---|
| Fluid overload | ||||||
| Symptoms and signs | ||||||
| Orthopnea, n (%) | 7 (19.4) | 6 (16.7) | 4 (11.1) | 6 (16.7) | 23 (16.0) | .611 |
| Bendopnea, n (%) | 3 (8.3) | 2 (5.6) | 1 (2.8) | 3 (8.3) | 9 (6.3) | .878 |
| Time to bendopnea, sec | 55±15 | 58±11 | 58±10 | 56±11 | 57±12 | .698 |
| Jugular engorgement, n (%) | 10 (27.8) | 8 (22.2) | 5 (13.9) | 2 (5.6) | 25 (17.4) | .008 |
| Peripheral edema, n (%) | 13 (36.1) | 8 (22.2) | 10 (27.8) | 10 (27.8) | 41 (28.5) | .563 |
| Ultrasonography | ||||||
| Pulmonary congestion, n (%) | 5 (13.9) | 5 (13.9) | 2 (5.6) | 5 (13.9) | 17 (11.8) | .729 |
| Renal congestiona, n (%) | 10 (32.3) | 4 (11.8) | 2 (6.1) | 5 (16.7) | 21 (16.4) | .043 |
| IVCb, mm | 18.9±4.7 | 18.4±5.1 | 18.1±5.5 | 17.5±4.1 | 18.2±4.8 | .220 |
| Circulating biomarkers | ||||||
| NT-proBNPb, pg/mL | 1818 (863, 2849) | 1272 (878, 2108) | 1333 (681, 2288) | 1053 (568, 2628) | 1272 (778, 2322) | .142 |
| CA125b, U/mL | 25.0 (12.5, 43.5) | 17.0 (11.5, 32.0) | 14.0 (10.5, 21.5) | 15.0 (10.5, 27.0) | 17.0 (11.0, 33) | .046 |
| Functional capacity | ||||||
| 6MWT, meters | 268±108 | 320±94 | 350±89 | 345±119 | 321±107 | .002 |
6MWT: 6-minute walking test; CA125: carbohydrate antigen 125; IC: inspiratory capacity; IVC: inferior vena cava; NT-proBNP: amino-terminal pro-brain natriuretic peptide; TLC: total lung capacity.
Values are expressed as mean (standard deviation) unless otherwise specified.
The proportion of patients with orthopnea did not differ across the IC/TLC quartiles (Table 2). Likewise, IC/TLC along the continuum was not independently associated with the presence of orthopnea (Fig. 1A).
Association between IC/TLC and parameters of pulmonary fluid overload. (A) Orthopnea. (B) Pulmonary congestion evaluated by ultrasound. (C) Bendopnea. IC/TLC: inspiratory-to-total lung capacity ratio. Dichotomous variables are presented as population-level, model-based estimated probabilities of symptom/sign presence derived from logistic regression models.
Rates were similar across the IC/TLC quartiles (Table 2). Similarly, the continuum of IC/TLC was not associated with dyspnea when bending (Fig. 1B).
Lung B-linesRates did not differ across the exposure groups (Table 2). Likewise, after multivariable adjustment, IC/TLC showed no association (Fig. 1C).
Systemic fluid overloadJugular engorgementRates of jugular engorgement were higher in lower IC/TLC quartiles (Table 2). Higher risk at lower IC/TLC was also present after multivariable adjustment (Fig. 2A). Compared with the 3 upper quartiles, those in the lower quartile (≤0.28) had a 4-fold increased risk (OR, 4.37; 95%CI, 1.48–12.9; P=.008).
Association between IC/TLC and parameters of systemic fluid overload. (A) Jugular engorgement. (B) Inferior vena cava diameter. (C) Peripheral edema. (D) Renal congestion. IC/TLC: inspiratory-to-total lung capacity ratio; IVC: inferior vena cava. Dichotomous variables are presented as population-level, model-based estimated probabilities of symptom/sign presence derived from logistic regression models.
The observed values did not differ across quartiles of IC/TLC (Table 2). After multivariable adjustment, there was a signal of a stepwise increase in IVC diameter at lower IC/TLC values (Fig. 2B).
Peripheral edemaRates of pedal edema did not differ across IC/TLC quartiles (Table 2). Under a multivariable setting, IC/TLC remained unrelated to pedal edema (Fig. 2C).
Ultrasound renal venous congestionWe found a stepwise increase in renal venous congestion rates when moving from higher to lower IC/TLC quartiles (Table 2). Indeed, the rates of renal congestion were at least double compared with the remaining quartiles. After multivariable adjustment, a lower IC/TLC ratio remained associated with a higher risk of discontinuous renal venous Doppler flow (Fig. 2D). Compared with the 3 upper quartiles, patients in the lower quartile displayed a greater than 3-fold increased risk (OR, 3.70; 95%CI, 1.34–10.24; P=.012).
IC/TLC and circulating biomarkers of fluid overloadNT-proBNPMedian NT-proBNP levels did not significantly differ across IC/TLC quartiles (Table 2). Likewise, the ratio along the continuum was not associated with NT-proBNP (Fig. 3A).
CA125Raw data showed higher median CA125 values at lower IC/TLC quartiles (Table 2). These differences were also present after multivariable adjustment (Fig. 3B). Lower IC/TLC was inversely and linearly associated with higher CA125 levels.
IC/TLC and functional capacity6MWTThe mean 6MWT distance was 321±107m. Mean 6MWT distance increased by approximately 70m from the lowest to the upper quartile (Table 2). This association remained linear and significant after multivariable analysis (Fig. 4). For every decrease of 0.1 in IC/TLC, the distance walked in 6min decreased by 22m (95%CI, −34 to −9).
In our cohort, 20 patients (13.9%) exhibited an IC/TLC≤0.25. These individuals showed numerical indications of more pronounced systemic congestion; however, statistically significant differences were observed only for higher NT-proBNP and CA125 levels, as well as a shorter distance walked during the 6-min walk test (Supplementary Table 2).
The role of alternative surrogates of lung hyperinflation and proxies of fluid overloadResidual volume/total lung capacity ratio (RV/TLC)When RV/TLC was evaluated as a surrogate of air trapping, we found similar findings (Supplementary Fig. 1). However, the magnitude and significance of the associations were weaker, and higher RV/TLC was significantly associated with higher jugular engorgement and plasmatic CA125 levels.
Chronic obstructive pulmonary disease (COPD)Given the relatively high prevalence of COPD in our cohort, we performed additional analyses according to COPD status. Clinical signs, biomarkers, and functional capacity between patients with and without COPD are described in Supplementary Table 3. When we evaluated the association between IC/TLC and the study endpoints across COPD strata, no significant interactions were observed with any of the evaluated endpoints, as shown in Supplementary Table 4.
Smoking statusAssociations between IC/TLC and proxies of FO were not significantly affected by smoking status (Supplementary Table 5).
DiscussionThe current study highlights the role of lung hyperinflation, approached by the IC/TLC ratio, in the pathophysiology of HF. Our findings show that a lower IC/TLC ratio was independently associated with proxies of systemic FO and reduced functional capacity in patients with chronic HF, including all LVEF categories (Graphical Abstract). The mean IC/TLC value suggests that severe hyperinflation was uncommon in this ambulatory HF cohort. Nonetheless, associations were observed across the continuum, indicating that even moderate reductions in inspiratory reserve may have clinical relevance. These results underscore the importance of evaluating lung mechanics in HF, particularly in patients with overlapping COPD.
Hyperinflation in COPDThe impact of hyperinflation on hemodynamics has been previously studied in COPD patients [1–4]; however, to our knowledge, this is the first study aiming to quantify the potential deleterious effects of lung hyperinflation in patients with chronic HF. These findings underscore the role of hyperinflation as a key component of heart–lung interactions. Dynamic lung hyperinflation in COPD leads to increased end-expiratory lung volumes caused by airflow limitation and loss of elastic recoil. As the lung parenchyma loses elasticity, lung recoil diminishes, resulting in a compensatory rise in functional residual capacity (FRC) and a subsequent reduction in IC [4,20,21]. This progression reflects worsening static lung hyperinflation, marked by a declining IC/TLC ratio. In COPD, the IC/TLC ratio is a strong predictor of mortality and correlates with reduced exercise capacity and quality of life [15,22–25] by impairing lung function (reduced inspiratory capacity, diaphragmatic function, and ventilatory efficiency) [4,20,21].
In the present study, we did not observe significant heterogeneity according to COPD diagnosis, suggesting that the effects associated with COPD and those related to static hyperinflation should not be considered interchangeable. COPD is a clinical entity defined by persistent airflow limitation, typically identified by spirometric criteria. In contrast, the IC/TLC ratio reflects a physiological indicator of lung hyperinflation, representing a shift of tidal breathing toward higher lung volumes. Importantly, this mechanical alteration may arise from multiple mechanisms beyond COPD, including age-related changes in lung mechanics, diaphragmatic dysfunction, and cardiopulmonary interactions associated with HF and pulmonary congestion. Therefore, reduced IC/TLC in our cohort likely reflects alterations in respiratory mechanics that are not exclusively attributable to obstructive lung disease. Accordingly, IC/TLC should be interpreted as a physiological surrogate of altered ventilatory mechanics rather than a direct mechanistic driver of congestion.
Pathophysiology of hyperinflation in HFIn a recent study, Leahy et al. reported that patients with HF with preserved ejection fraction who developed hyperinflation during exercise exhibited higher pulmonary capillary wedge pressures, suggesting that dysfunctional ventilatory mechanics and the resulting increase in intrathoracic pressure may contribute to this hemodynamic impairment [29].
In COPD, hyperinflation causes elevated intrathoracic pressures, which impair cardiac hemodynamics through multiple mechanisms [1,2,4,26]. These include reduced venous return and limited cardiac compliance, leading to decreased cardiac preload [1,2,4,26]. Additionally, air trapping may increase right ventricular afterload [4–6]. All these mechanisms may be associated with reduced cardiac output and increased venous pressure and volume overload [1,2,4]. In rats, severe hyperinflation leads to cardiorespiratory failure, characterized by arterial hypotension and myocardial injury, particularly in the right ventricle [28]. In COPD patients, hyperinflation-induced cardiac filling impairment due to mechanical constraints on the heart likely explains the smaller cardiac size reported with more severe air trapping [8,9,29,30]. These heart–lung interactions seem especially relevant in HF, where even minor preload and afterload perturbations may aggravate cardiac hemodynamics.
Consistent with these findings, our results show a consistent association between lower IC/TLC ratios and proxies of systemic congestion, including jugular venous distension and renal venous congestion. Moreover, we found a strong association between lung hyperinflation and higher circulating levels of CA125, an emerging biomarker of FO (mainly tissue congestion) [31,32]. Interestingly, the association between lower IC/TLC and FO was observed for highly specific parameters (jugular engorgement and ultrasound kidney congestion) and not for parameters with lower specificity, such as pedal edema [33].
No significant relationship was found between IC/TLC and markers of pulmonary congestion. This discrepancy between the effects of air trapping on left- and right-sided hemodynamics may stem from hyperinflation predominantly compressing systemic veins and right-sided structures rather than affecting left ventricular filling, which operates under significantly higher pressures. This could also explain the lack of association between hyperinflation and NT-proBNP levels, as elevated NT-proBNP is primarily linked to higher left ventricular end-diastolic and pulmonary venous pressures rather than right-sided HF [34].
Lastly, IC/TLC was significantly associated with reduced submaximal functional capacity, as measured by the 6MWT. In adjusted analyses, a 0.1 decrease in IC/TLC corresponded to a 22-m reduction in 6MWT. This magnitude is consistent with, or approaches, the lower range of MCID estimates for 6MWD reported in HF and COPD populations (∼15–30m), although thresholds vary according to population characteristics and methodological approach [35,36].
This relationship remained significant after multivariable adjustment, emphasizing the clinical relevance of hyperinflation in limiting exercise tolerance. In agreement with these findings, Chiari et al. reported that dynamic pulmonary hyperinflation during exercise correlated with reduced peak oxygen uptake in chronic HF [37]. In addition to the strong relationship with FO, hyperinflation imposes a greater workload on the respiratory muscles and reduces cardiac preload during inspiration, which may explain the observed functional impairment. This aligns with previous findings showing that a reduced IC/TLC ratio is associated with dyspnea and exercise limitations in patients with COPD and HF [19,25]. Additionally, the IC/TLC ratio affects the cardiac oxygen pulse, a marker of global cardiac function during exercise, underscoring the impact of hyperinflation on cardiac performance [38].
All these findings reinforce the notion that hyperinflation is not merely a pulmonary finding but also a potential contributor to systemic fluid imbalance, neurohormonal activation, and functional intolerance in HF patients.
Clinical implicationsThe current findings highlight the potential utility of recognizing lung hyperinflation as a contributor to HF progression, particularly in patients with concomitant COPD. Hyperinflation also emerges as a potential therapeutic target since current triple inhalation therapy reduces air trapping and cardiovascular events in COPD patients [39]. Additionally, amelioration of air trapping may underlie the suggested benefit observed with inspiratory muscle training in patients with HF with preserved ejection fraction [40]. Further dedicated studies should address the clinical benefit of treating hyperinflation in patients with HF.
LimitationsSeveral limitations need to be acknowledged. This was a single-center study with a relatively small sample size, which may limit the generalizability of the results and explain the lack of significant association for some comparisons. Second, given the observational and cross-sectional design, the findings should be interpreted as associations rather than causal relationships. Third, despite a thorough description of HF status, several unmeasured pulmonary and cardiac confounders may be playing a role. We cannot exclude residual confounding related to respiratory muscle strength or nutritional status, which were not directly quantified. Fourth, given the graded relationship found between IC/TLC and proxies of systemic congestion and potentially the limited statistical power of the current study, we could not infer a discriminative IC/TLC threshold for predicting significant systemic congestion. Fifth, we did not directly quantify extracellular volume. However, our assessment of volume expansion was performed using validated clinical, imaging, and biomarker tools [10,41]. Sixth, spirometry and plethysmography findings were not complemented by imaging data, and chest computed tomography was not systematically performed, precluding direct assessment of emphysema, air trapping, or lung volumes. DLCO reductions cannot be specifically attributed to parenchymal lung disease, as they may also reflect pulmonary vascular and interstitial alterations inherent to HF. Seventh, given the complex geometry and load dependence of the right ventricle, reliance on tricuspid annular plane systolic excursion without complementary indices or evaluation by cardiac magnetic resonance may have underestimated alterations in right ventricular function and RV-pulmonary arterial coupling. Finally, the present findings are restricted to clinically stable HF patients and should not be extrapolated to critically ill or mechanically ventilated populations.
Our findings suggest that the relationship between static hyperinflation and congestion profiles may not be exclusively driven by COPD and could also extend to patients with HF without overt obstructive lung disease. However, the present study was not specifically designed or powered to robustly evaluate this potential interaction. In particular, subgroup analyses according to spirometry-defined COPD were limited by the relatively small sample size, which increases the risk of unstable estimates and type II error. Although no heterogeneity was detected across COPD or smoking status, a type II error cannot be excluded given the limited statistical power.
ConclusionsIn patients with ambulatory HF, we found that a lower IC/TLC ratio, a marker of lung hyperinflation, was associated with several markers of systemic congestion and more impaired functional capacity. These findings support the critical role of heart–lung interactions in HF pathophysiology and highlight the need for integrated cardiopulmonary assessment in HF patients.
Take home messageIn patients with ambulatory HF pulmonary hyperinflation was associated with greater clinical, imaging, and circulating proxies of fluid overload and a lower functional capacity. #HeartFailure, #Pulmonary Hyperinflation, #Congestion, #Fluid overload.
Authors’ contributionG. Miñana, J. Núñez, and C. González conceived and designed the study, supervised data collection, performed statistical analyses, interpreted the results, and drafted the manuscript. J. Tarrasó, B. Silla, Y. García, I. Tur, and J. Signes were responsible for patient recruitment, plethysmography, and acquisition of clinical and imaging data. R. de la Espriella, A. Fernández-Cisnal, M. Lorenzo, and A. Mollar contributed to database management, data analysis, and interpretation of cardiopulmonary interactions. V. Bodí and J. Sanchis provided overall supervision, intellectual input, and critical revision of the manuscript. All authors contributed to manuscript revision, approved the final version, and take responsibility for the accuracy and integrity of the work.
Artificial intelligence involvementArtificial intelligence has not been used beyond grammatical review of the text.
FundingThis work was supported by a non-conditional grant from Sociedad Española de Cardiología (Proyectos Asociación Insuficiencia Cardiaca 2020) and CIBER Cardiovascular (grant number 16/11/00420).
Conflict of interestNone declared.















