Acute decompensated heart failure: systolic pulmonary artery pressure cut off value to define presence of congestion

Acute decompensated heart failure: systolic pulmonary artery pressure cut off value to define presence of congestion | 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 Acute decompensated heart failure: systolic pulmonary artery pressure cut off value to define presence of congestion Maria Giulia Bellicini This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6627975/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 and Aims: Pulmonary and/or peripheral venous congestion defines the clinical diagnosis of acute heart failure (HF). However, the systolic pulmonary arterial pressure (sPAP) thresholds at which pulmonary (chest X-ray) and inferior vena cava (IVC) congestion occur are not well established. This study aimed to identify a cut-off value of sPAP that reliably indicates acute HF. Methods and Results: We retrospectively included 380 consecutive patients hospitalized for acute HF at an Italian referral center, excluding those with severe tricuspid regurgitation. Receiver operating characteristic (ROC) curve analysis and Youden’s J statistic identified a threshold of sPAP ≥ 48.75 mmHg as the most accurate in predicting both pulmonary (sensitivity = 89.9%, specificity = 73%) and peripheral (sensitivity = 88.3%, specificity = 82.5%) fluid overload. The association between this sPAP threshold and both pulmonary and peripheral congestion was confirmed by chi-square testing (p < 0.001) and multivariate logistic regression (p < 0.001). After adjustment for confounders, sPAP ≥ 48.75 mmHg was independently associated with all-cause death or HF hospitalization (HR = 1.713; 95% CI 1.127–2.602; p = 0.012). Conclusions: Acute HF decompensation is characterized by at least moderately elevated sPAP values. Cardiac & Cardiovascular Systems Acute decompensated heart failure echocardiogram systolic pulmonary artery pressure central venous pressure Figures Figure 1 Figure 2 Figure 3 Figure 4 INTRODUCTION Heart failure (HF) is a clinical syndrome characterized by inadequate cardiac function. In most cases, dysfunction involves the left heart and leads to progressive fluid accumulation, resulting in pulmonary and systemic venous congestion. On echocardiographic evaluation, affected patients often show inferior vena cava (IVC) congestion and elevated systolic pulmonary artery pressure (sPAP). To date, however, no studies have clearly defined the sPAP thresholds that distinguish compensated from decompensated HF. Moreover, there is ongoing disagreement among authors regarding what constitutes a normal or pathological sPAP cut-off—especially in patients with comorbid conditions. Additional causes of HF exacerbation may involve right-sided heart failure mechanisms (McDonagh et al., 2021 ; Rommel et al., 2023 , 2024 ). In this study, we aimed to identify a specific sPAP cut-off value associated with pulmonary congestion in the setting of acute HF. METHODS Study Design and Patient Selection A total of 819 consecutive patients admitted for acute heart failure (HF) to the Cardiology Department of a major Italian referral center (Spedali Civili of Brescia) between January 2022 and November 2023 were retrospectively identified. The diagnosis of acute HF was established according to the European Society of Cardiology (ESC) criteria. Major exclusion criteria were: (1) severe tricuspid valve regurgitation—due to its potential to cause isolated right-sided congestion (Rommel et al., 2023 , 2024 ); (2) precapillary pulmonary hypertension; and (3) missing data for key variables of interest (sPAP, IVC, or chest X-ray). After applying these criteria, 380 patients were included in the final analysis. For each patient, demographic and baseline clinical characteristics, comorbidities, HF phenotype, vital signs, physical exam findings, cardiac rhythm, laboratory results, chest X-ray, and echocardiographic parameters at admission were reviewed. Prognostic outcomes—including rehospitalization and all-cause mortality—were also recorded. All data were collected anonymously. Echocardiography A comprehensive transthoracic echocardiogram at rest was performed upon presentation to the emergency department or shortly after admission to the cardiology ward. Studies were conducted by experienced operators using Philips Affiniti or Epiq ultrasound systems with a 1–5 MHz matrix array sector probe. Left and right ventricular dimensions and function, as well as the severity of valvular regurgitation, were assessed using standard two- and four-chamber views, in accordance with ESC recommendations (Lang et al., 2015 ). Left ventricular diastolic function was evaluated by pulsed Doppler analysis of transmitral inflow, specifically the E/A wave ratio. Systolic pulmonary artery pressure (sPAP) was estimated by measuring the peak velocity of tricuspid regurgitation and adding the estimated central venous pressure (CVP). CVP was derived from inferior vena cava (IVC) diameter and collapsibility, using thresholds defined by the ESC Pulmonary Hypertension Guidelines and expressed in multiples of 5 mmHg (Ambrosy et al., 2013 ). Chest Radiograph A chest X-ray was performed at admission in all patients. Pulmonary congestion was defined as the presence of pleural effusion, accentuation of the interstitial–vascular markings, or congestion of at least one pulmonary hilum (unilateral or bilateral). In patients with known chronic obstructive or restrictive pulmonary disease, or chronic pleural disease, the chest X-ray was considered diagnostic of acute HF decompensation only if pleural effusion was present or if acute interstitial changes were observed. In all other cases, findings were deemed non-specific (McDonagh et al., 2021 ). Statistical Analysis Baseline characteristics were expressed as frequencies and percentages for categorical variables, and as means with standard deviations for continuous variables. Comparisons between groups were performed using chi-square or Levene’s test, as appropriate. Patient characteristics, comorbidities, and cardiovascular outcomes were extracted from the hospital database and regional electronic health records. Univariate binary logistic regression was used to assess associations between baseline variables and the presence of pulmonary congestion on chest X-ray. Variables found to be statistically significant in univariate analysis were then evaluated using receiver operating characteristic (ROC) curves and Youden’s J statistic to identify optimal thresholds predictive of congestion. The resulting sPAP threshold was subsequently tested for its ability to predict IVC (peripheral) congestion using ROC analysis. Multivariate logistic regression was performed to identify independent predictors of chest X-ray congestion. Clinically relevant variables and those significant in univariate analysis (p < 0.05) were included, unless they exhibited multicollinearity with sPAP or CVP. Specifically, NYHA class, peripheral edema, lung rales and mitral regurgitation grade were excluded from the final model to preserve statistical validity. Collinearity was assessed through inspection of standard errors and confidence intervals; no relevant multicollinearity was detected. The prognostic significance of the identified sPAP cut-off was assessed using Cox proportional hazards models for the outcomes of all-cause mortality and the composite of cardiovascular and heart failure hospitalizations. All analyses were conducted using SPSS statistical software. RESULTS Patients’ baseline characteristics Baseline characteristics of the study population are summarized in Table 1 and Supplemetary Table 1. The mean age was 76.3 years (SD ±11.2), and 34.7% of patients were female. The mean left ventricular ejection fraction (LVEF) was 38.2% (SD ±13.4), with a mean systolic pulmonary artery pressure (sPAP) of 51.6 mmHg (SD ±13 mmHg) and a central venous pressure (CVP) of 10.8 mmHg (SD ±4.5 mmHg). Pulmonary congestion on chest X-ray was present in 72.1% of patients. The mean NT-proBNP level was 8,258 pg/mL (SD ±9,271 pg/mL). Table 1 . Baseline characteristics of the study population. baseline demographic, clinical, and echocardiographic characteristics of the study population are reported. Continuous variables are expressed as mean ± standard deviation; categorical variables as percentages. Variable Value (SD)/ Frequency (%) N=380 Demographics and comorbidities Age at inclusion (years) 76,3 ± 11,2 Female sex 132 (34,7%) Hypertension 259 (68,1%) Dyslipidemia 201 (52,9%) Diabetes mellitus 125 (32,9%) Permanent AF 88 (23,1%) Prior CAD diagnosis 122 (32,1%) Prior valve surgery 39 (10,2%) Prior percutaneous valve intervention 22 (5,7%) Objective examination NYHA class III 143 (37,6%) NYHA class IV 146 (38,4%) Declive oedema 163 (42,9%) Lung rales 169 (44,4%) Vital parameters SBP (mmHg) 138,8 ± 32,7 HR(bpm) 88,64 ± 25,856 Rhythm at ECG Sinus rhythm 260 (68,4%) AF 120 (31,6%) Echocardiogram LVEF(%) 38,23 ± 13,396 LA volume(mL) 108,08 ± 64,295 RV disfunction 47 (12,4%) Severe MR 160 (42,1%) Severe MS 4 (1,0%) Severe AS 34 (8,9%) Severe AR 17 (4,4%) sPAP (mmHg) 51,58 ± 12,98 CVP (mmHg) 10,81 ± 4,49 Lung congestion at chest X ray 274 (72,1%) Laboratory exams NTproBNP (pg/mL) 8258,35 ± 9271,41 Troponin T (ng/L) 532,90 ± 2689,84 Creatinine (mg/dL) 1,66 ± 1,14 Hemoglobin (g/dL) 12,13 ± 2,20 ALT (UI/L) 59,68 ± 215,41 Abbreviations: ALT = alanine aminotransferase; AF = atrial fibrillation; AR = aortic regurgitation; AS = aortic stenosis; CAD = coronary artery disease; CVP = central venous pressure; HR = heart rate; LA = left atrium; LVEF = left ventricular ejection fraction; MR = mitral regurgitation; MS = mitral stenosis; NTproBNP = N-terminal pro-B-type natriuretic peptide; NYHA = New York Heart Association; SBP = systolic blood pressure; SD = standard deviation; sPAP = systolic pulmonary artery pressure; TnT = troponin T Characteristics of the study population stratified by chest-X ray congestion Patients with evidence of congestion on chest X-ray at admission were more likely to present with higher weight (OR = 0.978, 95% CI 0.963–0.994, p = 0.006), NYHA class III or IV (OR = 2.674, 95% CI 1.914–3.737, p < 0.001), declive oedema (OR = 6.375, 95% CI 3.607–11.269, p < 0.001), severe mitral regurgitation (OR = 2.528, 95% CI 1.546–4.134, p < 0.001), elevated sPAP (OR = 1.13, 95% CI 1.098–1.163, p < 0.001), and increased CVP (OR = 1.406, 95% CI 1.304–1.516, p 3000 pg/mL (OR = 5.655, 95% CI 3.212–9.958, p 14 ng/L (OR = 4.136, 95% CI 1.570–10.894, p = 0.004), and higher AST levels (OR = 1.013, 95% CI 1.002–1.024, p = 0.02) (Supplemetary Table 2). After adjustment in a multivariate binary logistic regression, echocardiographic parameters and troponin T remained significantly associated with chest X-ray congestion (Table 2). No significant differences were observed between groups in terms of other clinical characteristics, comorbidities, or preadmission therapy (Supplementary Table 2). Table 2. Multivariate Logistic Regression for the Presence of Pulmonary Congestion on Chest X-Ray Variables independently associated with chest X-ray pulmonary congestion are shown. Odds ratios (Exp(B)) are reported with 95% confidence intervals. Variables were selected based on clinical relevance and univariate significance; collinear variables were excluded Variable P value Exp(B) (95% C.I.) Femalesex 0,737 0,821 (0,259; 2,598) Age 0,129 0,956 (0,902;1,013) Weight(kg) 0,004 0,955 (0,925; 0,985) Hypertension 0,064 3,162 (0,936; 10,678) LVEF(%) 0,659 1,010 (0,966; 1,057) sPAP (mmHg) 0,006 1,083 (1,023; 1,145) CVP (mmHg) 0,001 1,332 (1,121; 1,583) Troponin T >14 ng/L 0,027 9,162 (1,283; 65,447) NTproBNP >3000 mg/dL 0,102 2,420 (0,840; 6,971) ALT(UI/L) 0,468 1,007 (0,989; 1,026) sPAP cut off A receiver operating characteristic (ROC) curve analysis combined with Youden’s J statistic was used to evaluate the diagnostic performance of variables significantly associated with chest X-ray pulmonary congestion. Among them, systolic pulmonary artery pressure (sPAP) demonstrated the best discriminatory ability. An sPAP value ≥ 48.75 ± 1.25 mmHg was identified as the optimal cut-off, yielding a sensitivity of 89.9% and specificity of 73% for predicting pulmonary congestion (Table 3, Figure 1). Table 3 . Diagnostic Performance of Variables Associated with Chest X-Ray Congestion Variable AUC Sensitivity Specificity More performing cut off value Youden test NYHA class 0,736 0,885 0,621 3 0,508 Declive oedema (1/0) 0,713 0,65 0,232 1 0,118 Lung rales (1/0) 0,602 0,58 0,623 1 0,204 LVEF (%) 0,407 MR grade 0,641 0,494 0,721 severe 0,215 sPAP (mmHg) 0,823 0,899 0,735 48,75 ±1,25 0,634 CVP (multiple of 5; mmHg) 0,816 0,881 0,708 10 0,589 Troponin T (ng/L) 0,614 0,695 0,526 34,5±0,5 0,221 NTproBNP (pg/mL) 0,735 0,725 0,693 3124±25 0,419 ALT (UI/L) 0,61 0,372 0,98 32,5±0,5 0,153 The sPAP cut-off of 48.75 ± 1.25 mmHg also showed strong performance in predicting peripheral venous congestion on echocardiography, with a sensitivity of 88.3% and specificity of 82.5% (Figure 2). This threshold was further supported by chi-square testing and multivariate linear regression, which confirmed a significant association between sPAP ≥ 48.75 ± 1.25 mmHg and both peripheral venous and chest X-ray pulmonary congestion (p < 0.001). Prognostic significance of sPAP cutoff A total of 25% of patients died and 15% were rehospitalized for heart failure. The combined endpoint of all-cause death or heart failure hospitalization occurred in 38% of patients over a median follow-up of 521 days (95% CI: 455–586 days). In univariable analysis, sPAP value ≥ 48.75 ± 1.25 was significantly associated with an increased risk of the composite outcome (p = 0.019) and with first cardiovascular hospitalization (p = 0.030). Survival analysis using the Kaplan–Meier method demonstrated a significantly worse prognosis in patients with sPAP ≥ 48.75 ± 1.25 mmHg (Figure 3). After multivariable adjustment, residual clinical congestion defined by sPAP ≥ 48.75 ± 1.25 mmHg remained independently associated with an increased risk of the composite outcome of all-cause death or heart failure hospitalization (HR = 1.713; 95% CI: 1.127–2.602; p = 0.012) (Table 4, Figure 4). Kaplan–Meier analysis showing the probability of survival free from all-cause death or heart failure hospitalization according to sPAP ≥ 48.75 ± 1.25 mmHg. Patients above this threshold had significantly worse outcomes during follow-up (log-rank p < 0.05). Table 4. Cox regression for composite outcome all-cause death or HF hospitalization Variable P value Exp(B) (95% C.I.) sPAP ≥ 48,75 ±1,25 mmHg 0,021 1,672 (1,082; 2,584) Female sex 0,041 0,660 (0,442; 0,984) Age (years) 0,002 1,029 (1,011; 1,049) LVEF (%) 0,420 0,994 (0,980; 1,009) DISCUSSION Current Pulmonary Arterial Hypertension guidelines define 30 mmHg as the upper limit of normal for sPAP in healthy individuals (Humbert et al., 2022). However, the 2019 ESC consensus on the use of diuretics in heart failure does not establish specific sPAP thresholds for defining euvolemia or congestion. Instead, fluid status is primarily evaluated based on clinical signs such as peripheral edema, natriuretic peptide levels (e.g., NT-proBNP), and imaging findings including IVC congestion and chest X-ray abnormalities (Mullens et al., 2019). To date, no study has clearly defined sPAP ranges for compensated versus decompensated heart failure. The literature also shows disagreement regarding the definition of normal versus pathological sPAP values, particularly in patients with comorbidities. For instance, Fisher and colleagues proposed stratified thresholds for pulmonary hypertension: mild (35–49 mmHg), moderate (50–59 mmHg), and severe (≥60 mmHg) (Faqih et al., 2016; Fisher et al., 2009; Velidakis et al., 2023). Similarly, McQuillan et al. reported that only 28% of 3,790 echocardiographically normal individuals had an sPAP <30 mmHg. They also noted that the upper reference limit might reach 40 mmHg in older adults or patients with obesity or heart failure (McQuillan et al., 2001). Establishing sPAP cut-off values is challenging due to multiple confounders. One major issue is the frequent presence of severe tricuspid regurgitation in patients with decompensated HF, which often leads to IVC regardless of the patient's true volume status. Additionally, isolated right-sided congestion patterns do not always present with radiographic pulmonary congestion, further complicating the assessment. Another limitation in the literature stems from high inter-operator and inter-center variability in echocardiographic evaluation, particularly in emergency settings. Many studies lack complete imaging data at the time of admission, reducing diagnostic consistency. In contrast, our study was conducted in a high-volume tertiary care center with comprehensive clinical and echocardiographic assessments performed at admission and during hospitalization. By applying stringent exclusion criteria, we found that a moderately elevated sPAP value—specifically, ≥48.75 mmHg—was highly sensitive and specific for identifying both pulmonary and peripheral venous congestion in patients with acute HF. These findings suggest that this cut-off may serve as a practical reference point in routine clinical assessment. CONCLUSION In this study, we identified a systolic pulmonary arterial pressure (sPAP) threshold of ≥48.75 ± 1.25 mmHg as a reliable marker of acute heart failure decompensation due to fluid overload. This value was independently associated with both objective signs of congestion and adverse clinical outcomes, supporting its potential clinical utility in routine assessment. Abbreviations AF atrial fibrillation CKD chronic kidney disease COPD chronic obstructive pulmonary disease CVP central venous pressure HF heart failure IVC inferior vena cava LVEF left ventricular ejection fraction NYHA New York Heart Association RAASis renin–angiotensin–aldosterone system inhibitors SGLTis sodium–glucose cotransporter 2 inhibitors sPAP systolic pulmonary artery pressure Declarations This analysis was conducted within the scope of a research protocol on acute heart failure that had been previously approved by the Ethics Committee of ASST Spedali Civili di Brescia. The study is based on retrospective analysis of anonymized data collected during routine care, and no additional approval was required for this secondary use of data. Funding None. Conflict of Interest The authors declare no conflicts of interest. Ethical Statement This study was conducted retrospectively using anonymized clinical data collected during routine patient care. In accordance with national regulations and institutional policies, formal ethical approval was not required. References Ambrosy AP, Pang PS, Khan S, Konstam MA, Fonarow GC, Traver B, Maggioni AP, Cook T, Swedberg K, Burnett JC, Grinfeld L, Udelson JE, Zannad F, Gheorghiade M (2013) Clinical course and predictive value of congestion during hospitalization in patients admitted for worsening signs and symptoms of heart failure with reduced ejection fraction: Findings from the EVEREST trial. Eur Heart J 34(11):835–843. https://doi.org/10.1093/eurheartj/ehs444 Faqih SA, Noto-Kadou-Kaza B, Abouamrane LM, Mtiou N, Khayat E, Zamd S, Medkouri M, Benghanem G, M. G., Ramdani B (2016) Pulmonary hypertension: Prevalence and risk factors. IJC Heart Vasculature 11:87–89. https://doi.org/10.1016/j.ijcha.2016.05.012 Fisher MR, Forfia PR, Chamera E, Housten-Harris T, Champion HC, Girgis RE, Corretti MC, Hassoun PM (2009) Accuracy of doppler echocardiography in the hemodynamic assessment of pulmonary hypertension. Am J Respir Crit Care Med 179(7):615–621. https://doi.org/10.1164/rccm.200811-1691OC Humbert M, Kovacs G, Hoeper MM, Badagliacca R, Berger RMF, Brida M, Carlsen J, Coats AJS, Escribano-Subias P, Ferrari P, Ferreira DS, Ghofrani HA, Giannakoulas G, Kiely DG, Mayer E, Meszaros G, Nagavci B, Olsson KM, Pepke-Zaba J, Sivakumaran K (2022) 2022 ESC/ERS Guidelines for the diagnosis and treatment of pulmonary hypertension. In European Heart Journal (Vol. 43, Issue 38, pp. 3618–3731). 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Supplementary Files SupplementaryTablesspapcutoffbellicini1.docx supplemetary tables 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. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6627975","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":454293967,"identity":"5d3916c3-3d0f-4a12-a82f-8d8fb4503c97","order_by":0,"name":"Maria Giulia Bellicini","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIiWNgGAWjYFACHgaGBwUSDGwMjA0HEn7YMDAwA0UYDAhoSTAAa2l88LEnDaYFnx6wFjCL2XAG22GICD5rdBt4Dz5IMLCQ55NubpPm4TmfuJ2d9/AHhoI/OLWYHeBLNgA6zLBN5iBQi8XtxJ3NfGkS+BxmdoDHTAKoJYFNIhFky+3EDYd5zPD6BajF/AdCC9s5kBbjD4RsYYBqaQZ6/wBIiwF+hx3mS5YA+0UiERTIycYbDgP9kmBgjFvL8d6DHz5U1MnLz0h/AIxKO9kN588e/vDhjxxOLQzMWEUTcGsYBaNgFIyCUUAEAABGzU3KOpQAtgAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0002-0625-7555","institution":"Spedali Civili of Brescia","correspondingAuthor":true,"prefix":"","firstName":"Maria","middleName":"Giulia","lastName":"Bellicini","suffix":""}],"badges":[],"createdAt":"2025-05-09 11:16:40","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6627975/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6627975/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82615114,"identity":"fa6222ba-5a59-4967-8bf5-801b834cdf0b","added_by":"auto","created_at":"2025-05-13 11:35:00","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":341299,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eReceiver Operating Characteristic (ROC) Curves of Parameters Predicting Chest X-Ray Congestion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eROC curves are shown for all variables significantly associated with chest X-ray pulmonary congestion in univariate analysis. sPAP demonstrated the highest diagnostic performance (AUC = 0.823), while LVEF showed no discriminatory value (AUC = 0.407). Optimal cut-off values were identified using Youden’s J statistic where applicable\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6627975/v1/56bd705b49293c71471aa6f8.jpeg"},{"id":82616518,"identity":"14006170-5404-4841-b3cd-d323abf0505e","added_by":"auto","created_at":"2025-05-13 11:43:00","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":79726,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC Curve of sPAP for the Prediction of Peripheral Venous Congestion\u003c/strong\u003e\u003cbr\u003e\n \u003cem\u003eThe ROC curve illustrates the discriminatory ability of sPAP to predict peripheral venous congestion as assessed by echocardiography. The optimal cut-off value of ≥48.75 ± 1.25 mmHg yielded a sensitivity of 88.3% and a specificity of 82.5%, with an area under the curve (AUC) of 0.823.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6627975/v1/05246b9b98d900dfcf778291.jpeg"},{"id":82616519,"identity":"de4e0c2f-bdd6-44f7-82e3-e9717f877108","added_by":"auto","created_at":"2025-05-13 11:43:00","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":212057,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan–Meier Curve for the Composite Outcome of All-Cause Death or Heart Failure Hospitalization, Stratified by sPAP Cut-Off\u003c/strong\u003e\u003cbr\u003e\n \u003cem\u003eKaplan–Meier analysis showing the probability of survival free from all-cause death or heart failure hospitalization according to sPAP ≥ 48.75 ± 1.25 mmHg. Patients above this threshold had significantly worse outcomes during follow-up (log-rank p \u0026lt; 0.05).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6627975/v1/df4722ec237a052e17b430b0.jpeg"},{"id":82617226,"identity":"50cc836a-a9bf-43bd-b7e2-52722e0ff0e4","added_by":"auto","created_at":"2025-05-13 11:51:00","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":133884,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eForest Plot from Cox Regression for the Composite Outcome of All-Cause Death or Heart Failure Hospitalization\u003c/strong\u003e\u003cbr\u003e\n \u003cem\u003eForest plot displaying hazard ratios (HRs) with 95% confidence intervals for variables included in the multivariable Cox regression analysis. sPAP ≥ 48.75 ± 1.25 mmHg was independently associated with an increased risk of the composite outcome (HR = 1.713, 95% CI: 1.127–2.602; p = 0.012). Age and sex also showed significant associations. LVEF was not independently predictive of outcome\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6627975/v1/1610609a286ab49e2d4a6649.jpeg"},{"id":82618178,"identity":"366097ea-3fa0-4204-9f92-2fbfe7d1d902","added_by":"auto","created_at":"2025-05-13 11:59:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1769477,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6627975/v1/642937e5-72a8-4f6c-b400-8223c3896b25.pdf"},{"id":82615109,"identity":"777a7943-a033-4512-903d-ceb8998827b3","added_by":"auto","created_at":"2025-05-13 11:35:00","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":47120,"visible":true,"origin":"","legend":"\u003cp\u003esupplemetary tables\u003c/p\u003e","description":"","filename":"SupplementaryTablesspapcutoffbellicini1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6627975/v1/733f6008e1ac75edc2fc2710.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eAcute decompensated heart failure: systolic pulmonary artery pressure cut off value to define presence of congestion\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eHeart failure (HF) is a clinical syndrome characterized by inadequate cardiac function. In most cases, dysfunction involves the left heart and leads to progressive fluid accumulation, resulting in pulmonary and systemic venous congestion. On echocardiographic evaluation, affected patients often show inferior vena cava (IVC) congestion and elevated systolic pulmonary artery pressure (sPAP).\u003c/p\u003e \u003cp\u003eTo date, however, no studies have clearly defined the sPAP thresholds that distinguish compensated from decompensated HF. Moreover, there is ongoing disagreement among authors regarding what constitutes a normal or pathological sPAP cut-off\u0026mdash;especially in patients with comorbid conditions.\u003c/p\u003e \u003cp\u003eAdditional causes of HF exacerbation may involve right-sided heart failure mechanisms (McDonagh et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Rommel et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this study, we aimed to identify a specific sPAP cut-off value associated with pulmonary congestion in the setting of acute HF.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Patient Selection\u003c/h2\u003e \u003cp\u003eA total of 819 consecutive patients admitted for acute heart failure (HF) to the Cardiology Department of a major Italian referral center (Spedali Civili of Brescia) between January 2022 and November 2023 were retrospectively identified. The diagnosis of acute HF was established according to the European Society of Cardiology (ESC) criteria.\u003c/p\u003e \u003cp\u003eMajor exclusion criteria were: (1) severe tricuspid valve regurgitation\u0026mdash;due to its potential to cause isolated right-sided congestion (Rommel et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e); (2) precapillary pulmonary hypertension; and (3) missing data for key variables of interest (sPAP, IVC, or chest X-ray). After applying these criteria, 380 patients were included in the final analysis.\u003c/p\u003e \u003cp\u003eFor each patient, demographic and baseline clinical characteristics, comorbidities, HF phenotype, vital signs, physical exam findings, cardiac rhythm, laboratory results, chest X-ray, and echocardiographic parameters at admission were reviewed. Prognostic outcomes\u0026mdash;including rehospitalization and all-cause mortality\u0026mdash;were also recorded. All data were collected anonymously.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEchocardiography\u003c/h3\u003e\n\u003cp\u003eA comprehensive transthoracic echocardiogram at rest was performed upon presentation to the emergency department or shortly after admission to the cardiology ward. Studies were conducted by experienced operators using Philips Affiniti or Epiq ultrasound systems with a 1\u0026ndash;5 MHz matrix array sector probe.\u003c/p\u003e \u003cp\u003eLeft and right ventricular dimensions and function, as well as the severity of valvular regurgitation, were assessed using standard two- and four-chamber views, in accordance with ESC recommendations (Lang et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Left ventricular diastolic function was evaluated by pulsed Doppler analysis of transmitral inflow, specifically the E/A wave ratio.\u003c/p\u003e \u003cp\u003eSystolic pulmonary artery pressure (sPAP) was estimated by measuring the peak velocity of tricuspid regurgitation and adding the estimated central venous pressure (CVP). CVP was derived from inferior vena cava (IVC) diameter and collapsibility, using thresholds defined by the ESC Pulmonary Hypertension Guidelines and expressed in multiples of 5 mmHg (Ambrosy et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eChest Radiograph\u003c/h3\u003e\n\u003cp\u003eA chest X-ray was performed at admission in all patients. Pulmonary congestion was defined as the presence of pleural effusion, accentuation of the interstitial\u0026ndash;vascular markings, or congestion of at least one pulmonary hilum (unilateral or bilateral).\u003c/p\u003e \u003cp\u003eIn patients with known chronic obstructive or restrictive pulmonary disease, or chronic pleural disease, the chest X-ray was considered diagnostic of acute HF decompensation only if pleural effusion was present or if acute interstitial changes were observed. In all other cases, findings were deemed non-specific (McDonagh et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eBaseline characteristics were expressed as frequencies and percentages for categorical variables, and as means with standard deviations for continuous variables. Comparisons between groups were performed using chi-square or Levene\u0026rsquo;s test, as appropriate.\u003c/p\u003e \u003cp\u003ePatient characteristics, comorbidities, and cardiovascular outcomes were extracted from the hospital database and regional electronic health records. Univariate binary logistic regression was used to assess associations between baseline variables and the presence of pulmonary congestion on chest X-ray.\u003c/p\u003e \u003cp\u003eVariables found to be statistically significant in univariate analysis were then evaluated using receiver operating characteristic (ROC) curves and Youden\u0026rsquo;s J statistic to identify optimal thresholds predictive of congestion. The resulting sPAP threshold was subsequently tested for its ability to predict IVC (peripheral) congestion using ROC analysis.\u003c/p\u003e \u003cp\u003eMultivariate logistic regression was performed to identify independent predictors of chest X-ray congestion. Clinically relevant variables and those significant in univariate analysis (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were included, unless they exhibited multicollinearity with sPAP or CVP. Specifically, NYHA class, peripheral edema, lung rales and mitral regurgitation grade were excluded from the final model to preserve statistical validity. Collinearity was assessed through inspection of standard errors and confidence intervals; no relevant multicollinearity was detected.\u003c/p\u003e \u003cp\u003eThe prognostic significance of the identified sPAP cut-off was assessed using Cox proportional hazards models for the outcomes of all-cause mortality and the composite of cardiovascular and heart failure hospitalizations. All analyses were conducted using SPSS statistical software.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cstrong\u003ePatients\u0026rsquo; baseline characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBaseline characteristics of the study population are summarized in Table 1 and Supplemetary Table 1. The mean age was 76.3 years (SD \u0026plusmn;11.2), and 34.7% of patients were female. The mean left ventricular ejection fraction (LVEF) was 38.2% (SD \u0026plusmn;13.4), with a mean systolic pulmonary artery pressure (sPAP) of 51.6 mmHg (SD \u0026plusmn;13 mmHg) and a central venous pressure (CVP) of 10.8 mmHg (SD \u0026plusmn;4.5 mmHg). Pulmonary congestion on chest X-ray was present in 72.1% of patients. The mean NT-proBNP level was 8,258 pg/mL (SD \u0026plusmn;9,271 pg/mL).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e. \u003cstrong\u003eBaseline characteristics of the study population.\u003c/strong\u003e\u003cbr\u003e\u003cem\u003ebaseline demographic, clinical, and echocardiographic characteristics of the study population are reported. Continuous variables are expressed as mean \u0026plusmn; standard deviation; categorical variables as percentages.\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eValue \u0026nbsp;(SD)/ Frequency (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN=380\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDemographics and comorbidities\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eAge at inclusion (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e76,3 \u0026plusmn; 11,2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eFemale sex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e132 (34,7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e259 (68,1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eDyslipidemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e201 (52,9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eDiabetes mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e125 (32,9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003ePermanent AF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e88 (23,1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003ePrior CAD diagnosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e122 (32,1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003ePrior valve surgery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e39 (10,2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003ePrior percutaneous valve intervention\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e22 (5,7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eObjective examination\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eNYHA class III\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e143 (37,6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eNYHA class IV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e146 (38,4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eDeclive oedema\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e163 (42,9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eLung rales\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e169 (44,4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVital parameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eSBP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e138,8 \u0026plusmn; 32,7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eHR(bpm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e88,64 \u0026plusmn; 25,856\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRhythm at ECG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eSinus rhythm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e260 (68,4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eAF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e120 (31,6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEchocardiogram\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eLVEF(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e38,23 \u0026plusmn; 13,396\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eLA volume(mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e108,08 \u0026plusmn; 64,295\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eRV disfunction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e47 (12,4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eSevere MR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e160 \u0026nbsp;(42,1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eSevere MS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e4 (1,0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eSevere AS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e34 (8,9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eSevere AR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e17 (4,4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003esPAP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e51,58 \u0026plusmn; 12,98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eCVP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e10,81 \u0026plusmn; 4,49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLung congestion at chest X ray\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e274 (72,1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLaboratory exams\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eNTproBNP (pg/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e8258,35 \u0026plusmn; 9271,41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTroponin T (ng/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e532,90 \u0026plusmn; 2689,84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eCreatinine (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e1,66 \u0026plusmn; 1,14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eHemoglobin (g/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e12,13 \u0026plusmn; 2,20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eALT (UI/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e59,68 \u0026plusmn; 215,41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u003c/strong\u003e ALT = alanine aminotransferase; AF = atrial fibrillation; AR = aortic regurgitation; AS = aortic stenosis; CAD = coronary artery disease; CVP = central venous pressure; HR = heart rate; LA = left atrium; LVEF = left ventricular ejection fraction; MR = mitral regurgitation; MS = mitral stenosis; NTproBNP = N-terminal pro-B-type natriuretic peptide; NYHA = New York Heart Association; SBP = systolic blood pressure; SD = standard deviation; sPAP = systolic pulmonary artery pressure; TnT = troponin T\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCharacteristics of the study population stratified by chest-X ray congestion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatients with evidence of congestion on chest X-ray at admission were more likely to present with higher weight (OR = 0.978, 95% CI 0.963\u0026ndash;0.994, p = 0.006), NYHA class III or IV (OR = 2.674, 95% CI 1.914\u0026ndash;3.737, p \u0026lt; 0.001), declive oedema (OR = 6.375, 95% CI 3.607\u0026ndash;11.269, p \u0026lt; 0.001), severe mitral regurgitation (OR = 2.528, 95% CI 1.546\u0026ndash;4.134, p \u0026lt; 0.001), elevated sPAP (OR = 1.13, 95% CI 1.098\u0026ndash;1.163, p \u0026lt; 0.001), and increased CVP (OR = 1.406, 95% CI 1.304\u0026ndash;1.516, p \u0026lt; 0.001). Laboratory findings associated with congestion included NT-proBNP \u0026gt; 3000 pg/mL (OR = 5.655, 95% CI 3.212\u0026ndash;9.958, p \u0026lt; 0.001), troponin T \u0026gt; 14 ng/L (OR = 4.136, 95% CI 1.570\u0026ndash;10.894, p = 0.004), and higher AST levels (OR = 1.013, 95% CI 1.002\u0026ndash;1.024, p = 0.02) (Supplemetary Table 2).\u003cbr\u003e\u0026nbsp;After adjustment in a multivariate binary logistic regression, echocardiographic parameters and troponin T remained significantly associated with chest X-ray congestion (Table 2).\u003cbr\u003e\u0026nbsp;No significant differences were observed between groups in terms of other clinical characteristics, comorbidities, or preadmission therapy (Supplementary Table 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e \u003cstrong\u003eMultivariate Logistic Regression for the Presence of Pulmonary Congestion on Chest X-Ray\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eVariables independently associated with chest X-ray pulmonary congestion are shown. Odds ratios (Exp(B)) are reported with 95% confidence intervals. Variables were selected based on clinical relevance and univariate significance; collinear variables were excluded\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38.8889%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.2778%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45.8333%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eExp(B) (95% C.I.)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38.8889%;\"\u003e\n \u003cp\u003eFemalesex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.2778%;\"\u003e\n \u003cp\u003e0,737\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45.8333%;\"\u003e\n \u003cp\u003e0,821 (0,259; 2,598)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38.8889%;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.2778%;\"\u003e\n \u003cp\u003e0,129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45.8333%;\"\u003e\n \u003cp\u003e0,956 (0,902;1,013)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38.8889%;\"\u003e\n \u003cp\u003eWeight(kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.2778%;\"\u003e\n \u003cp\u003e0,004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45.8333%;\"\u003e\n \u003cp\u003e0,955 (0,925; 0,985)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38.8889%;\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.2778%;\"\u003e\n \u003cp\u003e0,064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45.8333%;\"\u003e\n \u003cp\u003e3,162 (0,936; 10,678)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38.8889%;\"\u003e\n \u003cp\u003eLVEF(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.2778%;\"\u003e\n \u003cp\u003e0,659\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45.8333%;\"\u003e\n \u003cp\u003e1,010 (0,966; 1,057)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38.8889%;\"\u003e\n \u003cp\u003esPAP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.2778%;\"\u003e\n \u003cp\u003e0,006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45.8333%;\"\u003e\n \u003cp\u003e1,083 (1,023; 1,145)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38.8889%;\"\u003e\n \u003cp\u003eCVP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.2778%;\"\u003e\n \u003cp\u003e0,001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45.8333%;\"\u003e\n \u003cp\u003e1,332 (1,121; 1,583)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38.8889%;\"\u003e\n \u003cp\u003eTroponin T \u0026gt;14 ng/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.2778%;\"\u003e\n \u003cp\u003e0,027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45.8333%;\"\u003e\n \u003cp\u003e9,162 (1,283; 65,447)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38.8889%;\"\u003e\n \u003cp\u003eNTproBNP \u0026gt;3000 mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.2778%;\"\u003e\n \u003cp\u003e0,102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45.8333%;\"\u003e\n \u003cp\u003e2,420 (0,840; 6,971)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38.8889%;\"\u003e\n \u003cp\u003eALT(UI/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.2778%;\"\u003e\n \u003cp\u003e0,468\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45.8333%;\"\u003e\n \u003cp\u003e1,007 (0,989; 1,026)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003esPAP cut off\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA receiver operating characteristic (ROC) curve analysis combined with Youden\u0026rsquo;s J statistic was used to evaluate the diagnostic performance of variables significantly associated with chest X-ray pulmonary congestion. Among them, systolic pulmonary artery pressure (sPAP) demonstrated the best discriminatory ability. An sPAP value \u0026ge; 48.75 \u0026plusmn; 1.25 mmHg was identified as the optimal cut-off, yielding a sensitivity of 89.9% and specificity of 73% for predicting pulmonary congestion (Table 3, Figure 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e. \u003cstrong\u003eDiagnostic Performance of Variables Associated with Chest X-Ray Congestion\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAUC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSensitivity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpecificity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMore performing cut off value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eYouden test\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNYHA class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,736\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,885\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,621\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,508\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDeclive oedema (1/0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,713\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,118\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLung rales (1/0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,602\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,623\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,204\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLVEF (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,407\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMR grade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,641\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,494\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,721\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003esevere\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,215\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003esPAP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,823\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,899\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,735\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e48,75 \u0026plusmn;1,25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,634\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCVP (multiple of 5; mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,816\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,881\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,708\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,589\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTroponin T (ng/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,614\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,695\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,526\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e34,5\u0026plusmn;0,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,221\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNTproBNP (pg/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,735\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,725\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,693\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3124\u0026plusmn;25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,419\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eALT (UI/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,372\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e32,5\u0026plusmn;0,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,153\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe sPAP cut-off of 48.75 \u0026plusmn; 1.25 mmHg also showed strong performance in predicting peripheral venous congestion on echocardiography, with a sensitivity of 88.3% and specificity of 82.5% (Figure 2). This threshold was further supported by chi-square testing and multivariate linear regression, which confirmed a significant association between sPAP \u0026ge; 48.75 \u0026plusmn; 1.25 mmHg and both peripheral venous and chest X-ray pulmonary congestion (p \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrognostic significance of sPAP cutoff\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 25% of patients died and 15% were rehospitalized for heart failure. The combined endpoint of all-cause death or heart failure hospitalization occurred in 38% of patients over a median follow-up of 521 days (95% CI: 455\u0026ndash;586 days). In univariable analysis, sPAP value \u0026ge; 48.75 \u0026plusmn; 1.25 was significantly associated with an increased risk of the composite outcome (p = 0.019) and with first cardiovascular hospitalization (p = 0.030).\u003cbr\u003e\u0026nbsp;Survival analysis using the Kaplan\u0026ndash;Meier method demonstrated a significantly worse prognosis in patients with sPAP \u0026ge; 48.75 \u0026plusmn; 1.25 mmHg (Figure 3).\u003c/p\u003e\n\u003cp\u003eAfter multivariable adjustment, residual clinical congestion defined by sPAP \u0026ge; 48.75 \u0026plusmn; 1.25 mmHg remained independently associated with an increased risk of the composite outcome of all-cause death or heart failure hospitalization (HR = 1.713; 95% CI: 1.127\u0026ndash;2.602; p = 0.012) (Table 4, Figure 4).\u003cbr\u003e\u003cem\u003eKaplan\u0026ndash;Meier analysis showing the probability of survival free from all-cause death or heart failure hospitalization according to sPAP \u0026ge; 48.75 \u0026plusmn; 1.25 mmHg. Patients above this threshold had significantly worse outcomes during follow-up (log-rank p \u0026lt; 0.05).\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.\u003c/strong\u003e Cox regression for composite outcome all-cause death or HF hospitalization\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eExp(B) (95% C.I.)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003esPAP \u0026ge; 48,75\u0026nbsp;\u0026plusmn;1,25 mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0,021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e1,672 (1,082; 2,584)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003eFemale sex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0,041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e0,660 (0,442; 0,984)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0,002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e1,029 (1,011; 1,049)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003eLVEF \u0026nbsp;(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0,420\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e0,994 (0,980; 1,009)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eCurrent Pulmonary Arterial Hypertension guidelines define 30 mmHg as the upper limit of normal for sPAP in healthy individuals (Humbert et al., 2022). However, the 2019 ESC consensus on the use of diuretics in heart failure does not establish specific sPAP thresholds for defining euvolemia or congestion. Instead, fluid status is primarily evaluated based on clinical signs such as peripheral edema, natriuretic peptide levels (e.g., NT-proBNP), and imaging findings including IVC congestion and chest X-ray abnormalities (Mullens et al., 2019).\u003c/p\u003e\n\u003cp\u003eTo date, no study has clearly defined sPAP ranges for compensated versus decompensated heart failure. The literature also shows disagreement regarding the definition of normal versus pathological sPAP values, particularly in patients with comorbidities. For instance, Fisher and colleagues proposed stratified thresholds for pulmonary hypertension: mild (35\u0026ndash;49 mmHg), moderate (50\u0026ndash;59 mmHg), and severe (\u0026ge;60 mmHg) (Faqih et al., 2016; Fisher et al., 2009; Velidakis et al., 2023). Similarly, McQuillan et al. reported that only 28% of 3,790 echocardiographically normal individuals had an sPAP \u0026lt;30 mmHg. They also noted that the upper reference limit might reach 40 mmHg in older adults or patients with obesity or heart failure (McQuillan et al., 2001).\u003c/p\u003e\n\u003cp\u003eEstablishing sPAP cut-off values is challenging due to multiple confounders. One major issue is the frequent presence of severe tricuspid regurgitation in patients with decompensated HF, which often leads to IVC regardless of the patient\u0026apos;s true volume status. Additionally, isolated right-sided congestion patterns do not always present with radiographic pulmonary congestion, further complicating the assessment.\u003c/p\u003e\n\u003cp\u003eAnother limitation in the literature stems from high inter-operator and inter-center variability in echocardiographic evaluation, particularly in emergency settings. Many studies lack complete imaging data at the time of admission, reducing diagnostic consistency. In contrast, our study was conducted in a high-volume tertiary care center with comprehensive clinical and echocardiographic assessments performed at admission and during hospitalization.\u003c/p\u003e\n\u003cp\u003eBy applying stringent exclusion criteria, we found that a moderately elevated sPAP value\u0026mdash;specifically, \u0026ge;48.75 mmHg\u0026mdash;was highly sensitive and specific for identifying both pulmonary and peripheral venous congestion in patients with acute HF. These findings suggest that this cut-off may serve as a practical reference point in routine clinical assessment.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eIn this study, we identified a systolic pulmonary arterial pressure (sPAP) threshold of ≥48.75 ± 1.25 mmHg as a reliable marker of acute heart failure decompensation due to fluid overload. This value was independently associated with both objective signs of congestion and adverse clinical outcomes, supporting its potential clinical utility in routine assessment.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eatrial fibrillation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCKD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003echronic kidney disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCOPD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003echronic obstructive pulmonary disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCVP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecentral venous pressure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eheart failure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIVC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003einferior vena cava\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLVEF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eleft ventricular ejection fraction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNYHA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNew York Heart Association\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRAASis\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003erenin\u0026ndash;angiotensin\u0026ndash;aldosterone system inhibitors\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSGLTis\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esodium\u0026ndash;glucose cotransporter 2 inhibitors\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003esPAP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esystolic pulmonary artery pressure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eThis analysis was conducted within the scope of a research protocol on acute heart failure that had been previously approved by the Ethics Committee of ASST Spedali Civili di Brescia. The study is based on retrospective analysis of anonymized data collected during routine care, and no additional approval was required for this secondary use of data.\n\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted retrospectively using anonymized clinical data collected during routine patient care. In accordance with national regulations and institutional policies, formal ethical approval was not required.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAmbrosy AP, Pang PS, Khan S, Konstam MA, Fonarow GC, Traver B, Maggioni AP, Cook T, Swedberg K, Burnett JC, Grinfeld L, Udelson JE, Zannad F, Gheorghiade M (2013) Clinical course and predictive value of congestion during hospitalization in patients admitted for worsening signs and symptoms of heart failure with reduced ejection fraction: Findings from the EVEREST trial. 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Multidisciplinary Digital Publishing Institute (MDPI). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/life13091793\u003c/span\u003e\u003cspan address=\"10.3390/life13091793\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Spedali Civili di Brescia","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":"Acute decompensated heart failure, echocardiogram, systolic pulmonary artery pressure, central venous pressure","lastPublishedDoi":"10.21203/rs.3.rs-6627975/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6627975/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground and Aims:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePulmonary and/or peripheral venous congestion defines the clinical diagnosis of acute heart failure (HF). However, the systolic pulmonary arterial pressure (sPAP) thresholds at which pulmonary (chest X-ray) and inferior vena cava (IVC) congestion occur are not well established. This study aimed to identify a cut-off value of sPAP that reliably indicates acute HF.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods and Results:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe retrospectively included 380 consecutive patients hospitalized for acute HF at an Italian referral center, excluding those with severe tricuspid regurgitation. Receiver operating characteristic (ROC) curve analysis and Youden’s J statistic identified a threshold of sPAP ≥ 48.75 mmHg as the most accurate in predicting both pulmonary (sensitivity = 89.9%, specificity = 73%) and peripheral (sensitivity = 88.3%, specificity = 82.5%) fluid overload. The association between this sPAP threshold and both pulmonary and peripheral congestion was confirmed by chi-square testing (p \u0026lt; 0.001) and multivariate logistic regression (p \u0026lt; 0.001). After adjustment for confounders, sPAP ≥ 48.75 mmHg was independently associated with all-cause death or HF hospitalization (HR = 1.713; 95% CI 1.127–2.602; p = 0.012).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAcute HF decompensation is characterized by at least moderately elevated sPAP values.\u003c/p\u003e","manuscriptTitle":"Acute decompensated heart failure: systolic pulmonary artery pressure cut off value to define presence of congestion","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-13 11:34:56","doi":"10.21203/rs.3.rs-6627975/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":"1c726892-15dc-49da-bced-1239541b7133","owner":[],"postedDate":"May 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":48309347,"name":"Cardiac \u0026 Cardiovascular Systems"}],"tags":[],"updatedAt":"2025-05-13T11:34:56+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-13 11:34:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6627975","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6627975","identity":"rs-6627975","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}