Multiparameter predictor model for Low Cardiac Output Syndrome After Pericardiectomy

Multiparameter predictor model for Low Cardiac Output Syndrome After Pericardiectomy | 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 Multiparameter predictor model for Low Cardiac Output Syndrome After Pericardiectomy Lanying Gao, Yongnan Li, Yong Luo, Jian Chen, Shilin Wei, Lijian Cheng, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7481051/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background Low cardiac output syndrome (LCOS) following pericardiectomy in patients with constrictive pericarditis significantly contributes to perioperative morbidity mortality. Predictive models specific to LCOS after pericardiectomy are currently lacking. This study aimed to identify independent risk factors for LCOS following pericardiectomy and develop a predictive model to guide clinical decision-making. Methods A retrospective cohort of 190 patients with constrictive pericarditis undergoing isolated pericardiectomy were divided into LCOS group (57 cases) and non-LCOS group (133 cases). Univariate and multivariate logistic regression analyses were performed to identify independent risk factors. A predictive model was developed and validated. Model performance was evaluated using receiver operating characteristic (ROC) curve, Hosmer-Lemeshow test, and 10-fold cross-validation. A visual nomogram was constructed for clinical application. Results Multivariate logistic regression identified preoperative New York Heart Association function (NYHA) classification, white blood cell counts (WBC), left ventricular end-diastolic dimension(LVEDD), and early diastolic trans-mitral velocity to mitral annular early diastolic velocity ratio (E/e' ratio) as independent risk factors. The model showed excellent performance (AUC = 0.951, P < 0.001; Hosmer-Lemeshow P = 0.176). Ten-fold cross-validation yielded 85.7% accuracy, 92.6% sensitivity, and 82.7% specificity. The nomogram demonstrated good calibration (bootstrap C-index = 0.951; corrected = 0.945). Conclusions Preoperative NYHA class, elevated WBC, reduced LVEDD, and decreased E/e' ratio predict LCOS after pericardiectomy in constrictive pericarditis. The model exhibits high clinical utility for early risk stratificaiton. constrictive pericarditis low cardiac output syndrome risk factors prediction model Figures Figure 1 Introduction Constrictive pericarditis (CP) is a progressive disease characterized by pericardial thickening and impaired ventricular filling. Without timely intervention, CP can progress to refractory heart failure, hepatorenal dysfunction, and even death. Pericardiectomy is the standard and definitive treatment for CP, providing effective relieving from pericardial constriction. However, postoperative complications persist, notably low cardiac output syndrome (LCOS), which occurs in 10–25% of cases [ 1 ] with a mortality rate ranging from 14.5–20.0%. [ 2 – 5 ] and represents a leading cause of increased morbidity, mortality in this population. Importantly, LCOS following pericardiectomy differs substantially from LCOS seen in other types of cardiac surgery. Unlike ischemia-driven or cardiopulmonary bypass-related LCOS, the post-pericardiectomy form arises from unique pathophysiological mechanisms: sudden loss of ventricular constraint, chronic myocardial remodeling, impaired diastolic reserve, and inflammatory activation. These specific mechanisms often make hemodynamic responses unpredictable, and patients may require circulatory support such as extracorporeal membrane oxygenation (ECMO). [ 6 ] Despite its clinical importance, current literature lacks predictive tools that address the specific risk profile of LCOS after pericardiectomy. Existing models [ 7 – 10 ] based on general cardiac surgery populations may not adequately capture the distinct risks in CP patients. Although several preoperative risk factors have been identified, such as advanced age, reduced LVEF, comorbid diabetes and CKD, malnutrition, and elevated biomarkers [ 11 ] —a disease-specific predictive model is particularly valuable given the unique pathophysiology of LCOS after pericardiectomy. Tailored risk stratification would better support individualized perioperative management in this population. We further hypothesize that CP-specific features—such as pericardial thickness, calcification, altered myocardial compliance, systemic inflammatory response, and preoperative echocardiographic parameters—may hold prognostic value and warrant focused investigation. This study aimed to identify independent risk factors for LCOS after pericardiectomy and constructing a comprehensive, clinically applicable predictive model for preoperative risk stratification. Materials and Methods Study Population This retrospective study involved patients with constrictive pericarditis who underwent isolated pericardiectomy at Chengdu Third People's Hospital from January 2020 to January 2025. Since 2020, the center has implemented the standardized complete pericardiectomy and consistent perioperative management protocols. Inclusion criteria were: confirmed diagnosis of CP; and undergoing pericardiectomy without concomitant cardiac procedures. Exclusion criteria included prior pericardiectomy, moderate-to-severe valvular disease requiring intervention, underlying cardiomyopathy. The diagnosis of constrictive pericarditis follows the 2015 ESC guidelines, based on signs of right heart failure and impaired diastolic filling confirmed by imaging such as echocardiography, CT, CMR. Typical echocardiographic features include septal bounce, pericardial thickening or calcification, and elevated mitral annular e′ velocity (> 8 cm/s). CT/CMR may show pericardial thickness > 3–4 mm and ventricular interdependence. [ 12 ] During surgery, the diagnosis was further confirmed based on pathological changes consistent with CP, including pericardial thickness > 2 mm, stiffness, calcification (mainly on the diaphragmatic surface and atrioventricular groove), and fibrous adhesions causing cardiac compression or deformation. The presence of at least two of these intraoperative features, in accordance with the criteria proposed by Welch et a was required for confirmation. [ 13 ] The study was approved by the Institutional Review Board of Chengdu Third People’s Hospital (Approval No: [2025-S-60]). Informed consent was waived due to the retrospective nature of the study and anonymous data processing in accordance with ethical guidelines. Definition of Low Cardiac Output Syndrome (LCOS): LCOS was defined as cardiac index (CI) < 2.0 L/(min·m²); systolic blood pressure 20% compared to preoperative levels [ 11 ] and requirement for inotropic support or mechanical circulatory assistance to maintain hemodynamic stability for more than 24 hours within three days post-pericardiectomy. Patients who only received noradrenaline for hemodynamic support were not considered to have LCOS. Perioperative Management Protocols Total pericardiectomy was performed off-pump via median sternotom and defined as resection of anterolateral pericardium between bilateral phrenic nerves, including pericardium over ventricular apex, diaphragmatic base, great vessels, and the junction from superior vena cava-right atrium to inferior vena cava-right atrium.Postoperatively, patients were transferred to the intensive care unit (ICU), where pulse indicated continuous cardiac output (PICCO) system was used in all the patients. Patients were subsequently moved to the general ward after achieving hemodynamic stability. Data Collection Perioperative data were retrospectively collected from electronic medical records. Laboratory tests and echocardiographic measurements were obtained within 48 hours before surgery. Echocardiographic assessments followed a standardized protocol using the apical four-chamber view. Statistical Analysis SAS 9.4 (SAS Institute Inc., Cary, NC, USA) was used for descriptive statistics and predictive model development. Categorical variables were described as counts and percentages, and compared using chi-square tests. Normally distributed continuous variables were presented as mean ± standard deviation (SD) and compared using independent t-tests. Non-normally distributed continuous variables were reported as median and interquartile range (IQR), and analyzed using Mann–Whitney U tests. Univariate logistic regression was performed to identify candidate predictors of postoperative LCOS. Variables with P < 0.05 entered into a multivariable logistic regression model using stepwise selection. Model discrimination and predictive value were evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. Internal validation was performed using 10-fold cross-validation. The optimal cutoff probability for classifying high-risk patients was determined by maximizing the Youden index, which balances sensitivity and specificity. Rms package, version 6.0.0 was used for visualization and nomogram construction. The final logistic model was translated into a nomogram using the rms package. Internal validation and calibration of the nomogram were conducted using 2,000 bootstrap resamples. Calibration was evaluated via calibration curves, and model discrimination was assessed using Harrell’s concordance index (C-index), with corrected values obtained from bootstrap validation. Two-sided P value < 0.05 was considered statistically significant. Results Baseline Characteristics and univariate analysis A total of 190 patients were enrolled in the study and divided into the non-LCOS group (n = 133) and the LCOS group (n = 57) according to the occurrence of low cardiac output syndrome within 72 hours after surgery.. Intergroup comparisons revealed significantly higher heart rate and higher New York Heart Association function classification (NYHA class) in the LCOS group (P < 0.05; Table 1). In addition, the LCOS group exhibited significantly elevated levels of white blood cell count (WBC), red blood cell count (RBC), lactate dehydrogenase (LDH), D-dimer, inferior vena cava diameter (IVC), early diastolic transmitral velocity to early diastolic mitral annular velocity ratio (E/e'), pericardial thickness, and a higher incidence of ascites and pleural effusion. Conversely, lower left ventricular end-diastolic dimension (LVEDD), cholinesterase (CHE), and serum sodium levels were observed in the LCOS group (P < 0.05; Table 2, Table 3). Table 1 Basic characteristics of the patients before surgery BMI : Body Mass Index; NYHA : New York Heart Function Variables Total no-LCOS (n = 133) LCOS (n = 57) P Male 124 (65.26%) 82 (61.65%) 42 (73.68%) 0.110 Age (year) 52.00 (29.00,64.00) 52.00 (30.00,63.00) 46.00 (25.00,64.00) 0.379 BMI (kg/m 2 ) 22.46 ± 3.50 22.39 ± 3.01 22.64 ± 4.46 0.695 Systolic pressure (mmHg) 111.66 ± 12.36 111.77 ± 12.72 111.40 ± 11.56 0.853 Diastolic pressure (mmHg) 74.29 ± 10.11 74.08 ± 10.55 74.79 ± 9.06 0.656 Pulse pressure (mmHg) 37.37 ± 11.06 37.69 ± 11.83 36.61 ± 9.04 0.495 Heart rate 89.42 ± 13.93 87.34 ± 14.73 92.93 ± 11.81 0.039 Comorbidity Disease course (Month) 4.00 (2.00,12.00) 4.00 (2.00,12.00) 3.00 (2.00,12.00) 0.349 Atrial fibrillation [n (%)] 16 (8.42%) 11 (8.27%) 5 (8.77%) 0.999 Hypertension [n (%)] 17 (8.95%) 15 (11.28%) 2 (3.51%) 0.086 Diabetes [n (%)] 16 (8.42%) 10 (7.52%) 6 (10.53%) 0.690 NYHA < 0.001* Class Ⅱ 96 (50.53%) 96 (72.18%) 0 (0.00%) Class Ⅲ 65 (34.21%) 32 (24.06%) 33 (57.89%) Class Ⅳ 29 (15.26%) 5 (3.76%) 24 (42.11%) Mortality within 30 days[n (%)] 11(5.79%) 2(1.50%) 9(15.79%) < 0.001 BMI: Body Mass Index; NYHA: New York Heart Function Table 2 Patients were examined 48 hours before surgery Variables Total no-LCOS (n = 133) LCOS (n = 57) P White Blood Cell Count (10^9/L) 5.89 ± 2.14 5.34 ± 1.88 7.16 ± 2.19 < 0.001* Thickness of pericardium (mm) 4.52 ± 0.60 4.46 ± 0.58 4.67 ± 0.63 0.026 Hemoglobin (g/L) 127.94 ± 18.32 126.53 ± 17.74 131.23 ± 19.36 0.105 Platelet Count (10^9/L) 156.50 (111.00,235.00) 148.00 (110.00,210.00) 188.00 (125.0,248.0) 0.020 Alanine Aminotransferase (U/L) 16.80 (11.90,25.60) 18.20 (11.20,25.40) 15.70 (12.60,25.70) 0.890 Aspartate Aminotransferase (U/L) 26.45 (21.00,34.50) 26.50 (21.10,33.50) 26.20 (20.80,35.60) 0.756 Total Bilirubin (mg/dL) 17.93 (11.18,27.70) 16.99 (11.15,27.60) 18.73 (12.88,27.89) 0.406 Direct Bilirubin (mg/dL) 6.87 (3.44,11.67) 6.36 (3.20,10.52) 7.62 (4.48,14.25) 0.068 Indirect Bilirubin (mg/dL) 10.45 (7.04,14.95) 10.72 (6.97,15.77) 9.80 (7.42,13.51) 0.756 Total Protein (g/L) 62.70 ± 9.50 63.30 ± 8.99 61.30 ± 10.55 0.185 Albumin (g/L) 34.06 ± 5.82 34.50 ± 5.93 33.04 ± 5.44 0.111 Prealbumin (g/L) 163.04 ± 71.07 168.42 ± 72.99 150.48 ± 65.28 0.111 Lactate Dehydrogenase (U/L) 199.15 ± 51.77 191.35 ± 42.46 217.36 ± 65.69 0.007 Gamma-Glutamyl Transferase (U/L) 84.80 (50.60,136.00) 84.80 (44.60,136.00) 86.90 (56.20,130.10) 0.333 Cholinesterase (U/L) 5028.53 ± 1927.04 5365.80 ± 1869.60 4241.57 ± 1842.08 < 0.001 Adenosine Deaminase (U/L) 13.20 (10.20,17.00) 13.20 (10.20,16.40) 13.20 (10.40,18.10) 0.346 Creatinine (mmol/L) 67.10 (56.10,78.20) 66.80 (57.00,77.00) 69.40 (56.00,79.90) 0.829 Uric Acid (mmol/L) 495.55 ± 205.72 481.41 ± 198.13 528.55 ± 220.71 0.148 Na (mmol/L) 138.88 ± 3.90 139.29 ± 3.93 137.92 ± 3.68 0.026 Potassium (mmol/L) 3.85 ± 0.53 3.81 ± 0.48 3.94 ± 0.63 0.183 Prothrombin Time (s) 14.27 ± 1.57 14.25 ± 1.67 14.32 ± 1.31 0.782 APPT (s) 89.38 ± 16.88 90.21 ± 17.75 87.46 ± 14.64 0.305 D-Dimer (µg/L) 1.36 (0.50,3.34) 1.13 (0.46,2.89) 2.02 (0.80,4.20) 0.019 APPT: activated partial thromboplastin time. Table 3 Echocardiography was performed 48h before surgery Variables Total no-LCOS (n = 133) LCOS (n = 57) P Left Atrium (mm) 39.16 ± 6.51 39.51 ± 6.63 38.35 ± 6.19 0.261 LVEDD (mm) 41.22 ± 4.49 41.87 ± 4.34 39.70 ± 4.50 0.002 * Right Atrium (-) (mm) 40.02 ± 5.89 40.25 ± 6.16 39.49 ± 5.24 0.419 Right Ventricle (mm) 20.00(19.00,22.00) 20.00(19.00,22.00) 20.00(18.00,21.00) 0.271 EF% 58.63 ± 7.04 58.74 ± 7.18 58.39 ± 6.77 0.754 Interventricular Septum (mm) 9.00(8.00,10.00) 9.00(8.00,10.00) 9.00(8.00,10.00) 0.235 IVC (cm) 22.45 ± 4.08 22.08 ± 4.32 23.32 ± 3.34 0.034 E (m/s) 0.72 ± 0.21 0.73 ± 0.22 0.70 ± 0.19 0.436 A (m/s) 0.53(0.41,0.67) 0.53(0.44,0.68) 0.46(0.40,0.66) 0.052 e’ Velocity (m/s) 10.23 ± 3.95 9.91 ± 3.78 10.96 ± 4.26 0.091 E/e’ 8.15 ± 4.49 8.56 ± 5.00 7.18 ± 2.76 0.015 * Thickness of pericardium (mm) 8.01 ± 3.73 7.66 ± 3.52 8.84 ± 4.09 0.044 Ventricular septal bounce 160(84.21%) 108(81.20%) 52(91.23%) 0.082 Pericardial calcification 75(39.68%) 52(39.39%) 23(40.35%) 0.902 Pericardial effusion 129(67.89%) 86(64.66%) 43(75.44%) 0.145 Hepatic vein dilation 42(22.22%) 27(20.45%) 15(26.32%) 0.374 EF% : Cardiac ejection fraction; IVC : Inferior Vena Cava; E : Early mitral valve velocity; A : Late mitral valve velocity Multivariable Analysis of Predictors for LCOS NYHA classification (OR = 22.424, 95% CI = 8.264–60.846), WBC (OR = 1.441, 95% CI = 1.175–1.766), LVEDD (OR = 0.852, 95% CI = 0.749–0.970), and E/e’ (OR = 0.878, 95% CI = 0.784–0.982) as independent risk factors for postoperative acute LCOS (Table 4). Postoperative Outcomes in LCOS vs. Non-LCOS Groups. Postoperative outcomes differed significantly between the LCOS and non-LCOS groups. Mortality within 30 days after surgery was substantially higher in the LCOS group compared to the non-LCOS group [15.79% vs. 1.5% (P < 0.001)]. (Table 1) Table 4 Multivariate logistic regression model Variable β Standardized coefficient β Standard error Wald χ2 P OR (95%CI) Constant 1.860 — 2.665 0.487 0.485 — NYHA 3.110 1.256 0.509 37.293 < 0.001 22.424(8.264,60.846) WBC (10^9/L) 0.365 0.431 0.104 12.368 < 0.001 1.441(1.175,1.766) LVEDD (mm) -0.160 -0.395 0.066 5.903 0.015 0.852(0.749,0.970) E/e’ -0.130 -0.323 0.057 5.158 0.023 0.878(0.784,0.982) WBC: White Blood Cell Count Development of the LCOS Prediction Model Based on multivariate logistic regression analysis, a prediction model for LCOS was developed, described by the formula: P = eY/ (1 + eY), where Y = 1.860 + 3.100×NYHA class + 0.365×WBC (×10⁹/L) − 0.160×LVEDD (mm) − 0.130×E/e’. The model demonstrated excellent goodness-of-fit, (Hosmer-Lemeshow χ² = 11.483, P = 0.176). The area under the receiver operating characteristic curve (AUC) was 0.951 (95% CI: 0.923–0.978, P < 0.001) (Supplementary Fig. 1). Based on the Youden index, a cutoff probability of ≥ 0.25 was selected, to classify patients with high risk of developing postoperative LCOS. Internal validation via 10-fold cross-validation showed robust performance: the model achieved an accuracy of 85.71%, sensitivity of 92.57%, and specificity of 82.74% in the validation cohort (Supplementary Table 1), demonstrating promising potential for predicting. Visualization of the Nomogram A nomogram was subsequently constructed based on the prediction model (Fig. 1), converting each variable into a visual scoring system for practical bedside use. A total score of ≥ 177 points corresponds to a risk of LCOS ≥ 25%. For example, if a patient has NYHA class III (score = 33), WBC of 14 × 10⁹/L (score = 53), LVEDD of 45 mm (score = 58), and E/e′ of 8 cm/s (score = 60), the total score would be 204, corresponding to an estimated LCOS probability of 0.8. After 2,000 bootstrap resampling iterations, the nomogram model yielded a C-index of 0.951, and the bias-corrected C-index was 0.945, indicating good discriminatory performance. The calibration curve (Supplementary Fig. 2) showed excellent agreement between predicted and observed LCOS probabilities, with a mean absolute error of 0.039. These findings support the model’s strong predictive performance and clinical applicability. Discussion This retrospective study identified four independent preoperative predictors of postoperative LCOS: NYHA functional class, WBC count, LVEDD, and E/e′, reflecting cardiac functional reserve, systemic inflammation, ventricular remodeling, and diastolic filling capacity. Together, they form a robust and clinically applicable risk prediction model. Our study found that NYHA class ≥ III conferred a more than 20-fold increased odds of developing postoperative LCOS. The predictive significance aligns with prior evidence indicating that symptomatic heart failure portends adverse outcomes in cardiac surgery [ 11 ] . In CP, elevated NYHA class indicates reduced cardiac reserve in the context of chronic constriction, where the myocardium is accustomed to operating under low preload and mechanical constraint. After pericardiectomy, these patients may face abrupt hemodynamic shifts that overwhelm their limited compensatory capacity, predisposing them to LCOS. Preoperative leukocytosis also emerged as a strong independent predictor.In the context of constrictive pericarditis—particularly with a high prevalence of tuberculous etiology—elevated WBC may reflect subclinical infection or persistent granulomatous inflammation. These inflammatory states can impair myocardial contractility through cytokine-mediated pathways and disrupt vascular tone and endothelial integrity, contributing to systemic vasoplegia and microcirculatory dysfunction [ 13 ] . Notably, a previous study also reported that elevated preoperative WBC levels were associated with increased postoperative complications in general cardiac surgery patients [ 14 ] , Our findings build upon this by specifically linking leukocytosis to LCOS risk after pericardiectomy, highlighting the need for more deliberate preoperative assessment and optimization of inflammatory status in CP patients. The echocardiographic findings in our study provide further insight into the structural and functional derangements underlying LCOS risk. A reduced LVEDD reflects chronic underfilling and disuse atrophy of the left ventricle, often secondary to long-standing pericardial constraint. These ventricles, frequently fibrotic and stiff, are ill-equipped to accommodate increased preload after pericardial release, leading to a mismatch between volume status and contractile capacity. Several prior studies [ 15 ] have similarly demonstrated that smaller LV dimensions are associated with higher postoperative morbidity and mortality in CP patients, likely due to irreversible remodeling and impaired myocardial compliance. Interestingly, lower E/e′values were independently associated with increased LCOS risk, contrary to findings in broader heart failure populations, where higher E/e′ indicates elevated filling pressures and poor outcomes [ 16 ] . In CP, this paradox is explained by the “annulus paradoxus” phenomenon [ 17 , 18 ] , in which longitudinal annular velocities (e′) may remain deceptively normal or elevated, while inflow velocities (E) are attenuated by restrictive preload. Thus, a low E/e′ may reflect advanced constriction, impaired preload reserve, and susceptibility to hemodynamic collapse after pericardial decompression. These findings appear to contrast with a previous study reporting that higher E velocity predicted worse long-term outcomes after pericardiectomy [ 18 ] . These likely reflect distinct end-point and mechanisms. Higher E velocity may indicate elevated left atrial pressure and more advanced pericardial disease, contributing to persistent diastolic dysfunction and atrial arrhythmias in the long term. In contrast, lower E/e′ in CP—due to preserved or elevated e′ and restricted preload (“annulus paradoxus”)—may identify patients with limited diastolic reserve who are more vulnerable to acute hemodynamic collapse after pericardial decompression. Thus, both parameters capture different dimensions of risk, and are complementary rather than contradictory. Recent studies have explored predictors of LCOS after pericardiectomy. Wang et al. identified atrial arrhythmia, renal dysfunction, hyponatremia, elevated CVP, and low cardiac index as independent LCOS predictors in a cohort of 212 patients ( Supplementary Table 2 ). [ 19 ] Huang et al., in a multicenter study of 826 patients, found that incomplete pericardial dissection, fluid overload, delayed treatment, and tuberculous etiology were significant risk factors. [ 20 ] In contrast to previous studies that emphasized intraoperative or congestion-related factors, our study focused on preoperative risk assessment, with particular attention to echocardiographic indicators. Most importantly, we developed and validated a predictive model with good performance, and translated it into a nomogram for individualized bedside stratification. Beyond LCOS, prior studies have explored predictors for other postoperative outcomes, or long-term follow-up adverse outcomes [ 21 – 34 ] , with several confirming NYHA class and LVEDD as key predictors. In addition, the study demonstrated that LCOS was significantly associated with increased in-hospital mortality in our cohort, consistent with previous studies highlighting LCOS as a critical determinant of postoperative outcomes [ 35 , 36 ] . Due to the low mortality rate and limited sample size, we did not analyze mortality risk factors separately, but instead focused on LCOS as a clinically actionable intermediate outcome. may allow timely interventions, including intensified monitoring, optimization of volume and inflammatory status, and preparedness for circulatory support. Limitations Several limitations should be acknowledged. First, this was a single-center retrospective study, which may limit the generalizability of findings. The high proportion of tuberculous CP may not reflect CP etiologies in other settings. Second, external validation was not performed. Finally, the study did not include analysis of other preoperative inflammatory markers such as CRP, procalcitonin, or IL-6, which may provide additional prognostic value. Conclusions In patients undergoing isolated pericardiectomy for constrictive pericarditis, LCOS remains a serious and unpredictable postoperative complication. This study is the first to propose and validate a multiparametric predictive model specifically tailored to LCOS risk in this population. Our model achieved excellent discriminative power and calibration, and was translated into a practical nomogram for clinical use. Abbreviations BMI: body mass index LCOS: low cardiac output syndrome LVEDD: left ventricular end-diastolic dimension CP: constrictive pericarditis LV: left ventricle WBC: white blood cell count NYHA: new york heart association PICCO: pulse Indicator continuous cardiac output CHE: cholinesterase RBC: red blood cell PCT: pericardial Thickness LVEDD: left ventricular end-diastolic diameter ATPP: activated partial thromboplastin Time CRP: c-reactive protein LDH: lactate dehydrogenase IVC: inferior vena cava e’: early mitral myocardial velocity E: early mitral valve velocity MVE: mitral orifice blood flow rate ECMO: extracorporeal membrane oxygenation A: late mitral valve velocity Declarations Ethics approval and consent to participate All patients provided written consent for surgical treatment. However, consent for study participation was waived due to its retrospective design, in accordance with China law, and local ethical committees were notified, obviating the need for formal approval. Consent for publication Not applicable. Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding This study was supported by Health Commission of Sichuan Province. (KY2024SJ0033) Author contributions Lanying Gao: Analysis, interpretation of data, drafting the work, revising the work; Yongnan Li: Conception, design of the work, acquisition, analysis, interpretation of data, drafting the work; Yong Luo: Interpretation of data, validation; Jian Chen: The acquisition, analysis; Shilin Wei: Data collection; Lijian Cheng: Interpretation of data; Lijie Jiang: Drafting the work; Shuzhen Wang: Interpretation of data; Shujie Yan: Conception, design of the work; Bin Jia: Conception, design of the work, analysis, drafting the work, project administration, revising the work. Acknowledgements ： Not applicable. Clinical trial number: N ot applicable. References Chowdhury UK, Sankhyan LK, Malik V, George N, Gudala V, et al. (2019) Low Cardiac Output Syndrome following Pericardiostomy and Pericardiectomy for Massive Pericardial Effusion and Chronic Constrictive Pericarditis: Myths and Realities at 100 Years. Int J Clin Case Stud Rep, 1(3): 46-60. 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J Thorac Cardiovasc Surg. 2016;152(2):448-458. Fang L, Yu G, Huang J, Zhao W, Ye B. Predictors of postoperative complication and prolonged intensive care unit stay after complete pericardiectomy in tuberculous constrictive pericarditis. J Cardiothorac Surg. 2020;15(1):148. Radakovic D, Opacic D, Börgermann J, et al. Model for end-stage liver disease predicts mortality after pericardiectomy for constrictive pericarditis. Interact Cardiovasc Thorac Surg. 2018;27(6):813-818. Komoda T, Frumkin A, Knosalla C, Hetzer R. Child-Pugh score predicts survival after radical pericardiectomy for constrictive pericarditis. Ann Thorac Surg. 2013;96(5):1679-1685. Schoonen, A, van Klei, WA, van Wolfswinkel, L, et al. Definitions of low cardiac output syndrome after cardiac surgery and their effect on the incidence of intraoperative LCOS: A literature review and cohort study. Front Cardiovasc Med. 2022; 9 Front Cardiovasc Med. Zhao, X, Gu, B, Li, Q, et al. Machine learning approach identified clusters for patients with low cardiac output syndrome and outcomes after cardiac surgery. Front Cardiovasc Med. 2022; 9 Front Cardiovasc Med. Additional Declarations No competing interests reported. Supplementary Files SupplementaryTable1.docx FigureS1.jpg Supplementary Figure 1: ROC curve of logistic regression model to predict LCOS. FigureS2.jpg Supplementary Figure 2: Calibration curve of the patient LOCS nomogram. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 13 Oct, 2025 Editor invited by journal 08 Sep, 2025 Editor assigned by journal 05 Sep, 2025 Submission checks completed at journal 05 Sep, 2025 First submitted to journal 28 Aug, 2025 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. 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1","display":"","copyAsset":false,"role":"figure","size":318843,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram for predicting LCOS in patients.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7481051/v1/6d6046ca3cc640f281c07670.jpg"},{"id":94490004,"identity":"83318e3b-3948-4f6b-985e-7197f52e0873","added_by":"auto","created_at":"2025-10-27 17:06:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1696074,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7481051/v1/260df6ac-51f4-450b-83a5-fdc98dca6b78.pdf"},{"id":94473962,"identity":"2eccacd9-31c5-40a7-88da-2fd584d14acb","added_by":"auto","created_at":"2025-10-27 15:46:27","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":13580,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7481051/v1/9505022d24cf4691bbd83d7a.docx"},{"id":94474276,"identity":"7696323e-8904-4c6f-948e-62fdcdb1e5b6","added_by":"auto","created_at":"2025-10-27 15:48:05","extension":"jpg","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":74149,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 1: \u003c/strong\u003eROC curve of logistic regression model to predict LCOS.\u003c/p\u003e","description":"","filename":"FigureS1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7481051/v1/97db857577f670d0c12a5d07.jpg"},{"id":94473870,"identity":"7f45b3ea-bd22-4de6-a27f-b964cdb59cf9","added_by":"auto","created_at":"2025-10-27 15:46:05","extension":"jpg","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":86794,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 2: \u003c/strong\u003eCalibration curve of the patient LOCS nomogram.\u003c/p\u003e","description":"","filename":"FigureS2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7481051/v1/7e497198f80546327bf3c2a6.jpg"}],"financialInterests":"No competing interests reported.","formattedTitle":"Multiparameter predictor model for Low Cardiac Output Syndrome After Pericardiectomy","fulltext":[{"header":"Introduction","content":"\u003cp\u003eConstrictive pericarditis (CP) is a progressive disease characterized by pericardial thickening and impaired ventricular filling. Without timely intervention, CP can progress to refractory heart failure, hepatorenal dysfunction, and even death. Pericardiectomy is the standard and definitive treatment for CP, providing effective relieving from pericardial constriction. However, postoperative complications persist, notably low cardiac output syndrome (LCOS), which occurs in 10\u0026ndash;25% of cases \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e with a mortality rate ranging from 14.5\u0026ndash;20.0%.\u003csup\u003e[\u003cspan additionalcitationids=\"CR3 CR4\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e and represents a leading cause of increased morbidity, mortality in this population.\u003c/p\u003e\u003cp\u003eImportantly, LCOS following pericardiectomy differs substantially from LCOS seen in other types of cardiac surgery. Unlike ischemia-driven or cardiopulmonary bypass-related LCOS, the post-pericardiectomy form arises from unique pathophysiological mechanisms: sudden loss of ventricular constraint, chronic myocardial remodeling, impaired diastolic reserve, and inflammatory activation. These specific mechanisms often make hemodynamic responses unpredictable, and patients may require circulatory support such as extracorporeal membrane oxygenation (ECMO).\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e Despite its clinical importance, current literature lacks predictive tools that address the specific risk profile of LCOS after pericardiectomy. Existing models\u003csup\u003e[\u003cspan additionalcitationids=\"CR8 CR9\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e based on general cardiac surgery populations may not adequately capture the distinct risks in CP patients. Although several preoperative risk factors have been identified, such as advanced age, reduced LVEF, comorbid diabetes and CKD, malnutrition, and elevated biomarkers\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e\u0026mdash;a disease-specific predictive model is particularly valuable given the unique pathophysiology of LCOS after pericardiectomy. Tailored risk stratification would better support individualized perioperative management in this population. We further hypothesize that CP-specific features\u0026mdash;such as pericardial thickness, calcification, altered myocardial compliance, systemic inflammatory response, and preoperative echocardiographic parameters\u0026mdash;may hold prognostic value and warrant focused investigation.\u003c/p\u003e\u003cp\u003eThis study aimed to identify independent risk factors for LCOS after pericardiectomy and constructing a comprehensive, clinically applicable predictive model for preoperative risk stratification.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Population\u003c/h2\u003e\u003cp\u003eThis retrospective study involved patients with constrictive pericarditis who underwent isolated pericardiectomy at Chengdu Third People's Hospital from January 2020 to January 2025. Since 2020, the center has implemented the standardized complete pericardiectomy and consistent perioperative management protocols. Inclusion criteria were: confirmed diagnosis of CP; and undergoing pericardiectomy without concomitant cardiac procedures. Exclusion criteria included prior pericardiectomy, moderate-to-severe valvular disease requiring intervention, underlying cardiomyopathy.\u003c/p\u003e\u003cp\u003e The diagnosis of constrictive pericarditis follows the 2015 ESC guidelines, based on signs of right heart failure and impaired diastolic filling confirmed by imaging such as echocardiography, CT, CMR. Typical echocardiographic features include septal bounce, pericardial thickening or calcification, and elevated mitral annular e\u0026prime; velocity (\u0026gt;\u0026thinsp;8 cm/s). CT/CMR may show pericardial thickness\u0026thinsp;\u0026gt;\u0026thinsp;3\u0026ndash;4 mm and ventricular interdependence.\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e During surgery, the diagnosis was further confirmed based on pathological changes consistent with CP, including pericardial thickness\u0026thinsp;\u0026gt;\u0026thinsp;2 mm, stiffness, calcification (mainly on the diaphragmatic surface and atrioventricular groove), and fibrous adhesions causing cardiac compression or deformation. The presence of at least two of these intraoperative features, in accordance with the criteria proposed by Welch et a was required for confirmation.\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e The study was approved by the Institutional Review Board of Chengdu Third People\u0026rsquo;s Hospital (Approval No: [2025-S-60]). Informed consent was waived due to the retrospective nature of the study and anonymous data processing in accordance with ethical guidelines.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eDefinition of Low Cardiac Output Syndrome (LCOS):\u003c/h3\u003e\n\u003cp\u003eLCOS was defined as cardiac index (CI)\u0026thinsp;\u0026lt;\u0026thinsp;2.0 L/(min\u0026middot;m\u0026sup2;); systolic blood pressure\u0026thinsp;\u0026lt;\u0026thinsp;90 mmHg or a decrease\u0026thinsp;\u0026gt;\u0026thinsp;20% compared to preoperative levels\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e and requirement for inotropic support or mechanical circulatory assistance to maintain hemodynamic stability for more than 24 hours within three days post-pericardiectomy. Patients who only received noradrenaline for hemodynamic support were not considered to have LCOS.\u003c/p\u003e\n\u003ch3\u003ePerioperative Management Protocols\u003c/h3\u003e\n\u003cp\u003eTotal pericardiectomy was performed off-pump via median sternotom and defined as resection of anterolateral pericardium between bilateral phrenic nerves, including pericardium over ventricular apex, diaphragmatic base, great vessels, and the junction from superior vena cava-right atrium to inferior vena cava-right atrium.Postoperatively, patients were transferred to the intensive care unit (ICU), where pulse indicated continuous cardiac output (PICCO) system was used in all the patients. Patients were subsequently moved to the general ward after achieving hemodynamic stability.\u003c/p\u003e\n\u003ch3\u003eData Collection\u003c/h3\u003e\n\u003cp\u003ePerioperative data were retrospectively collected from electronic medical records. Laboratory tests and echocardiographic measurements were obtained within 48 hours before surgery. Echocardiographic assessments followed a standardized protocol using the apical four-chamber view.\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eSAS 9.4 (SAS Institute Inc., Cary, NC, USA) was used for descriptive statistics and predictive model development. Categorical variables were described as counts and percentages, and compared using chi-square tests. Normally distributed continuous variables were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) and compared using independent t-tests. Non-normally distributed continuous variables were reported as median and interquartile range (IQR), and analyzed using Mann\u0026ndash;Whitney U tests.\u003c/p\u003e\u003cp\u003eUnivariate logistic regression was performed to identify candidate predictors of postoperative LCOS. Variables with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 entered into a multivariable logistic regression model using stepwise selection. Model discrimination and predictive value were evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. Internal validation was performed using 10-fold cross-validation. The optimal cutoff probability for classifying high-risk patients was determined by maximizing the Youden index, which balances sensitivity and specificity. Rms package, version 6.0.0 was used for visualization and nomogram construction. The final logistic model was translated into a nomogram using the rms package. Internal validation and calibration of the nomogram were conducted using 2,000 bootstrap resamples. Calibration was evaluated via calibration curves, and model discrimination was assessed using Harrell\u0026rsquo;s concordance index (C-index), with corrected values obtained from bootstrap validation. Two-sided P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\"\u003e\n \u003ch2\u003eBaseline Characteristics and univariate analysis\u003c/h2\u003e\n \u003cp\u003eA total of 190 patients were enrolled in the study and divided into the non-LCOS group (n = 133) and the LCOS group (n = 57) according to the occurrence of low cardiac output syndrome within 72 hours after surgery.. Intergroup comparisons revealed significantly higher heart rate and higher New York Heart Association function classification (NYHA class) in the LCOS group (P \u0026lt; 0.05; Table\u0026nbsp;1). In addition, the LCOS group exhibited significantly elevated levels of white blood cell count (WBC), red blood cell count (RBC), lactate dehydrogenase (LDH), D-dimer, inferior vena cava diameter (IVC), early diastolic transmitral velocity to early diastolic mitral annular velocity ratio (E/e'), pericardial thickness, and a higher incidence of ascites and pleural effusion. Conversely, lower left ventricular end-diastolic dimension (LVEDD), cholinesterase (CHE), and serum sodium levels were observed in the LCOS group (P \u0026lt; 0.05; Table\u0026nbsp;2, Table\u0026nbsp;3).\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003e\u003cstrong\u003eBasic characteristics of the patients before surgery BMI\u003c/strong\u003e: Body Mass Index; \u003cstrong\u003eNYHA\u003c/strong\u003e: New York Heart Function\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eno-LCOS (n = 133)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLCOS (n = 57)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e124 (65.26%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e82 (61.65%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42 (73.68%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge (year)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e52.00 (29.00,64.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e52.00 (30.00,63.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46.00 (25.00,64.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.379\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.46 ± 3.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.39 ± 3.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.64 ± 4.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.695\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSystolic pressure (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e111.66 ± 12.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e111.77 ± 12.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e111.40 ± 11.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiastolic pressure (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e74.29 ± 10.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e74.08 ± 10.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e74.79 ± 9.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.656\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePulse pressure (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37.37 ± 11.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37.69 ± 11.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36.61 ± 9.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.495\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHeart rate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e89.42 ± 13.93\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e87.34 ± 14.73\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e92.93 ± 11.81\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.039\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eComorbidity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDisease course (Month)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.00 (2.00,12.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.00 (2.00,12.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.00 (2.00,12.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAtrial fibrillation [n (%)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16 (8.42%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11 (8.27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5 (8.77%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHypertension [n (%)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17 (8.95%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15 (11.28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2 (3.51%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiabetes [n (%)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16 (8.42%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10 (7.52%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6 (10.53%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.690\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNYHA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClass Ⅱ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e96 (50.53%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e96 (72.18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0 (0.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eClass Ⅲ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e65 (34.21%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e32 (24.06%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e33 (57.89%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eClass Ⅳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e29 (15.26%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e5 (3.76%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e24 (42.11%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMortality within 30 days[n (%)]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e11(5.79%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e2(1.50%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e9(15.79%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003eBMI:\u0026nbsp;\u003c/strong\u003eBody Mass Index;\u0026nbsp;\u003cstrong\u003eNYHA:\u0026nbsp;\u003c/strong\u003eNew York Heart Function\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003ePatients were examined 48 hours before surgery\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eno-LCOS (n = 133)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLCOS (n = 57)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWhite Blood Cell Count (10^9/L)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e5.89 ± 2.14\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e5.34 ± 1.88\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e7.16 ± 2.19\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.001*\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eThickness of pericardium (mm)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e4.52 ± 0.60\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e4.46 ± 0.58\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e4.67 ± 0.63\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHemoglobin (g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e127.94 ± 18.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e126.53 ± 17.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e131.23 ± 19.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlatelet Count (10^9/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e156.50 (111.00,235.00)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e148.00 (110.00,210.00)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e188.00 (125.0,248.0)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.020\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlanine Aminotransferase (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.80 (11.90,25.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.20 (11.20,25.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.70 (12.60,25.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.890\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAspartate Aminotransferase (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.45 (21.00,34.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.50 (21.10,33.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.20 (20.80,35.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.756\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal Bilirubin (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.93 (11.18,27.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.99 (11.15,27.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.73 (12.88,27.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.406\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDirect Bilirubin (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.87 (3.44,11.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.36 (3.20,10.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.62 (4.48,14.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndirect Bilirubin (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.45 (7.04,14.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.72 (6.97,15.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.80 (7.42,13.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.756\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal Protein (g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.70 ± 9.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63.30 ± 8.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61.30 ± 10.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.185\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlbumin (g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.06 ± 5.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.50 ± 5.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.04 ± 5.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.111\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrealbumin (g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e163.04 ± 71.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e168.42 ± 72.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e150.48 ± 65.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.111\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLactate Dehydrogenase (U/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e199.15 ± 51.77\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e191.35 ± 42.46\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e217.36 ± 65.69\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.007\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGamma-Glutamyl Transferase (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84.80 (50.60,136.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84.80 (44.60,136.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86.90 (56.20,130.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.333\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCholinesterase (U/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e5028.53 ± 1927.04\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e5365.80 ± 1869.60\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e4241.57 ± 1842.08\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdenosine Deaminase (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.20 (10.20,17.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.20 (10.20,16.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.20 (10.40,18.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.346\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCreatinine (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67.10 (56.10,78.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66.80 (57.00,77.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69.40 (56.00,79.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.829\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUric Acid (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e495.55 ± 205.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e481.41 ± 198.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e528.55 ± 220.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.148\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNa (mmol/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e138.88 ± 3.90\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e139.29 ± 3.93\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e137.92 ± 3.68\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.026\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePotassium (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.85 ± 0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.81 ± 0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.94 ± 0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.183\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProthrombin Time (s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.27 ± 1.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.25 ± 1.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.32 ± 1.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.782\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAPPT (s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e89.38 ± 16.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90.21 ± 17.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87.46 ± 14.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.305\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eD-Dimer (µg/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.36 (0.50,3.34)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.13 (0.46,2.89)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.02 (0.80,4.20)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.019\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003eAPPT:\u003c/strong\u003e activated partial thromboplastin time.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eEchocardiography was performed 48h before surgery\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eno-LCOS (n = 133)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLCOS (n = 57)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLeft Atrium (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.16 ± 6.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.51 ± 6.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.35 ± 6.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.261\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLVEDD (mm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e41.22 ± 4.49\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e41.87 ± 4.34\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e39.70 ± 4.50\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002 *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRight Atrium (-) (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.02 ± 5.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.25 ± 6.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.49 ± 5.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.419\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRight Ventricle (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.00(19.00,22.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.00(19.00,22.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.00(18.00,21.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.271\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEF%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.63 ± 7.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.74 ± 7.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.39 ± 6.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.754\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInterventricular Septum (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.00(8.00,10.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.00(8.00,10.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.00(8.00,10.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.235\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eIVC (cm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e22.45 ± 4.08\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e22.08 ± 4.32\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e23.32 ± 3.34\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.034\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eE (m/s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.72 ± 0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.73 ± 0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.70 ± 0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.436\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA (m/s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.53(0.41,0.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.53(0.44,0.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.46(0.40,0.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ee’ Velocity (m/s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.23 ± 3.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.91 ± 3.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.96 ± 4.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eE/e’\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e8.15 ± 4.49\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e8.56 ± 5.00\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e7.18 ± 2.76\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.015 *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eThickness of pericardium (mm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e8.01 ± 3.73\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e7.66 ± 3.52\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e8.84 ± 4.09\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.044\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVentricular septal bounce\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e160(84.21%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e108(81.20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52(91.23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.082\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePericardial calcification\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75(39.68%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52(39.39%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23(40.35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.902\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePericardial effusion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e129(67.89%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86(64.66%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43(75.44%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.145\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHepatic vein dilation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42(22.22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27(20.45%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15(26.32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.374\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\u003cstrong\u003eEF%\u003c/strong\u003e: Cardiac ejection fraction; \u003cstrong\u003eIVC\u003c/strong\u003e: Inferior Vena Cava; \u003cstrong\u003eE\u003c/strong\u003e: Early mitral valve velocity; \u003cstrong\u003eA\u003c/strong\u003e: Late mitral valve velocity\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003ch3\u003eMultivariable Analysis of Predictors for LCOS\u003c/h3\u003e\n\u003cp\u003eNYHA classification (OR = 22.424, 95% CI = 8.264–60.846), WBC (OR = 1.441, 95% CI = 1.175–1.766), LVEDD (OR = 0.852, 95% CI = 0.749–0.970), and E/e’ (OR = 0.878, 95% CI = 0.784–0.982) as independent risk factors for postoperative acute LCOS (Table\u0026nbsp;4). Postoperative Outcomes in LCOS vs. Non-LCOS Groups. Postoperative outcomes differed significantly between the LCOS and non-LCOS groups. Mortality within 30 days after surgery was substantially higher in the LCOS group compared to the non-LCOS group [15.79% vs. 1.5% (P \u0026lt; 0.001)]. (Table\u0026nbsp;1)\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 4\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eMultivariate logistic regression model\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eβ\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStandardized coefficient β\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStandard\u003c/p\u003e\n \u003cp\u003eerror\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWald χ2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR (95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.860\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e—\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.665\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.487\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.485\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e—\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNYHA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.509\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37.293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.424(8.264,60.846)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWBC (10^9/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.431\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.368\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.441(1.175,1.766)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLVEDD (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.395\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.903\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.852(0.749,0.970)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eE/e’\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.878(0.784,0.982)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eWBC:\u003c/strong\u003e White Blood Cell Count\u003c/p\u003e\n\u003cdiv id=\"Sec11\"\u003e\n \u003ch2\u003eDevelopment of the LCOS Prediction Model\u003c/h2\u003e\n \u003cp\u003eBased on multivariate logistic regression analysis, a prediction model for LCOS was developed, described by the formula: P = eY/ (1 + eY), where Y = 1.860 + 3.100×NYHA class + 0.365×WBC (×10⁹/L) − 0.160×LVEDD (mm) − 0.130×E/e’. The model demonstrated excellent goodness-of-fit, (Hosmer-Lemeshow χ² = 11.483, P = 0.176). The area under the receiver operating characteristic curve (AUC) was 0.951 (95% CI: 0.923–0.978, P \u0026lt; 0.001) (Supplementary Fig.\u0026nbsp;1). Based on the Youden index, a cutoff probability of ≥ 0.25 was selected, to classify patients with high risk of developing postoperative LCOS. Internal validation via 10-fold cross-validation showed robust performance: the model achieved an accuracy of 85.71%, sensitivity of 92.57%, and specificity of 82.74% in the validation cohort (Supplementary Table\u0026nbsp;1), demonstrating promising potential for predicting.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\"\u003e\n \u003ch2\u003eVisualization of the Nomogram\u003c/h2\u003e\n \u003cp\u003eA nomogram was subsequently constructed based on the prediction model (Fig. 1), converting each variable into a visual scoring system for practical bedside use. A total score of ≥ 177 points corresponds to a risk of LCOS ≥ 25%. For example, if a patient has NYHA class III (score = 33), WBC of 14 × 10⁹/L (score = 53), LVEDD of 45 mm (score = 58), and E/e′ of 8 cm/s (score = 60), the total score would be 204, corresponding to an estimated LCOS probability of 0.8.\u003c/p\u003e\n \u003cp\u003eAfter 2,000 bootstrap resampling iterations, the nomogram model yielded a C-index of 0.951, and the bias-corrected C-index was 0.945, indicating good discriminatory performance. The calibration curve (Supplementary Fig.\u0026nbsp;2) showed excellent agreement between predicted and observed LCOS probabilities, with a mean absolute error of 0.039. These findings support the model’s strong predictive performance and clinical applicability.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis retrospective study identified four independent preoperative predictors of postoperative LCOS: NYHA functional class, WBC count, LVEDD, and E/e\u0026prime;, reflecting cardiac functional reserve, systemic inflammation, ventricular remodeling, and diastolic filling capacity. Together, they form a robust and clinically applicable risk prediction model.\u003c/p\u003e\u003cp\u003eOur study found that NYHA class\u0026thinsp;\u0026ge;\u0026thinsp;III conferred a more than 20-fold increased odds of developing postoperative LCOS. The predictive significance aligns with prior evidence indicating that symptomatic heart failure portends adverse outcomes in cardiac surgery\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. In CP, elevated NYHA class indicates reduced cardiac reserve in the context of chronic constriction, where the myocardium is accustomed to operating under low preload and mechanical constraint. After pericardiectomy, these patients may face abrupt hemodynamic shifts that overwhelm their limited compensatory capacity, predisposing them to LCOS.\u003c/p\u003e\u003cp\u003ePreoperative leukocytosis also emerged as a strong independent predictor.In the context of constrictive pericarditis\u0026mdash;particularly with a high prevalence of tuberculous etiology\u0026mdash;elevated WBC may reflect subclinical infection or persistent granulomatous inflammation. These inflammatory states can impair myocardial contractility through cytokine-mediated pathways and disrupt vascular tone and endothelial integrity, contributing to systemic vasoplegia and microcirculatory dysfunction\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Notably, a previous study also reported that elevated preoperative WBC levels were associated with increased postoperative complications in general cardiac surgery patients\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e, Our findings build upon this by specifically linking leukocytosis to LCOS risk after pericardiectomy, highlighting the need for more deliberate preoperative assessment and optimization of inflammatory status in CP patients.\u003c/p\u003e\u003cp\u003eThe echocardiographic findings in our study provide further insight into the structural and functional derangements underlying LCOS risk. A reduced LVEDD reflects chronic underfilling and disuse atrophy of the left ventricle, often secondary to long-standing pericardial constraint. These ventricles, frequently fibrotic and stiff, are ill-equipped to accommodate increased preload after pericardial release, leading to a mismatch between volume status and contractile capacity. Several prior studies\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e have similarly demonstrated that smaller LV dimensions are associated with higher postoperative morbidity and mortality in CP patients, likely due to irreversible remodeling and impaired myocardial compliance.\u003c/p\u003e\u003cp\u003eInterestingly, lower E/e\u0026prime;values were independently associated with increased LCOS risk, contrary to findings in broader heart failure populations, where higher E/e\u0026prime; indicates elevated filling pressures and poor outcomes\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. In CP, this paradox is explained by the \u0026ldquo;annulus paradoxus\u0026rdquo; phenomenon\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e, in which longitudinal annular velocities (e\u0026prime;) may remain deceptively normal or elevated, while inflow velocities (E) are attenuated by restrictive preload. Thus, a low E/e\u0026prime; may reflect advanced constriction, impaired preload reserve, and susceptibility to hemodynamic collapse after pericardial decompression.\u003c/p\u003e\u003cp\u003eThese findings appear to contrast with a previous study reporting that higher E velocity predicted worse long-term outcomes after pericardiectomy\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. These likely reflect distinct end-point and mechanisms. Higher E velocity may indicate elevated left atrial pressure and more advanced pericardial disease, contributing to persistent diastolic dysfunction and atrial arrhythmias in the long term. In contrast, lower E/e\u0026prime; in CP\u0026mdash;due to preserved or elevated e\u0026prime; and restricted preload (\u0026ldquo;annulus paradoxus\u0026rdquo;)\u0026mdash;may identify patients with limited diastolic reserve who are more vulnerable to acute hemodynamic collapse after pericardial decompression. Thus, both parameters capture different dimensions of risk, and are complementary rather than contradictory. Recent studies have explored predictors of LCOS after pericardiectomy. Wang et al. identified atrial arrhythmia, renal dysfunction, hyponatremia, elevated CVP, and low cardiac index as independent LCOS predictors in a cohort of 212 patients (\u003cem\u003eSupplementary Table\u0026nbsp;2\u003c/em\u003e).\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e Huang et al., in a multicenter study of 826 patients, found that incomplete pericardial dissection, fluid overload, delayed treatment, and tuberculous etiology were significant risk factors.\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e In contrast to previous studies that emphasized intraoperative or congestion-related factors, our study focused on preoperative risk assessment, with particular attention to echocardiographic indicators. Most importantly, we developed and validated a predictive model with good performance, and translated it into a nomogram for individualized bedside stratification.\u003c/p\u003e\u003cp\u003eBeyond LCOS, prior studies have explored predictors for other postoperative outcomes, or long-term follow-up adverse outcomes \u003csup\u003e[\u003cspan additionalcitationids=\"CR22 CR23 CR24 CR25 CR26 CR27 CR28 CR29 CR30 CR31 CR32 CR33\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e, with several confirming NYHA class and LVEDD as key predictors. In addition, the study demonstrated that LCOS was significantly associated with increased in-hospital mortality in our cohort, consistent with previous studies highlighting LCOS as a critical determinant of postoperative outcomes \u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. Due to the low mortality rate and limited sample size, we did not analyze mortality risk factors separately, but instead focused on LCOS as a clinically actionable intermediate outcome. may allow timely interventions, including intensified monitoring, optimization of volume and inflammatory status, and preparedness for circulatory support.\u003c/p\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eLimitations\u003c/h2\u003e\u003cp\u003eSeveral limitations should be acknowledged. First, this was a single-center retrospective study, which may limit the generalizability of findings. The high proportion of tuberculous CP may not reflect CP etiologies in other settings. Second, external validation was not performed. Finally, the study did not include analysis of other preoperative inflammatory markers such as CRP, procalcitonin, or IL-6, which may provide additional prognostic value.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn patients undergoing isolated pericardiectomy for constrictive pericarditis, LCOS remains a serious and unpredictable postoperative complication. This study is the first to propose and validate a multiparametric predictive model specifically tailored to LCOS risk in this population. Our model achieved excellent discriminative power and calibration, and was translated into a practical nomogram for clinical use.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eBMI: body mass index\u003c/p\u003e\n\u003cp\u003eLCOS: low cardiac output syndrome\u003c/p\u003e\n\u003cp\u003eLVEDD: left ventricular end-diastolic dimension\u003c/p\u003e\n\u003cp\u003eCP: constrictive pericarditis\u003c/p\u003e\n\u003cp\u003eLV: left ventricle\u003c/p\u003e\n\u003cp\u003eWBC: white blood cell count\u003c/p\u003e\n\u003cp\u003eNYHA: new york heart association\u003c/p\u003e\n\u003cp\u003ePICCO: pulse Indicator continuous cardiac output\u003c/p\u003e\n\u003cp\u003eCHE: cholinesterase\u003c/p\u003e\n\u003cp\u003eRBC: red blood cell\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePCT: pericardial Thickness\u003c/p\u003e\n\u003cp\u003eLVEDD: left ventricular end-diastolic diameter\u003c/p\u003e\n\u003cp\u003eATPP: activated partial thromboplastin Time\u003c/p\u003e\n\u003cp\u003eCRP: c-reactive protein\u003c/p\u003e\n\u003cp\u003eLDH: lactate dehydrogenase\u003c/p\u003e\n\u003cp\u003eIVC: inferior vena cava\u003c/p\u003e\n\u003cp\u003ee\u0026rsquo;: early mitral myocardial velocity\u003c/p\u003e\n\u003cp\u003eE: early mitral valve velocity\u003c/p\u003e\n\u003cp\u003eMVE: mitral orifice blood flow rate\u003c/p\u003e\n\u003cp\u003eECMO: extracorporeal membrane oxygenation\u003c/p\u003e\n\u003cp\u003eA: late mitral valve velocity\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll patients provided written consent for surgical treatment. However, consent for study participation was waived due to its retrospective design, in accordance with China law, and local ethical committees were notified, obviating the need for formal approval.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by Health Commission of Sichuan Province. (KY2024SJ0033)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLanying Gao: Analysis, interpretation of data, drafting the work, revising the work; Yongnan Li: Conception, design of the work, acquisition, analysis, interpretation of data, drafting the work; Yong Luo: Interpretation of data, validation; Jian Chen: The acquisition, analysis; Shilin Wei: Data collection; Lijian Cheng: Interpretation of data; Lijie Jiang: Drafting the work; Shuzhen Wang: Interpretation of data; Shujie Yan: Conception, design of the work; Bin Jia: Conception, design of the work, analysis, drafting the work, project administration, revising the work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number: N\u003c/strong\u003eot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eChowdhury UK, Sankhyan LK, Malik V, George N, Gudala V, et al. 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Front Cardiovasc Med. 2022; 9 Front Cardiovasc Med.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"constrictive pericarditis, low cardiac output syndrome, risk factors, prediction model","lastPublishedDoi":"10.21203/rs.3.rs-7481051/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7481051/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eLow cardiac output syndrome (LCOS) following pericardiectomy in patients with constrictive pericarditis significantly contributes to perioperative morbidity mortality. Predictive models specific to LCOS after pericardiectomy are currently lacking. This study aimed to identify independent risk factors for LCOS following pericardiectomy and develop a predictive model to guide clinical decision-making.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA retrospective cohort of 190 patients with constrictive pericarditis undergoing isolated pericardiectomy were divided into LCOS group (57 cases) and non-LCOS group (133 cases). Univariate and multivariate logistic regression analyses were performed to identify independent risk factors. A predictive model was developed and validated. Model performance was evaluated using receiver operating characteristic (ROC) curve, Hosmer-Lemeshow test, and 10-fold cross-validation. A visual nomogram was constructed for clinical application.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eMultivariate logistic regression identified preoperative New York Heart Association function (NYHA) classification, white blood cell counts (WBC), left ventricular end-diastolic dimension(LVEDD), and early diastolic trans-mitral velocity to mitral annular early diastolic velocity ratio (E/e' ratio) as independent risk factors. The model showed excellent performance (AUC\u0026thinsp;=\u0026thinsp;0.951, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Hosmer-Lemeshow P\u0026thinsp;=\u0026thinsp;0.176). Ten-fold cross-validation yielded 85.7% accuracy, 92.6% sensitivity, and 82.7% specificity. The nomogram demonstrated good calibration (bootstrap C-index\u0026thinsp;=\u0026thinsp;0.951; corrected\u0026thinsp;=\u0026thinsp;0.945).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003ePreoperative NYHA class, elevated WBC, reduced LVEDD, and decreased E/e' ratio predict LCOS after pericardiectomy in constrictive pericarditis. The model exhibits high clinical utility for early risk stratificaiton.\u003c/p\u003e","manuscriptTitle":"Multiparameter predictor model for Low Cardiac Output Syndrome After Pericardiectomy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-27 14:32:04","doi":"10.21203/rs.3.rs-7481051/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2025-10-13T12:24:02+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-08T05:05:37+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-05T06:21:34+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-05T06:21:10+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cardiovascular Disorders","date":"2025-08-28T13:52:59+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"679fbfda-949c-42c8-b50c-8d061ed87f66","owner":[],"postedDate":"October 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-10-27T14:32:04+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-27 14:32:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7481051","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7481051","identity":"rs-7481051","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}