Prognostic Significance of Cardiac Output in Postcapillary Pulmonary Hypertension: Estimated Fick vs. Thermodilution

Prognostic Significance of Cardiac Output in Postcapillary Pulmonary Hypertension: Estimated Fick vs. Thermodilution | 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 Prognostic Significance of Cardiac Output in Postcapillary Pulmonary Hypertension: Estimated Fick vs. Thermodilution Yue Zhang, Yanping Shi, Iokfai Cheang, Yuxuan Lou, Yuan Tang, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7325994/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: The prognostic value of cardiac output measured by the estimated Fick method and thermodilution in patients with postcapillary pulmonary hypertension (pc-PH) is unclear. We aimed to compare the correlations and consistency of cardiac output (CO) evaluated by the estimated Fick method and thermodilution, and to assess the prognostic validity of these two cardiac index (CI)-based methods with regard to all-cause mortality in patients with pc-PH. Methods: CO was simultaneously measured by thermodilution and the estimated Fick method using a Swan-Ganz catheter in 257 patients with pc-PH. Oxygen consumption was calculated by Dehmer, LaFarge, and Bergstra formulae. Results: The CO measured by the estimated Fick method combined with the three formulae, and by thermodilution exhibited moderate correlations (r = 0.706, r = 0.688, r = 0.702, respectively; all P < 0.010). The 95% limits of agreement were -2.73 to 1.51, -2.99 to 1.36, and -2.42 to 2.02 L/min, respectively. CI measured by thermodilution was associated with the mortality (HR: 0.75 [95% CI: 0.58-0.98]; P = 0.034). After adjustment, the risk of all-cause mortality was higher in the low CI group assessed by the Dehmer formula (HR: 1.92 [95% CI: 1.10-3.37]; P = 0.022) compared to the high CI group. Conclusions : There were moderate positive correlations and poor agreements between the CO measured by the estimated Fick method and thermodilution in patients with pc-PH. Thermodilution was superior to the estimated Fick method in improving risk stratification for all-cause mortality. Estimated Fick method Postcapillary pulmonary hypertension Cardiac output Thermodilution All-cause mortality Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Postcapillary pulmonary hypertension (pc-PH) is defined as a syndrome resulting from abnormal pulmonary vascular function or structure in the presence of left heart disease, leading to increased pulmonary arterial pressure 1 . It is also referred to as pulmonary hypertension associated with left heart disease, which accounts for approximately 65–80% of all pulmonary hypertension cases 2 – 3 . Patients with advanced pulmonary vascular lesions and a progressive decline in cardiac output (CO) are at a higher risk for all-cause mortality 4 . The 6-month mortality rate of acute left heart failure in patients with pc-PH is 21.8–48.3% 5 . CO is used to calculate pulmonary vascular resistance (PVR), guiding the diagnosis and risk stratification of pulmonary hypertension 6 . It can guide fluid resuscitation and assess the indications for advanced therapies such as left ventricular assist devices and heart transplantation 7 – 8 . The gold standard for measuring CO is the direct Fick method, which requires precision instruments to accurately measure systemic oxygen consumption (VO 2 ) and is not suitable for routine clinical practice 9 . Currently, the commonly used methods in clinical practice are thermodilution 10 and the estimated Fick method combined with VO 2 formulae. The latter can be implemented in basic healthcare settings and is relatively cost-effective 11 . Previous studies have compared the correlations between different methods for assessing CO in cohorts of critically ill patients 12 – 14 , left ventricular assist device implantation 15 – 16 , chronic dyspnea 17 , and pulmonary hypertension 18 – 19 . These studies have summarized the factors influencing the measurement of CO using different methods. The latest ESC/ERS guideline 2 has reclassified the diagnostic criteria for pc-PH, but the comparability of the estimated Fick method with thermodilution for risk prediction remains largely unknown. This study excluded known factors affecting the CO measurement and re-evaluated the correlation and consistency of the two methods. We aimed to compare the clinical application values of these two CO-based methods with regard to all-cause mortality in patients with pc-PH. Methods Study design and population This was a single-center retrospective study. We included patients with pc-PH who underwent Swan-Ganz catheterization in the First Affiliated Hospital of Nanjing Medical University from September 2013 to December 2021. The diagnostic criteria for pc-PH were based on the 2022 ESC/ERS guidelines 2 . Patients with the mean pulmonary arterial pressure (mPAP) > 20mmHg and the pulmonary arterial wedge pressure (PAWP) > 15mmHg were eligible. Exclusion criteria included (i) patients without baseline data, (ii) patients with shunts or regurgitations known to affect CO measurement, such as Qp/Qs > 1.5 and severe tricuspid insufficiency, (iii) patients with conditions that increases VO 2, such as fever, and/or those using sedatives that reduces VO 2 , (iv) patients with BMI > 40kg/m 2 or body fat > 30%, (v) patients with a history of malignancy, recent major surgery, and/or current pregnancy and lactation. There were 257 participants included in the analysis (Fig. 1 A). This study was approved by the independent Ethics Committee of the First Affiliated Hospital with Nanjing Medical University and the ethics number was 2023-SR-103. All patients signed the informed consent form. Follow-Up All enrolled patients were followed up from the time of their initial right heart catheterization (RHC) until April 2022, by outpatient visit and / or telephone interviews. The primary endpoint of the study was all-cause mortality. During the follow-up period, detailed information on patient survival status and any relevant clinical events was collected and recorded. Efforts were made to ensure complete data collection, and any missing data was documented and accounted for in the statistical analysis. Cardiac Output Measurement Thermodilution Method Thermodilution was proposed by Fegler in 1954 20 . RHC was performed on each patient using a six-lumen Swan-Ganz floating catheter. The temperature-sensitive guide wire warmed the right heart, causing temperature changes, which were then measured by a thermistor in the main pulmonary artery. A continuous hemodynamic detection system (Vigilance II Edwards, Irvine, CA, USA) measured the blood flow per unit time according to the Stewart-Hamilton equation. Cardiac index (CI) was calculated as CO divided by body surface area (BSA). The area under the curve (Fig. 1 B) was used to determine the cardiac output (TDCO) and cardiac index (TDCI). The monitoring panel updated the values every 30 to 60 seconds. The average value for the first 3 to 6 minutes was recorded as the TDCO/TDCI. Estimated Fick Method The estimated Fick method, first employed by Adolph Fick in 1870 21 , was simultaneously used to determine CO. During a specific time interval, the blood flow rate is calculated based on the quantity of matter entering the bloodstream and the difference in concentration of matter between the inlet and outlet points within the circulatory system. Specifically, the estimated Fick cardiac output (eFCO) and cardiac index (eFCI) can be calculated using the oxygen intake inhaled into the lungs and the difference in oxygen content between the pulmonary artery and vein (CaO 2 - CvO 2 ) (Fig. 1 B). In this study, the pressure and blood oxygen saturation of each segment were measured and recorded using a multi-function monitor (Phillips MP50). Peripheral arterial oxygen saturation was utilized to substitute for pulmonary venous oxygen saturation. CO and CI were estimated using three formulae for calculating VO 2 : LaFarge 22 , Dehmer 23 and Bergstra 24 ( Table S1 ),and were recorded as LCO, DCO, BCO and LCI, DCI, BCI respectively. Statistical analysis Categorical variables were expressed as the frequencies and percentages, and comparisons were made using the chi-square test or Fisher's exact test. Continuous variables were presented as means ± standard deviations and non-normal data with median and IQR. The modified Kolmogorov-Smirnov test was used to assess the normality of continuous variables. The paired t-test and Pearson correlation analysis were used for normally distributed variables. The paired Mann-Whitney U test and Spearman correlation analysis were used for non-normally distributed variables. Chi-square test is used for comparison of rates. The Bland-Altman test was used for bias and agreement analysis 25 . Inconsistency between the two methods was defined as a relative error of CO > 20% 26 and an absolute error > 1L/min. The optimal cut-off values of CI for survival analysis were determined by X-tile software 27 . Kaplan-Meier survival curves were used to compare all-cause mortality between groups. Cox proportional hazards regression analyses were performed to examine the associations between the CI and all-cause mortality. Statistical analyses were conducted using X-tile (v3.6.1), IBM SPSS 25.0, R software (v4.4.1; The R Foundation for Statistical Computing), and GraphPad Prism (v10.1.2). P value < 0.050 was considered statistically significant. Results Baseline characteristics A total of 257 pc-PH patients with a mean age of 54.80 ± 12.27 years were enrolled, of whom 85 were female (33.1%). Participants were divided into two groups based on survival status. 107 patients (41.63%) died after a median follow-up time of 71.17 months (Table 1 ). The death group was characterized by lower CO and CI estimated by both the thermodilution and estimated Fick method, while PAWP, mPAP and PVR were higher ( P < 0.050). Table 1 Baseline characteristics of patients with postcapillary pulmonary hypertension (n = 257) Variable Death (n=107) Survival (n=150) P- value Age, years 58.71 ± 10.35 52.01 ± 12.79 <0.001 Female, % 38 (35.5) 47 (31.3) 0.483 BMI, kg/m 2 23.43 ± 3.67 24.94 ± 4.49 0.006 BSA, m 2 1.72 ± 0.17 1.78 ± 0.21 0.011 NT-pro BNP, ng/L 2459 (1141, 5870) 1660 (806, 3801) 0.009 SaO 2 , % 96.07 ± 3.16 97.15 ± 2.24 0.001 Hb, g/L 134.63 ± 21.03 137.95 ± 19.39 0.193 Echocardiography LVEF, % 36.58 ± 15.19 35.90 ± 14.63 0.859 sPAP, mmHg 50.65 ± 16.74 47.13 ± 16.11 0.113 Right heart catheterization HR, bpm 86.50 ± 19.42 84.88 ± 16.75 0.477 SVCP, mmHg 15.12 ± 7.86 12.90 ± 5.64 0.062 S P O 2 , % 54.91 ± 10.32 63.02 ± 10.75 <0.001 mRAP, mmHg 10.74 ± 6.41 8.69 ± 4.77 0.031 PAWP(mmHg 23.57 ± 4.92 21.94 ± 5.28 0.013 sPAP, mmHg 58.36 ± 15.97 49.66 ± 13.95 <0.001 dPAP, mmHg 27.42 ± 9.16 24.21 ± 6.66 0.001 mPAP, mmHg 39.51 ± 10.46 34.56 ± 8.50 <0.001 SvO 2 , % 52.54 ± 11.65 61.53 ± 10.80 <0.001 PVR, Wood units 5.31 ± 3.14 3.18 ± 2.28 <0.001 TDCO, L/min 3.49 ± 1.24 4.38 ± 1.49 <0.001 eFCO, L/min Dehmer 2.99 ± 1.11 3.69 ± 1.23 <0.001 LaFarge 2.76 ± 1.04 3.50 ± 1.25 <0.001 Bergstra 3.32 ± 1.24 4.16 ± 1.42 <0.001 TDCI, L·min − 1 ·m − 2 2.04 ± 0.74 2.47 ± 0.80 <0.001 eFCI, L·min − 1 ·m − 2 Dehmer 1.75 ± 0.63 2.07 ± 0.66 <0.001 LaFarge 1.61 ± 0.57 1.95 ± 0.63 <0.001 Bergstra 1.93 ± 0.69 2.32 ± 0.73 <0.001 Data are presented as n (%), \(\:\stackrel{-}{\text{X}}\pm\:\text{S}\) and M (P25, P75). BMI, body mass index; BSA, body surface area; NT-pro BNP, N-terminal pro-brain natriuretic peptide; SaO 2 , peripheral arterial oxygen saturation; sPAP, systolic pulmonary artery pressure; SVCP, superior vena cava pressure; S P O 2 , superior vena cava oxygen saturation; mRAP, mean right atrial pressure; PAWP, pulmonary arterial wedge pressure; dPAP, diastolic pulmonary artery pressure; mPAP, mean pulmonary arterial pressure; SvO 2 , pulmonary venous oxygen saturation; PVR, pulmonary vascular resistance; TDCO, cardiac output measured with thermodilution; eFCO, cardiac output calculated with the estimated methods; TDCI, cardiac index measured with thermodilution; eFCI, cardiac index calculated with the estimated methods. Correlation and Consistency Between the Estimated Fick method and Thermodilution The mean values and standard deviations of cardiac output evaluated by the two methods were as follows: TDCO, 4.01 ± 1.46 L/min; DCO, 3.40 ± 1.23 L/min; LCO, 3.19 ± 1.22 L/min; and BCO, 3.81 ± 1.40 L/min. Significant differences were observed between the thermodilution method and the estimated Fick method using the three different VO 2 formulas (Fig. 2 A). The proportions of cardiac output differences greater than 1 L/min between DCO, LCO, BCO, and TDCO were 37.74%, 46.69%, and 34.24%, respectively. The relative errors between eFCO and TDCO were classified into three categories: less than 20%, 20–30%, and more than 30% (Fig. 2 B). The Bergstra formula had the most differences of less than 20% and the least differences of more than 30%, and showed no statistical difference with the Dehmer formula ( P = 0.493) but a significant difference with the LaFarge formula ( P < 0.050). Spearman correlation analysis demonstrated moderate correlations between DCO, LCO, BCO, and TDCO (Fig. 3 A): r = 0.706, r = 0.688, and r = 0.702, with all P < 0.010. Bland-Altman plots revealed bias and limits of agreement between the two methods. Limits of agreement refer to precision of the measurement tool, describing the reproducibility of measurements. Bias represents accuracy, defined as how close the measured value is to the true or reference value 25 . The biases between DCO, LCO, BCO, and TDCO were − 0.61 L/min, -0.82 L/min, and − 0.20 L/min, respectively, with 95% limits of agreement of -2.73 to 1.51 L/min, -2.99 to 1.36 L/min, and − 2.42 to 2.02 L/min, respectively (Fig. 2 B). The limits of agreement were wide, indicating substantial individual differences. Notably, the Bergstra formula exhibited the smallest mean difference and the highest accuracy. Cardiac Index and All-Cause Mortality Based on the optimal cut-off values of CI determined by X-tile software, patients were categorized into three groups: low, moderate and high CI group ( Table S2 ). Kaplan-Meier survival curves demonstrated stepwise increase in all-cause mortality with low CI group contrary to the moderate or high group (Fig. 4 ). No statistical differences in survival rates were observed among the three groups of the estimated Fick method (log-rank P = 0.150, 0.079, 0.185, respectively), whereas the difference between thermodilution groups was statistically significant (log-rank P = 0.015). Univariate Cox regression analysis indicated that TDCI (HR: 0.74 [95% CI: 0.57–0.97]; P = 0.029) exhibited a significant association with all-cause mortality when considered as a continuous variable, whereas eFCI did not. When cardiac index was categorized into groups, the mortality risk was higher in the low cardiac index group for thermodilution (HR: 2.07 [95% CI: 1.28–3.34]; P = 0.003), Dehmer (HR: 1.86 [95% CI: 1.07–3.25]; P = 0.029), and LaFarge (HR: 1.81 [95% CI: 1.03–3.18]; P = 0.039) formulae compared to the high cardiac index group as the reference. After adjusting for gender and age, a 1 L/min/m² increase in CI measured by thermodilution was associated with a lower risk of death (HR: 0.75 [95% CI: 0.58–0.98]; P = 0.034). The mortality risk remained higher in the low cardiac index groups of thermodilution (HR: 1.89 [95% CI: 1.16–3.08]; P = 0.011) and the Dehmer formula (HR: 1.92 [95% CI: 1.10–3.37]; P = 0.022) compared to the high groups. After extensive adjustment for sex, age, log 2 NT-proBNP, mPAP, PAWP, mean right atrial pressure, and pulmonary artery systolic pressure, neither eFCI nor TDCI could independently predict the risk of all-cause mortality, whether as continuous or grouping variables (Table 2 ). Table 2 COX regression analysis for the prediction of all-cause mortality Crude HR (95% CI) Model 1 a HR (95% CI) Model 2 b HR (95% CI) Cardiac index (thermodilution) Per 1 L/min/m² increase 0.74 (0.57–0.97) * 0.75 (0.58–0.98) * 0.93 (0.66–1.31) High level 1[Reference] 1[Reference] 1[Reference] Moderate level 1.39 (0.87–2.22) 1.49 (0.93–2.37) 1.45 (0.83–2.52) Low level 2.07 (1.28–3.34) ** 1.89 (1.16–3.08) * 1.56 (0.81–3.01) Cardiac index (Dehmer formula) Per 1 L/min/m² increase 1.00 (0.72–1.40) 1.00 (0.71–1.39) 1.42 (0.98–2.07) High level 1[Reference] 1[Reference] 1[Reference] Moderate level 1.01 (0.64–1.58) 0.97 (0.62–1.52) 0.68 (0.39–1.19) Low level 1.86 (1.07–3.25) * 1.92 (1.10–3.37) * 1.09 (0.52–2.30) Cardiac index (Lafarge formula) Per 1 L/min/m² increase 0.88 (0.61–1.27) 0.98 (0.68–1.41) 1.40 (0.93–2.10) High level 1[Reference] 1[Reference] 1[Reference] Moderate level 1.41 (0.92–2.17) 1.22 (0.79–1.89) 0.99 (0.60–1.64) Low level 1.81 (1.03–3.18) * 1.70 (0.96–3.01) 1.02 (0.47–2.21) Cardiac index (Bergstra formula) Per 1 L/min/m² increase 0.96 (0.71–1.30) 0.99 (0.73–1.33) 1.36 (0.97–1.92) High level 1[Reference] 1[Reference] 1[Reference] Moderate level 1.06 (0.67–1.67) 1.02 (0.64–1.61) 1.00 (0.60–1.70) Low level 1.62 (0.99–2.65) 1.53 (0.94–2.51) 0.76 (0.38–1.52) a Model 1: adjusted for age and sex. b Model 2: adjusted for age, sex, log 2 NT-proBNP, mPAP, PAWP, mean right atrial pressure, systolic pulmonary artery pressure. HR, hazard ratio; CI, confidence interval; * represents P < 0.050, ** represents P < 0.010. Categorization Based on Clinically Relevant Cutoff for Hypoperfusion Patients were categorized based on clinically relevant cutoff for hypoperfusion. It differentiates low cardiac index (< 2.2 L·min − 1 ·m − 2 ) from normal cardiac index (≥ 2.2 L·min − 1 ·m − 2 ), resulting in four distinct groups: normal eFCI + TDCI, low eFCI + TDCI, low eFCI + normal TDCI and low TDCI + normal eFCI group. There were no significant differences in the mortality rates of each group among the estimated Fick method of the three VO 2 formulas ( P = 0.787, 0.980, 0.967, 1.000, respectively). Contrary to the normal eFCI + TDCI group, Chi-square test showed no additional risks of all-cause mortality occurred in the low eFCI + normal TDCI and low TDCI + normal eFCI group (all P > 0.050) (Table 3 ). Table 3 All-cause mortality of different cardiac index groups Death, n (%) Normal eFCI Normal TDCI Low eFCI Low TDCI Low eFCI Normal TDCI Low TDCI Normal eFCI Dehmer formula 15 (22.06) 61 (54.46) * 27 (39.71) 4 (44.44) Lafarge formula 10 (19.23) 61 (53.51) * 32 (38.10) 4 (57.14) Bergstra formula 20 (24.69) 54 (54.54) * 22 (40.00) 11 (50.00) P -value 0.787 0.980 0.967 1.000 Data are presented as death number (death number / total number of the group). *Contrary to the normal eFCI and normal TDCI group, P < 0.050. TDCI, cardiac index measured with thermodilution; eFCI, cardiac index calculated with the estimated method. Discussion In this study, we assessed baseline characteristics and cardiac function in 257 patients with pc-PH, revealing that mortality was associated with lower CO and CI in limited adjustment, as well as higher unadjusted mPAP and PVR. Our study demonstrated that CO, evaluated by the estimated Fick method and thermodilution in patients with pc-PH, exhibited moderate correlation and poor consistency with significant individual variability. The Bergstra formula showed the highest consistency with TDCO among the eFCO methods. Survival analysis indicated that a low CI was associated with increased all-cause mortality, particularly when evaluated by thermodilution and with Dehmer formula as a grouping variable, though multivariate analysis did not support independent predictive value for mortality risk after adjusting for covariates. Categorization based on a clinically relevant hypoperfusion cutoff showed no significant mortality differences across groups, suggesting that neither method alone accurately predicts mortality risk in this patient population. Pc-PH is divided into isolated post-capillary (PVR ≤ 2 Wood) and combined post- and pre-capillary pulmonary hypertension (PVR > 2 Wood). The pathological structures, mortality risks, and treatments for the two entities are different 28 – 30 . PVR is calculated based on CO. Prior studies showed that 11–45% of patients with pulmonary hypertension were misclassified due to errors in CO assessment, and 10–20% of cases were misdiagnosed as cardiogenic shock 31 . Consequently, variances in CO and PVR influence the diagnostic classification, treatment planning, and prognosis evaluation of patients with pc-PH. In clinical practice, the two most commonly employed methods for evaluating CO are thermodilution and the estimated Fick method 32 . Each method has distinct advantages and disadvantages. Thermodilution can continuously monitor hemodynamic indicators and provide timely feedback; however, thermodilution requires the use of a Swan-Ganz floating catheter and supporting instruments, necessitating larger medical centers and higher costs 33 . The estimated Fick method, on the other hand, requires only a single-function right cardiac catheter to measure pulmonary arterial oxygen saturation. It can be implemented at the basic medical setting and is relatively cost-effective, but it involves blood draws for blood gas analysis, cannot continuously monitor hemodynamic changes, and relies on formulae to estimate VO 2 , which has large individual differences. Minor differences in arterial and venous oxygen saturation can amplify the error in VO 2 estimation. Previous literature has reported variable consistency between the two methods of CO assessment. Thermodilution is less reliable in patients with severe valvular regurgitation, intracardiac shunts, and low CO conditions 33 – 36 . The estimated Fick method is susceptible to errors in estimating VO 2 in obese patients, when oxygen concentration exceeds 0.85, and under sedation 23 , 37 – 38 . Early studies demonstrated good consistency between thermodilution and the estimated Fick method 31 , 39 , while others expressed concerns about this issue 18 . Recent large-scale studies have reported that both the estimated Fick method and thermodilution exhibit lower consistency compared to the direct Fick method 16 – 17 , 40 . The debate regarding the selection of an appropriate method to guide clinical practice in specific populations remains ongoing. Our study excluded cases with known factors affecting cardiac output measurements and re-evaluated the consistency between the estimated Fick method and thermodilution in patients with pc-PH. Although the Bergstra formula had the smallest absolute deviation among the three VO 2 formulae compared to thermodilution, large individual differences still existed. This finding is consistent with the study by Chase et al. 7 , who reported that the Bergstra formula has a significant error in estimating VO 2 and is not recommended for clinical use. Another study compared the VO 2 estimated by the Dehmer, LaFarge, and Bergstra formulae with accurately measured VO 2 and found significant deviations between them ( P < 0.001) 41 , suggesting that VO 2 should be directly measured in clinical practice. Several studies have compared disparities in the measurement of CO among diverse cohorts of individuals with pulmonary hypertension. Hoeper et al. 9 first explored the consistency of thermodilution and the estimated Fick method in 35 patients with pulmonary hypertension, and found that the mean difference between the two CO was 0.01 L/min, and the consistency limit was − 1.1 to 1.1 L/min. A recent large-scale study compared the estimated Fick method and thermodilution in 300 patients with chronic dyspnea (with pulmonary hypertension accounting for 76%), using the direct Fick method as the standard. The deviations > 1L/min accounted for 45.0% and 45.7% in the respective cases, demonstrating significant differences 17 . Abdullah et al. 18 reported that CO measured by thermodilution had a stronger correlation with right ventricular function indicators of echocardiographic than the estimated Fick method. Despite the varying definitions and judgment criteria for the correlation and consistency of different CO results across studies, most researchers agree that the estimated Fick method and thermodilution show poor consistency. However, these methods can be interchangeable in certain specific application scenarios. Volodarsky et al. 42 suggested that the estimated Fick methods and thermodilution can be used interchangeably for diagnostic classification when mPAP > 25mmHg. Sandeep et al. 43 found that the distribution of pulmonary hypertension hemodynamic groups did not significantly differ between thermodilution and the estimated Fick method when using either mPAP at end-expiration or mPAP averaged across the respiratory cycle. Our study was the first to investigate the risk predictive value of the cardiac index calculated with the estimated Fick method in pc-PH patients. Our findings showed that the cardiac index calculated using the estimated Fick method (Dehmer formula) had predictive value for all-cause mortality, but this value was limited to grouping variables. In contrast, the cardiac index measured with thermodilution had better prognostic value as both a continuous and a grouping variable, though it was not an independent predictor. Alexander R et al. 35 found that thermodilution had a higher ability than the estimated Fick method to predict all-cause mortality in 12,232 patients with a cardiac index of less than 2.2 L·min − 1 ·m − 2 at 90 days (HR: 1.71 vs. 1.42) and 1 year (HR: 1.53 vs. 1.35). However, there was no difference in individuals with normal cardiac index. We acknowledge some limitations in our study. First, the single-center retrospective study design and the limited sample size may introduce potential selection bias. We focus on a more narrowly defined population, while the use of multiple oxygen consumption formulas may provide clinically useful and formula-specific insights. Second, due to technical and clinical constraints, we were unable to use the direct Fick method, which is the gold standard, as a reference. Finally, the factors affecting cardiac output measurement in patients with pc-PH are multifaceted and varied, and some confounding factors remain unidentified. Despite these limitations, our study provides valuable insights into the predictive value of different methods for assessing CI in this population. Conclusions In summary, our study demonstrated a moderate correlation between COs evaluated by the estimated Fick method and thermodilution. However, the consistency between these two methods was poor. The CI calculated using the estimated Fick method combined with the Dehmer formula, when used as a grouping variable, was associated with the risk of all-cause mortality. It can be implemented at the basic medical setting when necessary. Thermodilution, on the other hand, showed superior performance in risk stratification for all-cause mortality, whether used as a continuous or grouping variable. These findings highlight the limitations of the estimated Fick method and the advantages of thermodilution in clinical settings, particularly for patients with post-capillary pulmonary hypertension. Abbreviations CO cardiac output CI cardiac index pc-PH postcapillary pulmonary hypertension VO 2 oxygen consumption PVR pulmonary vascular resistance mPAP mean pulmonary arterial pressure PAWP pulmonary arterial wedge pressure RHC right heart catheterization Declarations Ethics approval and consent to participate Our study complies with the Declaration of Helsinki. This study was approved by the independent Ethics Committee of the First Affiliated Hospital with Nanjing Medical University and the ethics number was 2023-SR-103. All the participants have signed informed consent. Consent for publication: Not applicable. Competing interests The authors declare that they have no competing interests. Authors' information 1 State Key Laboratory for Innovation and Transformation of Luobing Theory, Department of Cardiology, The First Affiliated Hospital with Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, China. 2 Department of Cardiology, Gusu School, The Affiliated Suzhou Hospital with Nanjing Medical University, Suzhou Municipal Hospital, Nanjing Medical University, 26 Daoqian Street, Suzhou, 215002, China. 3 Department of Cardiology, Jiangsu Province Hospital, 300 Guangzhou Road, Nanjing, 210029, China. 4 Southeast University School of Medicine, 87 Zhonghuan North Road, Nanjing, 210009, China. Funding This work was supported by State Key Laboratory for Innovation and Transformation of Luobing Theory, General Program of National Natural Science Foundation of China (82370389, 81970339 to XL Li, 82270394 to HF Zhang, 82200425 to RR Gao), The National High Technology Research and Development Program of China (2017YFC1700505 to XL Li), Project from Gusu School (GSRCKY20210204 to HF Zhang), Gusu Health Personnel Training Project (GSWS2021042 to HF Zhang), Excellent Young Scientists Fund of Jiangsu (BK20231538 to RR Gao), and Qing Lan Project of Jiangsu (RR Gao). Author Contribution YZ and YS contributed to design of study, data acquisition, analysis, interpretation, drafting of primary and subsequent manuscripts. IC, YL, YT, and RG contributed to data acquisition and manuscript editing. HZ, YS and XL contributed to supervision, and manuscript editing. All authors have approved the final version of the manuscript. Acknowledgement We are grateful to all the patients for their participation. Thanks to all the staff for managing the patients. Prof. XL Li and Prof. HF Zhang are Associate Fellows at the Collaborative Innovation Center for Cardiovascular Disease Translational Medicine. Dr. Cheang are Post-doctorate Follows at Nanjing Medical University. Data Availability The data underlying this article cannot be shared publicly due to the privacy of individuals that participated in the study. The data will be shared on reasonable request to the corresponding author. References Maron BA, Bortman G, De Marco T, Huston JH, Lang IM, Rosenkranz SH, et al. Pulmonary hypertension associated with left heart disease. Eur Respir J. 2024. 10.1183/13993003.01344-2024 . 64. Journal Article; Review. Humbert M, Kovacs G, Hoeper MM, Badagliacca R, Berger RMF, Brida M, et al. 2022 ESC/ERS Guidelines for the diagnosis and treatment of pulmonary hypertension. Eur Heart J. 2022;43:3618–731. 10.1093/eurheartj/ehac237 . Omote K, Sorimachi H, Obokata M, Reddy Y, Verbrugge FH, Omar M, et al. Pulmonary vascular disease in pulmonary hypertension due to left heart disease: pathophysiologic implications. Eur Heart J. 2022;43:3417–31. 10.1093/eurheartj/ehac184 . Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't. Huston JH, Shah SJ. Understanding the Pathobiology of Pulmonary Hypertension Due to Left Heart Disease. Circ. Res. 2022; 130: 1382–1403. Journal Article; Research Support, N.I.H., Extramural; Review. 10.1161/CIRCRESAHA.122.319967 Aronson D, Eitan A, Dragu R, Burger AJ. Relationship between reactive pulmonary hypertension and mortality in patients with acute decompensated heart failure. Circ Heart Fail. 2011;4:644–50. 10.1161/CIRCHEARTFAILURE.110.960864 . Journal Article; Multicenter Study; Randomized Controlled Trial. Vachiéry J, Tedford RJ, Rosenkranz S, Palazzini M, Lang I, Guazzi M, et al. Pulmonary hypertension due to left heart disease. Eur Respir J. 2019;53:1801897. 10.1183/13993003.01897-2018 . Chase PJ, Davis PG, Wideman L, Starnes JW, Schulz MR, Bensimhon DR. Comparison of Estimations Versus Measured Oxygen Consumption at Rest in Patients With Heart Failure and Reduced Ejection Fraction Who Underwent Right-Sided Heart Catheterization. Am J Cardiol. 2015;116:1724–30. 10.1016/j.amjcard.2015.08.051 . Bachmann KF, Vasireddy R, Heinisch PP, Jenni H, Vogt A, Berger D. Estimating cardiac output based on gas exchange during veno-arterial extracorporeal membrane oxygenation in a simulation study using paediatric oxygenators. Sci Rep. 2021;11. 10.1038/s41598-021-90747-w . 11528. Journal Article; Research Support, Non-U.S. Gov't. Hoeper MM, Maier R, Tongers J, Niedermeyer J, Hohlfeld JM, Hamm M et al. Determination of cardiac output by the Fick method, thermodilution, and acetylene rebreathing in pulmonary hypertension. Am J Respir Crit Care Med 1999; 160: 535–541. Comparative Study; Journal Article. 10.1164/ajrccm.160.2.9811062 Conway J. Clinical assessment of cardiac output. Eur. Heart J. 1990; 11 Suppl I: 148–150. Journal Article. 10.1093/eurheartj/11.suppl_i.148 Fagard R, Conway J. Measurement of cardiac output: Fick principle using catheterization. Eur Heart J. 1990;11:1–5. 10.1093/eurheartj/11.suppl_I.1 . Axler O, Tousignant C, Thompson CR, Dall'Ava-Santucci J, Phang PT, Russell JA, et al. Comparison of transesophageal echocardiographic, fick, and thermodilution cardiac output in critically ill patients. J Crit Care. 1996;11:109–16. 10.1016/s0883-9441(96)90006-4 . Clinical Trial; Comparative Study; Controlled Clinical Trial; Journal Article; Research Support, Non-U.S. Gov't. Dhingra VK, Fenwick JC, Walley KR, Chittock DR, Ronco JJ. Lack of agreement between thermodilution and fick cardiac output in critically ill patients. Chest 2002; 122: 990–997. Comparative Study; Journal Article. 10.1378/chest.122.3.990 Gonzalez J, Delafosse C, Fartoukh M, Capderou A, Straus C, Zelter M, et al. Comparison of bedside measurement of cardiac output with the thermodilution method and the Fick method in mechanically ventilated patients. Crit Care. 2003;7:171–8. 10.1186/cc1848 . Comparative Study; Evaluation Study; Journal Article; Research Support, Non-U.S. Gov't. Tehrani DM, Grinstein J, Kalantari S, Kim G, Sarswat N, Adatya S et al. Cardiac Output Assessment in Patients Supported with Left Ventricular Assist Device: Discordance Between Thermodilution and Indirect Fick Cardiac Output Measurements. Asaio J. 2017; 63: 433–437. Journal Article. 10.1097/MAT.0000000000000528 Azih NI, Read JM, Jackson GR, Inampudi C, Witer L, Kilic A et al. Cardiac output assessment methods in left ventricular assist device patients: A problem of heteroscedasticity. J Heart Lung Transplant 2023; 42: 145–149. Journal Article. 10.1016/j.healun.2022.10.021 Desole S, Obst A, Habedank D, Opitz CF, Knaack C, Hortien F et al. Comparison between thermodilution and Fick methods for resting and exercise-induced cardiac output measurement in patients with chronic dyspnea. Pulm Circ 2022; 12: e12128. Journal Article. 10.1002/pul2.12128 Alkhodair A, Tsang M, Cairns JA, Swiston JR, Levy RD, Lee L et al. Comparison of thermodilution and indirect Fick cardiac outputs in pulmonary hypertension. Int. J. Cardiol. 2018; 258: 228–231. Comparative Study; Journal Article. 10.1016/j.ijcard.2018.01.076 Genecand L, Adler D, Beghetti M, Lador F. Cardiac Output Determination in Precapillary Pulmonary Hypertension: A Systematic Review. Respiration. 2021;100:1243–50. 10.1159/000517084 . Systematic Review. FEGLER G. Measurement of cardiac output in anaesthetized animals by a thermodilution method. Q J Exp Physiol Cogn Med Sci 1954; 39: 153–164. Journal Article. 10.1113/expphysiol.1954.sp001067 Roguin A. Adolf Eugen Fick (1829–1901) - The Man Behind the Cardiac Output Equation. Am J Cardiol. 2020;133:162–5. 10.1016/j.amjcard.2020.07.042 . Biography; Historical Article; Journal Article; Portrait. LaFarge CG, Miettinen OS. The estimation of oxygen consumption. Cardiovasc. Res. 1970; 4: 23–30. Journal Article. 10.1093/cvr/4.1.23 Dehmer GJ, Firth BG, Hillis LD. Oxygen consumption in adult patients during cardiac catheterization. Clin Cardiol. 1982;5:436–40. 10.1002/clc.4960050803 . Journal Article; Research Support, U.S. Gov't, P.H.S. Bergstra A, van Dijk RB, Hillege HL, Lie KI, Mook GA. Assumed oxygen consumption based on calculation from dye dilution cardiac output: an improved formula. Eur. Heart J. 1995; 16: 698–703. Journal Article. 10.1093/oxfordjournals.eurheartj.a060976 Odor PM, Bampoe S, Cecconi M. Cardiac Output Monitoring: Validation Studies-how Results Should be Presented. Curr Anesthesiol Rep. 2017;7:410–5. 10.1007/s40140-017-0239-0 . Journal Article; Review. Critchley LA, Critchley JA. A meta-analysis of studies using bias and precision statistics to compare cardiac output measurement techniques. J. Clin. Monit. Comput. 1999; 15: 85–91. Comparative Study; Journal Article; Meta-Analysis. 10.1023/a:1009982611386 Camp RL, Dolled-Filhart M, Rimm DL. X-tile: a new bio-informatics tool for biomarker assessment and outcome-based cut-point optimization. Clin. Cancer Res. 2004; 10: 7252–7259. Journal Article; Research Support, Non-U.S. Gov't; Research Support, U.S. Gov't, Non-P.H.S.; Research Support, U.S. Gov't, P.H.S. 10.1158/1078-0432.CCR-04-0713 Allen BJ, Frye H, Ramanathan R, Caggiano LR, Tabima DM, Chesler NC et al. Biomechanical and Mechanobiological Drivers of the Transition From PostCapillary Pulmonary Hypertension to Combined Pre-/PostCapillary Pulmonary Hypertension. J. Am Heart Assoc 2023; 12: e028121. Journal Article; Research Support, N.I.H., Extramural; Review. 10.1161/JAHA.122.028121 Hoeper MM, Oerke B, Wissmüller M, Leuchte H, Opitz C, Halank M, et al. Tadalafil for Treatment of Combined Postcapillary and Precapillary Pulmonary Hypertension in Patients With Heart Failure and Preserved Ejection Fraction: A Randomized Controlled Phase 3 Study. Circulation. 2024;150:600–10. 10.1161/CIRCULATIONAHA.124.069340 . Clinical Trial, Phase III; Journal Article; Multicenter Study; Randomized Controlled Trial. Zhang H, Kan J, Zhang J, Xie D, Li X, Zhou W, et al. 3-Year Outcome in Patients With Combined Precapillary and Postcapillary Pulmonary Hypertension: Results From PADN-5 Trial. Jacc Heart Fail. 2023;11:1135–46. 10.1016/j.jchf.2023.05.016 . Journal Article; Randomized Controlled Trial; Research Support, Non-U.S. Gov't. Kresoja KP, Faragli A, Abawi D, Paul O, Pieske B, Post H et al. Thermodilution vs estimated Fick cardiac output measurement in an elderly cohort of patients: A single-centre experience. Plos One 2019; 14: e0226561. Comparative Study; Journal Article; Research Support, Non-U.S. Gov't. 10.1371/journal.pone.0226561 Grinstein J, Sinha SS, Goswami RM, Patel PA, Cyrille-Superville N, Neyestanak ME et al. Variation in Hemodynamic Assessment and Interpretation: A Call to Standardize the Right Heart Catheterization. J. Card. Fail. 2023; 29: 1507–1518. Journal Article. 10.1016/j.cardfail.2023.06.009 Argueta EE, Paniagua D. Thermodilution Cardiac Output. Cardiol Rev. 2019;27:138–44. 10.1097/CRD.0000000000000223 . Grafton G, Cascino TM, Perry D, Ashur C, Koelling TM. Resting Oxygen Consumption and Heart Failure: Importance of Measurement for Determination of Cardiac Output With the Use of the Fick Principle. J. Card. Fail. 2020; 26: 664–672. Journal Article. 10.1016/j.cardfail.2019.02.004 Opotowsky AR, Hess E, Maron BA, Brittain EL, Barón AE, Maddox TM, et al. Thermodilution vs Estimated Fick Cardiac Output Measurement in Clinical Practice: An Analysis of Mortality From the Veterans Affairs Clinical Assessment, Reporting, and Tracking (VA CART) Program and Vanderbilt University. Jama Cardiol. 2017;2:1090–9. 10.1001/jamacardio.2017.2945 . Comparative Study; Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't. Nishikawa T, Dohi S. Errors in the measurement of cardiac output by thermodilution. Can J Anaesth. 1993;40:142–53. 10.1007/BF03011312 . Journal Article; Review. Girdharry NR, Bentley RF, Valle FH, Karvasarski E, Osman S, Gurtu V et al. Body Habitus Considerations During Right Heart Catheterization. Cjc Open 2021; 3: 1108–1116. Journal Article. 10.1016/j.cjco.2021.04.015 Lin SY, Chang FC, Lin JR, Chou AH, Tsai YF, Liao CC, et al. Increased FIO2 influences SvO2 interpretation and accuracy of Fick-based cardiac output assessment in cardiac surgery patients: A prospective randomized study. Med (Baltim). 2021;100:e27020. 10.1097/MD.0000000000027020 . Journal Article; Randomized Controlled Trial. Opotowsky AR, Hess E, Maron BA, Brittain EL, Baron AE, Maddox TM, et al. Thermodilution vs Estimated Fick Cardiac Output Measurement in Clinical Practice: An Analysis of Mortality From the Veterans Affairs Clinical Assessment, Reporting, and Tracking (VA CART) Program and Vanderbilt University. Jama Cardiol. 2017;2:1090–9. 10.1001/jamacardio.2017.2945 . Comparative Study; Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't. Narang N, Thibodeau JT, Parker WF, Grodin JL, Garg S, Tedford RJ et al. Comparison of Accuracy of Estimation of Cardiac Output by Thermodilution Versus the Fick Method Using Measured Oxygen Uptake. Am. J. Cardiol. 2022; 176: 58–65. Journal Article. 10.1016/j.amjcard.2022.04.026 Narang N, Thibodeau JT, Levine BD, Gore MO, Ayers CR, Lange RA, et al. Inaccuracy of estimated resting oxygen uptake in the clinical setting. Circulation. 2014;129:203–10. 10.1161/CIRCULATIONAHA.113.003334 . Comparative Study; Journal Article; Research Support, Non-U.S. Gov't. Volodarsky I, Kerzhner K, Haberman D, Cuciuc V, Poles L, Blatt A et al. Comparison between Cardiac Output and Pulmonary Vascular Pressure Measured by Indirect Fick and Thermodilution Methods. J Pers Med 2023; 13. Journal Article. 10.3390/jpm13030559 Sahay S, Lane J, Sharpe MG, Toth D, Paul D, Siuba MT et al. Impact on Pulmonary Hypertension Hemodynamic Classification Based on the Methodology Used to Measure Pulmonary Artery Wedge Pressure and Cardiac Output. Ann Am Thorac Soc 2023; 20: 1752–1759. Journal Article. 10.1513/AnnalsATS.202303-216OC Additional Declarations No competing interests reported. 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08:36:30","extension":"html","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":152258,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7325994/v1/eb92a03cd47d0c3f78bad33e.html"},{"id":91827910,"identity":"b16508ea-4f4d-483f-bee2-26cc0323331c","added_by":"auto","created_at":"2025-09-22 08:52:29","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":279521,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudy flow chart.\u003c/strong\u003e (A) Flow chart of the study participants. (B) Cardiac output evaluation methods. TDCO, cardiac output measured with thermodilution; eFCO, cardiac output calculated with the estimated method; CaO\u003csub\u003e2\u003c/sub\u003e, peripheral arterial oxygen content; CvO\u003csub\u003e2\u003c/sub\u003e, pulmonary venous oxygen content.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7325994/v1/f66064d069a019585e7fa785.png"},{"id":91825710,"identity":"25f105d0-5af0-455d-b957-4838aed1094a","added_by":"auto","created_at":"2025-09-22 08:36:29","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":213397,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of CO by thermodilution and the estimated Fick method.\u003c/strong\u003e(*** represents \u003cem\u003eP \u003c/em\u003e\u0026lt; 0.001, * represents \u003cem\u003eP \u003c/em\u003e\u0026lt; 0.050). (A) Raincloud plots of cardiac output values estimated with the two methods. (B) Barplots of relative differences in cardiac output between three oxygen consumption equations of estimated Fick methods and thermodilution. TDCO, cardiac output measured with thermodilution; DCO, LCO and BCO, cardiac output calculated with the estimated methods of Dehmer, LaFarge and Bergstra formulae.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7325994/v1/7a87108bdd6eee2540645091.png"},{"id":91827911,"identity":"054bd363-eb92-4cc1-984f-8709b92ae8d1","added_by":"auto","created_at":"2025-09-22 08:52:29","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":160926,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpearmen’s correlation and Bland-Altman analysis. \u003c/strong\u003eTDCO, cardiac output measured with thermodilution; DCO, LCO and BCO, cardiac output calculated with the estimated methods of Dehmer, LaFarge and Bergstra formulae.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7325994/v1/2646821cc371d90917b5ca41.png"},{"id":91825704,"identity":"be9aca77-b8b0-496e-9d77-cc170b9cb86c","added_by":"auto","created_at":"2025-09-22 08:36:29","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":344755,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan-Meier survival analysis of all-cause mortality. \u003c/strong\u003e(A) According to the cardiac index categories measured with thermodilution. (B, C, D) According to the cardiac index calculated with the estimated methods of Dehmer, LaFarge and Bergstra formulae.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7325994/v1/1fc10711c100fe3523f02d7a.png"},{"id":108511239,"identity":"1995919b-0b95-4947-b055-a511ee9f4388","added_by":"auto","created_at":"2026-05-05 12:42:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1609240,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7325994/v1/fbaabee9-ec22-46ec-8657-5b284c3ea9f6.pdf"},{"id":91826919,"identity":"4bc672da-aa0f-4689-b4b1-4503ff79b177","added_by":"auto","created_at":"2025-09-22 08:44:29","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":20982,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-7325994/v1/889b2622254f36ee4d5d9a8e.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prognostic Significance of Cardiac Output in Postcapillary Pulmonary Hypertension: Estimated Fick vs. Thermodilution","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePostcapillary pulmonary hypertension (pc-PH) is defined as a syndrome resulting from abnormal pulmonary vascular function or structure in the presence of left heart disease, leading to increased pulmonary arterial pressure\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. It is also referred to as pulmonary hypertension associated with left heart disease, which accounts for approximately 65\u0026ndash;80% of all pulmonary hypertension cases\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Patients with advanced pulmonary vascular lesions and a progressive decline in cardiac output (CO) are at a higher risk for all-cause mortality\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. The 6-month mortality rate of acute left heart failure in patients with pc-PH is 21.8\u0026ndash;48.3%\u003csup\u003e5\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eCO is used to calculate pulmonary vascular resistance (PVR), guiding the diagnosis and risk stratification of pulmonary hypertension\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. It can guide fluid resuscitation and assess the indications for advanced therapies such as left ventricular assist devices and heart transplantation\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. The gold standard for measuring CO is the direct Fick method, which requires precision instruments to accurately measure systemic oxygen consumption (VO\u003csub\u003e2\u003c/sub\u003e) and is not suitable for routine clinical practice\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Currently, the commonly used methods in clinical practice are thermodilution\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e and the estimated Fick method combined with VO\u003csub\u003e2\u003c/sub\u003e formulae. The latter can be implemented in basic healthcare settings and is relatively cost-effective\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Previous studies have compared the correlations between different methods for assessing CO in cohorts of critically ill patients\u003csup\u003e\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, left ventricular assist device implantation\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, chronic dyspnea\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e, and pulmonary hypertension\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. These studies have summarized the factors influencing the measurement of CO using different methods.\u003c/p\u003e\u003cp\u003eThe latest ESC/ERS guideline\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e has reclassified the diagnostic criteria for pc-PH, but the comparability of the estimated Fick method with thermodilution for risk prediction remains largely unknown. This study excluded known factors affecting the CO measurement and re-evaluated the correlation and consistency of the two methods. We aimed to compare the clinical application values of these two CO-based methods with regard to all-cause mortality in patients with pc-PH.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy design and population\u003c/h2\u003e\u003cp\u003eThis was a single-center retrospective study. We included patients with pc-PH who underwent Swan-Ganz catheterization in the First Affiliated Hospital of Nanjing Medical University from September 2013 to December 2021. The diagnostic criteria for pc-PH were based on the 2022 ESC/ERS guidelines\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003ePatients with the mean pulmonary arterial pressure (mPAP)\u0026thinsp;\u0026gt;\u0026thinsp;20mmHg and the pulmonary arterial wedge pressure (PAWP)\u0026thinsp;\u0026gt;\u0026thinsp;15mmHg were eligible. Exclusion criteria included (i) patients without baseline data, (ii) patients with shunts or regurgitations known to affect CO measurement, such as Qp/Qs\u0026thinsp;\u0026gt;\u0026thinsp;1.5 and severe tricuspid insufficiency, (iii) patients with conditions that increases VO\u003csub\u003e2,\u003c/sub\u003e such as fever, and/or those using sedatives that reduces VO\u003csub\u003e2\u003c/sub\u003e, (iv) patients with BMI\u0026thinsp;\u0026gt;\u0026thinsp;40kg/m\u003csup\u003e2\u003c/sup\u003e or body fat\u0026thinsp;\u0026gt;\u0026thinsp;30%, (v) patients with a history of malignancy, recent major surgery, and/or current pregnancy and lactation.\u003c/p\u003e\u003cp\u003eThere were 257 participants included in the analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). This study was approved by the independent Ethics Committee of the First Affiliated Hospital with Nanjing Medical University and the ethics number was 2023-SR-103. All patients signed the informed consent form.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eFollow-Up\u003c/h3\u003e\n\u003cp\u003eAll enrolled patients were followed up from the time of their initial right heart catheterization (RHC) until April 2022, by outpatient visit and / or telephone interviews. The primary endpoint of the study was all-cause mortality. During the follow-up period, detailed information on patient survival status and any relevant clinical events was collected and recorded. Efforts were made to ensure complete data collection, and any missing data was documented and accounted for in the statistical analysis.\u003c/p\u003e\n\u003ch3\u003eCardiac Output Measurement\u003c/h3\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eThermodilution Method\u003c/h2\u003e\u003cp\u003eThermodilution was proposed by Fegler in 1954\u003csup\u003e20\u003c/sup\u003e. RHC was performed on each patient using a six-lumen Swan-Ganz floating catheter. The temperature-sensitive guide wire warmed the right heart, causing temperature changes, which were then measured by a thermistor in the main pulmonary artery. A continuous hemodynamic detection system (Vigilance II Edwards, Irvine, CA, USA) measured the blood flow per unit time according to the Stewart-Hamilton equation. Cardiac index (CI) was calculated as CO divided by body surface area (BSA). The area under the curve (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB) was used to determine the cardiac output (TDCO) and cardiac index (TDCI). The monitoring panel updated the values every 30 to 60 seconds. The average value for the first 3 to 6 minutes was recorded as the TDCO/TDCI.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eEstimated Fick Method\u003c/h3\u003e\n\u003cp\u003eThe estimated Fick method, first employed by Adolph Fick in 1870\u003csup\u003e21\u003c/sup\u003e, was simultaneously used to determine CO. During a specific time interval, the blood flow rate is calculated based on the quantity of matter entering the bloodstream and the difference in concentration of matter between the inlet and outlet points within the circulatory system. Specifically, the estimated Fick cardiac output (eFCO) and cardiac index (eFCI) can be calculated using the oxygen intake inhaled into the lungs and the difference in oxygen content between the pulmonary artery and vein (CaO\u003csub\u003e2\u003c/sub\u003e - CvO\u003csub\u003e2\u003c/sub\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). In this study, the pressure and blood oxygen saturation of each segment were measured and recorded using a multi-function monitor (Phillips MP50). Peripheral arterial oxygen saturation was utilized to substitute for pulmonary venous oxygen saturation. CO and CI were estimated using three formulae for calculating VO\u003csub\u003e2\u003c/sub\u003e: LaFarge\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, Dehmer\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e and Bergstra\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e (\u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e),and were recorded as LCO, DCO, BCO and LCI, DCI, BCI respectively.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eCategorical variables were expressed as the frequencies and percentages, and comparisons were made using the chi-square test or Fisher's exact test. Continuous variables were presented as means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations and non-normal data with median and IQR. The modified Kolmogorov-Smirnov test was used to assess the normality of continuous variables. The paired t-test and Pearson correlation analysis were used for normally distributed variables. The paired Mann-Whitney U test and Spearman correlation analysis were used for non-normally distributed variables. Chi-square test is used for comparison of rates. The Bland-Altman test was used for bias and agreement analysis\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Inconsistency between the two methods was defined as a relative error of CO\u0026thinsp;\u0026gt;\u0026thinsp;20%\u003csup\u003e26\u003c/sup\u003e and an absolute error\u0026thinsp;\u0026gt;\u0026thinsp;1L/min. The optimal cut-off values of CI for survival analysis were determined by X-tile software\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Kaplan-Meier survival curves were used to compare all-cause mortality between groups. Cox proportional hazards regression analyses were performed to examine the associations between the CI and all-cause mortality.\u003c/p\u003e\u003cp\u003eStatistical analyses were conducted using X-tile (v3.6.1), IBM SPSS 25.0, R software (v4.4.1; The R Foundation for Statistical Computing), and GraphPad Prism (v10.1.2). \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.050 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003eBaseline characteristics\u003c/h2\u003e\u003cp\u003eA total of 257 pc-PH patients with a mean age of 54.80\u0026thinsp;\u0026plusmn;\u0026thinsp;12.27 years were enrolled, of whom 85 were female (33.1%). Participants were divided into two groups based on survival status. 107 patients (41.63%) died after a median follow-up time of 71.17 months (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The death group was characterized by lower CO and CI estimated by both the thermodilution and estimated Fick method, while PAWP, mPAP and PVR were higher (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.050).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline characteristics of patients with postcapillary pulmonary hypertension (n\u0026thinsp;=\u0026thinsp;257)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDeath (n=107)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eSurvival (n=150)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge, years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e58.71\u0026thinsp;\u0026plusmn;\u0026thinsp;10.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e52.01\u0026thinsp;\u0026plusmn;\u0026thinsp;12.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale, %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e38 (35.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e47 (31.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.483\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e23.43\u0026thinsp;\u0026plusmn;\u0026thinsp;3.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24.94\u0026thinsp;\u0026plusmn;\u0026thinsp;4.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBSA, m\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e1.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNT-pro BNP, ng/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e2459 (1141, 5870)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1660 (806, 3801)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSaO\u003csub\u003e2\u003c/sub\u003e, %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e96.07\u0026thinsp;\u0026plusmn;\u0026thinsp;3.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e97.15\u0026thinsp;\u0026plusmn;\u0026thinsp;2.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHb, g/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e134.63\u0026thinsp;\u0026plusmn;\u0026thinsp;21.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e137.95\u0026thinsp;\u0026plusmn;\u0026thinsp;19.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.193\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEchocardiography\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLVEF, %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e36.58\u0026thinsp;\u0026plusmn;\u0026thinsp;15.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e35.90\u0026thinsp;\u0026plusmn;\u0026thinsp;14.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.859\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003esPAP, mmHg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e50.65\u0026thinsp;\u0026plusmn;\u0026thinsp;16.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e47.13\u0026thinsp;\u0026plusmn;\u0026thinsp;16.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.113\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRight heart catheterization\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHR, bpm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e86.50\u0026thinsp;\u0026plusmn;\u0026thinsp;19.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e84.88\u0026thinsp;\u0026plusmn;\u0026thinsp;16.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.477\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSVCP, mmHg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e15.12\u0026thinsp;\u0026plusmn;\u0026thinsp;7.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12.90\u0026thinsp;\u0026plusmn;\u0026thinsp;5.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.062\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eS\u003csub\u003eP\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e, %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e54.91\u0026thinsp;\u0026plusmn;\u0026thinsp;10.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e63.02\u0026thinsp;\u0026plusmn;\u0026thinsp;10.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003emRAP, mmHg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e10.74\u0026thinsp;\u0026plusmn;\u0026thinsp;6.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.69\u0026thinsp;\u0026plusmn;\u0026thinsp;4.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.031\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePAWP(mmHg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e23.57\u0026thinsp;\u0026plusmn;\u0026thinsp;4.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21.94\u0026thinsp;\u0026plusmn;\u0026thinsp;5.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003esPAP, mmHg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e58.36\u0026thinsp;\u0026plusmn;\u0026thinsp;15.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e49.66\u0026thinsp;\u0026plusmn;\u0026thinsp;13.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003edPAP, mmHg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e27.42\u0026thinsp;\u0026plusmn;\u0026thinsp;9.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24.21\u0026thinsp;\u0026plusmn;\u0026thinsp;6.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003emPAP, mmHg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e39.51\u0026thinsp;\u0026plusmn;\u0026thinsp;10.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e34.56\u0026thinsp;\u0026plusmn;\u0026thinsp;8.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSvO\u003csub\u003e2\u003c/sub\u003e, %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e52.54\u0026thinsp;\u0026plusmn;\u0026thinsp;11.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e61.53\u0026thinsp;\u0026plusmn;\u0026thinsp;10.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePVR, Wood units\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e5.31\u0026thinsp;\u0026plusmn;\u0026thinsp;3.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.18\u0026thinsp;\u0026plusmn;\u0026thinsp;2.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTDCO, L/min\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e3.49\u0026thinsp;\u0026plusmn;\u0026thinsp;1.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.38\u0026thinsp;\u0026plusmn;\u0026thinsp;1.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eeFCO, L/min\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDehmer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e2.99\u0026thinsp;\u0026plusmn;\u0026thinsp;1.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.69\u0026thinsp;\u0026plusmn;\u0026thinsp;1.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLaFarge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e2.76\u0026thinsp;\u0026plusmn;\u0026thinsp;1.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.50\u0026thinsp;\u0026plusmn;\u0026thinsp;1.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBergstra\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e3.32\u0026thinsp;\u0026plusmn;\u0026thinsp;1.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.16\u0026thinsp;\u0026plusmn;\u0026thinsp;1.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTDCI, L\u0026middot;min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u0026middot;m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e2.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eeFCI, L\u0026middot;min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u0026middot;m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDehmer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e1.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLaFarge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e1.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBergstra\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e1.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eData are presented as n (%), \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{\\text{X}}\\pm\\:\\text{S}\\)\u003c/span\u003e\u003c/span\u003e and M (P25, P75).\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eBMI, body mass index; BSA, body surface area; NT-pro BNP, N-terminal pro-brain natriuretic peptide; SaO\u003csub\u003e2\u003c/sub\u003e, peripheral arterial oxygen saturation; sPAP, systolic pulmonary artery pressure; SVCP, superior vena cava pressure; S\u003csub\u003eP\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e, superior vena cava oxygen saturation; mRAP, mean right atrial pressure; PAWP, pulmonary arterial wedge pressure; dPAP, diastolic pulmonary artery pressure; mPAP, mean pulmonary arterial pressure; SvO\u003csub\u003e2\u003c/sub\u003e, pulmonary venous oxygen saturation; PVR, pulmonary vascular resistance; TDCO, cardiac output measured with thermodilution; eFCO, cardiac output calculated with the estimated methods; TDCI, cardiac index measured with thermodilution; eFCI, cardiac index calculated with the estimated methods.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eCorrelation and Consistency Between the Estimated Fick method and Thermodilution\u003c/h2\u003e\u003cp\u003eThe mean values and standard deviations of cardiac output evaluated by the two methods were as follows: TDCO, 4.01\u0026thinsp;\u0026plusmn;\u0026thinsp;1.46 L/min; DCO, 3.40\u0026thinsp;\u0026plusmn;\u0026thinsp;1.23 L/min; LCO, 3.19\u0026thinsp;\u0026plusmn;\u0026thinsp;1.22 L/min; and BCO, 3.81\u0026thinsp;\u0026plusmn;\u0026thinsp;1.40 L/min. Significant differences were observed between the thermodilution method and the estimated Fick method using the three different VO\u003csub\u003e2\u003c/sub\u003e formulas (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). The proportions of cardiac output differences greater than 1 L/min between DCO, LCO, BCO, and TDCO were 37.74%, 46.69%, and 34.24%, respectively.\u003c/p\u003e\u003cp\u003eThe relative errors between eFCO and TDCO were classified into three categories: less than 20%, 20\u0026ndash;30%, and more than 30% (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). The Bergstra formula had the most differences of less than 20% and the least differences of more than 30%, and showed no statistical difference with the Dehmer formula (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.493) but a significant difference with the LaFarge formula (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.050).\u003c/p\u003e\u003cp\u003eSpearman correlation analysis demonstrated moderate correlations between DCO, LCO, BCO, and TDCO (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA): r\u0026thinsp;=\u0026thinsp;0.706, r\u0026thinsp;=\u0026thinsp;0.688, and r\u0026thinsp;=\u0026thinsp;0.702, with all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.010. Bland-Altman plots revealed bias and limits of agreement between the two methods. Limits of agreement refer to precision of the measurement tool, describing the reproducibility of measurements. Bias represents accuracy, defined as how close the measured value is to the true or reference value\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. The biases between DCO, LCO, BCO, and TDCO were \u0026minus;\u0026thinsp;0.61 L/min, -0.82 L/min, and \u0026minus;\u0026thinsp;0.20 L/min, respectively, with 95% limits of agreement of -2.73 to 1.51 L/min, -2.99 to 1.36 L/min, and \u0026minus;\u0026thinsp;2.42 to 2.02 L/min, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). The limits of agreement were wide, indicating substantial individual differences. Notably, the Bergstra formula exhibited the smallest mean difference and the highest accuracy.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eCardiac Index and All-Cause Mortality\u003c/h2\u003e\u003cp\u003eBased on the optimal cut-off values of CI determined by X-tile software, patients were categorized into three groups: low, moderate and high CI group (\u003cb\u003eTable S2\u003c/b\u003e). Kaplan-Meier survival curves demonstrated stepwise increase in all-cause mortality with low CI group contrary to the moderate or high group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). No statistical differences in survival rates were observed among the three groups of the estimated Fick method (log-rank \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.150, 0.079, 0.185, respectively), whereas the difference between thermodilution groups was statistically significant (log-rank \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.015).\u003c/p\u003e\u003cp\u003eUnivariate Cox regression analysis indicated that TDCI (HR: 0.74 [95% CI: 0.57\u0026ndash;0.97]; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.029) exhibited a significant association with all-cause mortality when considered as a continuous variable, whereas eFCI did not. When cardiac index was categorized into groups, the mortality risk was higher in the low cardiac index group for thermodilution (HR: 2.07 [95% CI: 1.28\u0026ndash;3.34]; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003), Dehmer (HR: 1.86 [95% CI: 1.07\u0026ndash;3.25]; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.029), and LaFarge (HR: 1.81 [95% CI: 1.03\u0026ndash;3.18]; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.039) formulae compared to the high cardiac index group as the reference. After adjusting for gender and age, a 1 L/min/m\u0026sup2; increase in CI measured by thermodilution was associated with a lower risk of death (HR: 0.75 [95% CI: 0.58\u0026ndash;0.98]; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.034). The mortality risk remained higher in the low cardiac index groups of thermodilution (HR: 1.89 [95% CI: 1.16\u0026ndash;3.08]; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011) and the Dehmer formula (HR: 1.92 [95% CI: 1.10\u0026ndash;3.37]; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.022) compared to the high groups.\u003c/p\u003e\u003cp\u003eAfter extensive adjustment for sex, age, log\u003csub\u003e2\u003c/sub\u003eNT-proBNP, mPAP, PAWP, mean right atrial pressure, and pulmonary artery systolic pressure, neither eFCI nor TDCI could independently predict the risk of all-cause mortality, whether as continuous or grouping variables (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCOX regression analysis for the prediction of all-cause mortality\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCrude\u003c/p\u003e\u003cp\u003e HR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModel 1\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e HR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModel 2\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e HR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCardiac index (thermodilution)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePer 1 L/min/m\u0026sup2; increase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.74 (0.57\u0026ndash;0.97) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.75 (0.58\u0026ndash;0.98) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.93 (0.66\u0026ndash;1.31)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1[Reference]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1[Reference]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1[Reference]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModerate level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.39 (0.87\u0026ndash;2.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.49 (0.93\u0026ndash;2.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.45 (0.83\u0026ndash;2.52)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.07 (1.28\u0026ndash;3.34) \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.89 (1.16\u0026ndash;3.08) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.56 (0.81\u0026ndash;3.01)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCardiac index (Dehmer formula)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePer 1 L/min/m\u0026sup2; increase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00 (0.72\u0026ndash;1.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00 (0.71\u0026ndash;1.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.42 (0.98\u0026ndash;2.07)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1[Reference]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1[Reference]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1[Reference]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModerate level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.01 (0.64\u0026ndash;1.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.97 (0.62\u0026ndash;1.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.68 (0.39\u0026ndash;1.19)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.86 (1.07\u0026ndash;3.25) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.92 (1.10\u0026ndash;3.37) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.09 (0.52\u0026ndash;2.30)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCardiac index (Lafarge formula)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePer 1 L/min/m\u0026sup2; increase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.88 (0.61\u0026ndash;1.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.98 (0.68\u0026ndash;1.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.40 (0.93\u0026ndash;2.10)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1[Reference]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1[Reference]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1[Reference]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModerate level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.41 (0.92\u0026ndash;2.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.22 (0.79\u0026ndash;1.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.99 (0.60\u0026ndash;1.64)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.81 (1.03\u0026ndash;3.18)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.70 (0.96\u0026ndash;3.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.02 (0.47\u0026ndash;2.21)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCardiac index (Bergstra formula)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePer 1 L/min/m\u0026sup2; increase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.96 (0.71\u0026ndash;1.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.99 (0.73\u0026ndash;1.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.36 (0.97\u0026ndash;1.92)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1[Reference]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1[Reference]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1[Reference]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModerate level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.06 (0.67\u0026ndash;1.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.02 (0.64\u0026ndash;1.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00 (0.60\u0026ndash;1.70)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.62 (0.99\u0026ndash;2.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.53 (0.94\u0026ndash;2.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.76 (0.38\u0026ndash;1.52)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003csup\u003ea\u003c/sup\u003eModel 1: adjusted for age and sex.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003csup\u003eb\u003c/sup\u003eModel 2: adjusted for age, sex, log\u003csub\u003e2\u003c/sub\u003eNT-proBNP, mPAP, PAWP, mean right atrial pressure, systolic pulmonary artery pressure.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eHR, hazard ratio; CI, confidence interval; * represents \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.050, ** represents \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.010.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eCategorization Based on Clinically Relevant Cutoff for Hypoperfusion\u003c/h2\u003e\u003cp\u003ePatients were categorized based on clinically relevant cutoff for hypoperfusion. It differentiates low cardiac index (\u0026lt;\u0026thinsp;2.2 L\u0026middot;min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u0026middot;m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e) from normal cardiac index (\u0026ge;\u0026thinsp;2.2 L\u0026middot;min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u0026middot;m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e), resulting in four distinct groups: normal eFCI\u0026thinsp;+\u0026thinsp;TDCI, low eFCI\u0026thinsp;+\u0026thinsp;TDCI, low eFCI\u0026thinsp;+\u0026thinsp;normal TDCI and low TDCI\u0026thinsp;+\u0026thinsp;normal eFCI group. There were no significant differences in the mortality rates of each group among the estimated Fick method of the three VO\u003csub\u003e2\u003c/sub\u003e formulas (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.787, 0.980, 0.967, 1.000, respectively). Contrary to the normal eFCI\u0026thinsp;+\u0026thinsp;TDCI group, Chi-square test showed no additional risks of all-cause mortality occurred in the low eFCI\u0026thinsp;+\u0026thinsp;normal TDCI and low TDCI\u0026thinsp;+\u0026thinsp;normal eFCI group (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.050) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAll-cause mortality of different cardiac index groups\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDeath, n (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNormal eFCI\u003c/p\u003e\u003cp\u003eNormal TDCI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eLow eFCI\u003c/p\u003e\u003cp\u003eLow TDCI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLow eFCI\u003c/p\u003e\u003cp\u003eNormal TDCI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLow TDCI\u003c/p\u003e\u003cp\u003eNormal eFCI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDehmer formula\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15 (22.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e61 (54.46) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e27 (39.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4 (44.44)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLafarge formula\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10 (19.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e61 (53.51) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e32 (38.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4 (57.14)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBergstra formula\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20 (24.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e54 (54.54) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e22 (40.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11 (50.00)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.787\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.980\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.967\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eData are presented as death number (death number / total number of the group).\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003e*Contrary to the normal eFCI and normal TDCI group, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.050.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eTDCI, cardiac index measured with thermodilution; eFCI, cardiac index calculated with the estimated method.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we assessed baseline characteristics and cardiac function in 257 patients with pc-PH, revealing that mortality was associated with lower CO and CI in limited adjustment, as well as higher unadjusted mPAP and PVR. Our study demonstrated that CO, evaluated by the estimated Fick method and thermodilution in patients with pc-PH, exhibited moderate correlation and poor consistency with significant individual variability. The Bergstra formula showed the highest consistency with TDCO among the eFCO methods. Survival analysis indicated that a low CI was associated with increased all-cause mortality, particularly when evaluated by thermodilution and with Dehmer formula as a grouping variable, though multivariate analysis did not support independent predictive value for mortality risk after adjusting for covariates. Categorization based on a clinically relevant hypoperfusion cutoff showed no significant mortality differences across groups, suggesting that neither method alone accurately predicts mortality risk in this patient population.\u003c/p\u003e\u003cp\u003ePc-PH is divided into isolated post-capillary (PVR\u0026thinsp;\u0026le;\u0026thinsp;2 Wood) and combined post- and pre-capillary pulmonary hypertension (PVR\u0026thinsp;\u0026gt;\u0026thinsp;2 Wood). The pathological structures, mortality risks, and treatments for the two entities are different\u003csup\u003e\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. PVR is calculated based on CO. Prior studies showed that 11\u0026ndash;45% of patients with pulmonary hypertension were misclassified due to errors in CO assessment, and 10\u0026ndash;20% of cases were misdiagnosed as cardiogenic shock\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Consequently, variances in CO and PVR influence the diagnostic classification, treatment planning, and prognosis evaluation of patients with pc-PH.\u003c/p\u003e\u003cp\u003eIn clinical practice, the two most commonly employed methods for evaluating CO are thermodilution and the estimated Fick method\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Each method has distinct advantages and disadvantages. Thermodilution can continuously monitor hemodynamic indicators and provide timely feedback; however, thermodilution requires the use of a Swan-Ganz floating catheter and supporting instruments, necessitating larger medical centers and higher costs\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. The estimated Fick method, on the other hand, requires only a single-function right cardiac catheter to measure pulmonary arterial oxygen saturation. It can be implemented at the basic medical setting and is relatively cost-effective, but it involves blood draws for blood gas analysis, cannot continuously monitor hemodynamic changes, and relies on formulae to estimate VO\u003csub\u003e2\u003c/sub\u003e, which has large individual differences. Minor differences in arterial and venous oxygen saturation can amplify the error in VO\u003csub\u003e2\u003c/sub\u003e estimation.\u003c/p\u003e\u003cp\u003ePrevious literature has reported variable consistency between the two methods of CO assessment. Thermodilution is less reliable in patients with severe valvular regurgitation, intracardiac shunts, and low CO conditions\u003csup\u003e\u003cspan additionalcitationids=\"CR34 CR35\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. The estimated Fick method is susceptible to errors in estimating VO\u003csub\u003e2\u003c/sub\u003e in obese patients, when oxygen concentration exceeds 0.85, and under sedation\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Early studies demonstrated good consistency between thermodilution and the estimated Fick method\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e, while others expressed concerns about this issue\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Recent large-scale studies have reported that both the estimated Fick method and thermodilution exhibit lower consistency compared to the direct Fick method\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. The debate regarding the selection of an appropriate method to guide clinical practice in specific populations remains ongoing.\u003c/p\u003e\u003cp\u003eOur study excluded cases with known factors affecting cardiac output measurements and re-evaluated the consistency between the estimated Fick method and thermodilution in patients with pc-PH. Although the Bergstra formula had the smallest absolute deviation among the three VO\u003csub\u003e2\u003c/sub\u003e formulae compared to thermodilution, large individual differences still existed. This finding is consistent with the study by Chase et al.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, who reported that the Bergstra formula has a significant error in estimating VO\u003csub\u003e2\u003c/sub\u003e and is not recommended for clinical use. Another study compared the VO\u003csub\u003e2\u003c/sub\u003e estimated by the Dehmer, LaFarge, and Bergstra formulae with accurately measured VO\u003csub\u003e2\u003c/sub\u003e and found significant deviations between them (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003csup\u003e41\u003c/sup\u003e, suggesting that VO\u003csub\u003e2\u003c/sub\u003e should be directly measured in clinical practice.\u003c/p\u003e\u003cp\u003eSeveral studies have compared disparities in the measurement of CO among diverse cohorts of individuals with pulmonary hypertension. Hoeper et al.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e first explored the consistency of thermodilution and the estimated Fick method in 35 patients with pulmonary hypertension, and found that the mean difference between the two CO was 0.01 L/min, and the consistency limit was \u0026minus;\u0026thinsp;1.1 to 1.1 L/min. A recent large-scale study compared the estimated Fick method and thermodilution in 300 patients with chronic dyspnea (with pulmonary hypertension accounting for 76%), using the direct Fick method as the standard. The deviations\u0026thinsp;\u0026gt;\u0026thinsp;1L/min accounted for 45.0% and 45.7% in the respective cases, demonstrating significant differences\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Abdullah et al.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e reported that CO measured by thermodilution had a stronger correlation with right ventricular function indicators of echocardiographic than the estimated Fick method.\u003c/p\u003e\u003cp\u003eDespite the varying definitions and judgment criteria for the correlation and consistency of different CO results across studies, most researchers agree that the estimated Fick method and thermodilution show poor consistency. However, these methods can be interchangeable in certain specific application scenarios. Volodarsky et al.\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e suggested that the estimated Fick methods and thermodilution can be used interchangeably for diagnostic classification when mPAP\u0026thinsp;\u0026gt;\u0026thinsp;25mmHg. Sandeep et al.\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e found that the distribution of pulmonary hypertension hemodynamic groups did not significantly differ between thermodilution and the estimated Fick method when using either mPAP at end-expiration or mPAP averaged across the respiratory cycle.\u003c/p\u003e\u003cp\u003eOur study was the first to investigate the risk predictive value of the cardiac index calculated with the estimated Fick method in pc-PH patients. Our findings showed that the cardiac index calculated using the estimated Fick method (Dehmer formula) had predictive value for all-cause mortality, but this value was limited to grouping variables. In contrast, the cardiac index measured with thermodilution had better prognostic value as both a continuous and a grouping variable, though it was not an independent predictor. Alexander R et al.\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e found that thermodilution had a higher ability than the estimated Fick method to predict all-cause mortality in 12,232 patients with a cardiac index of less than 2.2 L\u0026middot;min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u0026middot;m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e at 90 days (HR: 1.71 \u003cem\u003evs.\u003c/em\u003e 1.42) and 1 year (HR: 1.53 \u003cem\u003evs.\u003c/em\u003e 1.35). However, there was no difference in individuals with normal cardiac index.\u003c/p\u003e\u003cp\u003eWe acknowledge some limitations in our study. First, the single-center retrospective study design and the limited sample size may introduce potential selection bias. We focus on a more narrowly defined population, while the use of multiple oxygen consumption formulas may provide clinically useful and formula-specific insights. Second, due to technical and clinical constraints, we were unable to use the direct Fick method, which is the gold standard, as a reference. Finally, the factors affecting cardiac output measurement in patients with pc-PH are multifaceted and varied, and some confounding factors remain unidentified. Despite these limitations, our study provides valuable insights into the predictive value of different methods for assessing CI in this population.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn summary, our study demonstrated a moderate correlation between COs evaluated by the estimated Fick method and thermodilution. However, the consistency between these two methods was poor. The CI calculated using the estimated Fick method combined with the Dehmer formula, when used as a grouping variable, was associated with the risk of all-cause mortality. It can be implemented at the basic medical setting when necessary. Thermodilution, on the other hand, showed superior performance in risk stratification for all-cause mortality, whether used as a continuous or grouping variable. These findings highlight the limitations of the estimated Fick method and the advantages of thermodilution in clinical settings, particularly for patients with post-capillary pulmonary hypertension.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCO\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ecardiac output\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ecardiac index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003epc-PH\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003epostcapillary pulmonary hypertension\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eVO\u003csub\u003e2\u003c/sub\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eoxygen consumption\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePVR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003epulmonary vascular resistance\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003emPAP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003emean pulmonary arterial pressure\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePAWP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003epulmonary arterial wedge pressure\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eRHC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eright heart catheterization\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003cp\u003eOur study complies with the Declaration of Helsinki. This study was approved by the independent Ethics Committee of the First Affiliated Hospital with Nanjing Medical University and the ethics number was 2023-SR-103. All the participants have signed informed consent.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eCompeting interests\u003c/h2\u003e\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eAuthors' information\u003c/h2\u003e\u003cp\u003e\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003eState Key Laboratory for Innovation and Transformation of Luobing Theory, Department of Cardiology, The First Affiliated Hospital with Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, China. \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003eDepartment of Cardiology, Gusu School, The Affiliated Suzhou Hospital with Nanjing Medical University, Suzhou Municipal Hospital, Nanjing Medical University, 26 Daoqian Street, Suzhou, 215002, China. \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003eDepartment of Cardiology, Jiangsu Province Hospital, 300 Guangzhou Road, Nanjing, 210029, China. \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003eSoutheast University School of Medicine, 87 Zhonghuan North Road, Nanjing, 210009, China.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis work was supported by State Key Laboratory for Innovation and Transformation of Luobing Theory, General Program of National Natural Science Foundation of China (82370389, 81970339 to XL Li, 82270394 to HF Zhang, 82200425 to RR Gao), The National High Technology Research and Development Program of China (2017YFC1700505 to XL Li), Project from Gusu School (GSRCKY20210204 to HF Zhang), Gusu Health Personnel Training Project (GSWS2021042 to HF Zhang), Excellent Young Scientists Fund of Jiangsu (BK20231538 to RR Gao), and Qing Lan Project of Jiangsu (RR Gao).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eYZ and YS contributed to design of study, data acquisition, analysis, interpretation, drafting of primary and subsequent manuscripts. IC, YL, YT, and RG contributed to data acquisition and manuscript editing. HZ, YS and XL contributed to supervision, and manuscript editing. All authors have approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe are grateful to all the patients for their participation. Thanks to all the staff for managing the patients. Prof. XL Li and Prof. HF Zhang are Associate Fellows at the Collaborative Innovation Center for Cardiovascular Disease Translational Medicine. Dr. Cheang are Post-doctorate Follows at Nanjing Medical University.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data underlying this article cannot be shared publicly due to the privacy of individuals that participated in the study. The data will be shared on reasonable request to the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMaron BA, Bortman G, De Marco T, Huston JH, Lang IM, Rosenkranz SH, et al. Pulmonary hypertension associated with left heart disease. Eur Respir J. 2024. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1183/13993003.01344-2024\u003c/span\u003e\u003cspan address=\"10.1183/13993003.01344-2024\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. 64. Journal Article; Review.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHumbert M, Kovacs G, Hoeper MM, Badagliacca R, Berger RMF, Brida M, et al. 2022 ESC/ERS Guidelines for the diagnosis and treatment of pulmonary hypertension. Eur Heart J. 2022;43:3618\u0026ndash;731. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/eurheartj/ehac237\u003c/span\u003e\u003cspan address=\"10.1093/eurheartj/ehac237\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOmote K, Sorimachi H, Obokata M, Reddy Y, Verbrugge FH, Omar M, et al. Pulmonary vascular disease in pulmonary hypertension due to left heart disease: pathophysiologic implications. Eur Heart J. 2022;43:3417\u0026ndash;31. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/eurheartj/ehac184\u003c/span\u003e\u003cspan address=\"10.1093/eurheartj/ehac184\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuston JH, Shah SJ. Understanding the Pathobiology of Pulmonary Hypertension Due to Left Heart Disease. \u003cem\u003eCirc. Res.\u003c/em\u003e 2022; 130: 1382\u0026ndash;1403. Journal Article; Research Support, N.I.H., Extramural; Review. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1161/CIRCRESAHA.122.319967\u003c/span\u003e\u003cspan address=\"10.1161/CIRCRESAHA.122.319967\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAronson D, Eitan A, Dragu R, Burger AJ. Relationship between reactive pulmonary hypertension and mortality in patients with acute decompensated heart failure. Circ Heart Fail. 2011;4:644\u0026ndash;50. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1161/CIRCHEARTFAILURE.110.960864\u003c/span\u003e\u003cspan address=\"10.1161/CIRCHEARTFAILURE.110.960864\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Journal Article; Multicenter Study; Randomized Controlled Trial.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVachi\u0026eacute;ry J, Tedford RJ, Rosenkranz S, Palazzini M, Lang I, Guazzi M, et al. Pulmonary hypertension due to left heart disease. Eur Respir J. 2019;53:1801897. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1183/13993003.01897-2018\u003c/span\u003e\u003cspan address=\"10.1183/13993003.01897-2018\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChase PJ, Davis PG, Wideman L, Starnes JW, Schulz MR, Bensimhon DR. Comparison of Estimations Versus Measured Oxygen Consumption at Rest in Patients With Heart Failure and Reduced Ejection Fraction Who Underwent Right-Sided Heart Catheterization. Am J Cardiol. 2015;116:1724\u0026ndash;30. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.amjcard.2015.08.051\u003c/span\u003e\u003cspan address=\"10.1016/j.amjcard.2015.08.051\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBachmann KF, Vasireddy R, Heinisch PP, Jenni H, Vogt A, Berger D. Estimating cardiac output based on gas exchange during veno-arterial extracorporeal membrane oxygenation in a simulation study using paediatric oxygenators. Sci Rep. 2021;11. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-021-90747-w\u003c/span\u003e\u003cspan address=\"10.1038/s41598-021-90747-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. 11528. Journal Article; Research Support, Non-U.S. Gov't.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHoeper MM, Maier R, Tongers J, Niedermeyer J, Hohlfeld JM, Hamm M et al. Determination of cardiac output by the Fick method, thermodilution, and acetylene rebreathing in pulmonary hypertension. \u003cem\u003eAm J Respir Crit Care Med\u003c/em\u003e 1999; 160: 535\u0026ndash;541. Comparative Study; Journal Article. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1164/ajrccm.160.2.9811062\u003c/span\u003e\u003cspan address=\"10.1164/ajrccm.160.2.9811062\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eConway J. Clinical assessment of cardiac output. \u003cem\u003eEur. Heart J.\u003c/em\u003e 1990; 11 Suppl I: 148\u0026ndash;150. Journal Article. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/eurheartj/11.suppl_i.148\u003c/span\u003e\u003cspan address=\"10.1093/eurheartj/11.suppl_i.148\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFagard R, Conway J. Measurement of cardiac output: Fick principle using catheterization. Eur Heart J. 1990;11:1\u0026ndash;5. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/eurheartj/11.suppl_I.1\u003c/span\u003e\u003cspan address=\"10.1093/eurheartj/11.suppl_I.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAxler O, Tousignant C, Thompson CR, Dall'Ava-Santucci J, Phang PT, Russell JA, et al. Comparison of transesophageal echocardiographic, fick, and thermodilution cardiac output in critically ill patients. J Crit Care. 1996;11:109\u0026ndash;16. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/s0883-9441(96)90006-4\u003c/span\u003e\u003cspan address=\"10.1016/s0883-9441(96)90006-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Clinical Trial; Comparative Study; Controlled Clinical Trial; Journal Article; Research Support, Non-U.S. Gov't.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDhingra VK, Fenwick JC, Walley KR, Chittock DR, Ronco JJ. Lack of agreement between thermodilution and fick cardiac output in critically ill patients. \u003cem\u003eChest\u003c/em\u003e 2002; 122: 990\u0026ndash;997. Comparative Study; Journal Article. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1378/chest.122.3.990\u003c/span\u003e\u003cspan address=\"10.1378/chest.122.3.990\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGonzalez J, Delafosse C, Fartoukh M, Capderou A, Straus C, Zelter M, et al. Comparison of bedside measurement of cardiac output with the thermodilution method and the Fick method in mechanically ventilated patients. Crit Care. 2003;7:171\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/cc1848\u003c/span\u003e\u003cspan address=\"10.1186/cc1848\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Comparative Study; Evaluation Study; Journal Article; Research Support, Non-U.S. Gov't.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTehrani DM, Grinstein J, Kalantari S, Kim G, Sarswat N, Adatya S et al. Cardiac Output Assessment in Patients Supported with Left Ventricular Assist Device: Discordance Between Thermodilution and Indirect Fick Cardiac Output Measurements. \u003cem\u003eAsaio J.\u003c/em\u003e 2017; 63: 433\u0026ndash;437. Journal Article. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/MAT.0000000000000528\u003c/span\u003e\u003cspan address=\"10.1097/MAT.0000000000000528\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAzih NI, Read JM, Jackson GR, Inampudi C, Witer L, Kilic A et al. Cardiac output assessment methods in left ventricular assist device patients: A problem of heteroscedasticity. \u003cem\u003eJ Heart Lung Transplant\u003c/em\u003e 2023; 42: 145\u0026ndash;149. Journal Article. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.healun.2022.10.021\u003c/span\u003e\u003cspan address=\"10.1016/j.healun.2022.10.021\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDesole S, Obst A, Habedank D, Opitz CF, Knaack C, Hortien F et al. Comparison between thermodilution and Fick methods for resting and exercise-induced cardiac output measurement in patients with chronic dyspnea. \u003cem\u003ePulm Circ\u003c/em\u003e 2022; 12: e12128. Journal Article. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/pul2.12128\u003c/span\u003e\u003cspan address=\"10.1002/pul2.12128\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlkhodair A, Tsang M, Cairns JA, Swiston JR, Levy RD, Lee L et al. Comparison of thermodilution and indirect Fick cardiac outputs in pulmonary hypertension. \u003cem\u003eInt. J. Cardiol.\u003c/em\u003e 2018; 258: 228\u0026ndash;231. Comparative Study; Journal Article. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ijcard.2018.01.076\u003c/span\u003e\u003cspan address=\"10.1016/j.ijcard.2018.01.076\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGenecand L, Adler D, Beghetti M, Lador F. Cardiac Output Determination in Precapillary Pulmonary Hypertension: A Systematic Review. Respiration. 2021;100:1243\u0026ndash;50. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1159/000517084\u003c/span\u003e\u003cspan address=\"10.1159/000517084\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Systematic Review.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFEGLER G. Measurement of cardiac output in anaesthetized animals by a thermodilution method. \u003cem\u003eQ J Exp Physiol Cogn Med Sci\u003c/em\u003e 1954; 39: 153\u0026ndash;164. Journal Article. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1113/expphysiol.1954.sp001067\u003c/span\u003e\u003cspan address=\"10.1113/expphysiol.1954.sp001067\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRoguin A. Adolf Eugen Fick (1829\u0026ndash;1901) - The Man Behind the Cardiac Output Equation. Am J Cardiol. 2020;133:162\u0026ndash;5. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.amjcard.2020.07.042\u003c/span\u003e\u003cspan address=\"10.1016/j.amjcard.2020.07.042\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Biography; Historical Article; Journal Article; Portrait.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLaFarge CG, Miettinen OS. The estimation of oxygen consumption. \u003cem\u003eCardiovasc. Res.\u003c/em\u003e 1970; 4: 23\u0026ndash;30. Journal Article. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/cvr/4.1.23\u003c/span\u003e\u003cspan address=\"10.1093/cvr/4.1.23\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDehmer GJ, Firth BG, Hillis LD. Oxygen consumption in adult patients during cardiac catheterization. Clin Cardiol. 1982;5:436\u0026ndash;40. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/clc.4960050803\u003c/span\u003e\u003cspan address=\"10.1002/clc.4960050803\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Journal Article; Research Support, U.S. Gov't, P.H.S.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBergstra A, van Dijk RB, Hillege HL, Lie KI, Mook GA. Assumed oxygen consumption based on calculation from dye dilution cardiac output: an improved formula. \u003cem\u003eEur. Heart J.\u003c/em\u003e 1995; 16: 698\u0026ndash;703. Journal Article. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/oxfordjournals.eurheartj.a060976\u003c/span\u003e\u003cspan address=\"10.1093/oxfordjournals.eurheartj.a060976\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOdor PM, Bampoe S, Cecconi M. Cardiac Output Monitoring: Validation Studies-how Results Should be Presented. Curr Anesthesiol Rep. 2017;7:410\u0026ndash;5. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s40140-017-0239-0\u003c/span\u003e\u003cspan address=\"10.1007/s40140-017-0239-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Journal Article; Review.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCritchley LA, Critchley JA. A meta-analysis of studies using bias and precision statistics to compare cardiac output measurement techniques. \u003cem\u003eJ. Clin. Monit. Comput.\u003c/em\u003e 1999; 15: 85\u0026ndash;91. Comparative Study; Journal Article; Meta-Analysis. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1023/a:1009982611386\u003c/span\u003e\u003cspan address=\"10.1023/a:1009982611386\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCamp RL, Dolled-Filhart M, Rimm DL. X-tile: a new bio-informatics tool for biomarker assessment and outcome-based cut-point optimization. \u003cem\u003eClin. Cancer Res.\u003c/em\u003e 2004; 10: 7252\u0026ndash;7259. Journal Article; Research Support, Non-U.S. Gov't; Research Support, U.S. Gov't, Non-P.H.S.; Research Support, U.S. Gov't, P.H.S. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1158/1078-0432.CCR-04-0713\u003c/span\u003e\u003cspan address=\"10.1158/1078-0432.CCR-04-0713\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAllen BJ, Frye H, Ramanathan R, Caggiano LR, Tabima DM, Chesler NC et al. Biomechanical and Mechanobiological Drivers of the Transition From PostCapillary Pulmonary Hypertension to Combined Pre-/PostCapillary Pulmonary Hypertension. \u003cem\u003eJ. Am Heart Assoc\u003c/em\u003e 2023; 12: e028121. Journal Article; Research Support, N.I.H., Extramural; Review. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1161/JAHA.122.028121\u003c/span\u003e\u003cspan address=\"10.1161/JAHA.122.028121\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHoeper MM, Oerke B, Wissm\u0026uuml;ller M, Leuchte H, Opitz C, Halank M, et al. Tadalafil for Treatment of Combined Postcapillary and Precapillary Pulmonary Hypertension in Patients With Heart Failure and Preserved Ejection Fraction: A Randomized Controlled Phase 3 Study. Circulation. 2024;150:600\u0026ndash;10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1161/CIRCULATIONAHA.124.069340\u003c/span\u003e\u003cspan address=\"10.1161/CIRCULATIONAHA.124.069340\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Clinical Trial, Phase III; Journal Article; Multicenter Study; Randomized Controlled Trial.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang H, Kan J, Zhang J, Xie D, Li X, Zhou W, et al. 3-Year Outcome in Patients With Combined Precapillary and Postcapillary Pulmonary Hypertension: Results From PADN-5 Trial. Jacc Heart Fail. 2023;11:1135\u0026ndash;46. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jchf.2023.05.016\u003c/span\u003e\u003cspan address=\"10.1016/j.jchf.2023.05.016\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Journal Article; Randomized Controlled Trial; Research Support, Non-U.S. Gov't.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKresoja KP, Faragli A, Abawi D, Paul O, Pieske B, Post H et al. Thermodilution vs estimated Fick cardiac output measurement in an elderly cohort of patients: A single-centre experience. \u003cem\u003ePlos One\u003c/em\u003e 2019; 14: e0226561. Comparative Study; Journal Article; Research Support, Non-U.S. Gov't. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pone.0226561\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0226561\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGrinstein J, Sinha SS, Goswami RM, Patel PA, Cyrille-Superville N, Neyestanak ME et al. Variation in Hemodynamic Assessment and Interpretation: A Call to Standardize the Right Heart Catheterization. \u003cem\u003eJ. Card. Fail.\u003c/em\u003e 2023; 29: 1507\u0026ndash;1518. Journal Article. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.cardfail.2023.06.009\u003c/span\u003e\u003cspan address=\"10.1016/j.cardfail.2023.06.009\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eArgueta EE, Paniagua D. Thermodilution Cardiac Output. Cardiol Rev. 2019;27:138\u0026ndash;44. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/CRD.0000000000000223\u003c/span\u003e\u003cspan address=\"10.1097/CRD.0000000000000223\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGrafton G, Cascino TM, Perry D, Ashur C, Koelling TM. Resting Oxygen Consumption and Heart Failure: Importance of Measurement for Determination of Cardiac Output With the Use of the Fick Principle. \u003cem\u003eJ. Card. Fail.\u003c/em\u003e 2020; 26: 664\u0026ndash;672. Journal Article. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.cardfail.2019.02.004\u003c/span\u003e\u003cspan address=\"10.1016/j.cardfail.2019.02.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOpotowsky AR, Hess E, Maron BA, Brittain EL, Bar\u0026oacute;n AE, Maddox TM, et al. Thermodilution vs Estimated Fick Cardiac Output Measurement in Clinical Practice: An Analysis of Mortality From the Veterans Affairs Clinical Assessment, Reporting, and Tracking (VA CART) Program and Vanderbilt University. Jama Cardiol. 2017;2:1090\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/jamacardio.2017.2945\u003c/span\u003e\u003cspan address=\"10.1001/jamacardio.2017.2945\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Comparative Study; Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNishikawa T, Dohi S. Errors in the measurement of cardiac output by thermodilution. Can J Anaesth. 1993;40:142\u0026ndash;53. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/BF03011312\u003c/span\u003e\u003cspan address=\"10.1007/BF03011312\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Journal Article; Review.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGirdharry NR, Bentley RF, Valle FH, Karvasarski E, Osman S, Gurtu V et al. Body Habitus Considerations During Right Heart Catheterization. \u003cem\u003eCjc Open\u003c/em\u003e 2021; 3: 1108\u0026ndash;1116. Journal Article. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.cjco.2021.04.015\u003c/span\u003e\u003cspan address=\"10.1016/j.cjco.2021.04.015\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLin SY, Chang FC, Lin JR, Chou AH, Tsai YF, Liao CC, et al. Increased FIO2 influences SvO2 interpretation and accuracy of Fick-based cardiac output assessment in cardiac surgery patients: A prospective randomized study. Med (Baltim). 2021;100:e27020. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/MD.0000000000027020\u003c/span\u003e\u003cspan address=\"10.1097/MD.0000000000027020\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Journal Article; Randomized Controlled Trial.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOpotowsky AR, Hess E, Maron BA, Brittain EL, Baron AE, Maddox TM, et al. Thermodilution vs Estimated Fick Cardiac Output Measurement in Clinical Practice: An Analysis of Mortality From the Veterans Affairs Clinical Assessment, Reporting, and Tracking (VA CART) Program and Vanderbilt University. Jama Cardiol. 2017;2:1090\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/jamacardio.2017.2945\u003c/span\u003e\u003cspan address=\"10.1001/jamacardio.2017.2945\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Comparative Study; Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNarang N, Thibodeau JT, Parker WF, Grodin JL, Garg S, Tedford RJ et al. Comparison of Accuracy of Estimation of Cardiac Output by Thermodilution Versus the Fick Method Using Measured Oxygen Uptake. \u003cem\u003eAm. J. Cardiol.\u003c/em\u003e 2022; 176: 58\u0026ndash;65. Journal Article. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.amjcard.2022.04.026\u003c/span\u003e\u003cspan address=\"10.1016/j.amjcard.2022.04.026\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNarang N, Thibodeau JT, Levine BD, Gore MO, Ayers CR, Lange RA, et al. Inaccuracy of estimated resting oxygen uptake in the clinical setting. Circulation. 2014;129:203\u0026ndash;10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1161/CIRCULATIONAHA.113.003334\u003c/span\u003e\u003cspan address=\"10.1161/CIRCULATIONAHA.113.003334\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Comparative Study; Journal Article; Research Support, Non-U.S. Gov't.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVolodarsky I, Kerzhner K, Haberman D, Cuciuc V, Poles L, Blatt A et al. Comparison between Cardiac Output and Pulmonary Vascular Pressure Measured by Indirect Fick and Thermodilution Methods. \u003cem\u003eJ Pers Med\u003c/em\u003e 2023; 13. Journal Article. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/jpm13030559\u003c/span\u003e\u003cspan address=\"10.3390/jpm13030559\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSahay S, Lane J, Sharpe MG, Toth D, Paul D, Siuba MT et al. Impact on Pulmonary Hypertension Hemodynamic Classification Based on the Methodology Used to Measure Pulmonary Artery Wedge Pressure and Cardiac Output. \u003cem\u003eAnn Am Thorac Soc\u003c/em\u003e 2023; 20: 1752\u0026ndash;1759. Journal Article. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1513/AnnalsATS.202303-216OC\u003c/span\u003e\u003cspan address=\"10.1513/AnnalsATS.202303-216OC\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Estimated Fick method, Postcapillary pulmonary hypertension, Cardiac output, Thermodilution, All-cause mortality","lastPublishedDoi":"10.21203/rs.3.rs-7325994/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7325994/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e The prognostic value of cardiac output measured by the estimated Fick method and thermodilution in patients with postcapillary pulmonary hypertension (pc-PH) is unclear. We aimed to compare the correlations and consistency of cardiac output (CO) evaluated by the estimated Fick method and thermodilution, and to assess the prognostic validity of these two cardiac index (CI)-based methods with regard to all-cause mortality in patients with pc-PH.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e CO was simultaneously measured by thermodilution and the estimated Fick method using a Swan-Ganz catheter in 257 patients with pc-PH. Oxygen consumption was calculated by Dehmer, LaFarge, and Bergstra formulae.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e The CO measured by the estimated Fick method combined with the three formulae, and by thermodilution exhibited moderate correlations (r = 0.706, r = 0.688, r = 0.702, respectively; all \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.010). The 95% limits of agreement were -2.73 to 1.51, -2.99 to 1.36, and -2.42 to 2.02 L/min, respectively. CI measured by thermodilution was associated with the mortality (HR: 0.75 [95% CI: 0.58-0.98]; \u003cem\u003eP\u003c/em\u003e = 0.034). After adjustment, the risk of all-cause mortality was higher in the low CI group assessed by the Dehmer formula (HR: 1.92 [95% CI: 1.10-3.37]; \u003cem\u003eP\u003c/em\u003e = 0.022) compared to the high CI group.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e: There were moderate positive correlations and poor agreements between the CO measured by the estimated Fick method and thermodilution in patients with pc-PH. Thermodilution was superior to the estimated Fick method in improving risk stratification for all-cause mortality.\u003c/p\u003e","manuscriptTitle":"Prognostic Significance of Cardiac Output in Postcapillary Pulmonary Hypertension: Estimated Fick vs. Thermodilution","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-22 08:36:25","doi":"10.21203/rs.3.rs-7325994/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"734dabc2-2894-42af-9cf3-ac7adf5f2b63","owner":[],"postedDate":"September 22nd, 2025","published":true,"recentEditorialEvents":[{"type":"decision","content":"Rejected","date":"2026-05-05T12:36:56+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-05T12:41:42+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-22 08:36:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7325994","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7325994","identity":"rs-7325994","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}