Variations in central venous oxygen saturation and central venous-to-arterial carbon dioxide tension difference to define fluid responsiveness: a prospective observational study

Introduction: Fluid-induced variations in central venous oxygen saturation (ΔScvO₂) and central venous-to-arterial carbon dioxide tension difference [ΔP(cv-a)CO₂] have been proposed to define fluid responsiveness. This study aimed to determine whether their diagnostic accuracies are affected by baseline values or oxygen consumption (VO₂) responsiveness.

Materials and methods: This prospective observational study enrolled mechanically ventilated patients with circulatory shock. Hemodynamic variables and blood gas analysis were measured before and after a fluid challenge. Fluid responsiveness and VO₂ responsiveness were defined as a ≥10% increase in cardiac index and VO₂, respectively. The Spearman's rank correlation coefficient (rho) was computed to evaluate the association between variables. The diagnostic accuracy was assessed using the area under the receiver operating characteristic curve (AUC), with subgroup analyses based on baseline ScvO₂ and P(cv-a)CO₂ values and VO₂ responsiveness.

Results: Out of 58 patients enrolled, 30 were fluid responders. The fluid-induced changes in cardiac index were significantly correlated with ΔScvO₂ (rho = 0.36, P = 0.006) and ΔP(cv-a)CO₂ (rho = −0.35, P = 0.006). ΔScvO₂ and ΔP(cv-a)CO₂ defined fluid responsiveness with AUC values of 0.76 [95% confidence interval (CI): 0.63–0.86, P < 0.001] and 0.72 (95% CI: 0.59–0.83, P < 0.001), respectively. A cutoff value of 5% for ΔScvO₂ and −2 mmHg for ΔP(cv-a)CO₂ yielded positive predictive values of 88% and 75%, and negative predictive values of 63% and 61%, respectively. The gray zones for ΔScvO₂ (−3 to 4.6%) and ΔP(cv-a)CO₂ (−2.7 to 1 mmHg) comprised 51.7% and 48.3% of the patients, respectively. In the subgroup analyses, ΔScvO₂ potentially exhibited better accuracy for assessing fluid responsiveness in VO₂ non-responders (AUC of 0.91, 95% CI: 0.78–0.98; 40 patients) and patients with a baseline ScvO₂ < 70% (AUC of 0.84, 95% CI: 0.67–0.95; 32 patients). Meanwhile, the diagnostic accuracy of ΔP(cv-a)CO₂ was slightly improved in VO₂ non-responders (AUC of 0.78, 95% CI: 0.62–0.90; 40 patients) and patients with a baseline P(cv-a)CO₂ ≥ 6 mmHg (AUC of 0.78, 95% CI: 0.62–0.90; 39 patients).

Conclusion: ΔScvO₂ and ΔP(cv-a)CO₂ are potential indicators of fluid responsiveness in mechanically ventilated patients with circulatory shock, especially those with abnormal baseline values or VO₂ unresponsiveness.

Introduction

In the intensive care unit (ICU), volume expansion represents the most commonly used measure to correct hypotension and hypoperfusion, aiming to improve oxygen delivery (DO₂) by increasing cardiac output (CO), thereby ameliorating tissue perfusion. Whether volume expansion can elevate CO depends on whether the heart functions on the steep portion of the Frank-Starling curve, indicating fluid responsiveness (1). In recent years, the study of oxygen and carbon dioxide (CO₂) metabolism has gained attention for assessing fluid responsiveness (2–4). From a physiological perspective, oxygen and CO₂ metabolism are closely related to blood flow, as it provides oxygen to the tissues and removes CO₂ produced by them (5, 6).

In recent studies, variations in central venous oxygen saturation (ScvO₂) (ΔScvO₂) and central venous-to-arterial carbon dioxide tension difference (P(cv-a)CO₂) [ΔP(cv-a)CO₂] during volume expansion have been confirmed to assess fluid responsiveness (2–4). Indeed, according to the Fick principle, ΔScvO₂ during volume expansion can track changes in CO if oxygen content and oxygen consumption (VO₂) remain stable, and ΔP(cv-a)CO₂ during volume expansion is inversely proportional to the CO changes under consistent CO₂ production (5–8). However, VO₂ and CO₂ production may not always remain unchanged during volume expansion due to the VO₂/DO₂ dependency phenomenon and anaerobic CO₂ production (9–11). Furthermore, whether the diagnostic accuracies of ΔScvO₂ and ΔP(cv-a)CO₂ depend on their baseline values remains unknown, even though the baseline ScvO₂ and P(cv-a)CO₂ seem unable to identify fluid responsiveness (2). This study aimed to determine whether their diagnostic accuracies are affected by baseline ScvO₂ and P(cv-a)CO₂ values or the fluid-induced VO₂ responsiveness.

Materials and methods

This prospective observational study was conducted in the ICU of Ningbo No. 2 Hospital from January 2024 to December 2024. It was part of a study program registered with the Chinese Clinical Trial Registry (ChiCTR2100053665) and approved by the local institutional ethics committee (YJ-NBEY-KY-2022-147-01). This manuscript adheres to the applicable STROBE guidelines (12). Written informed consent was obtained from the patients’ relatives. This study was conducted in compliance with the Declaration of Helsinki.

Patients

The eligible subjects were mechanically ventilated adults (age ≥ 18 years) with circulatory shock and without spontaneous respiratory efforts, for whom the attending physician decided to perform a fluid challenge, where circulatory shock was defined as the presence of one or more of the following signs: 1) systolic arterial pressure < 90 mmHg, mean arterial pressure < 65 mmHg, or requiring vasopressor administration; 2) skin mottling; 3) urine output < 0.5 mL/kg/h for ≥ 2 h; 4) lactate level > 2 mmol/L. Patients would be excluded if they met the following criteria: no indwelling arterial or central venous catheterization, aortic valve surgery, equipped with extracorporeal membrane oxygenation or a pacemaker, contraindications to fluid challenge, poor echogenicity, atrial fibrillation, refractory shock expected to die within 24 h, or decline to participate.

Study protocol

All eligible patients received invasive radial arterial monitoring and central venous catheterization, with the catheter tip positioned in the superior vena cava or the right atrium. Pressure calibration was performed in the supine position, with pressure transducers zeroed at the phlebostatic axis, a position corresponding to the right atrium's level (the midpoint of the fourth intercostal space at the midaxillary line) (13). A pressure-controlled ventilation mode was set, and sedative and analgesic medications were continuously administered to avoid spontaneous breathing efforts. Once enrollment was confirmed, a baseline set of hemodynamic variables was measured, and transthoracic echocardiography (TTE) was performed. Meanwhile, arterial and central venous blood gases were simultaneously sampled and analyzed using a GEM Premier 3,500 blood gas analyzer (Instrumentation Laboratory Company, Bedford, MA, USA). Immediately after that, a fluid challenge test was conducted by administering a pressurized bolus of 500 mL of Ringer's solution over 15 min in the 45° semi-recumbent position. Immediately after the fluid challenge, a second set of the above measurements was taken. During the study period, no adjustments were made to body position, ventilator settings, vasopressors, inotropes, or sedative and analgesic drugs. Figure 1 illustrates the detailed process of this study.

Figure 1

Figure 1. Illustration of study selection and fluid challenge test. ECMO, extracorporeal membrane oxygenation; TTE, transthoracic echocardiography.

Data collection

We collected demographic information (including age, gender, body mass index, and comorbidities), causes of shock, ventilator-related parameters (including tidal volume, positive end-expiratory pressure, driving pressure, respiratory rate, and fraction of inspired oxygen), acute physiology and chronic health evaluation II score, sequential organ failure assessment score, sedative and analgesic drugs, and vasoactive agents at the time of enrollment. We measured and recorded hemodynamic variables [including heart rate (HR), central venous pressure, systolic arterial pressure, diastolic arterial pressure, and mean arterial pressure], echocardiographic parameters [including stroke volume (SV), cardiac index, and velocity-time integral (VTI)], arterial and central venous blood gases parameters (including potential of hydrogen (PH), arterial lactate level, arterial partial pressure of carbon dioxide (PaCO₂), arterial oxygen saturation (SaO₂), central venous partial pressure of carbon dioxide (PcvCO₂), and ScvO₂), and oxygen-CO₂ derived variables (including DO₂, VO₂, and P(cv-a)CO₂) at baseline and after the fluid challenge. The hemoglobin (Hb) concentration was measured together with arterial blood gas analysis using the GEM Premier 3,500 blood gas analyzer (Instrumentation Laboratory Company, Bedford, MA, USA). Patients were followed up until ICU discharge.

SV was computed as: VTI × LVOT area, where VTI was measured via continuous Doppler transaortic flow on an apical five-chamber view, and LVOT area was calculated as π × (LVOT diameter/2)² (LVOT refers to the left ventricular outflow tract). Then, cardiac index was calculated as (SV × HR)/body surface area. TTE examination was conducted with the CX50 ultrasound system (Philips Medical System, Suresnes, France), which was performed by an independent ICU physician who was blinded to the study outcomes. The representative value of echocardiographic parameters was obtained by averaging three consecutive measurements, regardless of the respiratory cycle.

Definition

ΔScvO₂ and ΔP(cv–a)CO₂ induced by volume expansion were calculated as absolute changes, that is, subtracting the baseline value from the value after volume expansion. The fluid-induced changes in cardiac index and VO₂ were calculated as relative changes: (the value after fluid infusion – the baseline value)/ the baseline value×100%. Fluid responsiveness and VO₂ responsiveness were defined by a ≥10% increase in cardiac index and VO₂ in response to volume expansion, respectively. According to the Fick principle, DO₂ was calculated as (SV × HR) × (1.34 × Hb × SaO₂ + 0.003 × PaO₂), and VO₂ was calculated as (SV × HR) × [(1.34 × Hb × SaO₂ + 0.003 × PaO₂) − (1.34 × Hb × ScvO₂ + 0.003 × PcvO₂)], where PaO₂ is the arterial oxygen tension, and PcvO₂ is the central venous oxygen tension.

Statistical analysis

Statistical analyses were performed using SPSS version 17.0 (IBM, New York, USA). The normal distribution of continuous variables was assessed using the Kolmogorov–Smirnov test. Normally distributed variables are presented as means ± standard deviation (SD), and skewed variables are reported as medians with interquartile ranges (IQR). Categorical variables are described as frequencies and percentages. For the continuous data, either the Student's t-test or the Mann–Whitney test was used for inter-group comparison, depending on the data distribution, and the Student's paired t-test was applied for the intra-group comparisons. The Chi-squared test or Fisher's exact test was utilized to compare categorical variables. The Spearman's rank correlation coefficient (rho) was computed to evaluate the association between the fluid-induced changes in cardiac index and ΔScvO₂ and ΔP(cv–a)CO₂.

The MedCalc Statistical Software (MedCalc Software bvba, Ostend, Belgium) was employed to construct ROC curves for assessing the diagnostic accuracy of ΔScvO₂ and ΔP(cv–a)CO₂ for fluid responsiveness. The optimal cutoff value was determined by maximizing the Youden index, while taking into account the smallest detectable differences (SDD) of ScvO₂ (±3%) and P(cv–a)CO₂ (±2 mmHg), as previously reported (14). Additionally, the gray zone approach was used to avoid the binary constraint of a “black-or-white” decision of the optimal cutoff value (15). We calculated the gray zone for ΔScvO₂ and ΔP(cv–a)CO₂ based on values that did not allow for having 10% of diagnosis tolerance (i.e., a sensitivity of <90% or a specificity of <90%) (15). To identify potential factors affecting the diagnostic accuracy, we performed subgroup analyses based on baseline ScvO₂ value (≥70% or <70%), baseline P(cv–a)CO₂ value (≥6 mmHg or <6 mmHg), and fluid-induced VO₂ responsiveness (yes or no). The DeLong's test was used to determine the difference in AUC between subgroups with a minimum calculated sample size (16).

The Power Analysis and Sample Size software (NCSS, LLC, Kaysville, UT, USA) was utilized to determine the statistical power. Previous studies indicated that both ΔScvO₂ and ΔP(cv–a)CO₂ had an area under the receiver operating characteristic (ROC) curve (AUC) of approximately 0.8 (2, 3). To achieve a power of 80% with an alpha risk of 0.05, it was determined that 26 subjects would be sufficient. Therefore, at least 52 patients were required to ensure adequate statistical power for each arm in the subgroup analyses. In addition, we randomly selected 10 patients to calculate the coefficient of variation (CV) and the least significant change (LSC) to assess the intra-operator reproducibility for VTI. A two-tailed P-value of less than 0.05 was considered statistically significant.

Results

A total of 58 consecutive patients were enrolled over one year, and 30 (51.7%) of them were classified as fluid responders (Figure 1). Distributive shock represented the primary cause of hypotension in this study (79.3%, 46/58), and the baseline characteristics and clinical outcomes were comparable between the responders and non-responders. Of note, all patients but one received norepinephrine infusion during the study period, and the intra-operator reproducibility for VTI was deemed acceptable with a CV of 4.0% [95% confidence interval (CI): 1.4%–6.6%] and a LSC of 6.4% (95% CI: 2.2%–10.5%). Table 1 presents the baseline characteristics of the patients.

Table 1

Table 1. Baseline characteristics.

Hemodynamic changes induced by fluid expansion

Before the fluid challenge, no significant differences in the baseline hemodynamic variables were observed between the responders and non-responders. After the fluid challenge, SV, cardiac index, and DO₂ were remarkably increased in the responders, but not in the non-responders. Hemoglobin was significantly decreased after fluid expansion in both groups. In the responders, volume expansion led to an elevated ScvO₂ and a reduced P(cv–a)CO₂, whereas these values remained unchanged in the non-responders. Table 2 shows the fluid-induced changes in hemodynamic variables in detail.

Table 2

Table 2. Changes in hemodynamic variables induced by volume expansion.

Relationship between fluid responsiveness and ΔScvO₂, and Δp(cv–a)CO₂

Spearman correlation analyses revealed that the fluid-induced changes in cardiac index were positively correlated with ΔScvO₂ (rho = 0.36, P = 0.006) and were negatively correlated with ΔP(cv–a)CO₂ (rho = −0.35, P = 0.006) (Figure 2).

Figure 2

Figure 2. Correlation between the fluid-induced cardiac index and ΔScvO₂ (panel A) and ΔP(cv-a)CO₂ (panel B).

ΔScvO₂ and ΔP(cv-a)CO₂ defined fluid responsiveness with AUC values of 0.76 (95% CI: 0.63–0.86; P < 0.001) and 0.72 (95% CI: 0.59–0.83; P < 0.001) (Figure 3), respectively. Based on the Youden index, the optimal cutoff value of ΔScvO₂ was 2%, with a sensitivity of 76.7% and a specificity of 75.0%. However, considering the repeatability of ScvO₂ (a SDD of ±3%), the optimal cutoff value was 5%, yielding a sensitivity of 50%, a specificity of 92.9%, a positive predictive value (PPV) of 88%, and a negative predictive value (NPV) of 63%. The gray zone approach identified a ΔScvO₂ range of −3% to 4.6%, which included 51.7% of the patients (Figure 3). According to the Youden index, the optimal cutoff value of ΔP(cv-a)CO₂ was −2 mmHg, which exceeded the SDD of P(cv-a)CO₂ (±2 mmHg). Thus, the optimal cutoff value of −2 mmHg yielded a sensitivity of 50%, a specificity of 82.1%, a PPV of 75%, and an NPV of 61%. A range of −2.7 mmHg to 1 mmHg represented the gray zone for ΔP(cv-a)CO₂ that comprised 48.3% of patients (Figure 3).

Figure 3

Figure 3. Receiver operating characteristic curves (panel A) and the gray zone for ΔScvO₂ (panel B) and ΔP(cv-a)CO₂ (panel C).

Subgroup analyses

Given the minimum calculated sample size, the findings from the subgroup analysis with a sample size of 26 cases or more were considered statistically valid. Subgroup analyses demonstrated that ΔScvO₂ potentially exhibited better accuracy for assessing fluid responsiveness in VO₂ non-responders and patients with a baseline ScvO₂ < 70%. Meanwhile, the diagnostic accuracy of ΔP(cv-a)CO₂ was slightly improved in VO₂ non-responders and patients with a baseline P(cv-a)CO₂ ≥ 6 mmHg (Table 3). The AUC of ΔScvO₂ in the subgroup with a baseline ScvO₂ < 70% was slightly higher than that in the subgroup with a baseline ScvO₂ ≥ 70%, despite no statistical significance (P = 0.219). However, the comparisons of AUC in other subgroups were not conducted because the minimum sample size in some subgroups was not reached.

Table 3

Table 3. Subgroup analyses for diagnostic accuracies of ΔScvO₂ and ΔP(cv-a)CO₂ in assessing fluid responsiveness.

Discussion

In this perspective observational study, the principal findings demonstrated that ΔScvO₂ and ΔP(cv-a)CO₂ during volume expansion possessed an acceptable diagnostic accuracy for identifying fluid responsiveness, and the diagnostic accuracies of ΔScvO₂ and ΔP(cv-a)CO₂ were likely associated with their baseline values and the fluid-induced VO₂ responsiveness.

Consistent with the optimal CI of ΔScvO₂ (3% to 5%) identified in our recent meta-analysis (3), we determined the optimal cutoff value for ΔScvO₂ as 5% in the current study, yielding a high PPV (88%) and a relatively low NPV (63%). Thus, we can almost confirm that a patient can benefit from volume expansion if the measured ΔScvO₂ is greater than 5%. However, we cannot make any decisions if the measured ΔScvO₂ is less than 5%, due to the low NPV. Indeed, a low ΔScvO₂ does not necessarily indicate a small change in CO induced by fluid challenge. According to the Fick principle, the close relationship between ScvO₂ and CO depends on stable oxygen content and VO₂ during volume expansion (7). However, a potential decrease in Hb concentration could somewhat reduce oxygen content. We observed a median reduction in Hb of 5.9% after volume expansion across the entire population studied, which was consistent with a recent meta-analysis (17). Furthermore, VO₂ does not always remain constant during volume expansion because of the VO₂/DO₂ dependency phenomenon. The VO₂/DO₂ dependency phenomenon refers to a linear correlation between DO₂ and VO₂ when DO₂ decreases below the critical value (18), which implies that VO₂ will change linearly with DO₂, thus resulting in a relatively constant oxygen extraction and ScvO₂ (3). In these situations, ScvO₂ would not change significantly (i.e., a low ΔScvO₂) despite a noticeable increase in CO and DO₂. This could explain why the diagnostic accuracy of ΔScvO₂ was improved considerably after excluding the VO₂ responders. Additionally, subgroup analysis revealed that the AUC of ΔScvO₂ was increased after excluding patients with a baseline ScvO₂ ≥ 70% (see Table 3), which suggested that the baseline ScvO₂ may be a determinant of the diagnostic accuracy of ΔScvO₂, even though the baseline ScvO₂ seems unable to identify fluid responsiveness (2, 19). Indeed, a normal or supranormal ScvO₂ value typically indicates an adequate CO to provide sufficient oxygen delivery and/or mitochondrial dysfunction or microcirculatory shunting. In this case, the magnitude of ΔScvO₂ induced by volume expansion may be limited and may not be parallel to the fluid-induced increases in CO.

In addition, we also confirmed the ability of ΔP(cv-a)CO₂ to define fluid responsiveness. However, the diagnostic accuracy of ΔP(cv-a)CO₂ (AUC of 0.72) in our study appears to be lower than that in a previous study (AUC of 0.831) (2). This discrepancy is not surprising given the complex relationship between ΔP(cv-a)CO₂ and CO. It should be recognized that the close association between ΔP(cv-a)CO₂ and CO relies on a stable CO₂ content-CO₂ partial pressure relationship, as well as a stable relationship between P(cv-a)CO₂ and CO (5, 20). However, the curvilinear CO₂ content-CO₂ partial pressure relationship can be influenced by metabolic acidosis, hematocrit, or the Haldane effect, which refers to the effect of oxygen saturation on CO₂ transport (5, 20). Consequently, varying baseline values for these variables may result in differing diagnostic accuracies of ΔP(cv-a)CO₂ across various studies. Furthermore, the relationship between P(cv-a)CO₂ and CO is also curvilinear. This means that for a constant total CO₂ production, fluid-induced changes in CO can cause a more significant alteration in P(cv-a) CO₂ at a low CO value than at a normal or high CO value (5). This may explain why ΔP(cv-a)CO₂ was more effective in defining fluid responsiveness in patients with a baseline P(cv-a)CO₂ ≥ 6 mmHg, as a high P(cv-a)CO₂ level typically indicates a low baseline CO. Similarly, a previous study found that fluid-induced CO increases engendered a reduction in P(cv-a) CO₂ only in patients with elevated baseline P(cv-a)CO₂ values (≥6 mmHg), but not in those with normal baseline levels (21). The subgroup analysis also revealed an improved diagnostic accuracy of ΔP(cv-a)CO₂ when excluding patients with VO₂ responsiveness. This finding aligns with the study conducted by Nassar et al. (4). Specifically, CO₂ production associated with anaerobic metabolism tends to occur at the VO₂/DO₂ dependency stage (5, 6). This phenomenon may attenuate the relationship between ΔP(cv-a)CO₂ and fluid-induced CO increases.

Our findings provide a clinical implication: when the CO measurement is not available, measuring ScvO₂ or P(cv-a)CO₂ before and after volume expansion can help identify which patients are likely to benefit from fluid therapy, particularly for patients with abnormal baseline values or with VO₂ unresponsiveness. However, several limitations in this study should be highlighted. First, the limited sample size in this study could overestimate the effect sizes, especially hampering us from drawing a firm conclusion in the subgroup analysis. Second, ScvO₂ was measured in this study instead of the mixed venous oxygen saturation. As ScvO₂ primarily reflects the DO₂-VO₂ relationship in the upper side of the body, it may not inform about the local perfusion disturbances in regional septic conditions (5). Despite this, the changes in ScvO₂ can track the global DO₂ changes, given the equivalent changing trend of ScvO₂ and the mixed venous oxygen saturation (22). Finally, there may be mathematical coupling issues in the estimation of DO₂ and VO₂ based on the Fick method, which may introduce bias in the results of subgroup analysis.

Conclusion

In mechanically ventilated patients, ΔScvO₂ and ΔP(cv-a)CO₂ induced by volume expansion are potential indicators for assessing fluid responsiveness and may be routinely measured to indicate fluid responsiveness in the absence of CO measurement. The diagnostic accuracies of ΔScvO₂ and ΔP(cv-a)CO₂ were likely associated with the baseline ScvO₂ and P(cv-a)CO₂ values and the fluid-induced VO₂ responsiveness.

Data availability statement

The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding authors.

Ethics statement

The studies involving humans were approved by the institutional ethics committee of Ningbo No.2 Hospital (YJ-NBEY-KY-2022-147-01). The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

Author contributions

XZ: Methodology, Supervision, Writing – original draft, Funding acquisition, Conceptualization, Formal analysis. HF: Formal analysis, Writing – original draft, Methodology. CX: Formal analysis, Writing – original draft, Data curation, Methodology, Investigation. JP: Data curation, Methodology, Investigation, Formal analysis, Writing – original draft. HW: Investigation, Data curation, Writing – original draft, Formal analysis, Methodology. TP: Writing – review & editing, Funding acquisition, Methodology, Formal analysis, Conceptualization. ZX: Conceptualization, Writing – review & editing, Funding acquisition, Formal analysis. BC: Writing – review & editing, Methodology, Supervision, Conceptualization, Formal analysis.

Funding

The author(s) declare financial support was received for the research and/or publication of this article. This study was supported by grants from the Zhejiang Medicine and Health Science and Technology Project (No. 2023KY1084), the Project of Ningbo Health Youth Backbone Talent Training (No. 2025QNJS-22, 2025JSPT-42), and the Project of Hospital-level Key Discipline of Ningbo No.2 Hospital (No. 2023-Y06). The funders had no role in the design of the study or collection, analysis, or interpretation of data, or writing the manuscript.

Conflict of interest

The authors declare that the research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest.

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Abbreviations

ICU, intensive care unit; CO, cardiac output; DO₂, oxygen delivery; VO₂, oxygen consumption; CO₂, carbon dioxide; ScvO₂, central venous oxygen saturation; P(cv-a)CO₂, central venous-to-arterial carbon dioxide tension difference; ΔScvO₂, the variation in ScvO₂; ΔP(cv-a)CO₂, the variation in P(cv-a)CO₂; PaCO₂, arterial partial pressure of carbon dioxide; PcvCO₂, central venous partial pressure of carbon dioxide; TTE, transthoracic echocardiography; VTI, aortic velocity-time integral; LVOT, left ventricular outflow tract; SV, stroke volume; HR, heart rate; SaO₂, arterial oxygen saturation, Hb, hemoglobin, PaO₂, arterial oxygen tension; PcvO₂, central venous oxygen tension; SDD, smallest detectable differences; CV, coefficient of variation; LSC, least significant change; ROC, receiver operating characteristic; AUC, area under the ROC curve; SD, standard deviation; IQR, interquartile range; CI, confidence interval; PPV, positive predictive value; NPV, negative predictive value.

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