Correlation between carotid Doppler ultrasound-derived parameters and cardiac output measurements: a systematic review and meta-analysis
Original Article

Correlation between carotid Doppler ultrasound-derived parameters and cardiac output measurements: a systematic review and meta-analysis

Feng Chen ORCID logo, Tao-Tao Peng, Hua-Wei Liu, Zhen-Xin Duan, Mi Yang, Hong Li ORCID logo

Department of Anesthesiology, The Second Affiliated Hospital of Army Medical University, Chongqing, China

Contributions: (I) Conception and design: H Li, F Chen; (II) Administrative support: H Li; (III) Provision of study materials or patients: TT Peng, HW Liu; (IV) Collection and assembly of data: ZX Duan, M Yang; (V) Data analysis and interpretation: F Chen, TT Peng; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors.

Correspondence to: Hong Li, PhD. Department of Anesthesiology, The Second Affiliated Hospital of Army Medical University, 83 Xinqiao Zheng Street, Shapingba District, Chongqing 400037, China. Email: lh78553@tmmu.edu.cn.

Background: The utility of carotid Doppler ultrasound (CDU) as a noninvasive tool for cardiac output (CO) monitoring has been a subject of considerable debate. This meta-analysis was conducted to synthesize the available evidence and quantitatively assess the correlation between CDU-derived parameters and standard CO measurements.

Methods: We searched the databases of PubMed, EMBASE, Science Direct/Elsevier, MEDLINE, Web of Science, Cochrane Library, China National Knowledge Infrastructure, and China Biology Medicine from the date of inception up to August 2024. Data were analyzed by extracting Pearson’s or Spearman’s correlation coefficients from each study and converting them to Fisher’s Z. The pooled r value was calculated using Fisher’s Z and standard error. STATA (Version 15.0) software was used to facilitate data synthesis.

Results: After screening titles and abstracts, 476 studies required full-text evaluation. Based on the inclusion and exclusion criteria, 17 observational studies were selected for meta-analysis. The results indicated a positive correlation between CDU-derived parameters and CO measurements [0.53, 95% confidence interval (CI): 0.39–0.64], with parameter change values being more relevant than absolute values (0.76, 95% CI: 0.49–0.90) versus (0.43, 95% CI: 0.27–0.57). Furthermore, the carotid velocity-time integral (CVTI) exhibited a stronger correlation (0.81, 95% CI: 0.62–0.91) than the other three parameters (carotid-corrected flow time 0.59, 95% CI: 0.31–0.77), carotid peak systolic velocity (0.40, 95% CI: 0.21–0.57), and carotid blood flow (0.33, 95% CI: 0.12–0.52) in the subgroup analysis. Egger regression analysis and the trim-and-fill method indicated no publication bias (P>0.05).

Conclusions: CDU-derived parameters demonstrated positive correlations with CO measurements, providing evidence for the use of CDU-derived parameters as surrogate CO monitors.

Keywords: Carotid arteries; ultrasound; cardiac output (CO); correlation; meta-analysis

Submitted Oct 28, 2024. Accepted for publication Jul 14, 2025. Published online Oct 19, 2025.

doi: 10.21037/qims-24-2362

Introduction

Accurate hemodynamic monitoring is crucial for the management of critically ill and pre- and post-surgery patients (1,2). However, many of the established techniques require specialized equipment, expensive devices, and invasive procedures. Traditional bolus thermodilution (TD) cardiac output (CO) and pulse contour analysis (PCA) of the pulse index continuous CO require arterial cannulation and/or central line insertion that entail complications (3,4). Other methods, such as transesophageal echocardiography (TEE), require complete sedation and paralysis of the patient. Moreover, patient movements, such as turning, may displace the TEE probe (5). Similarly, transthoracic echocardiography (TTE) requires specific operating conditions and cannot provide continuous monitoring (6).

In the search for superior hemodynamic monitoring solutions, carotid Doppler ultrasound (CDU) has emerged as a promising noninvasive modality, offering a viable alternative to established invasive techniques (7). The superficial location of the common carotid artery (CCA) facilitates its identification and visualization, potentially eliminating the need for extensive training. These favorable characteristics position CDU as a safe and cost-effective bedside alternative, aiding clinicians in early diagnosis and clinical decision-making (8). Although CDU has not yet replaced invasive “gold standard” CO monitoring, the present meta-analysis demonstrates that specific CDU-derived parameters show promise in functional hemodynamic monitoring applications.

Various studies have assessed the potential use of CDU parameters for determining CO monitoring (8-11). However, the current CDU literature exhibits diverse correlations between CDU-derived parameters and the corresponding CO measurements, ranging from high to negligible, with often contradictory findings (12-14). These studies included diverse patient populations, small sample sizes, varied assessment purposes, and study designs. Due to these inconsistent results, we opted for a systematic review and meta-analysis to summarize the current studies and obtain a consistent correlation. We present this article in accordance with the PRISMA reporting checklist (15) (available at https://qims.amegroups.com/article/view/10.21037/qims-24-2362/rc).

Methods

Literature search

A comprehensive and systematic search of the literature was performed independently by two reviewers to identify all relevant studies. We queried the following electronic databases from their inception through August 2024, with no restrictions on language or publication type: PubMed, EMBASE, Science Direct/Elsevier, MEDLINE, Web of Science, the Cochrane Library, China National Knowledge Infrastructure, and China Biology Medicine. In PubMed, we used the search syntax: ((“Arteries, Common Carotid”[MeSH Terms]) OR (“Artery, Common Carotid”[Title/Abstract]) OR (“Carotid Arteries, Common”[Title/Abstract]) OR (“Common Carotid Arteries”[Title/Abstract]) OR (“Common Carotid Artery”[Title/Abstract])) AND ((((Stroke Volumes[MeSH Terms]) OR (Volumes, Stroke[Title/Abstract])) OR (Volume, Stroke[Title/Abstract])) OR (Ventricular Ejection Fraction[Title/Abstract])) OR (Ejection Fractions, Ventricular[Title/Abstract])) OR (Ejection Fraction, Ventricular[Title/Abstract])) OR (Fractions, Ventricular Ejection[Title/Abstract])) OR (Fraction, Ventricular Ejection[Title/Abstract])) OR (Ventricular Ejection Fractions[Title/Abstract])) OR (Ventricular End-Diastolic Volume[Title/Abstract])) OR (End-Diastolic Volumes, Ventricular[Title/Abstract])) OR (End-Diastolic Volume, Ventricular[Title/Abstract])) OR (Ventricular End Diastolic Volume[Title/Abstract])) OR (Ventricular End-Diastolic Volumes[Title/Abstract])) OR (Volumes, Ventricular End-Diastolic[Title/Abstract])) OR (Volume, Ventricular End-Diastolic[Title/Abstract])) OR (Ventricular End-Systolic Volume[Title/Abstract])) OR (End-Systolic Volumes, Ventricular[Title/Abstract])) OR (End-Systolic Volume, Ventricular[Title/Abstract])) OR (Ventricular End Systolic Volume[Title/Abstract])) OR (Ventricular End-Systolic Volumes[Title/Abstract])) OR (Volumes, Ventricular End-Systolic[Title/Abstract])) OR (Volume, Ventricular End-Systolic[Title/Abstract])) OR (((“Cardiac Outputs”[MeSH Terms]) OR (“Output, Cardiac”[Title/Abstract]) OR (“Outputs, Cardiac”[Title/Abstract])))) OR (cardiac index[MeSH Terms])) OR (velocity-time integral[MeSH Terms])). All relevant articles and abstracts were retrieved.

Eligibility criteria

Inclusion criteria

Studies were deemed eligible for inclusion if they were observational in design (cohort, case-control, cross-sectional) and reported a quantitative correlation between one or more CDU-derived parameters and a reference measurement of CO. Critically, inclusion required the article to provide either a Pearson’s or Spearman’s correlation coefficient for data synthesis. No limitations were imposed on the study participants, who could be healthy volunteers, surgical patients, or critically ill patients. Since this was a correlation meta-analysis, there were no strict intervention and comparison group settings.

Exclusion criteria

Studies were excluded if they were case reports, review articles, duplicate publications, or lacked outcome data. Participants younger than 18 years were excluded in order to reduce developmental variability and lack of pediatric standards, highlighting the need for future research to establish age-appropriate references for minors. Future work could integrate advanced imaging and multicenter registries to address this gap (16,17).

Study selection and validity assessment

Two authors independently screened the articles based on titles and abstracts, following the preset inclusion criteria. When ambiguous decisions arose from the title and abstract reviews, the full texts were obtained for analysis. Subsequently, the full texts of all potentially relevant studies were downloaded to conduct two independent reviews of the selected literature. Disagreements were resolved through mutual discussion or by involving a third reviewer. The collaborative working mechanism of the three researchers exhibits significant iterative optimization features: In the first stage, parallel independent reviews maximize the diversity of perspectives; in the second stage, a structured dispute resolution framework (comprising three levels of dispute resolution: concept definition calibration, methodological appropriateness assessment, and evidence strength grading) integrates knowledge; in the third stage, a backpropagation algorithm dynamically adjusts the review criteria. This three-stage workflow of ’separation-aggregation-optimization’ lays a highly reliable and valid knowledge foundation for the theoretical construction of subsequent research.

Data extraction

Two authors independently extracted data, including general characteristics (first author, publication year, language, and research country/region), participant characteristics (sample size, average age, sex, and study population), other characteristics (study design and monitoring method), and outcomes (Pearson’s or Spearman’s correlation coefficients of the CDU-derived parameters and CO measurements), using a predefined data extraction form. Additional relevant information was also included.

Quality assessment

The methodological quality of each included study was independently evaluated by two reviewers using the 11-item checklist from the Agency for Healthcare Research and Quality (AHRQ), which is specifically designed for cross-sectional and observational studies (18). In line with the AHRQ scoring guide, studies were assigned a summary score and subsequently categorized as: low quality (score: 0–3), moderate quality (score: 4–7), or high quality (score: 8–11). Any discrepancies in the quality ratings between the two reviewers were resolved through discussion to achieve consensus. In cases where disagreement persisted, a third, senior reviewer was consulted for final adjudication.

Data analysis

We extracted the Pearson’s correlation coefficients (r) or Spearman’s correlation coefficients (rs) from each study and conducted a meta-analysis of the correlation between CDU-derived parameters and CO measurements using a random effects model, along with a 95% confidence interval (CI) and prediction intervals. WPS software (Kingsoft Office Software, Beijing, China) transformed the correlation coefficient (r=2sin[rsπ/6]) (19). After converting r into Fisher’s Z and standard error, the final effect size was calculated as the pooled r value and 95% CI. The heterogeneity of r values between studies was determined by calculating the Q statistic, derived from the chi-square test, and the inconsistency index (I2) (20).

Results

Literature search

A total of 1,597 studies were identified, with 1,341 remaining after removing duplicates. After screening titles and abstracts, 549 studies required full-text evaluation. Based on the exclusion criteria, 17 studies were selected according to the eligibility criteria (Figure 1). All reviewers concurred with the inclusion of 17 papers.

Figure 1 Flow diagram of selection of eligible studies.

Study characteristics

This systematic review included 17 studies involving 532 adults. All studies were cross-sectional observational studies. A total of 10 studies (8,10,13,14,21-26) monitored carotid blood flow (CBF), two studies (23,27) monitored carotid peak systolic velocity (CPSV), five studies (8,24,28-30) monitored carotid corrected flow time (CCFT), and six studies (9,10,29-32) monitored carotid velocity-time integral (CVTI) with their corresponding CO measurements. CO was monitored using TTE, TEE, PCA (Picco), PCA (Nexfin), PCA (Clearsight), and TD, respectively (Table 1).

Table 1

Basic characteristics of the included studies

Study Participant characteristics Male, n [%] Age (years), mean ± SD Study design Carotid parameters Reference for comparison Reference method r/rs
Patnaik et al., 2023 (21) ICU patients 28 [70] 46.97±16.3 Cross-sectional CBF CO TTE 0.6
CBF† CO† TTE 0.05
Roy et al., 2023 (27) ICU patients 15 [50] 49.6±13.4 Cross-sectional Pre CPSV LVOT VTI TTE 0.56
Post CPSV LVOT VTI TTE 0.37
Bu et al., 2023 (22) Surgery patients 69 [69] 70.50±5.2 Cross-sectional SCBF CO TEE 0.451
DCBF CO TEE 0.175
TCBF CO TEE 0.403
Van Houte et al., 2023 (28) Surgery patients 14 [78] 63±31.9 Cross-sectional CCFT CO PCA (Picco) 0.43
CCFT SV PCA (Picco) 0.33
Cheong et al., 2023 (9) ICU patients 25 [58] 61±23.7 Cross-sectional CSVTI LVOT VTI TTE 0.81
CSDVTI LVOT VTI TTE 0.89
Van Houte et al., 2022 (10) Surgery patients 14 [78] 63±31.9 Cross-sectional CBF CO PCA (Picco) 0.67
CVTI CO PCA (Picco) 0.25
CBF SV PCA (Picco) 0.41
CVTI SV PCA (Picco) 0.57
Kenny et al., 2021 (29) Healthy volunteers 7 [64] 27±11.1 Cross-sectional CVTI† SV† PCA (Nexfin) 0.93
CCFT† SV† PCA (Nexfin) 0.92
Kenny et al., 2020 (31) Healthy volunteers 4 [44] 34.5±2.9 Cross-sectional CVTI† LVOT VTI† TTE 0.89
CVTI† SV† PCA (Clearsight) 0.97
Kenny et al., 2020 (30) Healthy volunteers 7 [58] 32.1±11.1 Cross-sectional CVTI† SV† PCA (Clearsight) 0.9
CCFT† SV† PCA (Clearsight) 0.79
Sidor et al., 2020 (8) Healthy volunteers 11 [55] 29.7±5 Cross-sectional CBF CO TTE 0.67
CCFT CO TTE 0.57
Girotto et al., 2018 (23) ICU patients 22 [67] 67±14 Cross-sectional CBF CI PCA (Picco) 0.54
CPSV CI PCA (Picco) 0.26
Ma et al., 2017 (24) Patients 39 [76] 59.6±16.3 Cross-sectional CCFT CO TD 0.25
CBF CO TD 0.44
Roehrig et al., 2017 (25) ICU patients 24 [69] 62±11 Cross-sectional CBF CO TD 0.8
CBF† CO† TD 0.79
Peachey et al., 2016 (32) Healthy volunteers 17 [52] NR Cross-sectional CVTI† SV† TTE 0.29
Weber et al., 2016 (26) Surgery patients 17 [68] 67±10.7 Cross-sectional CBF CI TD 0.159
CBF† CI† TD −0.224
Weber et al., 2015 (13) Healthy volunteers 8 [73] 38±14.1 Cross-sectional LCBF CI PCA (Nexfin) −0.285
RCBF CI PCA (Nexfin) −0.376
Eicke et al., 2001 (14) Patients 13 [30] 60±13.7 Cross-sectional CBF EF TTE 0.39
CBF CI TTE −0.73

†, change values. CBF, carotid blood flow; CCFT, corrected carotid flow time; CI, cardiac index; CO, cardiac output; CSDVTI, carotid systodiastolic flow velocity-time integral; CSVTI, carotid systolic flow velocity-time integral; CVTI, carotid velocity-time integral; DCBF, diastolic carotid blood flow; EF, ejection fraction; ICU, intensive care unit; LCBF, left carotid blood flow; LVOT VTI, left ventricle outflow tract velocity-time integral; PCA, pulse contour analysis; post CPSV, post-fluid therapy carotid peak systolic velocity; pre CPSV, pre-fluid therapy carotid peak systolic velocity; RCBF, right carotid blood flow; SCBF, systolic carotid blood flow; SD, standard deviation; SV, stroke volume; TCBF, total (systolic and diastolic) carotid blood flow; TD, thermodilution; TEE, transesophageal echocardiography; TTE, transthoracic echocardiography.

Methodological quality

In this review, the AHRQ scale was employed to evaluate cross-sectional studies. There were four low-quality studies and 13 moderate-quality studies identified (Table 2).

Table 2

Risk assessment results of bias included in cross-sectional studies (score)

Study [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] Total scores
Patnaik et al., 2023 (21) 1 1 1 0 0 1 0 0 1 1 0 6
Roy et al., 2023 (27) 1 1 1 0 0 0 0 0 1 1 0 5
Bu et al., 2023 (22) 1 0 1 0 0 1 0 1 1 1 0 6
Van Houte et al., 2023 (28) 1 1 1 0 0 1 0 0 0 0 0 4
Cheong et al., 2023 (9) 1 1 1 0 0 1 0 0 1 0 0 5
Van Houte et al., 2022 (10) 1 1 1 0 0 0 0 0 0 0 0 3
Kenny et al., 2021 (29) 1 1 1 0 0 1 0 0 1 0 0 5
Kenny et al., 2020 (31) 1 1 1 0 0 1 1 0 1 0 0 6
Kenny et al., 2020 (30) 1 1 1 0 0 1 0 0 1 1 0 6
Sidor et al., 2020 (8) 1 0 1 0 0 0 0 0 0 1 0 3
Girotto et al., 2018 (23) 1 1 1 0 0 1 0 1 1 0 0 6
Ma et al., 2017 (24) 1 1 1 0 0 1 0 0 0 0 0 4
Roehrig et al., 2017 (25) 1 0 1 0 0 1 0 1 1 0 0 5
Peachey et al., 2016 (32) 1 0 1 0 0 0 1 0 1 1 0 5
Weber et al., 2016 (26) 1 0 1 0 0 0 0 1 0 0 0 3
Weber et al., 2015 (13) 1 0 1 0 0 0 0 0 0 1 0 3
Eicke et al., 2001 (14) 1 1 1 0 0 0 0 0 0 1 0 4

[1] Define the source of information (survey, record review). [2] List inclusion and exclusion criteria for exposed and unexposed subjects (cases and controls) or refer to previous publications. [3] Indicate the time period used for identifying patients. [4] Indicate whether subjects were consecutive if not population-based. [5] Indicate if evaluators of subjective components of the study were masked to other aspects of the status of the participants. [6] Describe any assessments undertaken for quality assurance purposes (e.g., tests/retests of primary outcome measurements). [7] Explain any patient exclusions from analysis. [8] Describe how confounding was assessed and/or controlled. [9] If applicable, explain how missing data were handled in the analysis. [10] Summarize patient response rates and completeness of data collection. [11] Clarify what follow-up, if any, was expected and the percentage of patients for which incomplete data or follow-up was obtained.

Meta-analysis

A meta-analysis of the included studies was conducted by extracting correlation coefficients comparing CDU-derived parameters and CO measurements from different studies and performing the appropriate conversions. As seen in the forest plot, the pooled effect size (z) was 0.58 (95% CI: 0.41–0.76). After conversion, the pooled r value was 0.53 (95% CI: 0.39–0.64), exhibiting significant heterogeneity among studies (I2=87.2%, P<0.001) (Figure 2A).

Figure 2 The correlation between carotid Doppler ultrasound-derived parameters and CO measurements (A), sensitivity analysis of the meta-analysis (B), and the funnel plot of publication bias (C). */**, different studies with the same first author; ^, change values. CBF, carotid blood flow; CCFT, corrected carotid flow time; CI, cardiac index; CO, cardiac output; CSDVTI, carotid systodiastolic flow velocity-time integral; CSVTI, carotid systolic flow velocity-time integral; CVTI, carotid velocity-time integral; DCBF, diastolic carotid blood flow; EF, ejection fraction; ES, effect size; LCBF, left carotid blood flow; LVOT VTI, left ventricle outflow tract velocity-time integral; post CPSV, post-fluid therapy carotid peak systolic velocity; pre CPSV, pre-fluid therapy carotid peak systolic velocity; RCBF, right carotid blood flow; SCBF, systolic carotid blood flow; SV, stroke volume; TCBF, total (systolic and diastolic) carotid blood flow; VTI, velocity-time integral.

Sensitivity analysis

Sensitivity analysis of the meta-analysis was also performed. Sequentially omitting each study revealed that no single study significantly altered the combined results, suggesting that the results were statistically stable and reliable (Figure 2B).

Publication bias

The funnel plots were symmetrical (Figure 2C). Egger regression analysis indicated no publication bias (P>0.05).

Subgroup analysis

Comparison of correlation coefficients of CDU-derived parameters and CO measurements regarding change values and absolute value measurements

Some reports have suggested that trending and functional monitoring study designs may be more clinically relevant than measuring absolute values (1). Thus, we analyzed the correlation coefficients of CDU-derived parameters and CO measurements with respect to change values and absolute value measurements in the included studies. A total of 13 studies (8-10,13,14,21-28) measured the absolute values with a pooled effect size z=0.46 (95% CI: 0.28–0.65) and a pooled r=0.43 (95% CI: 0.27–0.57), with significant heterogeneity (I2=86.8%, P<0.001). A total of 7 studies (21,25,26,29-32) stated change values with a pooled effect size z=0.99 (95% CI: 0.52–1.47) and a pooled r=0.76 (95% CI: 0.49–0.90) with significant heterogeneity among the studies (I2=88.6%, P<0.001) (Figure 3A). Egger’s regression analysis demonstrated no publication bias (P>0.05) in the absolute value measurement group and publication bias (P<0.05) in the dynamic-change group. For further validation of the publication bias, the trim and fill method was employed after including four virtual studies, and a meta-analysis of all studies was conducted again (Figure 3B). Our analysis showed significant heterogeneity (Q=137.32, P<0.001) and an index of synergism (IOR) at 0.50 (95% CI: 0.03–0.97) using a random-effects model.

Figure 3 The correlation between carotid Doppler ultrasound-derived parameters and CO measurements with change values and absolute value measurements (A), the trim and fill method of publication bias for the dynamic change group (B). */**, different studies with the same first author; ^, change values. CBF, carotid blood flow; CCFT, corrected carotid flow time; CI, cardiac index; CO, cardiac output; CSDVTI, carotid systodiastolic flow velocity-time integral; CSVTI, carotid systolic flow velocity-time integral; CVTI, carotid velocity-time integral; DCBF, diastolic carotid blood flow; EF, ejection fraction; ES, effect size; LCBF, left carotid blood flow; LVOT VTI, left ventricle outflow tract velocity-time integral; post CPSV, post-fluid therapy carotid peak systolic velocity; pre CPSV, pre-fluid therapy carotid peak systolic velocity; RCBF, right carotid blood flow; SCBF, systolic carotid blood flow; SV, stroke volume; TCBF, total (systolic and diastolic) carotid blood flow; VTI, velocity-time integral.

Comparison of correlation coefficients of different CDU-derived parameters with CO measurements

Although the current study demonstrated that certain CDU-derived parameters show promise for hemodynamic monitoring, the correlation between each CDU-derived parameter and CO measurements remains unclear (33,34). We analyzed the correlation coefficients of different CDU-derived parameters with the corresponding CO measurements in the included studies. This analysis revealed that 10 studies (8,10,13,14,21-26) mentioned CBF with a pooled effect size z=0.35 (95% CI: 0.12–0.57) and a pooled r=0.33 (95% CI: 0.12–0.52) with significant heterogeneity (I2=88.3%, P<0.001). Meanwhile, two studies (23,27) mentioned CPSV with a pooled effect size z=0.43 (95% CI: 0.21–0.64) and a pooled r=0.40 (95% CI: 0.21–0.57) with no heterogeneity (I2=0.0%, P>0.05). Additionally, five studies (8,24,28-30) mentioned CCFT with a pooled effect size z=0.67 (95% CI: 0.32–1.03) and a pooled r=0.59 (95% CI: 0.31–0.77) with significant heterogeneity (I2=68.4%, P<0.001). Finally, six studies (9,10,29-32) mentioned CVTI with a pooled effect size z=1.12 (95% CI: 0.72–1.51) and a pooled r=0.81 (95% CI: 0.62–0.91) with significant heterogeneity (I2=83.4%, P<0.001) (Figure 4A). Egger’s regression analysis indicated a publication bias (P>0.05) in the CCFT group and no publication bias (P<0.05) in the other three groups. For publication bias, the trim-and-fill method was employed after including two virtual studies, and a meta-analysis of all studies was conducted again (Figure 4B). The test for heterogeneity result showed Q=33.77, P<0.001 and the combined effect index result showed IOR =0.42 (95% CI: 0.01–0.82) using the random-effects model.

Figure 4 The correlation between different carotid Doppler ultrasound-derived parameters and CO measurements (A), the trim and fill method of publication bias for the corrected carotid flow time group (B). */**, different studies with the same first author; ^, change values. CBF, carotid blood flow; CCFT, corrected carotid flow time; CI, cardiac index; CO, cardiac output; CSDVTI, carotid systodiastolic flow velocity-time integral; CSVTI, carotid systolic flow velocity-time integral; CVTI, carotid velocity-time integral; DCBF, diastolic carotid blood flow; EF, ejection fraction; ES, effect size; LCBF, left carotid blood flow; LVOT VTI, left ventricle outflow tract velocity-time integral; post CPSV, post-fluid therapy carotid peak systolic velocity; pre CPSV, pre-fluid therapy carotid peak systolic velocity; RCBF, right carotid blood flow; SCBF, systolic carotid blood flow; SV, stroke volume; TCBF, total (systolic and diastolic) carotid blood flow; VTI, velocity-time integral.

Discussion

CDU is a non-invasive, radiation-free imaging technique used to evaluate carotid artery structure, blood flow, and pathologies. It combines B-mode imaging for anatomical visualization with color and spectral Doppler to assess blood flow velocity and direction. During the procedure, patients lie supine with their neck slightly extended, and high-frequency linear transducers (5–13 MHz) are used to image the common, internal, and external carotid arteries. Color Doppler identifies flow patterns and potential stenosis, whereas spectral Doppler measures parameters such as peak systolic velocity, end-diastolic velocity, resistive index, and pulsatility index. A typical exam lasts 20–45 minutes, though hemodynamic measurements require only 3–5 cardiac cycles (35,36).

The validity of CO assessment methods—TEE (37), PCA (38), and TD (39)—depends on their principles and clinical context. TD is often considered the gold standard, providing reliable CO measurements in stable conditions by tracking temperature changes in the pulmonary artery. However, its invasiveness and susceptibility to errors in arrhythmias or valvular issues limit its use in dynamic settings. TEE offers real-time, non-invasive visualization of aortic flow and cardiac function, correlating moderately with TD in perioperative scenarios, but its accuracy relies on operator expertise and can be affected by turbulent flow or poor probe positioning. PCA, derived from arterial waveform analysis, enables continuous CO monitoring and is minimally invasive, though its reliability decreases during vasopressor use or vascular tone instability, requiring periodic recalibration. Although TD is the most validated method, TEE and PCA provide complementary insights, with TEE excelling in structural evaluation and PCA tracking trends in real-time. No single method perfectly reflects CO universally; their validity is context-dependent, and integrating multiple approaches may enhance accuracy in complex clinical scenarios.

In this study, we extracted the correlation coefficients of each study to obtain a consistent result. Although significant heterogeneity was noted, based on I2 values, the results indicated a positive correlation between the different CDU-derived parameters and the corresponding CO measurements. The change in values of parameters were more relevant than measuring absolute values. CVTI demonstrated a stronger correlation with CO measurements than the other three parameters.

As previously mentioned, approximately 15% of the resting CO is distributed to the brain (40). Changes in arterial blood flow and CO may also be proportional (22). Thus, flow through the CCA may serve as a surrogate parameter for CO. However, earlier studies have reported conflicting results regarding the relationship between CO and CBF (25,26). This discrepancy may arise from cerebral autoregulatory processes and complex neurovascular coupling mechanisms that complicate data interpretation (41,42). Furthermore, human factors may contribute to errors in the Doppler velocity measurements (43), whereas the relationship between CO and CBF may vary depending on the clinical situation. Through meta-analysis, we determined that CDU-derived parameters exhibited a moderate effect size with the corresponding CO measurements (pooled r=0.53, 95% CI: 0.39–0.64) according to Cohen’s conventions (r=0.3–0.5, moderate; r>0.5, large) (44). The moderate correlation highlights CDU’s potential as a screening or adjunct tool but underscores the need for caution when interpreting absolute values in clinical decision-making, particularly in hemodynamically unstable patients where small errors could have significant consequences, emphasizing that CDU should complement, not replace, existing methods unless validated in specific populations (e.g., stable outpatient cohorts) (45,46). The large heterogeneity in participant selection, monitoring time and site, monitoring equipment and methods, and other factors may account for the differences in outcomes among these studies. Therefore, we employed a random-effects model for the analysis, and our sensitivity analysis revealed that no single study significantly altered the combined results, indicating that the results were statistically stable and reliable.

Volume responsiveness, namely, an increase in CO following a fluid bolus or a maneuver to centralize blood volume, is often employed to guide the administration of intravenous fluids (47). The clinical utility of carotid Doppler measurements in assessing volume responsiveness is determined by the level of correlation between the CO and the precision of tracking changes in the CO (34). We conducted a subgroup analysis to compare the correlation coefficients of the CDU-derived parameters and CO measurements with respect to change values and absolute values. Our study demonstrated a higher correlation between changes in CDU and CO measurements (0.76, 95% CI: 0.49–0.90) as compared to absolute measurements (0.43, 95% CI: 0.27–0.57). This finding also confirms that monitoring study designs employing trending and changing hemodynamics may be more clinically relevant than measuring absolute values.

In this investigation, four CDU-derived parameters and the corresponding CO monitoring results were used for the subgroup analysis. A frequently employed parameter in carotid ultrasound is the VTI that represents the area under the velocity-time curve of a pulsed wave Doppler signal (48). Several studies have proposed measuring the CVTI as a surrogate for stroke volume (29-31). This meta-analysis found that CVTI had a strong positive correlation with the corresponding CO measurements (0.81, 95% CI: 0.62–0.91) with I2=83.4%, P<0.001. This result suggests that CVTI can be used as a surrogate marker for CO measurements.

CPSV variation is a suitable surrogate marker for stroke volume variation. Roy et al. discovered a correlation between CPSV and left ventricular outflow tract VTI variability (27). Owing to limited research and data, we analyzed only three outcomes and found that the CPSV had a medium correlation with the corresponding CO measurements (0.40, 95% CI: 0.21–0.57). Accordingly, CPSV variations may also have the potential to be used in place of CO measurements.

CBF is calculated as VTI × cross-sectional area (CSA) of CCA × heart rate per minute (mL/min) (49). On average, approximately 15% of the resting CO is allocated to CBF (40). Changes in arterial blood flow and CO may also be proportional (50); however, contradictory results have been published on this issue (13,26). Our meta-analysis indicated that CBF had a weak positive correlation with corresponding CO measurements (0.33, 95% CI: 0.12–0.52). The underlying reason for this poor performance likely lies not in the physiological premise but in the technical execution—specifically, the calculation of the CSA of the carotid. This step introduces a critical source of measurement error. As the CSA is calculated from the diameter squared, even minor inaccuracies in vessel measurement—which can easily arise from subtle deviations from a true transverse imaging plane—are amplified quadratically, leading to substantial variability in the final CBF value (51). This leads to higher requirements for the accuracy of carotid artery diameter measurements.

Drawing an analogy to aortic flow time measured via echocardiography, the CCFT quantifies the duration of systolic ejection as observed in the carotid arterial waveform (52). To normalize for the confounding influence of heart rate and enable meaningful comparisons, this value is corrected to yield the CCFT (53,54). The CCFT is the systolic flow time corrected for the pulse rate. However, the relationship between CCFT and CO measurements remains uncertain. Our meta-analysis revealed that CCFT had a notable positive correlation with the corresponding CO measurements (0.59, 95% CI: 0.31–0.77). Perhaps due to the lack of carotid artery diameter measurements, CCFT exhibited a better correlation with CO measurements than CBF.

The findings of this meta-analysis substantiate the clinical utility of CDU as a non-invasive tool for monitoring CO. Its application is particularly advantageous in settings requiring rapid or serial assessments, such as outpatient clinics, emergency department triage, and for intraoperative hemodynamic trending. The robust correlation observed between the CVTI and CO measurements supports the use of CVTI as a reliable surrogate for monitoring dynamic changes in stroke volume. This capability is highly relevant for guiding therapeutic interventions such as fluid challenges or the administration of vasoactive agents, and offers a valuable method for steering fluid resuscitation in critical care, especially in clinical contexts where invasive monitoring is contraindicated or unfeasible. Furthermore, the utility of CDU in dynamic monitoring is underscored by the finding that correlations are significantly stronger for delta (change) values than for absolute measurements. This reinforces its value in scenarios such as the management of post-surgical or septic patients, where tracking hemodynamic trends over time is of greater clinical relevance than single, isolated readings. Nevertheless, it is crucial to acknowledge that the overall correlations, although significant, remain moderate, and CDU is subject to technical limitations, including operator dependency and anatomical constraints. Consequently, CDU should be regarded as a complementary adjunct to, rather than a replacement for, gold-standard invasive methods in the management of hemodynamically unstable patients. Conversely, in stable patient populations—for instance, during routine hypertension screening or within cardiac rehabilitation programs—CDU presents a compelling option to reduce the reliance on, and the associated risks of, invasive procedures.

This study has several limitations. First, the sample size of the included studies was relatively small, and the total number of cases was limited. Second, the results were heterogeneous. Due to the inclusion of trials from different countries and hospitals, it was impossible to avoid the effects of race, age, sex, and underlying diseases on the patients in these studies. Consequently, the findings of this study were limited by the overall low quality of evidence and the lack of robustness in higher-quality trials. Finally, this study focused on the CDU-derived parameters (CVTI, CBF, CCFT, and CPSV) using CO measurements. Owing to the limited number of reported clinical trials, limited outcome data were available for subgroup analysis. Although CDU shows promise for detecting gross changes in CO, its moderate correlation with reference standards limits its utility in settings requiring high precision. Future work should explore hybrid models combining CDU with machine learning or biomarkers of myocardial function to improve accuracy.

In summary, our meta-analysis demonstrated that CDU-derived parameters were significantly correlated with CO measurements. Considering the superficial location and easy accessibility of the carotid artery, CDU qualifies as an alternative technique for CO monitoring. However, the use of CDU may have technical limitations in several clinical situations. Severe atherosclerosis of the CCA, vascular abnormalities, a large habitus, or a muscular neck may obscure the lumen, making it difficult to obtain reliable measurements (55,56). Furthermore, comorbidities may affect flow velocity, such as in patients with disturbed cerebral autoregulation who experience unilateral or bilateral CBF alterations (57). Most critically, the accuracy of Doppler-derived measurements is highly contingent upon operator proficiency. Errors in angle correction, sample volume placement, or gain optimization can introduce significant measurement variability, demanding a high level of technical skill and standardized protocols (43). Therefore, the successful implementation of CDU for hemodynamic monitoring is conditional, hinging critically on careful patient selection to exclude those with prohibitive anatomical or physiological confounders, and on ensuring that measurements are performed by rigorously trained operators.

Conclusions

This meta-analysis suggests a positive correlation between CDU-derived parameters and CO measurements, with CVTI exhibiting a stronger correlation than the other three parameters. Additionally, the change in values demonstrates a better correlation than the absolute values. Considering the heterogeneity of the results analyzed here, future clinical trials of a higher quality with larger sample sizes are needed to validate carotid artery ultrasound parameters as alternatives for CO measurements.

Acknowledgments

We are grateful to the authors of the included trials, some of whom provided additional data for this systematic review and meta-analysis.

Footnote

Reporting Checklist: The authors have completed the PRISMA reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-24-2362/rc

Funding: This work was supported by the National Natural Science Foundation of China (Grant No. 82171265) and the Key Special Project for Technological Innovation and Application Development in Chongqing Municipality (Grant No. CSTB2022TIAD-KPX0179).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-24-2362/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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(English Language Editor: J. Jones)

Cite this article as: Chen F, Peng TT, Liu HW, Duan ZX, Yang M, Li H. Correlation between carotid Doppler ultrasound-derived parameters and cardiac output measurements: a systematic review and meta-analysis. Quant Imaging Med Surg 2025;15(11):11304-11319. doi: 10.21037/qims-24-2362

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