Clinical application of point-of-care ultrasound during cardiopulmonary resuscitation: a systematic review and meta-analysis

Introduction

Cardiopulmonary resuscitation (CPR) is an emergency response in patients with cardiac arrest and the timeliness and effectiveness of treatment may be of enormous significance to survival and neurological prognosis (1). In spite of the establishment and improvement of the CPR processes, reversible factors leading to cardiac arrest during CPR are difficult to monitor. Point-of-care ultrasound (PoCUS) is a non-invasive and bedside imaging technology, and it could potentially rapidly assess cardiac activity, pericardial effusion, and other life-threatening diseases by Rea et al. (2). Its ability to provide real-time visualizations may help clinicians to make decisions in time without interrupting resuscitations. Algorithms in advanced life support are gradually integrating PoCUS to provide assurance of higher diagnostic accuracy by Tseng et al. (3). Some of the observational studies suggest that PoCUS can be used to enhance the identification of cardiac standstill, right ventricle (RV) strain, and hypovolemia during arrest by Paul & Panzer (4). However, as noted by Betz et al. (5) and Wang et al. (6), a systematic review is yet to justify its clinical value assessment due to the heterogeneity of the research and the findings. The purpose of this review was to compile and quantitatively synthesize PoCUS use in CPR.

CPR PoCUS in the emergency department (ED) assists the medical specialists in evaluating cardiac activity and reversible causes like tamponade, pulmonary embolism, or severe hypovolemia as Saadi et al. (7). It assists in the identification of real cardiac arrest and pseudo-arrest and instructions are provided on the continuation of resuscitation or discontinuation of resuscitation as reported by Liu et al. (8) and Dinsmore & Venkatraghavan (9). CPR efficiency can be guided by observing direct ventricular compression rather than left ventricular outflow tract (LVOT) obstruction using PoCUS, according to Senman et al. (10). It is also capable of giving real-time procedural advice during emergencies of treatment, pericardiocentesis or fluid resuscitation. Combination of CPR and ultrasound must be provided with the minimal number of interruptions in compressions as possible and, therefore, must be properly trained and given standard procedures as emphasized by Xiong et al. (11) and Zhu et al. (12). According to other research, PoCUS influences clinical judgment, but it is still unclear how it affects survival outcomes, a gap highlighted by Stolz et al. (13) and Koratala et al. (14). PoCUS is an essential bedside imaging tool in critical cardiovascular care for immediate detection of potentially fatal pathologies and facilitating emergency clinical decisions, as Liu & Ye (15). In addition, structured training in PoCUS improves the competence of the operator and increases the use of PoCUS in the acute care setting, making it possible for PoCUS to be integrated in cardiac arrest evaluation and treatment protocol (16), as Peyrony et al. (16) confirmed.

The second important use of echocardiography in CPR includes the recognition of a resumption of cardiac movement to determine return of spontaneous circulation (ROSC). New evidence indicates that cardiac activity up to PoCUS has a significant correlation with ROSC. The sensitivity and specificity of PoCUS in predicting ROSC among patients presenting with pulseless electrical activity (PEA) were most recently examined in a meta-analysis by Jian et al. (17), which found that the sensitivity was 86% [95% confidence interval (CI): 0.67–0.95] and the specificity was 64% (95% CI: 0.51–0.75). Serial PoCUS tests conducted every two minutes while doing CPR were reported to have 100% specificity for non-ROSC in sustained cardiac standstill for ≥10 minutes in Heydari et al. (18) previous investigation, highlighting its prognostic value. Early echocardiographic standstill (around 8 minutes after the onset of enhanced life support) exhibited a positive predictive value of 84% (95% CI: 78–89%) for the lack of ROSC, with low end-tidal CO₂ levels serving as another predictor, according to Javaudin et al. (19) proposed example of out-of-hospital cardiac arrest (OHCA). In order to address this, Zaki et al. (20) have suggested that PoCUS should not be used as the only guidance for cessation decisions because operator expertise variability, procedural interruptions, and study heterogeneity all contribute to the low level of evidence, as Lalande et al. (21) argued. This article is presented in accordance with the PRISMA reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-2026-1-0191/rc).

Methods

This systematic review and meta-analysis were conducted following a predefined methodology; however, no protocol for the review was registered in the PROSPERO database.

Data sources & searchers

A comprehensive and systematic literature search was conducted across multiple electronic databases, including MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials, Web of Science, and Scopus, from inception to December 2024 and updated in September 2025. The search strategy combined Medical Subject Headings (MeSH) and free-text keywords related to “PoCUS”, “cardiopulmonary resuscitation”, “cardiac arrest”, “echocardiography”, “return of spontaneous circulation”, and “survival outcomes”, using Boolean operators (AND, OR).

Study selection and eligibility criteria

An electronic search was used to locate all published analytical quantitative research, randomized controlled trials, non-randomized controlled studies, and observational studies related to the review issue about the clinical use of the PoCUS approach in CPR. The database search was initially conducted from inception to December 2024 and was subsequently updated in September 2025 to include the most recent studies using PubMed, EMBASE, Cochran Central, Internet of Science, Scopus, and the clinical trial registries. The updated search was necessary to capture newly published evidence and ensure that the review reflects the most current state of research in this rapidly evolving field. Given the increasing number of recent studies on PoCUS in CPR, particularly those published in 2025, incorporating an updated search improves the completeness, validity, and clinical relevance of the meta-analysis findings. Institutional subscriptions offered full-text access when available. The reference lists of the included studies and earlier systematic reviews were manually searched for other relevant papers. Boolean operators (AND, OR) and MeSH keywords were employed to optimize the search strategy. “PoCUS”, “cardiopulmonary resuscitation”, “cardiac arrest”, “echocardiography”, “ROSC”, “survival to discharge”, “diagnostic accuracy”, along with “resuscitation outcomes” were the terms that were most important. It included human participants from all age groups, including adult and pediatric participants, and received approval for this review. The studies were accepted without any age or gender or demographic restrictions as long as they satisfied the established requirements for inclusion. The researchers excluded clinical case reports along with extremely small case series from their quantitative meta-analysis because these studies did not provide enough participants to meet statistical requirements, which would have allowed for generalization of their results. The researchers used this method to create a meta-analysis that relied on evidence that demonstrated greater credibility and a better ability to represent the population studied. Identical datasets and research relevant to PoCUS in cardiac arrest were eliminated, as were publications with insufficient data to create 2×2 diagnostic tables. With a direct impact on enhancing clinical outcomes, this method ensures the synthesis of trustworthy, medically pertinent information for pooled analysis.

Eligibility criteria

The systematic review process for the current review is shown in the flowchart. Of these, 3,142 records were from single databases, i.e., from MEDLINE, EMBASE, Scopus, Web of Science and Cochrane Central. Each database’s records were tracked individually to maintain accountability for the records prior to deduplication and screening. The title and abstract screening process began after 148 duplicate records were removed, which left 2,994 unique records for examination. The reviewers excluded 2,530 records from the study because the contents did not meet their relevance and quality standards. The researchers evaluated 464 articles, which they obtained through full-text assessment, to determine their eligibility status. The review process resulted in the exclusion of 427 articles because their content did not meet the requirements for review design, data sufficiency, or relevance. Nineteen papers were selected from the full-text articles that satisfied the inclusion criteria and were included in the qualitative synthesis. Of the full-text articles assessed, 18 were excluded, resulting in 19 studies included in the qualitative synthesis. Necessary data for inclusion in their quantitative meta-analysis. The high risk of bias, poor quality, or insufficient reporting of key outcomes required for analysis. Any work lacking adequate rigor or complete outcome information was not considered suitable for inclusion in the review and was therefore removed from further evaluation to maintain the reliability and validity of results shown in Figure 1.

Figure 1 Meta analysis flow chart of included and excluded studies.

Table 1 summarizes the predefined inclusion and exclusion criteria used for selecting studies in the systematic review and meta-analysis, based on study population, design, and outcome relevance.

Data extraction

A third investigator confirmed the work’s accuracy and consistency after two reviewers independently finished the data extraction process. To collect information about the trial population, operator type, ultrasound window type, beginning rhythm, and resuscitative outcomes such as ROSC, survival to the hospital, or death to hospital release. The other data set was diagnosis accuracy, which included true positives, false positives, false negatives, and true negatives. The values were subsequently compiled in 2×2 tables for contingencies to pool in the evaluation after this data extraction, following the approach of Kang et al. (22) and Kreiser et al. (23).

Quality assessment

Two reviewers independently assessed the included papers’ methodological quality using standardized bias techniques, and differences were settled by consensus and discussion. Non-randomized studies were assessed using the Risk of Bias in Non-randomized Studies of Interventions (ROBINS-I) instrument, and RCTs were assessed using the improved Risk of Bias 2 (RoB 2) tool. All the studies were taken into account in various important areas: selection of the participants, intervention classification, confounding, measurement of the results, and reporting bias. Studies were also assessed as technologically advanced, involving live-stream PoCUS during resuscitation, as demonstrated by Hafner et al. (24).

The analysis of visual risk-of-bias was done with the help of RIAT software tools, so a systematic comparison of studies was done as well, but it was also possible to see whether the existence of heterogeneity may occur. Detailed research, which should enlighten on the time and method of administering PoCUS in resuscitation, specifically, was found especially helpful, as it reduces variability by the operator, as Wastl et al. (25) specifically highlighted. These studies were supportive, e.g., narrative reviews and pediatric-based, however, failed to be included in the meta-analysis due to the methodological differences noted by Levitt et al. (26) and Miller et al. (27). Besides the systematic assessment, the quality assessment aimed to evaluate the methodological rigor and potential bias of included method, paying enough attention to such aspects as small sample size, difference in the training of the operators, or the level of reporting outcomes. The risk-of-bias assessment was conducted using domains adapted from ROBINS-I and RoB 2, enabling a consistent and structured evaluation across the included studies. These domains were harmonized to facilitate uniform reporting and improve comparability of bias judgments within a tabular framework. The adopted approach was specifically designed for clarity and coherence in presentation and is distinct from the QUADAS-2 assessment methodology.

Outcomes

The main outcome measurements that were considered in these studies included patient survival till death or hospital discharge within 30 days after cardiac arrest; secondary outcome measurements included ROSC and survival upon hospital admission to treat cardiac arrest. Other diagnostic endpoints studied included the absence or presence of spontaneous cardiac motions by PoCUS, investigating its prognostic values during CPR. Studies showed that early cardiac activities were strongly associated with higher ROSC, with cardiac standstill persisting and predicting poor outcomes.

Statistical analysis

Data extracted from included studies, comprising study attributes, patient population, ultrasound protocols, and clinical outcomes, were entered systemically in a pre-arranged manner consistent with the frameworks of Yamada et al. (28) and Betz et al. (5). Where possible, quantitative outcomes such as neurological outcomes, survival to hospital release, and ROSC were combined, drawing on the pooling methodology of Basmaji et al. (29). Dichotomous data were quantitatively analyzed with 95% risk ratios and confidence intervals, whereas continuous outcomes were presented with mean differences or standardized mean differences, in line with Martinez et al. (30). The I² statistic was used to calculate heterogeneity; values greater than 50% represented significant heterogeneity, following the threshold adopted by Osterwalder et al. (31). When heterogeneity was found to be significant, the random-effects model was used; otherwise, the fixed-effects model was employed as recommended by Wastl et al. (25). All of the highly biased articles were excluded, and sensitivity analysis was conducted, building on the approach of Park et al. (32). The subgroups were analyzed in terms of patient characteristics, ultrasound methods, and settings, as classified by Bieler et al. (33). Statistical analysis was carried out by R (version 4.3.0) and Review Manager (RevMan) (version 5.4) consistent with the analytical pipeline of Lin et al. (34). Data synthesis was carried out to confirm accuracy and reproducibility, as validated by Sutton et al. (35). The quality of evidence was assessed according to the GRADE protocol, the recommendation was rated for strength and level of evidence, and the results were reviewed with respect to their clinical relevance and the methodological quality of the studies used, in accordance with the GRADE framework applied by Hellenthal et al. (36).

Results

When 3,558 patients were pooled for analysis, clinical outcomes were observed to be very good with PoCUS during CPR. PoCUS-guided resuscitation showed better chances of survival until discharge from hospital (19% versus 12%) and a higher rate of recovery of circulatory function (43% versus 28%) than would be expected with routine CPR. These benefits were observed across a wide range of clinical settings, including EDs, intensive care units, prehospital environments, and hospital wards, in both adult and pediatric populations. All in all, the results highlight PoCUS as a reliable prognostic instrument to enhance decision-making and, possibly, reduce inter-operator variability in handling cardiac arrest. Comprising randomized controlled trials and prospective observational studies and retrospective observational studies and cohort studies and feasibility analyses. The study populations included both adult and pediatric patients who were treated in different medical environments, including in-hospital and out-of-hospital and ED and prehospital settings. The researchers conducted studies at different scales, which included both small single-centre studies and large multi-centre trials to investigate various research designs and medical settings. The different ways of using PoCUS during resuscitation procedures created multiple operating procedures, which resulted in different operator skills and different times of use throughout the resuscitation process.

Secondary outcomes

Table 2 includes the studies that assessed PoCUS in the resuscitation and emergency care setting, which were very heterogeneous. Multi-center RCTs included in Ienghong et al. (37) and Maganti et al. (38) offered level 1 evidence in the pediatric and ICU groups, and observational cohort studies included by Wolfe et al. (39) offered real-world information based on adult and prehospital settings, as reported by Vianen et al. (40) and Yanni et al. (41). The primary factors of operation that were examined in the course of feasibility and pilot studies included the capability to implement PoCUS in the situation of remote supervision or in the context of high-time-sensitive C PR guidelines, as explored by Lau et al. (42) and Kim et al. (43). At the same time, the combination of inculcatory types of studies also enhances evidence through repetitive nature of the essence of experimental design with real clinical experiences, as Hermann et al. (44) illustrated. As Persson et al. (45) noted, patient samples varied considerably, with single centres with under 100 participants in studies by Riishede et al. (46) and Patail et al. (47), and bigger ones with more than 300 participants, as in Reihan et al. (48) and Hanson et al. (49), and the multi-centres of beyond 1,000 participants. Pediatric research (43-45) was necessary to investigate age-related uses of PoCUS, while adult research more typically reported mean ages of between 55 and 65 years, the age group most impacted by cardiac arrest. Many studies did not report age or initial rhythm [non-shockable rhythm (NS)], a limitation noted by Manthena et al. (50), limiting comparability between cohorts, as Beaulac et al. (51) acknowledged. Despite these gaps, the range of clinical settings, from emergency rooms to helicopter emergency medical services, shows how adaptable PoCUS is as a diagnostic and prognostic tool in a variety of contexts as evidenced by Leviter et al. (52) and Thandar et al. (53). PoCUS during resuscitation, especially cardiac arrest, has been linked to better outcomes, including increased chances of ROSC and a more effective resuscitation, and, therefore, it is believed to be a useful component in CPR, as Zaki et al. (20) concluded. A small subset of case-based studies was retained for qualitative synthesis to offer supplementary clinical insights and aid in contextual interpretation. Nevertheless, these studies were not considered for quantitative meta-analysis owing to their limited sample sizes and insufficient statistical strength, which may affect the reliability and generalizability of the aggregated results. All things considered, this diversity increases the findings’ generalizability and makes it easier to integrate them into resuscitation procedures. The features of the included studies are displayed in Table 2.

Studies on PoCUS

Table 3 compiles many studies that look at the use of PoCUS in various patient contexts. Each study includes information about the author or authors, patient selection, index test, reference standard, flow and timing, and associated risk factors. The studies encompass heterogeneous patient groups, ranging from those with cardiac arrest, acute failure, or trauma, and evaluate the utilization of PoCUS training or distinct diagnostic protocols. All studies are classified according to their study design (prospective, observational, retrospective) and risk factors, with the majority of studies falling into the low-risk (LR) and high-risk (HR) group for patient selection, index tests, and reference standards. Risk levels are indicated for different factors, such as study flow and timing. The table offers a detailed comparison of how PoCUS is used in various clinical settings.

Geographic distribution of included studies

Figure 2 displays the regional spread of the studies included, with North America leading with a total of seven studies, followed by six studies in Europe and five studies in Asia. Africa is represented by a single study, highlighting a significant regional imbalance. This disproportionate distribution illustrates that research inputs are predominantly found in the developed world in the form of North America and Europe, whereas developing countries are underrepresented. These imbalances are indicative of possible gaps in international research coverage that can restrict the generalizability of findings across contexts. This imbalanced representation can also indicate differences in funding, infrastructure, and access to research material, which can affect the number and scale of research studies undertaken. Cultural, socio-economic, and policy variations between regions can also impact study design and transferability, further confining the universality of the conclusions reached. Bridging these regional disparities is essential in order to develop more universal, globally applicable evidence to guide policy and practice in a variety of settings.

Figure 2 Geographic distribution of included studies.

ROSC prediction by PoCUS

Figure 3 is a forest plot illustrating the ROSC prediction of ROSC by PoCUS with accuracy on the Y-axis and odds ratios with 95% CIs on the X-axis. Every horizontal line is a separate study, with the black dot being the point estimate of the odds ratio and the blue lines showing the confidence intervals. The vertical red dashed line at 1.0 is used as the line of no effect to compare if the odds ratio shows significant predictive capacity. The majority of studies present odds ratios above 1, indicating that PoCUS can be a beneficial tool in predicting ROSC. The grouping of accuracy values between 0.70 and 0.85 also reflects its stable performance. Broader confidence intervals in a few experiments represent variability and smaller sample sizes, while narrower ones demonstrate more precise estimates. The most accurate predictions occur near 0.85, which demonstrates high predictive power in a few situations. Some experiments, however, demonstrate lower accuracy near 0.65, which indicates restrictions in particular contexts. On the whole, this figure illustrates that PoCUS is generally a reliable and clinically useful prediction of ROSC, although there is heterogeneity among individual studies.

Figure 3 ROSC prediction by PoCUS. CI, confidence interval; PoCUS, point-of-care ultrasound; ROSC, return of spontaneous circulation.

Subgroup analysis

Figure 4 presents a subgroup analysis of the adult versus the pediatric population wherein the effect size (log odds ratio) has been used. The combination of the pediatric group (red) is the one with the highest effect size and the most significant, but with a wide confidence interval, which suggests that strong effects with significant variability occur in this group. Conversely, the individual pediatric studies (green) are more consistent with small confidence intervals that indicate the validity of the findings. In adults, the cumulative group (purple) has a moderate effect size with a broad confidence interval indicating more heterogeneity among the adult studies. At the same time, the individual adult studies (blue) are concentrated around the value of the effect size of about 0.5, which is a stable but moderate effect. On the whole, the number indicates heterogeneity between adult and pediatric populations, with more variability in the adult research.

Figure 4 Subgroup analysis for adult vs. pediatric populations.

Discussion

PoCUS is becoming more and more valued as a bedside technique for treating cardiac arrest, particularly in young patients. Its ability to quickly assess heart activity and guide resuscitation treatment has been demonstrated by research, which improves decision-making in life-threatening circumstances, as shown by Reihan et al. (48) and Chouhan et al. (54). The practicability of using PoCUS in high-pressure situations, including pediatric cardiac arrest, has been validated, indicating that clinicians are able to conduct focused exams without hindering major interruption of routine resuscitation protocols, findings corroborated by Reihan et al. (48) and Zaki et al. (20). Additionally, PoCUS permits real-time observation of cardiac performance, a process that can give early clues regarding the prognosis of the patient as well as inform therapeutic decisions, as Leviter et al. (55) reported. The technology facilitates non-invasive monitoring, minimizing the use of more invasive diagnostic tests. Its portability and ease of deployment make it ideal for emergency and critical care settings. Together, these findings each highlight PoCUS as a promising option to supplement traditional resuscitation efforts.

Beyond being feasible, PoCUS has been successfully implemented within training and simulation settings, increasing clinician proficiency with CPR, as Rymarczyk et al. (56) demonstrated. Simulation and virtual modules enable healthcare professionals to optimize image acquisition and interpretation, which is essential with the time-sensitive context of cardiac arrest situations as Manthena et al. (50) established. Educational interventions such as these result in improved guideline compliance while reducing time lost during patient care. Furthermore, safe practice protocols have been established to ensure that the use of ultrasound does not disrupt chest compressions or other resuscitative procedures, as outlined by Heiman et al. (57). These protocols are important for promoting high-quality CPR while tapping into the diagnostic advantages of PoCUS. Overall, the incorporation of formal training guarantees efficacy and safety in actual resuscitation efforts.

The development of holistic clinical models in the management of cardiac arrest is also related to PoCUS, with Beaulac et al. (51), Chouhan et al. (54), and Zaki et al. (20) each contributing to this evolving framework. Researchers emphasize that it is useful in the identification of modifiable sources of arrest and in real-time clinical decision-making, and may directly affect survival rates as a growing body of evidence from Beaulac et al. (51) through Chouhan et al. (54) suggests. It has developed protocols to facilitate its use by standardizing its use and making sure that high-risk pediatric and adult patients get the right and evidence-based interventions, as the implementation framework proposed by Loscalzo et al. (58) recommends. With the use of PoCUS as a part of resuscitation, clinicians can better triage interventions, anticipate complications, and record accurate functional cardiac status. These can be used in generalized application in several different clinical settings, bridging gaps between training, evidence, and practice. This will be essential as further refinement of guidelines and patient-centred outcomes are maximized through continuous evaluation as more adoption occurs.

The medical field requires standardized training programs combined with operational procedures to establish proper usage of PoCUS during CPR. Future practice should emphasize protocol-driven PoCUS use, incorporation into advanced life support algorithms, and the development of competency-based training frameworks. The implementation of real-time feedback systems together with artificial intelligence-based image analysis technologies will improve diagnostic precision and clinical decision-making processes throughout the resuscitation process.

Conclusions

In a range of environments, including prehospital, EDs, intensive care units, and hospital wards, PoCUS during CPR is effective in both adult and pediatric populations. Early detection of potentially reversible causes of cardiac arrest and prompt, targeted management are made possible by PoCUS. The technique reduces operator-dependent variability and enhances decision-making in high-stakes resuscitation scenarios. Innovations in technology, such as live-stream PoCUS, and methodological standards further enhance its reliability and reproducibility. In conclusion, the inclusion of PoCUS in CPR guidelines promotes evidence-based practice, enhances patient outcomes, and provides a foundation for uniform training and application. Major multi-center trials, artificial intelligence-enabled automated interpretation, and measurement of long-term neurological outcomes need to be looked into in future studies to optimize protocols and establish long-term benefits in varied clinical settings.

Acknowledgments

I would like to express my sincere gratitude to my fellow team members for their valuable assistance with the literature screening and data extraction processes.

Footnote

Reporting Checklist: The author has completed the PRISMA reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-2026-1-0191/rc

Funding: None.

Conflicts of Interest: The author has completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-2026-1-0191/coif). The author has no conflicts of interest to declare.

Ethical Statement: The author is 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.

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