Venous congestion in septic shock quantified with point-of-care ultrasound: a pilot prospective multicentre cohort study.

PURPOSE: Venous congestion is a pathophysiologic state that can result in organ dysfunction, particularly acute kidney injury (AKI). We sought to evaluate the feasibility of performing a definitive observational study to determine the impact of venous congestion quantified using point-of-care ultrasound (POCUS) in patients with septic shock. METHODS: We conducted a prospective observational feasibility study at two intensive care units (ICUs). We recruited adult patients with septic shock within 12 hr of ICU admission. Using the validated Venous Excess Ultrasound Score (VEXUS), we quantified venous congestion on day 1 and day 3 of ICU admission. The primary feasibility outcome was successful completion rate of the two VEXUS scores. We performed a survival analysis to quantify the hazard of renal replacement therapy (RRT). RESULTS: We enrolled 75 patients from January 2022 to January 2023. The success rate of completion for VEXUS scans was 94.5% (95% confidence interval [CI], 89.5 to 97.6). Severe venous congestion was present in 19% (14/75) of patients on ICU admission day 1 and in 16% (10/61) of patients on day 3. Venous congestion on ICU admission may be associated with a higher risk of requiring RRT (unadjusted hazard ratio, 3.35; 95% CI, 0.94 to 11.88; P = 0.06). CONCLUSIONS: It is feasible to conduct a definitive observational study exploring the association between venous congestion quantified with POCUS and clinical outcomes in patients with septic shock. We hypothesize that venous congestion may be associated with an increased hazard of receiving RRT.

RéSUMé: OBJECTIF: La congestion veineuse est un état physiopathologique qui peut entraîner un dysfonctionnement des organes, en particulier une insuffisance rénale aiguë (IRA). Nous avons cherché à évaluer la faisabilité de la réalisation d'une étude observationnelle définitive pour déterminer l'impact de la congestion veineuse quantifiée à l'aide de l'échographie ciblée (POCUS) chez des patient·es en choc septique. MéTHODE: Nous avons réalisé une étude de faisabilité observationnelle prospective dans deux unités de soins intensifs (USI). Nous avons recruté des patient·es adultes souffrant d'un choc septique dans les 12 heures suivant leur admission aux soins intensifs. À l'aide du score VEXUS (score d'échographie de l'excès veineux) validé, nous avons quantifié la congestion veineuse au jour 1 et au jour 3 de leur admission aux soins intensifs. Le principal critère de faisabilité était le taux de réussite des deux scores VEXUS. Nous avons réalisé une analyse de survie pour quantifier le risque de thérapie de substitution rénale (TSR). RéSULTATS: Nous avons recruté 75 patient·es de janvier 2022 à janvier 2023. Le taux de réussite des scores VEXUS était de 94,5 % (intervalle de confiance [IC] à 95 %, 89,5 à 97,6). Une congestion veineuse sévère était présente chez 19 % (14/75) des patient·es au jour 1 d'admission aux soins intensifs et chez 16 % (10/61) des patient·es au jour 3. La congestion veineuse lors de l'admission aux soins intensifs peut être associée à un risque plus élevé de nécessiter une TSR (rapport de risque non ajusté, 3,35; IC 95 %, 0,94 à 11,88; P = 0,06). CONCLUSION: Il est possible de mener une étude observationnelle définitive explorant l'association entre la congestion veineuse quantifiée par POCUS et les devenirs cliniques chez les patient·es en choc septique. Nous émettons l'hypothèse que la congestion veineuse peut être associée à un risque accru de recevoir une thérapie de substitution rénale.

Automated Real-Time Detection of Lung Sliding Using Artificial Intelligence: A Prospective Diagnostic Accuracy Study.

BACKGROUND: Rapid evaluation for pneumothorax is a common clinical priority. Although lung ultrasound (LUS) often is used to assess for pneumothorax, its diagnostic accuracy varies based on patient and provider factors. To enhance the performance of LUS for pulmonary pathologic features, artificial intelligence (AI)-assisted imaging has been adopted; however, the diagnostic accuracy of AI-assisted LUS (AI-LUS) deployed in real time to diagnose pneumothorax remains unknown. RESEARCH QUESTION: In patients with suspected pneumothorax, what is the real-time diagnostic accuracy of AI-LUS to recognize the absence of lung sliding? STUDY DESIGN AND METHODS: We performed a prospective AI-assisted diagnostic accuracy study of AI-LUS to recognize the absence of lung sliding in a convenience sample of patients with suspected pneumothorax. After calibrating the model parameters and imaging settings for bedside deployment, we prospectively evaluated its diagnostic accuracy for lung sliding compared with a reference standard of expert consensus. RESULTS: Two hundred forty-one lung sliding evaluations were derived from 62 patients. AI-LUS showed a sensitivity of 0.921 (95% CI, 0.792-0.973), specificity of 0.802 (95% CI, 0.735-0.856), area under the receiver operating characteristic curve of 0.885 (95% CI, 0.828-0.956), and accuracy of 0.824 (95% CI, 0.766-0.870) for the diagnosis of absent lung sliding. INTERPRETATION: Real-time AI-LUS shows high sensitivity and moderate specificity to identify the absence of lung sliding. Further research to improve model performance and optimize the integration of AI-LUS into existing diagnostic pathways is warranted.

Medico-legal risks of point-of-care ultrasound: a closed-case analysis of Canadian Medical Protective Association medico-legal cases.

BACKGROUND: Point-of-care ultrasound (POCUS) has become a core diagnostic tool for many physicians due to its portability, excellent safety profile, and diagnostic utility. Despite its growing use, the potential risks of POCUS use should be considered by providers. We analyzed the Canadian Medical Protective Association (CMPA) repository to identify medico-legal cases arising from the use of POCUS. METHODS: We retrospectively searched the CMPA closed-case repository for cases involving diagnostic POCUS between January 1st, 2012 and December 31st, 2021. Cases included civil-legal actions, medical regulatory authority (College) cases, and hospital complaints. Patient and physician demographics, outcomes, reason for complaint, and expert-identified contributing factors were analyzed. RESULTS: From 2012 to 2021, there were 58,626 closed medico-legal cases in the CMPA repository with POCUS determined to be a contributing factor for medico-legal action in 15 cases; in all cases the medico-legal outcome was decided against the physicians. The most common reasons for patient complaints were diagnostic error, deficient assessment, and failure to perform a test or intervention. Expert analysis of these cases determined the most common contributing factors for medico-legal action was failure to perform POCUS when indicated (7 cases, 47%); however, medico-legal action also resulted from diagnostic error, incorrect sonographic approach, deficient assessment, inadequate skill, inadequate documentation, or inadequate reporting. CONCLUSIONS: Although the most common reason associated with the medico-legal action in these cases is failure to perform POCUS when indicated, inappropriate use of POCUS may lead to medico-legal action. Due to limitations in granularity of data, the exact number of civil-legal, College cases, and hospital complaints for each contributing factor is unavailable. To enhance patient care and mitigate risk for providers, POCUS should be carefully integrated with other clinical information, performed by providers with adequate skill, and carefully documented.

Resuscitative transesophageal echocardiography during the acute resuscitation of trauma: A retrospective observational study.

BACKGROUND: Resuscitative transesophageal echocardiography (TEE) is an emerging POCUS modality that can be used to guide trauma resuscitation. METHODS: Trauma patients who underwent TEE within 24 h of admission from 2013 to 2022 were prospectively identified. We retrospectively analyzed resuscitative TEE reports and patient charts in duplicate. RESULTS: 29 providers performed TEE for 54 acute trauma patients. 28 (52%) died in hospital; 33 (61%) required operative intervention (<24 h). Median injury severity score was 29 [IQR 22-43]. The most common indications for TEE were hemodynamic instability (34, 63%), inadequate windows for transthoracic echocardiography (14, 26%) and cardiac arrest (11, 20%). There were no identified complications. A new diagnosis was made in 31 (57%) cases: most commonly right ventricular dysfunction (10, 19%), pericardial effusion (9, 17%), and hypovolemia (6, 11%). TEE ruled out major cardiac injury in 83% of cases. TEE changed resuscitative strategy, in 17 (32%) patients, diagnostic imaging approach in 6 (11%) patients, procedural or operative approach in 5 (9%) patients and disposition from the trauma bay in 4 (7%) patients. CONCLUSION: Resuscitative TEE during acute trauma care has an additional diagnostic yield to existing diagnostic pathways and may impact definitive management for some patients in the trauma bay.

Introducing medical students to deep learning through image labelling: a new approach to meet calls for greater artificial intelligence fluency among medical trainees.

Our approach addresses the urgent need for AI experience for the doctors of tomorrow. Through a medical education-focused approach to data labelling, we have fostered medical student competence in medical imaging and AI. We envision our framework being applied at other institutions and academic groups to develop robust labelling programs for research endeavours. Application of our approach to core visual modalities within medicine (e.g. interpretation of ECGs, diagnostic imaging, dermatologic findings) can lead to valuable student experience and competence in domains that feature prominently in clinical practice, while generating much needed data in fields that are ripe for AI integration.

Notre approche répond au besoin urgent de familiariser les médecins de demain avec l'IA. Nous avons cherché à développer leurs compétences en imagerie médicale et en IA par une approche à l'étiquetage de données axée sur la formation médicale. D'autres établissements et groupes universitaires souhaitant mettre sur pied des programmes d'étiquetage solides pour leurs projets de recherche pourraient adopter notre modèle. L'application de notre approche aux principales modalités visuelles en médecine (par exemple, l'interprétation des ECG, l'imagerie diagnostique, le diagnostic des lésions dermatologiques) peut permettre aux étudiants d'acquérir une expérience et des compétences précieuses dans des domaines importants de la pratique clinique, tout en procurant des données indispensables dans des secteurs qui sont mûrs pour une intégration de l'IA.

Doppler identified venous congestion in septic shock: protocol for an international, multi-centre prospective cohort study (Andromeda-VEXUS).

INTRODUCTION: Venous congestion is a pathophysiological state where high venous pressures cause organ oedema and dysfunction. Venous congestion is associated with worse outcomes, particularly acute kidney injury (AKI), for critically ill patients. Venous congestion can be measured by Doppler ultrasound at the bedside through interrogation of the inferior vena cava (IVC), hepatic vein (HV), portal vein (PV) and intrarenal veins (IRV). The objective of this study is to quantify the association between Doppler identified venous congestion and the need for renal replacement therapy (RRT) or death for patients with septic shock. METHODS AND ANALYSIS: This study is a prespecified substudy of the ANDROMEDA-SHOCK 2 (AS-2) randomised control trial (RCT) assessing haemodynamic resuscitation in septic shock and will enrol at least 350 patients across multiple sites. We will include adult patients within 4 hours of fulfilling septic shock definition according to Sepsis-3 consensus conference. Using Doppler ultrasound, physicians will interrogate the IVC, HV, PV and IRV 6-12 hours after randomisation. Study investigators will provide web-based educational sessions to ultrasound operators and adjudicate image acquisition and interpretation. The primary outcome will be RRT or death within 28 days of septic shock. We will assess the hazard of RRT or death as a function of venous congestion using a Cox proportional hazards model. Sub-distribution HRs will describe the hazard of RRT given the competing risk of death. ETHICS AND DISSEMINATION: We obtained ethics approval for the AS-2 RCT, including this observational substudy, from local ethics boards at all participating sites. We will report the findings of this study through open-access publication, presentation at international conferences, a coordinated dissemination strategy by investigators through social media, and an open-access workshop series in multiple languages. TRIAL REGISTRATION NUMBER: NCT05057611.

Start spreading the news: a deliberate approach to POCUS program development and implementation.

While there is an expanding body of literature on Point-of-Care Ultrasound (POCUS) pedagogy, administrative elements that are necessary for the widespread adoption of POCUS in the clinical environment have received little attention. In this short communication, we seek to address this gap by sharing our institutional experience with POCUS program development and implementation. The five pillars of our program, selected to tackle local barriers to POCUS uptake, are education, workflow, patient safety, research, and sustainability. Our program logic model outlines the inputs, activities, and outputs of our program. Finally, key indicators for the monitoring of program implementation efforts are presented. Though designed for our local context, this approach may readily be adapted toward other clinical environments. We encourage others leading the integration of POCUS at their centers to adopt this approach not only to achieve sustainable change but also to ensure that quality safeguards are in place.

Ten Influential Point-of-Care Ultrasound Papers: 2022 in Review.

Keeping up with the latest developments in the point-of-care ultrasound (POCUS) literature is challenging, as with any area of medicine. Our group of POCUS experts has selected 10 influential papers from the past 12 months and provided a short summary of each. We hope to provide emergency physicians, intensivists, and other acute care providers with a succinct update concerning some key areas of ultrasound interest.

Acquisition and retention of lung ultrasound skills by respiratory therapists: A curriculum for respiratory therapists.

Purpose: Lung point-of-care ultrasound (POCUS) is a versatile bedside tool. The utility of POCUS has grown during the coronavirus disease 2019 pandemic, as it allows clinicians to obtain real-time images without requiring transport of the patient outside the intensive care unit. As respiratory therapists (RTs) are involved in caring for those with respiratory failure, there is a good rationale for their adoption of lung ultrasound. However, no training standards have been defined. Our objective was to develop and implement a training programme for RTs to achieve and sustain competence in lung ultrasound. Methods: This was a single-centre, prospective, single-cohort observational study. A total of 10 RTs completed our curriculum and were tasked with independently completing and interpreting 10 initial lung ultrasound exams and 3 subsequent exams after a 6-week interim period. All exams were blindly overread by a local expert in lung ultrasound. Results: After completing the curriculum, RTs were able to acquire and accurately interpret their images over 85% of the time. They were more successful in the upper lung zone image acquisition and interpretation compared with the lower lung zones. After 6 weeks, the RTs' lung POCUS skills remained stable, and their lower lung zone image interpretation improved. The RTs reported that their confidence improved throughout the study. Conclusion: The RTs in our study have demonstrated competence in acquisition and interpretation of upper lung zone images. They have also reported confidence in acquiring and interpreting upper lung zone images. More experience appears to be required to gain competence and confidence in lower lung zone ultrasound. Next steps would be to repeat the present study with a higher number of RTs completing at least 20 lung POCUS studies.

Prospective Real-Time Validation of a Lung Ultrasound Deep Learning Model in the ICU.

OBJECTIVES: To evaluate the accuracy of a bedside, real-time deployment of a deep learning (DL) model capable of distinguishing between normal (A line pattern) and abnormal (B line pattern) lung parenchyma on lung ultrasound (LUS) in critically ill patients. DESIGN: Prospective, observational study evaluating the performance of a previously trained LUS DL model. Enrolled patients received a LUS examination with simultaneous DL model predictions using a portable device. Clip-level model predictions were analyzed and compared with blinded expert review for A versus B line pattern. Four prediction thresholding approaches were applied to maximize model sensitivity and specificity at bedside. SETTING: Academic ICU. PATIENTS: One-hundred critically ill patients admitted to ICU, receiving oxygen therapy, and eligible for respiratory imaging were included. Patients who were unstable or could not undergo an LUS examination were excluded. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: A total of 100 unique ICU patients (400 clips) were enrolled from two tertiary-care sites. Fifty-six patients were mechanically ventilated. When compared with gold standard expert annotation, the real-time inference yielded an accuracy of 95%, sensitivity of 93%, and specificity of 96% for identification of the B line pattern. Varying prediction thresholds showed that real-time modification of sensitivity and specificity according to clinical priorities is possible. CONCLUSIONS: A previously validated DL classification model performs equally well in real-time at the bedside when platformed on a portable device. As the first study to test the feasibility and performance of a DL classification model for LUS in a dedicated ICU environment, our results justify further inquiry into the impact of employing real-time automation of medical imaging into the care of the critically ill.

Indications, Clinical Impact, and Complications of Critical Care Transesophageal Echocardiography: A Scoping Review.

BACKGROUND: Critical care transesophageal echocardiography (ccTEE) is an increasingly popular tool used by intensivists to characterize and manage hemodynamics at the bedside. Its usage appears to be driven by expanded diagnostic scope as well as the limitations of transthoracic echocardiography (TTE) - lack of acoustic windows, patient positioning, and competing clinical interests (eg, the need to perform chest compressions). The objectives of this scoping review were to determine the indications, clinical impact, and complications of ccTEE. METHODS: MEDLINE, EMBASE, Cochrane, and six major conferences were searched without a time or language restriction on March 31st, 2021. Studies were included if they assessed TEE performed for adult critically ill patients by intensivists, emergency physicians, or anesthesiologists. Intraoperative or post-cardiac surgical TEE studies were excluded. Study demographics, indication for TEE, main results, and complications were extracted in duplicate. RESULTS: Of the 4403 abstracts screened, 289 studies underwent full-text review, with 108 studies (6739 patients) included. Most studies were retrospective (66%), performed in academic centers (84%), in the intensive care unit (73%), and were observational (55%). The most common indications for ccTEE were hemodynamic instability, trauma, cardiac arrest, respiratory failure, and procedural guidance. Across multiple indications, ccTEE was reported to change the diagnosis in 52% to 78% of patients and change management in 32% to79% patients. During cardiac arrest, ccTEE identified the cause of arrest in 25% to 35% of cases. Complications of ccTEE included two cases of significant gastrointestinal bleeding requiring intervention, but no other major complications (death or esophageal perforation) reported. CONCLUSIONS: The use of ccTEE has been described for the diagnosis and management of a broad range of clinical problems. Overall, ccTEE was commonly reported to offer additional diagnostic yield beyond TTE with a low observed complication rate. Additional high quality ccTEE studies will permit stronger conclusions and a more precise understanding of the trends observed in this scoping review.

Initial evaluation of extracorporeal immunomodulatory therapy for the treatment of critically ill COVID-19 infected patients.

Severe COVID-19 infection results in significant immune dysregulation resulting from excessive recruitment and activation of neutrophils. The aim of this study was to confirm feasibility, initial safety and detect signal of efficacy of a non-propriety device delivered using an intermittent extra-corporeal system (LMOD) allowing leucocytes modulation in the setting of Severe COVID-19 infection. Twelve patients were recruited. Inclusion criteria were > 18 years age, confirmed COVID-19, acute respiratory distress syndrome requiring mechanical support and hypotension requiring vasopressor support. Primary end point was vasopressor requirements (expressed as epinephrine dose equivalents) and principle secondary endpoints related to safety, ability to deliver the therapy and markers of inflammation assessed over five days after treatment initiation. LMOD treatment appeared safe, defined by hemodynamic stability and no evidence of white cell number depletion from blood. We demonstrated a significant decrease in vasopressor doses (-37%, p = 0.02) in patients receiving LMOD therapy (despite these patients having to tolerate an additional extracorporeal intermittent therapy). Vasopressor requirements unchanged/increasing in control group (+ 10%, p = 0.48). Although much about the use of this therapy in the setting of severe COVID-19 infection remains to be defined (e.g. optimal dose and duration), this preliminary study supports the further evaluation of this novel extracorporeal approach.

Enhancing Annotation Efficiency with Machine Learning: Automated Partitioning of a Lung Ultrasound Dataset by View.

BACKGROUND: Annotating large medical imaging datasets is an arduous and expensive task, especially when the datasets in question are not organized according to deep learning goals. Here, we propose a method that exploits the hierarchical organization of annotating tasks to optimize efficiency. METHODS: We trained a machine learning model to accurately distinguish between one of two classes of lung ultrasound (LUS) views using 2908 clips from a larger dataset. Partitioning the remaining dataset by view would reduce downstream labelling efforts by enabling annotators to focus on annotating pathological features specific to each view. RESULTS: In a sample view-specific annotation task, we found that automatically partitioning a 780-clip dataset by view saved 42 min of manual annotation time and resulted in 55±6 additional relevant labels per hour. CONCLUSIONS: Automatic partitioning of a LUS dataset by view significantly increases annotator efficiency, resulting in higher throughput relevant to the annotating task at hand. The strategy described in this work can be applied to other hierarchical annotation schemes.

Accurate assessment of the lung sliding artefact on lung ultrasonography using a deep learning approach.

Pneumothorax is a potentially life-threatening condition that can be rapidly and accurately assessed via the lung sliding artefact generated using lung ultrasound (LUS). Access to LUS is challenged by user dependence and shortage of training. Image classification using deep learning methods can automate interpretation in LUS and has not been thoroughly studied for lung sliding. Using a labelled LUS dataset from 2 academic hospitals, clinical B-mode (also known as brightness or two-dimensional mode) videos featuring both presence and absence of lung sliding were transformed into motion (M) mode images. These images were subsequently used to train a deep neural network binary classifier that was evaluated using a holdout set comprising 15% of the total data. Grad-CAM explanations were examined. Our binary classifier using the EfficientNetB0 architecture was trained using 2535 LUS clips from 614 patients. When evaluated on a test set of data uninvolved in training (540 clips from 124 patients), the model performed with a sensitivity of 93.5%, specificity of 87.3% and an area under the receiver operating characteristic curve (AUC) of 0.973. Grad-CAM explanations confirmed the model's focus on relevant regions on M-mode images. Our solution accurately distinguishes between the presence and absence of lung sliding artefacts on LUS.

Clinical characteristics, multiorgan dysfunction and outcomes of patients with COVID-19: a prospective case series.

BACKGROUND: Characterizing the multiorgan manifestations and outcomes of patients hospitalized with COVID-19 will inform resource requirements to address the long-term burden of this disease. We conducted a descriptive analysis using prospectively collected data to describe the clinical characteristics and spectrum of organ dysfunction, and in-hospital and longer-term clinical outcomes of patients hospitalized with COVID-19 during the first wave of the pandemic at a Canadian centre. METHODS: We conducted a prospective case series involving adult patients (aged ≥ 18 yr) with COVID-19 admitted to 1 of 2 hospitals in London, Ontario, from Mar. 17 to June 18, 2020, during the first wave of the pandemic. We recorded patients' baseline characteristics, physiologic parameters, measures of organ function and therapies administered during hospitalization among patients in the intensive care unit (ICU) and in non-ICU settings, and compared the characteristics of hospital survivors and nonsurvivors. Finally, we recorded follow-up thoracic computed tomography (CT) and echocardiographic findings after hospital discharge. RESULTS: We enrolled 100 consecutive patients (47 women) hospitalized with COVID-19, including 32 patients who received ICU care and 68 who received treatment in non-ICU settings. Respiratory sequelae were common: 23.0% received high-flow oxygen by nasal cannula, 9.0% received noninvasive ventilation, 24.0% received invasive mechanical ventilation and 2.0% received venovenous extracorporeal membrane oxygenation. Overall, 9.0% of patients had cerebrovascular events (3.0% ischemic stroke, 6.0% intracranial hemorrhage), and 6.0% had pulmonary embolism. After discharge, 11 of 19 patients had persistent abnormalities on CT thorax, and 6 of 15 had persistent cardiac dysfunction on echocardiography. INTERPRETATION: This study provides further evidence that COVID-19 is a multisystem disease involving neurologic, cardiac and thrombotic dysfunction, without evidence of hepatic dysfunction. Patients have persistent organ dysfunction after hospital discharge, underscoring the need for research on long-term outcomes of COVID-19 survivors.

Ten Influential Point-of-Care Ultrasound Papers: 2021 in Review.

The ongoing rapid expansion of point-of-care ultrasound (POCUS) and its corresponding supporting literature leaves the frontline clinician in a difficult position when trying to keep abreast of the latest developments. Our group of POCUS experts has selected ten influential POCUS-related papers from the past twelve months and provided a short summary of each. Our aim is to give to emergency physicians, intensivists, and other acute care providers key information, helping them to keep up to date on rapidly evolving POCUS literature.

Stroke Volume Determination by Echocardiography.

Basic critical care echocardiography emphasizes two-dimensional (2D) findings, such as ventricular function, inferior vena cava size, and pericardial assessment, while generally excluding quantitative findings and Doppler-based techniques. Although this approach offers advantages, including efficiency and expedited training, it complicates attempts to understand the hemodynamic importance of any 2D abnormalities detected. Stroke volume (SV), as the summative event of the cardiac cycle, is the most pragmatic available indicator through which a clinician can rapidly determine, no matter the 2D findings, whether aberrant cardiac physiology is contributing to the state of shock. An estimate of SV allows 2D findings to be placed into better context in terms of both hemodynamic significance and acuity. This article describes the technique of SV determination, reviews common confounding factors and pitfalls, and suggests a systematic approach for using SV measurements to help integrate important 2D findings into the clinical context.

Criteria, Processes, and Determination of Competence in Basic Critical Care Echocardiography Training: A Delphi Process Consensus Statement by the Learning Ultrasound in Critical Care (LUCC) Initiative.

BACKGROUND: With the paucity of high-quality studies on longitudinal basic critical care echocardiography (BCCE) training, expert opinion guidelines have guided BCCE competence educational standards and processes. However, existing guidelines lack precise detail due to methodological flaws during guideline development. RESEARCH QUESTIONS: To formulate methodologically robust guidelines on BCCE training using evidence and expert opinion, detailing specific criteria for every step, we conducted a modified Delphi process using the principles of the validated AGREE-II tool. Based on systematic reviews, the following domains were chosen: components of a longitudinal BCCE curriculum; pass-grade criteria for image-acquisition and image-interpretation; and formative/summative assessment and final competence processes. STUDY DESIGN AND METHODS: Between April 2020 and May 2021, a total of 21 BCCE experts participated in four rounds. Rounds 1 and 2 used five web-based questionnaires, including branching-logic software for directed questions to individual panelists. In round 3 (videoconference), the panel finalized the recommendations by vote. During the journal peer-review process, Round 4 was conducted as Web-based questionnaires. Following each round, the agreement threshold for each item was determined as ≥ 80% for item inclusion and ≤ 30% for item exclusion. RESULTS: Following rounds 1 and 2, agreement was reached on 62 of 114 items. To the 49 unresolved items, 12 additional items were added in round 3, with 56 reaching agreement and five items remaining unresolved. There was agreement that longitudinal BCCE training must include introductory training, mentored formative training, summative assessment for competence, and final cognitive assessment. Items requiring multiple rounds included two-dimensional views, Doppler, cardiac output, M-mode measurement, minimum scan numbers, and pass-grade criteria. Regarding objective criteria for image-acquisition and image-interpretation quality, the panel agreed on maintaining the same criteria for formative and summative assessment, to categorize BCCE findings as major vs minor and a standardized approach to errors, criteria for readiness for summative assessment, and supervisory options. INTERPRETATION: In conclusion, this expert consensus statement presents comprehensive evidence-based recommendations on longitudinal BCCE training. However, these recommendations require prospective validation.