The Evolution of Ultrasound in Critical Care: From Procedural Guidance to Hemodynamic Monitor

Establishing a Novel Diagnostic Framework Using Handheld Point-of-Care Focused-Echocardiography (HoPE) for Acute Left-Sided Cardiac Valve Emergencies: A Bayesian Approach for Emergency Physicians in Resource-Limited Settings

Acute severe cardiac valve emergencies, such as acute severe mitral regurgitation (AMR) and acute severe aortic regurgitation (AAR), present significant challenges in terms of diagnosis and management. Handheld point-of-care ultrasound devices have emerged as potentially pivotal tools in ensuring the prompt and accurate diagnosis of these left-sided valve emergencies by emergency physicians, particularly in resource-limited settings. Despite the increased utilisation of point-of-care ultrasound by emergency physicians for the management of patients in states of acute cardiorespiratory failure, current diagnostic protocols cannot perform sufficient quantitative assessments of the left-sided cardiac valves. This review elucidates and evaluates the diagnostic utility of handheld point-of-care focused-echocardiography (HoPE) in native AMR and AAR by reviewing the relevant literature and the use of clinical case examples from the Emergency Department at Port Shepstone Regional Hospital (PSRH-ED)-a rural, resource-limited hospital located in KwaZulu-Natal, South Africa. Combining the findings of the review and clinical case illustrations, this review proceeds to synthesise a novel, Bayesian-inspired, iterative diagnostic framework that integrates HoPE into the evaluation of patients with acute cardiorespiratory failure and suspected severe left-sided valve lesions.

Can Dialysis Patients Identify and Diagnose Pulmonary Congestion Using Self-Lung Ultrasound?

BACKGROUND

With the recent developments in automated tools, smaller and cheaper machines for lung ultrasound (LUS) are leading us toward the potential to conduct POCUS tele-guidance for the early detection of pulmonary congestion. This study aims to evaluate the feasibility and accuracy of a self-lung ultrasound study conducted by hemodialysis (HD) patients to detect pulmonary congestion, with and without artificial intelligence (AI)-based automatic tools.

METHODS

This prospective pilot study was conducted between November 2020 and September 2021. Nineteen chronic HD patients were enrolled in the Soroka University Medical Center (SUMC) Dialysis Clinic. First, we examined the patient's ability to obtain a self-lung US. Then, we used interrater reliability (IRR) to compare the self-detection results reported by the patients to the observation of POCUS experts and an ultrasound (US) machine with an AI-based automatic B-line counting tool. All the videos were reviewed by a specialist blinded to the performer. We examined their agreement degree using the weighted Cohen's kappa (Kw) index.

RESULTS

A total of 19 patients were included in our analysis. We found moderate to substantial agreement between the POCUS expert review and the automatic counting both when the patient performed the LUS (Kw = 0.49 [95% CI: 0.05-0.93]) and when the researcher performed it (Kw = 0.67 [95% CI: 0.67-0.67]). Patients were able to place the probe in the correct position and present a lung image well even weeks from the teaching session, but did not show good abilities in correctly saving or counting B-lines compared to an expert or an automatic counting tool.

CONCLUSIONS

Our results suggest that LUS self-monitoring for pulmonary congestion can be a reliable option if the patient's count is combined with an AI application for the B-line count. This study provides insight into the possibility of utilizing home US devices to detect pulmonary congestion, enabling patients to have a more active role in their health care.

Surgeon-Performed Point-of-Care Ultrasound in the Diagnosis of Acute Sigmoid Diverticulitis: A Pragmatic Prospective Multicenter Cohort Study

Background and purpose Early diagnosis and risk stratification of sigmoid diverticulitis rely heavily on timely imaging. Computerized tomography (CT), the gold standard diagnostic test, may be delayed due to resource constraints or patient comorbidity. Point-of-care ultrasound (POCUS) has an established role in trauma evaluation, and could potentially diagnose and stage acute diverticulitis, thus shortening the time to definitive treatment.  Aims This study aimed to benchmark the accuracy of surgeon-performed POCUS against CT in diagnosing and staging acute diverticulitis. A secondary aim was to evaluate the duration between the POCUS and the confirmatory CT scan report. Patients and methods A pragmatic prospective multicenter cohort study (ClinicalTrials.gov Identifier: NCT02682368) was conducted. Surgeons performed point-of-care ultrasound as first-line imaging for suspected acute diverticulitis. POCUS diagnosis and radiologic Hinchey classification were compared to CT as the reference standard. Results Of 45 patients with suspected acute diverticulitis, POCUS classified 37 (82.2%) as uncomplicated diverticulitis, four (8.8%) as complicated diverticulitis, and four (8.8%) as other diagnoses. The POCUS-estimated modified radiologic Hinchey classification was largely concordant with CT staging with an accuracy of 88.8% (95% CI, 75.95-96.2%), a sensitivity of 100% (95% CI, 90.2- 100%) and a specificity of 44.4% (95% CI, 13.7-78.8%). The positive predictive value (PPV) was 87.8% and the negative predictive value (NPV) was 100%. There was moderate agreement between CT and POCUS, with a Cohen's kappa coefficient of 0.56. The mean delay between CT and POCUS was 9.14 hours (range 0.33 to 43.5). Conclusion We examined the role of POCUS in the management of acute diverticulitis and our findings suggest that it is a promising imaging modality with the potential to reduce radiation exposure and treatment delays. Adding a POCUS training module to the surgical curriculum could enhance diagnosis and expedite the management of acute diverticulitis.

Protocol for a Delphi Consensus Study to Determine the Essential and Optional Ultrasound Skills for Medical Practitioners Working in District Hospitals in South Africa

With increasing access to point of care ultrasound (POCUS) at district hospitals in South Africa, there is a lack of standardisation of skillsets among medical practitioners working at this level of care. This study protocol aims to use the Delphi process to achieve expert consensus on the essential and optional ultrasound skills required for medical practitioners working in district hospitals in South Africa. In alignment with the Delphi method, several iterative rounds will be implemented from June to November 2022. Purposive sampling will be conducted, through the recruitment of two representatives from each academic department of family medicine and two medical doctors working in district hospitals in each province in the country (N = 36). The POCUS skillsets published by the American Academy of Family Physicians will be circulated in the first iterative round, following which participants may suggest further additions. Once a consensus target of 70% has been achieved, the Delphi process will be finalised. The Delphi process and data analysis will be facilitated by an online Delphi platform. Findings from the study will provide insight into the design of the curriculum for POCUS training for medical practitioners in district hospitals and registrars in family medicine departments across the country.

Artificial intelligence (AI) versus expert: A comparison of left ventricular outflow tract velocity time integral (LVOT-VTI) assessment between ICU doctors and an AI tool

Purpose

The application of point of care ultrasound (PoCUS) in medical education is a relatively new course. There are still great differences in the existence, quantity, provision, and depth of bedside ultrasound education. The left ventricular outflow tract velocity time integral (LVOT‐VTI) has been successfully used in several studies as a parameter for hemodynamic management of critically ill patients, especially in the evaluation of fluid responsiveness. While LVOT‐VTI has been broadly used, valuable applications using artificial intelligence (AI) in PoCUS is still limited. We aimed to identify the degree of correlation between auto LVOT‐VTI and the manual LVOT‐VTI acquired by PoCUS trained ICU doctors.

Methods

Among the 58 ICU doctors who attended PoCUS training from 1 September 2019 to 30 November 2020, 46 ICU doctors who trained for more than 3 months were enrolled. At the end of PoCUS training, each of the enrolled ICU doctors acquired echocardiography parameters of a new ICU patient in 2 h after new patient was admitted. One of the two bedside expert sonographers would take standard echocardiogram of new ICU patients within 24 h. For ICU doctors, manual LVOT‐VTI was obtained for reference and auto LVOT‐VTI was calculated instantly by using an AI software tool. Based on the image quality of the auto LVOT‐VTI, ICU patients was separated into ideal group (n = 31) and average group (n = 15).

Results

Left ventricular end‐diastolic dimension (LVEDd, p = 0.1028), left ventricular ejection fraction (LVEF, p = 0.3251), left atrial dimension (LA‐d, p = 0.0962), left ventricular E/A ratio (p = 0.160), left ventricular wall motion (p = 0.317) and pericardial effusion (p = 1) had no significant difference between trained ICU doctors and expert sonographer. ICU patients in average group had greater sequential organ failure assessment (SOFA) score (7.33 ± 1.58 vs. 4.09 ± 0.57, p = 0.022) and lactic acid (3.67 ± 0.86 mmol/L vs. 1.46 ± 0.12 mmol/L, p = 0.0009) with greater value of LVEDd (51.93 ± 1.07 vs. 47.57 ± 0.89, p = 0.0053), LA‐d (39.06 ± 1.47 vs. 35.22 ± 0.98, p = 0.0334) and percentage of decreased wall motion (p = 0.0166) than ideal group. There were no significant differences of δLVOT‐VTI (|manual LVOT‐VTI – auto LVOT‐VTI|/manual VTI*100%) between the two groups (8.8% ± 1.3% vs. 10% ± 2%, p = 0.6517). Statistically, significant correlations between manual LVOT‐VTI and auto LVOT‐VTI were present in the ideal group (R² = 0.815, p = 0.00) and average group (R² = 0.741, p = 0.00).

Conclusions

ICU doctors could achieve the satisfied level of expertise as expert sonographers after 3 months of PoCUS training. Nearly two thirds of the enrolled ICU doctors could obtain the ideal view and one third of them could acquire the average view. ICU patients with higher SOFA scores and lactic acid were less likely to acquire the ideal view. Manual and auto LVOT‐VTI had statistically significant agreement in both ideal and average groups. Auto LVOT‐VTI in ideal view was more relevant with the manual LVOT‐VTI than the average view. AI might provide real‐time guidance among novice operators who lack expertise to acquire the ideal standard view.

Assessing Fluid Intolerance with Doppler Ultrasonography: A Physiological Framework

Ultrasonography is becoming the favored hemodynamic monitoring utensil of emergentologists, anesthesiologists and intensivists. While the roles of ultrasound grow and evolve, many clinical applications of ultrasound stem from qualitative, image-based protocols, especially for diagnosing and managing circulatory failure. Often, these algorithms imply or suggest treatment. For example, intravenous fluids are opted for or against based upon ultrasonographic signs of preload and estimation of the left ventricular ejection fraction. Though appealing, image-based algorithms skirt some foundational tenets of cardiac physiology; namely, (1) the relationship between cardiac filling and stroke volume varies considerably in the critically ill, (2) the correlation between cardiac filling and total vascular volume is poor and (3) the ejection fraction is not purely an appraisal of cardiac function but rather a measure of coupling between the ventricle and the arterial load. Therefore, management decisions could be enhanced by quantitative approaches, enabled by Doppler ultrasonography. Both fluid ‘responsiveness’ and ‘tolerance’ are evaluated by Doppler ultrasound, but the physiological relationship between these constructs is nebulous. Accordingly, it is argued that the link between them is founded upon the Frank–Starling–Sarnoff relationship and that this framework helps direct future ultrasound protocols, explains seemingly discordant findings and steers new routes of enquiry.

Inferring the Frank-Starling Curve From Simultaneous Venous and Arterial Doppler: Measurements From a Wireless, Wearable Ultrasound Patch

The Frank–Starling relationship is a fundamental concept in cardiovascular physiology, relating change in cardiac filling to its output. Historically, this relationship has been measured by physiologists and clinicians using invasive monitoring tools, relating right atrial pressure (P_ra) to stroke volume (SV) because the P_ra-SV slope has therapeutic implications. For example, a critically ill patient with a flattened P_ra-SV slope may have low P_ra yet fail to increase SV following additional cardiac filling (e.g., intravenous fluids). Provocative maneuvers such as the passive leg raise (PLR) have been proposed to identify these “fluid non-responders”; however, simultaneously measuring cardiac filling and output via non-invasive methods like ultrasound is cumbersome during a PLR. In this Hypothesis and Theory submission, we suggest that a wearable Doppler ultrasound can infer the P_ra-SV relationship by simultaneously capturing jugular venous and carotid arterial Doppler in real time. We propose that this method would confirm that low cardiac filling may associate with poor response to additional volume. Additionally, simultaneous assessment of venous filling and arterial output could help interpret and compare provocative maneuvers like the PLR because change in cardiac filling can be confirmed. If our hypothesis is confirmed with future investigation, wearable monitors capable of monitoring both variables of the Frank–Starling relation could be helpful in the ICU and other less acute patient settings.

W-Net: Dense and diagnostic semantic segmentation of subcutaneous and breast tissue in ultrasound images by incorporating ultrasound RF waveform data

We study the use of raw ultrasound waveforms, often referred to as the "Radio Frequency" (RF) data, for the semantic segmentation of ultrasound scans to carry out dense and diagnostic labeling. We present W-Net, a novel Convolution Neural Network (CNN) framework that employs the raw ultrasound waveforms in addition to the grey ultrasound image to semantically segment and label tissues for anatomical, pathological, or other diagnostic purposes. To the best of our knowledge, this is also the first deep-learning or CNN approach for segmentation that analyzes ultrasound raw RF data along with the grey image. We chose subcutaneous tissue (SubQ) segmentation as our initial clinical goal for dense segmentation since it has diverse intermixed tissues, is challenging to segment, and is an underrepresented research area. SubQ potential applications include plastic surgery, adipose stem-cell harvesting, lymphatic monitoring, and possibly detection/treatment of certain types of tumors. Unlike prior work, we seek to label every pixel in the image, without the use of a background class. A custom dataset consisting of hand-labeled images by an expert clinician and trainees are used for the experimentation, currently labeled into the following categories: skin, fat, fat fascia/stroma, muscle, and muscle fascia. We compared W-Net and attention variant of W-Net (AW-Net) with U-Net and Attention U-Net (AU-Net). Our novel W-Net's RF-Waveform encoding architecture outperformed regular U-Net and AU-Net, achieving the best mIoU accuracy (averaged across all tissue classes). We study the impact of RF data on dense labeling of the SubQ region, which is followed by the analyses of the generalization capability of the networks to patients and analysis on the SubQ tissue classes, determining that fascia tissues, especially muscle fascia in particular, are the most difficult anatomic class to recognize for both humans and AI algorithms. We present diagnostic semantic segmentation, which is semantic segmentation carried out for the purposes of direct diagnostic pixel labeling, and apply it to breast tumor detection task on a publicly available dataset to segment pixels into malignant tumor, benign tumor, and background tissue class. Using the segmented image we diagnose the patient by classifying the breast lesion as either benign or malignant. We demonstrate the diagnostic capability of RF data with the use of W-Net, which achieves the best segmentation scores across all classes.

Diagnostic value of ONSD in sepsis associated encephalopathy of New Zealand rabbits

OBJECTIVE

The purpose of the present study was to assess whether optic nerve sheath diameter (ONSD) measured by ultrasound could predict brain injury in sepsis associated encephalopathy (SAE).

METHODS

A total of 48 male New Zealand White rabbits were used to establish sepsis model. The levels of neuro-specific enolase (NSE), S100B, myeloperoxidase (MPO), and tumor necrosis factor-α (TNF-α) were detected by enzyme-linked immuno sorbent assay and ONSD were measured before modeling, 6 h, 12 h and 24 h after modeling. Sixteen rabbits were sacrificed for hematoxylin-eosin (HE) staining of brain tissue and the brain water content at above time points. Rabbits demonstrated brain injury by HE staining were included in the SAE group, the others were enrolled in the control group. The correlation between ONSD and MPO, NSE and S100B in the SAE group were analyzed. Receiver operator characteristic curves were generated to analyze the area under the curve (AUC), specificity and sensitivity of ONSD values for SAE.

RESULTS

Twenty-nine of 48 rabbits had brain injury, while 19 cases were enrolled in the control group. The level of MPO, NSE, S100B, TNF-α at 6 h, 12 h and 24 h in SAE group were all higher than those of the control group with statistical significance. The ONSD in SAE group increased with time and significantly wider than those in the control group. Correlation analysis revealed that ONSD was positively correlated with MPO, NSE and S100B in the SAE group. The AUCs for the ONSD value in diagnosing SAE at 6 h, 12 h and 24 h were 0.864, 0.957, 0.877, respectively.

CONCLUSIONS

Alterations in ONSD strongly correlated with MPO, NSE and S100B among SAE rabbits. Monitoring of ONSD exhibited a high predictive value for SAE.

Training strategies for point of care ultrasound in the ICU

PURPOSE OF REVIEW

Ultrasound in critical care medicine (CCUS) is a relatively young tool that has been evolving rapidly as skillsets, applications and technology continue to progress. Although ultrasound is identified as a core competency in intensive care unit (ICU) training, there remains significant variability and inconsistencies in the delivery of ultrasound training. The goal of this narrative review is to explore areas of consensus and highlight areas where consensus is lacking to bring attention to future directions of ultrasound training in critical care medicine.

RECENT FINDINGS

There exists considerable variation in competencies identified as basic for CCUS. Recent efforts by the European Society of Intensive Care Medicine serve as the most up to date iteration however implementation is still limited by regional expertise and practice patterns. Major barriers to ultrasound training in the ICU include a lack of available experts for bedside teaching and a lack of familiarity with new technology.

SUMMARY

Though international uptake of CCUS has made many gains in the past 20 years, further adoption of technology will be required to overcome the traditional barriers of CCUS training. Although the availability and time constraints of experts will remain a limitation even with wireless capabilities, the ability to expand beyond the physical constraints of an ultrasound machine will vastly benefit efforts to standardize training and improve access to knowledge.

A novel, hands-free ultrasound patch for continuous monitoring of quantitative Doppler in the carotid artery

Quantitative Doppler ultrasound of the carotid artery has been proposed as an instantaneous surrogate for monitoring rapid changes in left ventricular output. Tracking immediate changes in the arterial Doppler spectrogram has value in acute care settings such as the emergency department, operating room and critical care units. We report a novel, hands-free, continuous-wave Doppler ultrasound patch that adheres to the neck and tracks Doppler blood flow metrics in the common carotid artery using an automated algorithm. String and blood-mimicking test objects demonstrated that changes in velocity were accurately measured using both manually and automatically traced Doppler velocity waveforms. In a small usability study with 22 volunteer users (17 clinical, 5 lay), all users were able to locate the carotid Doppler signal on a volunteer subject, and, in a subsequent survey, agreed that the device was easy to use. To illustrate potential clinical applications of the device, the Doppler ultrasound patch was used on a healthy volunteer undergoing a passive leg raise (PLR) as well as on a congestive heart failure patient at resting baseline. The wearable carotid Doppler patch holds promise because of its ease-of-use, velocity measurement accuracy, and ability to continuously record Doppler spectrograms over many cardiac and respiratory cycles.

Functional Hemodynamic Monitoring With a Wireless Ultrasound Patch

In this Emerging Technology Review, a novel, wireless, wearable Doppler ultrasound patch is described as a tool for resuscitation. The device is designed, foremost, as a functional hemodynamic monitor-a simple, fast, and consistent method for measuring hemodynamic change with preload variation. More generally, functional hemodynamic monitoring is a paradigm that helps predict stroke volume response to additional intravenous volume. Because Doppler ultrasound of the left ventricular outflow tract noninvasively measures stroke volume in realtime, it increasingly is deployed for this purpose. Nevertheless, Doppler ultrasound in this manner is cumbersome, especially when repeat assessments are needed. Accordingly, peripheral arteries have been studied and various measures from the common carotid artery Doppler signal act as windows to the left ventricle. Yet, handheld Doppler ultrasound of a peripheral artery is susceptible to human measurement error and statistical limitations from inadequate beat sample size. Therefore, a wearable Doppler ultrasound capable of continuous assessment minimizes measurement inconsistencies and smooths inherent physiologic variation by sampling many more cardiac cycles. Reaffirming clinical studies, the ultrasound patch tracks immediate SV change with excellent accuracy in healthy volunteers when cardiac preload is altered by various maneuvers. The wearable ultrasound also follows jugular venous Doppler, which qualitatively trends right atrial pressure. With further clinical research and the application of artificial intelligence, the monitoring modalities with this new technology are manifold.