Publications by authors named "Michael Blaivas"

170 Publications

Optic Nerve Sheath Diameter Ultrasound for Raised Intracranial Pressure: A Literature Review and Meta-analysis of its Diagnostic Accuracy.

J Ultrasound Med 2021 Apr 24. Epub 2021 Apr 24.

Department of Internal Medicine, University of South Carolina, School of Medicine, Columbia, South Carolina, USA.

Optic nerve sheath diameter (ONSD) ultrasound is becoming increasingly more popular for estimating raised intracranial pressure (ICP). We performed a systematic review and analysis of the diagnostic accuracy of ONSD when compared to the standard invasive ICP measurement.

Method: We performed a systematic search of PUBMED and EMBASE for studies including adult patients with suspected elevated ICP and comparing sonographic ONSD measurement to a standard invasive method. Quality of studies was assessed using the QUADAS-2 tool by two independent authors. We used a bivariate model of random effects to summarize pooled sensitivity, specificity, and diagnostic odds ratio (DOR). Heterogeneity was investigated by meta-regression and sub-group analyses.

Results: We included 18 prospective studies (16 studies including 619 patients for primary outcome). Only one study was of low quality, and there was no apparent publication bias. Pooled sensitivity was 0.9 [95% confidence intervals (CI): 0.85-0.94], specificity was 0.85 (95% CI: 0.8-0.89), and DOR was 46.7 (95% CI: 26.2-83.2) with partial evidence of heterogeneity. The Area-Under-the-Curve of the summary Receiver-Operator-Curve was 0.93 (95% CI: 0.91-0.95, P < .05). No covariates were significant in the meta-regression. Subgroup analysis of severe traumatic brain injury and parenchymal ICP found no heterogeneity. ICP and ONSD had a correlation coefficient of 0.7 (95% CI: 0.63-0.76, P < .05).

Conclusion: ONSD is a useful adjunct in ICP evaluation but is currently not a replacement for invasive methods where they are feasible.
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http://dx.doi.org/10.1002/jum.15732DOI Listing
April 2021

WFUMB position paper on reverberation artefacts in lung ultrasound: B-lines or comet-tails?

Med Ultrason 2021 Feb;23(1):70-73

Hirslanden Kliniken Bern, Beau Site, Salem and Permanence, Bern, Switzerland.

The analysis of vertical reverberation artefacts is an essential component of the differential diagnosis in pulmonary ultra-sound. Traditionally, they are often, but not exclusively, called B-line artefacts (BLA) and/or comet tail artefacts (CTA), but this view is misleading. In this position paper we clarify the terminology and relation of the two lung reverberation artefacts BLA and CTA to spe-cific clinical scenarios. BLA are defined by a normal pleura line and are a typical hallmark of cardiogenic pulmonary edema after exclusion of certain pathologies including pneumonia or lung contusion, whereas CTAs show an irregular pleura line representing a variety of parenchymal lung diseases. The dual approach using low frequency transducers to determine BLA and high frequency transducer to determine the pleural surface is recommended.
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http://dx.doi.org/10.11152/mu-2944DOI Listing
February 2021

The COVID-19 Worsening Score (COWS)-a predictive bedside tool for critical illness.

Echocardiography 2021 02 24;38(2):207-216. Epub 2021 Jan 24.

Department of Medicine, University of Udine, Udine, Italy.

Objectives: To evaluate the accuracy of a new COVID-19 prognostic score based on lung ultrasound (LUS) and previously validated variables in predicting critical illness.

Methods: We conducted a single-center retrospective cohort development and internal validation study of the COVID-19 Worsening Score (COWS), based on a combination of the previously validated COVID-GRAM score (GRAM) variables and LUS. Adult COVID-19 patients admitted to the emergency department (ED) were enrolled. Ten variables previously identified by GRAM, days from symptom onset, LUS findings, and peripheral oxygen saturation/fraction of inspired oxygen (P/F) ratio were analyzed. LUS score as a single predictor was assessed. We evaluated GRAM model's performance, the impact of adding LUS, and then developed a new model based on the most predictive variables.

Results: Among 274 COVID-19 patients enrolled, 174 developed critical illness. The GRAM score identified 51 patients at high risk of developing critical illness and 132 at low risk. LUS score over 15 (range 0 to 36) was associated with a higher risk ratio of critical illness (RR, 2.05; 95% confidence interval [CI], 1.52-2.77; area under the curve [AUC], 0.63; 95% CI 0.676-0.634). The newly developed COVID-19 Worsening Score relies on five variables to classify high- and low-risk patients with an overall accuracy of 80% and negative predictive value of 93% (95% CI, 87%-98%). Patients scoring more than 0.183 on COWS showed a RR of developing critical illness of 8.07 (95% CI, 4.97-11.1).

Conclusions: COWS accurately identify patients who are unlikely to need intensive care unit (ICU) admission, preserving resources for the remaining high-risk patients.
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http://dx.doi.org/10.1111/echo.14962DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8013873PMC
February 2021

Point-of-care ultrasound stewardship.

J Am Coll Emerg Physicians Open 2020 Dec 11;1(6):1326-1331. Epub 2020 Oct 11.

Department of Medicine University of South Carolina Columbia South Carolina USA.

Rapid adoption and widespread use of point-of-care ultrasound (POCUS) has impacted diagnostic testing and clinical care across medical disciplines. The benefits of POCUS must be weighed against certain pitfalls, such as the risk of misdiagnosis and false assurance. Beyond technical error in image acquisition and interpretation, an important pitfall is reliance on POCUS results without considering pre-test patient characteristics or the diagnostic accuracy of POCUS in varying clinical contexts. In this article, we introduce the concept of POCUS stewardship that emphasizes critical evaluation of clinical indications prior to performing POCUS as well as the individual patient and test characteristics of POCUS when integrating results into clinical decisionmaking. Adherence to these principles can lead to optimized POCUS application and improved patient care.
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http://dx.doi.org/10.1002/emp2.12279DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7771754PMC
December 2020

A Tale of Undiagnosed Coronavirus Disease 2019 and Continued Disabling Exertional Dyspnea in a Previously Healthy and Active Patient.

Authors:
Michael Blaivas

J Ultrasound Med 2020 Dec 3. Epub 2020 Dec 3.

Department of Medicine, University of South Carolina School of Medicine, Columbia, South Carolina, USA; and Department of Emergency Medicine, St Francis Hospital, Columbus, Georgia, USA.

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http://dx.doi.org/10.1002/jum.15590DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7753772PMC
December 2020

Residual Lung Injury in Patients Recovering From COVID-19 Critical Illness: A Prospective Longitudinal Point-of-Care Lung Ultrasound Study.

J Ultrasound Med 2020 Nov 13. Epub 2020 Nov 13.

Department of Emergency Medicine, St. Francis Hospital, Columbus, Georgia, USA.

Scarce data exist regarding the natural history of lung lesions detected on ultrasound in those who survive severe COVID-19 pneumonia.

Objective: We performed a prospective analysis of point-of-care ultrasound (POCUS) findings in critically ill COVID-19 patients during and after hospitalization.

Methods: We enrolled 171 COVID-19 intensive care unit patients. POCUS of the lungs was performed with phased array (2-4 MHz), convex (2-6 MHz) and linear (10-15 MHz) transducers, scanning 12 lung areas. Chest computed tomography angiography was performed to exclude suspected pulmonary embolism. Survivors were clinically and sonographically evaluated during a 4 month period for evidence of residual lung injury. Chest computed tomography angiography and echocardiography were used to exclude pulmonary hypertension (PH) and chest high-resolution-computed-tomography to exclude interstitial lung disease (ILD) in symptomatic survivors.

Results: Cox regression analysis showed that lymphocytopenia (hazard ratio [HR]: 0.88, 95% confidence intervals [CI]: 0.68-0.96, p = 0.048), increased lactate (HR: 1.17, 95% CI: 0.94-1.46, p = 0.049), and D-dimers (HR: 1.21, 95% CI: 1.03-1.44, p = 0.03) were mortality predictors. Non-survivors had increased incidence of pulmonary abnormalities (B-lines, pleural line irregularities, and consolidations) compared to survivors (p < 0.05). During follow-up, POCUS with clinical and laboratory parameters integrated in the semi-quantitative Riyadh-Residual-Lung-Injury scale had sensitivity of 0.82 (95% CI: 0.76-0.89) and specificity of 0.91 (95% CI: 0.94-0.95) in predicting ILD. The prevalence of PH and ILD (non-specific-interstitial-pneumonia) was 7% and 11.8%, respectively.

Conclusion: POCUS showed ability to monitor the evolution of severe COVID-19 pneumonia after hospital discharge, supporting its integration in clinical predictive models of residual lung injury.
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http://dx.doi.org/10.1002/jum.15563DOI Listing
November 2020

A novel measure for characterizing ultrasound device use and wear.

J Am Coll Emerg Physicians Open 2020 Oct 23;1(5):865-870. Epub 2020 Apr 23.

Harvard Medical School Massachusetts General Hospital Massachusetts Boston USA.

Point-of-care ultrasound (POCUS) equipment management is critical in optimizing daily clinical operations in emergency departments (EDs). Traditional consultative ultrasound laboratories are well practiced at operations management, but this is not the case for POCUS programs, because machine upgrade and replacement metrics have not been developed or tested. We present a data-driven method for assessment of POCUS equipment maintenance and replacement named the ULTrA (a data-driven approach to point-of-care ultrasound upgrade) score. This novel model of assessing each ultrasound machine by quantitative scoring in each of four mostly objective categories: use (U), likeability (L), trustworthiness (Tr), and age (A). We propose the ULTrA model as a method to identify underperforming devices which could be upgraded or eliminated, and to compare relative performance amongst a group of departmental ultrasound machines. This composite score may be a useful objective tool that could replace individual proxies for clinical effectiveness, such as age, use, or individual provider preference. Additional research in multiple centers would be needed to refine and validate the ULTrA score. Once fully developed, the ULTrA score could be deployed in EDs and other clinical settings where POCUS is used to help streamline resources to maintain a functional and state-of-the-art fleet of ultrasound machines over time.
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http://dx.doi.org/10.1002/emp2.12051DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7593474PMC
October 2020

Artificial intelligence versus expert: a comparison of rapid visual inferior vena cava collapsibility assessment between POCUS experts and a deep learning algorithm.

J Am Coll Emerg Physicians Open 2020 Oct 31;1(5):857-864. Epub 2020 Jul 31.

Michigan State University-East Lansing East Lansing Michigan USA.

Objectives: We sought to create a deep learning algorithm to determine the degree of inferior vena cava (IVC) collapsibility in critically ill patients to enable novice point-of-care ultrasound (POCUS) providers.

Methods: We used publicly available long short term memory (LSTM) deep learning basic architecture that can track temporal changes and relationships in real-time video, to create an algorithm for ultrasound video analysis. The algorithm was trained on public domain IVC ultrasound videos to improve its ability to recognize changes in varied ultrasound video. A total of 220 IVC videos were used, 10% of the data was randomly used for cross correlation during training. Data were augmented through video rotation and manipulation to multiply effective training data quantity. After training, the algorithm was tested on the 50 new IVC ultrasound video obtained from public domain sources and not part of the data set used in training or cross validation. Fleiss' κ was calculated to compare level of agreement between the 3 POCUS experts and between deep learning algorithm and POCUS experts.

Results: There was very substantial agreement between the 3 POCUS experts with κ = 0.65 (95% CI = 0.49-0.81). Agreement between experts and algorithm was moderate with κ = 0.45 (95% CI = 0.33-0.56).

Conclusions: Our algorithm showed good agreement with POCUS experts in visually estimating degree of IVC collapsibility that has been shown in previously published studies to differentiate fluid responsive from fluid unresponsive septic shock patients. Such an algorithm could be adopted to run in real-time on any ultrasound machine with a video output, easing the burden on novice POCUS users by limiting their task to obtaining and maintaining a sagittal proximal IVC view and allowing the artificial intelligence make real-time determinations.
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http://dx.doi.org/10.1002/emp2.12206DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7593461PMC
October 2020

The In-Plane, Long-Axis Ultrasound Approach to Vascular Access. Try It, You Might Like It.

Pediatr Crit Care Med 2020 11;21(11):1015-1017

Department of Medicine; and Department of Emergency Medicine, University of South Carolina School of Medicine, St. Francis Hospital, Columbus, GA.

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http://dx.doi.org/10.1097/PCC.0000000000002533DOI Listing
November 2020

Accuracy of Point-of-Care Ultrasound in Detecting Fractures in Children: A Validation Study.

Ultrasound Med Biol 2021 Jan 21;47(1):68-75. Epub 2020 Oct 21.

Emergency Department, Regina Margherita Children Hospital, Turin, Italy.

This study sought to compare point-of-care ultrasound (POCUS) and conventional X-rays for detecting fractures in children. This was a prospective, non-randomized, convenience-sample study conducted in five medical centers. It evaluated pediatric patients with trauma. POCUS and X-ray examination results were treated as dichotomous variables with fracture either present or absent. Descriptive statistics were calculated in addition to prevalence, sensitivity, specificity, positive predictive value and negative predictive value, including 95% confidence intervals (CIs). The Cohen κ coefficient was determined as a measurement of the level of agreement. A total of 554 examinations were performed with POCUS and X-ray. On physical examination, swelling, localized hematoma and functional limitation were found in 66.73%, 33.78% and 53.74% of participants, respectively. The most-studied areas were limbs and hands/feet (58.19% and 38.27%), whereas the thorax was less represented (3.54%). Sensitivity of POCUS was 91.67% (95% CI, 76.41-97.82%) for high-skill providers and 71.50 % (95% CI, 64.75-77.43%) for standard-skill providers. Specificity was 88.89% (95% CI, 73.00-96.34%) and 82.91% (95% CI, 77.82-87.06%) for high- and standard-skill providers, respectively. Positive predictive value was 89.19% (95% CI, 73.64-96.48%) and 75.90% (95% CI, 69.16-81.59%) for high- and standard-skill providers, respectively. Negative predictive value was 91.43% (95% CI, 75.81-97.76%) and 79.44% (95% CI, 74.21-83.87%) for high- and standard-skill providers, respectively. The Cohen κ coefficient showed very good agreement (0.81) for high-skill providers, but moderate agreement (0.54) for standard-skill providers. We noted good diagnostic accuracy of POCUS in evaluating fracture, with excellent sensitivity, specificity, and positive and negative predictive value for high-skill providers.
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http://dx.doi.org/10.1016/j.ultrasmedbio.2020.09.012DOI Listing
January 2021

Regulating Critical Care Ultrasound, It Is All in the Interpretation.

Pediatr Crit Care Med 2021 04;22(4):e253-e258

Department of Pediatrics, Stanford University School of Medicine, Palo Alto, CA.

Point-of-care ultrasound (POCUS) use is rapidly expanding as a practice in adult and pediatric critical care environments. In January 2020, the Joint Commission endorsed a statement from the Emergency Care Research Institute citing point-of-care ultrasound as a potential hazard to patients for reasons related to training and skill verification, oversight of use, and recordkeeping and accountability mechanisms for clinical use; however, no evidence was presented to support these concerns. Existing data on point-of-care ultrasound practices in pediatric critical care settings verify that point-of-care ultrasound use continues to increase, and contrary to the concerns raised, resources are becoming increasingly available for point-of-care ultrasound use. Many institutions have recognized a successful approach to addressing these concerns that can be achieved through multispecialty collaborations.
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http://dx.doi.org/10.1097/PCC.0000000000002600DOI Listing
April 2021

Development of a Deep Learning Network to Classify Inferior Vena Cava Collapse to Predict Fluid Responsiveness.

J Ultrasound Med 2020 Oct 10. Epub 2020 Oct 10.

Department of Medicine, Division of Pulmonary Critical Care and Sleep, Warren Alert Medical School of Brown University, Providence, Rhode Island, USA.

Objectives: To create a deep learning algorithm capable of video classification, using a long short-term memory (LSTM) network, to analyze collapsibility of the inferior vena cava (IVC) to predict fluid responsiveness in critically ill patients.

Methods: We used a data set of IVC ultrasound (US) videos to train the LSTM network. The data set was created from IVC US videos of spontaneously breathing critically ill patients undergoing intravenous fluid resuscitation as part of 2 prior prospective studies. We randomly selected 90% of the IVC videos to train the LSTM network and 10% of the videos to test the LSTM network's ability to predict fluid responsiveness. Fluid responsiveness was defined as a greater than 10% increase in the cardiac index after a 500-mL fluid bolus, as measured by bioreactance.

Results: We analyzed 211 videos from 175 critically ill patients: 191 to train the LSTM network and 20 to test it. Using standard data augmentation techniques, we increased our sample size from 191 to 3820 videos. Of the 175 patients, 91 (52%) were fluid responders. The LSTM network was able to predict fluid responsiveness moderately well, with an area under the receiver operating characteristic curve of 0.70 (95% confidence interval [CI], 0.43-1.00), a positive likelihood ratio of infinity, and a negative likelihood ratio of 0.3 (95% CI, 0.12-0.77). In comparison, point-of-care US experts using video review offline and manual diameter measurement via software caliper tools achieved an area under the receiver operating characteristic curve of 0.94 (95% CI, 0.83-0.99).

Conclusions: We demonstrated that an LSTM network can be trained by using videos of IVC US to classify IVC collapse to predict fluid responsiveness. Our LSTM network performed moderately well given the small training cohort but worse than point-of-care US experts. Further training and testing of the LSTM network with a larger data sets is warranted.
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http://dx.doi.org/10.1002/jum.15527DOI Listing
October 2020

DIY AI, deep learning network development for automated image classification in a point-of-care ultrasound quality assurance program.

J Am Coll Emerg Physicians Open 2020 Apr 1;1(2):124-131. Epub 2020 Mar 1.

Department of Critical Care Medicine Western University Ontario.

Background: Artificial intelligence (AI) is increasingly a part of daily life and offers great possibilities to enrich health care. Imaging applications of AI have been mostly developed by large, well-funded companies and currently are inaccessible to the comparatively small market of point-of-care ultrasound (POCUS) programs. Given this absence of commercial solutions, we sought to create and test a do-it-yourself (DIY) deep learning algorithm to classify ultrasound images to enhance the quality assurance work-flow for POCUS programs.

Methods: We created a convolutional neural network using publicly available software tools and pre-existing convolutional neural network architecture. The convolutional neural network was subsequently trained using ultrasound images from seven ultrasound exam types: pelvis, heart, lung, abdomen, musculoskeletal, ocular, and central vascular access from 189 publicly available POCUS videos. Approximately 121,000 individual images were extracted from the videos, 80% were used for model training and 10% each for cross validation and testing. We then tested the algorithm for accuracy against a set of 160 randomly extracted ultrasound frames from ultrasound videos not previously used for training and that were performed on different ultrasound equipment. Three POCUS experts blindly categorized the 160 random images, and results were compared to the convolutional neural network algorithm. Descriptive statistics and Krippendorff alpha reliability estimates were calculated.

Results: The cross validation of the convolutional neural network approached 99% for accuracy. The algorithm accurately classified 98% of the test ultrasound images. In the new POCUS program simulation phase, the algorithm accurately classified 70% of 160 new images for moderate correlation with the ground truth, α = 0.64. The three blinded POCUS experts correctly classified 93%, 94%, and 98% of the images, respectively. There was excellent agreement among the experts with α = 0.87. Agreement between experts and algorithm was good with α = 0.74. The most common error was misclassifying musculoskeletal images for both the algorithm (40%) and POCUS experts (40.6%). The algorithm took 7 minutes 45 seconds to review and classify the new 160 images. The 3 expert reviewers took 27, 32, and 45 minutes to classify the images, respectively.

Conclusions: Our algorithm accurately classified 98% of new images, by body scan area, related to its training pool, simulating POCUS program workflow. Performance was diminished with exam images from an unrelated image pool and ultrasound equipment, suggesting additional images and convolutional neural network training are necessary for fine tuning when using across different POCUS programs. The algorithm showed theoretical potential to improve workflow for POCUS program directors, if fully implemented. The implications of our DIY AI for POCUS are scalable and further work to maximize the collaboration between AI and POCUS programs is warranted.
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http://dx.doi.org/10.1002/emp2.12018DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7493582PMC
April 2020

Lung Ultrasound Point-of-View in Pediatric and Adult COVID-19 Infection.

J Ultrasound Med 2021 May 7;40(5):899-908. Epub 2020 Sep 7.

Geriatrics and Geriatric Emergency Care, Italian National Research Center on Aging (IRCCS- INRCA), Ancona, Italy.

From its start in China in December 2019, infection by the new SARS-CoV2 spread fast all over the world. It can present as severe respiratory distress in the elderly or a vasculitis in a child, most of whom are typically completely asymptomatic. This makes infection detection based on clinical grounds exceedingly difficult. Lung ultrasound has become an important tool in diagnosis and follow-up of patient with COVID-19 infection.Here we review available, up to date literature on ultrasound use for COVID-19 suspected pediatric patients and contrast it to published findings in adult patients.
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http://dx.doi.org/10.1002/jum.15475DOI Listing
May 2021

The role of lung ultrasonography in COVID-19 disease management.

J Am Coll Emerg Physicians Open 2020 Jul 21. Epub 2020 Jul 21.

Department of Medicine. Department of Emergency Medicine St. Francis Hospital, University of South Carolina School of Medicine Columbus Georgia USA.

Coronavirus disease 2019 (COVID-19) has created unprecedented disruption for global healthcare systems. Offices and emergency departments (EDs) were the first responders to the pandemic, followed by medical wards and intensive care unit (ICUs). Worldwide efforts sprouted to coordinate proper response by increasing surge capacity and optimizing diagnosis and containment. Within the complex scenario of the outbreak, the medical community shared scientific research and implemented best-guess imaging strategies in order to save time and additional staff exposures. Early publications showed agreement between chest computed tomography (CT) and lung sonography: widespread ground-glass findings resembling acute respiratory distress syndrome (ARDS) on CT of COVID-19 patients matched lung ultrasound signs and patterns. Well-established accuracy of bedside sonography for lung conditions and its advantages (such as no ionizing radiation; low-cost, real-time bedside imaging; and easier disinfection steps) prompted a wider adoption of lung ultrasound for daily assessment and monitoring of COVID-19 patients. Growing literature, webinars, online materials, and international networks are promoting lung ultrasound for the same purpose. We propose 11 lung ultrasound roles for different medical settings during the pandemic, starting from the out-of-hospital setting, where lung ultrasound has ergonomic and infection control advantages. Then we describe how medical wards and ICUs can safely integrate lung ultrasound into COVID-19 care pathways. Finally, we present outpatient use of lung ultrasound to aid follow-up of positive case contacts and of those discharged from the hospital.
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http://dx.doi.org/10.1002/emp2.12194DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7404352PMC
July 2020

Unexpected finding of myocardial depression in 2 healthy young patients with COVID-19 pneumonia: possible support for COVID-19-related myocarditis.

Authors:
Michael Blaivas

J Am Coll Emerg Physicians Open 2020 Jun 13. Epub 2020 Jun 13.

Department of Medicine University of South Carolina School of Medicine Columbia South Carolina USA.

COVID-19 is proving to be a devastating pandemic with both tragic economic and health consequences worldwide. Point-of-care ultrasound (POCUS) of the lungs has been thrust into the forefront of resources that could be used in the management of COVID-19 acute care patients. However, relatively little attention has been paid to POCUS utility in assessing the heart in COVID-19 patients. Anecdotal reports suggest encounters of likely COVID-19 induced pericardial effusions and myocardial electrical dysfunction. This article presents 2 cases of generally healthy patients who were noted to have classic COVID-19 bilateral pneumonia findings on lung ultrasound and incidentally discovered to have unsuspected left ventricular dysfunction likely resulting from myocarditis. POCUS videos are presented as illustrations of this potentially overlooked complication.
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http://dx.doi.org/10.1002/emp2.12098DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7323425PMC
June 2020

Prospective Longitudinal Evaluation of Point-of-Care Lung Ultrasound in Critically Ill Patients With Severe COVID-19 Pneumonia.

J Ultrasound Med 2021 Mar 14;40(3):443-456. Epub 2020 Aug 14.

Department of Medicine, University of South Carolina School of Medicine, Columbia, South Carolina, USA.

Objectives: To perform a prospective longitudinal analysis of lung ultrasound findings in critically ill patients with coronavirus disease 2019 (COVID-19).

Methods: Eighty-nine intensive care unit (ICU) patients with confirmed COVID-19 were prospectively enrolled and tracked. Point-of-care ultrasound (POCUS) examinations were performed with phased array, convex, and linear transducers using portable machines. The thorax was scanned in 12 lung areas: anterior, lateral, and posterior (superior/inferior) bilaterally. Lower limbs were scanned for deep venous thrombosis and chest computed tomographic angiography was performed to exclude suspected pulmonary embolism (PE). Follow-up POCUS was performed weekly and before hospital discharge.

Results: Patients were predominantly male (84.2%), with a median age of 43 years. The median duration of mechanical ventilation was 17 (interquartile range, 10-22) days; the ICU length of stay was 22 (interquartile range, 20.2-25.2) days; and the 28-day mortality rate was 28.1%. On ICU admission, POCUS detected bilateral irregular pleural lines (78.6%) with accompanying confluent and separate B-lines (100%), variable consolidations (61.7%), and pleural and cardiac effusions (22.4% and 13.4%, respectively). These findings appeared to signify a late stage of COVID-19 pneumonia. Deep venous thrombosis was identified in 16.8% of patients, whereas chest computed tomographic angiography confirmed PE in 24.7% of patients. Five to six weeks after ICU admission, follow-up POCUS examinations detected significantly lower rates (P < .05) of lung abnormalities in survivors.

Conclusions: Point-of-care ultrasound depicted B-lines, pleural line irregularities, and variable consolidations. Lung ultrasound findings were significantly decreased by ICU discharge, suggesting persistent but slow resolution of at least some COVID-19 lung lesions. Although POCUS identified deep venous thrombosis in less than 20% of patients at the bedside, nearly one-fourth of all patients were found to have computed tomography-proven PE.
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http://dx.doi.org/10.1002/jum.15417DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7436430PMC
March 2021

Focused Transesophageal Echocardiography During Cardiac Arrest Resuscitation: JACC Review Topic of the Week.

J Am Coll Cardiol 2020 08;76(6):745-754

Division of Ultrasound, Department of Emergency Medicine, The Ohio State University Wexner Medical Center, Columbus, Ohio.

Focused transthoracic echocardiography (TTE) during cardiac arrest resuscitation can enable the characterization of myocardial activity, identify potentially treatable pathologies, assist with rhythm interpretation, and provide prognostic information. However, an important limitation of TTE is the difficulty obtaining interpretable images due to external and patient-related limiting factors. Over the last decade, focused transesophageal echocardiography (TEE) has been proposed as a tool that is ideally suited to image patients in extremis-those in cardiac arrest and periarrest states. In addition to the same diagnostic and prognostic role provided by TTE images, TEE provides unique advantages including the potential to optimize the quality of chest compressions, shorten cardiopulmonary resuscitation interruptions, guide resuscitative procedures, and provides a continuous image of myocardial activity. This review discusses the rationale, supporting evidence, opportunities, and challenges, and proposes a research agenda for the use of focused TEE in cardiac arrest with the goal to improve resuscitation outcomes.
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http://dx.doi.org/10.1016/j.jacc.2020.05.074DOI Listing
August 2020

Are Convolutional Neural Networks Trained on ImageNet Images Wearing Rose-Colored Glasses?: A Quantitative Comparison of ImageNet, Computed Tomographic, Magnetic Resonance, Chest X-Ray, and Point-of-Care Ultrasound Images for Quality.

J Ultrasound Med 2021 Feb 5;40(2):377-383. Epub 2020 Aug 5.

Department of Emergency Medicine, University of South Carolina School of Medicine, Columbia, South Carolina, USA.

Objectives: Deep learning for medical imaging analysis uses convolutional neural networks pretrained on ImageNet (Stanford Vision Lab, Stanford, CA). Little is known about how such color- and scene-rich standard training images compare quantitatively to medical images. We sought to quantitatively compare ImageNet images to point-of-care ultrasound (POCUS), computed tomographic (CT), magnetic resonance (MR), and chest x-ray (CXR) images.

Methods: Using a quantitative image quality assessment technique (Blind/Referenceless Image Spatial Quality Evaluator), we compared images based on pixel complexity, relationships, variation, and distinguishing features. We compared 5500 ImageNet images to 2700 CXR, 2300 CT, 1800 MR, and 18,000 POCUS images. Image quality results ranged from 0 to 100 (worst). A 1-way analysis of variance was performed, and the standardized mean-difference effect size value (d) was calculated.

Results: ImageNet images showed the best image quality rating of 21.7 (95% confidence interval [CI], 0.41) except for CXR at 13.2 (95% CI, 0.28), followed by CT at 35.1 (95% CI, 0.79), MR at 31.6 (95% CI, 0.75), and POCUS at 56.6 (95% CI, 0.21). The differences between ImageNet and all of the medical images were statistically significant (P ≤ .000001). The greatest difference in image quality was between ImageNet and POCUS (d = 2.38).

Conclusions: Point-of-care ultrasound (US) quality is significantly different from that of ImageNet and other medical images. This brings considerable implications for convolutional neural network training with medical images for various applications, which may be even more significant in the case of US images. Ultrasound deep learning developers should consider pretraining networks from scratch on US images, as training techniques used for CT, CXR, and MR images may not apply to US.
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http://dx.doi.org/10.1002/jum.15413DOI Listing
February 2021

Change in Carotid Blood Flow and Carotid Corrected Flow Time Assessed by Novice Sonologists Fails to Determine Fluid Responsiveness in Spontaneously Breathing Intensive Care Unit Patients.

Ultrasound Med Biol 2020 10 31;46(10):2659-2666. Epub 2020 Jul 31.

Division of Pulmonary, Critical Care & Sleep Medicine, Department of Medicine, Warren Alpert School of Medicine, Brown University, Providence, Rhode Island, USA.

Measurement of carotid blood flow (CBF) and corrected carotid flow time (ccFT) has been proposed as a non-invasive means of determining fluid responsiveness. We evaluated the ability of CBF and ccFT as assessed by novice sonologists to determine fluid responsiveness in intensive care unit patients. Three novice physician sonologists performed carotid ultrasounds before and after a fluid bolus and calculated changes in CBF and ccFT. Fluid responsiveness was defined as a ≥10% increase in cardiac index as measured using bioreactance. Of 112 participants, 56 (50%) were fluid responders. Changes in CBF and ccFT performed poorly at determining fluid responsiveness: 19 mL/min (area under the receiver operating characteristic curve: 0.58, 95% confidence interval: 0.47-0.68) and 6 ms (0.59, 0.46-0.65) respectively. Novice physician sonologists are unable to determine fluid responsiveness using CBF or ccFT. Further research is needed to identify the key limiting factors in using carotid ultrasound to determine fluid responsiveness.
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http://dx.doi.org/10.1016/j.ultrasmedbio.2020.07.001DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7771259PMC
October 2020

Ultrasound-aided diagnosis of septic arthritis of the elbow in the emergency department.

J Ultrasound 2020 Jul 15. Epub 2020 Jul 15.

Department of Emergency Medicine, University of South Carolina School of Medicine, St Francis Hospital, Columbus, GA, USA.

Septic arthritis (SA) is an emergency orthopedic condition that carries significant patient morbidity and mortality. Clinical data and blood test analyses are fairly unreliable in making the diagnosis and, therefore, utilizing a feasible and reliable diagnostic tool is desirable, particularly in emergency settings where rapid diagnosis is pivotal. Here, we report the case of a 58-year-old male presenting to the emergency department with a swollen elbow. After demonstration of a large articular effusion with point-of-care ultrasound, the synovial fluid analysis was compatible with SA. The patient was treated with elbow arthrotomy and systemic antibiotics and discharged shortly thereafter, uneventfully. Finally, we discuss the impact of ultrasound in diagnosing SA and the many advantages that make it the first-line tool in urgent care.
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http://dx.doi.org/10.1007/s40477-020-00506-2DOI Listing
July 2020

Respiratory Variation in Carotid Artery Peak Systolic Velocity Is Unable to Predict Fluid Responsiveness in Spontaneously Breathing Critically Ill Patients When Assessed by Novice Physician Sonologists.

J Intensive Care Med 2020 Jun 29:885066620934392. Epub 2020 Jun 29.

Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, , Warren Alpert School of Medicine at Brown University, Providence, RI, USA.

Background: Respiratory variation in carotid artery peak systolic velocity (ΔVpeak) assessed by point-of-care ultrasound (POCUS) has been proposed as a noninvasive means to predict fluid responsiveness. We aimed to evaluate the ability of carotid ΔVpeak as assessed by novice physician sonologists to predict fluid responsiveness.

Methods: This study was conducted in 2 intensive care units. Spontaneously breathing, nonintubated patients with signs of volume depletion were included. Patients with atrial fibrillation/flutter, cardiogenic, obstructive or neurogenic shock, or those for whom further intravenous (IV) fluid administration would be harmful were excluded. Three novice physician sonologists were trained in POCUS assessment of carotid ΔVpeak. They assessed the carotid ΔVpeak in study participants prior to the administration of a 500 mL IV fluid bolus. Fluid responsiveness was defined as a ≥10% increase in cardiac index as measured using bioreactance.

Results: Eighty-six participants were enrolled, 50 (58.1%) were fluid responders. Carotid ΔVpeak performed poorly at predicting fluid responsiveness. Test characteristics for the optimum carotid ΔVpeak of 8.0% were: area under the receiver operating curve = 0.61 (95% CI: 0.48-0.73), sensitivity = 72.0% (95% CI: 58.3-82.56), specificity = 50.0% (95% CI: 34.5-65.5).

Conclusions: Novice physician sonologists using POCUS are unable to predict fluid responsiveness using carotid ΔVpeak. Until further research identifies key limiting factors, clinicians should use caution directing IV fluid resuscitation using carotid ΔVpeak.
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http://dx.doi.org/10.1177/0885066620934392DOI Listing
June 2020

Medical Student Ultrasound Education, a WFUMB Position Paper, Part II. A consensus statement of ultrasound societies.

Med Ultrason 2020 May;22(2):220-229

Department Allgemeine Innere Medizin (DAIM), Kliniken Hirslanden Beau Site, Salem und Permanence, Bern, Switzerland Ultrasound Department, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.

Ultrasound is becoming a fundamental first-line diagnostic tool for most medical specialties and an innovative tool to teach anatomy, physiology and pathophysiology to undergraduate and graduate students. However, availability of structured training programs during medical school is lagging behind and many physicians still acquire all their ultrasound skills during postgraduate training.There is wide variation in medical student ultrasound education worldwide. Sharing successful educational strategies from early adopter medical schools and learning from leading education programs should advance the integration of ultrasound into the university medical school curricula. In this overview, we present current approaches and suggestions by ultrasound societies concerning medical student educa-tion throughout the world. Based on these examples, we formulate a consensus statement with suggestions on how to integrate ultrasound teaching into the preclinical and clinical medical curricula.
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http://dx.doi.org/10.11152/mu-2599DOI Listing
May 2020

Creation and Testing of a Deep Learning Algorithm to Automatically Identify and Label Vessels, Nerves, Tendons, and Bones on Cross-sectional Point-of-Care Ultrasound Scans for Peripheral Intravenous Catheter Placement by Novices.

J Ultrasound Med 2020 Sep 17;39(9):1721-1727. Epub 2020 Mar 17.

Department of Critical Care Medicine, Western University, London, Ontario, Canada.

Objectives: We sought to create a deep learning (DL) algorithm to identify vessels, bones, nerves, and tendons on transverse upper extremity (UE) ultrasound (US) images to enable providers new to US-guided peripheral vascular access to identify anatomy.

Methods: We used publicly available DL architecture (YOLOv3) and deidentified transverse US videos of the UE for algorithm development. Vessels, bones, tendons, and nerves were labeled with bounding boxes. A total of 203,966 images were generated from videos, with corresponding label box coordinates in a YOLOv3 format. Training accuracy, losses, and learning curves were tracked. As a final real-world test, 50 randomly selected images from unrelated UE US videos were used to test the DL algorithm. Four different versions of the YOLOv3 algorithm were tested with varied amounts of training and sensitivity settings. The same 50 images were labeled by 2 blinded point-of-care ultrasound (POCUS) experts. The area under the curve (AUC) was calculated for the DL algorithm and POCUS expert performance.

Results: The algorithm outperformed POCUS experts in detection of all structures in the UE, with an AUC of 0.78 versus 0.69 and 0.71, respectively. When considering vessels, only one of the POCUS experts attained an AUC of 0.85, just ahead of the DL algorithm, with an AUC of 0.83.

Conclusions: Our DL algorithm proved accurate at identifying 4 common structures on cross-sectional US imaging of the UE, which would allow novice POCUS providers to more confidently and accurately target vessels for cannulation, avoiding other structures. Overall, the algorithm outperformed 2 blinded POCUS experts.
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http://dx.doi.org/10.1002/jum.15270DOI Listing
September 2020

Are All Deep Learning Architectures Alike for Point-of-Care Ultrasound?: Evidence From a Cardiac Image Classification Model Suggests Otherwise.

J Ultrasound Med 2020 Jun 24;39(6):1187-1194. Epub 2019 Dec 24.

Michigan State University, East Lansing, Michigan, USA.

Objectives: Little is known about optimal deep learning (DL) approaches for point-of-care ultrasound (POCUS) applications. We compared 6 popular DL architectures for POCUS cardiac image classification to determine whether an optimal DL architecture exists for future DL algorithm development in POCUS.

Methods: We trained 6 convolutional neural networks (CNNs) with a range of complexities and ages (AlexNet, VGG-16, VGG-19, ResNet50, DenseNet201, and Inception-v4). Each CNN was trained by using images of 5 typical POCUS cardiac views. Images were extracted from 225 publicly available deidentified POCUS cardiac videos. A total of 750,018 individual images were extracted, with 90% used for model training and 10% for cross-validation. The training time and accuracy achieved were tracked. A real-world test of the algorithms was performed on a set of 125 completely new cardiac images. Descriptive statistics, Pearson R values, and κ values were calculated for each CNN.

Results: Accuracy ranged from 96% to 85.6% correct for the 6 CNNs. VGG-16, one of the oldest and simplest CNNs, performed best at 96% correct with 232 minutes to train (R = 0.97; κ = 0.95; P < .00001). The worst-performing CNN was the newer DenseNet201, with 85.6% accuracy and 429 minutes to train (R = 0.92; κ = 0.82; P < .00001).

Conclusions: Six common image classification DL algorithms showed considerable variability in their accuracy and training time when trained and tested on identical data, suggesting that not all will perform optimally for POCUS DL applications. Contrary to well-established accuracies for CNNs, more modern and deeper algorithms yielded poorer results.
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http://dx.doi.org/10.1002/jum.15206DOI Listing
June 2020

A Cumbersome Rolling Stone.

J Ultrasound Med 2020 May 19;39(5):1037-1038. Epub 2019 Nov 19.

Unità Operativa Complessa Geriatria, Accettazione Geriatrica e Centro di Ricerca per l'Invecchiamento, Istituto Nazionale Ricovero e Cura per Anziani-Istituto di Ricovero e Cura a Carattere Scientifico, Ancona, Italy.

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http://dx.doi.org/10.1002/jum.15173DOI Listing
May 2020

Performance of a 25% Inferior Vena Cava Collapsibility in Detecting Fluid Responsiveness When Assessed by Novice Versus Expert Physician Sonologists.

J Intensive Care Med 2020 Dec 14;35(12):1520-1528. Epub 2019 Oct 14.

Department of Medicine, 12321Alert Medical School of Brown University, Providence, RI, USA.

Objectives: Inferior vena cava collapsibility (cIVC) measured by point-of-care ultrasound (POCUS) has been proposed as a noninvasive means of assessing fluid responsiveness. We aimed to prospectively evaluate the performance of a 25% cIVC cutoff value to detect fluid responsiveness among spontaneously breathing intensive care unit (ICU) patients when assessed with POCUS by novice versus expert physician sonologists.

Methods: Prospective observational study of spontaneously breathing ICU patients. Fluid responsiveness was defined as a 10% increase in cardiac index following a 500 mL fluid bolus, measured by bioreactance. Novice sonologist measured cIVC with POCUS. Their measurements were later compared to an expert physician sonologist who independently reviewed the POCUS images and assessed cIVCs.

Results: Of the 85 participants, 44 (52%) were fluid responders. A 25% cIVC cutoff value performed better when assessed by expert sonologists than novice physician sonologists (receiver-operator characteristic curve, ROC = 0.82 [0.74-0.88] vs ROC = 0.69 [0.60-0.77]).

Conclusions: A 25% cIVC cutoff value measured by POCUS detects fluid responsiveness. However, the experience of the physician sonologist affects test performance and should be considered when interpreting and clinically using cIVC to direct intravenous fluid resuscitation.
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http://dx.doi.org/10.1177/0885066619881123DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7153972PMC
December 2020

A Pilot Prospective Study to Validate Point-of-Care Ultrasound in Comparison to X-Ray Examination in Detecting Fractures.

Ultrasound Med Biol 2020 01 1;46(1):11-19. Epub 2019 Oct 1.

U.O.C. Geriatria, Dipartimento di Medicina Interna, Azienda Provinciale per i Servizi Sanitari di Trento (APSS), Rovereto, Italy; Scuola di Medicina e Chirurgia, Università degli Studi di Verona, Verona Italy.

Despite its limitations, conventional radiography is the method of choice for fracture evaluation in the emergency department. Only a few studies, moreover in limited populations, have evaluated the possible benefits of ultrasound (US), and especially of point-of-care ultrasound (POCUS), in the diagnosis of fractures. We sought to compare the accuracy of POCUS with that of conventional radiography in the diagnosis of bone fractures. This prospective study with a non-randomly allocated convenience sample was conducted at two academic medical centers. Four physicians, with focused training in musculoskeletal POCUS, evaluated consecutive patients with suspected orthopedic injury. US and X-ray examination results were treated as dichotomous variables with either fracture present or fracture absent. Descriptive statistics were calculated in addition to prevalence, sensitivity, specificity, positive predictive value and negative predictive value including 95% confidence intervals (CIs). Cohen's κ coefficient was determined as a measurement of the level of agreement. Four hundred sixty-nine patients (404 adult and 65 pediatric) ranging in age from 1-97 y were enrolled at two different hospitals. Seven hundred six examinations, both US and X-ray, were performed in 634 suspected fractures in adults (age ≥18 y) and 72 in children. On physical examination, swelling, localized hematoma and functional limitation were found in 64.61%, 34.97% and 53.52, respectively. The sensitivity of US examination was 93.89% (CI: 89.74%-96.49%) for all patients and 94.30% (CI: 89.77%-96.98%) and 91.67% (CI: 76.41%-97.82%) in adult and pediatric groups, respectively. Specificity was 94.13% (CI: 91.53-95.99), 94.56% (CI: 91.89-96.41) and 88.89% (CI: 73.00-96.38) for the whole group, adults and children, respectively. The positive predictive value was 88.48% (CI: 83.62%-92.08%), 88.35% (CI: 82.97%-92.24%) and 89.19% (CI: 73.64%-96.48%) for the whole group, adults and children, respectively. The negative predictive value was 96.98% (CI: 94.86%-98.27%), 97.43% (CI: 95.31%-98.64%) and 91.43% (CI: 75.81%-97.76%) in the three groups, respectively. Cohen's κ coefficient revealed high agreement of 0.87 for both the whole group and adult patients and 0.81 for pediatric patients. We found that POCUS has significant diagnostic accuracy in evaluating fracture compared with plain radiography, with excellent sensitivity, specificity and positive and negative predictive values.
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http://dx.doi.org/10.1016/j.ultrasmedbio.2019.09.006DOI Listing
January 2020

Medical Student Ultrasound Education, a WFUMB Position Paper, Part I, response to the letter to the Editor.

Ultrasound Med Biol 2019 07 11;45(7):1857-1859. Epub 2019 Apr 11.

University of South Carolina School of Medicine, Department of Emergency Medicine, St. Francis Hospital, Columbus, Georgia, USA.

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http://dx.doi.org/10.1016/j.ultrasmedbio.2019.02.020DOI Listing
July 2019