Accuracy of three-point compression ultrasound for the diagnosis of proximal deep-vein thrombosis in emergency department

Dehbozorgi, Afsaneh; Damghani, Fatemeh; Mousavi‐Roknabadi, Razieh Sadat; Sharifi, Mehrdad; Sajjadi, Seyed Mahmoudreza; Hosseini-Marvast, Seyed Rouhollah

doi:10.4103/jrms.jrms_1057_18

Cited by 13 publications

(3 citation statements)

References 24 publications

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“…In most cohort studies, the majority of focused ultrasound examinations for DVT in patients with suspected acute DVT are negative (56 to 77%) [ 13 – 15 ]. However, most of these patients had signs or symptoms that led to the clinical suspicion of acute DVT.…”

Section: Introductionmentioning

confidence: 99%

Prevalence and predictors of alternative diagnoses on whole-leg ultrasound negative for acute deep venous thrombosis

et al. 2020

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Background To investigate the prevalence, spectrum, and predictors of alternative diagnoses explaining leg symptoms in patients negative for suspected acute deep venous thrombosis (DVT), which can be detected with whole-leg ultrasound. Methods We retrospectively analyzed a cohort of 789 patients (median age 70 years, 50.6% women) evaluated with a whole-leg ultrasound examination for suspected acute DVT within one year. All findings in the radiology report were analyzed and electronic chart review was performed to collect clinical information. Results Ultrasound was negative for acute DVT in 531 patients (67.3%). Among these, alternative diagnoses explaining leg symptoms were seen in 349 patients (65.7%). The most frequent alternative diagnoses were chronic venous insufficiency (147 patients, 27.7%), followed by lymphedema (48 patients, 9.0%) and chronic post-thrombotic changes (41 patients, 7.7%). Patients with alternative diagnoses were older (median 71 vs. 66 years, p = 0.0226), as well as more likely to present with leg swelling (39.5% vs. 23.1%, p = 0.0002), difference in leg circumference (25.5% vs. 14.8%, p = 0.0055) and redness (7.7% vs. 2.7%, p = 0.0213) than patients without alternative diagnosis. Independent predictors of finding alternative diagnoses on whole-leg ultrasound were older age (odds ratio 1.014 per year, p = 0.0119), leg swelling (OR 1.949, p = 0.0020) and history of previous DVT (OR 2.235, p = 0.0154). Conclusions Alternative diagnoses explaining leg symptoms can be detected on whole-leg ultrasound in two thirds of patients with no evidence of acute DVT. Our data supports performing a comprehensive ultrasound evaluation beyond the venous system, particularly, in older patients, who present with leg swelling and a past history of DVT.

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Section: Introductionmentioning

confidence: 99%

Prevalence and predictors of alternative diagnoses on whole-leg ultrasound negative for acute deep venous thrombosis

et al. 2020

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“…Therefore, we recommend the use of three-point compression ultrasonography by an attending physician in an isolation ward for COVID-19 patients with high D-dimer levels. Mousavi-Roknabadi et al 22 ) reported that three-point compression ultrasonography identified the presence of lower-extremity DVT with an accuracy of 96.4% in comparison with whole-leg compression ultrasound. Moreover, three-point compression ultrasonography showed high specificity regardless of the skill of the clinician performing the investigation.…”

Section: Discussionmentioning

confidence: 99%

Assessment of the Need for Early Initiation of Rehabilitation Treatments in Patients with Coronavirus Disease 2019

Suzuki

Sakai

Hoshino

et al. 2020

PRM

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Objective: The aim of this study was to describe the clinical characteristics of coronavirus disease (COVID-19) patients, including risk factors for deep vein thrombosis and pulmonary embolism, and to evaluate the need for rehabilitation to prevent pulmonary embolism. Methods: A retrospective medical record review was conducted of patients admitted to the study hospital with COVID-19 between April 2 and April 23, 2020. The clinical characteristics and blood test results of patients with no history on admission of oral anticoagulant use were evaluated to assess the importance of inflammation and clotting function as risk factors for pulmonary embolism. Results: A total of 51 patients with COVID-19 were admitted during the study period. Their median age was 54.0 years (range: 41–63 years) and 38 of 51 (74.5%) were men. The most common comorbidities in men were diabetes (9/38, 23.7%) and hypertension (13/38, 34.2%). On admission, white blood cell counts were normal in both sexes, whereas C-reactive protein and hemostatic marker levels, except for the activated partial thromboplastin time, were significantly higher in men. Moreover, C-reactive protein and hemostatic marker levels were significantly higher in patients that required invasive ventilation. Two patients were diagnosed with acute pulmonary embolism, neither of whom required invasive ventilation. Conclusions: Hypercoagulability and hyperinflammation were observed in COVID-19 patients, especially in men with high oxygen demand. We recommend anticoagulant therapy and early rehabilitation intervention to prevent pulmonary embolism in COVID-19 patients.

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“…To exclude DVT an operator must follow a protocol as instructed by the software. This protocol resembles the clinical practice of three-point or two-point examinations 33 – 35 , which means doing compression ultrasound in two to three regions where the greatest risk of developing thrombosis occurs. For three-point compression protocols, these regions include: (1) the common femoral vein at the level of the inguinal crease (LM0–LM4), (2) the superficial femoral vein superior in the adductor canal (LM5–LM7), and (3) the popliteal vein and its trifurcation in the popliteal fossa (LM8–LM10).…”

Section: Methodsmentioning

confidence: 99%

Non-invasive diagnosis of deep vein thrombosis from ultrasound imaging with machine learning

Kainz

Heinrich

Makropoulos³

et al. 2021

npj Digit. Med.

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Deep vein thrombosis (DVT) is a blood clot most commonly found in the leg, which can lead to fatal pulmonary embolism (PE). Compression ultrasound of the legs is the diagnostic gold standard, leading to a definitive diagnosis. However, many patients with possible symptoms are not found to have a DVT, resulting in long referral waiting times for patients and a large clinical burden for specialists. Thus, diagnosis at the point of care by non-specialists is desired. We collect images in a pre-clinical study and investigate a deep learning approach for the automatic interpretation of compression ultrasound images. Our method provides guidance for free-hand ultrasound and aids non-specialists in detecting DVT. We train a deep learning algorithm on ultrasound videos from 255 volunteers and evaluate on a sample size of 53 prospectively enrolled patients from an NHS DVT diagnostic clinic and 30 prospectively enrolled patients from a German DVT clinic. Algorithmic DVT diagnosis performance results in a sensitivity within a 95% CI range of (0.82, 0.94), specificity of (0.70, 0.82), a positive predictive value of (0.65, 0.89), and a negative predictive value of (0.99, 1.00) when compared to the clinical gold standard. To assess the potential benefits of this technology in healthcare we evaluate the entire clinical DVT decision algorithm and provide cost analysis when integrating our approach into diagnostic pathways for DVT. Our approach is estimated to generate a positive net monetary benefit at costs up to £72 to £175 per software-supported examination, assuming a willingness to pay of £20,000/QALY.

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Accuracy of three-point compression ultrasound for the diagnosis of proximal deep-vein thrombosis in emergency department

Cited by 13 publications

References 24 publications

Prevalence and predictors of alternative diagnoses on whole-leg ultrasound negative for acute deep venous thrombosis

Prevalence and predictors of alternative diagnoses on whole-leg ultrasound negative for acute deep venous thrombosis

Assessment of the Need for Early Initiation of Rehabilitation Treatments in Patients with Coronavirus Disease 2019

Non-invasive diagnosis of deep vein thrombosis from ultrasound imaging with machine learning

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