The D-Dimer test in combination with a decision rule for ruling out deep vein thrombosis in primary care: diagnostic technology update

Plüddemann, Annette; Thompson, Matthew; Price, Christopher P.; Wolstenholme, Jane; Heneghan, Carl

doi:10.3399/bjgp12x641645

Cited by 5 publications

(5 citation statements)

References 13 publications

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“…[ 17 18 ] MD-CTPA as the gold diagnostic criteria is the most accurate test to diagnose APE and now regarded as the imaging test of first choice. [ 19 20 ] However, since MD-CTPA is an invasive examination and needs higher technology requirement, while radionuclide pulmonary ventilation/perfusion imaging and color Doppler ultrasonography were expensive diagnostic methods,[ 21 22 23 ] these examinations are of limited popularization in the primary health-care institutions in developing countries, such as China.…”

Section: Discussionmentioning

confidence: 99%

D-dimer testing for safe exclusion and risk stratification in patients with acute pulmonary embolism in primary care

Yin¹,

Chen²,

Xie³

et al. 2015

J Res Med Sci

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Background:Safe exclusion and risk stratification are currently recommended for the initial management of patients with acute pulmonary embolism (APE). The aim of this study was to assess the safe exclusion and risk stratification value of D-dimer (DD) for APE when tested at the beginning of admission.Materials and Methods:All consecutive Chinese APE patients and controls were recruited from January 2010 to December 2012. All measurements of serum indexes were made in duplicate and blinded to the patients’ status. All the 40 patients with the first episode of APE were confirmed by multi-detector computed tomographic pulmonary angiography. The plasma prothrombin time (PT), activated partial thromboplastin time, thrombin time, fibrinogen, and DD levels were measured within 24 h of admission. We used the Mann-Whitney U-test to determine the differences between groups and drew receiver operator characteristic curve to evaluate the indexes’ value in the APE screening.Results:The PT and DD in the APE group were significantly higher than those in the disease control group (P < 0.05). Taking PT and DD as the useful screening tests for APE and AUC was 0.765 and 0.822, respectively. DD yielded the higher screening efficiency, with DD >1820 μg/L as cut-off value, the sensitivity, specificity, positive and negative predictive value was 82.5%, 75.2%, 56.9%, and 91.6%, respectively.Conclusion:The patients with APE showed significant higher DD levels compared with disease controls, suggesting a negative qualitative DD test result can safely and efficiently exclude APE in primary care.

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Section: Discussionmentioning

confidence: 99%

D-dimer testing for safe exclusion and risk stratification in patients with acute pulmonary embolism in primary care

Yin¹,

Chen²,

Xie³

et al. 2015

J Res Med Sci

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“…DVT has a high level of morbidity. 30–50% of the surviving patients develop long-term symptoms in their affected leg (post-thrombotic syndrome) 5 .…”

Section: Introductionmentioning

confidence: 99%

“…Currently, no reliable test is available that can be used in a general healthcare setting (GP practice, community hospital, on a hospital ward) or be used remotely at the point of care (nursing home, patient’s home). Between 85 and 90% of patients presenting to their GP in high-income countries with a suspected DVT will be investigated only to find no evidence of a thrombus 5 . Many patients will receive unnecessary anticoagulants with numerous potential side-effects through an often-painful subcutaneous injection whilst waiting more than the recommended four hours for their scan.…”

Section: Introductionmentioning

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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“…Mortality from VTE is common, a European study estimated 534 000 deaths per year 3 and a similar study in the US reported 300 000 deaths per year 4 . Deep vein thrombosis (DVT) has a high level of morbidity and 30 - 50% of the surviving patients develop long-term symptoms in their affected leg (post-thrombotic syndrome) 5 .…”

Section: Introductionmentioning

confidence: 99%

“…Currently, no reliable test is available that can be used in a general healthcare setting (GP practice, community hospital, on a hospital ward) or be used remotely at the point of care (nursing home, patient’s home). Between 85 – 90% of patients presenting to their GP in high income countries with a suspected DVT will be investigated only to find no evidence of a thrombus 5 . Many patients will receive unnecessary anticoagulants with many potential side-effects through an often painful subcutaneous injection whilst waiting more than the recommended four hours for their scan.…”

Section: Introductionmentioning

confidence: 99%

Non-invasive Diagnosis of Deep Vein Thrombosis from Ultrasound with Machine Learning

Kainz

Makropoulos

Oppenheimer³

et al. 2021

Preprint

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Deep Vein Thrombosis (DVT) is a blood clot most 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 246 healthy volunteers and evaluate on a sample size of 51 prospectively enrolled patients from an NHS DVT diagnostic clinic. 32 DVT-positive patients and 19 DVT-negative patients were included. Algorithmic DVT diagnosis results in a sensitivity of 93.8% and a specificity of 84.2%, a positive predictive value of 90.9%, and a negative predictive value of 88.9% 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 a diagnostic pathway for DVT. Our approach is estimated to be cost effective at up to $150 per software examination, assuming a willingness to pay $26 000/QALY.

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The D-Dimer test in combination with a decision rule for ruling out deep vein thrombosis in primary care: diagnostic technology update

Cited by 5 publications

References 13 publications

D-dimer testing for safe exclusion and risk stratification in patients with acute pulmonary embolism in primary care

D-dimer testing for safe exclusion and risk stratification in patients with acute pulmonary embolism in primary care

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

Non-invasive Diagnosis of Deep Vein Thrombosis from Ultrasound with Machine Learning

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