Evidence-Based Diagnosis of Venous Thromboembolism: A Systematic Review
Bibliographic record
Abstract
Introduction: Venous thromboembolism (VTE), comprising deep vein thrombosis (DVT) and pulmonary embolism (PE), is a common, potentially lethal condition with acute morbidity. The purpose of this systematic review is to evaluate the literature for the evidence supporting the use of algorithms for the diagnosis of DVT and PE with a goal of a) ensuring the evidence is robust, b) determining if diagnostic approaches that don9t use algorithms are acceptable, and c) identifying knowledge gaps that require further research. Methods: this systematic review adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. A search of PubMed, EMBASE and MEDLINE (1990-May 2017) was conducted. The search was restricted to English-language only. The full text of all abstracts that met the basic eligibility criteria were assessed for inclusion or exclusion by two investigators independently. Disagreements were resolved by consensus.The key inclusion criteria were that the study was a high quality randomized clinical trial (RCT), meta-analysis, systematic review or prospective cohort study. The target outcomes of interest were the overall quality of development of the algorithms for the diagnosis of DVT and PE, the strength of evidence supporting their use, an assessment of the supporting evidence for diagnostic approaches that don9t use algorithms, and the strengths and limitations of the body of evidence. The Scottish Intercollegiate Guidelines Network and the Consolidated Standards of Reporting Trials checklists were used to assess the quality of included RCTs and prospective studies. Assessing the Methodological Quality of Systematic Reviews tool was used to rate the quality of meta-analyses or systematic reviews. Low quality studies were excluded. Results: Twenty-six meta-analyses or systematic reviews, 8 RCTs and 24 well-performed prospective studies informed our review. The approach to the diagnosis of VTE has evolved over the years. There is considerable high quality evidence that an algorithmic approach, which uses a combination of pretest clinical probability and D-dimer testing followed by accurate imaging tests in selected patients, will enable the safest and most cost-effective diagnostic approach in outpatients with suspected VTE. However, these algorithms have not been extensively studied in hospitalized patients, although a few moderate quality studies have shown that imaging without the use of probability and D-dimer could be appropriate in the hospitalized population until further research is available. Imaging tests alone are reasonably accurate but their use without consideration of pretest probability and D-dimer results in over testing with the associated costs and risks. Many other imaging techniques, such as magnetic resonance imaging and single-photon emission computed tomography, have been studied in the diagnosis of PE, however, studies assessing their accuracy and direct comparisons to computed tomography pulmonary angiogram (CTPA) are limited. The widespread availability of CTPA and ultrasound, coupled with the potentially fatal nature of VTE, has resulted in a dramatic increase in the number of patients tested for PE and DVT, resulting in lower rates of positive tests but without a reduction in mortality rates. This increased use of diagnostic imaging could be avoided as many good quality studies have proven that algorithm-based strategies allow for rapid and safe exclusion of DVT or PE, often without diagnostic imaging. For suspected recurrent DVT, ultrasound can result in a false positive test, especially in the setting of prior DVT. The best criteria for diagnosing DVT in patients with prior DVT in the symptomatic leg are not established yet. Since ultrasound abnormalities may persist indefinitely with DVT, the criterion of vein compressibility may not distinguish patients with acute recurrent DVT from patients with chronic findings. Conclusion: The use of clinical decision rules in combination with D-dimer has standardized the diagnostic approaches for VTE. Currently, in most cases, the use of algorithmic strategies is supported by strong evidence as they allow for safe, convenient and cost-effective investigation of outpatients. However, the algorithmic strategies are less useful in hospitalized patients. Further research is also desperately needed in patients with suspected upper extremity DVT and pregnant patients. Disclosures Wells: Bayer Healthcare: Other: Speaker Fees, Ad Board Meeting, Research Funding; Itreas: Other: Writing Committee; Janssen Scientific Affairs, LLC: Consultancy; BMS: Other: Grant Support; Pfizer: Other: Grant Support; Daiichi Sankyo: Other: Speaker Fees.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.036 | 0.164 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.013 |
| Bibliometrics | 0.018 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".