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Catheter-Directed Thrombolysis for Acute Deep Vein Thrombosis: Experience from a Canadian Thrombosis Referral Centre

2017· article· en· W2966217223 on OpenAlexaffabout
Andrea Charron, Neal Manning, James D. Douketis

Bibliographic record

VenueBlood · 2017
Typearticle
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineThrombolysisThrombosisPost-thrombotic syndromeThrombusSurgeryDeep veinRetrospective cohort studyVenous thrombosisMedical recordInternal medicineMyocardial infarction

Abstract

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BACKGROUND Conventional management strategies for DVT with parenteral and/or oral anticoagulants are effective in the prevention of thrombus extension and embolization but do not actively eliminate thrombus. This poses ongoing risk of injury to the venous valves and consequently development of the post-thrombotic syndrome (PTS). Catheter-directed thrombolysis (CDT) involves the local delivery of a low dose of thrombolytic agent directly to the venous thrombus, resulting in acute reduction in clot burden while limiting the systemic thrombolytic effect. Recommendations from clinical practice guidelines differ dramatically on the use of CDT for lower limb DVTs. Results from recent prospective randomized control trials suggest that those individuals who derive benefit must be carefully selected. We describe the clinical features and outcomes of patients who received CDT for lower limb DVT management at a major referral centre for thromboembolic disease. Additionally, cross-sectional follow-up of these patients was performed to estimate the incidence and severity of PTS. METHODS We conducted a retrospective cohort analysis on all adult patients who had undergone CDT for lower limb DVT management at St. Joseph9s Healthcare Hamilton (SJHH), ON, Canada. Retrospective data were collected through use of electronic and chart-based medical records. The presence and severity of PTS was measured using the Villalta scale. RESULTS From 2011 to 2016, 15 patients underwent CDT at SJHH (Table 1).12/15 patients were female. Average patient age was 43.2 years (range 20 to 86 years). All patients had risk factors for venous thromboembolism (VTE); 7/15 patients had 2 risk factors. The most commonly identified transient risk factor was use of the oral contraception pill (5/15). May Thurner syndrome (5/15) was the most common permanent VTE risk factor. All patients were deemed to be at low risk of bleeding (platelets > 100,000, INR All patients had DVTs that were iliofemoral in location; 2/15 presented with phlegmasia cerulea dolens. The average time between DVT diagnosis to CDT was 6.5 days. Prophylactic IVC filters were inserted prior to CDT in 3/15 patients. Pharmacomechanical thrombolysis with angioplasty was the most common form of CDT (6/15 patients). Complications from CDT were seen in 8/15 patients (Table 2). Recurrent VTE (3/15) and neurologic deficits in the affected leg (3/15) were the most common complications. There were no deaths attributable to CDT. All patients were managed with anticoagulant therapy post-procedure. The type and duration of anticoagulation was variable (Table 2). The Villalta score was calculated for 6/15 patients. Mild PTS was seen in 4/6 patients; 2/6 did not have PTS (average Villalta score 5.83). CONCLUSIONS Catheter-directed thrombolysis appears to be a well-tolerated treatment option for iliofemoral DVT. Complications were seen in 53% of patients and were frequently mild and self-resolving. The results of our study suggest that patients under 45 years of age who receive prompt thrombolysis, within 7-14 days of DVT diagnosis, are most likely to derive benefit from CDT. The clinical characteristics of patients described here are largely in accordance with those recommended by the 2016 CHEST guidelines. Due to the limited sample size in our study, it is unclear if CDT is beneficial in reducing rates of PTS. Additional studies are needed to clarify potential risks and benefits of this treatment approach. Disclosures No relevant conflicts of interest to declare.

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.867
Threshold uncertainty score0.267

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.033
GPT teacher head0.313
Teacher spread0.280 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2017
Admission routes2
Has abstractyes

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