Association Between Inherited and Acquired Thrombophilias and The Post Thrombotic Syndrome: A Systematic Review and Metaanalysis
Bibliographic record
Abstract
Introduction The post thrombotic syndrome (PTS) is a frequent complication of deep vein thrombosis (DVT), occurring in 20-40% of DVT patients. Identifying risk factors for PTS may be useful to provide patients with reliable prognostic information and target prevention strategies. Inherited and acquired thrombophilias are well established risk factors for DVT. We hypothesized that these markers may also increase the risk of PTS. Objective To conduct a systematic review to assess: Among patients with DVT, are inherited or acquired thrombophilias associated with risk of PTS? Methods We conducted a search of the electronic databases PubMed, EMBASE, Scopus, and Web of Science for studies published from 1990-2013. Studies were considered eligible if they reported assessing any thrombophilia in adult DVT patients and its association with the development of PTS. Risk of bias was assessed using a modified Newcastle-Ottawa scale. Meta-analysis was performed by pooling odds ratios (OR) using the random effects model, when three or more studies were available. Results After removal of duplicates, 2376 records were screened and 84 full text articles were assessed for eligibility. Sixteen studies were included in our review: 13 studies assessed Factor V Leiden (FVL), 10 assessed prothrombin gene mutation, 5 studies assessed protein S and C deficiencies, 3 assessed antithrombin deficiency, 4 assessed factor VIII activity, and 6 studies assessed antiphospholipid antibodies (6 studies assessed Lupus Anticoagulant (LAC) and 1 assessed Anticardiolipin Antibody). None of the meta-analyses performed identified any inherited or acquired thrombophilia to be predictive of PTS (Figure 1). We were unable to pool studies reporting the association between Factor VIII and PTS because of clinical heterogeneity, however, descriptively, the predictive value of FVIII for PTS seems to be limited. We conducted subgroup analyses stratifying studies by patient characteristics (Table 1). Both FVL and prothrombin mutations appeared to be protective for PTS among studies including patients with both first and recurrent DVT, and studies where >50% of patients had an unprovoked DVT. Our pooled estimate for the effect of antiphospholipid antibodies is largely based on studies that measured LAC while on anticoagulant treatment, hence may be an underestimate. However, the confidence intervals for the estimates overlapped the null for all of these subgroups. A sensitivity analysis was conducted to restrict to studies that used a more rigorous definition of PTS (using only the Villalta scale). FVL was associated with an OR 0.88 (6 studies; 95% CI: 0.66, 1.18) and prothrombin mutation with an OR 0.91 (6 studies; 95% CI: 0.55, 1.49). Hence, FVL appeared protective when we restricted to higher quality studies. Conclusions Our meta-analysis did not demonstrate a significant association between any of the thrombophilias assessed and the risk of PTS in DVT patients, however, we cannot rule out a mild protective effect of FVL and prothrombin mutation in certain subgroups. As the pathophysiology of PTS involves the complex interplay of persistent venous obstruction, valvular incompetence and inflammation, other biomarkers may be more predictive of the patient’s risk of PTS. Disclosures: No relevant conflicts of interest to declare.
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.028 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".