Is Thrombophilia a Risk Factor for Peripheral Vein Infusion Thrombophlebitis?.
Bibliographic record
Abstract
Abstract Background and Objectives. Peripheral vein infusion thrombophlebitis (PVIT) is a common complication in hospitalized patients receiving peripheral intravenous (IV) therapy. Although catheter-related risk factors such as catheter material have been well elucidated, patient-related factors have received little attention, despite evidence that (1) individuals vary in biologic vulnerability to developing PVIT; (2) there is biological evidence that thrombus formation may play a role in the pathogenesis of PVIT; and (3) thrombophilic disorders have been linked to central venous catheter thrombosis. We conducted a nested case-control study to determine whether patients who developed PVIT were more likely to have thrombophilia than patients without PVIT. Methods. A cohort of consecutive, hospitalized patients with peripheral IV catheters was prospectively assembled from 8 wards in 2 large tertiary care hospitals and followed until PVIT developed, the catheter was removed for reasons other than PVIT, or the patient was transferred with catheter in place to a non-study ward. Recruitment to the cohort continued until 100 patients, who developed PVIT, were eligible for case enrolment. For each case, 2 control patients who had not developed PVIT and who were matched to cases on catheter duration (days) were identified. PVIT was defined as the presence of two or more of the following at the catheter site: pain, tenderness, erythema, swelling, purulence, or a palpable cord. Factor V Leiden and prothrombin G20210A mutations and homocysteine levels were measured. Homocysteine levels ≥ 15μmol/L were considered elevated. The association between PVIT and thrombophilia was tested using multivariate conditional logistic regression analysis to account for the matched design. Results. From the cohort of 6426 patients with catheters, there were 113 PVIT episodes (PVIT incidence of 4.4 per 1000 catheter-days) of which 100 cases were eligible and matched to 200 randomly chosen controls. There were no differences between cases and controls with regard to age, sex distribution, history of previous venous thromboembolism (VTE) or presence of active cancer. Cases were less likely than controls to be taking anticoagulant medication(s) (9% vs. 16%, respectively, OR=0.46; 95% confidence interval (CI) [0.19, 1.12]). One or more prior episodes of PVIT was reported by 18% of cases vs. 6% of controls (OR=3.0; 95% CI [1.4, 6.2]). Prothrombin G20210A or Factor V Leiden was detected in 6% of cases and 6% of controls. Hyperhomocysteinemia was present in 24% of cases vs. 22% of controls (OR=1.23; 95% CI [0.69, 2.19]). Multivariable conditional regression analyses adjusted for age, sex and anticoagulant use showed that prior PVIT was an independent predictor of PVIT (OR=2.8; 95% CI [1.3, 6.0]), but thrombophilia did not predict PVIT (OR=1.02; 95% CI [0.57, 1.84]). Conclusions. We did not find an association between PVIT and Factor V Leiden, prothrombin gene mutation, or hyperhomocysteinemia, although our results suggest an unidentified patient-specific predisposition to PVIT. The pathogenesis of PVIT appears to be different than that of central venous catheter thrombosis, since thrombophilia, cancer and prior VTE, which are known risk factors for central venous catheter thrombosis, were not significantly associated with PVIT in our study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".