Incidence of Catheter-Related Venous Thromboembolism Events in Acute Leukemia patients; A Comparative, Retrospective Study of the Safety of Peripherally-Inserted Vs Centrally-Inserted Central Venous Catheters
Bibliographic record
Abstract
Abstract 1. Background Central venous catheters (CVCs) are a leading cause of upper extremity deep vein thrombosis (UE DVT). There is little data on patients with acute leukemia (AL). Long term CVCs are required for chemotherapy in AL. Concomitant severe thrombocytopenia makes anticoagulation for CVC related thrombosis a challenge. Incidence of UE DVT has been reported to be increased in those with peripherally inserted central venous catheters (PICC lines) vs those with centrally inserted lines. 2. Aims Our objective is to compare the incidence rate of VTE in leukemia inpatients with a PICC vs centrally-inserted CVC. 3. Methods We reviewed 420 charts for AL inpatients requiring a PICC line admitted to Hematology at the University of Alberta Hospital between 2003-2013. Baseline patient characteristics were recorded. All venous thromboembolic events were objectively confirmed on imaging studies. Incidence of catheter associated thrombosis was calculated. 4. Results 420 patients were identified. We present the preliminary results of the 337 patients that met our inclusion criteria, and received at least one PICC line insertion. 305 (90%) had AML, 144 (43%) were smokers, 126 (37.4%) had cardiovascular risk factor, and only 14 (4.2%) had previous VTE. Overall, there were 634 PICC line insertions, with the 5FR dual lumen being the most commonly used PICC line (80%). Out of the 634 insertions, there were 65 (10%) new ipsilateral upper extremity DVTs, 54 (83%) of which developed acutely (<1month), and 44 (68%) in thrombocytopenic patients (platelet<50). 7 (1.1%) and 15 (2.4%) patients developed recurrent and concurrent VTEs, respectively. There was an incidence of 1.85 DVT per 1000 catheter days. 5. Conclusions The incidence rate of DVT in our AL patients is higher than predicted for a general cancer patient population. This data will be compared to a similar cohort of AL inpatients presently being reviewed, who received a centrally-inserted CVC. Updated results will be included accordingly. Determining factors that are associated with a lower risk of DVT in this high bleeding risk population will be important to optimize patient care. Disclosures Wu: Leopharma: Membership on an entity's Board of Directors or advisory committees; Pfizer Canada: Membership on an entity's Board of Directors or advisory committees.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".