Does Venous Thromboembolism Prophylaxis Policy Have Any Impact on Clot Risk Reduction in Hospitalized Solid Tumor Patients at Princess Margaret Cancer Centre: A Longitudinal Study?
Bibliographic record
Abstract
Abstract Background We continued to witness a strong associations between cancer and the development of blood clots in the in-patient hospital setting. Patients are being treated for a venothromboembolism (VTE) event at any given time. The primary objective of this retrospective study is to determine the safety and efficacy impact of VTE prophylaxis (Prx.) by measuring the incidences of VTE before and after implementation of our prophylaxis policy over a span of 9 years, in order to gauge the clinical impact of this policy. Method Discharged Abstract Database (DAD) was used to extract post admitted PE and DVT events based on patients' primary diagnosis of solid tumor identified by the ICD-10-CA diagnosis codes. DAD identified the number of total patients or visits admitted to the in-patient units during the fiscal years of 2007 to 2016. Post-Admit VTE bleeding and death while on prophylaxis anticoagulant was also documented. From the pharmacy generated reports over the same period, the medication profile for each patient was cross-referenced with the DAD documented VTE events for accuracy. The timing of anticoagulant ordering for a given VTE event was checked against the information documented in the radiology/imaging section of the electronic patient record. Patients who came in with a full anticoagulant treatment regimen were removed from analysis. Similarly, patients who began anticoagulation treatment during the first 48 hours of hospital admission were also removed from analysis. The resulting vetted patient list containing only those who developed VTE 48 hours post-admission during their hospital stay, and receiving prophylaxis was the target population for analysis. The patients then were divided into two groups; pre-policy 2007-2012 and post-policy 2012-2016, policy implementation date was Sept. 2012. Each of these patient groups was analyzed and categorized by VTE risk according to the in-patient Padua risk score. When available, any information on bleeding in the presence of anticoagulation was reported. The proportion of patient population (*) receiving VTE prophylaxis was evolving in an increasing trend over the study period (from 2007 to policy 2012). The rate of Prx. post policy was close to 100%. Results Table 1. Comparisons between pre- and post- policy period in those received Prx. vs. no Prx., as well as risk scores and incidences of VTE within the two groups of patients with or without Prx. Conclusion The VTE Prx. policy in the in-patient setting has demonstrated positive impact in reducing the absolute number of incidence over the study period (p=0.0020). The bleeding incidence was low in the both pre- and post- periods. The documented 3 deaths in the pre- period was attributable to PE and none of these patients received Prx. Although VTE events in both pre- and post- periods were similar (18 and 20 respectively), these patients received routine Prx. The breakthrough VTE cases while on Prx. may be explained by the high Padua risk scores. In addition to cancer, obesity (Ave. wt. 77 kg, med. Wt. 75kg in pre- and Ave. Wt. 75.5kg, med Wt. 71kg in post-period), reduce mobility, acute infection and hormonal treatments (glucocortico-steroid) may have accounted for the significant VTE risks. Standard dose of enoxaparin 40mg once daily provided VTE prevention in the majority of our hospitalized patients, however a weight-based dosing schema may be needed for higher risk patients. Download : Download high-res image (340KB) Download : Download full-size image 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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".