Rate of Venous Thromboembolism in Obese Patients Undergoing General Surgery: A Retrospective Cohort Study
Bibliographic record
Abstract
Abstract Background: Implementation of prophylactic drug therapy for venous thromboembolism (VTE) can greatly reduce VTE-associated morbidity and mortality in patients undergoing surgery. Obesity has been demonstrated to be an independent risk factor for fatal pulmonary embolism following surgery; however, there is currently a lack of guidance with regard to the optimal drug regimen for VTE prophylaxis in obese patients. At the QEII Health Sciences Center (QEII HSC), a district-wide pre-printed order form is used to guide risk classification and corresponding VTE prophylactic regimens for a variety of nonorthopedic surgical populations. In the absence of a contraindication, such as heparin-induced thrombocytopenia, dalteparin is the drug of choice for the majority of patients. A standard fixed dose of dalteparin 5000 units given subcutaneously is employed for most patients except for those patients weighing less than 40 kg or over the age of 80, in which a lower fixed dose of 2500 units is recommended. There is no specific recommendation for obese patients with regard to dosing. Objectives: To determine the rate of objectively confirmed VTE during admission or up to six weeks post-discharge in obese patients (Body Mass Index (BMI) ≥ 35 kg/m2), compared to non-obese patients, that undergo general surgery at the QEII HSC. Risk factors associated with the development of VTE and practice patterns with regard to implementation of pharmacologic VTE prophylaxis were also evaluated. Methods:A retrospective chart review of 378 patients,18 years or older, who underwent general surgery (colorectal, surgical oncology, or hepatobiliary) from January 1, 2010 to December 31, 2013 was performed. Patient and procedure-related data was collected and analyzed using summary statistics and multivariate logistic regression. Results: The rate of VTE was not significantly different when comparing obese and non-obese patients (3.3% vs. 2.5%; p = NS). There were no risk factors identified to be significantly associated with VTE in patients undergoing general surgery. Although not included as an endpoint in our research objectives, data collected with regard to bleeding revealed a significant difference between obese and non-obese patients in the rate of major bleeding events (2.8% vs 7.6%; p=0.03). Being non-obese (OR 2.87, 95% CI 1.021 - 8.06; p=0.0456), having a higher dose per total body weight (OR 1.02, 95% CI 1.01 - 1.04; p=0.0346) and per BMI (OR 1.07, 95% CI 1.01 - 1.14; p=0.0330), having cancer (OR 2.65, 95% CI 1.07 - 6.58; p=0.0355), failure to ambulate early after surgery (OR 21.25, 95% CI 2.81 - 160.40; p=0.0030), and having a central venous catheter (OR 4.97, 95% CI 1.65 - 14.96); p=0.0043) and/or epidural catheter in place (OR 4.21, 95% CI 1.29 - 13.78; p=0.0174) increased the risk of a major bleeding event. Ninety-nine percent (374/378) of patients received some form of pharmacological prophylaxis. Of the 374 who received prophylactic therapy, 6 patients received pre-operative prophylaxis only and 368 patients received post-operative prophylaxis. Of the patients that received post-operative prophylaxis, 217 (59%) patients received dalteparin and 145 (39%) patients received unfractionated heparin (UFH), while the remaining 6 (2%) patients had a switch from one agent to the other. Of the 145 patients that received UFH, 24 patients (17%) had a recommended indication for its use as they required an epidural catheter while the remaining 121 (83%) did not. Conclusions: The rate of VTE in patients undergoing general surgery is low and obese patients (BMI ≥ 35 kg/m2) appear to be adequately protected against VTE. There is no need to alter our current VTE prophylaxis dosing strategy; however, in order to maintain a low rate of VTE, continued compliance with the implementation of pharmacological prophylaxis as well as assessment of VTE risk on a case by case basis is recommended. We likely failed to see an association of risk factors with the outcome of VTE as we had a low event rate and would have required a larger sample size to find significance. The rate of bleeding, although significantly different between groups, appears to be consistent with the rate reported in the literature. The percentage of patients receiving UFH outside of the recommended indications was fairly high and therefore further education is needed to ensure selection of the most appropriate pharmacologic agent. 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 | 0.001 | 0.002 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".