Quality Improvement of Venous Thromboembolism Prophylaxis in Neurological Surgery: Ochsner Health System Protocol
Bibliographic record
Abstract
BACKGROUND: Venous thromboembolism (VTE) is a common postoperative complication. The perioperative incidence of VTE in patients undergoing neurosurgery on VTE chemoprophylaxis is consistently between 1.7% and 3.5%, excluding patients with trauma. While the majority of VTEs are asymptomatic, symptomatic VTEs may result in serious adverse events or death. There are limited neurosurgery society guidelines making VTE prophylaxis recommendations for patients undergoing cranial or spine neurosurgery. Several societies have published VTE prophylaxis guidelines for neurosurgery, but inconsistent recommendations have led to varied practices based on neurosurgeon preference. OBJECTIVE: This study aims to assess whether implementing a single protocol of VTE guidelines at a large hospital system department of neurosurgery would decrease the rate of VTE incidence by 50% within a 12-month period. METHODS: This project uses a department-wide protocol of chemical prophylaxis for admitted patients after neurological surgery. This quality improvement (QI) project has been designed and reported in accordance with the SQUIRE 2.0 (Standards for Quality Improvement Reporting Excellence) guidelines. SQUIRE is the most appropriate framework for reporting system-level interventions aimed at improving health care quality, safety, and outcomes. This manuscript addresses all relevant SQUIRE elements. The primary outcome is the VTE incidence over 12 months following protocol implementation. Secondary outcomes include the incidence of pulmonary embolism, length of hospital stay, and unexpected treatment of patients, such as therapeutic anticoagulation, thrombolysis, or emergency surgical or catheter-directed embolectomy. Balancing measures and process measures are also considered. Interim analyses with department reports will be performed every 3 months, but every VTE event will trigger an analysis of the variables described in this protocol. Analyses will consider barriers to change and adequacy of the protocol to guide protocol modifications and continuous implementation of improvements. RESULTS: This QI project was approved on February 16, 2023 (protocol ID NEU20230216-01). Adaptation and training began on August 1, 2023. As of May 2024, data collection is ongoing and scheduled to conclude by December 2024. Final data analysis is anticipated in March 2025, with results submitted for publication by the end of 2025. CONCLUSIONS: It is anticipated that this QI project implementing a department-wide protocol for chemical prophylaxis in patients undergoing neurosurgery will improve the rates of VTE in patients on our service. In addition to a discussion of our results, we provide a comprehensive review of the literature and establish a gap in consistent published guidelines for VTE prophylaxis in patients undergoing neurosurgery. Results from this QI project will provide evidence for the effectiveness of a unified protocol of VTE prophylaxis in this patient group. Furthermore, outcomes from this study may be used as the basis for guideline recommendations and will benefit not only our department but may be used for QI of patient care at other hospitals. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/57278.
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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 | 0.194 | 0.171 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".