The Development and Application of a Clinical Severity Scoring Model for Post-Operative Venous Thromboembolism (VTE)
Bibliographic record
Abstract
Background: In studies evaluating the prevention of venous thromboembolic events (VTE - composite of deep vein thrombosis (DVT) and pulmonary embolism (PE)) following surgery, VTE is considered as a binary event (present or absent). There is no consensus in the literature in determining the clinical severity of a post-operative VTE. The objectives of our study were to derive a severity scoring model for VTE in the post-operative setting and then apply the model to outcomes from a clinical trial evaluating aspirin vs. rivaroxaban for extended prophylaxis following total hip or knee arthroplasty (EPCATII). Methods: Thirty-one clinical scenarios were written, each describing a VTE event after a total hip or knee arthroplasty procedure. Each scenario varied the severity of the patient's presenting symptoms, the extent of thrombosis observed on radiographic studies, and the presence or absence of long-term symptoms. These scenarios were incorporated into a web-based survey sent to thrombosis clinicians. Respondents were asked to score each scenario on a 9-point scale based on perceived clinical severity. Responses from 29 clinicians were analyzed using mixed-effects regression models to determine the weight of each variable on the respondents' overall severity scores. The sum of scores for each weighted factor present based on parameter estimates from the model was calculated to categorize the scenarios into high, moderate, or low clinical severity categories. The VTE clinical severity scoring model was applied independently by two clinicians to the 36 cases of confirmed VTE from the EPCAT II trial. Two further reviewers gave their clinical opinion of each case's severity. Kappa scores for inter-rater reliability, and for agreement between clinical opinion and the model were determined. The proportion of EPCAT II cases rated as mild, moderate and severe by the model were determined for rivaroxaban and aspirin groups and then compared using the Cochran-Armitage test for trend. Results: The following factors shown as parameter estimates were associated with higher levels of clinical severity: moderate (1.28, p<0.0001) or severe (2.61, p<0.0001) radiographic findings; high severity symptoms at initial presentation (0.72, p<0.0001); presence of long-term symptoms (1.01, p<0.0001); and PE compared to DVT (1.49, p<0.0001). Based on these parameters, a scoring model was created using the nearest half point for each doubled parameter estimate for ease of calculation. The model classifies post-operative VTE into mild (score <4.5), moderate (score 4.5-8), or severe (score >8) clinical severity categories. Independent application of the model to the EPCAT II VTE cases had agreement in 33 of 36 cases (κ=0.89). The agreement between the clinical opinion and the model was 28 of the 36 cases (κ=-0.14). In all cases of disagreement, the clinical gestalt was more severe than the model. Cases randomized to rivaroxaban were scored as 10 mild (55.6%), 4 moderate (22.2%), and 4 severe (22.2%). Cases randomized to aspirin were scored as 12 mild (66.7%), 4 moderate (22.2%) and 2 severe (11.1%). There were no differences in severity of VTE between the aspirin and rivaroxaban groups (p=0.38). Conclusion: A clinical severity scoring model for post-operative VTE was created based upon scenarios rated by experienced thrombosis clinicians and validated by applying it to EPCAT II cases of VTE. This model may be useful for categorizing clinical importance of VTE in the post-operative setting. There was no apparent difference in the severity of post-operative VTE following total hip or knee arthroplasty whether aspirin or rivaroxaban was used for extended prophylaxis. 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 | 0.016 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".