Predictive ability of modified Ottawa score for recurrence in patients with cancer-associated venous thromboembolisms: from the COMMAND VTE Registry
Bibliographic record
Abstract
Abstract Background/Introduction Patients with cancer-associated venous thromboembolisms (VTEs) have a markedly higher risk of recurrence as well as bleeding, compared to those without, leading to difficulty in achieving a good risk-to-benefit balance with anticoagulation therapy. Thus, the assessment of the risk of recurrence in an individual patient is essential. The modified Ottawa score has been developed to predict the risk of recurrence in patients with cancer-associated VTEs during anticoagulation therapy, however, the validity of the score is still controversial. Purpose We aimed to evaluate the utility and limitations of the modified Ottawa score in the risk stratification of recurrent VTEs in patients with cancer-associated VTEs. Methods The COMMAND VTE Registry is a multicenter retrospective registry enrolling 3027 consecutive patients with acute symptomatic VTEs among 29 Japanese centers between January 2010 and August 2014. The present study population consisted of 614 cancer-associated VTE patients with anticoagulation therapy beyond 10 days after the diagnosis, who were divided into 3 groups; High-risk group with a modified Ottawa score ≥1, Intermediate-risk group with a score = 0, and Low-risk group with a score ≤−1. To evaluate the discriminating power of the modified Ottawa score for recurrence, we described the receiver operating characteristic curve with a C-statistic, and evaluated the positive likelihood ratio as the predictive performance of the score for recurrence in each subgroup. Results The high-risk group accounted for 202 patients (33%), intermediate-risk group for 269 (44%), and low-risk group for 143 (23%). During the first 6 months of anticoagulation therapy, recurrent VTEs occurred in 39 patients. The cumulative incidence of recurrent VTEs substantially increased in the higher risk categories by the modified Ottawa score (High-risk group: 13.6%, Intermediate-risk group: 5.9%, and Low-risk group: 3.0%, Log-rank P=0.02) (Figure 1). The discriminating power of the score was modest with a C-statistic of 0.63 (95% CI 0.55–0.71). The positive likelihood ratios as the predictive performance of the score were 1.71 in the high-risk group, 0.81 in the intermediate-risk group, and 0.42 in the low-risk group. Women and patients with prior VTEs had numerically higher cumulative 6-month incidences of recurrent VTEs compared with those without, while patients with lung cancer, breast cancer, and without metastasis had numerically lower cumulative 6-month incidences of recurrent VTEs. Depending on the presence or absence of each score component, the risks of recurrence seemed to differ in the low-, intermediate-, and high-risk groups. Conclusions The risks of recurrence in patients with cancer-associated VTEs substantially increased in the higher risk categories by using the modified Ottawa score, but the discriminating power of the score for recurrence was modest with a widely variable impact of each score component on recurrence. Figure 1 Funding Acknowledgement Type of funding source: Foundation. Main funding source(s): Research Institute for Production Development, Mitsubishi Tanabe Pharma Corporation
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".