Development of a Clinical Prediction Rule for the Risk of Venous Thromboembolism in Patients with Acute Leukemia
Bibliographic record
Abstract
Abstract Background: The impact of venous thromboembolism (VTE) on morbidity and mortality is significant in hematological cancer patients, including acute leukemia (AL). However, VTE in AL receives little attention in comparison to other more frequently occurring complications. Identifying risk factors for the development of VTE among patients with AL will enable clinicians to stratify their patients according to their VTE risk and consider them accordingly for tailored surveillance or prophylaxis strategies. Methods: We aimed to derive a prediction rule for assessing VTE risk in AL patients. We conducted a retrospective cohort study of consecutive adult patients diagnosed with acute myeloid leukemia (AML) and acute lymphoid leukemia (ALL) diagnosed between June 2006 and June 2017 at the London Health Sciences Centre, a tertiary care center in London, Ontario, Canada. Included participants were followed from diagnosis until last follow up, the occurrence of VTE, or death. Potential predictors were compared between groups using χ2 or Fisher's exact tests for categorical variables and T tests for continuous variables. Potentially significant predictors were evaluated using logistic regression and Cox regression analysis. A risk score was derived based on weighed variables included in the final model and compared using Kaplan-Meier survival analysis. Results: A total of 396 patients with AL (335 AML and 61 ALL) were included in the preliminary analysis. Population characteristics are shown in the Table. VTE occurred in 68 (17%) patients. Median time to VTE was 3 months (Interquartile range 1-5). In univariate analysis a diagnosis of ALL, prior history of VTE, platelet count at presentation and the number of attempts for line insertion were potential predictors. However, in multivariate analysis only ALL diagnosis and prior history of VTE remained significant. A score was derived based on these predictors (ALL 1 point; Prior VTE 2 points). Kaplan-Meier analysis showed good discrimination between categories (Figure; Log-rank p<0.001). Conclusion: We derived a score predictive of VTE in acute leukemia patients. Further confirmatory and validation studies are ongoing. Disclaimer. FA-A is a fellow of the CanVECTOR Network; ALL-L is an investigator of the CanVECTOR Network. This study was funded by the CanVECTOR Network which receives grant funding from the Canadian Institutes of Health Research (Funding Reference: CDT-142654). Figure. Figure. 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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".