Risk Stratification for the Development of Venous Thromboembolism in Hospitalized Patients with Cancer
Bibliographic record
Abstract
Abstract Introduction: No method of venous thromboembolism (VTE) risk stratification exists for hospitalized cancer patients. The Khorana score is a validated tool in outpatients with cancer. The objective of this study was to assess the Khorana score for predicting development of VTE in cancer patients during admission to hospital. Methods: We conducted a retrospective analysis of data collected from healthcare records of consecutive, medically-ill cancer patients hospitalized between January and June 2010 in 3 academic medical centers in Canada. Objectively diagnosed symptomatic VTE during hospitalization, anticoagulant thromboprophylaxis (TP), and Khorana score variables were collected for every patient. Patients receiving therapeutic anticoagulation at admission, and those with incomplete data were excluded. The risk of VTE based on Khorana score category was evaluated using logistic regression. Continuous data were compared using a Student's t-test and expressed using the means and standard deviations. Categorical data were compared using the Pearson Chi-square test and were expressed as percentages. Statistical significance was defined as alpha less than 0.05. Results: 1398 patients were included. Mean age was 61.6 years, 51.2% were male, and mean BMI was 25.9 kg/m2. The most frequent tumor types were non-small cell lung carcinoma (12.7%) followed by lymphoma (10.9%). The median length of stay was 6 days (range 0-114 days). The most frequent reasons for hospitalization were chemotherapy (22.3%) followed by pain and palliation (21.4%). 34.5% received anticoagulant TP (n = 483/1398). The incidence of VTE was 2.9% (41/1398) overall, 5.4% (9/166) in high, 3.2% (26/817) in moderate, and 1.4% (6/415) in low Khorana score risk groups. High risk patients were significantly more likely than low risk patients to have VTE (p=0.016; OR 3.9, 95% CI 1.4-11.2). There was no difference in VTE incidence between patients who received anticoagulant TP and those who did not (3.5% vs 2.6%, p = 0.345). Patients with high risk Khorana score were more likely to receive anticoagulant TP than those with low risk Khorana score (46.4% vs. 23.9%, p <0.001, OR 2.8, 95% CI 1.9-4.0). Total incidence of major bleeding was 1.8% (25/1398). There was no difference in major bleeding between patients who received anticoagulant TP and those who did not (1.7% vs. 1.9%, p = 0.787). Conclusion: The Khorana score is predictive of VTE development in cancer patients who are hospitalized for medical illness and may be a useful tool for tailoring inpatient anticoagulant prophylaxis. Disclosures Lee: LEO: Consultancy, Honoraria; Bayer: Consultancy, Honoraria; Bristol Myers-Squibb: Consultancy, Honoraria, Research Funding; Pfizer: Consultancy, Honoraria. Carrier:BMS: Research Funding; Leo Pharma: Research Funding. Wu:Pfizer: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Leo Pharma: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".