Development and Validation of Risk Prediction Models for Coronary Heart Disease and Heart Failure After Treatment for Hodgkin Lymphoma
Bibliographic record
Abstract
PURPOSE: Previous efforts to predict absolute risk of treatment-related cardiovascular diseases (CVDs) have mostly focused on childhood cancer survivors. We aimed to develop prediction models for risk of coronary heart disease (CHD) and heart failure (HF) for survivors of adolescent/adult Hodgkin lymphoma (HL). METHODS: For model development, we used a multicenter cohort including 1,433 5-year HL survivors treated between 1965 and 2000 and age 18-50 years at HL diagnosis, with complete data on administered chemotherapy regimens, radiotherapy volumes and doses, and cardiovascular follow-up. Using cause-specific hazard models, covariate-adjusted cumulative incidences for CHD and HF were estimated in the presence of competing risks of death because of other causes than CHD and HF. Age at HL diagnosis, sex, smoking status, radiotherapy, and anthracycline treatment were included as predictors. External validation for the CHD model was performed using a Canadian cohort of 708 HL survivors treated between 1988 and 2004 and age 18-50 years at HL diagnosis. RESULTS: After a median follow-up of 24 years, 341 survivors had developed CHD and 102 had HF. We were able to predict CHD and HF risk at 20 and 30 years after treatment with moderate to good overall calibration and moderate discrimination (areas under the curve: 0.68-0.74), which was confirmed by external validation for the CHD model (areas under the curve: 0.73-0.74). On the basis of our model including prescribed mediastinal radiation dose, 30-year risks ranged from 4% to 78% for CHD and 3% to 46% for HF, depending on risk factors. CONCLUSION: We developed and validated prediction models for CHD and HF with good overall calibration and moderate discrimination. These models can be used to identify HL survivors who might benefit from targeted screening for CVD and early treatment for CVD risk factors.
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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 | 0.018 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".