Impact of Cancer Therapy-Related Cardiac Dysfunction on Risk of Heart Failure in Pregnancy
Bibliographic record
Abstract
Cancer treatment can lead to left ventricular (LV) dysfunction in female cancer survivors of reproductive age, and pregnancy-related hemodynamic stress may result in LV dysfunction or heart failure (HF). We performed a systematic review and meta-analysis to determine the incidence of LV systolic dysfunction or HF during or soon after pregnancy in cancer survivors and evaluated the impact of history of cancer therapeutics-related cardiac dysfunction (CTRCD). We systematically searched electronic databases (MEDLINE and EMBASE) from inception to January 2020 to identify cohort studies that examined cardiac disease in pregnant cancer survivors. Meta-analysis was performed using the inverse-variance fixed effects method. Potential sources of heterogeneity were explored using subgroup analyses and meta-regression. Of 13,782 identified articles, 6 studies consisting of 2,016 pregnancies, predominantly in childhood cancer survivors, were included. Overall, there were 33 cardiac events. The total weighted incidence of LV dysfunction or HF with pregnancy was 1.7% (95% confidence interval [CI]: 0.9% to 2.7%) overall; 28.4% (95% CI: 14.6% to 43.9%) in those with a history of CTRCD and 0.24% (95% CI: 0% to 0.81%) in those without, translating into an odds ratio of 47.4 (95% CI: 17.9 to 125.8). Interstudy heterogeneity was low (I2 = 17.5%). Metaregression did not reveal significant sources of heterogeneity. The incidence of LV dysfunction or HF during pregnancy in cancer survivors was low. Although risk estimates are limited by the small number of events, women with a history of CTRCD compared to those without had a 47.4-fold higher odds of experiencing pregnancy-related LV dysfunction or HF.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.025 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".