Baseline Electrocardiographic Abnormalities in Pre-Treatment Cancer Compared With Non-Cancer Patients: A Propensity Score Analysis
Bibliographic record
Abstract
Background: Most studies have compared post-treatment electrocardiogram (ECG) abnormalities in cancer patients to the general population. To assess baseline cardiovascular (CV) risk, we compared pre-treatment ECG abnormalities in cancer patients with a non-cancer surgical population. Methods: We conducted a combined prospective (n = 30) and retrospective (n = 229) cohort study of patients aged 18 - 80 years with diagnosis of hematologic or solid malignancy, compared with 267 pre-surgical, non-cancer, age- and sex-matched controls. Computerized ECG interpretations were obtained, and one-third of the ECGs underwent blinded interpretation by a board-certified cardiologist (agreement r = 0.94). We performed contingency table analyses using likelihood ratio Chi-square statistics, with calculated odds ratios. Data were analyzed after propensity score matching. Results: The mean age of cases was 60.97 ± 13.86; and 59.44 ± 11.83 years for controls. Pre-treatment cancer patients had higher likelihood of abnormal ECG (odds ratio (OR): 1.55; 95% confidence interval (CI): 1.05 to 2.30), and more ECG abnormalities (? 2 = 4.0502; P = 0.04) compared with non-cancer patients. ECG abnormalities were higher in black compared to non-black patients (P = 0.001). In addition, baseline ECGs among cancer patients prior to cancer therapy demonstrated less QT prolongation and intra-ventricular conduction defect (P = 0.04); but showed more arrhythmias (P < 0.01) and atrial fibrillation (AF) (P = 0.01) compared with the general patient population. Conclusions: Based on these findings, we recommend that all cancer patients receive an ECG, a low-cost and widely available tool, as part of their CV baseline screening, prior to cancer treatment. Cardiol Res. 2023;14(3):237-239 doi: https://doi.org/10.14740/cr1466
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 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".