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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".