Fragmented Surface ECG Was a Poor Predictor of Appropriate Therapies in Patients with Chagas’ Cardiomyopathy and ICD Implantation (Fragmented ECG in CHAgas’ Cardiomyopathy Study)
Bibliographic record
Abstract
BACKGROUND: Main causes of death in chronic Chagas' cardiomyopathy (CChC) are progressive congestive heart failure and sudden cardiac death. Implantable cardioverter defibrillators (ICD) have been proved an effective therapy to prevent sudden death in patients with CChC. Identification of predictors of sudden death remains a challenge. OBJECTIVE: To determine whether surface fragmented ECG (fQRS) helps identifying patients with CChC and ICDs at higher risk of presenting appropriate ICD therapies. METHODS: Multicenter retrospective study. All patients with CChC and ICDs were analyzed. Clinical demographics, surface ECG, and ICD therapies were collected. RESULTS: A total of 98 patients were analyzed. Another four cases were excluded due to pacing dependency. Mean age was 55.5 ± 10.4 years, male gender 65%, heart failure New York Heart Association class I 47% and II 38%. Mean left ventricular ejection fraction (LVEF) 39.6 ± 11.8%. The indication for ICD was secondary prevention in 70% of patients. fQRS was found in 56 patients (59.6%). Location of fragmentation was inferior (57.1%), lateral (35.7%), and anterior (44.6%). Rsr pattern was the more prevalent (57.1%). Predictors of appropriate therapy in the multivariate model were: increased age (P = 0.01), secondary prevention indication (P = 0.01), ventricular pacing >50% of the time (P = 0.004), and LVEF <30% (P = 0.01). The presence of fQRS did not identify patients at higher risk of presenting appropriate therapies delivered by the ICD (P = 0.87); regardless of QRS interval duration. CONCLUSIONS: fQRS is highly prevalent among patients with CChC. It has been found a poor predictor of appropriate therapies delivered by the ICD in this population.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".