Device Therapy in Patients with Heart Failure: Is Medication Use Optimized and Are We Targeting Those Patients Most Likely to Benefit?
Bibliographic record
Abstract
Background To determine what proportion of device-eligible patients with heart failure treated in a specialized clinic are on optimal guideline-directed medical therapy (GDMT) at the time of device referral and their benefit–risk status. Methods A cohort study was conducted of all patients seen between January 2013 and August 2024. We characterized GDMT by the modified Heart Function Collaboratory score at the time of device referral and grouped patients by Multicenter Automatic Defibrillator Implantation Trial-II (MADIT-ICD) benefit–risk score. Results Of 250 patients potentially eligible for a primary prophylaxis implantable cardioverter defibrillator (ICD), 136 (54.4%) were referred within 6 months of GDMT initiation—34 (25%) were in the lowest MADIT-ICD benefit–risk group and 6 (9.7%) of those not referred were in the highest benefit–risk group. No differences occurred in ICD referral based on sex ( P = 0.92) or New York Heart Association class ( P = 0.11), but younger patients ( P = 0.007), those with ischemic heart disease ( P = 0.006), and those with a longer QRS interval ( P = 0.001) were more likely to be referred. Of 54 patients who met cardiac resynchronization therapy indications, 32 (59.3%) were referred within 6 months of GDMT initiation. The median modified Heart Function Collaboratory score was 83.3 (interquartile range, 66.7-100) at the time of ICD referral, and 75.0 (interquartile range, 65.6–100) at the time of cardiac resynchronization therapy referral, but up to one third of patients had ≥ 1 element of GDMT dosed at < 50% target. Conclusions Within 6 months of initiating GDMT, more than half of eligible patients with heart failure had been referred for a device, but one quarter were in the group least likely to benefit from a device, and not all were on optimal GDMT.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".