Predicting Maximal HR in Heart Failure Patients on β-Blockade Therapy
Bibliographic record
Abstract
PURPOSE: Standards for estimating maximal HR are important when interpreting the adequacy of physiologic stress during exercise testing, assessing chronotropic response, and prescribing an exercise training regimen. The equation 220 - age is used to estimate maximum HR; however, it overestimates measured maximal HR in patients taking β-adrenergic blockade (βB) therapy. This study developed and validated a practical equation to predict maximal HR in patients with heart failure (HF) taking βB therapy. METHODS: Data from symptom-limited exercise tests completed on patients with systolic HF participating in the Heart Failure: A Controlled Trial Investigating Outcomes of Exercise Training trial and taking a βB agent were used to develop a simplified equation, which was validated using bootstrapping. RESULTS: The simplified derived equation was 119 + 0.5 (resting HR) - 0.5 (age) - (0, if test was completed using a treadmill; 5, if using a stationary bike). The R2 and SEE were 0.28 and 18 beats·min(-1), respectively. Validation of this equation yielded a mean R and SEE of 0.28 and 18 beats·min(-1), respectively. For the equation 220 - age, the R2 was -2.93, and the SEE was 43 beats·min(-1). CONCLUSIONS: We report a valid and simple population-specific equation for estimating peak HR in patients with HF taking βB therapy. This equation should be helpful when evaluating chronotropic response or assessing if a maximum effort was provided during exercise testing. We caution, however, that the magnitude of the variation (SEE = 18 beats·min(-1)) associated with this prediction equation may make it impractical when prescribing exercise intensity.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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".