Bibliographic record
Abstract
Nonpharmacologic approaches (ie, device-based therapies) for the treatment of congestive heart failure (CHF) are now the standard of care. Currently, implantable cardioverter-defibrillators (ICDs) are considered for the heart failure patient with both ischemic and nonischemic heart disease, New York Heart Association (NYHA) class II or III symptoms, and an ejection fraction (EF) ≤35%. Cardiac resynchronization therapy (CRT) should be considered in the heart failure patient with an EF ≤35% with sinus rhythm and NYHA class III or IV heart failure (HF) on optimal medical therapy with evidence of cardiac dyssynchrony. As the number of individuals with HF grows in the United States, more devices will likely be implanted. Moreover, the indications for device therapy may expand pending the outcome of ongoing trials.1 It is therefore imperative that the clinician become familiar with the management of HF patients in the device era. An important but frequently overlooked concern in this population—the impact of quality of life, anxiety, and depression in the device patient—is addressed by Sears and colleagues2 in this issue of Congestive Heart Failure. The purpose of their paper is 2-fold: (1) to review the development of device-based CHF management, and (2) to provide approaches on how to optimally manage patient outcomes. There are key points to cover while discussing the possibility of an implanted device with HF patients. One of the first issues to be addressed should be patient and caregiver expectations. The Multicenter InSync Randomized Clinical Evaluation (MIRACLE) trial3 clearly demonstrated significant clinical improvement with CRT, including quality-of-life indices in the patient with moderate-to-severe HF and an intraventricular conduction delay. The CRT patient should be well educated, however, regarding expected outcomes in the individual patient. Up to one third of CRT patients are “nonresponders.” Efforts to identify patients who will benefit most from CRT continue to be studied. One target is to focus on mechanical dyssynchrony identified by echocardiography rather than electrical dyssynchrony (QRS duration). In patients with NYHA class III or IV HF, up to one third of patients who meet standard criteria for CRT may have little or no dyssynchrony on echocardiography, and one third of patients with a narrow QRS will demonstrate significant mechanical dyssynchrony. The ongoing Predictors of Response to Cardiac Resynchronization Therapy (PROSPECT) trial4 will determine whether echocardiographic parameters of dyssynchrony can predict a favorable clinical response to CRT. The patient with an ICD should be carefully counseled regarding the goals of the device. Although indicated for the primary prevention of malignant arrhythmias for the HF patient with an EF ≤35%, the absolute risk of sudden death for this population is still low. In addition to appropriate shocks, inappropriate shocks can be quite distressing. Studies have confirmed, however, that most patients tolerate the device well and that the quality of life with ICD therapy is superior to that with antiarrhythmic drugs.5 Nevertheless, in patients with frequent shocks (a subgroup of about 15%), the percentage of psychologically distressed patients is >50%.6 Depression is increasingly recognized in the HF population. A recent meta-analysis found the prevalence of depression in the population to be 21.5%, which is 2–3 times the rate of that found in the general population.6 Moreover, there are higher rates of death, increased health care use, and hospitalizations in the clinically depressed HF patient. Therefore, identification of such patients is necessary so that appropriate treatment can be instituted. Patient self-assessment is increasingly recognized as an important component of evaluation of the HF patient. Studies suggest that clinically significant depression is identified more readily by questionnaire than by diagnostic interview.7 Sears and associates2 outline methods to screen the device population for anxiety and depression. Once a concern is identified, appropriate treatment should be initiated. The “ABC model” proposed by the authors incorporates appropriate education prescriptions, behavioral prescriptions, and cognitive coping prescriptions. This approach has not been rigorously evaluated in the device population, and the authors suggest that future studies are necessary to determine the utility of cognitive—behavioral treatments in the HF patient with a device. What this important article teaches us is that there are poorer clinical outcomes for the HF patient with depression, anxiety, or poor quality of life. Recent studies have confirmed that depression is common in the HF population and may be underrecognized. As more devices are implanted in this group, strategies to optimize patient outcome will need to be employed. Sears and coworkers have devised a method to identify patient concerns and target quality-of-life activities. We can all look forward to an explosion of research assessing cognitive—behavioral treatments in patients with CHF and implanted devices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| 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".