Right Heart Failure with Left Ventricular Assist Devices: Preoperative, Perioperative and Postoperative Management Strategies. A Clinical Consensus Statement of the Heart Failure Association (HFA) of the ESC
Bibliographic record
Abstract
Right heart failure (RHF) following implantation of a left ventricular assist device (LVAD) is a common and potentially serious condition with a wide spectrum of clinical presentations with an unfavourable effect on patient outcomes. Clinical scores that predict the occurrence of right ventricular (RV) failure have included multiple clinical, biochemical, imaging and haemodynamic parameters. However, unless the right ventricle is overtly dysfunctional with end-organ involvement, prediction of RHF post-LVAD implantation is, in most cases, difficult and inaccurate. For these reasons optimization of RV function in every patient is a reasonable practice aiming at preparing the right ventricle for a new and challenging haemodynamic environment after LVAD implantation. To this end, the institution of diuretics, inotropes and even temporary mechanical circulatory support may improve RV function, thereby preparing it for a better adaptation post-LVAD implantation. Furthermore, meticulous management of patients during the perioperative and immediate postoperative period should facilitate identification of RV failure refractory to medication. When RHF occurs late during chronic LVAD support, this is associated with worse long-term outcomes. Careful monitoring of RV function and characterization of the origination deficit should therefore continue throughout the patient's entire follow-up. Despite the useful information provided by the echocardiogram with respect to RV function, right heart catheterization frequently offers additional support for the assessment and optimization of RV function in LVAD-supported patients. In any patient candidate for LVAD therapy, evaluation and treatment of RV function and failure should be assessed in a multidimensional and multidisciplinary manner.
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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 | 0.000 | 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 | 0.001 |
| 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".