Efficacy of left ventricular unloading strategies during venoarterial extracorporeal membrane oxygenation in patients with cardiogenic shock: a protocol for a systematic review and Bayesian network meta-analysis
Bibliographic record
Abstract
Abstract Background: Venoarterial extracorporeal membrane oxygenation (VA-ECMO) has been widely used for patients with refractory cardiogenic shock (CS). A common side-effect of this technic is the resultant increase in left ventricular (LV) afterload which could potentially aggravate myocardial ischemia, delay ventricular recovery, and increase the risk of pulmonary congestion. Several LV unloading strategies have been proposed and implemented to mitigate these complications. However, it is still indistinct that which one is the best choice for clinical application. The objective of this Bayesian network meta-analysis (NMA) is to summarize the evidence and compare the efficacy of different LV unloading strategies during VA-ECMO.Methods: We will perform a systematic search to identify random controlled trials and cohort studies comparing different LV unloading strategies during VA-ECMO. PubMed, Embase, the Cochrane Library, and the International Clinical Trials Registry Platform (ICTRP) will be explored from their inception to 31 December 2020. The primary outcome will be in-hospital mortality. The secondary outcomes will include neurological complications, hemolysis, bleeding, limb ischemia, renal failure, gastrointestinal complications, sepsis, duration of mechanical ventilation, length of intensive care unit, and hospital stays. Pairwise and network meta-analysis will respectively be conducted using Stata (V.16, StataCorp) and Aggregate Data Drug Information System (ADDIS V.1.16.5), and the cumulative probability will be used to rank the included LV unloading strategies. The risk of bias will be conducted using the Cochrane Collaboration’s tool or Newcastle-Ottawa Quality Assessment Scale (NOS) according to their study design. Subgroup analysis, sensitivity analysis, and publication bias assessment will be performed. The Grading of Recommendations Assessment, Development and Evaluation (GRADE) will be conducted to explore the quality of evidence.Discussion: This Bayesian network meta-analysis (NMA) will address the problem that which strategy could achieve left ventricular (LV) unloading most effectively during venoarterial extracorporeal membrane oxygenation and increase cardiogenic shock patient survival benefit, and will provide evidence for clinical decision-making.Systematic review registration: PROSPERO registry number: CRD42020165093.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.037 | 0.066 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.031 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".