RESUMO
BACKGROUND AND AIMS: To develop a suite of quality indicators (QIs) for the evaluation of the care and outcomes for adults undergoing transcatheter aortic valve intervention (TAVI). METHODS: We followed the European Society of Cardiology (ESC) methodology for the development of QIs. Key domains were identified by constructing a conceptual framework for the delivery of TAVI care. A list of candidate QIs were developed by conducting a systematic review of the literature. A modified Delphi method was then used to select the final set of QIs. Finally, we mapped the QIs to the EuroHeart Data Standards for TAVI to ascertain the extent to which the EuroHeart TAVI registry captures information to calculate the QIs. RESULTS: We formed an international group of experts in quality improvement and TAVI, including representatives from the European Association of Percutaneous Cardiovascular Interventions, the European Association of Cardiovascular Imaging and the Association of Cardiovascular Nursing & Allied Professions. In total, 27 QIs were selected across eight domains of TAVI care, comprising 22 main (81%) and five secondary (19%) QIs. Of these, 19/27 (70%) are now being utilised in the EuroHeart TAVI registry. CONCLUSION: We present the 2023 ESC QIs for TAVI, developed using a standard methodology and in collaboration with ESC Associations. The EuroHeart TAVI registry allows calculation of the majority of the QIs, which may be used for benchmarking care and quality improvement initiatives.
RESUMO
AIMS: Multivariable prediction models can be used to estimate risk of incident heart failure (HF) in the general population. A systematic review and meta-analysis was performed to determine the performance of models. METHODS AND RESULTS: From inception to 3 November 2022 MEDLINE and EMBASE databases were searched for studies of multivariable models derived, validated and/or augmented for HF prediction in community-based cohorts. Discrimination measures for models with c-statistic data from ≥3 cohorts were pooled by Bayesian meta-analysis, with heterogeneity assessed through a 95% prediction interval (PI). Risk of bias was assessed using PROBAST. We included 36 studies with 59 prediction models. In meta-analysis, the Atherosclerosis Risk in Communities (ARIC) risk score (summary c-statistic 0.802, 95% confidence interval [CI] 0.707-0.883), GRaph-based Attention Model (GRAM; 0.791, 95% CI 0.677-0.885), Pooled Cohort equations to Prevent Heart Failure (PCP-HF) white men model (0.820, 95% CI 0.792-0.843), PCP-HF white women model (0.852, 95% CI 0.804-0.895), and REverse Time AttentIoN model (RETAIN; 0.839, 95% CI 0.748-0.916) had a statistically significant 95% PI and excellent discrimination performance. The ARIC risk score and PCP-HF models had significant summary discrimination among cohorts with a uniform prediction window. 77% of model results were at high risk of bias, certainty of evidence was low, and no model had a clinical impact study. CONCLUSIONS: Prediction models for estimating risk of incident HF in the community demonstrate excellent discrimination performance. Their usefulness remains uncertain due to high risk of bias, low certainty of evidence, and absence of clinical effectiveness research.