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Background and Purpose: Risk prediction with the Global Registry of Acute Coronary Events (GRACE) risk model is guideline-recommended in acute coronary syndrome (ACS) patients. However, the performance of more contemporary scores derived from ACTION (Acute Coronary Treatment and Intervention Outcomes Network) and National Cardiovascular Data (NCDR) registries remains incompletely understood. We aimed to compare these models in German ACS patients., Methods and Results: A total of 1567 patients with (Non-)ST-segment elevation myocardial infarction (NSTEMI: 1002 patients, STEMI: 565 patients) undergoing invasive management at University Hospital Düsseldorf (Germany) from 2014 to 2018 were included. Overall in-hospital mortality was 7.5% (NSTEMI 3.7%, STEMI 14.5%). Parameters for calculation of GRACE 1.0, GRACE 2.0, ACTION and NCDR risk models and in-hospital mortality were assessed and risk model performance was compared. The GRACE 1.0 risk model for prediction of in-hospital mortality discriminated risk superior (c-index 0.84) to its successor GRACE 2.0 (c-index 0.79, p GRACE1.0vsGRACE2.0 = 0.0008). The NCDR model performed best in discrimination of risk in ACS overall (c-index 0.89; p ACTIONvsNCDR < 0.0001; p GRACEvsNCDR < 0.0001) and showed superior performance compared to GRACE in NSTEMI and STEMI subgroups (p GRACEvsNCDR both < 0.02). ACTION and GRACE risk models performed comparable to each other (both c-index 0.84, p GRACEvsACTION = 0.68), with advantages for ACTION in NSTEMI patients (c-index 0.87 vs. 0.84 (GRACE); p GRACEvsACTION = 0.02). ACTION and GRACE 2.0 showed the most accurate calibration of all models., Conclusions: In a contemporary German patient population with ACS, modern NCDR and ACTION risk models showed superior performance in prediction of in-hospital mortality compared to the gold-standard GRACE model., Competing Interests: Declaration of Competing Interest All authors declare: no support from any industry or third-party organization for the submitted work; no financial relationships with any organizations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Background: Surgical risk prediction models are routinely used to guide decision-making for transcatheter aortic valve replacement (TAVR). New and updated TAVR-specific models have been developed to improve risk stratification; however, the best option remains unknown., Objective: To perform a comparative validation study of six risk models for the prediction of 30-day mortality in TAVR METHODS AND RESULTS: A total of 2946 patients undergoing transfemoral (TF, n = 2625) or transapical (TA, n = 321) TAVR from 2008 to 2018 from the German Rhine Transregio Aortic Diseases cohort were included. Six surgical and TAVR-specific risk scoring models (LogES I, ES II, STS PROM, FRANCE-2, OBSERVANT, GAVS-II) were evaluated for the prediction of 30-day mortality. Observed 30-day mortality was 3.7% (TF 3.2%; TA 7.5%), mean 30-day mortality risk prediction varied from 5.8 ± 5.0% (OBSERVANT) to 23.4 ± 15.9% (LogES I). Discrimination performance (ROC analysis, c-indices) ranged from 0.60 (OBSERVANT) to 0.67 (STS PROM), without significant differences between models, between TF or TA approach or over time. STS PROM discriminated numerically best in TF TAVR (c-index 0.66; range of c-indices 0.60 to 0.66); performance was very similar in TA TAVR (LogES I, ES II, FRANCE-2 and GAVS-II all with c-index 0.67). Regarding calibration, all risk scoring models-especially LogES I-overestimated mortality risk, especially in high-risk patients., Conclusions: Surgical as well as TAVR-specific risk scoring models showed mediocre performance in prediction of 30-day mortality risk for TAVR in the German Rhine Transregio Aortic Diseases cohort. Development of new or updated risk models is necessary to improve risk stratification.

Background and Purpose: The National Cardiovascular Data Registry (NCDR) risk scores for mortality, bleeding and acute kidney injury (AKI) are accurate outcome predictors of coronary catheterization procedures in North American populations. However, their application in German clinical practice remained elusive and we thus aimed to verify their use., Methods: NCDR scores for mortality, bleeding and AKI and corresponding clinical outcomes were retrospectively assessed in patients undergoing catheterization for ST-segment elevation myocardial infarction (STEMI), non-ST-segment elevation myocardial infarction (NSTEMI) or for elective coronary procedures at a German Heart Center from 2014 to 2017. Risk model performance was assessed using receiver-operating-characteristic curves (discrimination) and graphical analysis/logistic regression (calibration)., Results: A total of 1637 patients were included, procedures were performed for STEMI (565 patients, 34.5%), NSTEMI (572 patients, 34.9%) and elective purposes (500 patients, 30.5%); 6% (13% of STEMI and 5% of NSTEMI patients) presented in cardiogenic shock and 3% with resuscitated cardiac arrest. Radial access was used in 38% of procedures and cross-over was necessary in 5%; PCI was performed in 60% of procedures. In-hospital mortality was 6.3% (STEMI 14.5%; NSTEMI 3.7%; elective 0%) and major bleedings occurred in 5.6% (STEMI 10.6%; NSTEMI 5.4%; elective 0.2%); AKI was detected in 18.1% of patients (STEMI 23.7%; NSTEMI 27.3%; elective 1.4%), amounting to KDIGO stage I/II/III in 11.5%/3.5%/3.2%. NCDR risk models discriminated very well for mortality [AUC 0.93 with 95% confidence interval (CI) 0.91-0.95] and well for major bleeding (AUC 0.82, CI 0.78-0.86) and any AKI (AUC 0.83, CI 0.81-0.86). Discrimination in the subgroup of patients with PCI was comparable (mortality: AUC 0.90; major bleeding: AUC 0.78; any AKI: AUC 0.79). However, calibration showed considerable underestimation of mortality and AKI in high-risk patients, while major bleeding was consistently overestimated (Hosmer-Lemeshow p < 0.02 for all outcomes)., Conclusions: The NCDR risk models showed excellent performance in discriminating high-risk from low-risk patients in contemporary German interventional cardiology. Model calibration for adverse event probability prediction, however, is limited and demands recalibration, especially in high-risk patients.