Predicting Major Adverse Events in Patients Undergoing Transcatheter Left Atrial Appendage Occlusion.

BACKGROUND: The National Cardiovascular Data Registry Left Atrial Appendage Occlusion Registry (LAAO) includes the vast majority of transcatheter LAAO procedures performed in the United States. The objective of this study was to develop a model predicting adverse events among patients undergoing LAAO with Watchman FLX. METHODS: Data from 41 001 LAAO procedures with Watchman FLX from July 2020 to September 2021 were used to develop and validate a model predicting in-hospital major adverse events. Randomly selected development (70%, n=28 530) and validation (30%, n=12 471) cohorts were analyzed with 1000 bootstrapped samples, using forward stepwise logistic regression to create the final model. A simplified bedside risk score was also developed using this model. RESULTS: Increased age, female sex, low preprocedure hemoglobin, no prior attempt at atrial fibrillation termination, and increased fall risk most strongly predicted in-hospital major adverse events and were included in the final model along with other clinically relevant variables. The median in-hospital risk-standardized adverse event rate was 1.50% (range, 1.03%-2.84%; interquartile range, 1.42%-1.64%). The model demonstrated moderate discrimination (development C-index, 0.67 [95% CI, 0.65-0.70] and validation C-index, 0.66 [95% CI, 0.62-0.70]) with good calibration. The simplified risk score was well calibrated with risk of in-hospital major adverse events ranging from 0.26% to 3.90% for a score of 0 to 8, respectively. CONCLUSIONS: A transcatheter LAAO risk model using National Cardiovascular Data Registry and LAAO Registry data can predict in-hospital major adverse events, demonstrated consistency across hospitals and can be used for quality improvement efforts. A simple bedside risk score was similarly predictive and may inform shared decision-making.

Facilitating the identification of patients hospitalized for acute myocardial infarction and heart failure and the assessment of their readmission risk through the Patient Navigator Program.

BACKGROUND: Optimal transition care mitigates early hospital readmission risk. Given limited resources, hospitals need to identify patients with high readmission risk. This article examines whether a coordinated quality improvement campaign can help achieve this objective. METHODS: The American College of Cardiology Patient Navigator Program, a 2-year quality improvement campaign, sought to assess the impact of transition care interventions on 30-day readmission rates for patients with acute myocardial infarction (AMI) or heart failure (HF) at 35 hospitals. This article examines the change in 2 of the 36 performance metrics the campaign tracked: the number of AMI and HF patients identified predischarge and those whose readmission risk was assessed. RESULTS: The number of facilities identifying AMI and HF patients predischarge increased from 24 (68.6%) and 28 (80.0%), respectively, at baseline, to 34 (97.1%) (P = .0016) and 34 (97.1%) (P = .014), respectively, at 2 years. The number of facilities assessing the readmission risk of AMI and HF patients risk increased from 9 (25.7%) and 11 (31.4%), respectively, at baseline, to 32 (91.4%) (P < .0001) and 33 (94.5%) (P < .0001), respectively, at 2 years. Importantly, baseline reporting of performance for both metrics was poor, with >25% of the hospitals missing data. CONCLUSIONS: Implementation of a coordinated quality improvement campaign may increase the number of facilities identifying AMI and HF patients predischarge and assessing their readmission risk. Further research is needed to determine if increased identification reduces 30-day readmission or facilitates improvement in other important clinical outcomes.