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.

中文翻译：

---

预测接受经导管左心耳封堵术的患者的主要不良事件

背景：国家心血管数据登记处左心耳封堵登记处 (LAAO) 包括在美国进行的绝大多数经导管 LAAO 手术。本研究的目的是开发一个模型，预测使用 Watchman FLX 接受 LAAO 的患者的不良事件。 方法：使用 2020 年 7 月至 2021 年 9 月期间使用 Watchman FLX 进行的 41 001 例 LAAO 手术的数据来开发和验证预测院内不良事件的模型主要不良事件。使用 1000 个引导样本对随机选择的开发组（70%，n=28 530）和验证组（30%，n=12 471）进行分析，使用前向逐步逻辑回归创建最终模型。使用该模型还开发了简化的床边风险评分。 结果：年龄增加、女性性别、术前血红蛋白低、事先未尝试终止房颤以及跌倒风险增加最能预测院内主要不良事件，并被纳入研究范围内。最终模型以及其他临床相关变量。院内风险标准化不良事件发生率中位数为 1.50%（范围，1.03%–2.84%；四分位数范围，1.42%–1.64%）。该模型表现出中等的辨别力（开发 C 指数，0.67 [95% CI，0.65–0.70] 和验证 C 指数，0.66 [95% CI，0.62–0.70]）并具有良好的校准。简化的风险评分经过了很好的校准，院内主要不良事件的风险范围分别为 0.26% 至 3.90%（得分为 0 至 8）。结论：使用国家心血管数据登记处和 LAAO 登记处数据的经导管 LAAO 风险模型可以预测院内重大不良事件，证明整个医院的一致性，并可用于质量改进工作。简单的床边风险评分也具有类似的预测作用，并且可以为共同决策提供信息。