ImportanceThe existing models predicting right ventricular failure (RVF) after durable left ventricular assist device (LVAD) support might be limited, partly due to lack of external validation, marginal predictive power, and absence of intraoperative characteristics.ObjectiveTo derive and validate a risk model to predict RVF after LVAD implantation.Design, Setting, and ParticipantsThis was a hybrid prospective-retrospective multicenter cohort study conducted from April 2008 to July 2019 of patients with advanced heart failure (HF) requiring continuous-flow LVAD. The derivation cohort included patients enrolled at 5 institutions. The external validation cohort included patients enrolled at a sixth institution within the same period. Study data were analyzed October 2022 to August 2023.ExposuresStudy participants underwent chronic continuous-flow LVAD support.Main Outcome and MeasuresThe primary outcome was RVF incidence, defined as the need for RV assist device or intravenous inotropes for greater than 14 days. Bootstrap imputation and adaptive least absolute shrinkage and selection operator variable selection techniques were used to derive a predictive model. An RVF risk calculator (STOP-RVF) was then developed and subsequently externally validated, which can provide personalized quantification of the risk for LVAD candidates. Its predictive accuracy was compared with previously published RVF scores.ResultsThe derivation cohort included 798 patients (mean [SE] age, 56.1 [13.2] years; 668 male [83.7%]). The external validation cohort included 327 patients. RVF developed in 193 of 798 patients (24.2%) in the derivation cohort and 107 of 327 patients (32.7%) in the validation cohort. Preimplant variables associated with postoperative RVF included nonischemic cardiomyopathy, intra-aortic balloon pump, microaxial percutaneous left ventricular assist device/venoarterial extracorporeal membrane oxygenation, LVAD configuration, Interagency Registry for Mechanically Assisted Circulatory Support profiles 1 to 2, right atrial/pulmonary capillary wedge pressure ratio, use of angiotensin-converting enzyme inhibitors, platelet count, and serum sodium, albumin, and creatinine levels. Inclusion of intraoperative characteristics did not improve model performance. The calculator achieved a C statistic of 0.75 (95% CI, 0.71-0.79) in the derivation cohort and 0.73 (95% CI, 0.67-0.80) in the validation cohort. Cumulative survival was higher in patients composing the low-risk group (estimated <20% RVF risk) compared with those in the higher-risk groups. The STOP-RVF risk calculator exhibited a significantly better performance than commonly used risk scores proposed by Kormos et al (C statistic, 0.58; 95% CI, 0.53-0.63) and Drakos et al (C statistic, 0.62; 95% CI, 0.57-0.67).Conclusions and RelevanceImplementing routine clinical data, this multicenter cohort study derived and validated the STOP-RVF calculator as a personalized risk assessment tool for the prediction of RVF and RVF-associated all-cause mortality.

中文翻译：

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机器学习多中心风险模型可预测机械循环支持后的右心室衰竭


重要性现有的预测持久左心室辅助装置 (LVAD) 支持后右心室衰竭 (RVF) 的模型可能有限，部分原因是缺乏外部验证、边际预测能力和缺乏术中特征。预测 LVAD 植入后的 RVF。 设计、设置和参与者这是一项混合前瞻性回顾性多中心队列研究，于 2008 年 4 月至 2019 年 7 月进行，研究对象为需要连续流 LVAD 的晚期心力衰竭 (HF) 患者。衍生队列包括 5 个机构招募的患者。外部验证队列包括同一时期在第六家机构入组的患者。研究数据于 2022 年 10 月至 2023 年 8 月进行分析。研究参与者接受了慢性连续流 LVAD 支持。主要结果和措施主要结果是 RVF 发生率，定义为需要 RV 辅助装置或静脉正性肌力药物超过 14 天。使用自助插补和自适应最小绝对收缩以及选择算子变量选择技术来导出预测模型。随后开发了 RVF 风险计算器 (STOP-RVF)，并随后进行了外部验证，可以为 LVAD 候选人提供个性化的风险量化。将其预测准确性与之前发布的 RVF 评分进行比较。结果 衍生队列包括 798 名患者（平均 [SE] 年龄，56.1 [13.2] 岁；668 名男性 [83.7%]）。外部验证队列包括 327 名患者。推导队列中的 798 名患者中有 193 名 (24.2%) 发生了 RVF，验证队列中的 327 名患者中有 107 名 (32.7%) 发生了 RVF。 与术后 RVF 相关的植入前变量包括非缺血性心肌病、主动脉内球囊反搏、微轴经皮左心室辅助装置/静脉动脉体外膜氧合、LVAD 配置、机械辅助循环支持资料跨机构登记 1 至 2、右心房/肺毛细血管楔压比例、血管紧张素转换酶抑制剂的使用、血小板计数以及血清钠、白蛋白和肌酐水平。纳入术中特征并没有改善模型性能。计算器在推导队列中实现了 0.75（95% CI，0.71-0.79）的 C 统计量，在验证队列中实现了 0.73（95% CI，0.67-0.80）的 C 统计量。与高风险组的患者相比，低风险组(估计RVF风险＜20％)的患者的累积生存率较高。 STOP-RVF 风险计算器的性能明显优于 Kormos 等人（C 统计量，0.58；95% CI，0.53-0.63）和 Drakos 等人（C 统计量，0.62；95% CI，0.57）提出的常用风险评分。 -0.67).结论和相关性这项多中心队列研究通过常规临床数据，得出并验证了 STOP-RVF 计算器作为预测 RVF 和 RVF 相关全因死亡率的个性化风险评估工具。