Yamauchi Takashi, Takano Hiroshi, Miyata Hiroaki, Motomura Noboru, Takamoto Shinichi
Department of Cardiovascular Surgery, Dokkyo Medical University Saitama Medical Center Koshigaya Japan.
Department of Healthcare Quality Assessment, The University of Tokyo Tokyo Japan.
Circ Rep. 2020 Jan 24;1(3):131-136. doi: 10.1253/circrep.CR-19-0010.
The aim of this study was to determine adequate indication for transcatheter aortic valve replacement (TAVR). We analyzed risk factors of surgical aortic valve replacement (SAVR) not only for mortality, but also for morbidity, including long hospital stay (≥90 days) and patient activity at discharge, in patients who underwent SAVR for aortic stenosis (AS). Using the Japan Adult Cardiovascular Surgery Database (JCVSD), 13,961 patients with or without coronary artery bypass grafting who underwent elective SAVR for AS were identified from January 2008 to December 2012. The hospital mortality rate was 3.1%. The percentage of patients who had long hospital stay (≥90 days) and who had moderately or severely decompressed activity at discharge (modified Rankin scale ≥4) was 2.9% and 6.5%, respectively. Eleven and 20 preoperative predictors of hospital mortality and morbidity, respectively, including long hospital stay and compromised status at discharge, were identified. Based on these risk factors, the risk model predicted hospital mortality (area under the curve [AUC], 0.732) and morbidity (AUC, 0.694). Using JCVSD, a risk model of SAVR was developed for AS. This model can identify patients at high risk not only for mortality, but also for mortality and morbidity, including long hospital stay and status at discharge.
本研究的目的是确定经导管主动脉瓣置换术（TAVR）的适当适应症。我们分析了接受外科主动脉瓣置换术（SAVR）治疗主动脉瓣狭窄（AS）患者的手术主动脉瓣置换术的危险因素，不仅包括死亡率，还包括发病率，如住院时间长（≥90天）和出院时的患者活动能力。利用日本成人心血管外科数据库（JCVSD），从2008年1月至2012年12月识别出13961例接受择期SAVR治疗AS且有或无冠状动脉旁路移植术的患者。医院死亡率为3.1%。住院时间长（≥90天）以及出院时活动能力中度或严重受限（改良Rankin量表≥4）的患者百分比分别为2.9%和6.5%。分别确定了11个和20个术前医院死亡率和发病率的预测因素，包括住院时间长和出院时状况不佳。基于这些危险因素，风险模型预测了医院死亡率（曲线下面积[AUC]，0.732）和发病率（AUC，0.694）。利用JCVSD，为AS建立了SAVR的风险模型。该模型不仅可以识别高死亡风险患者，还可以识别包括住院时间长和出院时状况在内的死亡和发病高风险患者。
