Duke Clinical Research Institute, Duke University Medical Center, Durham, North Carolina.
Division of Cardiac Surgery, Johns Hopkins University School of Medicine, Baltimore, Maryland; Division of Cardiovascular Surgery, Johns Hopkins All Children's Heart Institute, St. Petersburg, Florida.
Ann Thorac Surg. 2018 May;105(5):1419-1428. doi: 10.1016/j.athoracsur.2018.03.003. Epub 2018 Mar 22.
The Society of Thoracic Surgeons (STS) uses statistical models to create risk-adjusted performance metrics for Adult Cardiac Surgery Database (ACSD) participants. Because of temporal changes in patient characteristics and outcomes, evolution of surgical practice, and additional risk factors available in recent ACSD versions, completely new risk models have been developed.
Using July 2011 to June 2014 ACSD data, risk models were developed for operative mortality, stroke, renal failure, prolonged ventilation, mediastinitis/deep sternal wound infection, reoperation, major morbidity or mortality composite, prolonged postoperative length of stay, and short postoperative length of stay among patients who underwent isolated coronary artery bypass grafting surgery (n = 439,092), aortic or mitral valve surgery (n = 150,150), or combined valve plus coronary artery bypass grafting surgery (n = 81,588). Separate models were developed for each procedure and endpoint except mediastinitis/deep sternal wound infection, which was analyzed in a combined model because of its infrequency. A surgeon panel selected predictors by assessing model performance and clinical face validity of full and progressively more parsimonious models. The ACSD data (July 2014 to December 2016) were used to assess model calibration and to compare discrimination with previous STS risk models.
Calibration in the validation sample was excellent for all models except mediastinitis/deep sternal wound infection, which slightly underestimated risk and will be recalibrated in feedback reports. The c-indices of new models exceeded those of the last published STS models for all populations and endpoints except stroke in valve patients.
New STS ACSD risk models have generally excellent calibration and discrimination and are well suited for risk adjustment of STS performance metrics.
胸外科医师学会（STS）使用统计模型为成人心脏手术数据库（ACSD）参与者创建风险调整后的绩效指标。由于患者特征和结果、手术实践的演变以及最近 ACSD 版本中可用的其他风险因素的时间变化，完全开发了新的风险模型。
使用 2011 年 7 月至 2014 年 6 月的 ACSD 数据，为接受单纯冠状动脉旁路移植术（n=439092）、主动脉瓣或二尖瓣手术（n=150150）或联合瓣膜加冠状动脉旁路移植术（n=81588）的患者开发了手术死亡率、中风、肾衰竭、通气延长、纵隔炎/深部胸骨伤口感染、再次手术、主要发病率或死亡率复合、术后住院时间延长和术后住院时间缩短的风险模型。除纵隔炎/深部胸骨伤口感染外，每个程序和终点都分别开发了单独的模型，因为其发生率较低，所以在一个联合模型中进行了分析。外科医生小组通过评估模型性能和完整及逐步更简约模型的临床表面有效性来选择预测因子。使用 ACSD 数据（2014 年 7 月至 2016 年 12 月）评估模型校准，并与以前的 STS 风险模型进行比较。
除了纵隔炎/深部胸骨伤口感染外，所有模型在验证样本中的校准都非常出色，而纵隔炎/深部胸骨伤口感染则略微低估了风险，将在反馈报告中重新校准。新模型的 c 指数超过了之前发布的 STS 模型的 c 指数，除了瓣膜患者的中风外，对于所有人群和终点都是如此。
新的 STS ACSD 风险模型通常具有出色的校准和区分能力，非常适合 STS 绩效指标的风险调整。
