Thoracic Surgery Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York.
Thoracic Surgery Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York; Key Laboratory of Carcinogenesis and Translational Research, Department of Thoracic Surgery II, Peking University Cancer Hospital & Institute, Beijing, China.
Ann Thorac Surg. 2021 May;111(5):1643-1651. doi: 10.1016/j.athoracsur.2020.08.021. Epub 2020 Oct 16.
Accurate preoperative risk assessment is critical for informed decision making. The Surgical Risk Preoperative Assessment System (SURPAS) and the National Surgical Quality Improvement Program (NSQIP) Surgical Risk Calculator (SRC) predict risks of common postoperative complications. This study compares observed and predicted outcomes after pulmonary resection between SURPAS and NSQIP SRC.
Between January 2016 and December 2018, 2514 patients underwent pulmonary resection and were included. We entered the requisite patient demographics, preoperative risk factors, and procedural details into the online NSQIP SRC and SURPAS formulas. Performance of the prediction models was assessed by discrimination and calibration.
No statistically significant differences were found between the 2 models in discrimination performance for 30-day mortality, urinary tract infection, readmission, and discharge to a nursing or rehabilitation facility. The ability to discriminate between a patient who will develop a complication and a patient who will not was statistically indistinguishable between NSQIP and SURPAS, except for renal failure. With a C index closer to 1.0, the NSQIP performed significantly better than the SURPAS SRC in discriminating risk of renal failure (C index, 0.798 vs 0.694; P = .003). The calibration curves of predicted and observed risk for each model demonstrate similar performance with a tendency toward overestimation of risk, apart from renal failure.
Overall, SURPAS and NSQIP SRC performed similarly in predicting outcomes for pulmonary resections in this large, single-center validation study with moderate to good discrimination of outcomes. Notably, SURPAS uses a smaller set of input variables to generate the preoperative risk assessment. The addition of thoracic-specific input variables may improve performance.
准确的术前风险评估对于知情决策至关重要。SURPAS（外科风险术前评估系统）和 NSQIP（国家外科质量改进计划）手术风险计算器（SRC）预测常见术后并发症的风险。本研究比较了 SURPAS 和 NSQIP SRC 后肺切除术后观察到的和预测的结果。
在 2016 年 1 月至 2018 年 12 月期间，共有 2514 例患者接受了肺切除术并被纳入研究。我们将必需的患者人口统计学数据、术前危险因素和手术细节输入到在线 NSQIP SRC 和 SURPAS 公式中。通过区分度和校准评估预测模型的性能。
在 30 天死亡率、尿路感染、再入院和出院至护理或康复设施方面，这两种模型在预测结果方面没有发现统计学上的显著差异。区分是否会发生并发症的患者的能力在 NSQIP 和 SURPAS 之间没有统计学上的区别，除了肾衰竭。NSQIP 的 C 指数更接近 1.0，在区分肾衰竭风险方面的表现明显优于 SURPAS SRC（C 指数分别为 0.798 和 0.694；P=0.003）。每个模型的预测和观察风险校准曲线显示出相似的性能，除了肾衰竭外，都有高估风险的趋势。
总的来说，在这项大型单中心验证研究中，SURPAS 和 NSQIP SRC 在预测肺切除术后结果方面表现相似，对结果的区分度适中至良好。值得注意的是，SURPAS 使用一组较小的输入变量来生成术前风险评估。增加胸部特定的输入变量可能会提高性能。
