准确的术前风险评估对于做出明智的决策至关重要。手术风险术前评估系统 (SURPAS) 和国家手术质量改进计划 (NSQIP) 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 肺切除术后观察到的和预测的结果。

2016 年 1 月至 2018 年 12 月期间，2514 名患者接受了肺切除术并被纳入。我们在在线 NSQIP SRC 和 SURPAS 公式中输入了必要的患者人口统计数据、术前风险因素和程序细节。通过判别和校准评估预测模型的性能。

两种模型在 30 天死亡率、尿路感染、再入院和出院到护理或康复机构的辨别性能方面没有发现统计学上的显着差异。NSQIP 和 SURPAS 之间区分会出现并发症的患者和不会出现并发症的患者的能力在统计学上无法区分，肾功能衰竭除外。C 指数接近 1.0 时，NSQIP 在区分肾衰竭风险方面的表现明显优于 SURPAS SRC（C 指数，0.798 对 0.694；P  = .003）。每个模型的预测和观察到的风险的校准曲线显示出相似的性能，除了肾功能衰竭外，还有高估风险的趋势。

总体而言，在这项大型、单中心验证研究中，SURPAS 和 NSQIP SRC 在预测肺切除术的结果方面表现相似，结果区分度为中度至良好。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。添加胸部特定的输入变量可以提高性能。

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Background

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.