IMPORTANCE Despite limited capacity and expensive cost, there are minimal objective data to guide postoperative allocation of intensive care unit (ICU) beds. The Surgical Risk Preoperative Assessment System (SURPAS) uses 8 preoperative variables to predict many common postoperative complications, but it has not yet been evaluated in predicting postoperative ICU admission. OBJECTIVE To determine if the SURPAS model could accurately predict postoperative ICU admission in a broad surgical population. DESIGN, SETTING, AND PARTICIPANTS This decision analytical model was a retrospective, observational analysis of prospectively collected patient data from the 2012 to 2018 American College of Surgeons (ACS) National Surgical Quality Improvement Program (NSQIP) database, which were merged with individual patients' electronic health record data to capture postoperative ICU use. Multivariable logistic regression modeling was used to determine how the 8 preoperative variables of the SURPAS model predicted ICU use compared with a model inputting all 28 preoperatively available NSQIP variables. Data included in the analysis were collected for the ACS NSQIP at 5 hospitals (1 tertiary academic center, 4 academic affiliated hospitals) within the University of Colorado Health System between January 1, 2012, and December 31, 2018. Included patients were those undergoing surgery in 9 surgical specialties during the 2012 to 2018 period. Data were analyzed from May 29 to July 30, 2021. EXPOSURE Surgery in 9 surgical specialties, including general, gynecology, orthopedic, otolaryngology, plastic, thoracic, urology, vascular, and neurosurgery. MAIN OUTCOMES AND MEASURES Use of ICU care up to 30 days after surgery. RESULTS A total of 34 568 patients were included in the analytical data set: 32 032 (92.7%) in the cohort without postoperative ICU use and 2545 (7.4%) in the cohort with postoperative ICU use (no ICU use: mean [SD] age, 54.9 [16.6] years; 18 188 women [56.8%]; ICU use: mean [SD] age, 60.3 [15.3] years; 1333 men [52.4%]). For the internal chronologic validation of the 7-variable SURPAS model, data from 2012 to 2016 were used as the training data set (n = 24 250, 70.2% of the total sample size of 34 568) and data from 2017 to 2018 were used as the test data set (n = 10 318, 29.8% of the total sample size of 34 568). The C statistic improved in the test data set compared with the training data set (0.933; 95% CI, 0.924-0.941 vs 0.922; 95% CI, 0.917-0.928), whereas the Brier score was slightly worse in the test data set compared with the training data set (0.045; 95% CI, 0.042-0.048 vs 0.045; 95% CI, 0.043-0.047). The SURPAS model compared favorably with the model inputting all 28 NSQIP variables, with both having good calibration between observed and expected outcomes in the Hosmer-Lemeshow graphs and similar Brier scores (model inputting all variables, 0.044; 95% CI, 0.043-0.048; SURPAS model, 0.045; 95% CI, 0.042-0.046) and C statistics (model inputting all variables, 0.929; 95% CI, 0.925-0.934; SURPAS model, 0.925; 95% CI, 0.921-0.930). CONCLUSIONS AND RELEVANCE Results of this decision analytical model study revealed that the SURPAS prediction model accurately predicted postoperative ICU use across a diverse surgical population. These results suggest that the SURPAS prediction model can be used to help with preoperative planning and resource allocation of limited ICU beds.

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开发和验证术后重症监护病房在广泛手术人群中停留的多变量预测模型。

重要性 尽管能力有限且成本昂贵，但指导重症监护病房 (ICU) 床位术后分配的客观数据很少。手术风险术前评估系统 (SURPAS) 使用 8 个术前变量来预测许多常见的术后并发症，但尚未在预测术后入住 ICU 方面进行评估。目的 确定 SURPAS 模型是否可以准确预测广泛手术人群术后入住 ICU。设计、设置和参与者 该决策分析模型是对 2012 年至 2018 年美国外科医师学会 (ACS) 国家外科质量改进计划 (NSQIP) 数据库中前瞻性收集的患者数据的回顾性观察分析，这些数据与个别患者的 电子健康记录数据，以获取术后 ICU 使用情况。多变量逻辑回归模型用于确定 SURPAS 模型的 8 个术前变量与输入所有 28 个术前可用 NSQIP 变量的模型相比如何预测 ICU 使用。分析中包含的数据是在 2012 年 1 月 1 日至 2018 年 12 月 31 日期间在科罗拉多大学卫生系统内的 5 家医院（1 家三级学术中心、4 家学术附属医院）收集的 ACS NSQIP 数据。纳入的患者是接受手术的患者2012 年至 2018 年期间在 9 个外科专科。分析了 2021 年 5 月 29 日至 7 月 30 日的数据。 曝光 9 个外科专科的手术，包括普通科、妇科、骨科、耳鼻喉科、整形科、胸科、泌尿科、血管科和神经外科。主要结果和测量 手术后 30 天内使用 ICU 护理。结果 共有 34 568 名患者被纳入分析数据集：32 032 名 (92.7%) 属于术后未使用 ICU 的队列，2545 名 (7.4%) 属于术后使用 ICU 的队列（未使用 ICU：平均值 [SD]年龄，54.9 [16.6] 岁；18188 名女性 [56.8%]；ICU 使用：平均 [SD] 年龄，60.3 [15.3] 岁；1333 名男性 [52.4%]）。对于 7 变量 SURPAS 模型的内部时间顺序验证，使用 2012 年至 2016 年的数据作为训练数据集（n = 24 250，占总样本量 34 568 的 70.2%），并使用 2017 年至 2018 年的数据作为测试数据集（n = 10 318，占总样本量 34 568 的 29.8%）。与训练数据集相比，测试数据集的 C 统计量有所改善（0.933；95% CI，0.924-0.941 vs 0.922；95% CI，0.917-0.928），而与训练数据集相比，测试数据集中的 Brier 评分稍差（0.045；95% CI，0.042-0.048 对 0.045；95% CI，0.043-0.047）。SURPAS 模型优于输入所有 28 个 NSQIP 变量的模型，两者在 Hosmer-Lemeshow 图中的观察结果和预期结果之间具有良好的校准和类似的 Brier 分数（输入所有变量的模型，0.044；95% CI，0.043-0.048； SURPAS 模型，0.045；95% CI，0.042-0.046）和 C 统计（输入所有变量的模型，0.929；95% CI，0.925-0.934；SURPAS 模型，0.925；95% CI，0.921-0.930）。结论和相关性 该决策分析模型研究的结果表明，SURPAS 预测模型准确地预测了不同手术人群术后 ICU 的使用情况。