Surgical Risk Preoperative Assessment System (SURPAS) III. Accurate Preoperative Prediction of 8 Adverse Outcomes Using 8 Predictor Variables
Surgical Risk Preoperative Assessment System (SURPAS) III. Accurate Preoperative Prediction of 8 Adverse Outcomes Using 8 Predictor Variables
复制标题
手术风险术前评估系统（SURPAS） III．使用 8 个预测变量准确术前预测 8 种不良结果
DOI：
10.1097/sla.0000000000001678
复制
发表时间：
2016-07-01
影响因子：
9
通讯作者：
Henderson, William G.
中科院分区：
文献类型：
--
作者：
Meguid, Robert A.;Bronsert, Michael R.;Henderson, William G.
目的：为了开发准确的术前风险预测模型，用于多种不良术后结局，适用于广泛的手术人群，使用一组简约的共同风险变量和outcome.Summary背景数据：目前，术前评估手术风险主要是基于主观的临床经验。我们提出了一个范式的转变，从目前的术后风险调整跨医院比较，以患者为中心的定量风险评估在术前evaluation.Methods：我们确定了最常见的和重要的预测变量的术后死亡率，总体发病率，和6个并发症集群从以前发表的预测分析，采用正向选择逐步Logistic回归。然后，我们仅使用8个最常见和最重要的预测变量重新拟合预测模型，并使用c指数、Hosmer-Lemeshow分析和Brier评分将这些模型与原始全变量模型的区分和校准进行比较。结果：使用一组8个术前风险变量开发了30天死亡率、总体发病率和6组并发症的准确风险模型。8个变量模型的C指数在包含多达28个变量的完整模型的C指数的97.9%和99.2%之间，表明使用较少的预测变量具有良好的区分度。Hosmer-Lemeshow分析显示，观察到的预期事件发生率之间的简约模型和完整的模型，都表现出良好的calibration.Conclusions：准确的术前风险评估的术后死亡率，总体发病率，和6个并发症集群在广泛的手术人群可以实现与少至8个术前预测变量，提高手术患者的常规术前风险评估的可行性。
Objective: To develop accurate preoperative risk prediction models for multiple adverse postoperative outcomes applicable to a broad surgical population using a parsimonious common set of risk variables and outcomes.Summary Background Data: Currently, preoperative assessment of surgical risk is largely based on subjective clinician experience. We propose a paradigm shift from the current postoperative risk adjustment for cross-hospital comparison to patient-centered quantitative risk assessment during the preoperative evaluation.Methods: We identify the most common and important predictor variables of postoperative mortality, overall morbidity, and 6 complication clusters from previously published prediction analyses that used forward selection stepwise logistic regression. We then refit the prediction models using only the 8 most common and important predictor variables, and compare the discrimination and calibration of these models to the original full-variable models using the c-index, Hosmer-Lemeshow analysis, and Brier scores.Results: Accurate risk models for 30-day outcomes of mortality, overall morbidity, and 6 clusters of complications were developed using a set of 8 preoperative risk variables. C-indexes of the 8 variable models are between 97.9% and 99.2% of those of the full models containing up to 28 variables, indicating excellent discrimination using fewer predictor variables. Hosmer-Lemeshow analyses showed observed to expected event rates to be nearly identical between parsimonious models and full models, both showing good calibration.Conclusions: Accurate preoperative risk assessment of postoperative mortality, overall morbidity, and 6 complication clusters in a broad surgical population can be achieved with as few as 8 preoperative predictor variables, improving feasibility of routine preoperative risk assessment for surgical patients.