Henderson William G, Bronsert Michael R, Hammermeister Karl E, Lambert-Kerzner Anne, Meguid Robert A
1Surgical Outcomes and Applied Research program, Department of Surgery, University of Colorado School of Medicine, Aurora, CO USA.
2Adult and Child Center for Health Outcomes Research and Delivery Science, University of Colorado School of Medicine, Aurora, CO USA.
Patient Saf Surg. 2019 Aug 20;13:28. doi: 10.1186/s13037-019-0208-2. eCollection 2019.
The Surgical Risk Preoperative Assessment System (SURPAS) is a parsimonious set of models providing accurate preoperative prediction of common adverse outcomes for individual patients. However, focus groups with surgeons and patients have developed a list of questions about and recommendations for how to further improve SURPAS's usability and usefulness. Eight issues were systematically evaluated to improve SURPAS.
The eight issues were divided into three groups: concerns to be addressed through further analysis of the database; addition of features to the SURPAS tool; and the collection of additional outcomes. Standard multiple logistic regression analysis was performed using the 2005-2015 American College of Surgeons National Surgical Quality Improvement Participant Use File (ACS NSQIP PUF) to refine models: substitution of the preoperative sepsis variable with a procedure-related risk variable; testing of an indicator variable for multiple concurrent procedure codes in complex operations; and addition of outcomes to increase clinical applicability. Automated risk documentation in the electronic health record and a patient handout and supporting documentation were developed. Long term functional outcomes were considered.
Model discrimination and calibration improved when preoperative sepsis was replaced with a procedure-related risk variable. Addition of an indicator variable for multiple concurrent procedures did not significantly improve the models. Models were developed for a revised set of eleven adverse postoperative outcomes that separated bleeding/transfusion from the cardiac outcomes, UTI from the other infection outcomes, and added a predictive model for unplanned readmission. Automated documentation of risk assessment in the electronic health record, visual displays of risk for providers and patients and an "About" section describing the development of the tool were developed and implemented. Long term functional outcomes were considered to be beyond the scope of the current SURPAS tool.
Refinements to SURPAS were successful in improving the accuracy of the models, while reducing manual entry to five of the eight variables. Adding a predictor variable to indicate a complex operation with multiple current procedure codes did not improve the accuracy of the models. We developed graphical displays of risk for providers and patients, including a take-home handout and automated documentation of risk in the electronic health record. These improvements should facilitate easier implementation of SURPAS.
手术风险术前评估系统（SURPAS）是一组简约的模型，可为个体患者的常见不良结局提供准确的术前预测。然而，与外科医生和患者的焦点小组针对如何进一步提高SURPAS的可用性和实用性提出了一系列问题及建议。对八个问题进行了系统评估以改进SURPAS。
这八个问题分为三组：需通过对数据库进行进一步分析来解决的问题；SURPAS工具的功能添加；以及额外结局的收集。使用2005 - 2015年美国外科医师学会国家外科质量改进参与者使用文件（ACS NSQIP PUF）进行标准多元逻辑回归分析以完善模型：用与手术相关的风险变量替代术前脓毒症变量；对复杂手术中多个并发手术编码的指示变量进行测试；以及添加结局以提高临床适用性。开发了电子健康记录中的自动风险记录、患者手册及支持文档。考虑了长期功能结局。
当用与手术相关的风险变量替代术前脓毒症时，模型的区分度和校准得到改善。添加多个并发手术的指示变量并未显著改善模型。针对一组修订后的11种术后不良结局开发了模型，这些结局将出血/输血与心脏相关结局分开，将尿路感染与其他感染结局分开，并增加了计划外再入院的预测模型。开发并实施了电子健康记录中的风险评估自动记录、为医护人员和患者提供的风险可视化显示以及描述该工具开发情况的“关于”部分。长期功能结局被认为超出了当前SURPAS工具的范围。
对SURPAS的改进成功提高了模型的准确性，同时将八个变量中的五个变量的手动输入减少。添加一个预测变量以表明存在多个当前手术编码的复杂手术并未提高模型的准确性。我们为医护人员和患者开发了风险的图形显示，包括一份带回家的手册以及电子健康记录中的风险自动记录。这些改进应有助于更轻松地实施SURPAS。
