Division of Research and Optimal Patient Care, American College of Surgeons, Chicago, IL; Department of Surgery, The Ohio State University Wexner Medical Center, Columbus, OH.
Division of Research and Optimal Patient Care, American College of Surgeons, Chicago, IL; Department of Surgery, University of Alabama at Birmingham Medical Center, Birmingham, AL.
J Am Coll Surg. 2020 Jan;230(1):88-100.e1. doi: 10.1016/j.jamcollsurg.2019.09.017. Epub 2019 Oct 28.
The American College of Surgeons (ACS) NSQIP Surgical Risk Calculator (SRC) plays an important role in risk prediction and decision-making. We sought to enhance the existing ACS NSQIP SRC with functionality to predict geriatric-specific outcomes and assess the predictive value of geriatric-specific risk factors by comparing performance in outcomes prediction using the traditional ACS NSQIP SRC with models that also included geriatric risk factors.
Data were collected from 21 ACS NSQIP Geriatric Surgery Pilot Project hospitals between 2014 and 2017. Hierarchical regression models predicted 4 postoperative geriatric outcomes (ie pressure ulcer, delirium, new mobility aid use, and functional decline) using the traditional 21-variable ACS NSQIP SRC models and 27-variable models that included 6 geriatric risk factors (ie living situation, fall history, mobility aid use, cognitive impairment, surrogate-signed consent, and palliative care on admission).
Data from 38,048 patients 65 years or older undergoing 197 unique operations across 10 surgical subspecialties were used. Stable model discrimination and calibration between developmental and validation datasets confirmed predictive validity. Models with and without geriatric risk factors demonstrated excellent performance (C statistic >0.8) with inclusion of geriatric risk factors improving performance. Of the 21 ACS NSQIP variables, CPT code, COPD, age, functional dependence, sex, disseminated cancer, diabetes, and sepsis were the strongest risk predictors, and impaired cognition, fall history, and mobility aid use were the strongest geriatric predictors.
The ACS NSQIP SRC can predict 4 unique outcomes germane to geriatric surgical patients, with improvement of predictive capability after accounting for geriatric risk factors. Augmentation of ACS NSQIP SRC can enhance shared decision-making to improve the quality of surgical care in older adults.
美国外科医师学院（ACS）的 NSQIP 手术风险计算器（SRC）在风险预测和决策中发挥着重要作用。我们试图通过比较使用传统 ACS NSQIP SRC 进行预测的模型和还包括老年风险因素的模型在预测结果方面的性能，来增强现有的 ACS NSQIP SRC，以预测老年特定的结果，并评估老年特定风险因素的预测价值。
数据来自 2014 年至 2017 年期间的 21 家 ACS NSQIP 老年外科试点项目医院。使用传统的 21 变量 ACS NSQIP SRC 模型和包含 6 个老年风险因素（即生活状况、跌倒史、使用助行器、认知障碍、替代签署同意书和入院时的姑息治疗）的 27 变量模型，对 4 个术后老年特定结局（即压疮、谵妄、新使用移动辅助工具和功能下降）进行了分层回归模型预测。
共使用了来自 10 个外科亚专业的 38048 名 65 岁或以上的患者进行的 197 种不同手术的数据。在发展和验证数据集之间稳定的模型区分度和校准证实了预测的有效性。具有和不具有老年风险因素的模型均表现出出色的性能（C 统计量>0.8），纳入老年风险因素可提高性能。在 21 个 ACS NSQIP 变量中，CPT 代码、COPD、年龄、功能依赖、性别、弥散性癌症、糖尿病和败血症是最强的风险预测因素，认知障碍、跌倒史和使用移动辅助工具是最强的老年预测因素。
ACS NSQIP SRC 可以预测与老年外科患者相关的 4 个独特结局，在考虑老年风险因素后，预测能力得到提高。增强 ACS NSQIP SRC 可以增强共同决策，提高老年人的外科护理质量。
