Kim Sunghye, Neiberg Rebecca, Rejeski W Jack, Marsh Anthony P, Kritchevsky Stephen B, Leng Xiaoyan I, Groban Leanne
1Department of Internal Medicine, Section of General Internal Medicine, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, NC 27157 USA.
2Sticht Center for Healthy Aging and Alzheimer's Prevention, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, NC 27157 USA.
Perioper Med (Lond). 2018 Jun 19;7:12. doi: 10.1186/s13741-018-0095-6. eCollection 2018.
The American College of Surgeons National Surgical Quality Improvement Program (NSQIP®) developed a surgical risk calculator using data from 1.4 million patients and including 1557 unique Current Procedural Terminology (CPT) codes. Although this calculator demonstrated excellent performance in predicting postoperative mortality, morbidity, and six surgical complications, it was not developed specifically for use in older surgical patients who have worse surgical outcomes and additional unique risk factors compared to younger adults. We aimed to test the ability of a simple self-reported mobility tool to predict postoperative outcomes in the older surgical population compared to the NSQIP.
We used data from a prospective cohort study that enrolled 197 older surgical patients (≥ 69 years) undergoing various elective surgeries and assessed 30-day surgical outcomes. Statistical models included data from the Mobility Assessment Tool-short form (MAT-sf) alone, covariates alone, and MAT-sf data and covariates. We used leave-one-out (LOO) cross-validation of the models within our cohort and compared their performance for predicting postoperative outcomes against the NSQIP calculator based on receiver operating characteristic area under the curve (ROC AUC).
Patients with poor self-reported mobility experienced higher rates of postoperative complications and nursing home placement. There was no difference in performance between any of our models and the NSQIP calculator ( > 0.1), with AUC between 0.604 and 0.697 for predicting postoperative complications and 0.653 and 0.760 for predicting nursing home placement. All models also predicted a length of stay (LOS) similar to the actual LOS.
Mobility assessment alone using MAT-sf can predict postoperative complications, nursing home placement, and LOS for older surgical patients, with accuracy comparable to that of the NSQIP calculator. The simplicity of this noninvasive risk assessment tool makes it an attractive alternative to the NSQIP calculator that requires 20 patient predictors and the planned procedure, or CPT code to predict the chance that patients will have 15 different adverse outcomes following surgery.
美国外科医师学会国家外科质量改进计划（NSQIP®）利用140万例患者的数据开发了一种手术风险计算器，其中包括1557个独特的当前手术操作术语（CPT）代码。尽管该计算器在预测术后死亡率、发病率和六种手术并发症方面表现出色，但它并非专门为老年手术患者开发，与年轻成年人相比，老年手术患者的手术结果更差且有其他独特的风险因素。我们旨在测试一种简单的自我报告的活动能力工具与NSQIP相比，预测老年手术人群术后结果的能力。
我们使用了一项前瞻性队列研究的数据，该研究纳入了197例接受各种择期手术的老年手术患者（≥69岁），并评估了30天的手术结果。统计模型包括仅来自简易活动能力评估工具（MAT-sf）的数据、仅协变量的数据以及MAT-sf数据和协变量。我们在队列中对模型进行留一法（LOO）交叉验证，并根据曲线下面积（ROC AUC）的受试者操作特征，将它们预测术后结果的性能与NSQIP计算器进行比较。
自我报告活动能力差的患者术后并发症发生率和入住养老院的比例更高。我们的任何模型与NSQIP计算器之间的性能均无差异（>0.1），预测术后并发症的AUC在0.604至0.697之间，预测入住养老院的AUC在0.653至0.760之间。所有模型预测的住院时间（LOS）也与实际住院时间相似。
仅使用MAT-sf进行活动能力评估可以预测老年手术患者的术后并发症、入住养老院情况和住院时间，准确性与NSQIP计算器相当。这种非侵入性风险评估工具的简单性使其成为NSQIP计算器的有吸引力的替代方案，NSQIP计算器需要20个患者预测指标和计划的手术或CPT代码来预测患者术后出现15种不同不良结局的可能性。
