Department of Anesthesiology, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, United States.
School of Nursing, University of Wisconsin-Madison, Madison, WI, United States.
Int J Geriatr Psychiatry. 2019 Jul;34(7):1018-1028. doi: 10.1002/gps.5104. Epub 2019 Apr 23.
Delirium is an important postoperative complication, yet predictive risk factors for postoperative delirium severity remain elusive. We hypothesized that the NSQIP risk calculation for serious complications (NSQIP-SC) or risk of death (NSQIP-D), and cognitive tests of executive function (Trail Making Tests A and B [TMTA and TMTB]), would be predictive of postoperative delirium severity. Further, we demonstrate how advanced statistical techniques can be used to identify candidate predictors.
METHODS/DESIGN: Data from an ongoing perioperative prospective cohort study of 100 adults (65 y old or older) undergoing noncardiac surgery were analyzed. In addition to NSQIP-SC, NSQIP-D, TMTA, and TMTB, participant age, sex, American Society of Anesthesiologists (ASA) score, tobacco use, surgery type, depression, Framingham risk score, and preoperative blood pressure were collected. The Delirium Rating Scale-R-98 (DRS) measured delirium severity; the Confusion Assessment Method (CAM) identified delirium. LASSO and best subsets linear regression were employed to identify predictive risk factors.
Ninety-seven participants with a mean age of 71.68 ± 4.55, 55% male (31/97 CAM+, 32%), and a mean peak DRS of 21.5 ± 6.40 were analyzed. LASSO and best subsets regression identified NSQIP-SC and TMTB to predict postoperative delirium severity (P < 00.001, adjusted R : 0.30). NSQIP-SC and TMTB were also selected as predictors for postoperative delirium incidence (AUROC 0.81, 95% CI, 0.72-0.90).
In this cohort, we identified NSQIP risk score for serious complications and a measure of executive function, TMT-B, to predict postoperative delirium severity using advanced modeling techniques. Future studies should investigate the utility of these variables in a formal delirium severity prediction model.
谵妄是一种重要的术后并发症，但预测术后谵妄严重程度的风险因素仍难以捉摸。我们假设 NSQIP 严重并发症风险计算（NSQIP-SC）或死亡风险（NSQIP-D）以及执行功能的认知测试（Trail Making Tests A 和 B [TMTA 和 TMTB]）将预测术后谵妄的严重程度。此外，我们展示了如何使用高级统计技术来识别候选预测因素。
方法/设计：对正在进行的一项围手术期前瞻性队列研究中的 100 名成年人（65 岁或以上）进行了分析，这些成年人接受了非心脏手术。除了 NSQIP-SC、NSQIP-D、TMTA 和 TMTB 外，还收集了参与者的年龄、性别、美国麻醉师协会（ASA）评分、吸烟情况、手术类型、抑郁、弗雷明汉风险评分和术前血压。使用 Delirium Rating Scale-R-98（DRS）测量谵妄严重程度；使用 Confusion Assessment Method（CAM）识别谵妄。使用 LASSO 和最佳子集线性回归来识别预测风险因素。
对 97 名平均年龄为 71.68 ± 4.55 岁、55%为男性（CAM+31/97，32%）和平均峰值 DRS 为 21.5 ± 6.40 的参与者进行了分析。LASSO 和最佳子集回归确定了 NSQIP-SC 和 TMTB 可以预测术后谵妄的严重程度（P < 00.001，调整后的 R：0.30）。NSQIP-SC 和 TMTB 也被选为预测术后谵妄发生率的因素（AUROC 0.81，95%CI，0.72-0.90）。
在本队列中，我们使用高级建模技术确定了 NSQIP 严重并发症风险评分和执行功能的一个衡量标准，即 TMT-B，以预测术后谵妄的严重程度。未来的研究应在正式的谵妄严重程度预测模型中研究这些变量的实用性。
