From the Departments of *Anesthesiology, †Anesthesiology, Division of Critical Care Medicine, and ‡Surgery, Mayo Clinic, Rochester, MN.
Anesth Analg. 2013 Dec;117(6):1338-46. doi: 10.1213/ANE.0b013e3182a46d6d.
Computerized reviews of patient data promise to improve patient care through early and accurate identification of at-risk and well patients. The significance of sampling strategy for patient vital signs data is not known. In the instance of the surgical Apgar score (SAS), we hypothesized that larger sampling intervals would improve the specificity and overall predictive ability of this tool.
We used electronic intraoperative data from general and vascular surgical patients in a single-institution registry of the American College of Surgeons National Surgical Quality Improvement Program. The SAS, consisting of lowest heart rate, lowest mean arterial blood pressure, and estimated blood loss between incision and skin closure, was calculated using 5 methods: instantaneously and using intervals of of 5 and 10 minutes with and without interval overlap. Major complications including death were assessed at 30 days postoperatively.
Among 3000 patients, 272 (9.1%) experienced major complications or death. As the sampling interval increased from instantaneous (shortest) to 10 minutes without overlap (largest), the sensitivity, positive predictive value, and negative predictive value did not change significantly, but significant improvements were noted for specificity (79.5% to 82.9% across methods, P for trend <0.001) and accuracy (76.0% to 79.3% across methods, P for trend <0.01). In multivariate modeling, the predictive utility of the SAS as measured by the c-statistic nearly increased from Δc = +0.012 (P = 0.038) to Δc = +0.021 (P < 0.002) between the shortest and largest sampling intervals, respectively. Compared with a preoperative risk model, the net reclassification improvement and integrated discrimination improvement for the shortest versus largest sampling intervals of the SAS were net reclassification improvement 0.01 (P = 0.8) vs 0.06 (P = 0.02), and for integrated discrimination improvement, they were 0.008 (P < 0.01) vs 0.015 (P < 0.001).
When vital signs data are recorded in compliance with American Society of Anesthesiologists' standards, the sampling strategy for vital signs significantly influences performance of the SAS. Computerized reviews of patient data are subject to the choice of sampling methods for vital signs and may have the potential to be optimized for safe, efficient patient care.
通过早期准确识别高危和健康患者，计算机化的患者数据审查有望改善患者护理。患者生命体征数据的采样策略的意义尚不清楚。在手术 Apgar 评分 (SAS) 的情况下，我们假设较大的采样间隔会提高该工具的特异性和整体预测能力。
我们使用了美国外科医师学会国家外科质量改进计划单一机构注册处的普通外科和血管外科患者的术中电子数据。SAS 由切口至皮肤闭合期间的最低心率、最低平均动脉压和估计失血量组成，使用 5 种方法计算：即时和间隔 5 分钟和 10 分钟以及是否有间隔重叠。术后 30 天评估主要并发症（包括死亡）。
在 3000 名患者中，有 272 名（9.1%）发生了主要并发症或死亡。随着采样间隔从最短的即时（最短）增加到 10 分钟且无重叠（最长），敏感性、阳性预测值和阴性预测值没有显著变化，但特异性（方法间从 79.5%到 82.9%，趋势 P<0.001）和准确性（方法间从 76.0%到 79.3%，趋势 P<0.01）有显著提高。在多变量建模中，SAS 的预测效用由 c 统计量衡量，从最短和最长采样间隔之间的 Δc=+0.012（P=0.038）增加到Δc=+0.021（P<0.002）。与术前风险模型相比，SAS 最短与最长采样间隔之间的净重新分类改善和综合鉴别改善分别为净重新分类改善 0.01（P=0.8）和 0.06（P=0.02），综合鉴别改善分别为 0.008（P<0.01）和 0.015（P<0.001）。
当生命体征数据按照美国麻醉师协会的标准记录时，生命体征的采样策略会显著影响 SAS 的性能。患者数据的计算机化审查受到生命体征采样方法的选择的影响，并且有可能针对安全、高效的患者护理进行优化。
