Spine Center, Schulthess Klinik, Lengghalde 2, 8008, Zurich, Switzerland.
Department of Anaesthesia, Schulthess Klinik, Lengghalde 2, 8008, Zurich, Switzerland.
Eur Spine J. 2020 Dec;29(12):2941-2952. doi: 10.1007/s00586-020-06595-1. Epub 2020 Sep 18.
The American Society of Anaesthesiologists' Physical Status Score (ASA) is a key variable in predictor models of surgical outcome and "appropriate use criteria". However, at the time when such tools are being used in decision-making, the ASA rating is typically unknown. We evaluated whether the ASA class could be predicted statistically from Charlson Comorbidy Index (CCI) scores and simple demographic variables.
Using established algorithms, the CCI was calculated from the ICD-10 comorbidity codes of 11'523 spine surgery patients (62.3 ± 14.6y) who also had anaesthetist-assigned ASA scores. These were randomly split into training (N = 8078) and test (N = 3445) samples. A logistic regression model was built based on the training sample and used to predict ASA scores for the test sample and for temporal (N = 341) and external validation (N = 171) samples.
In a simple model with just CCI predicting ASA, receiver operating characteristics (ROC) analysis revealed a cut-off of CCI ≥ 1 discriminated best between being ASA ≥ 3 versus < 3 (area under the curve (AUC), 0.70 ± 0.01, 95%CI,0.82-0.84). Multiple logistic regression analyses including age, sex, smoking, and BMI in addition to CCI gave better predictions of ASA (Nagelkerke's pseudo-R for predicting ASA class 1 to 4, 46.6%; for predicting ASA ≥ 3 vs. < 3, 37.5%). AUCs for discriminating ASA ≥ 3 versus < 3 from multiple logistic regression were 0.83 ± 0.01 (95%CI, 0.82-0.84) for the training sample and 0.82 ± 0.01 (95%CI, 0.81-0.84), 0.85 ± 0.02 (95%CI, 0.80-0.89), and 0.77 ± 0.04 (95%CI,0.69-0.84) for the test, temporal and external validation samples, respectively. Calibration was adequate in all validation samples.
It was possible to predict ASA from CCI. In a simple model, CCI ≥ 1 best distinguished between ASA ≥ 3 and < 3. For a more precise prediction, regression algorithms were created based on CCI and simple demographic variables obtainable from patient interview. The availability of such algorithms may widen the utility of decision aids that rely on the ASA, where the latter is not readily available.
美国麻醉师协会的身体状况评分（ASA）是手术结果和“适当使用标准”预测模型的关键变量。然而，在使用此类工具进行决策时，ASA 等级通常是未知的。我们评估了 ASA 类别是否可以从 Charlson 合并症指数（CCI）评分和简单的人口统计学变量进行统计学预测。
使用既定的算法，从 11'523 名脊柱手术患者（62.3±14.6 岁）的 ICD-10 合并症代码中计算 CCI，这些患者还接受了麻醉师分配的 ASA 评分。这些数据被随机分为训练（N=8078）和测试（N=3445）样本。基于训练样本建立逻辑回归模型，并用于预测测试样本以及时间（N=341）和外部验证（N=171）样本的 ASA 评分。
在一个仅使用 CCI 预测 ASA 的简单模型中，受试者工作特征（ROC）分析显示 CCI≥1 最佳区分 ASA≥3 与<3（曲线下面积（AUC），0.70±0.01，95%CI，0.82-0.84）。包括年龄、性别、吸烟和 BMI 在内的多元逻辑回归分析除了 CCI 外，还能更好地预测 ASA（预测 ASA 等级 1 至 4 的 Nagelkerke 伪 R 为 46.6%；预测 ASA≥3 与<3 的为 37.5%）。来自多元逻辑回归的区分 ASA≥3 与<3 的 AUC 分别为训练样本的 0.83±0.01（95%CI，0.82-0.84）和 0.82±0.01（95%CI，0.81-0.84）、0.85±0.02（95%CI，0.80-0.89）和 0.77±0.04（95%CI，0.69-0.84）、测试、时间和外部验证样本。在所有验证样本中，校准均足够。
可以从 CCI 预测 ASA。在一个简单的模型中，CCI≥1 最佳区分 ASA≥3 和<3。为了更精确的预测，可以基于 CCI 和从患者访谈中获得的简单人口统计学变量创建回归算法。此类算法的可用性可能会扩大依赖 ASA 的决策辅助工具的实用性，因为后者不易获得。
