美国麻醉医师协会身体状况预测模型的建立与验证。
Development and validation of a predictive model for American Society of Anesthesiologists Physical Status.
机构信息
Anesthesiology and Perioperative Care Service, Veterans Affairs Palo Alto Health Care System, 3801 Miranda Avenue, Palo Alto, CA, 94402, USA.
Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, 291 Campus Drive, Stanford, CA, 94305, USA.
出版信息
BMC Health Serv Res. 2019 Nov 21;19(1):859. doi: 10.1186/s12913-019-4640-x.
BACKGROUND
The American Society of Anesthesiologists Physical Status (ASA-PS) classification system was developed to categorize the fitness of patients before surgery. Increasingly, the ASA-PS has been applied to other uses including justification of inpatient admission. Our objectives were to develop and cross-validate a statistical model for predicting ASA-PS; and 2) assess the concurrent and predictive validity of the model by assessing associations between model-derived ASA-PS, observed ASA-PS, and a diverse set of 30-day outcomes.
METHODS
Using the 2014 American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) Participant Use Data File, we developed and internally cross-validated multinomial regression models to predict ASA-PS using preoperative NSQIP data. Accuracy was assessed with C-Statistics and calibration plots. We assessed both concurrent and predictive validity of model-derived ASA-PS relative to observed ASA-PS and 30-day outcomes. To aid further research and use of the ASA-PS model, we implemented it into an online calculator.
RESULTS
Of the 566,797 elective procedures in the final analytic dataset, 8.9% were ASA-PS 1, 48.9% were ASA-PS 2, 39.1% were ASA-PS 3, and 3.2% were ASA-PS 4. The accuracy of the 21-variable model to predict ASA-PS was C = 0.77 +/- 0.0025. The model-derived ASA-PS had stronger association with key indicators of preoperative status including comorbidities and higher BMI (concurrent validity) compared to observed ASA-PS, but less strong associations with postoperative complications (predictive validity). The online ASA-PS calculator may be accessed at https://s-spire-clintools.shinyapps.io/ASA_PS_Estimator/ CONCLUSIONS: Model-derived ASA-PS better tracked key indicators of preoperative status compared to observed ASA-PS. The ability to have an electronically derived measure of ASA-PS can potentially be useful in research, quality measurement, and clinical applications.
背景
美国麻醉医师协会身体状况（ASA-PS）分类系统旨在对手术前患者的健康状况进行分类。ASA-PS 越来越多地应用于其他用途，包括住院治疗的正当性。我们的目标是开发和交叉验证一个用于预测 ASA-PS 的统计模型；并 2）通过评估模型衍生的 ASA-PS、观察到的 ASA-PS 与一组多样化的 30 天结果之间的关联，评估该模型的同时和预测有效性。
方法
使用 2014 年美国外科医师学会国家外科质量改进计划（ACS NSQIP）参与者使用数据文件，我们使用术前 NSQIP 数据开发并内部交叉验证了用于预测 ASA-PS 的多项回归模型。使用 C-统计量和校准图评估准确性。我们评估了模型衍生的 ASA-PS 相对于观察到的 ASA-PS 和 30 天结果的同时和预测有效性。为了进一步研究和使用 ASA-PS 模型，我们将其实现到一个在线计算器中。
结果
在最终分析数据集的 566797 例择期手术中，8.9%为 ASA-PS 1，48.9%为 ASA-PS 2，39.1%为 ASA-PS 3，3.2%为 ASA-PS 4。21 变量模型预测 ASA-PS 的准确性为 C=0.77 +/- 0.0025。与观察到的 ASA-PS 相比，模型衍生的 ASA-PS 与包括合并症和更高 BMI 在内的术前状态关键指标的关联更强（同时有效性），但与术后并发症的关联较弱（预测有效性）。在线 ASA-PS 计算器可在 https://s-spire-clintools.shinyapps.io/ASA_PS_Estimator/ 访问。
结论
与观察到的 ASA-PS 相比，模型衍生的 ASA-PS 更好地跟踪了术前状态的关键指标。能够获得电子衍生的 ASA-PS 测量值在研究、质量测量和临床应用中可能很有用。
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