Development and internal validation of a predictive model of cognitive decline 36 months following elective surgery.
Development and internal validation of a predictive model of cognitive decline 36 months following elective surgery.
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DOI：
10.1002/dad2.12201
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Inouye SK
中科院分区：
文献类型：
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作者：
Jones RN;Tommet D;Steingrimsson J;Racine AM;Fong TG;Gou Y;Hshieh TT;Metzger ED;Schmitt EM;Tabloski PA;Travison TG;Vasunilashorn SM;Abdeen A;Earp B;Kunze L;Lange J;Vlassakov K;Dickerson BC;Marcantonio ER;Inouye SK
我们的目标是确定手术患者的特征，可以很容易地从医疗图表或简短的采访中获得，可以用来预测那些可能在手术后经历更快的认知能力下降。我们分析了一项观察性研究的数据，该研究纳入了560名接受重大择期非心脏手术的无痴呆老年人（≥70岁）。认知功能下降是通过术后2至36个月的总体复合指标的变化来衡量的。预测特征被确定为从图表审查或简短的患者评估中容易获得的变量。我们开发了认知下降（斜率）的预测模型，并在临床确定的切割下预测二分认知下降。在保持测试集中，正则化回归预测模型实现了0.146的均方根误差（RMSE）和0.31的模型r平方（R2）。预测“快速”下降作为一个组达到了0.75的曲线下面积（AUC）。我们的一些模型可以预测加速认知能力下降风险增加的人，其准确性高于依赖于机会，这一结果可能有助于对手术患者进行分层，以纳入未来的临床试验。
Our goal was to determine if features of surgical patients, easily obtained from the medical chart or brief interview, could be used to predict those likely to experience more rapid cognitive decline following surgery. We analyzed data from an observational study of 560 older adults (≥70 years) without dementia undergoing major elective non‐cardiac surgery. Cognitive decline was measured using change in a global composite over 2 to 36 months following surgery. Predictive features were identified as variables readily obtained from chart review or a brief patient assessment. We developed predictive models for cognitive decline (slope) and predicting dichotomized cognitive decline at a clinically determined cut. In a hold‐out testing set, the regularized regression predictive model achieved a root mean squared error (RMSE) of 0.146 and a model r‐square (R2) of .31. Prediction of “rapid” decliners as a group achieved an area under the curve (AUC) of .75. Some of our models could predict persons with increased risk for accelerated cognitive decline with greater accuracy than relying upon chance, and this result might be useful for stratification of surgical patients for inclusion in future clinical trials.