Department of Anaesthesia, Critical Care and Perioperative Medicine, York and Scarborough Teaching Hospitals National Health Service Foundation Trust, York, United Kingdom; and Centre for Health and Population Science, Hull York Medical School, York, United Kingdom.
Department of Outcomes Research, Cleveland Clinic, Cleveland, Ohio.
Anesthesiology. 2024 Sep 1;141(3):443-452. doi: 10.1097/ALN.0000000000004989.
The Hypotension Prediction Index (the index) software is a machine learning algorithm that detects physiologic changes that may lead to hypotension. The original validation used a case control (backward) analysis that has been suggested to be biased. This study therefore conducted a cohort (forward) analysis and compared this to the original validation technique.
A retrospective analysis of data from previously reported studies was conducted. All data were analyzed identically with two different methodologies, and receiver operating characteristic curves were constructed. Both backward and forward analyses were performed to examine differences in area under the receiver operating characteristic curves for the Hypotension Prediction Index and other hemodynamic variables to predict a mean arterial pressure (MAP) less than 65 mmHg for at least 1 min 5, 10, and 15 min in advance.
The analysis included 2,022 patients, yielding 4,152,124 measurements taken at 20-s intervals. The area under the curve for the index predicting hypotension analyzed by backward and forward methodologies respectively was 0.957 (95% CI, 0.947 to 0.964) versus 0.923 (95% CI, 0.912 to 0.933) 5 min in advance, 0.933 (95% CI, 0.924 to 0.942) versus 0.923 (95% CI, 0.911 to 0.933) 10 min in advance, and 0.929 (95% CI, 0.918 to 0.938) versus 0.926 (95% CI, 0.914 to 0.937) 15 min in advance. No variable other than MAP had an area under the curve greater than 0.7. The areas under the curve using forward analysis for MAP predicting hypotension 5, 10, and 15 min in advance were 0.932 (95% CI, 0.920 to 0.940), 0.929 (95% CI, 0.918 to 0.938), and 0.932 (95% CI, 0.921 to 0.940), respectively. The R2 for the variation in the index due to MAP was 0.77.
Using an updated methodology, the study found that the utility of the Hypotension Prediction Index to predict future hypotensive events is high, with an area under the receiver operating characteristics curve similar to that of the original validation method.
低血压预测指数(指数)软件是一种机器学习算法,可检测可能导致低血压的生理变化。最初的验证使用了病例对照(回溯)分析,该分析已被证明存在偏差。因此,本研究进行了队列(前向)分析,并将其与原始验证技术进行了比较。
对先前报道的研究数据进行回顾性分析。所有数据均使用两种不同的方法进行了相同的分析,并构建了接受者操作特征曲线。分别进行回溯和前向分析,以检查预测平均动脉压(MAP)至少 1 分钟 5、10 和 15 分钟以下的低血压预测指数和其他血流动力学变量的接受者操作特征曲线下面积的差异。
该分析包括 2022 名患者,共产生 4152124 次 20 秒间隔测量值。通过回溯和前向方法分析指数预测低血压的曲线下面积分别为 0.957(95%CI,0.947 至 0.964)和 0.923(95%CI,0.912 至 0.933),分别提前 5 分钟,0.933(95%CI,0.924 至 0.942)和 0.923(95%CI,0.911 至 0.933),分别提前 10 分钟,0.929(95%CI,0.918 至 0.938)和 0.926(95%CI,0.914 至 0.937),分别提前 15 分钟。除了 MAP 之外,没有其他变量的曲线下面积大于 0.7。前向分析用于预测 MAP 提前 5、10 和 15 分钟的低血压的曲线下面积分别为 0.932(95%CI,0.920 至 0.940)、0.929(95%CI,0.918 至 0.938)和 0.932(95%CI,0.921 至 0.940)。由于 MAP 导致的指数变化的 R2 为 0.77。
使用更新的方法,该研究发现,低血压预测指数预测未来低血压事件的效用很高,其接受者操作特征曲线下面积与原始验证方法相似。