University of Tennessee Health Science Center, Memphis, Tennessee.
Department of Biomedical Engineering, Georgia Institute of Technology, University of Tennessee, Knoxville, Tennessee.
Shock. 2021 Jul 1;56(1):58-64. doi: 10.1097/SHK.0000000000001670.
Sepsis is a life-threatening condition with high mortality rates. Early detection and treatment are critical to improving outcomes. Our primary objective was to develop artificial intelligence capable of predicting sepsis earlier using a minimal set of streaming physiological data in real time.
A total of 29,552 adult patients were admitted to the intensive care unit across five regional hospitals in Memphis, Tenn, over 18 months from January 2017 to July 2018. From these, 5,958 patients were selected after filtering for continuous (minute-by-minute) physiological data availability. A total of 617 (10.4%) patients were identified as sepsis cases, using the Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3) criteria. Physiomarkers, a set of signal processing features, were derived from five physiological data streams including heart rate, respiratory rate, and blood pressure (systolic, diastolic, and mean), captured every minute from the bedside monitors. A support vector machine classifier was used for classification. The model accurately predicted sepsis up to a mean and 95% confidence interval of 17.4 ± 0.22 h before sepsis onset, with an average test accuracy of 83.0% (average sensitivity, specificity, and area under the receiver operating characteristics curve of 0.757, 0.902, and 0.781, respectively).
This study demonstrates that salient physiomarkers derived from continuous bedside monitoring are temporally and differentially expressed in septic patients. Using this information, minimalistic artificial intelligence models can be developed to predict sepsis earlier in critically ill patients.
败血症是一种危及生命的疾病，死亡率很高。早期发现和治疗对改善预后至关重要。我们的主要目标是开发一种人工智能，能够使用实时的最小生理数据流提前预测败血症。
在 18 个月的时间里（2017 年 1 月至 2018 年 7 月），田纳西州孟菲斯市的五家地区医院共收治了 29552 名成年重症监护病房患者。从这些患者中，经过筛选后保留了连续（每分钟）生理数据可用的 5958 名患者。使用第三版国际败血症和败血症性休克共识定义（Sepsis-3）标准，共确定了 617 例（10.4%）败血症病例。生理标志物是从五个生理数据流（包括心率、呼吸率和血压（收缩压、舒张压和平均压））中提取的一组信号处理特征，每分钟从床边监护仪捕获一次。使用支持向量机分类器进行分类。该模型在败血症发作前平均和 95%置信区间 17.4±0.22 h 之前准确地预测了败血症，平均测试准确率为 83.0%（平均灵敏度、特异性和接受者操作特征曲线下面积分别为 0.757、0.902 和 0.781）。
这项研究表明，从连续床边监测中提取的显著生理标志物在败血症患者中具有时间和差异表达。利用这些信息，可以开发出极简主义的人工智能模型，以便更早地预测重症患者的败血症。
