Hypotension Risk Prediction via Sequential Contrast Patterns of ICU Blood Pressure.
Hypotension Risk Prediction via Sequential Contrast Patterns of ICU Blood Pressure.
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DOI：
10.1109/jbhi.2015.2453478
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发表时间：
2016-09
影响因子：
7.7
通讯作者：
Li J
中科院分区：
文献类型：
--
作者：
Ghosh S;Feng M;Nguyen H;Li J
急性低血压是重症监护病房(ICU)住院死亡率的重要危险因素。长期低血压会导致组织灌流不足，导致细胞功能障碍和多个器官的严重损伤。因此，及时的医疗干预对于处理急性低血压发作(AHE)极其重要。在这种情况下，用于患者风险分层的人群水平预后评分系统是次优的。然而，设计一个有效的风险预测系统可以极大地帮助识别重症监护患者，他们有可能在未来一段时间内发展为急性肝病。针对这一目标，采用模式挖掘算法从血流动力学数据中提取信息序列对比模式，用于预测低血压发作。低血压和血压正常的患者组是从MIMIC-II重症监护研究数据库中提取出来的，遵循适当的临床纳入标准。该方法包括数据预处理步骤，利用符号聚合近似算法将血压时间序列转换为符号序列。然后，使用序列对比挖掘算法识别出区分的子序列。这些子序列用于在由用户定义的间隔间隔分隔的未来时间窗口中预测AHE的发生。结果表明，该方法在预测性能和生成具有临床意义的序列模式方面表现良好。因此，序列模式的新颖性在于它们可以作为潜在的生理生物标记物，用于建立最佳的患者风险分层系统和进一步临床研究重症监护患者中感兴趣的模式。
Acute hypotension is a significant risk factor for in-hospital mortality at intensive care units (ICUs). Prolonged hypotension can cause tissue hypoperfusion, leading to cellular dysfunction and severe injuries to multiple organs. Prompt medical interventions are thus extremely important for dealing with acute hypotensive episodes (AHE). Population level prognostic scoring systems for risk stratification of patients are suboptimal in such scenarios. However, the design of an efficient risk prediction system can significantly help in the identification of critical care patients, who are at risk of developing an AHE within a future time span. Towards this objective, a pattern mining algorithm is employed to extract informative sequential contrast patterns from hemodynamic data, for the prediction of hypotensive episodes. The hypotensive and normotensive patient groups are extracted from the MIMIC-II critical care research database, following an appropriate clinical inclusion criteria. The proposed method consists of a data preprocessing step to convert the blood pressure time series into symbolic sequences, using a symbolic aggregate approximation algorithm. Then, distinguishing subsequences are identified using the sequential contrast mining algorithm. These subsequences are used to predict the occurrence of an AHE in a future time window separated by a user-defined gap interval. Results indicate that the method performs well in terms of the prediction performance as well as in the generation of sequential patterns of clinical significance. Hence, the novelty of sequential patterns is in their usefulness as potential physiological biomarkers for building optimal patient risk stratification systems and for further clinical investigation of interesting patterns in critical care patients.