학술논문

Predicting hyperlactatemia in the MIMIC II database
Document Type
Conference
Source
2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE. :985-988 Aug, 2015
Subject
Engineered Materials, Dielectrics and Plasmas
Blood pressure
Biomedical monitoring
Electric shock
Heart rate
Market research
Silicon
Databases
Language
ISSN
1094-687X
1558-4615
Abstract
Sepsis, which occurs when an infection leads to a systemic inflammatory response, is believed to contribute to one in two to three hospital deaths in the United States. Using the Multiparameter Intelligent Monitoring in Intensive Care (MIMIC II) database of electronic medical records from Boston's Beth Israel Deaconess Medical Center (BIDMC), we worked to characterize sepsis at BIDMC's intensive care units. Additionally, we developed a real-time algorithm to stratify patients with infectious complaints into different risk categories for progressing to septic shock. From time series of heart rate and arterial blood pressure, as well as estimates of cardiac output and total peripheral resistance, we developed a variety of classifiers to predict high serum lactate levels, a proxy for hypoperfusion and imminent circulatory shock. The records from 146 patients met our selection criteria. In discriminating patients whose measured serum lactate stays below 2.5 mmol/L from those whose value drifts above, the best of our classifiers perform with area under the receiver operating characteristic exceeding 0.8 on test data.