Early Detection of Transition to Multiple Organ Dysfunction Syndrome Using Physiological Time Series Data

multiple organ dysfunction syndrome;time series data;early detection;generalized linear model;pediatric;Biomedical Engineering

Chyn, MichelleSarma, Sridevi V ;

Johns Hopkins University

关键词: multiple organ dysfunction syndrome;    time series data;    early detection;    generalized linear model;    pediatric;    Biomedical Engineering;   

Others  :  https://jscholarship.library.jhu.edu/bitstream/handle/1774.2/60166/CHYN-THESIS-2018.pdf?sequence=1&isAllowed=y

瑞士|英语

来源: JOHNS HOPKINS DSpace Repository

【 摘 要 】

Multiple organ dysfunction syndrome (MODS) has an incidence rate of between 11 to 56\% in the PICU. Early prevention and treatment of MODS is important in the pediatric population as it increases mortality and leads to possible negative functional outcomes in adulthood. MODS severity is measured using a few different metrics, among which the Pediatric Logistic Organ Dysfunction 2 Score (PELOD-2) is the most recent, pediatric multi-center validated scoring system. This study attempted to build a generalized linear model to detect risk of PICU patients at Johns Hopkins Children;;s Center from a retrospectively gathered cohort, using PELOD-2 Score>=6 to define MODS severity and minute to minute physiological data as model covariates. Patient specific models were built with a two hour window for transitioning into severe state, the positive class, and the non-severe state was undersampled to balance classes. A global model was built across the majority of the patient population with similar parameters in order to create a more useful, clinical applicable model. The accuracy, sensitivity, and specificity of training and testing sets were calculated for each model. Patient specific models performed well, but performance decayed for the global model, where predictions at the patient level for risk of transitioning had high sensitivity and very low specificity. Future research should continue to refine the definition of a severe state of MODS and calibrate the sampling scheme with regards to ratio of data points labeled as healthy versus at risk in order to improve global model performance.