DocumentCode :
710772
Title :
Prediction of post-surgical adverse outcomes using procedural data
Author :
Betts, Clayton ; Hardardt, James ; Friedland, Dawson ; Wergeles, Steven ; Hernandez, Joel ; Terner, Zachary ; Colquhoun, Douglas ; Brown, Donald E.
Author_Institution :
Univ. of Virginia, Charlottesville, VA, USA
fYear :
2015
fDate :
24-24 April 2015
Firstpage :
200
Lastpage :
205
Abstract :
Modern surgical procedures have saved lives; however, the occurrence of post-surgical complications is still a concern. This study investigates the relationships between patients´ surgical data and the post-surgical adverse outcomes of death, cardiac injury, renal injury, and respiratory failure. We analyze these relationships through the statistical modelling of predictor variables from patients´ preoperative data and intraoperative physiological time series data, as well as the intraoperative time series of procedures performed by anesthesiologists and surgeons. In order to include this procedural data we create novel predictors to represent each patient´s time series in models. Three different statistical models are described: Random Forest (RF), Generalized Linear Models (GLM), and L1 Regularized Logistic Regression (L1). We evaluate the model results using Receiver Operating Characteristic (ROC) curves and their corresponding area under the curve (AUC) values. The GLM model performs the best for death with an AUC value of .887, and the RF model performs best for cardiac injury, renal injury and respiratory failure, with AUC values of .857, .873 and .831, respectively. For all adverse outcomes, the L1 models minimized false negatives at a threshold of .01. This paper describes the results of these models and identifies the strongest predictors of each post-surgical adverse outcome. Additionally, we create a Mixed Effects (ME) model to isolate interactions between patient types and other significant predictors. This model suggests significant interaction effects between the placement of an arterial line and patient types when predicting renal injury and respiratory failure.
Keywords :
injuries; physiology; regression analysis; statistical distributions; surgery; time series; AUC; GLM; L1 regularized logistic regression; RF; ROC curve; area under the curve; cardiac injury; death; generalized linear model; intraoperative physiological time series data; post-surgical adverse outcome prediction; procedural data; random forest; receiver operating characteristic; renal injury; respiratory failure; statistical modelling; Biological system modeling; Data models; Injuries; Predictive models; Radio frequency; Surgery; Time series analysis; Breakpoints; Mixed Effect Models; Statistical Models; Surgical Procedures;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Systems and Information Engineering Design Symposium (SIEDS), 2015
Conference_Location :
Charlottesville, VA
Print_ISBN :
978-1-4799-1831-7
Type :
conf
DOI :
10.1109/SIEDS.2015.7116974
Filename :
7116974
Link To Document :
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