Diabetes mellitus is associated with coronary artery disease, and diabetic patients are frequently referred for coronary bypass graft surgery. It is well known that HbA1c, which reflects long-term glycemic control, is related to diabetic morbidity and mortality. It is not known whether HbA1c is related to postoperative length of stay among patients who undergo coronary artery bypass surgery. The authors evaluated 135 patients who underwent bypass surgery at the Westchester Medical Center (Valhalla, NY). HbA1c was measured in all patients preoperatively; a value of 7% or greater was used as a threshold for uncontrolled hyperglycemia. A postoperative length of stay of 6 days or more was used as the cutoff for an extended length of stay. Linear regression was used to assess the relationship between HbA1c, adjusted for age, and length of stay in days. Logistic regression, with length of stay a binary variable <6, > or =6 days, was used to assess the accuracy of HbA1c <7%, > or =7%, adjusted for age, in predicting length of stay. An HbA1c of 7% or greater was found to be a strong predictor of a length of stay of 6 days or longer. These data suggest that HbA1c can be used as a surrogate marker for cardiac and noncardiac morbidity that prolongs hospitalization after coronary artery bypass surgery.
In this paper we develop a Hierarchical Switching Linear Dynamical System (HSLDS) for the detection of sepsis in neonates in an intensive care unit. The Factorial Switching LDS (FSLDS) of Quinn et al. (2009) is able to describe the observed vital signs data in terms of a number of discrete factors, which have either physiological or artifactual origin. In this paper we demonstrate that by adding a higher-level discrete variable with semantics sepsis/non-sepsis we can detect changes in the physiological factors that signal the presence of sepsis. We demonstrate that the performance of our model for the detection of sepsis is not statistically different from the auto-regressive HMM of Stanculescu et al. (2013), despite the fact that their model is given "ground truth" annotations of the physiological factors, while our HSLDS must infer them from the raw vital signs data.
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