Estimation and personalization of clinical insulin therapy parameters
| dc.contributor.advisor | Edgar, Thomas F. | |
| dc.contributor.advisor | Heller, Adam | |
| dc.creator | Palma, Ramiro Cesar, IV | en |
| dc.date.accessioned | 2013-09-27T20:32:32Z | en |
| dc.date.accessioned | 2017-05-11T22:34:10Z | |
| dc.date.available | 2017-05-11T22:34:10Z | |
| dc.date.issued | 2013-08 | en |
| dc.date.submitted | August 2013 | en |
| dc.date.updated | 2013-09-27T20:32:32Z | en |
| dc.description | text | en |
| dc.description.abstract | Despite considerable effort considerable cost in both time and money, as many as two out of three persons with type 1 diabetes are not in control of their disease. As a result, 40% of these individuals will go on to develop at least one serious complication including retinopathy, nephropathy, neuropathy and cardiomyopathy. It is further estimated that as much as $4 billion could be saved annually if all persons with type 1 diabetes in the US were properly controlled. Adequate treatment of type 1 diabetes is predicated on the estimation of three clinical insulin therapy parameters: the basal dose, the insulin sensitivity factor and the insulin-to-carbohydrate ratio. Currently, these therapy parameters are determined by iterative titration procedures based on expert opinion. Unfortunately, there is evidence suggesting that for the majority of individuals, these titration protocols do not provide good results. In this work we develop an alternative to traditional insulin titration protocols that allows clinical insulin therapy parameters to be estimated directly from a set of easily acquired measurements. First, a simple model of type 1 diabetes is used to derive a series of equations connecting the model's parameters to the clinically important insulin therapy parameters of insulin sensitivity factor, insulin-to-carbohydrate ratio and basal insulin dose. The simplifying assumptions used to derive these equations are tested and shown to be valid and the Fisher Information Matrix is used to demonstrate parameter identifiability. Parameter estimation is then performed on two cohorts of virtual subjects, as well as two segments of real continuous glucose monitoring data from a person with type 1 diabetes. Identification of the true insulin therapy parameters is successful under most conditions for both cohorts of virtual subjects. Parameter estimation for one of the two segments of real continuous glucose monitoring data is also successful. Finally, because continuous glucose monitors are instrumental to successful implementation of our insulin therapy framework, the physiological environment in which continuous glucose monitoring takes place is modeled and a fundamental limitation on measurement precision is shown to exist. An examination of physiological variability in the parameters indicates that many of the challenges observed in real world continuous glucose monitoring may have a relationship to changes in capillary bed perfusion. A rationale for anecdotally reported sensor faults is also proposed based on the physical mechanisms explored. | en |
| dc.description.department | Chemical Engineering | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.uri | http://hdl.handle.net/2152/21375 | en |
| dc.language.iso | en_US | en |
| dc.subject | Type 1 diabetes | en |
| dc.subject | Clinical decision support | en |
| dc.subject | Personalized medicine | en |
| dc.subject | Medical informatics | en |
| dc.subject | Insulin therapy | en |
| dc.title | Estimation and personalization of clinical insulin therapy parameters | en |