Diabetic Ketoacidosis

Transcription

1 6/9/2018 Management of Diabetic Ketoacidosis STAT 183 Individual Project Prepared for: Dr. Karen Huaying Xu Prepared by: Albert Sosa UNIVERSITY OF CALIFORNIA, RIVERSIDE

2 Table of Contents I Introduction 3 II Findings 4 III Other Influential Factors 5 Type 1 Diabetes Type 2 Diabetes Hypertension IV The Model 8 V Conclusion 10

3 I. Introduction Diabetic Ketoacidosis (DKA) is a well-known serious acute metabolic complication. It happens when the body lacks insulin and cells use fat for energy instead of sugar. As a result of burning fat, acids, called ketones, are generated. As cells continue to burn fat, ketones pile up in blood and thus become very acidic. This phenomenon can be very life-threatening to people diagnosed with diabetes. The side effects include, but not limited to, excessive fluid/electrolyte loss, nausea, coma, and even death. Severe cases of DKA are managed in the intensive care units. One way to detect DKA is by measuring the anion gap of patients. A large anion gap indicates a patient undergoing DKA. There are two different types of DKA treatment. The one-bag system approach utilizes a continuous insulin infusion and one bag of intravenous electrolyte and dextrose solution is initiated based on the clinical abnormalities at that stage of management. As the blood glucose level falls, a new bag with different dextrose concentrations would be ordered and multiple sequential bag changes would occur during the course of treatment. The two-bag system approach was first described in the early 1990 s in pediatric patients. It utilizes two bags of fluids with identical electrolyte content but different dextrose concentrations, 0% and 10%. The two bags are connected in a Y fashion and by adjusting the infusion rates from each bag, the concentration of dextrose can be customized to prevent unpredictable excursions in blood glucose. The purpose of this research is to determine if a two-bag-system significantly influenced the anion gap closure time in adult patients with diabetic ketoacidosis, using the anion gap closure time as the indicator. In addition, this report will also determine other patient factors that influence the time to close the anion. Methodology The study was carried out using the patients admitted for DKA between the years of 2010 to 2015 in Riverside University Health System Medical Center. All adult male and female patients over 18 years old with a diagnosis of DKA was identified from the medical records for the purpose of this study. Patients with a diagnosis of HHS were excluded from the study. Ketosis of any other etiology besides DKA was not included in this study. 249 patients admitted with DKA received treatment one-bag system, while the other 134 patients received the treatment two-bag system.

4 Time in Minutes II. Findings Looking at the average anion gap closure time for the two treatments, one-bag system has a higher closure time than two-bag system, with 1, to minutes. After conducting a two-sample t-test, we conclude the difference to be significant with a one-sided p-value of Thus, there s sufficient evidence to indicate that the true mean anion gap closure time using treatment one-bag system is greater than using treatment two-bag system, with alpha = 0.05 significance level. Figure 1. Average Closure Time for One Bag and Two Bag Mean Anion Gap Closure Time 1, ONE BAG System Approach TWO BAG

5 III. Other Influential Factors We further look into the comorbidities that had influence on the anion gap closure time. Type 1 Diabetes Those diagnosed with Type 1 Diabetes had significantly less anion gap closure time as compared to those not diagnosed. However, this does not take into account the treatment type. If we consider the treatment type, we found that if a patient was treated with the two-bag system, Type 1 Diabetic patients did not experience a significantly lower anion gap closure time as compared to patients not diagnosed with Type 1 Diabetes. For those treated with one-bag system, having a Type 1 diabetes did significantly decrease the gap closure time. Figure 2. Average Closure Time for Type 1 Non-Diabetic (0) and Diabetic (1)

6 Type 2 Diabetes Unlike type 1 diabetes, the anion gap closure time for those diagnosed with type 2 diabetes is greater than those not diagnosed with type 2 diabetes. And if we consider the treatment type, type 2 diabetes only made a significant difference if the one-bag system treatment is used. For the two-bag system treatment, having a type 2 diabetes did not significantly increase the anion gap closure time. Figure 2. Average Closure Time for Type 2 Non-Diabetic (0) and Diabetic (1)

7 Hypertension The last comorbidity this research studies is hypertension. Similarly, with type 2 diabetes, having a comorbidity of hypertension significantly increased the anion gap closure time. Considering the type of treatment used, having hypertension as a comorbidity only made a significant increase in the anion gap closure time if the patient was treated using the one-bag system, not the two-bag system. Figure 3. Average Closure Time for Non-Hypertension (0) and Hypertension (1)

8 IV. The Model We now look into a model that predicts the anion gap closure time. Since the response variable, anion gap closure time, is continuous, we fit a multiple linear regression model. The final model utilizes the Box Cox transformation with the lambda of -0.5 to satisfy the normality of residuals assumption. After conducting a step-wise selection, our simplified model is as follows: Delta Minutes 0.5 = Type TwoBag Bun Creat CCI Where Delta Minutes = Anion Gap Closure Time Type1 = Diagnosed with Type 1 Diabetes (1=yes,0=no) TwoBag = Used Two-Bag System as treatment (1=yes,0=no) Bun = Blood Urea Nitrogen (mg/dl) Creat = Creatinine (md/dl) CCI = Charlson s Comorbidity Index Model Accuracy This regression model produced an adjusted R-Squared value of 0.065, which states that 6.5% of the variation in the Box Cox transformation of delta minutes can be explained by our model. This R-squared adjusted value maybe small, however, it is significantly greater than 0, which states that it is a significant model in predicting the anion gap closure time. Model Adequacy Checking the assumptions of the model, we see that the model does not violate the normality assumption, as seen in figure 4. As for the constant variance assumption, the model does not violate this since the Fitted vs. Absolute Residuals does not show any particular pattern, as seen in figure 5. Lastly, checking for any multicollinearity between the predictors, the VIF s for all predictors are fairly low, as seen in figure 6.

9 Figure 4. QQ-Plot Figure 5. Fitted vs. Absolute Residuals Figure 6. Variation Inflation Factors of the Model Predictor VIF Type 1 Diabetes Two Bag System 2.43 Blood Urea Nitrogen 1.97 Creatinine Charlson s Comorbidity Index Age 2.69 Type 1 * Two Bag 3.047

10 V. Conclusion Diabetic Ketoacidosis (DKA) is a serious health concern especially for those diagnosed with diabetes. A fast and effective treatment is vital to the management of DKA. As we learn from this research, the two-bag system approach significantly decreases the anion gap closure time, which is the measurement of the severity of DKA. Other patient factors that decrease the anion gap closure time are being diagnosed with type 1 diabetes and increase in blood urea nitrogen level. On the other hand, as the creatinine level and Charlson s Comorbidity Index increase, the anion gap closure time also increases. Future Work If I were to continue on with this research, I would further look into the components of both the one-bag and two-bag system to identify specifically what chemicals, or processes, are effective in decreasing the anion gap closure time. Moreover, access to more data would represent a wider range of population, for example the whole United States. Since the sample are patients from Riverside, inferences concerning patients in other parts of the U.S. are not valid.