Kriz-Ann R Hernandez
Jose R. Reyes Memorial Medical Center, Philippines
Posters & Accepted Abstracts: J Diabetes Metab
Background: Diabetes mellitus (DM) is a major cause of premature mortality globally. One of its acute complications is diabetic ketoacidosis (DKA). DKA is a medical emergency wherein abrupt and correct management could prevent patient mortality. Prediction of mortality from DKA could be done using patient�??s demographics, clinical profile and laboratory parameters. However, locally, there is no prediction model developed yet to predict mortality. Objective: This study aims to create an assessment tool that could accurately predict the risk of mortality among DKA patients within the first 24 hours of admission and correlate patient�??s demographics, clinical profile and laboratory parameters with improvement of survival rate. Methods: This is a retrospective, cohort study which included 129 admitted adult DKA patients. Statistical analysis used was logistic binary regression. Receiving operating characteristic (ROC) curve was done to validate prediction models. Results & Analysis: 6 variables identified to predict mortality are patient�??s age �?�60 years, severe DKA, non-insulin dependent status, GCS<15, non-normal platelet count and non-normal estimated creatinine clearance. Prediction models developed included and omitted age profile. Cut-off scores of prediction models were validated with the ROC curve. Cut-off score with age was 5 with sensitivity of 73.91% and specificity of 74.70% and the area under the curve is 0.751 which is significant (p=0.0001). On the other hand, cut-off score of the prediction model without age is 4 with sensitivity of 65.22% and specificity of 67.47% and the area under the curve is 0.719 which is significant (p=0.0001). Conclusion: This study was able to prove that mortality in DKA can be predicted within the first 24 hours of admission using patient�??s demographics and significant clinical profile in the prediction models developed.
