Predictors and Predictive Score of In-Hospital Mortality in Diabetic Ketoacidosis: A Retrospective Cohort Study

Abstract

Background and Objectives: Diabetic ketoacidosis (DKA) is a critical complication of diabetes mellitus (DM). The primary objective of this study was to identify relevant clinical and biochemical predictors and create a predictive score for in-hospital DKA mortality. Materials and Methods: A 6-year retrospective cohort study of adult patients diagnosed with DKA and admitted to Chiang Mai University Hospital, a tertiary care center in Chiang Mai, Thailand, from 1 January 2015 to 31 December 2021, was conducted. Baseline clinical data and laboratory investigations were collected. The primary outcome was in-hospital mortality. Multivariable logistic regression analysis, clustered by type of diabetes, was performed to identify significant predictors. A predictive risk score was created using significant predictive factors identified by multivariable analysis. The results were presented as odds ratios (ORs) and 95% confidence intervals (CIs), with a significant p-value set at <0.05. Results: Ninety-three patients diagnosed with DKA were included in the study. Ten patients died during admission. Significant predictors for in-hospital mortality of DKA included age > 55 years (OR 7.8, p = 0.007), female gender (OR 3.5, p < 0.001), anion gap > 30 mEq/L (OR 2.6, p = 0.003), hemoglobin levels < 10 g/dL (OR 16.9, p < 0.001), and the presence of cardiovascular disease (OR 1.3, p = 0.046). The predictive risk score ranged from 1 to 14 for low risk, and 14.5–23.5 for high risk of in-hospital mortality. The predictive performance of the scoring system was 0.82 based on the area under the curve, with a sensitivity of 73.8% and specificity of 96.4%. Conclusions: Multiple clinical and biochemical factors, along with a predictive risk score, could assist in predicting in-hospital mortality of DKA and serve as a guide for physicians to identify patients at high risk. Nevertheless, as the predictive score was internally validated with data from a single institution, external validation in diverse healthcare settings with larger datasets or prospective cohorts is crucial to confirm the model’s generalizability and predictive accuracy.