Seventy-two hour mortality prediction model in patients with diabetic ketoacidosis: A retrospective cohort study

Nia Novianti Siregar, Pradana Soewondo, Imam Subekti, Muhadi Muhadi

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

Abstract

Objective. This study aims to identify predictors of 72-hour mortality in patients with diabetic ketoacidosis (DKA). Methodology. In this retrospective cohort study, data were obtained from medical records of adult patients with DKA in Cipto Mangunkusumo General Hospital from January 2011 to June 2017. Associations of predictors (age, type of diabetes, history of DKA, comorbidities, level of consciousness, renal function, bicarbonate, potassium, lactate, betahydroxybutyrate levels, and anion gap status) and 72-hour mortality were analyzed. The mortality prediction model was formulated by dividing the coefficient B by the standard error for all variables with p<0.05 in the multivariate analysis. Results. Eighty-six of 301 patients did not survive 72 hours after hospital admission. Comorbidities (HR 2.407; 95% CI 1.181–4.907), level of consciousness (HR 10.345; 95% CI 4.860–22.019), history of DKA (HR 2.126; 95% CI 1.308–3.457), and lactate level (HR 5.585; 95% CI 2.966–10.519) were significant predictors from multivariate analysis and were submitted to the prediction model. The prediction model had good performance. Patients with total score less than 3 points were at 15.41 % risk of mortality, 3 – 4 points were 78.01% and 5 – 6 points were 98.22% risk of mortality. Conclusion. The 72-hour mortality rate in Cipto Mangunkusumo General Hospital was 28.57%. The mortality prediction model had a good performance and consisted of comorbidities, history of DKA, level of consciousness and lactate level.

Original languageEnglish
Pages (from-to)124-129
Number of pages6
JournalJournal of the ASEAN Federation of Endocrine Societies
Volume33
Issue number2
DOIs
Publication statusPublished - 1 Nov 2018

Keywords

  • Diabetic ketoacidosis
  • Mortality
  • Prediction model

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