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. 2025 May 23;17(5):e84672.
doi: 10.7759/cureus.84672. eCollection 2025 May.

Clinical Characteristics and Hypokalemia Risk Prediction in Patients With Hyperosmolar Hyperglycemic State and Diabetic Ketoacidosis (HHS-DKA): A Single-Center, Retrospective Observational Study

Yuichiro Iwamoto  1 , Tomohiko Kimura  1 , Yuichi Morimoto  2 , Ayaka Harada  1 , Kazunori Dan  1 , Hideyuki Iwamoto  1 , Yoshiro Fushimi  1 , Junpei Sanada  1 , Masashi Shimoda  1 , Shuhei Nakanishi  1 , Tomoatsu Mune  1 , Kohei Kaku  1 , HIdeaki Kaneto  1
Affiliations

Clinical Characteristics and Hypokalemia Risk Prediction in Patients With Hyperosmolar Hyperglycemic State and Diabetic Ketoacidosis (HHS-DKA): A Single-Center, Retrospective Observational Study

Yuichiro Iwamoto et al. Cureus. .

Abstract

Background: People with combined diabetic ketoacidosis (DKA) and hyperosmolar hyperglycemic state (HHS) often present with more severe metabolic derangements than those with DKA or HHS alone. This study aimed to clarify the clinical characteristics of HHS-DKA and explore predictive models for complications, including hypokalemia.

Methods: We retrospectively analyzed data from 99 patients admitted with hyperglycemic emergencies between April 1, 2010, and October 31, 2024, and classified them into DKA, HHS, and HHS-DKA groups. A decision tree model was also developed to predict the risk of post-continuous insulin infusion (CII) hypokalemia. The decision tree model was created using machine learning with the Python language (Python Software Foundation, Wilmington, Delaware).

Results: HHS-DKA patients had significantly higher rates of acute kidney injury (84%) and hyperkalemia (58%) compared to those with DKA or HHS alone. A decision tree model predicted post-CII hypokalemia with 80% accuracy, identifying key predictors such as initial blood glucose and insulin flow rates.

Conclusion: HHS-DKA represents a distinct and severe clinical entity with unique characteristics and complications. Predictive models developed in this study will likely assist in risk stratification and improve patient management during hyperglycemic crises in emergency settings. However, as this was a single-center retrospective study without external validation, further studies are warranted to confirm these findings.

Keywords: continuous insulin infusion therapy; diabetes mellitus; diabetic ketoacidosis (dka); hyperosmolar hyperglycemic state (hhs); retrospective observational study.

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Conflict of interest statement

Human subjects: Consent for treatment and open access publication was obtained or waived by all participants in this study. Institutional Review Board of Kawasaki Medical School issued approval 6229-00. The protocol for the research project has been approved by the Institutional Review Board of Kawasaki Medical School (No.: 6229-00). Ethical consideration or informed consent of the patient: Consent was obtained for participants in this study via opt-out on the Kawasaki Medical School website. Animal subjects: All authors have confirmed that this study did not involve animal subjects or tissue. Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following: Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work. Financial relationships: Shuhei Nakanishi declare(s) personal fees from Novo Nordisk Pharma, Kowa and Daiichi Sankyo. S.N. has received honoraria for lectures from Novo Nordisk Pharma, Kowa, and Daiichi Sankyo. Kohei Kaku declare(s) a grant, personal fees and non-financial support from Novo Nordisk Pharma, Sanwa Kagaku, Taisho Pharma, Sumitomo Pharma, Astellas, Boehringer Ingelheim. K.K. has been an advisor to, received honoraria for lectures from, and received scholarship grants from Novo Nordisk Pharma, Sanwa Kagaku, Taisho Pharma, Sumitomo Pharma, Astellas, and Boehringer Ingelheim. Tomohiko Kimura declare(s) personal fees from Sumitomo Pharma and Novo Nordisk Pharma. T.K. has received honoraria for lectures from Sumitomo Pharma and Novo Nordisk Pharma. Hideaki Kaneto declare(s) a grant and personal fees from Novo Nordisk Pharma, Sanofi, Eli Lilly, Boehringer Ingelheim, Sumitomo Pharma, Daiichi Sankyo, Mitsubishi Tanabe Pharma, Manpei Suzuki Diabetes Foundation, Japan Arteriosclerosis Prevention Fund, Japan Association for Diabetes Education and Care. H.K. has received honoraria for lectures, received scholarship grants from Novo Nordisk Pharma, Sanofi, Eli Lilly, Boehringer Ingelheim, Sumitomo Pharma, Daiichi Sankyo, Mitsubishi Tanabe Pharma, Manpei Suzuki Diabetes Foundation, Japan Arteriosclerosis Prevention Fund, and Japan Association for Diabetes Education and Care. Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work.

Figures

Figure 1
Figure 1. Reactivity after CII initiation in the three groups: DKA alone, HHS alone, and DKA-HSS combination.
(A) Blood glucose level before CII (white box) and re-checked blood glucose level after CII (gray box). (B) Insulin flow rate from the start of CII to a re-examination of blood glucose level. (C) Infusion drip flow rate from the start of CII to the time of re-examination of blood glucose level. (D) Time from the beginning of CII to the time of re-examination of blood glucose level. * p < 0.05 vs. DKA, # p < 0.05; ## p < 0.005 before vs. after CII. The Kruskal-Wallis test was used for analysis, and the Steel-Dwass test was used as a post-hoc test. CII, continuous insulin infusion; DKA, diabetic ketoacidosis; HHS, hyperosmolar hyperglycemic state.
Figure 2
Figure 2. Correlation with various complications and clinical parameters.
On admission, various complications were associated with blood glucose, total ketones, serum osmolality, and bicarbonate ion concentration. (A) Gastroenteritis, (B) acute necrotizing esophagitis (ANE), (C) acute kidney injury (AKI), (D) hyperkalemia, (E) acute myocardial infarction (AMI) or acute cerebral infarction (ACI), and (F) infection. * p = 0.05, ** p = 0.005, and *** p = 0.001. The Wilcoxon signed-rank test was used for analysis.
Figure 3
Figure 3. The decision tree was created to predict the risk of hypokalemia after CII initiation.
(A) Correlation of hypokalemia after CII initiation with blood glucose, serum potassium, insulin flow rate per body weight, and infusion drip flow rate per body weight at CII initiation. * p < 0.05, *** p < 0.001. The Wilcoxon signed-rank test was used for analysis. (B) Decision tree for hypokalemia prediction after CII initiation. Each node shows the number of patients and the proportion of hypokalemia cases. (C) The confusion matrix shows the model’s performance in the test set, including sensitivity and specificity for high- and low-risk classifications. (D) The receiver operating characteristic (ROC) curve for the created decision tree model illustrates its performance across different thresholds. The decision tree was made using Python 3.10.12 and Scikit-learn version 1.6.0. CII, continuous insulin infusion.
Figure 4
Figure 4. The final evaluation of CII in this study.
The final evaluation was made when blood glucose levels were below 300 mg/dL or when oral intake was resumed, and the following were evaluated: (A) final blood glucose level, (B) total insulin dose, (C) total infusion dosage, and (D) time elapsed since the start of CII. * p < 0.05, ** p < 0.005 vs. DKA, # p < 0.05 vs. HHS. The Kruskal-Wallis test was used for analysis, and the Steel-Dwass test was used as a post-hoc test. CII, continuous insulin infusion; DKA, diabetic ketoacidosis; HHS, hyperosmolar hyperglycemic state.
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