Evidence map›Paper›PMID 40546498›Full record

ArticleCureus2025

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, Tomohiko Kimura, Yuichi Morimoto, Ayaka Harada, Kazunori Dan, Hideyuki Iwamoto, Yoshiro Fushimi, Junpei Sanada, Masashi Shimoda, Shuhei Nakanishi and 3 more

Abstract read
In one paragraph

Article in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Yuichiro IwamotoDepartment of Diabetes, Endocrinology and Metabolism, Kawasaki Medical School, Kurashiki, JPN.
Tomohiko KimuraDepartment of Diabetes, Endocrinology and Metabolism, Kawasaki Medical School, Kurashiki, JPN.
Yuichi MorimotoDepartment of Pediatrics, Kindai University, Osakasayama, JPN.
Ayaka HaradaDepartment of Diabetes, Endocrinology and Metabolism, Kawasaki Medical School, Kurashiki, JPN.
Kazunori DanDepartment of Diabetes, Endocrinology and Metabolism, Kawasaki Medical School, Kurashiki, JPN.
Hideyuki IwamotoDepartment of Diabetes, Endocrinology and Metabolism, Kawasaki Medical School, Kurashiki, JPN.
Yoshiro FushimiDepartment of Diabetes, Endocrinology and Metabolism, Kawasaki Medical School, Kurashiki, JPN.
Junpei SanadaDepartment of Diabetes, Endocrinology and Metabolism, Kawasaki Medical School, Kurashiki, JPN.
Masashi ShimodaDepartment of Diabetes, Endocrinology and Metabolism, Kawasaki Medical School, Kurashiki, JPN.
Shuhei NakanishiDepartment of Diabetes, Endocrinology and Metabolism, Kawasaki Medical School, Kurashiki, JPN.
Tomoatsu MuneDepartment of Diabetes, Endocrinology and Metabolism, Kawasaki Medical School, Kurashiki, JPN.
Kohei KakuDepartment of Diabetes, Endocrinology and Metabolism, Kawasaki Medical School, Kurashiki, JPN.
HIdeaki KanetoDepartment of Diabetes, Endocrinology and Metabolism, Kawasaki Medical School, Kurashiki, JPN.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPeople 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.

methodsWe 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).

resultsHHS-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.

conclusionHHS-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.

Indexed as

continuous insulin infusion therapydiabetes mellitusdiabetic ketoacidosis (dka)hyperosmolar hyperglycemic state (hhs)retrospective observational study

Identifiers

PMID40546498
PMCPMC12182720

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.