Prediabetes Directly Deteriorates into Diabetic Ketoacidosis and Hyperosmolar Hyperglycemic Syndrome Triggered by Acute Pancreatitis: A Case Report Illustrating a "Chicken and Egg" Paradigm in Ketosis-Prone Diabetes

A Cross-Sectional Study of Capillary Blood Ketone Concentrations in Heart Failure Based on Sodium-Glucose Co-Transporter-2 Inhibitor Use and Heart Failure Type

BACKGROUND

Sodium-glucose co-transporter 2 inhibitors (SGLT2i) are standard-of-care treatment in heart failure (HF). The risk of ketosis in patients with HF is unclear, especially during hospitalisation.

AIM

We aimed to evaluate the normal ketone concentration range in HF patients.

METHOD

We performed a cross-sectional study of inpatients with acutely decompensated HF and outpatients with stable HF. Ketone concentrations were measured and analysed based on SGLT2i use. Baseline demographic data (age, gender, body mass index [BMI]), time since last meal, HF type, type 2 diabetes status, insulin use, and blood parameters (creatinine, glycosylated haemoglobin A1c [HbA1c] and N-terminal pro-B-type natriuretic peptide) were collected from patients or medical records. The primary outcome was capillary blood ketone concentration in patients with acute decompensated HF and stable chronic HF stratified by SGLT2i use. Multivariate regression was also performed using ketones as the outcome variable, with age, gender, BMI, glucose levels, HbA1c, time since last meal and presence of insulin therapy as predictor variables.

RESULTS

A total of 20 individuals with decompensated HF (n=5 SGLT2i treated) and 47 with stable chronic HF (n=22 SGLT2i treated) were recruited. Median ketone concentrations were similar in all groups irrespective of SGLT2i use and the presence of acute decompensation (0.1 mmol/L, biggest interquartile range 0.2 mmol/L, p=0.49). Apart from time from last meal, multivariate regression analysis showed no association of ketone concentration with SGLT2i use, age, gender, BMI, type 2 diabetes status, insulin use and blood glucose level.

CONCLUSIONS

Ketone concentrations were low in individuals with HF regardless of SGLT2i use or the presence of acute decompensation.

Risk factors for prediabetes in community-dwelling adults: A generalized estimating equation logistic regression approach with natural language processing insights

The global prevalence of prediabetes is expected to reach 8.3% (587 million people) by 2045, with 70% of people with prediabetes developing diabetes during their lifetimes. We aimed to classify community-dwelling adults with a high risk for prediabetes based on prediabetes-related symptoms and to identify their characteristics, which might be factors associated with prediabetes. We analyzed homecare nursing records (n = 26,840) of 1628 patients aged over 20 years. Using a natural language processing algorithm, we classified each nursing episode as either low-risk or high-risk for prediabetes based on the detected number and category of prediabetes-symptom words. To identify differences between the risk groups, we employed t-tests, chi-square tests, and data visualization. Risk factors for prediabetes were identified using multiple logistic regression models with generalized estimating equations. A total of 3270 episodes (12.18%) were classified as potentially high-risk for prediabetes. There were significant differences in the personal, social, and clinical factors between groups. Results revealed that female sex, age, cancer coverage as part of homecare insurance coverage, and family caregivers were significantly associated with an increased risk of prediabetes. Although prediabetes is not a life-threatening disease, uncontrolled blood glucose can cause unfavorable outcomes for other major diseases. Thus, medical professionals should consider the associated symptoms and risk factors of prediabetes. Moreover, the proposed algorithm may support the detection of individuals at a high risk for prediabetes. Implementing this approach could facilitate proactive monitoring and early intervention, leading to reduced healthcare expenses and better health outcomes for community-dwelling adults.

Association Between Albumin-Corrected Anion Gap and In-Hospital Mortality and Sepsis-Associated Acute Kidney Injury

BACKGROUND This study aimed to investigate the association between albumin-corrected anion gap (ACAG) and in-hospital mortality in sepsis-associated acute kidney injury (S-AKI). MATERIAL AND METHODS We conducted this retrospective study based on data from the Medical Information Mart for Intensive Care IV database, and assessed the prognostic capabilities of ACAG in comparison with albumin (ALB) and anion gap (AG) to predict in-hospital mortality of patients with S-AKI. Binomial logistic regression analysis was performed to identify whether ACAG was an independent risk factor for in-hospital mortality for the patients, and receiver operating characteristic (ROC) curves were plotted to clarify its efficacy in predicting in-hospital mortality. We also performed a decision curve analysis (DCA) to determine whether there were net clinical benefits for patients when ACAG was used to predict in-hospital mortality. RESULTS Binary logistic regression analysis showed that ACAG was an independent risk factor for in-hospital mortality in patients with S-AKI, with an area under the ROC (AUC) curve of 0.675 (moderate predictive value) for the prediction of in-hospital mortality, higher than that of ALB or AG alone, with the highest Youden's index (0.2675). The DCA substantiated the superiority of ACAG in net clinical benefits at various threshold probability, enhancing its clinical applicability. CONCLUSIONS The research emphasizes the potential of ACAG as a valuable predictive tool for in-hospital mortality in S-AKI patients, which is better than albumin and AG, encouraging its consideration in clinical practice.

Diabetic Ketoalkalosis: A Common Yet Easily Overlooked Alkalemic Variant of Diabetic Ketoacidosis Associated with Mixed Acid-Base Disorders

BACKGROUND

Diabetic ketoacidosis (DKA) is commonly complicated by mixed acid-base disorders. Therefore, patients with DKA can present with pH > 7.3 or bicarbonate > 18 mmol/L, which falls outside the values defined by the current traditional DKA criteria (pH ≤ 7.3 or bicarbonate ≤ 18 mmol/L).

OBJECTIVE

We aimed to study the spectrum of acid-base clinical presentations of DKA and the prevalence of diabetic ketoalkalosis.

METHODS

This study included all adult patients at a single institution admitted in 2018-2020 with diabetes, positive beta-hydroxybutyric acid, and increased anion gap ≥ 16 mmol/L. Mixed acid-base disorders were analyzed to determine the spectrum of presentation of DKA.

RESULTS

There were 259 encounters identified under the inclusion criteria. Acid-base analysis was available in 227 cases. Traditional acidemic DKA (pH ≤ 7.3), DKA with mild acidemia (7.3 < pH ≤ 7.4), and diabetic ketoalkalosis (pH > 7.4) account for 48.9% (111/227), 27.8% (63/227), and 23.3% (53/227) of cases, respectively. Of the 53 cases with diabetic ketoalkalosis, increased anion gap metabolic acidosis was present in all, and concurrent metabolic alkalosis, respiratory alkalosis, and respiratory acidosis were present in 47.2% (25/53), 81.1% (43/53), and 11.3% (6/53) encounters, respectively. In addition, 34.0% (18/53) of those with diabetic ketoalkalosis were found to have severe ketoacidosis, defined by beta-hydroxybutyric acid ≥ 3 mmol/L.

CONCLUSIONS

DKA can present as traditional acidemic DKA, DKA with mild acidemia, and diabetic ketoalkalosis. Diabetic ketoalkalosis is a common yet easily overlooked alkalemic variant of DKA associated with mixed acid-base disorders, and a high proportion of these presentations have severe ketoacidosis and thus, require the same treatment as traditional DKA.

Developing a Classification Algorithm for Prediabetes Risk Detection From Home Care Nursing Notes: Using Natural Language Processing

This study developed and validated a rule-based classification algorithm for prediabetes risk detection using natural language processing from home care nursing notes. First, we developed prediabetes-related symptomatic terms in English and Korean. Second, we used natural language processing to preprocess the notes. Third, we created a rule-based classification algorithm with 31 484 notes, excluding 315 instances of missing data. The final algorithm was validated by measuring accuracy, precision, recall, and the F1 score against a gold standard testing set (400 notes). The developed terms comprised 11 categories and 1639 words in Korean and 1181 words in English. Using the rule-based classification algorithm, 42.2% of the notes comprised one or more prediabetic symptoms. The algorithm achieved high performance when applied to the gold standard testing set. We proposed a rule-based natural language processing algorithm to optimize the classification of the prediabetes risk group, depending on whether the home care nursing notes contain prediabetes-related symptomatic terms. Tokenization based on white space and the rule-based algorithm were brought into effect to detect the prediabetes symptomatic terms. Applying this algorithm to electronic health records systems will increase the possibility of preventing diabetes onset through early detection of risk groups and provision of tailored intervention.

Sodium-Glucose Cotransporter-2 Inhibitor-Associated Euglycemic Diabetic Ketoacidosis After Orthotopic Heart Transplant in a Prediabetic Patient: A Case Report

Postoperative euglycemic diabetic ketoacidosis (euDKA) associated with sodium-glucose cotransporter-2 (SGLT2) inhibitor use has been well-documented and carries a Food and Drug Administration recommendation to hold SGLT2 inhibitors 3 to 4 days before a planned surgical procedure. Unfortunately, many surgical procedures, such as orthotopic heart transplant (OHT), are unplanned and unpredictable. With the increasing use of SGLT2 inhibitors in diabetic and non-diabetic heart failure patients, new challenges in patient management and perioperative risk have arisen. We report a case in which SGLT2 inhibitor-associated euDKA complicated the postoperative course of a prediabetic patient who had undergone OHT.

Ketosis-prone diabetes mellitus in an obese adolescent: A case report

RATIONALE

In recent years, there are more new insights into the clinical susceptibility, pathophysiological mechanism, and progression of classification and treatment of ketosis-prone diabetes mellitus (KPDM), which was once described as Idiopathic Type 1 Diabetes, Type 1B Diabetes or Flatbush Diabetes. ketosis-prone diabetes mellitus is still a heterogeneous syndrome reported in African-American or western Sub-Sahara-African, Hispanic descendant, and recently in Asian.

PATIENT CONCERNS

An obese 17-year-old student was admitted to a tertiary referral hospital (teaching hospital), presenting with thirst, polyuria fatigue, and a 9 kg weight loss in the preceding two weeks.

DIAGNOSES

Physical examination showed body mass index (BMI) was 32.77 kg/m, arterial blood gas revealed a pH of 7.31. Serum glucose was 27.8 mmol/L with strong positive uric ketones (++++). Hemoglobin A1c (HbA1c) was 13.6%. The glucose disposal ratio (GDR) during the steady-state of euglycemic clamp test was 5.62 mg/kg/min and M value was 2.87 mg/kg/min during hyperglycemic clamp test. Those findings were sufficient to establish a diagnosis of ketosis-prone diabetes mellitus.

INTERVENTIONS

This obese patient with KPDM received intensive insulin therapy and fluids infusion, and during the remainder of hospitalization his insulin requirement was approximately 1.5 U per kilogram of body weight per day. Blood glucose monitoring was rigorous until the diabetic ketoacidosis under control.

OUTCOMES

He achieved the near-nomalglycemic remission uneventfully. At 12-month follow-up, his treatment was adjusted from insulin subcutaneous injection to oral hypoglycemic drugs.

LESSON

The present study of this obese adolescent with negative auto-antibodies but unprovoked diabetic ketoacidosis and partially preserved beta cell functional reserve after the acute of diabetic ketosis suggested that he has the phenotype of "A-β" KPDM. Further study of this syndrome will help illustrate the inadequacy of current classification and targeted therapies.