Superior ophthalmic vein thrombosis in unique double origin: a case report.

Superior ophthalmic vein thrombosis (SOVT) is a rare orbital pathology. It can cause serious complications if it isn´t diagnosed appropriately. It can be secondary to many etiologies, septic or aseptic ones. Diabetic ketoacidosis (DKA) may disturb the vascular endothelium and promote a prothrombotic state. The presence of which is related to a significantly increased risk of morbidity and mortality. We report the case of a 45-year-old woman who presented a SOVT revealing DKA. Orbit magnetic resonance imaging (MRI) showed thrombosis of the right superior ophthalmic vein. A treatment based on thrombolytic treatment, associated with antibiotic coverage and a glycemic balance was initiated. This case highlights the importance of considering both infection and diabetes as an important part of the diagnosis and management of SOVT.

Predicting type 1 diabetes in children using electronic health records in primary care in the UK: development and validation of a machine-learning algorithm.

BACKGROUND: Children presenting to primary care with suspected type 1 diabetes should be referred immediately to secondary care to avoid life-threatening diabetic ketoacidosis. However, early recognition of children with type 1 diabetes is challenging. Children might not present with classic symptoms, or symptoms might be attributed to more common conditions. A quarter of children present with diabetic ketoacidosis, a proportion unchanged over 25 years. Our aim was to investigate whether a machine-learning algorithm could lead to earlier detection of type 1 diabetes in primary care. METHODS: We developed the predictive algorithm using Welsh primary care electronic health records (EHRs) linked to the Brecon Dataset, a register of children newly diagnosed with type 1 diabetes. Children were included from their first primary care record within the study period of Jan 1, 2000, to Dec 31, 2016, until either type 1 diabetes diagnosis, they turned 15 years of age, or study end. We developed an ensemble learner (SuperLearner) using 26 potential predictors. Validation of the algorithm was done in English EHRs from the Clinical Practice Research Datalink (primary care) and Hospital Episode Statistics, focusing on the ability of the algorithm to identify children who went on to develop type 1 diabetes and the time by which diagnosis could be anticipated. FINDINGS: The development dataset comprised 34 754 400 primary care contacts, relating to 952 402 children, and the validation dataset comprised 43 089 103 primary care contacts, relating to 1 493 328 children. Of these, 1829 (0·19%) children younger than 15 years in the development dataset, and 1516 (0·10%) in the validation dataset had a reliable date of type 1 diabetes diagnosis. If set to give an alert in 10% of contacts, an estimated 71·6% (95% CI 68·8-74·4) of the children with type 1 diabetes would receive an alert by the algorithm in the 90 days before diagnosis, with diagnosis anticipated, on average, by an estimated 9·34 days (95% CI 7·77-10·9). INTERPRETATION: If implemented into primary care settings, this predictive algorithm could substantially reduce the proportion of patients with new-onset type 1 diabetes presenting in diabetic ketoacidosis. Acceptability of alert thresholds should be explored in primary care. FUNDING: Diabetes UK.

High Prevalence of A-ß+ Ketosis-Prone Diabetes in Children with Type 2 Diabetes and Diabetic Ketoacidosis at Diagnosis: Evidence from the Rare and Atypical Diabetes Network (RADIANT).

Background: A-ß+ ketosis-prone diabetes (KPD) in adults is characterized by presentation with diabetic ketoacidosis (DKA), negative islet autoantibodies, and preserved ß-cell function in persons with a phenotype of obesity-associated type 2 diabetes (T2D). The prevalence of KPD has not been evaluated in children. We investigated children with DKA at "T2D" onset and determined the prevalence and characteristics of pediatric A-ß+ KPD within this cohort. Methods: We reviewed the records of 716 children with T2D at a large academic hospital and compared clinical characteristics of those with and without DKA at onset. In the latter group, we identified patients with A-ß+ KPD using criteria of the Rare and Atypical Diabetes Network (RADIANT) and defined its prevalence and characteristics. Results: Mean age at diagnosis was 13.7 ± 2.4 years: 63% female; 59% Hispanic, 29% African American, 9% non-Hispanic White, and 3% other. Fifty-six (7.8%) presented with DKA at diagnosis and lacked islet autoantibodies. Children presenting with DKA were older and had lower C-peptide and higher glucose concentrations than those without DKA. Twenty-five children with DKA (45%) met RADIANT A-ß+ KPD criteria. They were predominantly male (64%), African American or Hispanic (96%), with substantial C-peptide (1.3 ± 0.7 ng/mL) at presentation with DKA and excellent long-term glycemic control (HbA1c 6.6% ± 1.9% at follow-up (median 1.3 years postdiagnosis)). Conclusions: In children with a clinical phenotype of T2D and DKA at diagnosis, approximately half meet criteria for A-ß+ KPD. They manifest the key characteristics of obesity, preserved ß-cell function, male predominance, and potential to discontinue insulin therapy, similar to adults with A-ß+ KPD.

Feature selection and risk prediction for diabetic patients with ketoacidosis based on MIMIC-IV.

Background: Diabetic ketoacidosis (DKA) is a frequent acute complication of diabetes mellitus (DM). It develops quickly, produces severe symptoms, and greatly affects the lives and health of individuals with DM.This article utilizes machine learning methods to examine the baseline characteristics that significantly contribute to the development of DKA. Its goal is to identify and prevent DKA in a targeted and early manner. Methods: This study selected 2382 eligible diabetic patients from the MIMIC-IV dataset, including 1193 DM patients with ketoacidosis and 1186 DM patients without ketoacidosis. A total of 42 baseline characteristics were included in this research. The research process was as follows: Firstly, important features were selected through Pearson correlation analysis and random forest to identify the relevant physiological indicators associated with DKA. Next, logistic regression was used to individually predict DKA based on the 42 baseline characteristics, analyzing the impact of different physiological indicators on the experimental results. Finally, the prediction of ketoacidosis was performed by combining feature selection with machine learning models include logistic regression, XGBoost, decision tree, random forest, support vector machine, and k-nearest neighbors classifier. Results: Based on the importance analysis conducted using different feature selection methods, the top five features in terms of importance were identified as mean hematocrit (haematocrit_mean), mean hemoglobin (haemoglobin_mean), mean anion gap (aniongap_mean), age, and Charlson comorbidity index (charlson_comorbidity_index). These features were found to have significant relevance in predicting DKA. In the individual prediction using logistic regression, these five features have been proven to be effective, with F1 scores of 1.000 for hematocrit mean, 0.978 for haemoglobin_mean, 0.747 for age, 0.692 for aniongap_mean and 0.666 for charlson_comorbidity_index. These F1 scores indicate the effectiveness of each feature in predicting DKA, with the highest score achieved by mean hematocrit. In the prediction of DKA using machine learning models, including logistic regression, XGBoost, decision tree, and random forest demonstrated excellent results, achieving an F1 score of 1.000. Additionally, by applying feature selection techniques, noticeable improvements were observed in the experimental performance of the support vector machine and k-nearest neighbors classifier. Conclusion: The study found that hematocrit, hemoglobin, anion gap, age, and Charlson comorbidity index are closely associated with ketoacidosis. In clinical practice, these five baseline characteristics should be given with the special attention to achieve early detection and treatment, thus reducing the incidence of the disease.

Does an episode of diabetic ketoacidosis affect thyroid function tests in pediatric patients?

OBJECTIVES: The aim of our study was to investigate the changes in thyroid hormone levels during and after acute metabolic disorder in patients with diabetic ketoacidosis (DKA). METHODS: Eighty five patients diagnosed with DKA were included in the study. Patients with control thyroid function test (TFT) values at admission (the first blood sample) and 1 month later were included in the study. Thyroid function tests obtained during diabetic ketoacidosis and at the first month follow-up were compared. Euthyroidism and euthyroid sick syndrome were defined and grouped according to current guidelines. The mild and moderate groups, according to DKA classification, were combined and compared with the severe group. RESULTS: A significant increase was observed between the first admission and the control TFT values 1 month later. However, there was no significant difference found in TFT between mild/moderate and severe groups taken at the time of DKA. Difference between two groups, euthyroid sick syndrome and euthyroid, was examined and the result that was different from the literature was the difference between TSH levels. We found that low FT4 levels were associated with higher HgbA1c, although the correlation was weak. CONCLUSIONS: Thyroid hormone levels may not reflect a thyroid disease during severe DKA attack. Therefore, it is unnecessary to check thyroid function tests.

Persistent chronic calcific pancreatitis with intraductal calculi associated with secondary diabetes mellitus type 3 and diabetic ketoacidosis - A case report.

Diabetes mellitus type 3 refers to diabetes secondary to an existing disease or condition of the exocrine pancreas and is an uncommon cause of diabetes occurring due to pancreatogenic pathology. It accounts for 15-20% of diabetic patients in Indian and Southeast Asian continents. This is case report of a rare case of type 3 diabetes mellitus (T3DM) presenting with diabetic ketoacidosis (DKA). The patient was admitted for DKA along with complaint of hyperglycemia, blood glucose of 405 mg/dl with HbA1c level of 13.7%. Computed tomography evidence revealed chronic calcific pancreatitis with intraductal calculi and dilated pancreatic duct.

Inaccurate diagnosis of diabetes type in youth: prevalence, characteristics, and implications.

Classifying diabetes at diagnosis is crucial for disease management but increasingly difficult due to overlaps in characteristics between the commonly encountered diabetes types. We evaluated the prevalence and characteristics of youth with diabetes type that was unknown at diagnosis or was revised over time. We studied 2073 youth with new-onset diabetes (median age [IQR] = 11.4 [6.2] years; 50% male; 75% White, 21% Black, 4% other race; overall, 37% Hispanic) and compared youth with unknown versus known diabetes type, per pediatric endocrinologist diagnosis. In a longitudinal subcohort of patients with data for ≥ 3 years post-diabetes diagnosis (n = 1019), we compared youth with steady versus reclassified diabetes type. In the entire cohort, after adjustment for confounders, diabetes type was unknown in 62 youth (3%), associated with older age, negative IA-2 autoantibody, lower C-peptide, and no diabetic ketoacidosis (all, p < 0.05). In the longitudinal subcohort, diabetes type was reclassified in 35 youth (3.4%); this was not statistically associated with any single characteristic. In sum, among racially/ethnically diverse youth with diabetes, 6.4% had inaccurate diabetes classification at diagnosis. Further research is warranted to improve accurate diagnosis of pediatric diabetes type.

Does Treatment with Sodium-Glucose Cotransporter-2 Inhibitors Affect Adherence to International Society Criteria for Diabetic Ketoacidosis in Adult Patients with Type 2 Diabetes? A Retrospective Cohort Analysis.

Design: Retrospective observational study. Setting. Inpatients at two teaching hospitals in Queensland, Australia. Primary Outcome Measure(s). The number of patients meeting the Joint British Diabetes Society (JBDS) and American Association of Clinical Endocrinology/American College of Endocrinology (AACE/ACE) diagnostic criteria for DKA. Patients were divided into two groups by treatment with SGLT2i at the time of diagnosis. Participants. Adult patients (>18 years old) with type 2 diabetes diagnosed with DKA from April 2015 to January 2022. Patients without type 2 diabetes were excluded. Results: One hundred and sixty-five patients were included in this study-comprising 94 patients in the SGLT2i cohort and 70 in the non-SGLT2i cohort. A significantly smaller proportion of patients in the SGLT2i vs. non-SGLT2i cohorts met both JBDS (56% vs. 72%, p = 0.035) and AACE/ACE (63% vs. 82%, p = 0.009) criteria for diagnosis of DKA. Conclusion: Patients with type 2 diabetes treated with SGLT2i may be more likely to be diagnosed with DKA despite not meeting the criteria. Despite recent adjustments to account the physiological effects of SGLT2i, significant variation in criteria between major society guidelines presents ongoing challenges to clinicians. The proportion of patients diagnosed using both JDBS and AACE/ACE were comparable, suggesting a reasonable degree of agreement.

Nanowire Array Breath Acetone Sensor for Diabetes Monitoring.

Diabetic ketoacidosis (DKA) is a life-threatening acute complication of diabetes characterized by the accumulation of ketone bodies in the blood. Breath acetone, a ketone, directly correlates with blood ketones. Therefore, monitoring breath acetone can significantly enhance the safety and efficacy of diabetes care. In this work, the design and fabrication of an InP/Pt/chitosan nanowire array-based chemiresistive acetone sensor is reported. By incorporation of chitosan as a surface-functional layer and a Pt Schottky contact for efficient charge transfer processes and photovoltaic effect, self-powered, highly selective acetone sensing is achieved. The sensor has exhibited an ultra-wide acetone detection range from sub-ppb to >100 000 ppm level at room temperature, covering those in the exhaled breath from healthy individuals (300-800 ppb) to people at high risk of DKA (>75 ppm). The nanowire sensor has also been successfully integrated into a handheld breath testing prototype, the Ketowhistle, which can successfully detect different ranges of acetone concentrations in simulated breath samples. The Ketowhistle demonstrates the immediate potential for non-invasive ketone monitoring for people living with diabetes, in particular for DKA prevention.

Prevalence and prognosis of fulminant type 1 diabetes mellitus in The Middle East: a comparative analysis in a 5-year nationwide cohort.

PURPOSE: To analyze the prevalence and progression of fulminant type 1 diabetes (FT1D) in Qatar. METHODS: This retrospective study analyzed consecutive index- diabetic ketoacidosis (DKA) admissions (2015-2020) among patients with new-onset T1D (NT1D) in Qatar. RESULTS: Of the 242 patients, 2.5% fulfilled the FT1D diagnostic criteria. FT1D patients were younger (median-age 4-years vs.15-years in classic-T1D). Gender distribution in FT1D was equal, whereas the classic-T1D group showed a female predominance at 57.6% (n = 136). FT1D patients had a mean C-peptide of 0.11 ± 0.09 ng/ml, compared to 0.53 ± 0.45 ng/ml in classic-T1D. FT1D patients had a median length of stay (LOS) of 1 day (1-2.2) and a DKA duration of 11.25 h (11-15). The median (length of stay) LOS and DKA duration in classic-T1D patients were 2.5 days (1-3.9) and 15.4 h (11-23), respectively. The FT1D subset primarily consisted of moderate (83.3%) and severe 916.7%) DKA, whereas classic T1D had 25.4% mild, 60.6% moderate, and 14% severe DKA cases. FT1D was associated with a higher median white cell count (22.3 × 103/uL) at admission compared to classic T1D (10.6 × 103/uL). ICU admission was needed for 66.6% of FT1D patients, compared to 38.1% of classic-T1D patients. None of the patients in the FT1D group had mortality, while two died in the classic-T1D group. CONCLUSION: This is the first study establishing the existence of FT1D in ME, which presented distinctively from classic-T1D, exhibiting earlier age onset and higher critical care requirements. However, the clinical outcomes in patients with FT1D seem similar to classic T1D.

Fulminant type 1 diabetes, an underrecognized and unique subtype of type 1 diabetes: A case series from Singapore.

Fulminant type 1 diabetes (FT1D) is a unique subtype of type 1 diabetes, characterized by acute absolute insulin deficiency, severe ketosis, and increased risk of hypoglycemia, glycemic variability and microvascular complications. Seven people with FT1D were identified from two tertiary centers in Singapore. Six were Chinese, the mean age was 35 years and all were lean (mean body mass index 20.3 kg/m2). All presented with diabetes ketosis or ketoacidosis and low C-peptide. All but one had low glutamic acid decarboxylase antibodies. Nearly half had a missed/delayed diagnosis of FT1D. Three had frequent hypoglycemia, which improved after transition to continuous subcutaneous insulin infusion therapy. Individuals with FT1D experience unique diagnostic and management challenges associated with rapid absolute insulin deficiency. Greater awareness about this clinical entity is required.

[Sodium glucose cotransporter-2 inhibitors (SGLT2i) and risk of ketoacidosis]. / SGLT2-hämmare och ketoacidos.

SGLT2i can induce euglycemic diabetic ketoacidosis (eDKA) in conditions with relative insulin deficiency, such as infections, surgery, or fasting state. In comparison with classical DKA, eDKA typically presents with lower blood glucose levels and more diffuse symptoms like tiredness, tachypnea, nausea and abdominal pain. The diagnosis is commonly delayed, and signs are often attributed to other factors. Early diagnosis and prevention are critical due to the risk of lethal outcome or prolonged hospital stay. Generous screening for ketonemia in risk situations allows identification of eDKA. To minimize the risk, we propose that SGLT2i should be discontinued 3-4 days before surgery (1-2 weeks prior to bariatric surgery) and during infections, acute disease, or poor oral intake. Postoperative slow infusion of low-dose insulin may prevent eDKA if SGLT2i could not be stopped in time or in prolonged fasting state. In this overview, the pathogenesis behind eDKA is discussed.

Blood Urea Nitrogen to Serum Albumin Ratio as A New Prognostic Indicator in Critically Ill Patients with Diabetic Ketoacidosis: A Retrospective Cohort Study.

OBJECTIVE: To investigate the predictive value of the blood urea nitrogen to serum albumin ratio for in-hospital and out-of-hospital mortality in critically ill patients with diabetic ketoacidosis. METHODS: Data were obtained from the Medical Information Mart for Intensive Care III (MIMIC III) database, and all eligible participants were categorized into two groups based on the BAR cutoff value. Multiple logistic regression analysis was conducted to determine the association between BAR and in-hospital mortality. The Kaplan-Meier (K-M) analysis was performed to evaluate the predictive performance of BAR. Propensity score matching (PSM) was applied to control confounding factors between the low and high BAR groups. RESULTS: A total of 589 critically ill patients with diabetic ketoacidosis were enrolled. Patients with diabetic ketoacidosis with a higher BAR level were associated with higher in- and out-hospital mortality (all p<0.001). A significant 4-year survival difference was observed between the low and high BAR groups (p<0.0001). After PSM analysis, two PSM groups (202 pairs, n=404) were generated, and similar results were observed in the K-M curve (p<0.0001). DISCUSSION: Elevated BAR levels were associated with an increased risk of in-hospital mortality in critically ill patients with diabetic ketoacidosis, and BAR could serve as an independent prognostic factor in in-hospital and out-of-hospital mortality for patients diagnosed with diabetic ketoacidosis.

Clinical nomogram prediction model to assess the risk of prolonged ICU length of stay in patients with diabetic ketoacidosis: a retrospective analysis based on the MIMIC-IV database.

BACKGROUND: The duration of hospitalization, especially in the intensive care unit (ICU), for patients with diabetic ketoacidosis (DKA) is influenced by patient prognosis and treatment costs. Reducing ICU length of stay (LOS) in patients with DKA is crucial for optimising healthcare resources utilization. This study aimed to establish a nomogram prediction model to identify the risk factors influencing prolonged LOS in ICU-managed patients with DKA, which will serve as a basis for clinical treatment, healthcare safety, and quality management research. METHODS: In this single-centre retrospective cohort study, we performed a retrospective analysis using relevant data extracted from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Clinical data from 669 patients with DKA requiring ICU treatment were included. Variables were selected using the Least Absolute Shrinkage and Selection Operator (LASSO) binary logistic regression model. Subsequently, the selected variables were subjected to a multifactorial logistic regression analysis to determine independent risk factors for prolonged ICU LOS in patients with DKA. A nomogram prediction model was constructed based on the identified predictors. The multivariate variables included in this nomogram prediction model were the Oxford acute severity of illness score (OASIS), Glasgow coma scale (GCS), acute kidney injury (AKI) stage, vasoactive agents, and myocardial infarction. RESULTS: The prediction model had a high predictive efficacy, with an area under the curve value of 0.870 (95% confidence interval [CI], 0.831-0.908) in the training cohort and 0.858 (95% CI, 0.799-0.916) in the validation cohort. A highly accurate predictive model was depicted in both cohorts using the Hosmer-Lemeshow (H-L) test and calibration plots. CONCLUSION: The nomogram prediction model proposed in this study has a high clinical application value for predicting prolonged ICU LOS in patients with DKA. This model can help clinicians identify patients with DKA at risk of prolonged ICU LOS, thereby enhancing prompt intervention and improving prognosis.

Revised one-bag IV fluid protocol for pediatric DKA: a feasible approach and retrospective comparative study.

BACKGROUND: This study compared the effectiveness of the traditional and revised one-bag protocols for pediatric diabetic ketoacidosis (DKA) management. METHODS: This single-center retrospective cohort study included children diagnosed with DKA upon admission between 2012 and 2019. Our institution reevaluated and streamlined the traditional one-bag protocol (revised one-bag protocol). The revised one-bag protocol rehydrated all pediatric DKA patients with dextrose (5 g/100 ml) containing 0.45% NaCl at a rate of 3500 ml/m2 per 24 h after the first 1 h bolus of normal saline, regardless of age or degree of dehydration. This study examined acidosis recovery times and the frequency of healthcare provider interventions to maintain stable blood glucose levels. RESULTS: The revised one-bag protocol demonstrated a significantly shorter time to acidosis recovery than the traditional protocol (12.67 and 18.20 h, respectively; p < 0.001). The revised protocol group required fewer interventions for blood glucose control, with an average of 0.25 dextrose concentration change orders per patient, compared to 1.42 in the traditional protocol group (p < 0.001). Insulin rate adjustments were fewer in the revised protocol group, averaging 0.52 changes per patient, vs. 2.32 changes in the traditional protocol group (p < 0.001). CONCLUSION: The revised one-bag protocol for pediatric DKA is both practical and effective. This modified DKA management achieved acidosis recovery more quickly and reduced blood glucose fluctuations compared with the traditional one-bag protocol. Future studies, including randomized controlled trials, should assess the safety and effectiveness of the revised protocol in a broad range of pediatric patients with DKA.

Managing Diabetes Mellitus in the Emergency Department.

Diabetes mellitus (DM) is a chronic medical condition that continues to increase in prevalence. Complications of DM, including diabetic ketoacidosis and hyperglycemic hyperosmolar state, often present in the emergency department requiring emergent management. Prompt assessment, diagnosis, evaluation of laboratory values, treatment, monitoring, and strict follow-up education are essential to the successful management of this complex disease. Common medications and management strategies are key elements to control DM. This article presents an overview of DM, including its prevalence, pathophysiology, presentations, and management.