ABSTRACT
AIMS: To evaluate the correlation between C-peptide index (CPI) at 2 h post-meal and endogenous insulin secretory capacity and to develop clinical models to predict the possibility of withdrawal from insulin therapy in patients with type 2 diabetes. METHOD: This was a single-centre retrospective study of patients with type 2 diabetes admitted to our hospital. Patients were divided into a withdrawal group (n = 72) and a non-withdrawal group (n = 75) based on whether they were able to withdraw from insulin therapy at discharge, and the correlation between CPI at 2 h after meal and diabetes-related parameters was evaluated. In addition, we created two clinical models to predict the possibility of withdrawal from insulin therapy using machine learning. RESULTS: The glycated haemoglobin values of the study participants were 87.8 ± 22.6 mmol/mo. The CPI at 2 h post-meal was 1.93 ± 1.28 in the non-withdrawal group and 2.97 ± 2.07 in the withdrawal group (p < 0.001). CPI at 2 h post-meal was an independent predictor of withdrawal from insulin therapy. In addition, CPI at 2 h post-meal was a better predictor than fasting CPI. Six factors associated with insulin therapy withdrawal (age, duration of diabetes, creatinine, alanine aminotransferase, insulin therapy until hospitalization, and CPI at 2 h post-meal) were used to generate two clinical models by machine learning. The accuracy of the generated clinical models ranged from 78.3% to 82.6%. CONCLUSION: The CPI at 2 h post-meal is a clinically useful measure of endogenous insulin secretory capacity under non-fasting conditions.
Subject(s)
C-Peptide , Diabetes Mellitus, Type 2 , Hypoglycemic Agents , Insulin Secretion , Insulin , Postprandial Period , Humans , Diabetes Mellitus, Type 2/drug therapy , Diabetes Mellitus, Type 2/blood , Male , Female , Middle Aged , Retrospective Studies , C-Peptide/blood , Insulin/therapeutic use , Insulin/administration & dosage , Aged , Hypoglycemic Agents/therapeutic use , Glycated Hemoglobin/analysis , Glycated Hemoglobin/metabolism , Withholding Treatment/statistics & numerical data , Machine Learning , MealsABSTRACT
Acute necrotizing esophagitis (ANE) is a rare and potentially life-threatening complication of diabetic ketoacidosis (DKA). While its association with DKA is established, specific clinical characteristics that predict ANE in DKA patients remain less understood. This study aimed to identify these characteristics by analyzing data from 30 DKA patients admitted from January 2018 to September 2022. Seven patients in this study presented with ANE, forming the ANE group. The remaining 23 constituted the non-ANE group. We compared the clinical parameters and computed tomography (CT) between the groups. The mean age of participants was 57.7 ± 20.4 years, and their mean HbA1c was 11.1 ± 3.3%. Notably, ethanol intake was significantly higher in the ANE group (44.4 ± 25.4 g/day) compared to the non-ANE group (6.8 ± 14.0 g/day; p = 0.013). Additionally, sodium-glucose transport protein 2 inhibitor use was significantly more prevalent in the ANE group (p = 0.013). Gastrointestinal symptoms were also significantly more pronounced in the ANE group, with vomiting occurring in 85.7% of patients compared to only 13.0% in the non-ANE group. Admission CT scans revealed further distinguishing features, with the ANE group showing significantly higher rates of esophageal wall thickening, intra-esophageal effusion, and calcification of the celiac artery origin (p < 0.0001, 0.0038, 0.0038, respectively). In conclusion, our study suggests that heavy alcohol consumption and strong gastrointestinal symptoms in DKA patients warrant a heightened suspicion of ANE. Early consideration of CT or upper gastrointestinal endoscopy is recommended in such cases.
Subject(s)
Diabetic Ketoacidosis , Esophagitis , Humans , Diabetic Ketoacidosis/complications , Middle Aged , Female , Male , Esophagitis/complications , Esophagitis/pathology , Adult , Aged , Tomography, X-Ray Computed , Necrosis , Retrospective Studies , Acute DiseaseABSTRACT
We report a case of testicular torsion in an 8-year-old who was referred to our hospital for right groin pain. He was diagnosed with right retractile testis during a 12-month check-up. However, instead of performing orchiopexy, he was placed under observation until the age of 5, after which he did not seek medical attention. Physical examination revealed swelling and tenderness in the right inguinal region and no palpable testis in the right scrotum. Ultrasound and computed tomography revealed right testicular torsion, and emergency surgery was performed. Intraoperative findings revealed a dark and ischemic testis that was twisted at 180°in the right inguinal region. There was no improvement in blood flow even after the testicular torsion was released; therefore, right orchidectomy with left orchiopexy was performed. Although the incidence of testicular torsion is higher in patients with an undescended testis than in those with a normally positioned scrotal position testis, reports of testicular torsion associated with a retractile testis are rare.
Subject(s)
Cryptorchidism , Spermatic Cord Torsion , Testicular Diseases , Male , Humans , Child , Spermatic Cord Torsion/surgery , Testis , Orchiectomy , Testicular Diseases/surgery , Cryptorchidism/complications , Cryptorchidism/diagnosis , Cryptorchidism/surgeryABSTRACT
BACKGROUND: The purpose of this study was to develop a model that can predict the postoperative visual acuity in eyes that had undergone vitrectomy for an epiretinal membrane (ERM). The Light Gradient Boosting Machine (LightGBM) was used to evaluate the accuracy of the prediction and the contribution of the explanatory variables. Two models were designed to predict the postoperative visual acuity in 67 ERM patients. Model 1 used the age, sex, affected eye, axial length, preoperative visual acuity, Govetto's classification stage, and OCT-derived vector information as features to predict the visual acuity at 1, 3, and 6 months postoperatively. Model 2 incorporated the early postoperative visual acuity as an additional variable to predict the visual acuity at 3, and 6 months postoperatively. LightGBM with 100 iterations of 5-fold cross-validation was used to tune the hyperparameters and train the model. This involved addressing multicollinearity and selecting the explanatory variables. The generalized performance of these models was evaluated using the root mean squared error (RMSE) in a 5-fold cross-validation, and the contributions of the explanatory variables were visualized using the average Shapley Additive exPlanations (SHAP) values. RESULTS: The RMSEs for the predicted visual acuity of Model 1 were 0.14 ± 0.02 logMAR units at 1 month, 0.12 ± 0.03 logMAR units at 3 months, and 0.13 ± 0.04 logMAR units at 6 months. High SHAP values were observed for the preoperative visual acuity and the ectopic inner foveal layer (EIFL) area with significant and positive correlations across all models. Model 2 that incorporated the postoperative visual acuity was used to predict the visual acuity at 3 and 6 months, and it had superior accuracy with RMSEs of 0.10 ± 0.02 logMAR units at 3 months and 0.10 ± 0.04 logMAR units at 6 months. High SHAP values were observed for the postoperative visual acuity in Model 2. CONCLUSION: Predicting the postoperative visual acuity in ERM patients is possible using the preoperative clinical data and OCT images with LightGBM. The contribution of the explanatory variables can be visualized using the SHAP values, and the accuracy of the prediction models improved when the postoperative visual acuity is included as an explanatory variable. Our data-driven machine learning models reveal that preoperative visual acuity and the size of the EIFL significantly influence postoperative visual acuity. Early intervention may be crucial for achieving favorable visual outcomes in eyes with an ERM.