Predictive Value of Glycosylated Hemoglobin for Post-operative Acute Kidney Injury in Non-cardiac Surgery Patients

Clinical Profile of Acute Kidney Injury in Type 2 Diabetes Mellitus Adult Patients Presenting With Diabetic Ketoacidosis: A Cross-Sectional Study

OBJECTIVE

 The purpose of this cross-sectional study was to identify the characteristics of acute kidney injury (AKI) in adult patients with type 2 diabetes mellitus (T2DM) who were admitted with diabetic ketoacidosis (DKA).

METHODOLOGY

 One hundred patients were selected based on the inclusion and exclusion criteria. Kidney function was assessed using kidney estimated glomerular filtration rate (KeGFR) calculations based on the Kidney Disease: Improving Global Outcomes (KDIGO) criteria. Data collection included clinical, biological, and demographic information. Univariate and multivariate logistic regression analyses were performed, with a p-value of less than 0.05 considered statistically significant. The study also examined risk factors, intensive care unit (ICU) treatments, and clinical outcomes.

RESULTS

AKI was significantly associated with ICU length of stay (p = 0.002), and all patients with prolonged ICU admission developed this condition. A higher incidence of AKI was observed in patients requiring mechanical ventilation, renal replacement therapy, or inotropic support. Among the 100 patients included in the study, 94 achieved clinical recovery, including 76 who had AKI, while six patients did not survive.

CONCLUSION

AKI is a common complication of DKA in adults with T2DM, particularly in hot climate regions. Most cases were detected within the first 24 hours, classified as stage 1 severity, and were pre-renal. Early detection plays a crucial role in preventing complications and improving patient recovery. KeGFR calculations proved an effective tool for monitoring kidney function changes in these patients.

Association of baseline and trajectory of triglyceride-glucose index with the incidence of cardiovascular autonomic neuropathy in type 2 diabetes mellitus

BACKGROUND

Cardiovascular autonomic neuropathy (CAN), characterized by disrupted autonomic regulation of the cardiovascular system, is a frequent complication associated with diabetes. The triglyceride-glucose (TyG) index represents a precise insulin resistance indicator. However, the influence of baseline and prolonged TyG index patterns on CAN risk in type 2 diabetes remains unclear.

METHODS

Based on the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial, multivariate logistic regression models and restricted cubic splines (RCS) were deployed for elucidating the relation between baseline TyG index and the incidence of CAN. The area under the curve (AUC) of receiver operating characteristic (ROC) curve was used to assess the diagnostic value of the TyG index in predicting the risk of CAN. The relationship between TyG trajectory and the occurrence of CAN in individuals with diabetes was examined using Kaplan-Meier curve and a multivariable Cox proportional hazards regression model. Subgroup analysis was used to assess the robustness of the results. Additionally, we explored the impact of intensive glycemia treatment on the relationship between trajectory of TyG index and CAN risk.

RESULTS

In this study, these in the top quartile of the TyG index had a greater likelihood of developing CAN (TyG index Q4 vs. Q1 in Model II, OR = 1.29, 95% CI 1.03-1.62, P = 0.027). RCS indicated a rising trend in the TyG index in relation to the incidence of CAN. The AUC of the TyG index for predicting the occurrence of CAN was 0.636 (95% CI 0.620-0.651; P < 0.001), with the cut-off value of 0.208. During a 7-year follow-up period, three unique TyG trajectories were recognized: class 1 (n = 431, 23.26%), class 2 (n = 798, 27.57%), and class 3 (n = 293, 31.71%). Notable discrepancies in CAN risk across various trajectories were identified in Kaplan-Meier curve (P < 0.001). Cox regression analysis indicated that individuals in class 3 experienced a greater incidence of CAN in comparison to those in class 1 after adjusting for all covariates. Subgroup analysis found no significant effect modification in this relationship. Additionally, in the intensive glycemia group, class 2 had a reduced risk of CAN, while class 3 had an increased risk when compared to standard glycemia group.

CONCLUSION

Increased baseline levels and long-term trajectory of TyG index are associated with an increased incidence of CAN. Intensive glycemic therapy might influence the association between the trajectory of TyG index and the chance of developing CAN.

Impact of preoperative haemoglobin A1c levels on postoperative outcomes in adults undergoing major noncardiac surgery: A systematic review

AIMS

Diabetes is known to increase morbidity and mortality after major surgery. However, literature is conflicting on whether elevated preoperative haemoglobin A1c (HbA1c) levels are associated with worse outcomes following major noncardiac surgery. We aimed to investigate the effect of incremental preoperative HbA1c levels on postoperative outcomes in adults who had undergone major noncardiac surgery.

METHODS

We systematically searched PubMed, EMBASE and the Cochrane Library databases for eligible studies published between January 2012 and July 2023. Randomised controlled trials and observational studies (cohort and case-control studies) which measured HbA1c within 6 months before surgery and compared outcomes between at least three incremental subgroups or analysed HbA1c as a continuous variable were included. The systematic review protocol was registered with PROSPERO (CRD42023391946).

RESULTS

Twenty observational studies investigating outcomes across multiple surgical types were included. Higher preoperative HbA1c levels were associated with increased odds of overall postoperative complications, postoperative acute kidney injury, anastomotic leak, surgical site infections and increased length of stay. Each 1% increase in preoperative HbA1c was associated with increased odds of these complications. No association with reoperations and 30-day mortality was identified. The literature was highly variable with respect to composite major complications, perioperative cardiovascular events, hospital readmissions, postoperative pneumonia and systemic thromboembolism.

CONCLUSIONS

Current evidence suggested that higher preoperative HbA1c levels were associated with increased odds of postoperative complications and extended length of stay in adults undergoing major noncardiac surgery. Further high-quality studies would be needed to quantify the risks posed and determine whether early intervention improves outcomes.

Prediction of glycosylated hemoglobin level in patients with cardiovascular diseases and type 2 diabetes mellitus with respect to anti-diabetic medication

Blood glycosylated hemoglobin level can be affected by various factors in patients with type 2 diabetes and cardiovascular diseases. Frequent measurements are expensive, and a suitable estimation method could improve treatment outcomes.

Patients and methods

93 patients were recruited in this research. We analyzed a number of parameters such as age, glucose level, blood pressure, Body Mass Index, cholesterol level, echocardiography et al. Patients were prescribed metformin. One group (n=60) additionally was taking sitagliptin. We applied eight machine learning methods (k nearest neighbors, Random Forest, Support Vector Machine, Extra Trees, XGBoost, Linear Regression including Lasso, and ElasticNet) to predict exact values of glycosylated hemoglobin in two years.

Results

We applied a feature selection approach using step-by-step removal of them, Linear Regression on remaining features, and Pearson's correlation coefficient on the validation set. As a result, we got four different subsets for each group. We compared all eight Machine Learning methods using different hyperparameters on validation sets and chose the best models. We tested the best models on the external testing set and got R2 = 0.88, C Index = 0.857, Accuracy = 0.846, and MAE (Mean Absolute Error) = 0.65 for the first group, R2 = 0.86, C Index = 0.80, Accuracy = 0.75, and MAE = 0.41 for the second group.

Conclusion

The resulting algorithms could be used to assist clinical decision-making on prescribing anti-diabetic medications in pursuit of achieving glycemic control.