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Background: Glycemic control for patients with diabetes in the surgical department is often unsatisfactory. Compounding this issue is the fact that conventional glucose management models are often inefficient and difficult to monitor over time. Objective: To investigate the impact of inpatient glucose team-based management on glycemic control and hospital days in surgical patients with diabetes. Methods: A retrospective analysis was conducted on 4156 patients with diabetes in the surgical department who received inpatient management of diabetes at a tertiary medical center from June 2020 to May 2022. Based on whether they received inpatient glucose team-based management, the surgical patients with diabetes were divided into two groups: the inpatient glucose team-based management (GM group, consisting of 1698 participants) and the conventional blood glucose management group (control group, consisting of 2458 participants). We compared the two groups in terms of glycemic control, hospital days, and health-care costs. Multiple logistic regression analysis was performed to build the hospital days prediction model and nomogram. Finally, the performance of the model was evaluated. Results: The rate of glucose detection was higher in the GM group at 2 h postprandial (P < 0.01). The incidence of hypoglycemia and severe hyperglycemia, blood glucose attainment time, pre-operative preparation days, hospital days, and health-care costs were lower in the GM group than in the control group (P < 0.01). The linear regression model revealed that blood glucose attainment time, incidence of hypoglycemia (< 3.9mmol/L), preoperative preparation days, perioperative complications, and health-care costs were the factors influencing the hospital days (Total Point 83.4 points, mean hospital days 9.37 days). Receiver operating characteristic (ROC) curve analysis demonstrated that the nomogram had good accuracy for predicting hospital days (area under the ROC curve 0.83, 95% confidence interval [CI], 0.74 to 0.92). Conclusion: Inpatient glucose team-based management demonstrated significant improvements in glycemic control among surgical patients with diabetes, resulting in reduced hospital days and associated costs. The developed nomogram also exhibited promising potential in predicting hospital days, offering valuable clinical applications. [ABSTRACT FROM AUTHOR]

Aims/Introduction: To compare the application value of different machine learning (ML) algorithms for diabetes risk prediction. Materials and Methods: This is a 3‐year retrospective cohort study with a total of 3,687 participants being included in the data analysis. Modeling variable screening and predictive model building were carried out using logistic regression (LR) analysis and 10‐fold cross‐validation, respectively. In total, six different ML algorithms, including random forests, light gradient boosting machine, extreme gradient boosting, adaptive boosting (AdaBoost), multi‐layer perceptrons and gaussian naive bayes were used for model construction. Model performance was mainly evaluated by the area under the receiver operating characteristic curve. The best performing ML model was selected for comparison with the traditional LR model and visualized using Shapley additive explanations. Results: A total of eight risk factors most associated with the development of diabetes were identified by univariate and multivariate LR analysis, and they were visualized in the form of a nomogram. Among the six different ML models, the random forests model had the best predictive performance. After 10‐fold cross‐validation, its optimal model has an area under the receiver operating characteristic value of 0.855 (95% confidence interval [CI] 0.823–0.886) in the training set and 0.835 (95% CI 0.779–0.892) in the test set. In the traditional LR model, its area under the receiver operating characteristic value is 0.840 (95% CI 0.814–0.866) in the training set and 0.834 (95% CI 0.785–0.884) in the test set. Conclusions: In the real‐world epidemiological research, the combination of traditional variable screening and ML algorithm to construct a diabetes risk prediction model has satisfactory clinical application value. [ABSTRACT FROM AUTHOR]

Aim: To explore whether serum pro‐gastric releasing peptide (proGRP) is elevated in nephropathy patients and evaluate the diagnostic value of proGRP in chronic kidney disease (CKD) patients. Methods: A total of 498 nephropathy patients and 170 healthy were selected in Zhongshan Hospital, Medical College of Xiamen University, from February 2016 to September 2017. The clinical data of the different groups including serum proGRP, CKD grading, and other serum and urine renal function biomarkers were analyzed by group comparison, correlation analysis and receiver operating characteristic curve. Results: Serum proGRP levels were significantly higher in the acute kidney injury and CKD groups compared with the other groups of kidney disease patients (P < 0.01), and increased with CKD grading (P < 0.01). Serum proGRP was substantially correlated with serum creatinine (r = 0.637, P < 0.01) and cystain C (0.837, P < 0.01). Serum proGRP had moderate correlations with urine β2‐macroglobulin (β2‐m; r = 0.587, P < 0.01) and α1‐macroglobulin (α1‐m; r = 0.557, P < 0.01). There were fair associations of serum proGRP with albumin (r = 0.10, P = 0.067), 24 h proteinuria (24 h‐TPU; r = 0.092, P = 0.099), urinary albumin/urocreatinine (uAlb/Cr; r = 0.29, P < 0.01) and urinary N‐acetyl‐β‐D‐glucosidase (r = −0.142, P < 0.01). The sensitivity of proGRP was superior to that of simplified modification of diet in renal disease (MDRD) formula in diagnosing CKD I + II (81.25% vs 66.67%), CKD III (86.42% vs 74.36%) and CKD IV (71.19% vs 69.64%), while its specificity was inferior to that of simplified MDRD formula in diagnosing CKD I + II (37.65% vs 66.97%), CKD III (56.25% vs 86.67%) and CKD IV (75.31% vs 88.46%). Conclusion: Serum proGRP is elevated in acute renal injury and CKD patients and increases with CKD grading. Serum proGRP is mainly affected by glomerular filtration rate and could be used for CKD staging, although the overall diagnostic sufficiency is inferior to simplified MDRD formula. SUMMARY AT A GLANCE: Serum pro‐gastric releasing peptide increases with declining glomerular filtration rate both in acute and chronic kidney disease in a Chinese population. However, it is not superior to the creatinine‐based modification of diet in renal disease formula for predicting renal function. [ABSTRACT FROM AUTHOR]

Aimed to investigate the predictive value of procalcitonin (PCT) in early detection of infections in elderly patients with type 2 diabetes, and to discover the optimum cut-off points of PCT. A retrospective study was conducted with type 2 diabetic patients (≥65 years) with lung infection (LI), urinary tract infection (UTI) or skin and soft tissue infection (SSTI). The receiver operating characteristic (ROC) curves of the 3 markers (PCT, WBC count, and CRP) were constructed and compared to assess their accuracies in diagnosing. Among the three different groups with LI, UTI or SSTI, the area under the ROC curve (AUC) of PCT was 0.98 (95% confidence interval (CI): 0.96–0.99, p < 0.05) for the LI group, 0.98 (95% CI: 0.96–0.99, p < 0.05) for the UTI group, and 0.97 (95% CI: 0.94–1.00, p < 0.05) for the SSTI group. The optimum cut-off point of PCT level was 0.73 ng/mL (Sn 89.7%, Sp 97.7%) for the LI group, 1.48 ng/mL (Sn 88.9%, Sp 100%) for the UTI group, and 0.73 ng/mL (Sn 85.7%, Sp 97.7%) for the SSTI group. PCT demonstrated the strongest correlation with each of the infection types, indicating significant diagnostic value. Optimum cut-off points of PCT levels in elderly diabetes were higher. [ABSTRACT FROM AUTHOR]