Cardiac autonomic neuropathy predicts diabetic retinopathy progression in Asian population with type 2 diabetes mellitus.

PURPOSE: To investigate the role of cardiac autonomic neuropathy (CAN), vascular condition, and sensory function in diabetic retinopathy (DR) progression. METHODS: This 3-year cohort study conducted in a community hospital included 4850 patients over 20 with type 2 diabetes mellitus. Participants were assessed in 2017 at baseline and were followed up in 2020. Patients were divided into two groups based on whether they had DR progression or not and were compared using the chi-square test or two-sample t-test. Beta coefficient and odds ratio (OR) with 95% confidence intervals were calculated using binary logistic regression. The receiver operating characteristic (ROC) curve of various independent variables for DR progression was provided with C-statistics. RESULTS: Abnormal hemoglobin A1c (HbA1c) level/variation, estimated glomerular filtration rate, urine albumin-to-creatinine ratio, R-R interval variation, standard deviation of the average NN intervals, autonomic nervous system function, power of high-frequency (HF) bands, balance, cardio-ankle vascular index (CAVI), and warm stimulation (WS) were associated with DR progression. Average HbA1c, HF, and proliferative diabetic retinopathy were independent factors for patients developing DR progression. The top three areas under the curve of ROCs were HF + baseline DR grading, WS + baseline DR grading, and CAVI + baseline DR grading. These variable combinations were the most reliable predictors of DR progression. CONCLUSION: CAN, abnormal vascular condition, and sensory function are associated with DR progression. The combination of HF, WS, and CAVI with baseline DR grading provides the most accurate predictive model for DR progression. Early detection of these factors is important to prevent DR progression.

Metabolic score tool for personalized acute pancreatitis prognosis: A multicenter analysis.

Increasing evidence suggests that body composition is associated with the development of acute pancreatitis (AP). This study aimed to investigate the applicability of body composition in predicting AP severity. Data of 213 patients with AP from Affiliated Hospital of Putian University (AHOPTU) were included in this study, whilst data of 173 patients with AP from Fujian Medical University Union Hospital (FMUUH) were used for external validation. Patients were classified into the non-severe and severe groups according to AP severity. After seven days of treatment, in patients from AHOPTU, the difference in skeletal muscle index before and after treatment (ΔSMI) was significantly higher (P = 0.002), while the skeletal muscle radiodensity before treatment (PreSMR) was significantly lower (P = 0.042) in the non-severe group than in the severe group. The multivariate logistic regression model also revealed that the ΔSMI and PreSMR were independent risk factors for AP severity. The optimal cut-off values of ΔSMI and PreSMR were 1.0 and 43.7, respectively. The following metabolic score (SMS) was established to predict AP severity: 0: ΔSMI < 1.0 and PreSMR < 43.7; 1: ΔSMI ≥ 1.0 and PreSMR < 43.7 or ΔSMI < 1.0 and PreSMR ≥ 43.7; 3: ΔSMI ≥ 1.0 and PreSMR ≥ 43.7. In patients from AHOPTU and FMUUH, the areas under the curves (AUC) for this model were 0.764 and 0.741, respectively. ΔSMI and PreSMR can accurately predict AP severity. It is recommended to routinely evaluate the statuses of patients with AP using the predictive model presented in this study for individualized treatment.

Development and validation of a preoperative radiomics-based nomogram to identify patients who can benefit from splenic hilar lymphadenectomy: a pooled analysis of three prospective trials.

BACKGROUND: The authors aimed to use preoperative computed tomography images to develop a radiomic nomogram to select patients who would benefit from spleen-preserving splenic hilar (No.10) lymphadenectomy (SPSHL). METHODS: A pooled analysis of three distinct prospective studies was performed. The splenic hilar lymph node (SHLN) ratio (sLNR) was established as the quotient of the number of metastatic SHLN to the total number of SHLN. Radiomic features reflecting the phenotypes of the primary tumor (RS1) and SHLN region (RS2) were extracted and used as predictive factors for sLNR. RESULTS: This study included 733 patients: 301 in the D2 group and 432 in the D2+No.10 group. The optimal sLNR cutoff value was set at 0.4, and the D2+No.10 group was divided into three groups: sLNR=0, sLNR ≤0.4, and sLNR >0.4. Patients in the D2+No. 10 group were randomly divided into the training ( n =302) and validation ( n =130) cohorts. The AUCs value of the nomogram, including RS1 and RS2, were 0.952 in the training cohort and 0.888 in the validation cohort. The entire cohort was divided into three groups based on the nomogram scores: low, moderate, and high SHLN metastasis burden groups (LMB, MMB, and HMB, respectively). A similar 5-year OS rate was found between the D2 and D2+No. 10 groups in the LMB and HMB groups. In the MMB group, the 5-year OS of the D2+No. 10 group (73.4%) was significantly higher than that of the D2 group (37.6%) ( P <0.001). CONCLUSIONS: The nomogram showed good predictive ability for distinguishing patients with various SHLN metastasis burdens. It can accurately identify patients who would benefit from SPSHL.

A scoring model for predicting early recurrence of gastric cancer with normal preoperative tumor markers: A multicenter study.

INTRODUCTION: Prognostic factors for postoperative early recurrence (ER) of gastric cancer (GC) in patients with normal or abnormal preoperative tumor markers (pre-TMs) remain unclear. MATERIALS AND METHODS: 2875 consecutive patients with GC who underwent radical gastrectomy (RG) between January 2010 and December 2016 were enrolled and randomly divided into training and internal validation groups. ER was defined as recurrence within two years of gastrectomy. Normal pre-TMs were defined as CEA≤5 ng/mL and CA199 ≤ 37 U/mL. Least absolute shrinkage selection operator (LASSO) Cox regression analysis was used to screen ER predictors. The scoring model was validated using 546 patients from another hospital. RESULTS: A total of 3421 patients were included. Multivariate Cox analysis showed that pre-TMs was an independent prognostic factor for ER. Survival after ER was equally poor in the normal and abnormal pre-TMs groups (P = 0.160). Based on LASSO Cox regression, the ER of patients with abnormal pre-TMs was only associated with the pT and pN stages; however, in patients with normal pre-TMs, it was also associated with tumor size, perineural invasion, and prognostic nutritional index. Scoring model constructed for patients with normal pre-TMs had better predictive performance than TNM staging (concordance-index:0.826 vs. 0.807, P < 0.001) and good reproducibility in both validation sets. Moreover, through risk stratification, the scoring model could not only identify the risk of ER but also distinguish ER patterns and adjuvant chemotherapy benefit subgroups. CONCLUSION: pre-TMs is an independent prognostic factor for ER in GC after RG. The established scoring model demonstrates excellent predictive performance and clinical utility.