Objective:To compared theclinic effectiveness ofBikini incision with that of traditional incisionbased on a short-term following and provide a suggesting reference for surgeons.Methods:The data of patients who accepted total hip arthroplasty (THA) through direct anterior approach (DAA) from January to November in 2019 were collected, male 13 and female 73, age 67.5±11.8 years (42-90 years). Forty-three patients were recruited to analyzeas one retrospective study in each group. Bikini incision: male 5 and female 38; age 69.7±10.8 years (51-90 years), following up 5.7 months (3.0-8.5 months). Traditional incision: male 8 and female 35; age 66.1±10.6 years (42-90 years), following up 6.5 months (3.0-10.0 months). The length area of incision, duration of operation, intraoperative blood loss, the rate of infection, lateral femoral cutaneous nerve (LFCN) deficits, and the Harris score were analyzed between those two groups. The scars were evaluated according toPatients Scar Assessment Scale and Vancouver Scar Scale.Results:There were no significant difference in age, sex, BMI and diseases distribution between Bikini and traditional incision group. The length of incision, duration of operation, analgesic drugs dosage and hip Harris score wereanalysed no significant difference between those two groups. No patients in those twogroupssuffered from infection of incision. As to LFCN deficits, Bikini group had lower than traditional group (11.6% vs 32.6%, P=0.019). The scar area of Bikini group 1.0±0.4 cm 2 wasmeasured smaller rate than that of traditional group 16.5±28.7 cm 2. As to the Patients Scar Assessment Scale and Vancouver Scar Scale, Bikini group was 8.2±3.2 and 2.4±1.8, which was better than traditional group which was 17.0±5.8 and 4.2±3.0. Conclusion:Compared to traditional incision, Bikini incision was evaluated having significant lower rate of LFCN deficits and smaller scar,good cosmetic appearance and clinical application value.

Objective:To construct a diabetes foot prediction model for adult patients with type 2 diabetes based on retrospective cohort study using data from a regional health data platform.Methods:Using Yinzhou Health Information Platform of Ningbo, adult patients with newly diagnosed type 2 diabetes from January 1, 2015 to December 31, 2022 were included in this study and divided randomly the train and test sets according to the ratio of 7∶3. LASSO regression model and bidirectional stepwise regression model were used to identify risk factors, and model comparisons were conducted with net reclassification index, integrated discrimination improvement and concordance index. Univariate and multivariate Cox proportional hazard regression models were constructed, and a nomogram plot was drawn. Area under the curve (AUC) was calculated as a discriminant evaluation indicator for model validation test its calibration ability, and calibration curves were drawn to test its calibration ability.Results:No significant difference existed between LASSO regression model and bidirectional stepwise regression model, but the better bidirectional stepwise regression model was selected as the final model. The risk factors included age of onset, gender, hemoglobin A1c, estimated glomerular filtration rate, taking angiotensin receptor blocker and smoking history. AUC values (95% CI) of risk outcome prediction at year 5 and 7 were 0.700 (0.650-0.749) and 0.715(0.668-0.762) for the train set and 0.738 (0.667-0.801) and 0.723 (0.663-0.783) for the test set, respectively. The calibration curves were close to the ideal curve, and the model discrimination and calibration powers were both good. Conclusions:This study established a convenient prediction model for diabetic foot and classified the risk levels. The model has strong interpretability, good discrimination power, and satisfactory calibration and can be used to predict the incidence of diabetes foot in adult patients with type 2 diabetes to provide a basis for self-assessment and clinical prediction of diabetic foot disease risk.

Objective:To develop a prediction model for the risk of diabetic retinopathy (DR) in patients with newly diagnosed type 2 diabetes mellitus (T2DM).Methods:Patients with new diagnosis of T2DM recorded in Yinzhou Regional Health Information Platform between January 1, 2015 and December 31, 2022 were included in the study. The predictor variables were selected by using Lasso-Cox proportional hazards regression model. Cox proportional hazards regression models were used to establish the prediction model for the risk of DR. Bootstrap method (500 resamples) was used for internal validation, and the performance of the model was assessed by C-index, the receiver operating characteristic curve and area under the curve (AUC), and calibration curve.Results:The predictor variables included in the final model were age of T2DM onset, education level, fasting plasma glucose, glycated hemoglobin A1c, urinary albumin, estimated glomerular filtration rate, and history of lipid-lowering agent and angiotensin converting enzyme inhibitor uses. The C-index of the final model was 0.622, and the mean corrected C-index was 0.623 (95% CI: 0.607-0.634). The AUC values for predicting the risk of DR after 3, 5, and 7 years were 0.631, 0.620, and 0.624, respectively, with a high degree of overlap of the calibration curves with the ideal curves. Conclusion:In this study, a simple and practical risk prediction model for DR risk prediction was developed, which could be used as a reference for individualized DR screening and intervention in newly diagnosed T2DM patients.

Objective:To construct a risk prediction model for diabetes kidney disease (DKD).Methods:Patients newly diagnosed with type 2 diabetes mellitus (T2DM) between January 1, 2015, and December 31, 2022, were selected as study subjects from the Yinzhou Regional Health Information Platform in Ningbo City. The Lasso method was used to screen the risk factors, and the DKD risk prediction model was established using Cox proportional hazard regression models. Bootstrap 500 resampling was applied for internal validation.Results:The study included 49 706 subjects, with an median ( Q1, Q3) age of 60.00 (50.00, 68.00) years old, and 55% were male. A total of 4 405 subjects eventually developed DKD. Age at first diagnosis of T2DM, BMI, education level, fasting plasma glucose, glycated hemoglobin A1c, urinary albumin, past medical history (hyperuricemia, rheumatic diseases), triglycerides, and estimated glomerular filtration rate were included in the final model. The final model's C-index was 0.653, with an average of 0.654 after Bootstrap correction. The final model's area under the receiver operating characteristic curve for predicting 4-year, 5-year, and 6-year was 0.657, 0.659, and 0.664, respectively. The calibration curve was closely aligned with the ideal curve. Conclusions:This study constructed a DKD risk prediction model for newly diagnosed T2DM patients based on real-world data that is simple, easy to use, and highly practical. It provides a reliable basis for screening high-risk groups for DKD.