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  • A Nomogram Prediction Model and Validation Study on the Risk of Complicated Diabetic Nephropathy in Type 2 Diabetes Patients

    Subjects: Medicine, Pharmacy >> Preventive Medicine and Hygienics submitted time 2023-11-15 Cooperative journals: 《中国全科医学》

    Abstract: Background  Diabetes nephropathy(DN)is a common complication of diabetes patients. The prediction and validation of its risk will help identify high-risk patients in advance and take intervention measures to avoid or delay the progress of nephropathy. Objective  To analyze the risk factors affecting the complication of DN in patients with type 2 diabetes mellitus(T2DM),construct a risk prediction model for the risk of DN in T2DM patients and validate it. Methods  A total of 5 810 patients with T2DM admitted to the First Affiliated Hospital of Xinjiang Medical University from January 2016 to June 2021 were selected as the study subjects and divided into the DN group(n=481)and non-DN group(n=5 329)according to the complication of DN. A 1 ∶ 1 case-control matching was performed on 481 of these DN patients by gender and age(±2 years),and the matched 962 T2DM patients were randomly divided into the training group(n=641)and validation group(n=321)based on a 2:1 ratio. Basic data of patients,such as clinical characteristics,laboratory test results and other related data,were collected. LASSO regression was applied to optimize the screening variables,and a nomogram prediction model was developed using multivariate Logistic regression analysis. The discriminability,calibration and clinical validity of the prediction model were evaluated by using the receiver operating characteristic(ROC)curve,calibration curve Hosmer-Lemeshow,and decision curve analysis(DCA),respectively. Results  There were significant differences in gender,age,BMI,course of diabetes,white blood cell count(WBC),total cholesterol(TC),triacylglycerol(TG),low-density lipoprotein cholesterol(LDL-C),serum creatinine(Scr),hypertension,systolic blood pressure(SBP),diastolic blood pressure(DBP),glycosylated hemoglobin(HbA1c),apolipoprotein B(ApoB),24-hour urinary micro total protein(Up),qualitative urinary protein(Upn) between the DN and non-DN group(P<0.05). Five predictor variables associated with the risk of DN in patients with T2DM were screened using LASSO regression analysis,and the results combined with multivariate Logistic regression analysis showed that duration of diabetes,TC,Scr,hypertension,and Upn were risk factors for the complication of DN in T2DM patients(P<0.05).The area under the ROC curve(AUC)for the risk of DN in the training group of the model was 0.866(95%CI=0.839-0.894),and the AUC for predicting the risk of DN in the validation group was 0.849(95%CI=0.804-0.889) based on the predictor variables. The calibration curve Hosmer-Lemeshow fit was good(P=0.748 for the training group;P=0.986 for the validation group). DCA showed that the use of nomogram prediction model was more beneficial in predicting DN when the threshold probability of patients was 0.15 to 0.95. Conclusion  The nomogram prediction model containing five predictor variables(diabetes duration,TC,Whbp,Scr,Upn)developed in this study can be used to predict the risk of DN in patients with T2DM.

  • 从“理性人”到“行为人”:公共政策研究的行为科学转向

    Subjects: Psychology >> Developmental Psychology submitted time 2023-03-28 Cooperative journals: 《心理科学进展》

    Abstract: With the criticism by behavioral economics on the rational man assumption and the accumulation of empirical evidences in the field of judgement and decision making, public policy researchers increasingly paying closer attention to the exploration of psychological and behavioral mechanism of human being in real life. These studies tend to use psychological measures, such as satisfaction and trust indicators that embody public interests and subjective feelings, to assess the performance of public policy. Besides, relevant psychological effects and technologies are employed to improve quality and efficiency of public administration and foster social fairness and justice. It is recommended that studies of public policy in China should learn the experience of two matured organizations, which are Behavioral Insights Team in UK and Social and Behavioral Sciences Team in US, clarify the connotations of this discipline, establish think tanks, and conduct researches based on China’s actual conditions.