RESUMO
Objective:To explore the construction of a Logistic prediction model and countermeasures for type 2 diabetic nephropathy based on clinical data.Methods:The patients with type 2 diabetic nephropathy admitted to Shijiazhuang Second Hospital from September 2019 to September 2021 (study group) were selected and the patients were selected according to a 1∶1 ratio using individual matching (control group), each group with 200 patients. Single and multiple factors analysis were used to analyze the factors influencing type 2 diabetic nephropathy, and Logistic regression equation models were developed to verify their predictive value.Results:Logistic regression equation model showed that the course of type 2 diabetes, glycosylated hemoglobin (HbA 1c), fasting plasma glucose (FPG), homocysteine (Hcy), urinary microalbumin, and serum creatinine (Scr) were high risk factors for type 2 diabetic nephropathy ( P<0.05). The results of Logistic regression model evaluation showed that the model was established with statistical significance, and the coefficients of the regression equations had statistically significant differences. The Hosmer-Lemeshow goodness-of-fit test showed that the model fitting effect was good. Logistic regression model was used to statistically analyzed the data set, and the receiver operating characteristic (ROC) curve of type 2 diabetic nephropathy was drawn, the area under the curve was 0.949(95% CI 0.922 - 0.968), the prediction sensitivity was 81.50%, the specificity was 95.50%, the calibration curve showed that the predicted results was in good agreement with the observed results. Conclusions:The independent predictors of type 2 diabetic nephropathy involve HbA 1c, FPG, Hcy, urinary microalbumin. The Logistic prediction model based on these predictors has reliable predictive value and can help guide clinical diagnosis and treatment.
RESUMO
Objective To build anautoverification system for hematology analysis system and validate the system based on commercialized labXpert software.Methods Preliminary validation rules was established base on 41 Items of International Consensus Review Rules and instructions for Mindray CAL8000 auto hematology analyzer,and input the rules into labX pert sample validation system.999 clinical samples were collected from Beijing Hospital Ministry of Health to test the preliminary rules and parameters including false positive rate,false negative rate and autoverification pass rate were calculated,based on which to adjust and customize the original protocol.Then 15 934 samples were tested,respectively,for autoverification by calculating the autoverification pass rate,proportion of manual verification and microscopic verification.Autoverification were compared as well as the turnover time (TAT,timefrom receipt of sample to report of result) before and after application of autoverification system.Results Preliminary verification results showed that false negative rates in both hospitals were less than 2％,and the false negative mainly caused by low promyelocytic cells value (blasts and promyelocytes less than 3 ％),abnormal erythrocyte morphology,and abnormal platelet morphology.No sample with excess blasts or percentage of blasts and promyelocytes higher 3％ with tested with false negative result,indicating relatively low clinical risks.Both hospitals reported with relatively high false positive rates,up to nearly 18％,using preliminary programs,which may affect the autoverification rate of the system.Based on the analyzing result of false positive results,the program was adjusted to significantly reduce the false positive rate while remaining the false negative rate low,therefore resulted with 4 remarkable increase of autoverification pass rate.Over 10,000 samples were tested with improved program,and the autoverification pass rates for hospital was 78.4 ％,respectively.Primary reason causing failure of autoverification included increased IMG％,flag for immature cells and WBC exceeding set limit.Application of system reduced the TAT by 5 min (P＜0.05).Conclusion Autoverificationsystem using Mindray CAL8000 auto hematology analyzer andlabXpert has been confirmed effective in reducing TAT and enhancing working efficiency while remaining low false negative rate.The autoverification pass rate tested 75％,which suggested that laboratory workers can spare more time on reexamination of abnormal samples for better blood routine report.