Objective: To establish a set of rules for autoverification of blood analysis, in order to provide a way to validate autoverification rules for different analytical systems, which can ensure the accuracy of test results as well as shorten turnaround time (TAT) of test reports. Methods: A total of 34 629 EDTA-K2 anticoagulated blood samples were collected from multicenter cooperative units including the First Hospital of Jinlin University during January 2017 to November 2017. These samples included: 3 478 cases in Autoverification Establishment Group, including 288 cases for Delta check rules; 5 362 cases in Autoverification Validation Group, including 2 494 cases for Delta check; 25 789 cases in Clinical Application Trial Group. All these samples were analyzed for blood routine tests using Sysmex XN series automatic blood analyzers.Blood smears, staining and microscopic examination were done for each sample; then the clinical information, instrument parameters, test results and microscopic results were summarized; screening and determination of autoverification conditions including parameters and cutoff values were done using statistical analysis. The autoverification rules were input into Sysmex Laboman software and undergone stage Ⅰ validation using simulated data, and stage Ⅱ validation for post-analytical samples successively. True negative, false negative, true positive, false positive, autoverification pass rate and passing accuracy were calculated. Autoverification rules were applied to autoverification blood routine results and missed detection rates were validated, and also data of autoverification pass rate and TAT were obtained. Results: (1)The selected autoverification conditions and cutoff values included 43 rules involving WBC, RBC, PLT, Delta check and abnormal characteristics. (2)Validation of 3 190 cases in Autoverification Establishment Group showed the false negative rate was 1.94%(62/3 190)(P<0.001), autoverification pass rate was 76.74%, passing accuracy was 97.47%; Validation of 2 868 cases in Autoverification Validation Group, the false negative rate was 3.38%(97/2 868)(P=0.002), autoverification pass rate was 42.26%, passing accuracy was 92.00%; Validation of Delta check on 288 cases in Autoverification Establishment Group and 2 494 cases in Autoverification Validation Group showed the false negative rates were respectively 1.39% and 2.61%(P<0.001). (3)Three hospitals adopted these rules of autoverification for 25 789 blood routine samples, and found that the average TAT of blood routine test reports were shortened by 24min, 32min and 7min respectively, the rate of samples reported within 30min were elevated by 33%, 53% and 7%. The autoverification pass rates were 72%-74%. Conclusions The application of this set of 43 autoverification rules in blood sample analysis can ensure test quality while shortenTAT and improve work efficiency. It is worth pointing out that for the same analytical systems in this research, validation is necessary before application of this set of rules, and periodic validation is required during application to make necessary adjustment; for different analytical systems, as this research provide a way to establish autoverification rules for blood routine tests.Clinical labs may establish their own suitable autoverification rules on the basis of technological parameters. (Chin J Lab Med, 2018, 41: 601-607)

Objective:To establish autoverification rules for coagulation tests in multicenter cooperative units, in order to reduce workload for manual review of suspected results and shorten turnaround time (TAT) of test reports, while ensure the accuracy of results.Methods:A total of 14 394 blood samples were collected from fourteen hospitals during December 2019 to March 2020. These samples included: Rules Establishment Group 11 230 cases, including 1 182 cases for Delta check rules; Rules Validation Group 3 164 cases, including 487cases for Delta check; Clinical Application Trial Group 77 269 cases. Samples were analyzed for coagulation tests using Sysmex CS series automatic coagulation analyzers, and the clinical information, instrument parameters, test results, clinical diagnosis, medication history of anticoagulant and other relative results such as HCT, TG, TBIL, DBIL were summarized; on the basis of historical data, the 2.5 and 97.5 percentile of all data arranged from low to high were initially accumulated; on the basis of clinical suggestions, critical values and specific drug use as well as relative guidelines, autoverification rules and limits were established.The rules were then input into middleware, in which Stage I/Stage II validation was done. Positive coincidence, negative coincidence, false negative, false positive, autoverification pass rate, passing accuracy (coincidence of autoverification and manual verification) were calculated. Autoverification rules underwent trial application in coagulation results reports.Results:(1) The autoverification algorisms involve 33 rules regarding PT/INR, APTT, FBG, D-dimer, FDP,Delta check, reaction curve and sample abnormalities; (2)Autoverification Establishment Group showed autoverification pass rate was 68.42% (7 684/11 230), the false negative rate was 0%(0/11230), coincidence of autoverification and manual verification was 98.51%(11 063/11 230), in which positive coincidence and negative coincidence were respectively 30.09% (3 379/11 230) and 68.42%(7 684/11 230); Autoverification Validation Group showed autoverification pass rate was 60.37%(1 910/3 164), the false negative rate was 0%(0/11 230), coincidence of autoverification and manual verification was 97.79%(3 094/3 164), in which positive coincidence and negative coincidence were respectively 37.42%(1 184/3 164) and 60.37%(1 910/3 164); (3) Trialed implementation of these autoverification rules on 77 269 coagulation samples showed that the average TAT shortened by 8.5 min-83.1 min.Conclusions:This study established 33 autoverification rules in coagulation tests. Validation showedthese rules could ensure test quality while shortening TAT and lighten manual workload.

ObjectiveTo investigate the genetic association between nonalcoholic fatty liver disease （NAFLD） and type 2 diabetes mellitus （T2DM） using bidirectional two-sample Mendelian randomization （MR）， as well as the causal relationship between NAFLD and T2DM across different body mass index （BMI） categories. MethodsThe data were derived from genome-wide association studies conducted in European populations， with a sample size of 32 941 cases for NAFLD， 312 646 cases for T2DM， and 681 275 cases for BMI. The univariate and multivariate MR methods were used to assess the bidirectional causal relationship between NAFLD and T2DM in the general population and across different BMI subtypes. The methods of inverse-variance weighting， MR-Egger regression， constrained maximum likelihood and model averaging， and weighted median were used to conduct the MR analysis， and MR-Pleiotropy Residual Sum and Outlier， radial MR， the MR-Egger intercept method， and the Cochrane Q test were used for sensitivity analysis. ResultsThe univariate MR analysis revealed a bidirectional causal relationship between NAFLD and T2DM in the general population （forward analysis： odds ratio ［OR］=9.75， 95% confidence interval ［CI］： 2.57‍ ‍—‍ ‍37.00， P<0.001； reverse analysis： OR=1.01， 95%CI： 1.00‍ ‍—‍ ‍1.01， P<0.01）. After adjustment for BMI， the multivariate MR analysis showed that the causal relationship between NAFLD and T2DM remained significant in the general population （OR=33.12， 95%CI： 7.57‍ ‍—‍ ‍144.95， P<0.000 1）. The subgroup analysis showed a causal relationship between NAFLD and T2DM across all BMI subtypes （lean subgroup： OR=12.19， 95%CI： 3.35‍ ‍—‍ ‍44.40， P<0.001； overweight subgroup： OR=4.30， 95%CI： 1.69‍ ‍—‍ ‍10.92， P<0.01； obese subgroup： OR=1.67， 95%CI： 1.14‍ ‍—‍ ‍2.44， P<0.01）. ConclusionThis study reveals the causal relationship between NAFLD and T2DM in the general population of NAFLD and across different BMI subtypes from a genetic perspective.