A multicenter study on the establishment and validation of autoverification rules for coagulation tests
Qu Linlin, Wu Jun, Wu Wei, Wang Beili, Liu Xiangyi, Jiang Hong, Huang Xunbei, Yang Dagan, Li Yongzhe, Du Yandan, Guo Wei, Sun Dehua, Wang Yuming, Ma Wei, Zhu Mingqing, Wang Xian, Sui Hong, Shou Weiling, Li Qiang, Chi Lin, Li Shuang, Liu Xiaolu, Wang Zhuo, Cao Jun, Bao Chunxi, Xia Yongquan, Cao Hui, An Beiying, Guo Fuyu, Feng Houmei, Yan Yan, Huang Guangri, Xu Wei
Abstract
ObjectiveTo 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.
MethodsA 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.
ConclusionsThis study established 33 autoverification rules in coagulation tests. Validation showedthese rules could ensure test quality while shortening TAT and lighten manual workload.
Key words:
Blood coagulation tests; Clinical laboratory techniques; Clinical laboratory information systems; Multicenter study
Contributor Information
Qu Linlin
the First Hospital of Jilin University, Changchun 130021, China
Wu Jun
Beijing Jishuitan Hospital, Beijing 100035, China
Wu Wei
Peking Union Medical College Hospital, Beijing100730, China
Wang Beili
Zhongshan Hospital of Fudan University, Shanghai 200032, China
Liu Xiangyi
Beijing Tongren Hospital, Beijing 100730, China
Jiang Hong
West China Hospital, Sichuan University, Chengdu 610041, China
Huang Xunbei
West China Hospital, Sichuan University, Chengdu 610041, China
Yang Dagan
the First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou 310009, China
Li Yongzhe
Peking Union Medical College Hospital, Beijing100730, China
Du Yandan
Inner Mongolia Forestry General Hospital, Yakeshi 022150, China
Guo Wei
Zhongshan Hospital of Fudan University, Shanghai 200032, China
Sun Dehua
Nanfang Hospital of Nanfang University, Guangzhou 510515, China
Wang Yuming
the Second Hospital of Kunming Medical University, Kunming 650101, China
Ma Wei
Suzhou Municipal Hospital, Suzhou 215000, China
Zhu Mingqing
the First Affiliated Hospital of Suzhou University, Suzhou 215006, China
Wang Xian
Nanjing Drum Tower Hospital, Affiliated Hospital of Nanjing University, Nanjing 210008, China
Sui Hong
Dongguan Kanghua Hospital, Dongguan 523080, China
Shou Weiling
Peking Union Medical College Hospital, Beijing100730, China
Li Qiang
Nanfang Hospital of Nanfang University, Guangzhou 510515, China
Chi Lin
Beijing Tongren Hospital, Beijing 100730, China
Li Shuang
Beijing Tongren Hospital, Beijing 100730, China
Liu Xiaolu
the First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou 310009, China
Wang Zhuo
the Second Hospital of Kunming Medical University, Kunming 650101, China
Cao Jun
the First Affiliated Hospital of Suzhou University, Suzhou 215006, China
Bao Chunxi
Inner Mongolia Forestry General Hospital, Yakeshi 022150, China
Xia Yongquan
Nanjing Drum Tower Hospital, Affiliated Hospital of Nanjing University, Nanjing 210008, China
Cao Hui
Beijing Jishuitan Hospital, Beijing 100035, China
An Beiying
the First Hospital of Jilin University, Changchun 130021, China
Guo Fuyu
Inner Mongolia Forestry General Hospital, Yakeshi 022150, China
Feng Houmei
Nanfang Hospital of Nanfang University, Guangzhou 510515, China
Yan Yan
Suzhou Municipal Hospital, Suzhou 215000, China
Huang Guangri
Dongguan Kanghua Hospital, Dongguan 523080, China
Xu Wei
the First Hospital of Jilin University, Changchun 130021, China
