Objective To analyze nonconformities between 2012 and 2022 version of ISO 15189,and provide strategies of transitioning to new standard for laboratories.Methods A total of 522 nonconformities from 32 on-site audits in 24 laboratories against ISO 15189:2012 were collected and mapped them to the relevant clauses of ISO 15189:2022.Strategies for transitioning ISO 15189 to new version were explored based on the standard requirements,literature review and current laboratory practices.Results On average,16 noncon-formities(range from 8 to 31)were identified in every on-site audit.Most of them were related to ISO 15189:2022 clauses 7.3 Exami-nation Processes(165 nonconformities).The others were clause 6.5 Equipment Calibration and Metrological Traceability(43)and clause 6.6 Reagents and Consumables(40).Relatively fewer nonconformities involved new/enhanced requirements,such as risk man-agement,patient-related processes and point-of-care testing.Conclusion The main nonconformities in ISO 15189:2022 predominantly involved in the link of examination processes.It should be suggested that the laboratories strengthen the management in this area by a-dopting digital/intelligent technologies in order to smoothly implement the requirements of the new version of ISO 15189:2022 standard.A comprehensive strategy,including incorporating training,gap analysis,document revision,implementation involved in all staff,stringent risk management and continuous improvement should be recommended to ensure successful transition progress for replacement of ISO 15189:2022.

Objective:To establish an interpretive reporting system for urinalysis based on artificial intelligence (AI).Methods:Urine tests were collected from the First Affiliated Hospital, College of Medicine, Zhejiang University from 2008 to 2018, including 2 899 917 patient tests and 710 971 physical check-up tests. Then we set up a large population distribution with the frequency of different results of each item and established a health index of each sample and an abnormal level of each item according to data distribution, importance and degree of abnormality. We collected data of seven diseases, such as diabetes mellitus, urinary tract infection, glomerulonephritis and nephrotic syndrome, and matched them with a same number of healthy control group by gender and age. An integrated learner based on the AdaBoost algorithm was used to establish a diagnostic model and assess its algorithm performance. JAVA was used to develop data presentation software. The accuracy of the AI model for disease judgment was assessed by manual verification using 199 abnormal urine tests.Results:Each report could be graded as four levels: normal, abnormal, ill and critical. Each item could be judged as normal, mild, moderate, severe or extreme and the population distribution was provided with big data. The training accuracy, true positive rate and area under the curve were ≥88.3%, ≥80.0%, and ≥0.954 respectively using the machine learning model based on AdaBoost. The developed JAVA software presented the above results and displayed medical records and results, historical results, personalized advice, patient education and position in large population data. By manual verification, the accuracy rate of the AI model for disease judgment was 82.41% (166/199).Conclusion:This study established an intelligent interpretive reporting system for urine test results. It can distinguish the abnormality of each report, predict the disease of patients, and make personalized clinical decisions.

Objective:A multi-center and large sample volume study was conducted on the verification and improvement of the early established criteria for intelligent routine urinalysis validation (including the microscopic review rules and manual validation rules, referred to as intelligent criteria for short), in order to improve the clinical application of this intelligent criteria.Methods:A total of 31 456 urine specimens were collected from the inpatients and outpatients in six hospitals in China, from March to September 2019. Firstly, 3105 specimens were analyzed for preliminary verification and improvement of the intelligent criteria based on the results of the microscopic examination and manual validation. Secondly, 28 351 specimens were used to verify the clinical application of the improved intelligent criteria. All samples were manually validated as reference.Results:The approval inconsistency rate of the manual validation rules in the original intelligent criteria was 8.59% (202/2 352), and the interception inconsistency rate was 8.84% (208/2 352). The false negative rate and the microscopic review rate of the microscopic review rules were similar to the previous results. Based on an in-depth analysis of big data and the discussions by senior technicians from eight hospitals, one microscopic review rules and four manual validation rules were added, meanwhile two manual validation rule was deleted. The manual validation standards were unified. Finally, the intelligent criteria was improved. Based on the improved intelligent criteria, for microscopic review rules, the false positive rate, false negative rate (misdiagnosis rate), and microscopic review rate did not change significantly, which were 14.72% (457/3 105), 4.06% (126/3 105), and 24.73% (768/3 105), respectively. The approval inconsistency rate and the interception inconsistency rate of manual validation rules were both reduced to 0; the total manual validation rate of the intelligent criteria was 50.89% (1 580/3 105), and the auto-validation rate was 49.11% (1 525/3 105). The large sample volume verification results were consistent with the preliminary verification results of the improved intelligent criteria.Conclusion:This multi-center and large sample volume study had shown that the improved intelligent criteria had better clinical performance.

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.

BACKGROUND: Several latest guidelines and consensus statements from Europe and the United States specify that there is no need for fasting prior to routine lipid tests. However, the latest Chinese guidelines still recommend fasting tests owing to a lack of local evidence. This study aimed to investigate postprandial lipid concentrations and daytime biological variation of lipids in a healthy Chinese population. METHODS: Venous blood samples were collected from 41 ostensibly healthy Chinese volunteers at five time points during the day (06:30, 09:00, 12:00, 15:00, and 18:30). The same batch of reagents was used to determine lipid concentrations. A nested ANOVA was performed to calculate within-subject biological variation (CVI) and between-subject biological variation (CVG). RESULTS: Postprandial concentrations of triglyceride were higher than fasting concentrations, with the maximum change occurring at 12:00 (0.5 hours after lunch, 0.21±0.65 mmol/L difference). The daytime biological variation of triglycerides was relatively high (CVI=25%, CVG=35.9%). The postprandial concentrations of total cholesterol, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, apolipoprotein A1, and apolipoprotein B were mostly lower than the fasting concentrations, and their daytime biological variations were relatively low (CVI=2.4–4.4%, CVG=11.8–18.7%). CONCLUSIONS: As most daytime lipid concentrations changed only slightly, non-fasting samples could be used for routine lipid tests. However, in cases of abnormal postprandial triglyceride concentrations, dietary factors and fasting time should be considered when interpreting the results.
Apolipoprotein A-I ; Apolipoproteins ; Asian Continental Ancestry Group* ; Cholesterol ; Consensus ; Europe ; Fasting ; Humans ; Indicators and Reagents ; Lipoproteins ; Lunch ; Triglycerides ; United States ; Volunteers

Objective To explore the clinical users′experience on using the intelligent laboratory test system so as to provide reference for its optimization.Methods Fifteen laboratory staff with different sub specialty was selected for in-depth interviews and data were analyzed using phenomenological approach.Results Intelligent laboratory system could improve the quality of test result verification and interpretation,shorten the turnaround time and improve the work efficiency.It took about 1-2 months for users to accept the automatic verification function.After that,the users could believe in the system and used it in their daily work.The sys-tem could reduce the work stress and verification duty,while balancing the technical gap between laboratory technicians.Users could only understand the general rule of the intelligent system and it was difficult for them to manage the rule repository.Conclu-sion The intelligent laboratory system is one of the artificial intelligent system used for the medical laboratory,it can provide com-prehensive clinical decision support for the laboratory staff.

The medical consulting service, disease inquiry and seeking medical advice functions of 10 commonly available APP software in China for seeking medical advice, such asRapid Asking Doctors,Handholding Doc-tor Selection, andChunyu Handholding Doctors, were compared, which showed that although the functions of APP software in China for seeking medical advice could meet the requirement of people for seeking medical advice, importance should be further attached to their accurate and integrative information and establishment of profit-making model in order to promote the sustainable development of medical APP software.

Objective To investigate the prevalence and trends of human papillomavirus (HPV)infections in gynecology outpatients in Zhejiang province.Methods Samples of cervical exfoliated cells were collected from gynecology outpatients in 11 sentinel hospitals in Zhejiang Province from January 2011 to December 2013.Twenty one HPV subtypes were detected by flow-through hybridization technique.Chisquare test was performed to analyze the prevalence rates of HPV infections in different years and in different age groups.Results A total of 14 569 patients were enrolled in the study,among whom 3 552 (24.38％)were positive for HPV.HPV-16 (5.77％,840/14 569),HPV-52 (4.71％,686/14 569) and HPV-58 (4.52％,659/14 569) were the most prevalent subtypes.Among all patients,2 244 (15.40％) were infected with a single high-risk subtype,426 (2.92％) were infected with a single low-risk subtype,and 882 (6.05％) were infected with multiple subtypes.The rate of multiple infection was on the rise during 2011 and 2013 (x2 =23.65,P ＜0.01).The positive rates of HPV in patients with 15-24 y age group and ＞54 y age group were 27.91％ (211/756) and 27.73％ (439/1 583) respectively,which were higher than those in other age groups (x2 =18.664,P ＜ 0.01).Conclusion HPV infection is popular in gynecology outpatients in Zhejiang province,especially in patients aged 15-24 y and ＞ 54 y,and a certain proportion of patients are infected with multiple subtypes.

Objective To analyze the detection rate of HBV serological makers in non-hepatic inpatients in the past six years. Methods Serum samples of 70 582 non-hepatic inpatients from three large hospitals were collected during 2003 to 2008. Serological markers of HBV ( HBsAg, anti-HBs, HBeAg, antiHBe and anti-HBc) were detected by the AxSYM MEIA system (Abbott Laboratories,Abbott Park,IL).Combining the test results of serological makers with other clinical data, several analysis models for this retrospective study were set up to evaluate the year-to-year changes in serological makers and the detection rates of each model. Results The order from high to low of detection rate of the 5 HBV serological markers was anti-HBc (55. 17% ), anti-HBs (49. 57% ), anti-HBe (28.42%), HBsAg ( 8. 92% ) and HBeAg (2. 12% ), and all of them had a downward trend in the past six years. The positive rate of HBsAg went down from 9. 30% (2003) to 8.70% (2008). The positive rate of HBsAg among people who were born after 1992 (2. 28% ) were significantly lower than that of the overall population (8. 92% ) and fell from 3.57%(2003) to 1.85% (2008). Each detection rate of all serological makers had male sexual side effect [HBsAg ( 12. 38%/7. 25% ), HBeAg ( 2. 72%/1.58% ), anti-HBc ( 56. 57%/53.43% ), anti-HBe (41.50%/28. 35% ) and anti-HBs (65.48%/50. 00% ), male/female]. The differences were statistically significant (Chi-square values of HBsAg, HBeAg, anti-HBc, anti-HBe and anti-HBs were 509.74,105.78, 69.66, 1 321.61 and 1 726.91, respectively; all P ＜ 0. 01).Twenty-six models of HBV serological makers from 70 582 inpatients were summed up, and 8 models had positive rates geater than or equal to 1%. The "All Negative" model ranked No. 1 and had no significant change from year to year. During the past six years, models representing "A11 Negative" and "anti-HBs Positive alone" were mainly in individuals younger than or equal to 20-year-old, while the models representing "anti-HBc and/or anti-HBe,anti-HBs Positive" were mostly in people older than 20-year-old. The distribution curve of models representing "HBsAg, HbeAg and anti-HBc Positive" and "HBsAg, anti-HBc, anti-HBe Positive"etc. showed a bell-shape, covering the population from 20-year-old to 70-year-old. Conclusions The slowlydescending tendency of the detection rates of HBV serological makers was observed during the past six years.The detection rates of HBV in the younger generation decreased significantly. However, the HBV infection rates of overall population is still high, so it is a high time that we made continuous improvement for the serum HBV screening technique in order to reduce the HBV infection ratess.

Objective Analyze the historical data of critical values lists,providing scientific evidence for continuous improvement of critical value systems.Methods Screen out critical value lists data of 2006 from laboratory information system,after pretreatment and transformation of data,calculate the percentage of critical value,and its daily distribution,weekly distribution and department distribution, evaluate the range and turnaround time for critical value.Results The rate of critical value was 1.67%.It was mainly concentrated from 8 to 13 O'clock.Monday and Thursday have more critical value than other days.From the perspective of department,the majority critical value was from hematology department and transplantation department.After the evaluation of distribution diagram of critical value range,the lower critical value limit of blood potassium was adjusted from 3.0 mmol/L to 2.8 mmol/L,the blood platelet and leukocyte counts for parlents with hematology disease were a(Ijusted from 20×109/L,1.5×109/L to 10×1O9/L,1.0×109/L respectively.The laboratory turnaround time for 76.2% critical value was less than 1 hour.Conclusion Review and analyze critical value lists data regularly can improve the work efficiency and quality for the laboratory and clinic department and better meet patients' safety needs.