Construction of platelet count-optical method reflex test rules using Micro-RBC#, Macro-RBC%, "PLT clumps?" flag, and "PLT abnormal histogram" flag on the Mindray BC-6800plus hematology analyzer in clinical practice.
{"title":"Construction of platelet count-optical method reflex test rules using Micro-RBC#, Macro-RBC%, \"PLT clumps?\" flag, and \"PLT abnormal histogram\" flag on the Mindray BC-6800plus hematology analyzer in clinical practice.","authors":"Yang Fei, Zhi-Gang Xiong, Liang Huang, Chi Zhang","doi":"10.1515/cclm-2024-0739","DOIUrl":null,"url":null,"abstract":"<p><strong>Objectives: </strong>Utilizing RBC or PLT-related parameters to establish rules for the PLT-O reflex test can assist laboratories in quickly identifying specimens with interfered PLT-I that require PLT-O retesting.</p><p><strong>Methods: </strong>Prospective PLT-I and PLT-O testing was performed on 6857 EDTA-anticoagulated whole blood samples, split randomly into training and validation cohorts at a 2:3 ratio. Reflex and non-reflex groups were distinguished based on the differences between PLT-I and PLT-O results. By comparing RBC and PLT parameter differences and flags in the training set, we pinpointed factors linked to PLT-O reflex testing. Utilizing Lasso regression, then refining through univariate and multivariate logistic regression, candidate parameters were selected. A predictive nomogram was constructed from these parameters and subsequently validated using the validation set. ROC curves were also plotted.</p><p><strong>Results: </strong>Significant differences were observed between the reflex and non-reflex groups for 19 parameters including RBC, MCV, MCH, MCHC, RDW-CV, RDW-SD, Micro-RBC#, Micro-RBC%, Macro-RBC#, Macro-RBC%, MPV, PCT, P-LCC, P-LCR, PLR,\"PLT clumps?\" flag, \"PLT abnormal histogram\" flag, \"IDA Anemia?\" flag, and \"RBC abnormal histogram\" flag. After further analysis, Micro-RBC#, Macro-RBC%,\"PLT clumps?\", and \"PLT abnormal histogram\" flag were identified as candidate parameters to develop a nomogram with an AUC of 0.636 (95 %CI: 0.622-0.650), sensitivity of 42.9 % (95 %CI: 37.8-48.1 %), and specificity of 90.5 % (95 %C1: 89.6-91.3 %).</p><p><strong>Conclusions: </strong>The established rules may help laboratories improve efficiency and increase accuracy in determining platelet counts as a supplement to ICSH41 guidelines.</p>","PeriodicalId":10390,"journal":{"name":"Clinical chemistry and laboratory medicine","volume":" ","pages":""},"PeriodicalIF":3.8000,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Clinical chemistry and laboratory medicine","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1515/cclm-2024-0739","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MEDICAL LABORATORY TECHNOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Objectives: Utilizing RBC or PLT-related parameters to establish rules for the PLT-O reflex test can assist laboratories in quickly identifying specimens with interfered PLT-I that require PLT-O retesting.
Methods: Prospective PLT-I and PLT-O testing was performed on 6857 EDTA-anticoagulated whole blood samples, split randomly into training and validation cohorts at a 2:3 ratio. Reflex and non-reflex groups were distinguished based on the differences between PLT-I and PLT-O results. By comparing RBC and PLT parameter differences and flags in the training set, we pinpointed factors linked to PLT-O reflex testing. Utilizing Lasso regression, then refining through univariate and multivariate logistic regression, candidate parameters were selected. A predictive nomogram was constructed from these parameters and subsequently validated using the validation set. ROC curves were also plotted.
Results: Significant differences were observed between the reflex and non-reflex groups for 19 parameters including RBC, MCV, MCH, MCHC, RDW-CV, RDW-SD, Micro-RBC#, Micro-RBC%, Macro-RBC#, Macro-RBC%, MPV, PCT, P-LCC, P-LCR, PLR,"PLT clumps?" flag, "PLT abnormal histogram" flag, "IDA Anemia?" flag, and "RBC abnormal histogram" flag. After further analysis, Micro-RBC#, Macro-RBC%,"PLT clumps?", and "PLT abnormal histogram" flag were identified as candidate parameters to develop a nomogram with an AUC of 0.636 (95 %CI: 0.622-0.650), sensitivity of 42.9 % (95 %CI: 37.8-48.1 %), and specificity of 90.5 % (95 %C1: 89.6-91.3 %).
Conclusions: The established rules may help laboratories improve efficiency and increase accuracy in determining platelet counts as a supplement to ICSH41 guidelines.
期刊介绍:
Clinical Chemistry and Laboratory Medicine (CCLM) publishes articles on novel teaching and training methods applicable to laboratory medicine. CCLM welcomes contributions on the progress in fundamental and applied research and cutting-edge clinical laboratory medicine. It is one of the leading journals in the field, with an impact factor over 3. CCLM is issued monthly, and it is published in print and electronically.
CCLM is the official journal of the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) and publishes regularly EFLM recommendations and news. CCLM is the official journal of the National Societies from Austria (ÖGLMKC); Belgium (RBSLM); Germany (DGKL); Hungary (MLDT); Ireland (ACBI); Italy (SIBioC); Portugal (SPML); and Slovenia (SZKK); and it is affiliated to AACB (Australia) and SFBC (France).
Topics:
- clinical biochemistry
- clinical genomics and molecular biology
- clinical haematology and coagulation
- clinical immunology and autoimmunity
- clinical microbiology
- drug monitoring and analysis
- evaluation of diagnostic biomarkers
- disease-oriented topics (cardiovascular disease, cancer diagnostics, diabetes)
- new reagents, instrumentation and technologies
- new methodologies
- reference materials and methods
- reference values and decision limits
- quality and safety in laboratory medicine
- translational laboratory medicine
- clinical metrology
Follow @cclm_degruyter on Twitter!