Literature DB >> 24690478

Critical values in hematology.

A McFarlane1, B Aslan, A Raby, G Bourner, R Padmore.   

Abstract

INTRODUCTION: Critical values are life-threatening results that require immediate notification to the patient's healthcare provider. Accreditation bodies require laboratories to establish critical values. A survey of Ontario laboratories was conducted to determine current practice for critical values in hematology.
METHODS: The survey was sent to 182 participants questioning sources for establishing critical values, levels, review frequency, delta checks, and reporting. The survey was completed by laboratory managers, supervisors, technical specialists, senior technologists, and bench technologists working in hematology.
RESULTS: The majority of participating laboratories have established critical values limits for hemoglobin, leukocyte counts, and platelet counts. Most laboratories also include the presence of malaria parasites and blast cells. Some laboratories reported the presence of plasma cells, sickle cells, schistocytes, and spherocytes as critical values. Multiple sources are used for establishing a critical value policy. There was variability for the frequency of critical values review. Rules may differ for a first-time patient sample vs. a repeat patient sample. Delta checks are seldom used to determine whether a result should be called a critical value. Most participants require the individual taking the critical result(s) to read back and confirm that they are directly involved with the patient's care.
CONCLUSION: There is a lack of consensus for critical values reporting in hematology. As critical value reporting is crucial for patient safety, standardization of this practice would be beneficial.
© 2014 John Wiley & Sons Ltd.

Entities:  

Keywords:  Laboratory practice; general hematology

Mesh:

Year:  2014        PMID: 24690478     DOI: 10.1111/ijlh.12226

Source DB:  PubMed          Journal:  Int J Lab Hematol        ISSN: 1751-5521            Impact factor:   2.877


  8 in total

1.  Change ratio of hemoglobin has predictive value for upper gastrointestinal bleeding.

Authors:  Minoru Tomizawa; Fuminobu Shinozaki; Rumiko Hasegawa; Yoshinori Shirai; Yasufumi Motoyoshi; Takao Sugiyama; Shigenori Yamamoto; Naoki Ishige
Journal:  Biomed Rep       Date:  2016-09-08

2.  An evaluation of adult critical result policies in haematology in a teaching hospital in China.

Authors:  Dagan Yang; Qian Cai; Xinglun Qi; Lili Xu; Yunxian Zhou
Journal:  Ann Transl Med       Date:  2019-02

3.  An Automated Draft Report Generator for Peripheral Blood Smear Examinations Based on Complete Blood Count Parameters.

Authors:  Young Gon Kim; Jung Ah Kwon; Yeonsook Moon; Seong Jun Park; Sangwook Kim; Hyun A Lee; Sun Young Ko; Eun Ah Chang; Myung Hyun Nam; Chae Seung Lim; Soo Young Yoon
Journal:  Ann Lab Med       Date:  2018-11       Impact factor: 3.464

4.  Normal values of neutrophil-to-lymphocyte ratio, lymphocyte-to-monocyte ratio and platelet-to-lymphocyte ratio among Iranian population: Results of Tabari cohort.

Authors:  Mahmood Moosazadeh; Iradj Maleki; Reza Alizadeh-Navaei; Motahareh Kheradmand; Akbar Hedayatizadeh-Omran; Amir Shamshirian; Agil Barzegar
Journal:  Caspian J Intern Med       Date:  2019

5.  Evaluation of automated hematology analyzer DYMIND DH76 compared to SYSMEX XN 1000 system.

Authors:  Milena Velizarova; Teodora Yacheva; Mariana Genova; Dobrin Svinarov
Journal:  J Med Biochem       Date:  2021-09-03       Impact factor: 3.402

6.  Baseline assessment of staff perception of critical value practices in government hospitals in Kuwait.

Authors:  Talal ALFadhalah; Buthaina Al Mudaf; Haya Al Tawalah; Wadha A Al Fouzan; Gheed Al Salem; Hanaa A Alghanim; Samaa Zenhom Ibrahim; Hossam Elamir; Hamad Al Kharji
Journal:  BMC Health Serv Res       Date:  2022-08-03       Impact factor: 2.908

7.  Evaluation on clinical performance of the new low-value platelet measurement mode (PLT-8X) of BC-6800Plus.

Authors:  Yimin Shen; Dongmei Liu; Yong Wang; Jun Cao; Shuaishuai Zhang; Hui Wen; Qiuqiu Dong; Dong Zheng; Jun Qiu
Journal:  Ann Transl Med       Date:  2022-06

8.  Predicting 2-Day Mortality of Thrombocytopenic Patients Based on Clinical Laboratory Data Using Machine Learning.

Authors:  Frank Lien; Hsin-Yao Wang; Jang-Jih Lu; Ying-Hao Wen; Tzong-Shi Chiueh
Journal:  Med Care       Date:  2021-03-01       Impact factor: 3.178

  8 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.