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JAK2-tree: a simple CBC-based decision rule to guide appropriate JAK2 V617F mutation testing

AIMS: The JAK2 V617F mutation is highly recurrent in many of the myeloproliferative neoplasms, a molecular variant that can be easily detected using sensitive and minimally invasive techniques. Given the ease of JAK2 V617F testing, this test may be improperly requested for the purposes of patient ‘s...

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Detalles Bibliográficos
Autores principales: Mahe, Etienne, Pedersen, Kasper Mønsted, Çolak, Yunus, Bojesen, Stig Egil, Lynch, Tarah, Sinclair, Gary, Khan, Faisal, Shabani-Rad, Meer-Taher
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6388913/
https://www.ncbi.nlm.nih.gov/pubmed/30514740
http://dx.doi.org/10.1136/jclinpath-2018-205527
Descripción
Sumario:AIMS: The JAK2 V617F mutation is highly recurrent in many of the myeloproliferative neoplasms, a molecular variant that can be easily detected using sensitive and minimally invasive techniques. Given the ease of JAK2 V617F testing, this test may be improperly requested for the purposes of patient ‘screening’ and to optimise laboratory resource utilisation, it behooves clinicians and laboratorians to perform JAK2 V617F testing only when most appropriate. METHODS: To assist with the screening of patients being considered for JAK2 V617F testing, we developed a clinical decision rule, “JAK2-tree”, which can be easily applied to basic CBC parameters (haemoglobin, platelet and white blood cell counts). RESULTS: We tested JAK2-tree on two independent datasets, one an unselected population-based sample (the Copenhagen General Population Study) and the other an historical clinical laboratory referral set, with sensitivities for JAK2 V617F detection of 91% and 94%, respectively. As applied to the historical laboratory referral dataset, moreover, the JAK2-tree algorithm would have reduced JAK2 V617F testing volume over the period of evaluation by 15%. CONCLUSIONS: Our work supports a simple decision-tree-based screening approach to optimize the selection of patients most appropriate for JAK2 V617F testing.