A Rational Approach to JAK2 Mutation Testing in Patients with Elevated Hemoglobin: Results from the JAK2 Prediction Cohort (JAKPOT) Study

Chin‐Yee, Benjamin; Bhai, Pratibha; Cheong, Ian; Matyashin, Maxim; Hsia, Cyrus C.; Kawata, Eri; Ho, Jenny; Levy, Michael A.; Stuart, Alan; Lin, Hanxin; Chin‐Yee, Ian; Kadour, Mike; Sadiković, Bekim; Lazo‐Langner, Alejandro

doi:10.1007/s11606-022-07963-x

Cited by 2 publications

(3 citation statements)

References 16 publications

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“…Chin-Yee and his team set out to craft a diagnostic methodology for patients referred due to high hemoglobin levels. [ 13 ] They used a prediction rule grounded on CBC and white blood cell differential parameters to steer JAK2 mutation testing. This rule was formulated and confirmed through a significant retrospective clinical group named the JAK2 Prediction Cohort or JAKPOT.…”

Section: Discussionmentioning

confidence: 99%

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JAKCalc: A machine-learning approach to rationalized JAK2 testing in patients with elevated hemoglobin levels

Koseoglu,

Keklik Karadag,

Bulbul

et al. 2024

Medicine

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The demand for Janus Kinase-2 (JAK2) testing has been disproportionate to the low yield of positive results, which highlights the need for more discerning test strategies. The aim of this study is to introduce an artificial intelligence application as a more rational approach for testing JAK2 mutations in cases of erythrocytosis. Test results were sourced from samples sent to a tertiary hospital’s genetic laboratory between 2017 and 2023, meeting 2016 World Health Organization criteria for JAK2V617F mutation testing. The JAK2 Somatic Mutation Screening Kit was used for genetic testing. Machine learning models were trained and tested using Python programming language. Out of 458 cases, JAK2V617F mutation was identified in 13.3%. There were significant differences in complete blood count parameters between mutation carriers and non-carriers. Various models were trained with data, with the random forest (RF) model demonstrating superior precision, recall, F1-score, accuracy, and area under the receiver operating characteristic, all reaching 100%. Gradient boosting (GB) model also showed high scores. When compared with existing algorithms, the RF and GB models displayed superior performance. The RF and GB models outperformed other methods in accurately identifying and classifying erythrocytosis cases, offering potential reductions in unnecessary testing and costs.

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Section: Discussionmentioning

confidence: 99%

“…[10] Given this, many researchers search for smarter testing methods to reduce unnecessary tests and hence costs. [11][12][13] Our objective here is to introduce an artificial intelligence application as a more rational approach for testing JAK2 mutations in case of erythrocytosis.…”

Section: Introductionmentioning

confidence: 99%

JAKCalc: A machine-learning approach to rationalized JAK2 testing in patients with elevated hemoglobin levels

Koseoglu,

Keklik Karadag,

Bulbul

et al. 2024

Medicine

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“…NGS has become widely used in patients with suspected hematologic malignancies, raising concerns over high costs from indiscriminate utilization [ 10 ]. Given these concerns, there is growing interest in developing triage tools for investigation of myeloid malignancies [ 11 ], including prediction rules to help improve utilization of molecular testing in patients with suspected MPNs [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

An Approach to the Investigation of Thrombocytosis: Differentiating between Essential Thrombocythemia and Secondary Thrombocytosis

Almanaseer,

Chin-Yee,

et al. 2024

Advances in Hematology

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Background. Thrombocytosis is a common reason for referral to Hematology. Differentiating between secondary causes of thrombocytosis and essential thrombocythemia (ET) is often clinically challenging. A practical diagnostic approach to identify secondary thrombocytosis could reduce overinvestigation such as next generation sequencing (NGS) panel. Methods and Results. All adult patients with thrombocytosis (≥450 × 109/L) who underwent molecular testing at a single tertiary care centre between January 1, 2018 and May 31, 2021 were evaluated. Clinical and laboratory variables were compared between patients with secondary thrombocytosis vs. ET. Clinical variables included smoking, thrombosis, splenectomy, active malignancy, chronic inflammatory disease, and iron deficiency anemia. Laboratory variables included complete blood count (CBC), ferritin, and myeloid mutations detected by NGS. The overall yield of molecular testing was 52.4%; 92.1% of which were mutations in JAK2, CALR, and/or MPL. Clinical factors predictive of ET included history of arterial thrombosis (p<0.05); active malignancy, chronic inflammatory disease, splenectomy, and iron deficiency were associated with secondary thrombocytosis (p<0.05). A diagnosis of ET was associated with higher hemoglobin, mean corpuscular volume (MCV), red cell distribution width (RDW), and mean platelet volume (MPV), while secondary thrombocytosis was associated with higher body mass index, white blood cells, and neutrophils (p<0.01). Conclusion. A practical approach to investigating patients with persistent thrombocytosis based on clinical characteristics such as active malignancy, chronic inflammatory disease, splenectomy, and iron deficiency may assist in accurately identifying patients more likely to have secondary causes of thrombocytosis and reduce overinvestigation, particularly costly molecular testing.

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A Rational Approach to JAK2 Mutation Testing in Patients with Elevated Hemoglobin: Results from the JAK2 Prediction Cohort (JAKPOT) Study

Cited by 2 publications

References 16 publications

JAKCalc: A machine-learning approach to rationalized JAK2 testing in patients with elevated hemoglobin levels

JAKCalc: A machine-learning approach to rationalized JAK2 testing in patients with elevated hemoglobin levels

An Approach to the Investigation of Thrombocytosis: Differentiating between Essential Thrombocythemia and Secondary Thrombocytosis

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