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Record W4405038536 · doi:10.1182/blood-2024-206260

Diagnostic Accuracy of Erythropoietin and Jakpot in Predicting JAK2-Postiive Erythrocytosis: A Retrospective Cohort Study

2024· article· en· W4405038536 on OpenAlexaffabout
Justin Bruni Senecal, Yasmine Madan, Sabina Rajkumar, Rabia Tahir, Mark Crowther, Siraj Mithoowani

Bibliographic record

VenueBlood · 2024
Typearticle
Languageen
FieldMedicine
TopicMyeloproliferative Neoplasms: Diagnosis and Treatment
Canadian institutionsSt. Joseph's HospitalMcMaster University
Fundersnot available
KeywordsErythropoietinMedicineRetrospective cohort studyCohortInternal medicine

Abstract

fetched live from OpenAlex

Introduction: Erythrocytosis affects 0.5 - 4% of ambulatory patients and is a common reason for Hematology referral. Very few patients are ultimately diagnosed with a myeloproliferative neoplasm such as polycythemia vera. The JAKPOT score (Chin-Yee et al) incorporating platelet, neutrophil, and erythrocyte counts shows promise at identifying patients at low risk of JAK2-positive erythrocytosis, but has not been externally validated. Our aim was to compare the diagnostic accuracy of serum erythropoietin (EPO) with the JAKPOT score to identify patients with JAK2-positive erythrocytosis. Methods: Retrospective cohort study including all patients who had EPO and JAK2 molecular testing (V617F or exon 12) for undifferentiated erythrocytosis at a tertiary care centre in Hamilton, Canada between Dec. 2014 - Dec. 2022. Patients with hemoglobin < 125 g/L were excluded. Demographics, comorbidities, medications, and laboratory parameters were collected. Univariate analysis to detect predictors of JAK2 positivity was performed with Mann Whitney U-tests, two-tailed Welch t-tests and Chi-squared tests where appropriate. Multivariate logistic regression analysis was performed with a subset of the predictors. We determined the sensitivity (Sn), specificity (Sp), positive and negative likelihood ratios (+LR, -LR) of EPO, JAKPOT score, and a combination of serum EPO and JAKPOT (EPO-JAKPOT) to diagnose JAK2-positive erythrocytosis. Results: The initial cohort included 237 patients (74 female, mean age 57.7 years). Twenty-four (10%) patients were excluded because they were known to have donated blood or had been phlebotomized in the 3 months preceding laboratory investigation, leaving 213 patients for analysis. The mean hemoglobin and hematocrit were 174 g/L and 0.52 L/L respectively. Forty patients (19%) had positive JAK2 molecular testing. Multivariate logistic regression showed that increasing age (p=0.001), increasing platelet count (p=0.006), decreasing serum ferritin (p=0.018) and low EPO (< 3.8 mU/mL) (p<0.001) were associated with JAK2-positive erythrocytosis, while the other components of the JAKPOT score, neutrophils (p=0.292) and erythrocytes (p=0.670), were not. Low EPO had a Sn of 0.77 (95% CI, 0.62 - 0.87), Sp of 0.98 (95% CI, 0.94 - 0.99), +LR of 33 and -LR of 0.23 for the diagnosis of JAK2-positive erythrocytosis. A positive JAKPOT score had a Sn of 0.88 (95% CI, 0.73 - 0.94), Sp of 0.65 (95% CI, 0.57 - 0.72), +LR of 2.5 and -LR of 0.19 to diagnose JAK2-positive erythrocytosis. A JAKPOT score ≥ 1 or low EPO (EPO-JAKPOT+) had a Sn of 0.95 (95% CI, 0.83 - 0.98), Sp of 0.65 (95% CI, 0.58 - 0.72), +LR of 2.7 and -LR of 0.07. Restricting JAK2 testing to patients who were EPO-JAKPOT+ would have led to 55% fewer molecular tests in our cohort but would have missed 2 (5%) of JAK2+ patients. Conclusions: Individually, low EPO and the JAKPOT score had modest sensitivity for JAK2-positive erythrocytosis. Combining EPO and JAKPOT into the EPO-JAKPOT score increased the sensitivity to 95% and had a -LR of 0.07. While further validation is needed, this score has the potential to lessen molecular testing for erythrocytosis by identifying a population at low risk of disease.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.277
Teacher spread0.267 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations2
Published2024
Admission routes2
Has abstractyes

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