Dataset Information

Development and validation of a sequential two-step algorithm for the screening of individuals with potential polycythaemia vera.

ABSTRACT: In 2016, the WHO included haemoglobin values within normal ranges as a diagnostic criterion for Polycythaemia Vera (PV). Since then, concerns have arisen that a large number of patients are undergoing unnecessary screening for PV. To address this issue, we estimated the prevalence of JAK2 p.V617F in individuals with elevated haemoglobin or haematocrit and developed and validated a screening algorithm for PV. A total of 15,366 blood counts performed in seven non-consecutive days were reviewed, of which 1001 were selected for subsequent JAK2 p.V617F mutation screening. Eight (0.8%) new JAK2 p.V617F-mutated cases were detected. From ROC curves, a two-step algorithm was developed based on the optimal cut-off for the detection of the JAK2 p.V617F mutation. The algorithm was prospectively validated in an independent cohort of 15,298 blood counts. A total of 1595 (10.4%) cases met the criterion for haemoglobin or haematocrit, of whom 581 passed to step 2 (3.8% of the total). The JAK2 p.V617F mutation was detected in 7 of the 501 patients tested, which accounts for 0.04% of the total cohort and 0.4% of patients with erythrocytosis. In conclusion, this data show that our two-step algorithm improves the selection of candidates for JAK2 p.V617F testing.

SUBMITTER: Piris-Villaespesa M

PROVIDER: S-EPMC7794224 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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Development and validation of a sequential two-step algorithm for the screening of individuals with potential polycythaemia vera.

Piris-Villaespesa Miguel M Álvarez-Larrán Alberto A Saez-Marín Adolfo A Nuñez-Torrón Claudia C Muñoz-Martin Gloria G Sánchez Ricardo R Del Castillo Francisco J FJ Villarrubia Jesús J Lopez-Jimenez Javier J Martinez-Lopez Joaquin J Garcia-Gutierrez Valentin V

Scientific reports 20210108 1

In 2016, the WHO included haemoglobin values within normal ranges as a diagnostic criterion for Polycythaemia Vera (PV). Since then, concerns have arisen that a large number of patients are undergoing unnecessary screening for PV. To address this issue, we estimated the prevalence of JAK2 p.V617F in individuals with elevated haemoglobin or haematocrit and developed and validated a screening algorithm for PV. A total of 15,366 blood counts performed in seven non-consecutive days were reviewed, of ...[more]

PMID: 33420222

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Project description:BackgroundStep counts are increasingly used in public health and clinical research to assess wellbeing, lifestyle, and health status. However, estimating step counts using commercial activity trackers has several limitations, including a lack of reproducibility, generalizability, and scalability. Smartphones are a potentially promising alternative, but their step-counting algorithms require robust validation that accounts for temporal sensor body location, individual gait characteristics, and heterogeneous health states.ObjectiveOur goal was to evaluate an open-source step-counting method for smartphones under various measurement conditions against step counts estimated from data collected simultaneously from different body locations ("internal" validation), manually ascertained ground truth ("manual" validation), and step counts from a commercial activity tracker (Fitbit Charge 2) in patients with advanced cancer ("wearable" validation).MethodsWe used eight independent datasets collected in controlled, semi-controlled, and free-living environments with different devices (primarily Android smartphones and wearable accelerometers) carried at typical body locations. Five datasets (N=103) were used for internal validation, two datasets (N=107) for manual validation, and one dataset (N=45) used for wearable validation. In each scenario, step counts were estimated using a previously published step-counting method for smartphones that uses raw sub-second level accelerometer data. We calculated mean bias and limits of agreement (LoA) between step count estimates and validation criteria using Bland-Altman analysis.ResultsIn the internal validation datasets, participants performed 751.7±581.2 (mean±SD) steps, and the mean bias was -7.2 steps (LoA -47.6, 33.3) or -0.5%. In the manual validation datasets, the ground truth step count was 367.4±359.4 steps while the mean bias was -0.4 steps (LoA -75.2, 74.3) or 0.1 %. In the wearable validation dataset, Fitbit devices indicated mean step counts of 1931.2±2338.4, while the calculated bias was equal to -67.1 steps (LoA -603.8, 469.7) or a difference of 0.3 %.ConclusionsThis study demonstrates that our open-source step counting method for smartphone data provides reliable step counts across sensor locations, measurement scenarios, and populations, including healthy adults and patients with cancer.

Dataset Information

Development and validation of a sequential two-step algorithm for the screening of individuals with potential polycythaemia vera.

Publications

Development and validation of a sequential two-step algorithm for the screening of individuals with potential polycythaemia vera.

Similar Datasets

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