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Record W4414740957 · doi:10.1093/clinchem/hvaf086.346

A-362 Impact of lot-to-lot variation in absolute neutrophil count measured by a point-of-care device in patients receiving clozapine treatment

2025· article· en· W4414740957 on OpenAlexaff
Mary Kathryn Bohn, Paul S. F. Yip

Bibliographic record

VenueClinical Chemistry · 2025
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare Operations and Scheduling Optimization
Canadian institutionsHealth Sciences CentreSunnybrook Health Science CentreToronto East General HospitalHospital for Sick Children
Fundersnot available
KeywordsClozapineAbsolute neutrophil countConcordanceNeutropeniaAdverse effectRelative riskAtypical antipsychoticAntipsychotic

Abstract

fetched live from OpenAlex

Abstract Background Clozapine is the antipsychotic of choice in the treatment of patients with schizophrenia that are resistant to other therapeutics or at high risk of recurrent suicidal behavior. However, use of clozapine is associated with hematological adverse events, including neutropenia and agranulocytosis, requiring regular monitoring of absolute neutrophil count (ANC). The CSAN® Pronto™ is a point-of-care device that uses image-based technology to measure WBC and ANC in capillary or venous whole blood. ANC results for patients on clozapine are classified for treatment safety as green (greater than or equal to 2.0 x10^9/L), yellow (1.5-1.9 x10^9/L), or red (less than 1.5 x10^9/L). It is recommended to repeat all yellow and red flags, and immediately discontinue treatment if the result is confirmed as <1.5 x10^9/L. The objective of this study was to evaluate the impact of lot-to-lot variation on ANC results. Methods Retrospective patient data were extracted since implementation of the Pronto device at a community hospital (2023/10/01 to 2024/11/01). Distribution of ANC results, frequency of green, yellow, and red flags, and rate and concordance of repeated results were determined across four separate different test strip lots. Patient comparisons between a laboratory analyzer (Sysmex XN-20) and Pronto device were completed for each lot. Correlation and bias relative to the laboratory method were assessed through regression and Bland-Altman statistics, respectively. Performance was compared to allowable performance limits of ±20% for results greater than or equal to 4.0 x10^9/L or ±0.6 x10^9/L for results less than 4.0 x10^9/L relative to the laboratory analyzer. Results A total of 522 patient results across lots were extracted with ANC means ranging from 3.5 to 4.5 x10^9/L. Percentage of results with yellow or red alerts varied markedly with lot number (yellow: 4.4 to 10.2%, red: 2.2 to 7.8%). Reclassification rates among repeated samples was 65%. Of discordant results (N=20), 11 had one sample with a red flag interpretation (<1.5 x10^9/L) and the other with a yellow or green flag (>1.5 x10^9/L). Patient comparisons between the Pronto device and laboratory method for ANC demonstrated good correlation across lots (R: 0.933 to 0.997, N=9-17 per lot). Mean ANC bias relative to the laboratory demonstrated lot-specific shifts and ranged from -0.8 to 0.6 x10^9/L. Across lots, percentage of samples within allowable performance limits ranged from 64.7 to 88.9%. The lot with the largest negative bias also demonstrated the highest red alert rate and repeat rate in real-time patient data. Conclusion Patients receiving clozapine treatment require monitoring of ANC. Point-of-care measurement offers significant advantages in this population, including rapid clinical decision-making and reduced risk of attrition. Our study combines patient-level data with laboratory method comparisons to highlight the impact of lot variation of the Pronto device on patient results. This variation could lead to downstream consequences on patient care, including increased rates of repeated blood draw and changes in management. These findings emphasize the need for additional quality metrics and provider education in the use of the Pronto device for measurement of ANC in individuals on clozapine treatment.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.004
Threshold uncertainty score0.582

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.061
GPT teacher head0.469
Teacher spread0.408 · 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 teacher head, 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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Citations0
Published2025
Admission routes1
Has abstractyes

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