Impact of lot-to-lot variation in absolute neutrophil count measured by a point-of-care device in patients receiving clozapine treatment
Bibliographic record
Abstract
Background Clozapine is indicated for patients with schizophrenia who are refractory to standard antipsychotic treatment. Due to risk of severe neutropenia, routine monitoring of absolute neutrophil count (ANC) is required. Image-based point-of-care devices for ANC measurement in capillary blood present an opportunity to reduce treatment barriers. The objective of this study was to evaluate the impact of lot-to-lot variation on ANC results using a point-of-care device (CSAN® Pronto™). Methods Retrospective patient data were extracted over a 13-month period. Results were classified for treatment safety per vendor as green (≥2.0 × 10 9 /L), yellow (1.5–1.9 × 10 9 /L), or red (<1.5 × 10 9 /L). Distribution of ANC results, flagging rates, and rate and concordance of repeated results were determined. Patient comparisons between a laboratory analyzer (Sysmex XN-10) and Pronto were completed for ANC and WBC for each lot using linear regression and Bland-Altman statistics. Results 522 patient results across four lots were reviewed. Percentage of results classified as yellow or red varied with lot (yellow: 4.4–10.1 %, red: 2.2–7.8 %). Results for 31 patients were repeated and a reclassification rate of 65 % was observed. Patient comparisons between Pronto and laboratory analyzer for ANC and WBC demonstrated good correlation (Pearson R: ≥0.970). A negative bias was observed for ANC relative to the laboratory that varied with lot (−0.80 to −0.53 × 10 9 /L). The lot with the largest bias demonstrated the highest red alert rate and repeat rate in real-time patient data. Conclusion Our study combines patient-level data with method comparisons to highlight the impact of lot variation on results with potential consequences, including increased rates of repeated blood sampling. While point-of-care devices may facilitate lowering barriers for clozapine use, provider education and additional quality metrics are needed to inform testing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".