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BACKGROUND: Clozapine is the most effective antipsychotic for treatment-resistant psychosis. However, clozapine is underutilised in part because of potential agranulocytosis. Accumulating evidence indicates that below-threshold haematological readings in isolation are not diagnostic of life-threatening clozapine-induced agranulocytosis (CIA). AIMS: To examine the prevalence and timing of CIA using different diagnostic criteria and to explore demographic differences of CIA in patients registered on the UK Central Non-Rechallenge Database (CNRD). METHOD: We analysed data of all patients registered on the UK Clozaril® Patient Monitoring Service Central Non-Rechallenge Database (at least one absolute neutrophil count (ANC) < 1.5 × 109/L and/or white blood cell count < 3.0 × 109/L) between May 2000 and February 2021. We calculated prevalence rates of agranulocytosis using threshold-based and pattern-based criteria, stratified by demographic factors (gender, age and ethnicity). Differences in epidemiology based on rechallenge status and clozapine indication were explored. The proportion of patients who recorded agranulocytosis from a normal ANC was explored. RESULTS: Of the 3029 patients registered on the CNRD with 283 726 blood measurements, 593 (19.6%) were determined to have threshold-based agranulocytosis and 348 (11.4%) pattern-based agranulocytosis. In the total sample (75 533), the prevalence of threshold-based agranulocytosis and pattern-based agranulocytosis was 0.8% and 0.5%, respectively. The median time to threshold-based agranulocytosis was 32 weeks (IQR 184) and 15 (IQR 170) weeks for pattern-based agranulocytosis. Among age groups, the prevalence of pattern-based agranulocytosis and threshold-based agranulocytosis was highest in the >48 age group. Prevalence rates were greatest for White (18%) and male individuals (13%), and lowest for Black individuals (0.1%). The proportion of people who were determined to have pattern-based agranulocytosis without passing through neutropenia was 70%. CONCLUSIONS: Threshold-based definition of agranulocytosis may over-diagnose CIA. Monitoring schemes should take into consideration neutrophil patterns to correctly identify clinically relevant CIA. In marked contrast to previous studies, CIA occurred least in Black individuals and most in White individuals.
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Aims: Current deep learning algorithms for automatic ECG analysis have shown notable accuracy but are typically narrowly focused on singular diagnostic conditions. This exploratory study aims to investigate the capability of a single deep learning model to predict a diverse range of both cardiac and non-cardiac discharge diagnoses based on a single ECG collected in the emergency department. Methods and results: In this study, we assess the performance of a model trained to predict a broad spectrum of diagnoses. We find that the model can reliably predict 253 ICD codes (81 cardiac and 172 non-cardiac) in the sense of exceeding an AUROC score of 0.8 in a statistically significant manner. Conclusion: The model demonstrates proficiency in handling a wide array of cardiac and non-cardiac diagnostic scenarios, indicating its potential as a comprehensive screening tool for diverse medical encounters.
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Cardiovascular diseases remain the leading global cause of mortality. Age is an important covariate whose effect is most easily investigated in a healthy cohort to properly distinguish the former from disease-related changes. Traditionally, most of such insights have been drawn from the analysis of electrocardiogram (ECG) feature changes in individuals as they age. However, these features, while informative, may potentially obscure underlying data relationships. In this paper we present the following contributions: (1) We employ a deep-learning model and a tree-based model to analyze ECG data from a robust dataset of healthy individuals across varying ages in both raw signals and ECG feature format. (2) We use explainable AI methods to identify the most discriminative ECG features across age groups.(3) Our analysis with tree-based classifiers reveals age-related declines in inferred breathing rates and identifies notably high SDANN values as indicative of elderly individuals, distinguishing them from younger adults. (4) Furthermore, the deep-learning model underscores the pivotal role of the P-wave in age predictions across all age groups, suggesting potential changes in the distribution of different P-wave types with age. These findings shed new light on age-related ECG changes, offering insights that transcend traditional feature-based approaches.