Real-World Evidence on Adverse Events and Healthcare Resource Utilization in Patients with Chronic Lymphocytic Leukaemia in Spain Using Natural Language Processing: The SRealCLL Study
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View abstract on PubMed
Summary
This summary is machine-generated.Patients undergoing first-line (1L) or second-line (2L) treatment for chronic lymphocytic leukemia (CLL) experienced more adverse events (AEs), particularly cytopenias, leading to increased healthcare utilization compared to those on watch and wait (W&W). Advanced AI methods identified higher AE rates, emphasizing the need for safer treatments.
Area Of Science
- Hematology
- Oncology
- Health Informatics
Background
- Chronic lymphocytic leukemia (CLL) is a common hematologic malignancy.
- Real-world data (RWD) is crucial for understanding treatment outcomes and healthcare resource utilization in CLL.
- Artificial intelligence (AI) offers novel methods for extracting and analyzing RWD from electronic health records (EHRs).
Purpose Of The Study
- To evaluate adverse events (AEs) and healthcare resource utilization in CLL patients using AI-driven RWD analysis.
- To compare AE rates and healthcare resource use across different CLL management strategies: watch and wait (W&W), first-line (1L) treatment, and second-line (2L) treatment.
- To assess the effectiveness of AI in identifying AE rates potentially underreported by traditional RWD methods.
Main Methods
- The SRealCLL study collected RWD from seven Spanish hospitals (Jan 2016–Dec 2018).
- AI-powered EHRead® technology, utilizing natural language processing and machine learning, extracted data from 385,904 EHRs.
- Data focused on 534 CLL patients categorized into W&W, 1L, and 2L groups.
Main Results
- Higher rates of anemia and thrombocytopenia were observed in 1L and 2L groups compared to W&W (p ≤ 0.05).
- AEs like major bleeding, digestive symptoms, and Richter syndrome were more frequent in the 1L group (p ≤ 0.05).
- Hospitalization rates due to AEs were significantly higher in treated groups (1L and 2L) versus W&W (p ≤ 0.05).
Conclusions
- CLL patients on 1L or 2L treatments incur substantial healthcare resource utilization due to AEs, especially cytopenias.
- AI-driven RWD analysis may capture higher AE rates than conventional methods, highlighting potential underreporting.
- Optimizing patient safety and healthcare resource management through effective treatments is critical for this elderly, often comorbid CLL population.
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