c9592b8165baa51dacbe4673441be35d 310173.pdf 011bb8459f06373ff1485b2ce931481e11a6bf04 310173.pdf 9994b831ca7e10f88dfd123ca2e5290b018018bc9fd260a58ffbe8945b8ee5fc 310173.pdf Title: 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 Subject: Objectives: The SRealCLL study described the occurrence of adverse events (AEs) and healthcare resource utilization in patients with chronic lymphocytic leukaemia (CLL) using artificial intelligence in a real-world scenario in Spain. Methods: We collected real-world data on patients with CLL from seven Spanish hospitals between January 2016 and December 2018, focusing on their AE and healthcare service utilization. Data extraction from electronic health records of 385,904 patients was performed using the EHRead® technology, which is based on natural language processing and machine learning. Results: Among the 534 CLL patients finally included, 270 (50.6%) were categorized as watch and wait (W&W), 230 (43.1%) as first-line treatment (1L), and 58 (10.9%) as relapse/refractory with second-line treatment (2L). The median study follow-up periods were 14.4, 8.4, and 6 months for W&W, 1L, and 2L, respectively. The most common antineoplastic treatments were ibrutinib (64.8%) and bendamustine + rituximab (12.6%) in 1L, and ibrutinib (62.1%) and venetoclax (15.5%) in 2L. Among the most frequent AEs, anaemia and thrombocytopenia presented higher rates in the treated groups (1L and 2L) compared with W&W (2.01 and 2.32 vs. 0.93; p 0.05 and 1.29 and 1.62 vs. 0.42; p 0.05). Moreover, several AEs, such as major bleeding, digestive symptoms, general symptoms, or Richter syndrome, were more frequent in 1L than W&W (all p 0.05). No differences were shown between groups in the rates of outpatient visits. However, rates of outpatient visits due to AE were higher in 1L than in W&W (1.07 vs. 0.65, p 0.05). The rates of patients being hospitalized were higher in the treated groups compared to W&W (1.68 and 1.9 vs. 0.88; p 0.05), and those due to AE were higher in 1L than W&W (1.23 vs. 0.60; p 0.05). Conclusions: Patients with CLL in 1L or 2L treatments often require healthcare resources due to AEs, particularly cytopenias. The methodology used in this study likely enabled us to identify higher rates of AEs that may be underreported using other real-world approaches. Addressing AEs with effective agents that maximize patient safety and optimize healthcare resource use is crucial in this typically older and comorbid population. Keywords: chronic lymphocytic leukaemia; electronic health records; natural language processing; real-world evidence; artificial intelligence; adverse events; healthcare resource utilization Author: Pau Abrisqueta-Costa, José Antonio García-Marco, Antonio Gutiérrez, José Ángel Hernández-Rivas, Rafael Andreu-Lapiedra, Miguel Arguello-Tomas, Carolina Leiva-Farré, María Dolores López-Roda, Ángel Callejo-Mellén, Esther Álvarez-García and Javier Loscertales Creator: LaTeX with hyperref Producer: pdfTeX-1.40.25 CreationDate: Fri Nov 29 10:29:20 2024 CET ModDate: Fri Nov 29 10:49:42 2024 CET Custom Metadata: no Metadata Stream: no Tagged: no UserProperties: no Suspects: no Form: none JavaScript: no Pages: 15 Encrypted: no Page size: 595.276 x 841.89 pts (A4) Page rot: 0 File size: 2124488 bytes Optimized: no PDF version: 1.7 name type encoding emb sub uni object ID ------------------------------------ ----------------- ---------------- --- --- --- --------- FFAYKL+VnURWPalladioL-Bold Type 1 Custom yes yes yes 10 0 HPADWV+URWPalladioL-Bold Type 1 Custom yes yes yes 16 0 RBKICU+URWPalladioL-Roma Type 1 Custom yes yes yes 22 0 QEPCFW+URWPalladioL-Ital Type 1 Custom yes yes yes 27 0 EIFDAO+VnURWPalladioL Type 1 Custom yes yes yes 32 0 IKKWKP+CMSY10 Type 1 Builtin yes yes yes 62 0 JKAEIH+PalatinoLinotype-Bold CID TrueType Identity-H yes yes yes 76 0 JKAEJH+PalatinoLinotype-Roman CID TrueType Identity-H yes yes yes 82 0 JKAEJI+PalatinoLinotype-Italic CID TrueType Identity-H yes yes yes 88 0 JKAEJJ+PalatinoLinotype-Italic TrueType WinAnsi yes yes no 94 0 JKAEJK+PalatinoLinotype-Bold TrueType WinAnsi yes yes no 97 0 JKAEJL+PalatinoLinotype-Roman TrueType WinAnsi yes yes no 100 0 JKAENL+PalatinoLinotype-Roman TrueType MacRoman yes yes no 103 0 JKAEIH+PalatinoLinotype-Bold CID TrueType Identity-H yes yes yes 116 0 JKAEJH+PalatinoLinotype-Roman CID TrueType Identity-H yes yes yes 122 0 JKAEJI+PalatinoLinotype-Italic CID TrueType Identity-H yes yes yes 128 0 JKAEJJ+PalatinoLinotype-Italic TrueType WinAnsi yes yes no 134 0 JKAEJK+PalatinoLinotype-Bold TrueType WinAnsi yes yes no 137 0 JKAEJL+PalatinoLinotype-Roman TrueType WinAnsi yes yes no 140 0 JKAENL+PalatinoLinotype-Roman TrueType MacRoman yes yes no 143 0 JKAEIH+PalatinoLinotype-Bold CID TrueType Identity-H yes yes yes 153 0 JKAEJH+PalatinoLinotype-Roman CID TrueType Identity-H yes yes yes 159 0 JKAEJI+PalatinoLinotype-Italic CID TrueType Identity-H yes yes yes 165 0 JKAEJJ+PalatinoLinotype-Italic TrueType WinAnsi yes yes no 171 0 JKAEJK+PalatinoLinotype-Bold TrueType WinAnsi yes yes no 174 0 JKAEJL+PalatinoLinotype-Roman TrueType WinAnsi yes yes no 177 0 JKAENL+PalatinoLinotype-Roman TrueType MacRoman yes yes no 180 0 JKAEIH+PalatinoLinotype-Bold CID TrueType Identity-H yes yes yes 187 0 JKAEJH+PalatinoLinotype-Roman CID TrueType Identity-H yes yes yes 193 0 JKAEJI+PalatinoLinotype-Italic CID TrueType Identity-H yes yes yes 199 0 JKAEJJ+PalatinoLinotype-Italic TrueType WinAnsi yes yes no 205 0 JKAEJK+PalatinoLinotype-Bold TrueType WinAnsi yes yes no 208 0 JKAEJL+PalatinoLinotype-Roman TrueType WinAnsi yes yes no 211 0 JKAEIH+PalatinoLinotype-Bold CID TrueType Identity-H yes yes yes 221 0 JKAEJH+PalatinoLinotype-Roman CID TrueType Identity-H yes yes yes 227 0 JKAEJI+PalatinoLinotype-Italic CID TrueType Identity-H yes yes yes 233 0 JKAEJJ+PalatinoLinotype-Italic TrueType WinAnsi yes yes no 239 0 JKAEJK+PalatinoLinotype-Bold TrueType WinAnsi yes yes no 242 0 JKAEJL+PalatinoLinotype-Roman TrueType WinAnsi yes yes no 245 0 JKAENL+PalatinoLinotype-Roman TrueType MacRoman yes yes no 248 0 Jhove (Rel. 1.28.0, 2023-05-18) Date: 2025-04-04 02:43:15 CEST RepresentationInformation: 310173.pdf ReportingModule: PDF-hul, Rel. 1.12.4 (2023-03-16) LastModified: 2025-04-03 14:37:02 CEST Size: 2124488 Format: PDF Version: 1.7 Status: Well-Formed and valid SignatureMatches: PDF-hul MIMEtype: application/pdf PDFMetadata: Objects: 481 FreeObjects: 1 IncrementalUpdates: 0 DocumentCatalog: PageLayout: SinglePage PageMode: UseNone Outlines: Item: Title: Introduction Destination: section.1 Item: Title: Methods Destination: section.2 Children: Item: Title: Study Design and Data Source Destination: subsection.2.1 Item: Title: Study Population Destination: subsection.2.2 Item: Title: Extracting Unstructured and Structured Data from EHRs Destination: subsection.2.3 Item: Title: Statistical Data Analyses Destination: subsection.2.4 Item: Title: Results Destination: section.3 Children: Item: Title: Occurrence of Adverse Events Destination: subsection.3.1 Item: Title: Healthcare Services Utilization Destination: subsection.3.2 Item: Title: Discussion Destination: section.4 Item: Title: Conclusions Destination: section.5 Item: Title: References Destination: appendix.A. Info: Title: 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 Author: Pau Abrisqueta-Costa, José Antonio García-Marco, Antonio Gutiérrez, José Ángel Hernández-Rivas, Rafael Andreu-Lapiedra, Miguel Arguello-Tomas, Carolina Leiva-Farré, María Dolores López-Roda, Ángel Callejo-Mellén, Esther Álvarez-García and Javier Loscertales Subject: Objectives: The SRealCLL study described the occurrence of adverse events (AEs) and healthcare resource utilization in patients with chronic lymphocytic leukaemia (CLL) using artificial intelligence in a real-world scenario in Spain. Methods: We collected real-world data on patients with CLL from seven Spanish hospitals between January 2016 and December 2018, focusing on their AE and healthcare service utilization. Data extraction from electronic health records of 385,904 patients was performed using the EHRead® technology, which is based on natural language processing and machine learning. Results: Among the 534 CLL patients finally included, 270 (50.6%) were categorized as watch and wait (W&W), 230 (43.1%) as first-line treatment (1L), and 58 (10.9%) as relapse/refractory with second-line treatment (2L). The median study follow-up periods were 14.4, 8.4, and 6 months for W&W, 1L, and 2L, respectively. The most common antineoplastic treatments were ibrutinib (64.8%) and bendamustine + rituximab (12.6%) in 1L, and ibrutinib (62.1%) and venetoclax (15.5%) in 2L. Among the most frequent AEs, anaemia and thrombocytopenia presented higher rates in the treated groups (1L and 2L) compared with W&W (2.01 and 2.32 vs. 0.93; p 0.05 and 1.29 and 1.62 vs. 0.42; p 0.05). Moreover, several AEs, such as major bleeding, digestive symptoms, general symptoms, or Richter syndrome, were more frequent in 1L than W&W (all p 0.05). No differences were shown between groups in the rates of outpatient visits. However, rates of outpatient visits due to AE were higher in 1L than in W&W (1.07 vs. 0.65, p 0.05). The rates of patients being hospitalized were higher in the treated groups compared to W&W (1.68 and 1.9 vs. 0.88; p 0.05), and those due to AE were higher in 1L than W&W (1.23 vs. 0.60; p 0.05). Conclusions: Patients with CLL in 1L or 2L treatments often require healthcare resources due to AEs, particularly cytopenias. The methodology used in this study likely enabled us to identify higher rates of AEs that may be underreported using other real-world approaches. Addressing AEs with effective agents that maximize patient safety and optimize healthcare resource use is crucial in this typically older and comorbid population. 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