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[NEWS]

Research on Prephase Steroid Treatment in Pediatric Leukemia Published in Clin Epidemiol

A research article, entitled “A Study to Evaluate the Effectiveness and Safety of Prephase Steroid Treatment before Remission Induction Chemotherapy in Patients with Pediatric Acute Lymphoblastic Leukemia Using Common Data Model-Based Real-World Data: A Retrospective Observational Study”, authored by Yoona Choi, Bo Kyung Kim, Jung-Hyun Won, Jae Won Yoo, Wona Choi, Surin Jung, Jae Yoon Kim, In Young Choi, Nack-Gyun Chung, Jae Wook Lee , Jung Yoon Choi, Hyoung Jin Kang, Howard Lee, has been published in Clin Epidemiol (5 year impact factor, 5.10) on Apr 22, 2024.

In 2022, the CCADD's proposal was selected for the 1st Health and Medical Technology R&D Project by the Ministry of Health and Welfare. The project aimed to develop a disease registry and utilized electronic health record-based real-world data for pediatric acute lymphoblastic leukemia (ALL). CCADD is collaborating with the Medical Informatics lab of the College of Medicine, The Catholic University of Korea (PI: Prof. In Young Choi) and hemato-oncologists from the Department of Pediatrics at Seoul National University Hospital and The Catholic University of Korea, Seoul St. Mary’s Hospital. This publication in Clin Epidemiol is a result of this ongoing project.

The study analyzed data from 2012 to 2021 extracted from the Common Data Model databases of Seoul National University Hospital and Catholic University Seoul St. Mary’s Hospital, to assess the effectiveness and safety of prephase steroid treatment in pediatric ALL patients undergoing remission induction chemotherapy (RIC). This study revealed an 88% decrease in the incidence of tumor lysis syndrome (TLS) among pediatric ALL patients receiving prephase steroids compared to those who did not. This significant reduction highlights the potential of prephase steroid treatment to enhance safety of initial chemotherapy for pediatric ALL patients.

This study emphasizes the role of precise steroid dosing in treating pediatric ALL treatment, illustrating how such precision can decrease complications such as TLS and enhance survival outcomes. CCADD is dedicated to leveraging real-world data to build clinical evidence, addressing questions that randomized controlled trials (RCTs) may not fully resolve.

[PUBLICATIONS]

Choi Y, Kim BK, Won JH, Yoo JW, Choi W, Jung S, Kim JY, Choi IY, Chung NG, Lee JW, Choi JY, Kang HJ, Lee H. A Study to Evaluate the Effectiveness and Safety of Prephase Steroid Treatment before Remission Induction Chemotherapy in Patients with Pediatric Acute Lymphoblastic Leukemia Using Common Data Model-Based Real-World Data: A Retrospective Observational Study. Clin Epidemiol. 2024 Apr 22;16:293-304. doi: 10.2147/CLEP.S454263. PMID: 38681782; PMCID: PMC11049150.

[NEWS]

Prof. Howard and his team attended EACL 2024

Prof. Howard and his team (Dr. Kim, Jung Hyun, and David) presented their work, "CReSE: Benchmark Data and Automatic Evaluation Framework for Recommending Eligibility Criteria from Clinical Trial Information," at the 2024 European Chapter of the Association for Computational Linguistics (EACL). The presentation was well received and sparked lively debates among the attendees.

The EACL conference, hosted in Malta from March 17th to 22nd, was not just a venue for presenting their findings; it also provided enriching learning opportunities. The event covered a wide range of topics, from the challenges posed by large language models (LLMs), as seen in studies like "Leak, Cheat, Repeat: Data Contamination and Evaluation Malpractices in Closed-Source LLMs," to their innovative applications in healthcare, as exemplified by "Leveraging ChatGPT in Pharmacovigilance Event Extraction: An Empirical Study." These discussions spurred our team to consider how we could integrate these insights into our research.

Additionally, the conference featured thought-provoking keynote speeches that challenged prevailing views on language models. Dr. Lapata, in particular, argued against the notion that LLMs are a solution-for-all. Instead, she advocated for a modular approach, employing specialized, smaller language models to efficiently and sustainably address various natural language processing tasks, such as summarizing and crosslingual semantic parsing.

The main conference, spanning three days, was followed by workshops that offered practical learning experiences. "Computational Approaches to Language Data Pseudonymization" was notably relevant to our work, focusing on the anonymization of sensitive text data, a critical consideration when dealing with clinical texts. A standout session, "When Is a Name Sensitive? Eponyms in Clinical Text and Implications for De-Identification," delved into how transformer-based models could minimize false positives in de-identifying clinical texts, a concern often raised by the presence of eponyms, and it got us thinking deeply about how to properly deidentify personal health information in clinical notes.

All in all, the week-long EACL conference served dual purposes: it was an avenue for sharing our contributions with the NLP community and a fertile ground for acquiring new knowledge. Such opportunities are invaluable, affirming our progress and direction in the field.

[PUBLICATIONS]

Lee KW, Han SW, Kim TW, Ahn JB, Baek JY, Cho SH, Lee H, Kim JW, Kim JW, Kim TY, Hong YS, Beom SH, Cha Y, Choi Y, Kim S, Bang YJ. A Phase Ib/2a Study of GC1118 with 5-Fluorouracil, Leucovorin and Irinotecan (FOLFIRI) in Patients with Recurrent or Metastatic Colorectal Cancer. Cancer Res Treat. 2023 Dec 7. dot: 10.4143/crt.2023.1117