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Elsaid MI, Meara AS, Owen DH. Role for Artificial Intelligence in the Detection of Immune-Related Adverse Events. J Clin Oncol 2024:JCO2401570. [PMID: 39356977 DOI: 10.1200/jco-24-01570] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2024] [Revised: 08/20/2024] [Accepted: 08/22/2024] [Indexed: 10/04/2024] Open
Affiliation(s)
- Mohamed I Elsaid
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH
- Division of Medical Oncology, College of Medicine, The Ohio State University, Columbus, OH
| | - Alexa Simon Meara
- Division of Medical Oncology, College of Medicine, The Ohio State University, Columbus, OH
- Division Rheumatology and Immunology College of Medicine, The Ohio State University, Columbus, OH
| | - Dwight H Owen
- Division of Medical Oncology, College of Medicine, The Ohio State University, Columbus, OH
- Pelotonia Institute for Immuno-Oncology, The Ohio State University Comprehensive Cancer Center-James Cancer Hospital and Solove Research Institute, The Ohio State University, Columbus, OH
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Sun VH, Heemelaar JC, Hadzic I, Raghu VK, Wu CY, Zubiri L, Ghamari A, LeBoeuf NR, Abu-Shawer O, Kehl KL, Grover S, Singh P, Suero-Abreu GA, Wu J, Falade AS, Grealish K, Thomas MF, Hathaway N, Medoff BD, Gilman HK, Villani AC, Ho JS, Mooradian MJ, Sise ME, Zlotoff DA, Blum SM, Dougan M, Sullivan RJ, Neilan TG, Reynolds KL. Enhancing Precision in Detecting Severe Immune-Related Adverse Events: Comparative Analysis of Large Language Models and International Classification of Disease Codes in Patient Records. J Clin Oncol 2024:JCO2400326. [PMID: 39226489 DOI: 10.1200/jco.24.00326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Revised: 05/20/2024] [Accepted: 06/24/2024] [Indexed: 09/05/2024] Open
Abstract
PURPOSE Current approaches to accurately identify immune-related adverse events (irAEs) in large retrospective studies are limited. Large language models (LLMs) offer a potential solution to this challenge, given their high performance in natural language comprehension tasks. Therefore, we investigated the use of an LLM to identify irAEs among hospitalized patients, comparing its performance with manual adjudication and International Classification of Disease (ICD) codes. METHODS Hospital admissions of patients receiving immune checkpoint inhibitor (ICI) therapy at a single institution from February 5, 2011, to September 5, 2023, were individually reviewed and adjudicated for the presence of irAEs. ICD codes and an LLM with retrieval-augmented generation were applied to detect frequent irAEs (ICI-induced colitis, hepatitis, and pneumonitis) and the most fatal irAE (ICI-myocarditis) from electronic health records. The performance between ICD codes and LLM was compared via sensitivity and specificity with an α = .05, relative to the gold standard of manual adjudication. External validation was performed using a data set of hospital admissions from June 1, 2018, to May 31, 2019, from a second institution. RESULTS Of the 7,555 admissions for patients on ICI therapy in the initial cohort, 2.0% were adjudicated to be due to ICI-colitis, 1.1% ICI-hepatitis, 0.7% ICI-pneumonitis, and 0.8% ICI-myocarditis. The LLM demonstrated higher sensitivity than ICD codes (94.7% v 68.7%), achieving significance for ICI-hepatitis (P < .001), myocarditis (P < .001), and pneumonitis (P = .003) while yielding similar specificities (93.7% v 92.4%). The LLM spent an average of 9.53 seconds/chart in comparison with an estimated 15 minutes for adjudication. In the validation cohort (N = 1,270), the mean LLM sensitivity and specificity were 98.1% and 95.7%, respectively. CONCLUSION LLMs are a useful tool for the detection of irAEs, outperforming ICD codes in sensitivity and adjudication in efficiency.
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Affiliation(s)
- Virginia H Sun
- Harvard Medical School, Boston, MA
- Cardiovascular Imaging Research Center, Massachusetts General Hospital, Boston, MA
| | - Julius C Heemelaar
- Harvard Medical School, Boston, MA
- Cardiovascular Imaging Research Center, Massachusetts General Hospital, Boston, MA
- Leiden University Medical Center, Leiden, the Netherlands
| | - Ibrahim Hadzic
- Harvard Medical School, Boston, MA
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Boston, MA
- Brigham and Women's Hospital, Boston, MA
- Maastricht University, Maastricht, the Netherlands
| | - Vineet K Raghu
- Harvard Medical School, Boston, MA
- Cardiovascular Imaging Research Center, Massachusetts General Hospital, Boston, MA
| | - Chia-Yun Wu
- Division of Hematology and Oncology, Department of Medicine, Massachusetts General Hospital, Boston, MA
- Far Eastern Memorial Hospital, New Taipei City, Taiwan
| | - Leyre Zubiri
- Harvard Medical School, Boston, MA
- Division of Hematology and Oncology, Department of Medicine, Massachusetts General Hospital, Boston, MA
| | - Azin Ghamari
- Harvard Medical School, Boston, MA
- Cardiovascular Imaging Research Center, Massachusetts General Hospital, Boston, MA
| | - Nicole R LeBoeuf
- Harvard Medical School, Boston, MA
- Department of Dermatology, Brigham and Women's Hospital, Boston, MA
- Center for Cutaneous Oncology, Dana-Farber Cancer Institute, Boston, MA
| | - Osama Abu-Shawer
- Department of Internal Medicine, Cleveland Clinic, Cleveland, OH
| | - Kenneth L Kehl
- Harvard Medical School, Boston, MA
- Division of Population Sciences, Dana-Farber Cancer Institute, Boston, MA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA
| | - Shilpa Grover
- Harvard Medical School, Boston, MA
- Division of Gastroenterology, Hepatology, and Endoscopy, Brigham and Women's Hospital, Boston, MA
| | - Prabhsimranjot Singh
- Harvard Medical School, Boston, MA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA
| | - Giselle A Suero-Abreu
- Harvard Medical School, Boston, MA
- Cardiovascular Imaging Research Center, Massachusetts General Hospital, Boston, MA
- Division of Cardiology, Massachusetts General Hospital, Boston, MA
| | - Jessica Wu
- Harvard Medical School, Boston, MA
- Cardiovascular Imaging Research Center, Massachusetts General Hospital, Boston, MA
| | - Ayo S Falade
- Internal Medicine Department, Massachusetts General Brigham Salem Hospital, Salem, MA
| | - Kelley Grealish
- Division of Hematology and Oncology, Department of Medicine, Massachusetts General Hospital, Boston, MA
| | - Molly F Thomas
- Division of Gastroenterology, Oregon Health and Science University, Portland, OR
- Department of Medicine, Oregon Health and Science University, Portland, OR
- Department of Cell, Developmental, and Cancer Biology, Oregon Health and Science University, Portland, OR
| | - Nora Hathaway
- Division of Hematology and Oncology, Department of Medicine, Massachusetts General Hospital, Boston, MA
| | - Benjamin D Medoff
- Harvard Medical School, Boston, MA
- Division of Pulmonary and Critical Care Medicine, Massachusetts General Hospital, Boston, MA
| | - Hannah K Gilman
- Harvard Medical School, Boston, MA
- Cardiovascular Imaging Research Center, Massachusetts General Hospital, Boston, MA
| | - Alexandra-Chloe Villani
- Harvard Medical School, Boston, MA
- Center for Immunology and Inflammatory Diseases (CIID), Massachusetts General Hospital Krantz Family Center for Cancer Research, Boston, MA
- Broad Institute of MIT and Harvard, Cambridge, MA
| | - Jor Sam Ho
- Harvard Medical School, Boston, MA
- Cardiovascular Imaging Research Center, Massachusetts General Hospital, Boston, MA
| | - Meghan J Mooradian
- Harvard Medical School, Boston, MA
- Division of Hematology and Oncology, Department of Medicine, Massachusetts General Hospital, Boston, MA
| | - Meghan E Sise
- Harvard Medical School, Boston, MA
- Division of Nephrology, Massachusetts General Hospital, Boston, MA
| | - Daniel A Zlotoff
- Harvard Medical School, Boston, MA
- Division of Cardiology, Massachusetts General Hospital, Boston, MA
| | - Steven M Blum
- Harvard Medical School, Boston, MA
- Division of Hematology and Oncology, Department of Medicine, Massachusetts General Hospital, Boston, MA
- Center for Immunology and Inflammatory Diseases (CIID), Massachusetts General Hospital Krantz Family Center for Cancer Research, Boston, MA
- Broad Institute of MIT and Harvard, Cambridge, MA
| | - Michael Dougan
- Harvard Medical School, Boston, MA
- Division of Gastroenterology, Massachusetts General Hospital, Boston, MA
| | - Ryan J Sullivan
- Harvard Medical School, Boston, MA
- Division of Hematology and Oncology, Department of Medicine, Massachusetts General Hospital, Boston, MA
| | - Tomas G Neilan
- Harvard Medical School, Boston, MA
- Cardiovascular Imaging Research Center, Massachusetts General Hospital, Boston, MA
- Division of Cardiology, Massachusetts General Hospital, Boston, MA
| | - Kerry L Reynolds
- Harvard Medical School, Boston, MA
- Division of Hematology and Oncology, Department of Medicine, Massachusetts General Hospital, Boston, MA
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Zitu MM, Zhang S, Owen DH, Chiang C, Li L. Generalizability of machine learning methods in detecting adverse drug events from clinical narratives in electronic medical records. Front Pharmacol 2023; 14:1218679. [PMID: 37502211 PMCID: PMC10368879 DOI: 10.3389/fphar.2023.1218679] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2023] [Accepted: 06/26/2023] [Indexed: 07/29/2023] Open
Abstract
We assessed the generalizability of machine learning methods using natural language processing (NLP) techniques to detect adverse drug events (ADEs) from clinical narratives in electronic medical records (EMRs). We constructed a new corpus correlating drugs with adverse drug events using 1,394 clinical notes of 47 randomly selected patients who received immune checkpoint inhibitors (ICIs) from 2011 to 2018 at The Ohio State University James Cancer Hospital, annotating 189 drug-ADE relations in single sentences within the medical records. We also used data from Harvard's publicly available 2018 National Clinical Challenge (n2c2), which includes 505 discharge summaries with annotations of 1,355 single-sentence drug-ADE relations. We applied classical machine learning (support vector machine (SVM)), deep learning (convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM)), and state-of-the-art transformer-based (bidirectional encoder representations from transformers (BERT) and ClinicalBERT) methods trained and tested in the two different corpora and compared performance among them to detect drug-ADE relationships. ClinicalBERT detected drug-ADE relationships better than the other methods when trained using our dataset and tested in n2c2 (ClinicalBERT F-score, 0.78; other methods, F-scores, 0.61-0.73) and when trained using the n2c2 dataset and tested in ours (ClinicalBERT F-score, 0.74; other methods, F-scores, 0.55-0.72). Comparison among several machine learning methods demonstrated the superior performance and, therefore, the greatest generalizability of findings of ClinicalBERT for the detection of drug-ADE relations from clinical narratives in electronic medical records.
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Affiliation(s)
- Md Muntasir Zitu
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, United States
| | - Shijun Zhang
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, United States
| | - Dwight H. Owen
- Department of Internal Medicine, College of Medicine, The Ohio State University, Columbus, OH, United States
| | - Chienwei Chiang
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, United States
| | - Lang Li
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, United States
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Johns AC, Yang M, Wei L, Grogan M, Spakowicz D, Patel SH, Li M, Husain M, Kendra KL, Otterson GA, Rosko AE, Andersen BL, Carbone DP, Owen DH, Presley CJ. Risk Factors for Immune Checkpoint Inhibitor Immunotherapy Toxicity Among Older Adults with Cancer. Oncologist 2023:7135996. [PMID: 37085156 PMCID: PMC10400153 DOI: 10.1093/oncolo/oyad097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 03/09/2023] [Indexed: 04/23/2023] Open
Abstract
OBJECTIVES Immune checkpoint inhibitor immunotherapy (IO) is revolutionizing cancer care but can lead to significant toxicity. This study seeks to describe potential risk factors for immune-related adverse events (irAEs) specifically among older adults. MATERIALS AND METHODS This was a retrospective study at a single academic comprehensive cancer center based on chart review data abstracted by physicians. For patients aged ≥70 years, frequency, type, and grade of irAEs and their association with baseline patient demographics, comorbidities, mobility, and functional status were characterized using bivariate analysis. Based on those results, multivariable logistic regressions were constructed to model the association between these characteristics with any grade and grade 3 or higher irAEs. RESULTS Data were analyzed for 238 patients aged ≥70 years who received IO for mostly (≥90%) advanced cancer between 2011 and 2018. Thirty-nine percent of older adults experienced an irAE and 13% experienced one that was grade 3 or higher. In the multivariable analysis, depression was associated with an increased incidence of any grade irAE, while decreased life-space mobility was associated with an increased incidence of grade ≥3 irAEs. CONCLUSION Most characteristics of special interest among older adults, include fall risk, weight loss, cognitive limitations, and hearing loss, were not associated with irAEs in our study. However, decreased life-space mobility and depression are potential risk factors for IO toxicity among older adults with advanced cancer. Interventions designed to evaluate and mitigate modifiable risk factors for treatment-related toxicity are needed, and the results of this study may be useful for guiding those efforts.
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Affiliation(s)
- Andrew C Johns
- Department of Internal Medicine, The Ohio State University Wexner Medical Center, Columbus, OH, USA
| | - Mike Yang
- College of Medicine, The Ohio State University, Columbus, OH, USA
| | - Lai Wei
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA
| | - Madison Grogan
- Division of Medical Oncology, Department of Internal Medicine, The Ohio State University Comprehensive Cancer Center, Columbus, OH, USA
| | - Daniel Spakowicz
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA
- Division of Medical Oncology, Department of Internal Medicine, The Ohio State University Comprehensive Cancer Center, Columbus, OH, USA
| | - Sandipkumar H Patel
- Division of Medical Oncology, Department of Internal Medicine, The Ohio State University Comprehensive Cancer Center, Columbus, OH, USA
| | - Mingjia Li
- Division of Hospital Medicine, Department of Internal Medicine, The Ohio State University Wexner Medical Center, Columbus, OH, USA
| | - Marium Husain
- Division of Medical Oncology, Department of Internal Medicine, The Ohio State University Comprehensive Cancer Center, Columbus, OH, USA
| | - Kari L Kendra
- Division of Medical Oncology, Department of Internal Medicine, The Ohio State University Comprehensive Cancer Center, Columbus, OH, USA
| | - Gregory A Otterson
- Division of Medical Oncology, Department of Internal Medicine, The Ohio State University Comprehensive Cancer Center, Columbus, OH, USA
| | - Ashley E Rosko
- Division of Hematology, Department of Internal Medicine, The Ohio State University Wexner Medical Center, Columbus, OH, USA
| | | | - David P Carbone
- Division of Medical Oncology, Department of Internal Medicine, The Ohio State University Comprehensive Cancer Center, Columbus, OH, USA
| | - Dwight H Owen
- Division of Medical Oncology, Department of Internal Medicine, The Ohio State University Comprehensive Cancer Center, Columbus, OH, USA
| | - Carolyn J Presley
- Division of Medical Oncology, Department of Internal Medicine, The Ohio State University Comprehensive Cancer Center, Columbus, OH, USA
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Murphy RM, Dongelmans DA, Kom IYD, Calixto I, Abu-Hanna A, Jager KJ, de Keizer NF, Klopotowska JE. Drug-related causes attributed to acute kidney injury and their documentation in intensive care patients. J Crit Care 2023; 75:154292. [PMID: 36959015 DOI: 10.1016/j.jcrc.2023.154292] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 03/14/2023] [Accepted: 03/14/2023] [Indexed: 03/25/2023]
Abstract
PURPOSE To investigate drug-related causes attributed to acute kidney injury (DAKI) and their documentation in patients admitted to the Intensive Care Unit (ICU). METHODS This study was conducted in an academic hospital in the Netherlands by reusing electronic health record (EHR) data of adult ICU admissions between November 2015 to January 2020. First, ICU admissions with acute kidney injury (AKI) stage 2 or 3 were identified. Subsequently, three modes of DAKI documentation in EHR were examined: diagnosis codes (structured data), allergy module (semi-structured data), and clinical notes (unstructured data). RESULTS n total 8124 ICU admissions were included, with 542 (6.7%) ICU admissions experiencing AKI stage 2 or 3. The ICU physicians deemed 102 of these AKI cases (18.8%) to be drug-related. These DAKI cases were all documented in the clinical notes (100%), one in allergy module (1%) and none via diagnosis codes. The clinical notes required the highest time investment to analyze. CONCLUSIONS Drug-related causes comprise a substantial part of AKI in the ICU patients. However, current unstructured DAKI documentation practice via clinical notes hampers our ability to gain better insights about DAKI occurrence. Therefore, both automating DAKI identification from the clinical notes and increasing structured DAKI documentation should be encouraged.
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Affiliation(s)
- Rachel M Murphy
- Amsterdam UMC location University of Amsterdam, Department of Medical Informatics, Meibergdreef 9, Amsterdam, the Netherlands; Amsterdam Public Health, Digital Health, Amsterdam, the Netherlands; Amsterdam Public Health, Quality of Care, Amsterdam, the Netherlands.
| | - Dave A Dongelmans
- Amsterdam Public Health, Quality of Care, Amsterdam, the Netherlands; Amsterdam UMC location University of Amsterdam, Department of Intensive Care Medicine, Meibergdreef 9, Amsterdam, the Netherlands
| | - Izak Yasrebi-de Kom
- Amsterdam UMC location University of Amsterdam, Department of Medical Informatics, Meibergdreef 9, Amsterdam, the Netherlands; Amsterdam Public Health, Methodology, Amsterdam, the Netherlands
| | - Iacer Calixto
- Amsterdam UMC location University of Amsterdam, Department of Medical Informatics, Meibergdreef 9, Amsterdam, the Netherlands; Amsterdam Public Health, Methodology, Amsterdam, the Netherlands; Amsterdam Public Health, Mental Health, Amsterdam, the Netherlands
| | - Ameen Abu-Hanna
- Amsterdam UMC location University of Amsterdam, Department of Medical Informatics, Meibergdreef 9, Amsterdam, the Netherlands; Amsterdam Public Health, Methodology, Amsterdam, the Netherlands; Amsterdam Public Health, Aging & Later Life, Amsterdam, the Netherlands
| | - Kitty J Jager
- Amsterdam UMC location University of Amsterdam, Department of Medical Informatics, Meibergdreef 9, Amsterdam, the Netherlands; Amsterdam Public Health, Quality of Care, Amsterdam, the Netherlands; Amsterdam Public Health, Aging & Later Life, Amsterdam, the Netherlands; Amsterdam Cardiovascular Sciences, Pulmonary hypertension & thrombosis, Amsterdam, the Netherlands
| | - Nicolette F de Keizer
- Amsterdam UMC location University of Amsterdam, Department of Medical Informatics, Meibergdreef 9, Amsterdam, the Netherlands; Amsterdam Public Health, Digital Health, Amsterdam, the Netherlands; Amsterdam Public Health, Quality of Care, Amsterdam, the Netherlands
| | - Joanna E Klopotowska
- Amsterdam UMC location University of Amsterdam, Department of Medical Informatics, Meibergdreef 9, Amsterdam, the Netherlands; Amsterdam Public Health, Digital Health, Amsterdam, the Netherlands; Amsterdam Public Health, Quality of Care, Amsterdam, the Netherlands
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Issa M, Tang J, Guo Y, Coss C, Mace TA, Bischof J, Phelps M, Presley CJ, Owen DH. Risk factors and predictors of immune-related adverse events: implications for patients with non-small cell lung cancer. Expert Rev Anticancer Ther 2022; 22:861-874. [PMID: 35786142 DOI: 10.1080/14737140.2022.2094772] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
INTRODUCTION Immune checkpoint inhibitors (ICI) are now utilized as a standard of care treatment for multiple cancers, including in both the metastatic setting as well as in earlier stages of disease. The identification of unique immune-related adverse events (irAE) that occur during ICI treatment has led to intense research to identify potential risk factors and biomarkers that may assist in clinical decision making. Although initial studies in ICI were primarily in advanced stage disease, the use of ICI in earlier stages of disease as adjuvant therapies requires a better understanding of patient risk stratification to mitigate or prevent serious irAE. AREAS COVERED In this review, we set out to describe the current state of research regarding potential risk factors for irAE in patients with non-small cell lung cancer, as well as explore the barriers to understanding irAE. We review data from irAE that occur in large phase 3 trials and prospective studies focusing on irAE, as well as the many retrospective studies that currently form the bulk of our understanding of irAE.
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Affiliation(s)
- Majd Issa
- Division of Medical Oncology, Department of Internal Medicine, the Ohio State University Wexner Medical Center - Comprehensive Cancer Center, Columbus, USA
| | - Joy Tang
- Division of Medical Oncology, Department of Internal Medicine, the Ohio State University Wexner Medical Center - Comprehensive Cancer Center, Columbus, USA
| | - Yizhen Guo
- College of Pharmacy, the Ohio State University Wexner Medical Center - Comprehensive Cancer Center, Columbus, USA
| | - Chris Coss
- College of Pharmacy, the Ohio State University Wexner Medical Center - Comprehensive Cancer Center, Columbus, USA
| | - Thomas A Mace
- Division of Gastroenterology, Hepatology & Nutrition, Department of Internal Medicine, the Ohio State University Wexner Medical Center, Columbus, USA
| | - Jason Bischof
- Department of Emergency Medicine, the Ohio State University Wexner Medical Center - Comprehensive Cancer Center, Columbus, USA
| | - Mitch Phelps
- College of Pharmacy, the Ohio State University Wexner Medical Center - Comprehensive Cancer Center, Columbus, USA
| | - Carolyn J Presley
- Division of Medical Oncology, Department of Internal Medicine, the Ohio State University Wexner Medical Center - Comprehensive Cancer Center, Columbus, USA
| | - Dwight H Owen
- Division of Medical Oncology, Department of Internal Medicine, the Ohio State University Wexner Medical Center - Comprehensive Cancer Center, Columbus, USA
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Anstadt EJ, Chu B, Yegya-Raman N, Han X, Doucette A, Poirier K, Mohiuddin JJ, Maity A, Facciabene A, Amaravadi RK, Karakousis GC, Cohen JV, Mitchell TC, Schuchter LM, Lukens JN. Moderate Colitis Not Requiring IV Steroids Is Associated with Improved Survival in Stage IV Melanoma after Anti-CTLA4 Monotherapy, But Not Combination Therapy. Oncologist 2022; 27:799-808. [PMID: 35666292 PMCID: PMC9438915 DOI: 10.1093/oncolo/oyac108] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Accepted: 04/08/2022] [Indexed: 01/01/2023] Open
Abstract
Background For patients with melanoma, gastrointestinal immune-related adverse events are common after receipt of anti-CTLA4 therapy. These present difficult decision points regarding whether to discontinue therapy. Detailing the situations in which colitis might predict for improved survival and how this is affected by discontinuation or resumption of therapy can help guide clinical decision-making. Materials and Methods Patients with stage IV melanoma receiving anti-CTLA4 therapy from 2008 to 2019 were analyzed. Immune-related colitis treated with ≥50 mg prednisone or equivalent daily or secondary immunosuppression was included. Moderate colitis was defined as receipt of oral glucocorticoids only; severe colitis was defined as requiring intravenous glucocorticoids or secondary immunosuppression. The primary outcome was overall survival (OS). Results In total, 171 patients received monotherapy, and 91 received dual checkpoint therapy. In the monotherapy group, 25 patients developed colitis and a nonsignificant trend toward improved OS was observed in this group. Notably, when colitis was categorized as none, moderate or severe, OS was significantly improved for moderate colitis only. This survival difference was not present after dual checkpoint therapy. There were no differences in known prognostic variables between groups, and on multivariable analysis neither completion of all ipilimumab cycles nor resumption of immunotherapy correlated with OS, while the development of moderate colitis did significantly affect OS. Conclusion This single-institution retrospective series suggests moderate colitis correlates with improved OS for patients with stage IV melanoma treated with single-agent anti-CTLA4, but not dual agent, and that this is true regardless of whether the immune-checkpoint blockade is permanently discontinued.
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Affiliation(s)
- Emily J Anstadt
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, PA, USA
| | - Brian Chu
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Nikhil Yegya-Raman
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, PA, USA
| | - Xiaoyan Han
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Abigail Doucette
- Abramson Cancer Center, University of Pennsylvania, Philadelphia, PA, USA
| | - Kendra Poirier
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, PA, USA
| | - Jahan J Mohiuddin
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, PA, USA
| | - Amit Maity
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, PA, USA
| | - Andrea Facciabene
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, PA, USA
| | - Ravi K Amaravadi
- Division of Hematology and Oncology, Department of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Giorgos C Karakousis
- Division of Endocrine and Oncologic Surgery, Department of Surgery, University of Pennsylvania, Philadelphia, PA, USA
| | - Justine V Cohen
- Division of Hematology and Oncology, Department of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Tara C Mitchell
- Division of Hematology and Oncology, Department of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Lynn M Schuchter
- Division of Hematology and Oncology, Department of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - John N Lukens
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, PA, USA
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Campochiaro C, De Luca G, Dagna L. Cardiac immune-related adverse events: an immune-cardio-oncology puzzle. Eur J Heart Fail 2021; 23:1748-1749. [PMID: 34383998 DOI: 10.1002/ejhf.2329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Accepted: 08/08/2021] [Indexed: 11/06/2022] Open
Affiliation(s)
- Corrado Campochiaro
- Unit of Immunology, Rheumatology, Allergy and Rare Diseases, IRCCS San Raffaele Hospital, Milan, Italy.,Vita-Salute San Raffaele University, Milan, Italy
| | - Giacomo De Luca
- Unit of Immunology, Rheumatology, Allergy and Rare Diseases, IRCCS San Raffaele Hospital, Milan, Italy.,Vita-Salute San Raffaele University, Milan, Italy
| | - Lorenzo Dagna
- Unit of Immunology, Rheumatology, Allergy and Rare Diseases, IRCCS San Raffaele Hospital, Milan, Italy.,Vita-Salute San Raffaele University, Milan, Italy
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