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Validation of an online application to identify potential immune-related adverse events associated with immune checkpoint inhibitors based on the patient’s symptoms
BACKGROUND: Immune checkpoint inhibitors (ICIs) are increasingly being used to treat malignancies. Some patients experience immune-related adverse events (irAEs), which may affect any organ/tissue. IrAEs are occasionally fatal and usually have nonspecific symptoms. We developed a three-step applicat...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8923505/ https://www.ncbi.nlm.nih.gov/pubmed/35290407 http://dx.doi.org/10.1371/journal.pone.0265230 |
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author | Osawa, Takahiro Abe, Takashige Kikuchi, Hiroshi Matsumoto, Ryuji Murai, Sachiyo Nakao, Takafumi Tanaka, Shinji Watanabe, Ayu Shinohara, Nobuo |
author_facet | Osawa, Takahiro Abe, Takashige Kikuchi, Hiroshi Matsumoto, Ryuji Murai, Sachiyo Nakao, Takafumi Tanaka, Shinji Watanabe, Ayu Shinohara, Nobuo |
author_sort | Osawa, Takahiro |
collection | PubMed |
description | BACKGROUND: Immune checkpoint inhibitors (ICIs) are increasingly being used to treat malignancies. Some patients experience immune-related adverse events (irAEs), which may affect any organ/tissue. IrAEs are occasionally fatal and usually have nonspecific symptoms. We developed a three-step application (https://irae-search.com/) to provide healthcare professionals with information on the diagnosis, treatment options, and published reports for 38 categories of irAEs encountered in clinical practice. METHODS: IrAEs reported in ≥5 cases were identified from articles published between October 2018 and August 2020 by searching Japanese (SELIMIC, JAPIC-Q Service, and JMED Plus) and international (MEDLINE, EMBASE, Derwent Drug File) databases. The cases’ symptoms were entered into the application to identify irAEs, which were verified using the reported diagnosis, to evaluate the application’s sensitivity and specificity. RESULTS: Overall, 1209 cases (1067 reports) were analyzed. The three most common categories of irAEs were pituitary or adrenal disorders (14% of cases), skin disorders (13%), and diabetes mellitus (10%). The top three primary diseases were lung cancer (364 cases), melanoma (286 cases), and renal cell carcinoma (218 cases). The average sensitivity was 90.8% (range 44.4%–100.0%) initially, and improved to 94.8% (range 83.3%–100.0%) after incorporating the symptoms reported in published cases into the application’s logic for two irAE categories. The average specificity was 79.3% (range 59.1% [thyroid disorders]–98.2% [arthritis]). CONCLUSION: irAE Search is an easy-to-use application designed to help healthcare professionals identify potential irAEs in ICI-treated patients in a timely manner to facilitate prompt management/treatment. The application showed high sensitivity and moderate-to-high specificity for detecting irAEs. |
format | Online Article Text |
id | pubmed-8923505 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-89235052022-03-16 Validation of an online application to identify potential immune-related adverse events associated with immune checkpoint inhibitors based on the patient’s symptoms Osawa, Takahiro Abe, Takashige Kikuchi, Hiroshi Matsumoto, Ryuji Murai, Sachiyo Nakao, Takafumi Tanaka, Shinji Watanabe, Ayu Shinohara, Nobuo PLoS One Research Article BACKGROUND: Immune checkpoint inhibitors (ICIs) are increasingly being used to treat malignancies. Some patients experience immune-related adverse events (irAEs), which may affect any organ/tissue. IrAEs are occasionally fatal and usually have nonspecific symptoms. We developed a three-step application (https://irae-search.com/) to provide healthcare professionals with information on the diagnosis, treatment options, and published reports for 38 categories of irAEs encountered in clinical practice. METHODS: IrAEs reported in ≥5 cases were identified from articles published between October 2018 and August 2020 by searching Japanese (SELIMIC, JAPIC-Q Service, and JMED Plus) and international (MEDLINE, EMBASE, Derwent Drug File) databases. The cases’ symptoms were entered into the application to identify irAEs, which were verified using the reported diagnosis, to evaluate the application’s sensitivity and specificity. RESULTS: Overall, 1209 cases (1067 reports) were analyzed. The three most common categories of irAEs were pituitary or adrenal disorders (14% of cases), skin disorders (13%), and diabetes mellitus (10%). The top three primary diseases were lung cancer (364 cases), melanoma (286 cases), and renal cell carcinoma (218 cases). The average sensitivity was 90.8% (range 44.4%–100.0%) initially, and improved to 94.8% (range 83.3%–100.0%) after incorporating the symptoms reported in published cases into the application’s logic for two irAE categories. The average specificity was 79.3% (range 59.1% [thyroid disorders]–98.2% [arthritis]). CONCLUSION: irAE Search is an easy-to-use application designed to help healthcare professionals identify potential irAEs in ICI-treated patients in a timely manner to facilitate prompt management/treatment. The application showed high sensitivity and moderate-to-high specificity for detecting irAEs. Public Library of Science 2022-03-15 /pmc/articles/PMC8923505/ /pubmed/35290407 http://dx.doi.org/10.1371/journal.pone.0265230 Text en © 2022 Osawa et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Osawa, Takahiro Abe, Takashige Kikuchi, Hiroshi Matsumoto, Ryuji Murai, Sachiyo Nakao, Takafumi Tanaka, Shinji Watanabe, Ayu Shinohara, Nobuo Validation of an online application to identify potential immune-related adverse events associated with immune checkpoint inhibitors based on the patient’s symptoms |
title | Validation of an online application to identify potential immune-related adverse events associated with immune checkpoint inhibitors based on the patient’s symptoms |
title_full | Validation of an online application to identify potential immune-related adverse events associated with immune checkpoint inhibitors based on the patient’s symptoms |
title_fullStr | Validation of an online application to identify potential immune-related adverse events associated with immune checkpoint inhibitors based on the patient’s symptoms |
title_full_unstemmed | Validation of an online application to identify potential immune-related adverse events associated with immune checkpoint inhibitors based on the patient’s symptoms |
title_short | Validation of an online application to identify potential immune-related adverse events associated with immune checkpoint inhibitors based on the patient’s symptoms |
title_sort | validation of an online application to identify potential immune-related adverse events associated with immune checkpoint inhibitors based on the patient’s symptoms |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8923505/ https://www.ncbi.nlm.nih.gov/pubmed/35290407 http://dx.doi.org/10.1371/journal.pone.0265230 |
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