Assessing the Relevance of Non-molecular Prognostic Systems for Myelodysplastic Syndrome in the Era of Next-Generation Sequencing.

IF 4 2区 医学 Q1 MEDICAL LABORATORY TECHNOLOGY Annals of Laboratory Medicine Pub Date : 2025-01-01 Epub Date: 2024-07-26 DOI:10.3343/alm.2024.0089
Marco Lincango, Verónica Andreoli, Hernán García Rivello, Andrea Bender, Ana I Catalán, Marilina Rahhal, Rocío Delamer, Mariana Asinari, Adrián Mosquera Orgueira, María Belén Castro, María José Mela Osorio, Alicia Navickas, Sofia Grille, Evangelina Agriello, Jorge Arbelbide, Ana Lisa Basquiera, Carolina B Belli
{"title":"Assessing the Relevance of Non-molecular Prognostic Systems for Myelodysplastic Syndrome in the Era of Next-Generation Sequencing.","authors":"Marco Lincango, Verónica Andreoli, Hernán García Rivello, Andrea Bender, Ana I Catalán, Marilina Rahhal, Rocío Delamer, Mariana Asinari, Adrián Mosquera Orgueira, María Belén Castro, María José Mela Osorio, Alicia Navickas, Sofia Grille, Evangelina Agriello, Jorge Arbelbide, Ana Lisa Basquiera, Carolina B Belli","doi":"10.3343/alm.2024.0089","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>The Molecular International Prognostic Scoring System (IPSS-M) has improved the prediction of clinical outcomes for myelodysplastic syndromes (MDS). The Artificial Intelligence Prognostic Scoring System for MDS (AIPSS-MDS), based on classical clinical parameters, has outperformed the IPSS, revised version (IPSS-R). For the first time, we validated the IPSS-M and other molecular prognostic models and compared them with the established IPSS-R and AIPSS-MDS models using data from South American patients.</p><p><strong>Methods: </strong>Molecular and clinical data from 145 patients with MDS and 37 patients with MDS/myeloproliferative neoplasms were retrospectively analyzed.</p><p><strong>Results: </strong>Prognostic power evaluation revealed that the IPSS-M (Harrell's concordance [C]-index: 0.75, area under the receiver operating characteristic curve [AUC]: 0.68) predicted overall survival better than the European MDS (EuroMDS; C-index: 0.72, AUC: 0.68) and Munich Leukemia Laboratory (MLL) (C-index: 0.70, AUC: 0.64) models. The IPSS-M prognostic discrimination was similar to that of the AIPSS-MDS model (C-index: 0.74, AUC: 0.66) and outperformed the IPSS-R model (C-index: 0.70, AUC: 0.61). Considering simplified low- and high-risk groups for clinical management, after restratifying from IPSS-R (57% and 32%, respectively, hazard ratio [HR]: 2.8; <i>P</i>=0.002) to IPSS-M, 12.6% of patients were upstaged, and 5% were downstaged (HR: 2.9; <i>P</i>=0.001). The AIPSS-MDS recategorized 51% of the low-risk cohort as high-risk, with no patients being downstaged (HR: 5.6; <i>P</i><0.001), consistent with most patients requiring disease-modifying therapy.</p><p><strong>Conclusions: </strong>The IPSS-M and AIPSS-MDS models provide more accurate survival prognoses than the IPSS-R, EuroMDS, and MLL models. The AIPSS-MDS model is a valid option for assessing risks for all patients with MDS, especially in resource-limited centers where molecular testing is not currently a standard clinical practice.</p>","PeriodicalId":8421,"journal":{"name":"Annals of Laboratory Medicine","volume":" ","pages":"44-52"},"PeriodicalIF":4.0000,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11609712/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annals of Laboratory Medicine","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.3343/alm.2024.0089","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/7/26 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"MEDICAL LABORATORY TECHNOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

Background: The Molecular International Prognostic Scoring System (IPSS-M) has improved the prediction of clinical outcomes for myelodysplastic syndromes (MDS). The Artificial Intelligence Prognostic Scoring System for MDS (AIPSS-MDS), based on classical clinical parameters, has outperformed the IPSS, revised version (IPSS-R). For the first time, we validated the IPSS-M and other molecular prognostic models and compared them with the established IPSS-R and AIPSS-MDS models using data from South American patients.

Methods: Molecular and clinical data from 145 patients with MDS and 37 patients with MDS/myeloproliferative neoplasms were retrospectively analyzed.

Results: Prognostic power evaluation revealed that the IPSS-M (Harrell's concordance [C]-index: 0.75, area under the receiver operating characteristic curve [AUC]: 0.68) predicted overall survival better than the European MDS (EuroMDS; C-index: 0.72, AUC: 0.68) and Munich Leukemia Laboratory (MLL) (C-index: 0.70, AUC: 0.64) models. The IPSS-M prognostic discrimination was similar to that of the AIPSS-MDS model (C-index: 0.74, AUC: 0.66) and outperformed the IPSS-R model (C-index: 0.70, AUC: 0.61). Considering simplified low- and high-risk groups for clinical management, after restratifying from IPSS-R (57% and 32%, respectively, hazard ratio [HR]: 2.8; P=0.002) to IPSS-M, 12.6% of patients were upstaged, and 5% were downstaged (HR: 2.9; P=0.001). The AIPSS-MDS recategorized 51% of the low-risk cohort as high-risk, with no patients being downstaged (HR: 5.6; P<0.001), consistent with most patients requiring disease-modifying therapy.

Conclusions: The IPSS-M and AIPSS-MDS models provide more accurate survival prognoses than the IPSS-R, EuroMDS, and MLL models. The AIPSS-MDS model is a valid option for assessing risks for all patients with MDS, especially in resource-limited centers where molecular testing is not currently a standard clinical practice.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
评估下一代测序时代骨髓增生异常综合征非分子预后系统的相关性。
背景:分子国际预后评分系统(IPSS-M分子国际预后评分系统(IPSS-M)改善了骨髓增生异常综合征(MDS)临床预后的预测。基于经典临床参数的 MDS 人工智能预后评分系统(AIPSS-MDS)的表现优于 IPSS 修订版(IPSS-R)。我们首次利用南美患者的数据验证了 IPSS-M 和其他分子预后模型,并将其与已建立的 IPSS-R 和 AIPSS-MDS 模型进行了比较:回顾性分析了145例MDS患者和37例MDS/骨髓增殖性肿瘤患者的分子和临床数据:结果:预后能力评估显示,IPSS-M(Harrell's concordance [C]-index:0.75,接收者操作特征曲线下面积[AUC]:0.68)比欧洲 MDS(EuroMDS;C-指数:0.72,AUC:0.68)和慕尼黑白血病实验室(MLL)(C-指数:0.70,AUC:0.64)模型更能预测总生存期。IPSS-M预后判别能力与AIPSS-MDS模型相似(C-index:0.74,AUC:0.66),优于IPSS-R模型(C-index:0.70,AUC:0.61)。考虑到简化的临床管理低风险组和高风险组,从 IPSS-R 模型(分别为 57% 和 32%,危险比 [HR]:2.8;P=0.002)调整为 IPSS-M 模型后,12.6% 的患者被上调分期,5% 的患者被下调分期(HR:2.9;P=0.001)。AIPSS-MDS 将 51% 的低危患者重新归类为高危,没有患者被降级(HR:5.6;P=0.001):与 IPSS-R、EuroMDS 和 MLL 模型相比,IPSS-M 和 AIPSS-MDS 模型能提供更准确的生存预后。AIPSS-MDS 模型是评估所有 MDS 患者风险的有效选择,尤其是在资源有限的中心,因为目前分子检测还不是标准的临床实践。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Annals of Laboratory Medicine
Annals of Laboratory Medicine MEDICAL LABORATORY TECHNOLOGY-
CiteScore
8.30
自引率
12.20%
发文量
100
审稿时长
6-12 weeks
期刊介绍: Annals of Laboratory Medicine is the official journal of Korean Society for Laboratory Medicine. The journal title has been recently changed from the Korean Journal of Laboratory Medicine (ISSN, 1598-6535) from the January issue of 2012. The JCR 2017 Impact factor of Ann Lab Med was 1.916.
期刊最新文献
Clinical Outcomes and Molecular Characteristics of Bacteroides fragilis Infections. TP53 Mutation Status in Myelodysplastic Neoplasm and Acute Myeloid Leukemia: Impact of Reclassification Based on the 5th WHO and International Consensus Classification Criteria: A Korean Multicenter Study. Performance Evaluation of the LabGenius C-CT/NG-BMX Assay for Chlamydia trachomatis and Neisseria gonorrhoeae Detection. A Machine Learning Approach for Predicting In-Hospital Cardiac Arrest Using Single-Day Vital Signs, Laboratory Test Results, and International Classification of Disease-10 Block for Diagnosis. Artificial Intelligence in Diagnostics: Enhancing Urine Test Accuracy Using a Mobile Phone-Based Reading System.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1