Felicitas Schulz, Carolin Kellersmann, Beate Betz, Barbara Hildebrandt, Annika Kasprzak, Corinna Strupp, Felicitas Thol, Michael Heuser, Christina Ganster, Fabian Beier, Katja Sockel, Wolf-Karsten Hofmann, Andrea Kuendgen, Paul Jaeger, Michael Pfeilstoecker, Michael Lauseker, Sascha Dietrich, Nobert Gattermann, Kathrin Nachtkamp, Detlef Haase, Ulrich Germing
{"title":"在日常临床实践中分子数据有限的情况下,IPSS-M、IPSS-R和AIPSS-MDS预测预后的比较","authors":"Felicitas Schulz, Carolin Kellersmann, Beate Betz, Barbara Hildebrandt, Annika Kasprzak, Corinna Strupp, Felicitas Thol, Michael Heuser, Christina Ganster, Fabian Beier, Katja Sockel, Wolf-Karsten Hofmann, Andrea Kuendgen, Paul Jaeger, Michael Pfeilstoecker, Michael Lauseker, Sascha Dietrich, Nobert Gattermann, Kathrin Nachtkamp, Detlef Haase, Ulrich Germing","doi":"10.1007/s00277-025-06570-0","DOIUrl":null,"url":null,"abstract":"<p><p>The IPSS-M was developed to revolutionize the prediction of MDS patients' survival by incorporating molecular data. To compensate for lack of access to molecular analyses, the AIPSS-MDS, a supervised machine learning algorithm exclusively based on clinical and cytogenetic data, was developed by the Spanish MDS Group. We used data of the Düsseldorf MDS Registry and included 207 of more than 8500 registry patients whose IPSS-M-requested complete molecular data were known to compare and validate prognostication regarding OS and LFS of the IPSS-M, IPSS-R and AIPSS-MDS. All three tools reliably prognosticated median OS of patients even in a comparatively small patient cohort. The IPSS-M provided the most accurate prediction of median OS while the frequent lack of molecular data persists as an obstacle in daily clinical practice. Due to these circumstances, the IPSS-R remains the prognostication tool with the widest applicability. Based on our data, prognostication using the AIPSS-MDS is also feasible but less precise.</p>","PeriodicalId":8068,"journal":{"name":"Annals of Hematology","volume":" ","pages":""},"PeriodicalIF":2.4000,"publicationDate":"2025-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Comparison of prognostication by IPSS-M, IPSS-R and AIPSS-MDS in the context of limited availability of molecular data in daily clinical practice.\",\"authors\":\"Felicitas Schulz, Carolin Kellersmann, Beate Betz, Barbara Hildebrandt, Annika Kasprzak, Corinna Strupp, Felicitas Thol, Michael Heuser, Christina Ganster, Fabian Beier, Katja Sockel, Wolf-Karsten Hofmann, Andrea Kuendgen, Paul Jaeger, Michael Pfeilstoecker, Michael Lauseker, Sascha Dietrich, Nobert Gattermann, Kathrin Nachtkamp, Detlef Haase, Ulrich Germing\",\"doi\":\"10.1007/s00277-025-06570-0\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>The IPSS-M was developed to revolutionize the prediction of MDS patients' survival by incorporating molecular data. To compensate for lack of access to molecular analyses, the AIPSS-MDS, a supervised machine learning algorithm exclusively based on clinical and cytogenetic data, was developed by the Spanish MDS Group. We used data of the Düsseldorf MDS Registry and included 207 of more than 8500 registry patients whose IPSS-M-requested complete molecular data were known to compare and validate prognostication regarding OS and LFS of the IPSS-M, IPSS-R and AIPSS-MDS. All three tools reliably prognosticated median OS of patients even in a comparatively small patient cohort. The IPSS-M provided the most accurate prediction of median OS while the frequent lack of molecular data persists as an obstacle in daily clinical practice. Due to these circumstances, the IPSS-R remains the prognostication tool with the widest applicability. Based on our data, prognostication using the AIPSS-MDS is also feasible but less precise.</p>\",\"PeriodicalId\":8068,\"journal\":{\"name\":\"Annals of Hematology\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":2.4000,\"publicationDate\":\"2025-09-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Annals of Hematology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1007/s00277-025-06570-0\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"HEMATOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annals of Hematology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s00277-025-06570-0","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"HEMATOLOGY","Score":null,"Total":0}
Comparison of prognostication by IPSS-M, IPSS-R and AIPSS-MDS in the context of limited availability of molecular data in daily clinical practice.
The IPSS-M was developed to revolutionize the prediction of MDS patients' survival by incorporating molecular data. To compensate for lack of access to molecular analyses, the AIPSS-MDS, a supervised machine learning algorithm exclusively based on clinical and cytogenetic data, was developed by the Spanish MDS Group. We used data of the Düsseldorf MDS Registry and included 207 of more than 8500 registry patients whose IPSS-M-requested complete molecular data were known to compare and validate prognostication regarding OS and LFS of the IPSS-M, IPSS-R and AIPSS-MDS. All three tools reliably prognosticated median OS of patients even in a comparatively small patient cohort. The IPSS-M provided the most accurate prediction of median OS while the frequent lack of molecular data persists as an obstacle in daily clinical practice. Due to these circumstances, the IPSS-R remains the prognostication tool with the widest applicability. Based on our data, prognostication using the AIPSS-MDS is also feasible but less precise.
期刊介绍:
Annals of Hematology covers the whole spectrum of clinical and experimental hematology, hemostaseology, blood transfusion, and related aspects of medical oncology, including diagnosis and treatment of leukemias, lymphatic neoplasias and solid tumors, and transplantation of hematopoietic stem cells. Coverage includes general aspects of oncology, molecular biology and immunology as pertinent to problems of human blood disease. The journal is associated with the German Society for Hematology and Medical Oncology, and the Austrian Society for Hematology and Oncology.