Development of machine learning models for predicting non-remission in early RA highlights the robust predictive importance of the RAID score-evidence from the ARCTIC study
IntroductionAchieving remission is a critical therapeutic goal in the management of rheumatoid arthritis (RA). Despite methotrexate being the cornerstone of early RA treatment, a significant proportion of patients fail to achieve remission. This study aims to predict 6-month non-remission in 222 dis...
Saved in:
| Main Authors: | , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-02-01
|
| Series: | Frontiers in Medicine |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2025.1526708/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1823856617460858880 |
|---|---|
| author | Gaoyang Li Shrikant S. Kolan Franco Grimolizzi Joseph Sexton Giulia Malachin Guro Goll Tore K. Kvien Tore K. Kvien Nina Paulshus Sundlisæter Manuela Zucknick Siri Lillegraven Espen A. Haavardsholm Espen A. Haavardsholm Bjørn Steen Skålhegg |
| author_facet | Gaoyang Li Shrikant S. Kolan Franco Grimolizzi Joseph Sexton Giulia Malachin Guro Goll Tore K. Kvien Tore K. Kvien Nina Paulshus Sundlisæter Manuela Zucknick Siri Lillegraven Espen A. Haavardsholm Espen A. Haavardsholm Bjørn Steen Skålhegg |
| author_sort | Gaoyang Li |
| collection | DOAJ |
| description | IntroductionAchieving remission is a critical therapeutic goal in the management of rheumatoid arthritis (RA). Despite methotrexate being the cornerstone of early RA treatment, a significant proportion of patients fail to achieve remission. This study aims to predict 6-month non-remission in 222 disease-modifying anti-rheumatic drug (DMARD)-naïve RA patients initiating methotrexate monotherapy, using baseline patient characteristics from the ARCTIC trial.MethodsMachine learning models were developed utilizing twenty-one baseline demographic, clinical and laboratory features to predict non-remission according to ACR/EULAR Boolean, SDAI and CDAI criteria. The model employed a super learner algorithm that combine three base algorithms of elastic net, random forest and support vector machine. The model performance was evaluated through five independent unseen tests with nested 5-fold cross-validation. The predictive power of each feature was assessed using a composite measure derived from individual algorithm estimates.ResultsThe model demonstrated a mean AUC-ROC of 0.75-0.76, with mean sensitivity of 0.77-0.81, precision (also referred to as Positive Predictive Value) of 0.77-0.79 and specificity of 0.63-0.66 across the criteria. Predictive power analysis of each feature identified the baseline Rheumatoid Arthritis Impact of Disease (RAID) score as the strongest predictor of non-remission. A simplified model using RAID score alone demonstrated comparable performance to the full-feature model.ConclusionThese findings highlight the potential utility of baseline RAID score-based model as an effective tool for early identification of patients at risk of non-remission in clinical practise. |
| format | Article |
| id | doaj-art-c799fa1536384faaace1276bfd5a503e |
| institution | Kabale University |
| issn | 2296-858X |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Medicine |
| spelling | doaj-art-c799fa1536384faaace1276bfd5a503e2025-02-12T07:26:21ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2025-02-011210.3389/fmed.2025.15267081526708Development of machine learning models for predicting non-remission in early RA highlights the robust predictive importance of the RAID score-evidence from the ARCTIC studyGaoyang Li0Shrikant S. Kolan1Franco Grimolizzi2Joseph Sexton3Giulia Malachin4Guro Goll5Tore K. Kvien6Tore K. Kvien7Nina Paulshus Sundlisæter8Manuela Zucknick9Siri Lillegraven10Espen A. Haavardsholm11Espen A. Haavardsholm12Bjørn Steen Skålhegg13Division of Molecular Nutrition, Department of Nutrition, Institute of Basic Medical Sciences, University of Oslo, Oslo, NorwayDivision of Molecular Nutrition, Department of Nutrition, Institute of Basic Medical Sciences, University of Oslo, Oslo, NorwayDivision of Molecular Nutrition, Department of Nutrition, Institute of Basic Medical Sciences, University of Oslo, Oslo, NorwayCenter for Treatment of Rheumatic and Musculoskeletal Diseases (REMEDY), Division of Rheumatology and Research, Diakonhjemmet Hospital, Oslo, NorwayDivision of Molecular Nutrition, Department of Nutrition, Institute of Basic Medical Sciences, University of Oslo, Oslo, NorwayCenter for Treatment of Rheumatic and Musculoskeletal Diseases (REMEDY), Division of Rheumatology and Research, Diakonhjemmet Hospital, Oslo, NorwayCenter for Treatment of Rheumatic and Musculoskeletal Diseases (REMEDY), Division of Rheumatology and Research, Diakonhjemmet Hospital, Oslo, NorwayInstitute of Clinical Medicine, University of Oslo, Oslo, NorwayCenter for Treatment of Rheumatic and Musculoskeletal Diseases (REMEDY), Division of Rheumatology and Research, Diakonhjemmet Hospital, Oslo, NorwayOslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, Oslo, NorwayCenter for Treatment of Rheumatic and Musculoskeletal Diseases (REMEDY), Division of Rheumatology and Research, Diakonhjemmet Hospital, Oslo, NorwayCenter for Treatment of Rheumatic and Musculoskeletal Diseases (REMEDY), Division of Rheumatology and Research, Diakonhjemmet Hospital, Oslo, NorwayInstitute of Clinical Medicine, University of Oslo, Oslo, NorwayDivision of Molecular Nutrition, Department of Nutrition, Institute of Basic Medical Sciences, University of Oslo, Oslo, NorwayIntroductionAchieving remission is a critical therapeutic goal in the management of rheumatoid arthritis (RA). Despite methotrexate being the cornerstone of early RA treatment, a significant proportion of patients fail to achieve remission. This study aims to predict 6-month non-remission in 222 disease-modifying anti-rheumatic drug (DMARD)-naïve RA patients initiating methotrexate monotherapy, using baseline patient characteristics from the ARCTIC trial.MethodsMachine learning models were developed utilizing twenty-one baseline demographic, clinical and laboratory features to predict non-remission according to ACR/EULAR Boolean, SDAI and CDAI criteria. The model employed a super learner algorithm that combine three base algorithms of elastic net, random forest and support vector machine. The model performance was evaluated through five independent unseen tests with nested 5-fold cross-validation. The predictive power of each feature was assessed using a composite measure derived from individual algorithm estimates.ResultsThe model demonstrated a mean AUC-ROC of 0.75-0.76, with mean sensitivity of 0.77-0.81, precision (also referred to as Positive Predictive Value) of 0.77-0.79 and specificity of 0.63-0.66 across the criteria. Predictive power analysis of each feature identified the baseline Rheumatoid Arthritis Impact of Disease (RAID) score as the strongest predictor of non-remission. A simplified model using RAID score alone demonstrated comparable performance to the full-feature model.ConclusionThese findings highlight the potential utility of baseline RAID score-based model as an effective tool for early identification of patients at risk of non-remission in clinical practise.https://www.frontiersin.org/articles/10.3389/fmed.2025.1526708/fullrheumatoid arthritismethotrexateremissionpredictionmachine learning |
| spellingShingle | Gaoyang Li Shrikant S. Kolan Franco Grimolizzi Joseph Sexton Giulia Malachin Guro Goll Tore K. Kvien Tore K. Kvien Nina Paulshus Sundlisæter Manuela Zucknick Siri Lillegraven Espen A. Haavardsholm Espen A. Haavardsholm Bjørn Steen Skålhegg Development of machine learning models for predicting non-remission in early RA highlights the robust predictive importance of the RAID score-evidence from the ARCTIC study Frontiers in Medicine rheumatoid arthritis methotrexate remission prediction machine learning |
| title | Development of machine learning models for predicting non-remission in early RA highlights the robust predictive importance of the RAID score-evidence from the ARCTIC study |
| title_full | Development of machine learning models for predicting non-remission in early RA highlights the robust predictive importance of the RAID score-evidence from the ARCTIC study |
| title_fullStr | Development of machine learning models for predicting non-remission in early RA highlights the robust predictive importance of the RAID score-evidence from the ARCTIC study |
| title_full_unstemmed | Development of machine learning models for predicting non-remission in early RA highlights the robust predictive importance of the RAID score-evidence from the ARCTIC study |
| title_short | Development of machine learning models for predicting non-remission in early RA highlights the robust predictive importance of the RAID score-evidence from the ARCTIC study |
| title_sort | development of machine learning models for predicting non remission in early ra highlights the robust predictive importance of the raid score evidence from the arctic study |
| topic | rheumatoid arthritis methotrexate remission prediction machine learning |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2025.1526708/full |
| work_keys_str_mv | AT gaoyangli developmentofmachinelearningmodelsforpredictingnonremissioninearlyrahighlightstherobustpredictiveimportanceoftheraidscoreevidencefromthearcticstudy AT shrikantskolan developmentofmachinelearningmodelsforpredictingnonremissioninearlyrahighlightstherobustpredictiveimportanceoftheraidscoreevidencefromthearcticstudy AT francogrimolizzi developmentofmachinelearningmodelsforpredictingnonremissioninearlyrahighlightstherobustpredictiveimportanceoftheraidscoreevidencefromthearcticstudy AT josephsexton developmentofmachinelearningmodelsforpredictingnonremissioninearlyrahighlightstherobustpredictiveimportanceoftheraidscoreevidencefromthearcticstudy AT giuliamalachin developmentofmachinelearningmodelsforpredictingnonremissioninearlyrahighlightstherobustpredictiveimportanceoftheraidscoreevidencefromthearcticstudy AT gurogoll developmentofmachinelearningmodelsforpredictingnonremissioninearlyrahighlightstherobustpredictiveimportanceoftheraidscoreevidencefromthearcticstudy AT torekkvien developmentofmachinelearningmodelsforpredictingnonremissioninearlyrahighlightstherobustpredictiveimportanceoftheraidscoreevidencefromthearcticstudy AT torekkvien developmentofmachinelearningmodelsforpredictingnonremissioninearlyrahighlightstherobustpredictiveimportanceoftheraidscoreevidencefromthearcticstudy AT ninapaulshussundlisæter developmentofmachinelearningmodelsforpredictingnonremissioninearlyrahighlightstherobustpredictiveimportanceoftheraidscoreevidencefromthearcticstudy AT manuelazucknick developmentofmachinelearningmodelsforpredictingnonremissioninearlyrahighlightstherobustpredictiveimportanceoftheraidscoreevidencefromthearcticstudy AT sirilillegraven developmentofmachinelearningmodelsforpredictingnonremissioninearlyrahighlightstherobustpredictiveimportanceoftheraidscoreevidencefromthearcticstudy AT espenahaavardsholm developmentofmachinelearningmodelsforpredictingnonremissioninearlyrahighlightstherobustpredictiveimportanceoftheraidscoreevidencefromthearcticstudy AT espenahaavardsholm developmentofmachinelearningmodelsforpredictingnonremissioninearlyrahighlightstherobustpredictiveimportanceoftheraidscoreevidencefromthearcticstudy AT bjørnsteenskalhegg developmentofmachinelearningmodelsforpredictingnonremissioninearlyrahighlightstherobustpredictiveimportanceoftheraidscoreevidencefromthearcticstudy |