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Lundberg K, Qin L, Aulin C, van Spil WE, Maurits MP, Knevel R. Population-based user-perceived experience of Rheumatic?: a novel digital symptom-checker in rheumatology. RMD Open 2023; 9:rmdopen-2022-002974. [PMID: 37094982 PMCID: PMC10152040 DOI: 10.1136/rmdopen-2022-002974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Accepted: 04/03/2023] [Indexed: 04/26/2023] Open
Abstract
OBJECTIVE Digital symptom-checkers (SCs) have potential to improve rheumatology triage and reduce diagnostic delays. In addition to being accurate, SCs should be user friendly and meet patient's needs. Here, we examined usability and acceptance of Rheumatic?-a new and freely available online SC (currently with >44 000 users)-in a real-world setting. METHODS Study participants were recruited from an ongoing prospective study, and included people ≥18 years with musculoskeletal complaints completing Rheumatic? online. The user experience survey comprised five usability and acceptability questions (11-point rating scale), and an open-ended question regarding improvement of Rheumatic? Data were analysed in R using t-test or Wilcoxon rank test (group comparisons), or linear regression (continuous variables). RESULTS A total of 12 712 people completed the user experience survey. The study population had a normal age distribution, with a peak at 50-59 years, and 78% women. A majority found Rheumatic? useful (78%), thought the questionnaire gave them an opportunity to describe their complaints well (76%), and would recommend Rheumatic? to friends and other patients (74%). Main shortcoming was that 36% thought there were too many questions. Still, 39% suggested more detailed questions, and only 2% suggested a reduction of questions. CONCLUSION Based on real-world data from the largest user evaluation study of a digital SC in rheumatology, we conclude that Rheumatic? is well accepted by women and men with rheumatic complaints, in all investigated age groups. Wide-scale adoption of Rheumatic?, therefore, seems feasible, with promising scientific and clinical implications on the horizon.
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Affiliation(s)
- Karin Lundberg
- Division of Rheumatology, Department of Medicine Solna, Karolinska Institutet and Center for Molecular Medicine, Karolinska University Hospital, Stockholm, Sweden
- Elsa Science AB, Stockholm, Sweden
| | - Ling Qin
- Department of Rheumatology, Leiden University Medical Center, Leiden, Netherlands
| | - Cecilia Aulin
- Division of Rheumatology, Department of Medicine Solna, Karolinska Institutet and Center for Molecular Medicine, Karolinska University Hospital, Stockholm, Sweden
| | | | - Marc P Maurits
- Department of Rheumatology, Leiden University Medical Center, Leiden, Netherlands
| | - Rachel Knevel
- Department of Rheumatology, Leiden University Medical Center, Leiden, Netherlands
- Rheumatology, Newcastle University Translational and Clinical Research Institute, Newcastle upon Tyne, UK
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Maurits MP, Wouters F, Niemantsverdriet E, Huizinga TWJ, van den Akker EB, Le Cessie S, van der Helm-van Mil AHM, Knevel R. The Role of Genetics in Clinically Suspect Arthralgia and Rheumatoid Arthritis Development: A Large Cross-Sectional Study. Arthritis Rheumatol 2023; 75:178-186. [PMID: 36514807 PMCID: PMC10107764 DOI: 10.1002/art.42323] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 06/21/2022] [Accepted: 07/30/2022] [Indexed: 02/02/2023]
Abstract
OBJECTIVE To investigate whether established genetic predictors for rheumatoid arthritis (RA) differentiate healthy controls, patients with clinically suspect arthralgia (CSA), and RA patients. METHODS Using analyses of variance, chi-square tests, and mean risk difference analyses, we investigated the association of an RA polygenic risk score (PRS) and HLA shared epitope (HLA-SE) with all participant groups, both unstratified and stratified for anti-citrullinated protein antibody (ACPA) status. We used 3 separate data sets sampled from the same Dutch population (1,015 healthy controls, 479 CSA patients, and 1,146 early classified RA patients). CSA patients were assessed for conversion to inflammatory arthritis over a period of 2 years, after which they were classified as either CSA converters (n = 84) or CSA nonconverters (n = 395). RESULTS The PRS was increased in RA patients (mean ± SD PRS 1.31 ± 0.96) compared to the complete CSA group (1.07 ± 0.94) and compared to CSA converters (1.12 ± 0.94). In ACPA- strata, PRS distributions differed strongly when comparing the complete CSA group (mean ± SD PRS 1.05 ± 0.94) and CSA converters (0.97 ± 0.87) to RA patients (1.20 ± 0.94), while in the ACPA+ strata, the complete CSA group (1.25 ± 0.99) differed clearly from healthy controls (1.05 ± 0.94) and RA patients (1.41 ± 0.96). HLA-SE was more prevalent in the RA group (prevalence 0.64) than the complete CSA group (0.45), with small differences between RA patients and CSA converters (0.64 versus 0.60) and larger differences between CSA converters and CSA nonconverters (0.60 versus 0.42). HLA-SE prevalence differed more strongly within the ACPA+ strata as follows: healthy controls (prevalence 0.43), CSA nonconverters (0.48), complete CSA group (0.59), CSA converters (0.66), and RA patients (0.79). CONCLUSION We observed that genetic predisposition increased across pre-RA participant groups. The RA PRS differed in early classified RA and inflammatory pre-disease stages, regardless of ACPA stratification. HLA-SE prevalence differed between arthritis patients, particularly ACPA+ patients, and healthy controls. Genetics seem to fulfill different etiologic roles.
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Affiliation(s)
- Marc P Maurits
- Department of Rheumatology, Leiden University Medical Center, The Netherlands
| | - Fenne Wouters
- Department of Rheumatology, Leiden University Medical Center, The Netherlands
| | | | - Thomas W J Huizinga
- Department of Rheumatology, Leiden University Medical Center, The Netherlands
| | - Erik B van den Akker
- Department of Biomedical Data Sciences Leiden, Leiden University Medical Center, The Netherlands
| | - Saskia Le Cessie
- Department of Medical Statistics, Leiden University Medical Center, The Netherlands
| | | | - Rachel Knevel
- Department of Rheumatology, Leiden University Medical Center, The Netherlands, and Translational & Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
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Maurits MP, Korsunsky I, Raychaudhuri S, Murphy SN, Smoller JW, Weiss ST, Petukhova LM, Weng C, Wei WQ, Huizinga TWJ, Reinders MJT, Karlson EW, van den Akker EB, Knevel R. A framework for employing longitudinally collected multicenter electronic health records to stratify heterogeneous patient populations on disease history. J Am Med Inform Assoc 2022; 29:761-769. [PMID: 35139533 PMCID: PMC9122640 DOI: 10.1093/jamia/ocac008] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 11/24/2021] [Accepted: 01/27/2022] [Indexed: 11/23/2022] Open
Abstract
OBJECTIVE To facilitate patient disease subset and risk factor identification by constructing a pipeline which is generalizable, provides easily interpretable results, and allows replication by overcoming electronic health records (EHRs) batch effects. MATERIAL AND METHODS We used 1872 billing codes in EHRs of 102 880 patients from 12 healthcare systems. Using tools borrowed from single-cell omics, we mitigated center-specific batch effects and performed clustering to identify patients with highly similar medical history patterns across the various centers. Our visualization method (PheSpec) depicts the phenotypic profile of clusters, applies a novel filtering of noninformative codes (Ranked Scope Pervasion), and indicates the most distinguishing features. RESULTS We observed 114 clinically meaningful profiles, for example, linking prostate hyperplasia with cancer and diabetes with cardiovascular problems and grouping pediatric developmental disorders. Our framework identified disease subsets, exemplified by 6 "other headache" clusters, where phenotypic profiles suggested different underlying mechanisms: migraine, convulsion, injury, eye problems, joint pain, and pituitary gland disorders. Phenotypic patterns replicated well, with high correlations of ≥0.75 to an average of 6 (2-8) of the 12 different cohorts, demonstrating the consistency with which our method discovers disease history profiles. DISCUSSION Costly clinical research ventures should be based on solid hypotheses. We repurpose methods from single-cell omics to build these hypotheses from observational EHR data, distilling useful information from complex data. CONCLUSION We establish a generalizable pipeline for the identification and replication of clinically meaningful (sub)phenotypes from widely available high-dimensional billing codes. This approach overcomes datatype problems and produces comprehensive visualizations of validation-ready phenotypes.
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Affiliation(s)
- Marc P Maurits
- Department of Rheumatology, Leiden University Medical Center, Leiden, The Netherlands
- Leiden Computational Biology Center, Leiden University Medical Center, Leiden, The Netherlands
| | - Ilya Korsunsky
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Soumya Raychaudhuri
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Shawn N Murphy
- Research Information Science and Computing, Mass General Brigham, Boston, MA, USA
| | - Jordan W Smoller
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Scott T Weiss
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Lynn M Petukhova
- Lynn M. Petukhova, Department of Dermatology at NewYork-Presbyterian/Columbia University Medical Center (CUMC)
| | - Chunhua Weng
- Chunhua Weng, Biomedical Informatics - Columbia University
| | - Wei-Qi Wei
- Wei-Qi Wei, Biomedical Informatics in the School of Medicine at Vanderbilt University Wei
| | - Thomas W J Huizinga
- Department of Rheumatology, Leiden University Medical Center, Leiden, The Netherlands
| | - Marcel J T Reinders
- Leiden Computational Biology Center, Leiden University Medical Center, Leiden, The Netherlands
- The Delft Bioinformatics Lab, Delft University of Technology, Delft, The Netherlands
| | - Elizabeth W Karlson
- Division of Rheumatology, Inflammation and Immunity, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Erik B van den Akker
- Leiden Computational Biology Center, Leiden University Medical Center, Leiden, The Netherlands
- Section of Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Rachel Knevel
- Department of Rheumatology, Leiden University Medical Center, Leiden, The Netherlands
- Division of Rheumatology, Inflammation and Immunity, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
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Wouters F, Maurits MP, van Boheemen L, Verstappen M, Mankia K, Matthijssen XME, Dorjée AL, Emery P, Knevel R, van Schaardenburg D, Toes REM, van der Helm-van Mil AHM. Determining in which pre-arthritis stage HLA-shared epitope alleles and smoking exert their effect on the development of rheumatoid arthritis. Ann Rheum Dis 2021; 81:48-55. [PMID: 34285049 DOI: 10.1136/annrheumdis-2021-220546] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Accepted: 07/07/2021] [Indexed: 11/03/2022]
Abstract
OBJECTIVES The human leukocyte antigen-shared epitope (HLA-SE) alleles and smoking are the most prominent genetic and environmental risk factors for rheumatoid arthritis (RA). However, at which pre-arthritis stage (asymptomatic/symptomatic) they exert their effect is unknown. We aimed to determine whether HLA-SE and smoking are involved in the onset of autoantibody positivity, symptoms (clinically suspect arthralgia (CSA)) and/or progression to clinical arthritis. METHODS We performed meta-analyses on results from the literature on associations of HLA-SE and smoking with anti-citrullinated protein antibodies (ACPAs) in the asymptomatic population. Next, we studied associations of HLA-SE and smoking with autoantibody positivity at CSA onset and with progression to clinical inflammatory arthritis (IA) during follow-up. Associations in ACPA-positive patients with CSA were validated in meta-analyses with other arthralgia cohorts. Analyses were repeated for rheumatoid factor (RF), anti-carbamylated protein antibodies (anti-CarP) and anti-acetylated protein antibodies (AAPA). RESULTS Meta-analyses showed that HLA-SE is not associated with ACPA positivity in the asymptomatic population (OR 1.06 (95% CI:0.69 to 1.64)), whereas smoking was associated (OR 1.37 (95% CI: 1.15 to 1.63)). At CSA onset, both HLA-SE and smoking associated with ACPA positivity (OR 2.08 (95% CI: 1.24 to 3.49), OR 2.41 (95% CI: 1.31 to 4.43)). During follow-up, HLA-SE associated with IA development (HR 1.86 (95% CI: 1.23 to 2.82)), in contrast to smoking. This was confirmed in meta-analyses in ACPA-positive arthralgia (HR 1.52 (95% CI: 1.08 to 2.15)). HLA-SE and smoking were not associated with RF, anti-CarP or AAPA-positivity at CSA onset. Longitudinally, AAPA associated with IA development independent from ACPA and RF (HR 1.79 (95% CI: 1.02 to 3.16)), anti-CarP did not. CONCLUSIONS HLA-SE and smoking act at different stages: smoking confers risk for ACPA and symptom development, whereas HLA-SE mediates symptom and IA development. These data enhance the understanding of the timing of the key risk factors in the development of RA.
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Affiliation(s)
- Fenne Wouters
- Department of Rheumatology, Leiden University Medical Center, Leiden, The Netherlands
| | - Marc P Maurits
- Department of Rheumatology, Leiden University Medical Center, Leiden, The Netherlands
| | - Laurette van Boheemen
- Department of Rheumatology, Amsterdam Rheumatology and Immunology Center, Reade, Amsterdam, The Netherlands
| | - Marloes Verstappen
- Department of Rheumatology, Leiden University Medical Center, Leiden, The Netherlands
| | - Kulveer Mankia
- Leeds Institute of Rheumatic and Musculoskeletal Medicine, University of Leeds, Leeds, UK
- NIHR Leeds Biomedical Research Unit, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | | | - Annemarie L Dorjée
- Department of Rheumatology, Leiden University Medical Center, Leiden, The Netherlands
| | - Paul Emery
- Leeds Institute of Rheumatic and Musculoskeletal Medicine, University of Leeds, Leeds, UK
- NIHR Leeds Biomedical Research Unit, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - Rachel Knevel
- Department of Rheumatology, Leiden University Medical Center, Leiden, The Netherlands
| | - Dirkjan van Schaardenburg
- Department of Rheumatology, Amsterdam Rheumatology and Immunology Center, Reade, Amsterdam, The Netherlands
| | - René E M Toes
- Department of Rheumatology, Leiden University Medical Center, Leiden, The Netherlands
| | - Annette H M van der Helm-van Mil
- Department of Rheumatology, Leiden University Medical Center, Leiden, The Netherlands
- Department of Rheumatology, Erasmus Medical Center, Rotterdam, The Netherlands
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Maarseveen TD, Maurits MP, Niemantsverdriet E, van der Helm-van Mil AHM, Huizinga TWJ, Knevel R. Handwork vs machine: a comparison of rheumatoid arthritis patient populations as identified from EHR free-text by diagnosis extraction through machine-learning or traditional criteria-based chart review. Arthritis Res Ther 2021; 23:174. [PMID: 34158089 PMCID: PMC8218515 DOI: 10.1186/s13075-021-02553-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 06/02/2021] [Indexed: 11/10/2022] Open
Abstract
Background Electronic health records (EHRs) offer a wealth of observational data. Machine-learning (ML) methods are efficient at data extraction, capable of processing the information-rich free-text physician notes in EHRs. The clinical diagnosis contained therein represents physician expert opinion and is more consistently recorded than classification criteria components. Objectives To investigate the overlap and differences between rheumatoid arthritis patients as identified either from EHR free-text through the extraction of the rheumatologist diagnosis using machine-learning (ML) or through manual chart-review applying the 1987 and 2010 RA classification criteria. Methods Since EHR initiation, 17,662 patients have visited the Leiden rheumatology outpatient clinic. For ML, we used a support vector machine (SVM) model to identify those who were diagnosed with RA by their rheumatologist. We trained and validated the model on a random selection of 2000 patients, balancing PPV and sensitivity to define a cutoff, and assessed performance on a separate 1000 patients. We then deployed the model on our entire patient selection (including the 3000). Of those, 1127 patients had both a 1987 and 2010 EULAR/ACR criteria status at 1 year after inclusion into the local prospective arthritis cohort. In these 1127 patients, we compared the patient characteristics of RA cases identified with ML and those fulfilling the classification criteria. Results The ML model performed very well in the independent test set (sensitivity=0.85, specificity=0.99, PPV=0.86, NPV=0.99). In our selection of patients with both EHR and classification information, 373 were recognized as RA by ML and 357 and 426 fulfilled the 1987 or 2010 criteria, respectively. Eighty percent of the ML-identified cases fulfilled at least one of the criteria sets. Both demographic and clinical parameters did not differ between the ML extracted cases and those identified with EULAR/ACR classification criteria. Conclusions With ML methods, we enable fast patient extraction from the huge EHR resource. Our ML algorithm accurately identifies patients diagnosed with RA by their rheumatologist. This resulting group of RA patients had a strong overlap with patients identified using the 1987 or 2010 classification criteria and the baseline (disease) characteristics were comparable. ML-assisted case labeling enables high-throughput creation of inclusive patient selections for research purposes. Supplementary Information The online version contains supplementary material available at 10.1186/s13075-021-02553-4.
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Affiliation(s)
- T D Maarseveen
- Department of Rheumatology, Leiden University Medical Center, Leiden, The Netherlands
| | - M P Maurits
- Department of Rheumatology, Leiden University Medical Center, Leiden, The Netherlands
| | - E Niemantsverdriet
- Department of Rheumatology, Leiden University Medical Center, Leiden, The Netherlands
| | | | - T W J Huizinga
- Department of Rheumatology, Leiden University Medical Center, Leiden, The Netherlands
| | - R Knevel
- Department of Rheumatology, Leiden University Medical Center, Leiden, The Netherlands.
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van Leeuwen NM, Liem SIE, Maurits MP, Ninaber M, Marsan NA, Allaart CF, Huizinga TWJ, Knevel R, de Vries-Bouwstra JK. Disease progression in systemic sclerosis. Rheumatology (Oxford) 2021; 60:1565-1567. [PMID: 33404661 PMCID: PMC7937017 DOI: 10.1093/rheumatology/keaa911] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Revised: 12/08/2020] [Accepted: 12/11/2020] [Indexed: 11/13/2022] Open
Affiliation(s)
| | | | | | | | - Nina Ajmone Marsan
- Department of Cardiology, Leiden University Medical Center, Leiden, The Netherlands
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