Population-based user-perceived experience of Rheumatic?: a novel digital symptom-checker in rheumatology

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.Pathophysiology and treatment of rheumatic disease

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

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.Pathophysiology and treatment of rheumatic disease

Study protocol: Cost effectiveness of two strategies to implement the NVOG guidelines on hypertension in pregnancy: An innovative strategy including a computerised decision support system compared to a common strategy of professional audit and feedback, a randomized controlled trial

Background: Hypertensive disease in pregnancy remains the leading cause of maternal mortality in the Netherlands. Seventeen percent of the clinical pregnancies are complicated by hypertension and 2% by preeclampsia. The Dutch Society of Obstetrics and Gynaecology (NVOG) has developed evidence-based guidelines on the management of hypertension in pregnancy and chronic hypertension. Previous studies showed a low adherence rate to other NVOG guidelines and a large variation in usual care in the different hospitals. An explanation is that the NVOG has no general strategy of practical implementation and evaluation of its guidelines. The development of an effective and cost effective implementation strategy to improve adherence to the guidelines on hypertension in pregnancy is needed.Methods/Design: The objective of this study is to assess the cost effectiveness of an innovative implementation strategy of the NVOG guidelines on hypertension including a computerised decision support system (BOS) compared to a common strategy of professional audit and feedback. A cluster randomised controlled trial with an economic evaluation alongside will be performed. Both pregnant women who develop severe hypertension or pre-eclampsia and professionals involved in the care for these women will participate. The main outcome measures are a combined rate of major maternal complications and process indicators extracted from the guidelines. A total of 472 patients will be included in both groups. For analysis, descriptive as well as regression techniques will be used. A cost effectiveness and cost utility analysis will be performed according to the intention-to-treat principle and from a societal perspective. Cost effectiveness ratios will be calculated using bootstrapping techniques

Disease progression in systemic sclerosis

Cardiolog

A framework for employing longitudinally collected multicenter electronic health records to stratify heterogeneous patient populations on disease history

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.Molecular Epidemiolog

Factors Associated with Revision Surgery after Internal Fixation of Hip Fractures

Background: Femoral neck fractures are associated with high rates of revision surgery after management with internal fixation. Using data from the Fixation using Alternative Implants for the Treatment of Hip fractures (FAITH) trial evaluating methods of internal fixation in patients with femoral neck fractures, we investigated associations between baseline and surgical factors and the need for revision surgery to promote healing, relieve pain, treat infection or improve function over 24 months postsurgery. Additionally, we investigated factors associated with (1) hardware removal and (2) implant exchange from cancellous screws (CS) or sliding hip screw (SHS) to total hip arthroplasty, hemiarthroplasty, or another internal fixation device. Methods: We identified 15 potential factors a priori that may be associated with revision surgery, 7 with hardware removal, and 14 with implant exchange. We used multivariable Cox proportional hazards analyses in our investigation. Results: Factors associated with increased risk of revision surgery included: female sex, [hazard ratio (HR) 1.79, 95% confidence interval (CI) 1.25-2.50; P = 0.001], higher body mass index (fo

The Role of Genetics in Clinically Suspect Arthralgia and Rheumatoid Arthritis Development: A Large Cross-Sectional Study

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.Pathophysiology and treatment of rheumatic disease

A framework for employing longitudinally collected multicenter electronic health records to stratify heterogeneous patient populations on disease history

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

Structural insights into the contactin 1 – neurofascin 155 adhesion complex

Cell-surface expressed contactin 1 and neurofascin 155 control wiring of the nervous system and interact across cells to form and maintain paranodal myelin-axon junctions. The molecular mechanism of contactin 1 – neurofascin 155 adhesion complex formation is unresolved. Crystallographic structures of complexed and individual contactin 1 and neurofascin 155 binding regions presented here, provide a rich picture of how competing and complementary interfaces, post-translational glycosylation, splice differences and structural plasticity enable formation of diverse adhesion sites. Structural, biophysical, and cell-clustering analysis reveal how conserved Ig1-2 interfaces form competing heterophilic contactin 1 – neurofascin 155 and homophilic neurofascin 155 complexes whereas contactin 1 forms low-affinity clusters through interfaces on Ig3-6. The structures explain how the heterophilic Ig1-Ig4 horseshoe’s in the contactin 1 – neurofascin 155 complex define the 7.4 nm paranodal spacing and how the remaining six domains enable bridging of distinct intercellular distances.Funding Information: We thank the staff of the DLS beamlines I03 and I24 for help with X-ray diffraction data collection and of beamline B21 for help with SAXS data collection. L.M.P.C. thanks Nick Pearce, Jitse van der Horn, and Gijs van der Schot, for the instructional conversations regarding crystallography. K562 cells were a kind gift from Dr. Bas van Steensel at Netherlands Cancer Institute. This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program with grant agreement No. 677500 (to B.J.C.J.). D.H.M. acknowledges support from Parents in KIND grant, sponsored by the Kavli Institute of Nanoscience, the Department of Bionanoscience in Delft, and the NWO Spinoza Prize. M.A.d.B. and A.J.R.H. acknowledge support from the Netherlands Organization for Scientific Research (NWO) funding the Netherlands Proteomics Centre through the X-omics Road Map program (project 184.034.019). Publisher Copyright: © 2022, The Author(s).BN/Dimphna Meijer LabBN/Bionanoscienc