Understanding the Comparative Fit Index: It\u27s all about the base!

Despite the sensitivity of fit indices to various model and data characteristics in structural equation modeling, these fit indices are used in a rigid binary fashion as a mere rule of thumb threshold value in a search for model adequacy. Here, we address the behavior and interpretation of the popular Comparative Fit Index (CFI) by stressing that its metric for model assessment is the amount of misspecification in a baseline model and by further decomposition into its fundamental components: sample size, number of variables and the degree of multivariate dependence in the data. Simulation results show how these components influence the performance of CFI and its rule of thumb in practice. We discuss the usefulness of additional qualifications when applying the CFI rule of thumb and potential adjustments to its threshold value as a function of data characteristics. In conclusion, we at a minimum recommend a dual reporting strategy to provide the necessary context and base for meaningful interpretation and even more optimal, a move to using CFI as a real incremental fit index intended to evaluate the relative effect size of cumulative theoretically motivated model restrictions in terms of \% reduction in misspecification as measured by the baseline model

The detection and modeling of direct effects in latent class analysis

Several approaches have been proposed for latent class modeling with external variables, including one-step, two-step and three-step estimators. However, very little is known yet about the performance of these approaches when direct effects of the external variable to the indicators of latent class membership are present. In the current article, we compare those approaches and investigate the consequences of not modeling these direct effects when present, as well as the power of residual and fir statistics to identify such effects. The results of the simulations show that not modeling direct effect can lead to severe parameter bias, especially with a weak measurement model. Both residual and fit statistics can be used to identify such effects, as long as the number and strength of these effects is low and the measurement model is sufficiently strong

Long-Term Follow-Up of Patients with Scleritis After Rituximab Treatment Including B Cell Monitoring

Purpose: We report the long-term effect of rituximab (RTX) in scleritis and determine the value of B-cell monitoring for the prediction of relapses. Methods: We retrospectively studied 10 patients with scleritis, who were treated with RTX. Clinical characteristics were collected, and blood B-cell counts were measured before the start of RTX, and at various time points after treatment. Results:Clinical activity of scleritis decreased after RTX treatment in all patients within a median time of 8 weeks (range 3–13), and all reached remission. The median follow-up was 101 months (range 9–138). Relapses occurred in 6 out of 10 patients. All relapses, where B-cell counts were measured (11 out of 19), were heralded by returning B cells. However, B cells also returned in patients with long-term remissions.Conclusions: RTX is a promising therapeutic option for scleritis. Recurrence of B cells after initial depletion does not always predict relapse of scleritis.</p

Text Mining of Electronic Health Records Can Accurately Identify and Characterize Patients With Systemic Lupus Erythematosus

Objective: Electronic health records (EHR) are increasingly being recognized as a major source of data reusable for medical research and quality monitoring, although patient identification and assessment of symptoms (characterization) remain challenging, especially in complex diseases such as systemic lupus erythematosus (SLE). Current coding systems are unable to assess information recorded in the physician’s free-text notes. This study shows that text mining can be used as a reliable alternative. Methods: In a multidisciplinary research team of data scientists and medical experts, a text mining algorithm on 4607 patient records was developed to assess the diagnosis of 14 different immune-mediated inflammatory diseases and the presence of 18 different symptoms in the EHR. The text mining algorithm included key words in the EHR, while mining the context for exclusion phrases. The accuracy of the text mining algorithm was assessed by manually checking the EHR of 100 random patients suspected of having SLE for diagnoses and symptoms and comparing the outcome with the outcome of the text mining algorithm. Results: After evaluation of 100 patient records, the text mining algorithm had a sensitivity of 96.4% and a specificity of 93.3% in assessing the presence of SLE. The algorithm detected potentially life-threatening symptoms (nephritis, pleuritis) with good sensitivity (80%-82%) and high specificity (97%-97%). Conclusion: We present a text mining algorithm that can accurately identify and characterize patients with SLE using routinely collected data from the EHR. Our study shows that using text mining, data from the EHR can be reused in research and quality control

A serum B-lymphocyte activation signature is a key distinguishing feature of the immune response in sarcoidosis compared to tuberculosis

Sarcoidosis and tuberculosis (TB) are two granulomatous diseases that often share overlapping clinical features, including uveitis. We measured 368 inflammation-related proteins in serum in both diseases, with and without uveitis from two distinct geographically separated cohorts: sarcoidosis from the Netherlands and TB from Indonesia. A total of 192 and 102 differentially expressed proteins were found in sarcoidosis and active pulmonary TB compared to their geographical healthy controls, respectively. While substantial overlap exists in the immune-related pathways involved in both diseases, activation of B cell activating factor (BAFF) signaling and proliferation-inducing ligand (APRIL) mediated signaling pathways was specifically associated with sarcoidosis. We identified a B-lymphocyte activation signature consisting of BAFF, TNFRSF13B/TACI, TRAF2, IKBKG, MAPK9, NFATC1, and DAPP1 that was associated with sarcoidosis, regardless of the presence of uveitis. In summary, a difference in B-lymphocyte activation is a key discriminative immunological feature between sarcoidosis/ocular sarcoidosis (OS) and TB/ocular TB (OTB).</p

Microarray testing in patients with systemic lupus erythematosus identifies a high prevalence of CpG DNA-binding antibodies

OBJECTIVE: Many autoantibodies are known to be associated with SLE, although their role in clinical practice is limited because of low sensitivity and weak associations with clinical manifestations. There has been great interest in the discovery of new autoantibodies to use in clinical practice. In this study, we investigated 57 new and known antibodies and their potential for diagnostics or risk stratification. METHODS: Between 2014 and 2017, residual sera of all anti-dsDNA tests in the UMC Utrecht were stored in a biobank. This included sera of patients with SLE, patients with a diagnosis of another immune-mediated inflammatory disease (IMID), patients with low (non-IMID) or medium levels of clinical suspicion of SLE but no IMID diagnosis (Rest), and self-reported healthy blood bank donors. Diagnosis and (presence of) symptoms at each blood draw were retrospectively assessed in the patient records with the Utrecht Patient-Oriented Database using a newly developed text mining algorithm. Sera of patients were analysed for the presence of 57 autoantibodies with a custom-made immunofluorescent microarray. Signal intensity cut-offs for all antigens on the microarray were set to the 95th percentile of the non-IMID control group. Differences in prevalence of autoantibodies between patients with SLE and control groups were assessed. RESULTS: Autoantibody profiles of 483 patients with SLE were compared with autoantibody profiles of 1397 patients from 4 different control groups. Anti-dsDNA was the most distinguishing feature between patients with SLE and other patients, followed by antibodies against Cytosine-phosphate-Guanine (anti-CpG) DNA motifs (p<0.0001). Antibodies against CMV (cytomegalovirus) and ASCA (anti-Saccharomyces cerevisiae antibodies) were more prevalent in patients with SLE with (a history of) lupus nephritis than patients with SLE without nephritis. CONCLUSION: Antibodies against CpG DNA motifs are prevalent in patients with SLE. Anti-CMV antibodies are associated with lupus nephritis

Microarray analysis of autoantibodies can identify future Systemic Lupus Erythematosus patients

OBJECTIVE: Reliable early ascertainment in patients with SLE is important to prevent the accumulation of irreversible organ damage. Autoantibodies are often present in the serum of patients before the first symptoms arise, therefore they are of potential use as early diagnostic tools. METHODS: We used a custom-made antibody microarray containing 57 autoantigens to analyze serum samples of 1519 patients previously tested for anti-dsDNA and 361 samples of self-reported healthy blood bank donors (BBD). The 1519 patients included 483 patients with SLE, 346 patients with other immune mediated inflammatory diseases (IMID), 218 patient controls without relevant clinical symptoms (Non-IMID), and 472 patients that did not fit in any of the previous groups (Rest). The Non-IMID and BBD groups were used individually to create multivariable prediction models to distinguish samples of patients with SLE from these control groups. We subsequently used these models to predict the outcome for samples of patients who developed SLE while in follow-up (pre-SLE). RESULTS: Out of 1036 patients with no diagnosis of SLE at the moment of sample collection, 17 patients developed SLE while in follow-up (mean time to diagnosis 7.2 months). The best performing model (AUC 0.83) identified 9 out of 17 (53%) pre-SLE samples as SLE, with a specificity of 94%. CONCLUSION: Serum samples of patients who will develop SLE in the future already show a shift of the autoantibody profile prior to diagnosis. In this study, we show that these autoantibody profiles can be used to identify these future SLE patients

Vascular Ehlers-Danlos Syndrome:A Comprehensive Natural History Study in a Dutch National Cohort of 142 Patients

BACKGROUND: Vascular Ehlers-Danlos syndrome (vEDS) is a rare connective tissue disorder with a high risk for arterial, bowel, and uterine rupture, caused by heterozygous pathogenic variants in COL3A1. The aim of this cohort study is to provide further insights into the natural history of vEDS and describe genotype-phenotype correlations in a Dutch multicenter cohort to optimize patient care and increase awareness of the disease. METHODS: Individuals with vEDS throughout the Netherlands were included. The phenotype was charted by retrospective analysis of molecular and clinical data, combined with a one-time physical examination. RESULTS: A total of 142 individuals (50% female) participated the study, including 46 index patients (32%). The overall median age at genetic diagnosis was 41.0 years. More than half of the index patients (54.3%) and relatives (53.1%) had a physical appearance highly suggestive of vEDS. In these individuals, major events were not more frequent (P=0.90), but occurred at a younger age (P=0.01). A major event occurred more often and at a younger age in men compared with women (P&lt;0.001 and P=0.004, respectively). Aortic aneurysms (P=0.003) and pneumothoraces (P=0.029) were more frequent in men. Aortic dissection was more frequent in individuals with a COL3A1 variant in the first quarter of the collagen helical domain (P=0.03). CONCLUSIONS: Male sex, type and location of the COL3A1 variant, and physical appearance highly suggestive of vEDS are risk factors for the occurrence and early age of onset of major events. This national multicenter cohort study of Dutch individuals with vEDS provides a valuable basis for improving guidelines for the diagnosing, follow-up, and treatment of individuals with vEDS.</p

Characteristics of COVID-19 infection and antibody formation in patients known at a tertiary immunology department

Background: Knowledge about COVID-19 infections is expanding, although knowledge about the disease course and antibody formation in patients with an auto-immune disease or immunodeficiency is not fully unraveled yet. It could be hypothesized that immunodeficient patients, due to immunosuppressive drugs or their disease, have a more severe disease course due to their immunocompromised state. However, it could also be hypothesized that some of the immunosuppressive drugs protect against a hyperinflammatory state. Methods: We collected data on the incidence of COVID-19, disease course and SARS-CoV-2 antibody formation in COVID-19 positive patients in a cohort of patients (n ​= ​4497) known at the Clinical Immunology outpatient clinic in a tertiary care hospital in the Netherlands. Results: In the first six months of the pandemic, 16 patients were identified with COVID-19, 14 by nasal swab PCR, and 2 pa

Identification and prediction of difficult-to-treat rheumatoid arthritis patients in structured and unstructured routine care data: results from a hackathon

Background: The new concept of difficult-to-treat rheumatoid arthritis (D2T RA) refers to RA patients who remain symptomatic after several lines of treatment, resulting in a high patient and economic burden. During a hackathon, we aimed to identify and predict D2T RA patients in structured and unstructured routine care data. Methods: Routine care data of 1873 RA patients were extracted from the Utrecht Patient Oriented Database. Data from a previous cross-sectional study, in which 152 RA patients were clinically classified as either D2T or non-D2T, served as a validation set. Machine learning techniques, text mining, and feature importance analyses were performed to identify and predict D2T RA patients based on structured and unstructured routine care data. Results: We identified 123 potentially new D2T RA patients by applying the D2T RA definition in structured and unstructured routine care data. Additionally, we developed a D2T RA identification model derived from a feature importance analysis of all available structured data (AUC-ROC 0.88 (95% CI 0.82–0.94)), and we demonstrated the potential of longitudinal hematological data to differentiate D2T from non-D2T RA patients using supervised dimension reduction. Lastly, using data up to the time of starting the first biological treatment, we predicted future development of D2TRA (AUC-ROC 0.73 (95% CI 0.71–0.75)). Conclusions: During this hackathon, we have demonstrated the potential of different techniques for the identification and prediction of D2T RA patients in structured as well as unstructured routine care data. The results are promising and should be optimized and validated in future research