Latent Class Trajectory Modeling of 2-Component Disease Activity Score in 28 Joints Identifies Multiple Rheumatoid Arthritis Phenotypes of Response to Biologic Disease-Modifying Antirheumatic Drugs

Identifying longitudinal cognitive resilience from cross-sectional amyloid, tau, and neurodegeneration

BACKGROUND

Leveraging Alzheimer's disease (AD) imaging biomarkers and longitudinal cognitive data may allow us to establish evidence of cognitive resilience (CR) to AD pathology in-vivo. Here, we applied latent class mixture modeling, adjusting for sex, baseline age, and neuroimaging biomarkers of amyloid, tau and neurodegeneration, to a sample of cognitively unimpaired older adults to identify longitudinal trajectories of CR.

METHODS

We identified 200 Harvard Aging Brain Study (HABS) participants (mean age = 71.89 years, SD = 9.41 years, 59% women) who were cognitively unimpaired at baseline with 2 or more timepoints of cognitive assessment following a single amyloid-PET, tau-PET and structural MRI. We examined latent class mixture models with longitudinal cognition as the dependent variable and time from baseline, baseline age, sex, neocortical Aβ, entorhinal tau, and adjusted hippocampal volume as independent variables. We then examined group differences in CR-related factors across the identified subgroups from a favored model. Finally, we applied our favored model to a dataset from the Alzheimer's Disease Neuroimaging Initiative (ADNI; n = 160, mean age = 73.9 years, SD = 7.6 years, 60% women).

RESULTS

The favored model identified 3 latent subgroups, which we labelled as Normal (71% of HABS sample), Resilient (22.5%) and Declining (6.5%) subgroups. The Resilient subgroup exhibited higher baseline cognitive performance and a stable cognitive slope. They were differentiated from other groups by higher levels of verbal intelligence and past cognitive activity. In ADNI, this model identified a larger Normal subgroup (88.1%), a smaller Resilient subgroup (6.3%) and a Declining group (5.6%) with a lower cognitive baseline.

CONCLUSION

These findings demonstrate the value of data-driven approaches to identify longitudinal CR groups in preclinical AD. With such an approach, we identified a CR subgroup who reflected expected characteristics based on previous literature, higher levels of verbal intelligence and past cognitive activity.

Distinct long-term disease activity trajectories differentiate early on treatment with etanercept in both rheumatoid arthritis and spondylarthritis patients: a prospective cohort study

To characterize disease activity trajectories and compare long-term drug retention between rheumatoid (RA) and spondylarthritis (SpA) patients initiating tumor necrosis factor inhibitor (TNFi) treatment (etanercept). Prospective observational study of RA, axial (AxSpA) and peripheral SpA (PerSpA) patients initiating etanercept during 2004-2020. Kaplan-Meier plots were used for drug retention comparisons and multivariable Cox regression models for predictors of discontinuation. Long-term disease activity trajectories were identified by latent class growth models using DAS28-ESR or ASDAS-CRP as outcome for RA and AxSpA respectively. We assessed 711 patients (450 RA, 178 AxSpA and 83 PerSpA) with a median (IQR) follow-up of 12 (5-32) months. At 5 years, 22%, 30% and 21% of RA, AxSpA and PerSpA patients, respectively, remained on therapy. Etanercept discontinuation was independent of the diagnosis and was predicted by gender and obesity in both RA and SpA groups. Four disease activity (DA) trajectories were identified from 6th month of treatment in both RA and AxSpA. RA patients in remission-low DA groups (33.7%) were younger, had shorter disease duration, fewer comorbidities and lower baseline disease activity compared to moderate (40.6%) & high DA (25.7%) groups. In AxSpA 74% were in inactive-low DA and they were more often males, non-obese and had lower number of comorbidities compared to higher ASDAS-CRP trajectories. In RA and AxSpA patients, disease activity trajectories revealed heterogeneity of TNFi treatment responses and prognosis. Male gender, lower baseline disease activity and fewer comorbidities, characterize a favourable outcome in terms of disease burden accrual and TNFi survival.

Four trajectories of 24-hour urine protein levels in real-world lupus nephritis cohorts

OBJECTIVES

A 24-hour urine protein (24hUP) is a key measurement in the management of lupus nephritis (LN); however, trajectories of 24hUP in LN is poorly defined.

METHODS

Two LN cohorts that underwent renal biopsies at Renji Hospital were included. Patients received standard of care in a real-world setting and 24hUP data were collected over time. Trajectory patterns of 24hUP were determined using the latent class mixed modelling (LCMM). Baseline characters were compared among trajectories and multinomial logistic regression was used to determine independent risk factors. Optimal combinations of variables were identified for model construction and user-friendly nomograms were developed.

RESULTS

The derivation cohort composed of 194 patients with LN with 1479 study visits and a median follow-up of 17.5 (12.2-21.7) months. Four trajectories of 24hUP were identified, that is, Rapid Responders, Good Responders, Suboptimal Responders and Non-Responders, with the KDIGO renal complete remission rates (time to complete remission, months) of 84.2% (4.19), 79.6% (7.94), 40.4% (not applicable) and 9.8% (not applicable), respectively (p<0.001). The 'Rapid Responders' distinguish itself from other trajectories and a nomogram, composed of age, systemic lupus erythematosus duration, albumin and 24hUP yielded C-indices >0.85. Another nomogram to predict 'Good Responders' yielded C-indices of 0.73~0.78, which composed of gender, new-onset LN, glomerulosclerosis and partial remission within 6 months. When applied to the validation cohort with 117 patients and 500 study visits, nomograms effectively sorted out 'Rapid Responders' and 'Good Responders'.

CONCLUSION

Four trajectories of LN shed some light to guide the management of LN and further clinical trials design.

Disease activity trajectories for early and established rheumatoid arthritis: Real-world data from a rheumatoid arthritis cohort

Objectives

Disease activity status described at fixed time points does not accurately reflect disease course in chronic and relapsing diseases such as rheumatoid arthritis (RA). We described longitudinal disease activity trajectories in early and established RA.

Methods

Patients with available 28-Joint Disease Activity Score-erythrocyte sedimentation rate (DAS28-ESR) and Clinical Disease Activity Index (CDAI) over two years were included. Using latent growth curve modelling (LCGM), subgroups of patients following distinct patterns were identified.

Results

1920 patients were included with 34.4% in early RA (< 2 years’ disease duration). Three subgroups were identified using DAS28-ESR in early RA: 1) low disease activity to remission (LDA-REM: 19.1%); 2) moderate disease to remission (MD-REM: 54%); 3) high to moderate disease (HD-MD: 26.9%). The HD-MD group had a significantly higher number of comorbidities, biologic and steroid use and lower post-secondary education. Using CDAI, we identified seven subgroups with only 1.9% remission in early RA. In established RA, seven subgroups were identified using either DAS28-ESR or CDAI. Using DAS28-ESR 27.8% with HD showed improvement in disease status (14.2% HD-REM, 10.3% HD-LDA and 3.3% HD-MD) while using CDAI 17.9% showed improvement.

Conclusion

Disease course was different in early and established RA. Only 14.2% of established RA reached DAS28-ESR remission compared to 73.1% of early RA. Using CDAI only 1.9% of early RA and none of the established RA achieved remission, likely reflecting the impact of the patient global assessment on this score. Findings also illustrate the impact of sociodemographic characteristics and early treatment on disease course.

Nothing about us without us: involving patient collaborators for machine learning applications in rheumatology

Novel machine learning methods open the door to advances in rheumatology through application to complex, high-dimensional data, otherwise difficult to analyse. Results from such efforts could provide better classification of disease, decision support for therapy selection, and automated interpretation of clinical images. Nevertheless, such data-driven approaches could potentially model noise, or miss true clinical phenomena. One proposed solution to ensure clinically meaningful machine learning models is to involve primary stakeholders in their development and interpretation. Including patient and health care professionals' input and priorities, in combination with statistical fit measures, allows for any resulting models to be well fit, meaningful, and fit for practice in the wider rheumatological community. Here we describe outputs from workshops that involved healthcare professionals, and young people from the Your Rheum Young Person's Advisory Group, in the development of complex machine learning models. These were developed to better describe trajectory of early juvenile idiopathic arthritis disease, as part of the CLUSTER consortium. We further provide key instructions for reproducibility of this process.Involving people living with, and managing, a disease investigated using machine learning techniques, is feasible, impactful and empowering for all those involved.

Factors associated with drug survival on first biologic therapy in patients with rheumatoid arthritis: a population-based cohort study

Lack of sufficient head-to-head trials comparing biologic disease-modifying antirheumatic drugs (bDMARDs) in rheumatoid arthritis (RA), makes the choice of the first bDMARD a matter of rheumatologist's preference. Longer drug survival on the first bDMARD usually correlates with early remission. We aimed to identify factors associated with longer drug survival. We conducted a population-based retrospective longitudinal cohort study. We identified RA patients using the relevant International Classification of Disease 9th codes. "True" RA patients were defined as patients fulfilling, additionally, at least one of the following: receiving conventional DMARDs (cDMARDs), being positive for rheumatoid factor or anti-cyclic citrullinated peptide, or being diagnosed by a rheumatologist. We compared drug survival times and identified factors associated with longer drug survival. We identified 4268 true RA patients between the years of 2000-2017. 820 patients (19.2%) received at least one bDMARD. The most commonly prescribed bDMARDs were etanercept (352, 42.9%), adalimumab (143, 17.4%), infliximab (142, 17.3%) and tocilizumab (58, 7.1%). Infliximab was associated with the longest drug survival (47.1 months ± 46.3) while golimumab was associated with the shortest drug survival (14.9 months ± 15.1). Male gender [hazard ratio (HR) = 0.76, 95% confidence interval (CI), 0.63-0.86, p = 0.001], concurrent conventional DMARDs use (HR = 0.79, 95% CI 0.68 - 0.98, p = .031) and initiating bDMARD therapy in earlier calendric years (HR = 1.12, 95% CI 1.10 -1.18, p = 0.0001) were associated with longer drug survival. Male gender, concomitant cDMARDs and initiating biologic therapy at earlier calendric years are associated with longer drug survival.

Distinct patterns of disease activity over time in patients with active SLE revealed using latent class trajectory models

BACKGROUND

Systemic lupus erythematosus (SLE) is a heterogeneous systemic autoimmune condition for which there are limited licensed therapies. Clinical trial design is challenging in SLE due at least in part to imperfect outcome measures. Improved understanding of how disease activity changes over time could inform future trial design. The aim of this study was to determine whether distinct trajectories of disease activity over time occur in patients with active SLE within a clinical trial setting and to identify factors associated with these trajectories.

METHODS

Latent class trajectory models were fitted to a clinical trial dataset of a monoclonal antibody targeting CD22 (Epratuzumab) in patients with active SLE using the numerical BILAG-2004 score (nBILAG). The baseline characteristics of patients in each class and changes in prednisolone over time were identified. Exploratory PK-PD modelling was used to examine cumulative drug exposure in relation to latent class membership.

RESULTS

Five trajectories of disease activity were identified, with 3 principal classes: non-responders (NR), slow responders (SR) and rapid-responders (RR). In both the SR and RR groups, significant changes in disease activity were evident within the first 90 days of the trial. The SR and RR patients had significantly higher baseline disease activity, exposure to epratuzumab and activity in specific BILAG domains, whilst NR had lower steroid use at baseline and less change in steroid dose early in the trial.

CONCLUSIONS

Longitudinal nBILAG scores reveal different trajectories of disease activity and may offer advantages over fixed endpoints. Corticosteroid use however remains an important confounder in lupus trials and can influence early response. Changes in disease activity and steroid dose early in the trial were associated with the overall disease activity trajectory, supporting the feasibility of performing adaptive trial designs in SLE.

Improved Disease Course Associated With Early Initiation of Biologics in Polyarticular Juvenile Idiopathic Arthritis: Trajectory Analysis of a Childhood Arthritis and Rheumatology Research Alliance Consensus Treatment Plans Study

OBJECTIVE

To investigate the effects of early introduction of biologic disease-modifying antirheumatic drugs (bDMARDs) on the disease course in untreated polyarticular juvenile idiopathic arthritis (JIA).

METHODS

We analyzed data on patients with polyarticular JIA participating in the Start Time Optimization of Biologics in Polyarticular JIA (STOP-JIA) study (n = 400) and a comparator cohort (n = 248) from the Childhood Arthritis and Rheumatology Research Alliance Registry. Latent class trajectory modeling (LCTM) was applied to identify subgroups of patients with distinct disease courses based on disease activity (clinical Juvenile Arthritis Disease Activity Score in 10 joints) over 12 months from baseline.

RESULTS

In the STOP-JIA study, 198 subjects (49.5%) received bDMARDs within 3 months of baseline assessment. LCTM analyses generated 3 latent classes representing 3 distinct disease trajectories, characterized by slow, moderate, or rapid disease activity improvement over time. Subjects in the rapid improvement trajectory attained inactive disease within 6 months from baseline. Odds of being in the rapid improvement trajectory versus the slow improvement trajectory were 3.6 times as high (95% confidence interval 1.32-10.0; P = 0.013) for those treated with bDMARDs ≤3 months from baseline compared with subjects who started bDMARDs >3 months after baseline, after adjusting for demographic characteristics, clinical attributes, and baseline disease activity. Shorter disease duration at first rheumatology visit approached statistical significance as a predictor of favorable trajectory without bDMARD treatment.

CONCLUSION

Starting bDMARDs within 3 months of baseline assessment is associated with more rapid achievement of inactive disease in subjects with untreated polyarticular JIA. These results demonstrate the utility of trajectory analysis of disease course as a method for determining treatment efficacy.

Longitudinal Trajectories of Hair Cortisol: Hypothalamic-Pituitary-Adrenal Axis Dysfunction in Early Childhood

The objective of this study was to examine if longitudinal trajectories of hair cortisol concentrations (HCC) measured at two or three yearly time points can identify 1-3 year old children at risk for altered hypothalamic-pituitary-adrenal (HPA)-axis function due to early life stress (ELS). HCC was measured (N = 575) in 265 children using a validated enzyme-linked immunosorbent assay. Hair was sampled in Clinic Visits (CV) centered at years 1, 2, and 3 (n = 45); 1 and 2 (n = 98); 1 and 3 (n = 27); 2 and 3 (n = 95). Log-transformed HCC values were partitioned using latent class mixed models (LCMM) to minimize the Bayesian Information Criterion. Multivariable linear mixed effects models for ln-HCC as a function of fixed effects for age in months and random effects for participants (to account for repeated measures) were generated to identify the factors associated with class membership. Children in Class 1 (n = 69; 9% Black) evidenced declining ln-HCC across early childhood, whereas Class 2 members (n = 196; 43% Black) showed mixed trajectories. LCMM with only Class 2 members revealed Class 2A (n = 17, 82% Black) with sustained high ln-HCC and Class 2B (n = 179, 40% Blacks) with mixed ln-HCC profiles. Another LCMM limited to only Class 2B members revealed Class 2B1 (n = 65, 57% Black) with declining ln-HCC values (at higher ranges than Class 1), and Class 2B2 (n = 113, 30% Black) with sustained high ln-HCC values. Class 1 may represent hair cortisol trajectories associated with adaptive HPA-axis profiles, whereas 2A, 2B1, and 2B2 may represent allostatic load with dysregulated profiles of HPA-axis function in response to varying exposures to ELS. Sequential longitudinal hair cortisol measurements revealed the allostatic load associated with ELS and the potential for developing maladaptive or dysregulated HPA-axis function in early childhood.