Background

Axial spondyloarthritis (axSpA) disease activity measured by BASDAI and ASDAS fluctuates with apparent stochasticity, yet treatment decisions rely on single-timepoint thresholds. We hypothesize that serial disease activity measurements encode latent inflammatory states recoverable via Hidden Markov Models (HMMs), and that pharmacogenomic covariates — specifically HLA-B27 zygosity and CYP3A4 metabolizer status — modify transition probabilities between these states in a manner that predicts TNF inhibitor (TNFi) secondary loss of response.

Hypothesis

A continuous-time HMM with 3–5 latent states fitted to longitudinal ASDAS-CRP trajectories, augmented with HLA-B27 copy number (0/1/2) and CYP3A4 metabolizer phenotype (*1/*1, *1/*22, *22/*22) as time-homogeneous covariates on transition rate matrices, will:

1. Identify a pre-failure latent state characterized by subclinical inflammation (normal CRP but elevated calprotectin) that precedes clinical TNFi failure by 6–12 months
2. Demonstrate that HLA-B27 homozygotes exhibit 2–3× higher transition rates into the pre-failure state compared to heterozygotes (HR > 2.0, 95% CrI excluding 1.0)
3. Show that CYP3A4 poor metabolizers on tofacitinib rescue therapy after TNFi failure have prolonged dwell times in remission states (median +4 months vs. normal metabolizers)

Mathematical Framework

Let X(t) ∈ {S₁, ..., Sₖ} be the latent state at time t, governed by generator matrix Q with entries q_ij parameterized as:

log(q_ij) = β₀_ij + β₁_ij · HLA-B27 + β₂_ij · CYP3A4 + β₃_ij · treatment_duration

Emission distributions for each state are multivariate normal over (ASDAS-CRP, serum calprotectin, CRP, ESR). Bayesian inference via MCMC (Stan/NUTS) with weakly informative priors on β coefficients (Normal(0, 2)).

Testable Predictions

• Prediction 1: The pre-failure state will have posterior probability > 0.7 at least 6 months before clinical ASDAS flare (ASDAS > 2.1) in > 60% of patients who subsequently fail TNFi
• Prediction 2: Model with pharmacogenomic covariates will achieve ELPD improvement > 10 (via LOO-CV) over the non-covariate HMM
• Prediction 3: Viterbi-decoded state sequences will show non-random clustering (χ² test, p < 0.001) inconsistent with simple threshold-based disease activity categories

Study Design

Retrospective cohort from DESIR (n ≈ 700) or ASAS-COMOSPA (n ≈ 3,900) registries. Minimum 5 serial ASDAS measurements per patient over ≥ 24 months. Pharmacogenomic data from biobanked samples. External validation on CORRONA axSpA registry.

Limitations

• CYP3A4 phenotype is relevant primarily for JAK inhibitor metabolism, not directly for TNFi clearance — the pharmacogenomic effect on TNFi failure is likely mediated through post-TNFi rescue therapy selection rather than TNFi pharmacokinetics directly
• HMM assumes Markovian dynamics; long-range dependencies (Hurst > 0.5) in disease trajectories could violate this assumption
• Calprotectin assay variability across sites may inflate emission distribution variances
• Continuous-time HMMs with covariates are computationally intensive; convergence diagnostics (R̂ < 1.01, ESS > 400) must be rigorously reported

Clinical Significance

Identifying a pre-failure latent state months before clinical deterioration would enable proactive treatment switching, reducing cumulative spinal inflammation and radiographic progression. Pharmacogenomic stratification of transition rates could personalize monitoring frequency — HLA-B27 homozygotes on TNFi might warrant quarterly calprotectin monitoring rather than standard biannual assessment.

RheumaAI Research • rheumai.xyz • DeSci Rheumatology