TB-SCREEN: Latent Tuberculosis Risk Stratification Before Biologic Therapy in Rheumatic Diseases with Bayesian Test Interpretation and Monte Carlo Uncertainty Estimation
TB-SCREEN: Latent Tuberculosis Risk Stratification Before Biologic Therapy in Rheumatic Diseases with Bayesian Test Interpretation and Monte Carlo Uncertainty Estimation
Authors
Erick Adrián Zamora Tehozol, DNAI, RheumaAI
Abstract
Biologic disease-modifying antirheumatic drugs (bDMARDs), particularly TNF inhibitors, substantially increase the risk of tuberculosis reactivation from latent TB infection (LTBI). Current screening relies on binary interpretation of IGRA or TST results, ignoring the quantitative impact of regional incidence, immunosuppression-related false negatives, BCG-driven false positives, and drug-specific reactivation risk. TB-SCREEN applies Bayesian post-test probability calculation with Monte Carlo uncertainty propagation to generate a continuous posterior LTBI probability, 1-year reactivation risk estimate, and guideline-aligned treatment and monitoring recommendations. Validated against three clinical scenarios spanning moderate to high TB incidence regions with varying immunosuppression levels and biologic classes. All outputs are fully transparent, reproducible (seeded RNG), and executable as a pure-Python agent skill with zero external dependencies.
Introduction
The ACR 2022 guidelines mandate LTBI screening before biologic initiation, yet provide no quantitative framework for integrating multiple risk dimensions. A positive IGRA in a BCG-vaccinated patient from a low-incidence region has a very different positive predictive value than the same result in an immunosuppressed patient from an endemic area. Similarly, a negative IGRA in a patient on high-dose glucocorticoids and rituximab may represent a false negative.
TB-SCREEN addresses this gap by implementing Bayesian test interpretation that integrates: (1) regional TB incidence as prior probability, (2) test-specific sensitivity and specificity from meta-analytic data, (3) immunosuppression-adjusted sensitivity, (4) BCG-adjusted specificity for TST, (5) host comorbidity risk factors, and (6) biologic-specific TB reactivation multipliers.
Methods
Prior Probability Estimation
The prior LTBI probability derives from WHO 2023 regional incidence data using a simplified cumulative infection model: P(LTBI) = min(0.60, incidence_per_100k × 0.003), representing approximate 30-year cumulative infection risk.
Bayesian Post-Test Calculation
For positive tests: P(LTBI|+) = (Sens × Prior) / (Sens × Prior + (1-Spec) × (1-Prior)) For negative tests: P(LTBI|−) = ((1-Sens) × Prior) / ((1-Sens) × Prior + Spec × (1-Prior))
Test Performance Parameters
Derived from Ai et al. 2016 JAMA Internal Medicine meta-analysis:
- QuantiFERON-TB Gold: Sensitivity 0.80 (SE 0.04), Specificity 0.97 (SE 0.01)
- T-SPOT.TB: Sensitivity 0.84 (SE 0.05), Specificity 0.95 (SE 0.02)
- TST ≥10mm: Sensitivity 0.75 (SE 0.05), Specificity 0.97 (SE 0.02)
- TST ≥5mm: Sensitivity 0.85 (SE 0.05), Specificity 0.90 (SE 0.03)
Immunosuppression Adjustment
Sensitivity is multiplied by a reduction factor: none (1.0), mild (0.92), moderate (0.82), severe (0.65), based on published data on IGRA sensitivity under immunosuppression (Bartalesi 2009, Bocchino 2008).
Monte Carlo Uncertainty Propagation
10,000 iterations sample test sensitivity and specificity from Gaussian distributions (using published standard errors), prior probability with ±15% uniform perturbation, and base reactivation rate with ±20% uncertainty. The 2.5th and 97.5th percentiles define 95% credible intervals.
Biologic-Specific Risk
Drug-specific TB reactivation multipliers from Cantini 2015, Winthrop 2017, and Dixon 2010: infliximab (4.0×), adalimumab (3.5×), certolizumab/golimumab (2.5×), etanercept (1.5×), JAK inhibitors (1.8–2.0×), IL-17/IL-23 inhibitors (1.0–1.1×).
Results
Scenario 1: RA, Moderate TB Region, Positive IGRA, Adalimumab
Posterior LTBI probability: 91.7% (95% CI: 86.0%–96.9%). 1-year reactivation risk: 12.02%. Classification: HIGH — Treat LTBI before biologic.
Scenario 2: SLE, Low TB Region, Negative IGRA, Severe Immunosuppression, Rituximab
Posterior LTBI probability: 10.3% (95% CI: 8.0%–12.7%). Classification: LOW with caveat — consider dual testing given immunosuppression.
Scenario 3: PsA, High TB Region, Indeterminate T-SPOT, Infliximab
Posterior LTBI probability: 88.0%. Host risk multiplier 4.5×. Classification: INDETERMINATE — repeat testing, but given extreme risk profile, treat empirically.
Discussion
TB-SCREEN transforms the binary screen-then-decide paradigm into a probabilistic risk assessment. Key clinical implications:
- False-negative awareness: Scenario 2 shows that a negative IGRA in severely immunosuppressed patients retains ~10% residual LTBI probability, justifying dual testing.
- Regional context matters: The same positive IGRA has dramatically different PPV in a 5/100k vs 200/100k incidence setting.
- Drug selection matters: Choosing etanercept over infliximab reduces TB reactivation risk by ~2.5×, which may influence biologic selection in high-risk patients.
Limitations
- Prior probability model is simplified; real LTBI prevalence varies by population surveys
- Test performance parameters assume general adult populations
- Drug risk multipliers derive primarily from European registries
- Does not model treatment efficacy for LTBI regimens
References
- ACR 2022 Guideline for Screening, Testing, and Treatment of LTBI. Arthritis Care Res.
- WHO 2023 Consolidated Guidelines on Tuberculosis. Module 1: Prevention.
- Ai JW et al. 2016. Rapid diagnosis of LTBI: a meta-analysis. JAMA Intern Med.
- Cantini F et al. 2015. Risk of TB reactivation in patients with rheumatic diseases. Ann Rheum Dis.
- Winthrop KL. 2017. The emerging safety profile of JAK inhibitors. Nat Rev Rheumatol.
- Dixon WG et al. 2010. Rates of TB on anti-TNF treatment. Ann Rheum Dis.
- ATS/IDSA/CDC. 2017. Diagnosis and treatment of LTBI. Am J Respir Crit Care Med.
Reproducibility: Skill File
Use this skill file to reproduce the research with an AI agent.
# TB-SCREEN Latent Tuberculosis Risk Stratification Before Biologic Therapy in Rheumatic Diseases with Bayesian Test Interpretation and Monte Carlo Uncertainty Estimation. ## Authors Erick Adrián Zamora Tehozol, DNAI, RheumaAI ## What It Does TB-SCREEN computes the post-test probability of latent TB infection (LTBI) given a patient's IGRA or TST result, regional TB incidence, immunosuppression level, and planned biologic. It applies Bayesian reasoning with Monte Carlo sampling to propagate uncertainty, then generates risk classification, treatment recommendations, and monitoring plans aligned with ACR 2022, WHO 2023, and ATS/IDSA/CDC 2017 guidelines. ## Clinical Problem Biologic DMARDs (especially TNF inhibitors) dramatically increase TB reactivation risk. Screening is mandatory before initiation, but test interpretation is non-trivial: sensitivity drops with immunosuppression, specificity drops with prior BCG, and regional incidence shifts the prior probability. A simple positive/negative binary misses this nuance. ## Inputs | Parameter | Type | Description | |-----------|------|-------------| | region | str | TB incidence zone: very_low, low, moderate, high, very_high | | test_type | str | igra_qft, igra_tspot, tst_10mm, tst_5mm | | test_result | str | positive, negative, indeterminate | | immunosuppression | str | none, mild, moderate, severe | | planned_biologic | str | Drug name (infliximab, adalimumab, etanercept, etc.) | | host_factors | list | diabetes, hiv, ckd_dialysis, smoking, etc. | | prior_bcg | bool | BCG vaccination history | | chest_xray_abnormal | bool | Abnormal chest radiograph | ## Outputs - Posterior LTBI probability with 95% CI - 1-year TB reactivation risk on planned biologic - Risk category (HIGH/MODERATE/LOW/INDETERMINATE) - Treatment recommendation (isoniazid vs rifampin regimen) - Monitoring plan - Test interpretation narrative ## Evidence Base - ACR 2022 Screening Guidelines for LTBI Before Biologics - WHO 2023 Consolidated TB Guidelines - ATS/IDSA/CDC 2017 Diagnosis and Treatment of LTBI - Cantini et al. 2015 Ann Rheum Dis — TNFi TB risk meta-analysis - Ai et al. 2016 JAMA Internal Medicine — IGRA sensitivity/specificity meta-analysis - Winthrop 2017 Nat Rev Rheumatol — TB risk across biologic classes - Dixon et al. 2010 Ann Rheum Dis — BSRBR TB rates with anti-TNF ## Usage ```bash python3 tb_screen.py ``` No external dependencies. Pure Python stdlib.
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