Validation of prediction models for anti-tumor necrosis factor treatment response in pediatric Crohn's disease: a systematic review and prospective cohort study.
Omer Rotem-Tryfus, Ben Kang, Esther Orlanski-Meyer, Oren Ledder, Raffi Lev Tzion, Sujin Choi, Byung-Ho Choe, Youra Kang, Dotan Yogev, Ibrahim Shemasne, Muhammed Shawar, Gili Focht, Dan Turner, Ohad Atia
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Abstract
Background: External validation of predictors of anti-tumor necrosis factor (TNF) outcomes remains limited, particularly in children. We conducted a systematic review (SR) of the literature to identify predictors of therapeutic success and validated them in a prospective pediatric cohort.
Methods: We searched PubMed and Embase for studies reporting clinical and laboratory predictors of anti-TNF outcomes in Crohn's disease (CD). Identified predictors were evaluated in a prospective cohort of 186 children with CD initiating anti-TNF. Univariable logistic regression assessed individual predictors, and previously published multivariable models were validated using the area under the curve (AUC).
Results: Of the 4,840 studies screened, 42 were included; 7 (17%) focused on children and only four were rated as low risk of bias. We identified 24 individual predictors and five multi-item models. Of these, prior corticosteroid use (odds ratio [OR], 2.84, 95% CI, 1.12-7.15) and immunomodulator combination therapy (OR 6.36, 95% CI, 2.39-17.10) were associated with increased risk of primary non-response. Disease activity at 4 months, reflected by C-reactive protein and disease activity indices, predicted remission at 12 months. Loss of response was associated with elevated inflammatory markers at 4 months and with partial clinical response. The five multivariable models demonstrated varying performance in children (AUC 0.54-0.76).
Conclusion: Only a few of the variables suggested to predict response to anti-TNF showed acceptable performance in pediatric CD, mainly those that included post-induction indicators. These findings highlight the limited generalizability of existing predictors and the importance of external validation before clinical implementation of prediction rules.