Development of a Simple, Serum Biomarker-based Model Predictive of the Need for Early Biologic Therapy in Crohn's Disease

J Crohns Colitis. 2021 Apr 6;15(4):583-593. doi: 10.1093/ecco-jcc/jjaa194.

Abstract

Background: Early or first-line treatment with biologics, as opposed to conventional immunomodulators, is not always necessary to achieve remission in Crohn's disease [CD] and may not be cost-effective. This study aimed to develop a simple model to predict the need for early biologic therapy, in order to risk-stratify CD patients and guide initial treatment selection.

Methods: A model-building study using supervised statistical learning methods was conducted using a retrospective cohort across two tertiary centres. All biologic-naïve CD patients who commenced an immunomodulator between January 1, 2004 and December 31, 2016, were included. A predictive score was derived using Cox regression modelling of immunomodulator failure, and was internally validated using bootstrap resampling.

Results: Of 410 patients [median age 37 years, 47% male, median disease duration 4.7 years], 229 [56%] experienced immunomodulator failure [39 required surgery, 24 experienced a new stricture, 44 experienced a new fistula/abscess, 122 required biologic escalation] with a median time to failure of 16 months. Independent predictors of treatment failure included raised C-reactive protein [CRP], low albumin, complex disease behaviour, younger age, and baseline steroids. Highest CRP and lowest albumin measured within the 3 months preceding immunomodulator initiation outperformed baseline measurements. After model selection, only highest CRP and lowest albumin remained and the resultant Crohn's Immunomodulator CRP-Albumin [CICA] index demonstrated robust optimism-corrected discriminative performance at 12, 24, and 36 months (area under the curve [AUC] 0.84, 0.83, 0.81, respectively).

Conclusions: The derived CICA index based on simple, widely available markers is feasible, internally valid, and has a high utility in predicting immunomodulator failure. This requires external, prospective validation.

Keywords: Precision medicine; prediction; predictive model; statistical learning.

Publication types

  • Multicenter Study

MeSH terms

  • Adult
  • Albumins / metabolism*
  • Australia
  • Biological Products / administration & dosage*
  • Biomarkers / blood*
  • C-Reactive Protein / metabolism*
  • Crohn Disease / blood*
  • Crohn Disease / drug therapy*
  • Crohn Disease / surgery
  • Female
  • Humans
  • Immunologic Factors / administration & dosage
  • Male
  • Middle Aged
  • Predictive Value of Tests
  • Remission Induction
  • Retrospective Studies

Substances

  • Albumins
  • Biological Products
  • Biomarkers
  • Immunologic Factors
  • C-Reactive Protein