Machine learning for Prediction of Intra-abdominal Abscesses in Patients with Crohn’s Disease on Abdominal Imaging

Background: Intra-abdominal abscess (IA) is an important clinical complication of Crohn’s disease (CD). A high index of clinical suspicion is needed as abdominal imaging is not routinely used during hospital admission. This study aimed to identify clinical predictors of an IA on abdominal imaging among hospitalized patients with CD using machine learning.

Methods: We created an electronic data repository of all patients with CD who visited the emergency department (ED) of our tertiary medical center between 2012-2018. We searched for the presence of an IA on abdominal imaging within 7 days from the visit. Machine-learning models were trained to predict the presence of an IA. A logistic regression model was compared to a random forest model.

Results: Overall, 309 patients with CD were hospitalized and underwent abdominal imaging within 7 days. Forty patients (12.9%) were diagnosed with an IA on imaging (39 CT scans, 1 MRE). On multivariate analysis, high C-reactive protein (CRP) (above 65 mg/L, aOR 16 [95% CI 5.51–46.18]), leukocytosis (above 10.5 K/microL, aOR 4.47 [95% CI 1.91–10.45]), thrombocytosis (above 322.5 K/microL, aOR 4.1 [95% CI 2–8.73]) and tachycardia (over 97 beats per minute, aOR 2.7 [95% CI 1.37–5.3]) were independently associated with an IA. A random forest model showed an AUC 0.817±0.065 with 6 features (CRP, HGB, WBC, age, current biologic, BUN).

Conclusion: In our large tertiary center cohort, the machine-learning model identified features associated with the presentation of an IA on abdominal imaging. Such a decision support tool may assist in triaging CD patients for imaging to exclude this potentially life-threatening complication.