Derivation and Internal Validation of a Clinical Prediction Tool to Predict Nonalcoholic Fatty Liver Disease in Patients With Crohn's Disease

Abstract

Background: Crohn's disease (CD) patients have more than double the risk of nonalcoholic fatty liver disease (NAFLD) compared with the general population after considering traditional risk factors. NAFLD remains underappreciated because routine imaging and liver biochemistries are neither sensitive nor specific for the diagnosis. Here we developed a Clinical Prediction Tool for NAFLD in CD (CPN-CD) using readily accessible parameters to diagnose NAFLD, as determined by magnetic resonance proton density fat fraction (PDFF).

Methods: A total of 311 consecutive CD patients who underwent magnetic resonance enterography from June 1, 2017, to May 31, 2018, were screened for NAFLD, defined as a PDFF >5.5% after excluding other liver diagnoses. CPN-CD was derived using binary multivariate logistic regression and internally validated with a 10-fold cross-validation. CPN-CD was compared with the Hepatic Steatosis Index (HSI) by the C-statistic and categorical Net Reclassification Improvement (NRI).

Results: CPN-CD included age, sex, ethnicity/race, serum alanine aminotransferase, body mass index, known cardiometabolic diagnoses, CD duration, and current use of azathioprine/6-mercaptopurine. At <20% risk, NAFLD could be excluded with a sensitivity of 86% (negative predictive value, 86%). At ≥50% risk, NAFLD was diagnosed with a specificity of 87% (positive predictive value, 75%). CPN-CD exhibited good discrimination (C-statistic 0.85) compared with fair discrimination of the HSI (C-statistic, 0.76). CPN-CD was superior to the HSI by net reclassification improvement (+0.20; P < 0.001) and decision curve analysis.

Conclusions: CPN-CD outperforms HSI in detecting NAFLD in patients with CD. Future directions include external validation, outcome validation, and testing generalizability to patients with ulcerative colitis.

Keywords: Crohn’s disease; clinical prediction tool; nonalcoholic fatty liver disease.

FIGURE 1.

Receiver operating curve for CPN-CD and the HSI. The receiver operating curves for the risk model’s native and recalibrated (where the beta coefficients are the same but with a different constant) versions are identical; however, the diagnostic operating characteristics differ due to risk category reclassification.

FIGURE 2.

Decision curve analysis for CPN-CD, HSI, screening only obese patients, and screening all patients. Decision curve analysis is a method to assess the utility of a risk prediction tool to guide a screening program by plotting the risk threshold to screen against the net benefit. The net benefit of a risk model is equal to $\left(\frac{TruePositives}{N}\right)-\left(\frac{FalsePositives}{N}\right)\times \left(\frac{RiskProbability}{1-RiskProbability}\right)$.