Risk Prediction Model for Crohn's Disease Based on Hematological Indicators

The similarity between Crohn's disease (CD) and non-CD, especially with ulcerative colitis (UC) or intestinal tuberculosis (ITB), makes the diagnostic error rate not low. Therefore, there is an urgent need for an efficient, fast, and simple predictive model that can be applied in clinical practice. The purpose of this study is to establish the risk prediction model for CD based on five routine laboratory tests by logistic-regression algorithm, to construct the early warning model for CD and the corresponding visual nomograph, and to provide an accurate and convenient reference for the risk determination and differential diagnosis of CD, in order to assist clinicians to better manage CD and reduce patient suffering.

Using a retrospective analysis, a total of 310 cases were collected from 2020 to 2022 at The Sixth Affiliated Hospital, Sun Yat-sen University, who were diagnosed by comprehensive clinical diagnosis, including 100 patients with CD, 50 patients with ulcerative colitis (UC), 110 patients with non-inflammatory bowel disease (non-IBD) diseases (65 cases of intestinal tuberculosis, radioactive enterocolitis 39, and colonic diverticulitis 6), and 50 healthy individuals (NC) in the non-CD group. Risk prediction models were established by measuring ESR, Hb, WBC, ALb, and CH levels in hematology. The models were evaluated and visualized using logistic-regression algorithm.

1) ESR, WBC, and WBC/CH ratios in the CD group were higher than those in the non-CD group, while ALb, Hb, CH, WBC/ESR ratio, and Hb/WBC ratio were lower than those in the non-CD group, and the differences were statistically significant (all p < 0.05). 2) CD occurrence had a strong correlation with the WBC/CH ratio, with the correlation coefficient exceeding 0.4; CD occurrence was correlated with other indicators. 3) A risk prediction model containing age, gender, ESR, ALb, Hb, CH, WBC, WBC/CH, WBC/ESR, and Hb/WBC characteristics was constructed using a logistic-regression algorithm. The sensitivity, specificity, positive predictive value, negative predictive value, and area under the curve of the model were 83.0%, 76.2%, 59.0%, 90.5%, and 0.86, respectively. The model based on the corresponding index also had high diagnostic accuracy (AUC = 0.88) for differentiating CD from ITB. Visual nomograph based on the logistic-regression algorithm was also constructed for clinical application reference.

In this study, a CD risk prediction model was established and visualized by five conventional hema-tological indices: ESR, Hb, WBC, ALb, and CH, in addition to a high diagnostic accuracy for the differential diagnosis of CD and ITB.