Serological Biomarker-Based Machine Learning Models for Predicting the Relapse of Ulcerative Colitis

Abstract

Purpose To explore whether machine learning models using serological markers can predict the relapse of Ulcerative colitis (UC). Patients and Methods This clinical cohort study included 292 UC patients, and serological markers were obtained when patients were discharged from the hospital. Subsequently, four machine learning models including the random forest (RF) model, the logistic regression model, the decision tree, and the neural network were compared to predict the relapse of UC. A nomogram was constructed, and the performance of these models was evaluated by accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC). Results Based on the patients’ characteristics and serological markers, we selected the relevant variables associated with relapse and developed a LR model. The novel model including gender, white blood cell count, percentage of leukomonocyte, percentage of monocyte, absolute value of neutrophilic granulocyte, and erythrocyte sedimentation rate was established for predicting the relapse. In addition, the average AUC of the four machine learning models was 0.828, of which the RF model was the best. The AUC of the test group was 0.889, the accuracy was 76.4%, the sensitivity was 78.5%, and the specificity was 76.4%. There were 45 variables in the RF models, and the relative weight coefficients of these variables were determined. Age has the greatest impact on classification results, followed by hemoglobin concentration, white blood cell count, and platelet distribution width. Conclusion Machine learning models based on serological markers had high accuracy in predicting the relapse of UC. The model can be used to noninvasively predict patient outcomes and can be an effective tool for determining personalized treatment plans.

Akkermansia muciniphila and Parabacteroides distasonis as prognostic markers for relapse in ulcerative colitis patients

“…Nevertheless, this study demonstrates a novel promising model that can provide relevant information for UC clinical management, since it can be used to infer relapses in patients that show higher risk for remission. The evaluation of specific bacterial species (in this case, A. muciniphila and P. distasonis ), in combination with demographic parameters and biochemical markers can constitute an important tool to predict remission, as previously defined by other research groups ( Hosseini et al., 2015 ; Pang et al., 2023 ).…”

NF-κB in biology and targeted therapy: new insights and translational implications

“…Ulcerative colitis (UC) is a persistent inflammatory disorder affecting the mucosal lining of the colon that primarily affects the rectum and extends through part or all of the colon in a continuous manner. 338 , 339 The pathogenesis of UC involves dysregulation of cytokine production and signaling mechanisms in intestinal epithelial cells, lymphocytes, and macrophages, with the transcription factor NF-κB playing a significant regulatory role in this complex process 340 (Fig. 4g ).…”

Machine Learning for Predicting Biologic Agent Efficacy in Ulcerative Colitis: An Analysis for Generalizability and Combination with Computational Models

“…Moreover, ML models based on serological markers have demonstrated high accuracy in predicting UC relapse. These models may help determine personalized treatment plans [13].…”