Objective:To identify early predictors of factors influencing the efficacy of infliximab (IFX) treatment in patients with Crohn's disease (CD) .Methods:This study is a nested case-control study, including CD patients treated with IFX at the Second Affiliated Hospital of Soochow University from November 2015 to April 2021 and at the First Affiliated Hospital of Nanjing Medical University from November 2015 to December 2022. All the patients were followed up until June 2023 and categorized into IFX non-response and treatment-effective groups based on changes in clinical symptoms and endoscopic image during the follow-up. Laboratory data of inflammatory markers, post-induction trough IFX concentration and antibody levels in both groups were retrospectively collected and compared. Logistic regression models were employed to identify potential factors associated with the risk of IFX non-responsiveness. Machine learning using random forest analysis was utilized to quantitatively assess the predictive features for IFX treatment efficacy and ROC curves was used to evaluate the model's accuracy.Results:This study included 147 CD patients undergoing IFX treatment, with 58 from the Second Affiliated Hospital of Soochow University and 89 from the First Affiliated Hospital of Nanjing Medical University. Among them, 38 were classified as the IFX non-response group, and 109 as the effective group. Patients in the IFX non-response group had lower trough concentration ( P < 0.001), higher antibody levels ( P < 0.001), and a less pronounced reduction in ESR during the induction therapy ( P < 0.001). Univariate and multi-variate Logistic regression models demonstrated that IFX trough concentration and the ratio of ESR before and after induction therapy was associated with the risk of non-responsiveness. After the induction period, for each unit increase in IFX trough concentration (1 μg/ml), the risk of IFX non-response decreased by 23% ( RR = 0.77, 95% CI = 0.68-0.89), while each doubling of the ESR ratio after induction was associated with a 1.43-fold increase in the risk of non-response ( RR = 2.43, 95% CI = 1.48-4.00). Random forest machine learning analysis revealed that IFX trough concentration below 1.5 μg/ml could predict IFX non-response, with area under the ROC curve was 0.722. Conclusion:Lower post-induction IFX trough concentrations is predictive of IFX non-response, while a lack of significant decrease in ESR during the induction phase is also significantly associated with IFX non-response.

Objective:To identify early predictors of factors influencing the efficacy of infliximab (IFX) treatment in patients with Crohn's disease (CD) .Methods:This study is a nested case-control study, including CD patients treated with IFX at the Second Affiliated Hospital of Soochow University from November 2015 to April 2021 and at the First Affiliated Hospital of Nanjing Medical University from November 2015 to December 2022. All the patients were followed up until June 2023 and categorized into IFX non-response and treatment-effective groups based on changes in clinical symptoms and endoscopic image during the follow-up. Laboratory data of inflammatory markers, post-induction trough IFX concentration and antibody levels in both groups were retrospectively collected and compared. Logistic regression models were employed to identify potential factors associated with the risk of IFX non-responsiveness. Machine learning using random forest analysis was utilized to quantitatively assess the predictive features for IFX treatment efficacy and ROC curves was used to evaluate the model's accuracy.Results:This study included 147 CD patients undergoing IFX treatment, with 58 from the Second Affiliated Hospital of Soochow University and 89 from the First Affiliated Hospital of Nanjing Medical University. Among them, 38 were classified as the IFX non-response group, and 109 as the effective group. Patients in the IFX non-response group had lower trough concentration ( P < 0.001), higher antibody levels ( P < 0.001), and a less pronounced reduction in ESR during the induction therapy ( P < 0.001). Univariate and multi-variate Logistic regression models demonstrated that IFX trough concentration and the ratio of ESR before and after induction therapy was associated with the risk of non-responsiveness. After the induction period, for each unit increase in IFX trough concentration (1 μg/ml), the risk of IFX non-response decreased by 23% ( RR = 0.77, 95% CI = 0.68-0.89), while each doubling of the ESR ratio after induction was associated with a 1.43-fold increase in the risk of non-response ( RR = 2.43, 95% CI = 1.48-4.00). Random forest machine learning analysis revealed that IFX trough concentration below 1.5 μg/ml could predict IFX non-response, with area under the ROC curve was 0.722. Conclusion:Lower post-induction IFX trough concentrations is predictive of IFX non-response, while a lack of significant decrease in ESR during the induction phase is also significantly associated with IFX non-response.