Observational studyDigestive diseases and sciences2026
Advancing Predictive Modeling of Inflammatory Bowel Disease (IBD) Flares: A Data-Driven Approach Using Lifestyle and Psychosocial Factors from a Remote Monitoring Platform.
Observational study in Digestive diseases and sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
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13 authors.
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Abstract
backgroundRecurrent flares are associated with disease progression and have a pronounced impact on the quality of life of people with Inflammatory Bowel Disease (IBD). Models using clinical characteristics only moderately predict flares and therefore difficult to implement in clinical practice. With the rise of remote monitoring platforms such as myIBDcoach, which capture besides clinical disease activity, modifiable lifestyle and psychosocial risk factors and patient-reported outcome measures (PROMs), harnessing real-world data may help improve flare prediction. The aim of this study was to develop and compare five predictive models for flares.
methodThe baseline demographic and clinical data and PROMs related to lifestyle and psychosocial factors were collected from the myIBDcoach telemedicine platform from November 2022 to June 2024. Associations between flares, baseline clinical variables alone, and PROMs variable categories from the myIBDcoach platform were estimated using stepwise group-LASSO logistic regression (G-LASSO) model, which was evaluated with performance matrices using accuracy, area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).
resultFour hundred and twenty-nine patients from a prospective observational cohort were selected to create the models. The performance of the G-LASSO regression model with baseline variables and myIBDcoach PROMs (psychosocial and lifestyle factors) was better (accuracy: 71%, ROC-AUC: 77%, sensitivity: 59%, specificity: 91%, PPV: 91%, NPV: 59%) than that of the model with baseline data alone (accuracy: 63%, ROC-AUC: 65%, sensitivity: 51%, specificity: 81%, PPV: 81%, NPV: 52%).
conclusionThe inclusion of subjective health and modifiable lifestyle and psychosocial data improved flare prediction in contrast to clinical characteristics alone, which was evidenced in the model performance matrices (accuracy, AUC, sensitivity, specificity, PPV, NPV). In multifactorial disorders such as IBD, lifestyle, and psychological stressors may intensify inflammatory responses, all of which can be controlled by lifestyle choices including diet, exercise, and stress management, for which this model underscores the need.
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