ArticleFrontiers in public health2026
Predicting patient dropout: a nomogram for loss to follow-up after
Article in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Objective: This study aimed to investigate the risk factors associated with LTFU after Methods: We conducted a prospective cohort study (April 2023-September 2024) enrolling treatment-naïve patients from a tertiary gastroenterology clinic. Following data collection via questionnaires and follow-ups, a nomogram for predicting loss to follow-up (LTFU) was developed by applying LASSO regression for variable selection and logistic regression for model building. The model was evaluated by its area under the ROC curve (AUC), calibration, and decision curve analysis (DCA), with internal validation performed via 500 bootstrap resamples to confirm reliability. Results: A total of 145 (37.76%) patients failed to follow up. From 19 potential predictors, 6 variables were independent predictive factors. They were included in the risk score: BMI > 30 kg/m Conclusion: The nomogram effectively assessed the risk of LTFU after
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