# A Clinical Prediction Model for Early Colectomy in Patients With Severe Ulcerative Colitis Treated With Tacrolimus

## TL;DR

This study creates a model to predict early colectomy risk in severe ulcerative colitis patients starting tacrolimus treatment.

## Contribution

A novel clinical prediction model using baseline factors to estimate early colectomy risk in tacrolimus-treated patients.

## Key findings

- Three predictors identified: serum albumin, hemoglobin, and age at treatment initiation.
- The model achieved 87.5% sensitivity and 63.4% specificity for predicting early colectomy.
- High-risk patients had significantly higher colectomy rates confirmed by Kaplan–Meier analysis.

## Abstract

Tacrolimus is an effective treatment option for refractory ulcerative colitis; however, some patients still require colectomy due to insufficient response. Early assessment of surgical risk is clinically important, as delayed decision‐making may worsen the patient's condition and increase the risk of postoperative complications. This study aimed to identify predictors of colectomy within 3 months of initiating tacrolimus therapy and to develop a clinically applicable prediction model.

We conducted a retrospective analysis of hospitalized patients with severe ulcerative colitis treated with tacrolimus between 2011 and 2025. Fourteen clinical background variables were evaluated using LASSO‐penalized logistic regression with cross‐validation to construct the prediction model.

Among 114 patients, 24 (21.1%) underwent colectomy, including 16 (14.0%) within 3 months of treatment initiation. The LASSO regression identified three predictive variables: serum albumin level, hemoglobin level, and age at tacrolimus initiation. The resulting model demonstrated good discriminative performance, with an area under the curve of 0.78. Using a cutoff value of logit(p), the model achieved a sensitivity of 87.5% and a specificity of 63.4%. Kaplan–Meier analysis revealed a significantly higher cumulative colectomy rate in the high‐risk group (p < 0.001), supporting the model's predictive utility.

We developed a clinical prediction model that accurately estimates the risk of early colectomy based on baseline clinical factors at the start of tacrolimus therapy. This model may serve as a practical tool to guide decision‐making regarding surgical timing and overall treatment strategy.