Association of prealbumin with risk of abdominal surgery and endoscopic recurrence in Crohn’s disease: A machine learning study

Abstract

BACKGROUND

Prealbumin (PA) reflects inflammatory and nutritional status and may therefore serve as a pragmatic biomarker for risk stratification and personalized management in Crohn’s disease (CD). However, its associations with abdominal surgical risk and postoperative endoscopic recurrence remain unclear.

AIM

To explore its associations with abdominal surgical risk and postoperative endoscopic recurrence.

METHODS

This retrospective study enrolled 1060 newly diagnosed CD patients and 282 patients who underwent abdominal surgery at our institution. Cox regression, Kaplan-Meier, and restricted cubic spline analyses were used to assess the associations between PA and disease outcomes. Mediation analysis explored the inflammatory, coagulation, and nutritional pathways. Finally, seven machine learning (ML) models were developed to predict disease outcomes, and their predictive performance was compared.

RESULTS

Trend and restricted cubic spline analyses showed that higher PA levels were associated with lower risks of abdominal surgery and endoscopic recurrence [Q4 vs Q1: Surgery, hazard ratio (HR) = 0.45; recurrence, HR = 0.20; both P < 0.001]. Inflection points at 208.149 mg/L and 148.816 mg/L were used to define PA categories, and accordingly, high PA levels were associated with reduced risks of surgery (HR = 0.39) and recurrence (HR = 0.52). PA also showed good discrimination, with a 5-year area under the curve of 0.697 for surgery and a 2-year area under the curve of 0.756 for recurrence. Mediation analysis indicated that fibrinogen and body mass index mediated 3.8% and 8.1% of the associations with surgery and recurrence, respectively, whereas inflammatory markers accounted for about 7% in both settings. Moreover, among the ML models, the PA-based gradient boosting machine model performed best for surgery prediction, while the random survival forest model performed best for recurrence prediction, with C-index values of 0.784 and 0.764, respectively; both also provided clear prognostic stratification (both P < 0.001). Notably, biologic therapy was associated with improved outcomes mainly in patients with low PA levels or those classified as intermediate- or high-risk. Consistently, serum PA levels showed approximately 70% concordance with ML-derived risk stratification. Finally, an online clinical calculator was developed to support treatment decision-making in CD.

CONCLUSION

Serum PA is a strong predictor of abdominal surgery and postoperative endoscopic recurrence in CD. Incorporating PA into ML-based risk stratification may improve early risk prediction and support more individualized treatment decisions.

Keywords: Crohn’s disease; Prealbumin; Abdominal surgery; Postoperative endoscopic recurrence; Machine learning; Predictive and personal medicine

Core Tip: Serum prealbumin (PA), reflecting inflammatory and nutritional status, may serve as a pragmatic biomarker for risk stratification in Crohn’s disease. Higher PA levels were associated with lower risks of abdominal surgery and postoperative endoscopic recurrence (highest vs lowest quartile: Hazard ratio = 0.45 and 0.20, respectively). Incorporating PA into machine learning models further improved prognostic performance (C-index = 0.784 for surgery and 0.764 for recurrence). Moreover, serum PA showed approximately 70% concordance with machine learning-derived risk tiers and helped identify patients more likely to benefit from biologic therapy, thereby supporting earlier risk prediction and more individualized treatment decisions.