Novel Prediction Score for Arterial-Esophageal Fistula in Patients with Esophageal Cancer Bleeding: A Multicenter Study

Cancers (Basel). 2024 Feb 16;16(4):804. doi: 10.3390/cancers16040804.

Abstract

Purpose: To develop and internally validate a novel prediction score to predict the occurrence of arterial-esophageal fistula (AEF) in esophageal cancer bleeding.

Methods: This retrospective cohort study enrolled patients with esophageal cancer bleeding in the emergency department. The primary outcome was the diagnosis of AEF. The patients were randomly divided into a derivation group and a validation group. In the derivation stage, a predictive model was developed using logistic regression analysis. Subsequently, internal validation of the model was conducted in the validation cohort during the validation stage to assess its discrimination ability.

Results: A total of 257 patients were enrolled in this study. All participants were randomized to a derivation cohort (n = 155) and a validation cohort (n = 102). AEF occurred in 22 patients (14.2%) in the derivation group and 14 patients (13.7%) in the validation group. A predictive model (HEARTS-Score) comprising five variables (hematemesis, active bleeding, serum creatinine level >1.2 mg/dL, prothrombin time >13 s, and previous stent implantation) was established. The HEARTS-Score demonstrated a high discriminative ability in both the derivation and validation cohorts, with c-statistics of 0.90 (95% CI 0.82-0.98) and 0.82 (95% CI 0.72-0.92), respectively.

Conclusions: By employing this novel prediction score, clinicians can make more objective risk assessments, optimizing diagnostic strategies and tailoring treatment approaches.

Keywords: arterial–esophageal fistula; cancer bleeding; esophageal cancer; gastrointestinal bleeding; predictive score.

Grants and funding

This study was funded by Chang-Gung Research Grant (CMRPVVN0181 and CORPVVN0091). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.