ArticleDiagnostics (Basel, Switzerland)2024
Complication Prediction after Esophagectomy with Machine Learning.
Article in Diagnostics (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- AI Trustworthiness in the Perioperative Period for Patients with Serious Illness: Scoping Narrative Review.JMIR perioperative medicine · 2026Article
- The Cutting Edge: A Systematic Review of Artificial Intelligence and Machine Learning in Predicting Esophagectomy Outcomes.Annals of thoracic surgery short reports · 2026Article
- One-Lung Ventilation Duration Is a Risk Factor for Pneumonia in Minimally Invasive and Robotic Esophagectomy.Journal of clinical medicine · 2026Article
- Identification and validation of an explainable machine learning model for early postoperative pulmonary complications after esophagectomy in patients with esophageal cancer.Journal of gastrointestinal oncology · 2026Article
- Nomographic predictive models for complications after minimally invasive esophagectomy: Current status and future perspectives.World journal of gastrointestinal surgery · 2025Review
- "Could She/He Walk Out of the Hospital?": Implementing AI Models for Recovery Prediction and Doctor-Patient Communication in Major Trauma.Diagnostics (Basel, Switzerland) · 2025Article
- The value of perioperative neutrophil-to-lymphocyte ratio combined with the prognostic nutritional index for predicting anastomotic leakage after minimally invasive esophagectomy.Journal of thoracic disease · 2025Article
- [Predictability of anastomotic leaks in visceral surgery].Chirurgie (Heidelberg, Germany) · 2024Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Esophageal cancer can be treated effectively with esophagectomy; however, the postoperative complication rate is high. In this paper, we study to what extent machine learning methods can predict anastomotic leakage and pneumonia up to two days in advance. We use a dataset with 417 patients who underwent esophagectomy between 2011 and 2021. The dataset contains multimodal temporal information, specifically, laboratory results, vital signs, thorax images, and preoperative patient characteristics. The best models scored mean test set AUROCs of 0.87 and 0.82 for leakage 1 and 2 days ahead, respectively. For pneumonia, this was 0.74 and 0.61 for 1 and 2 days ahead, respectively. We conclude that machine learning models can effectively predict anastomotic leakage and pneumonia after esophagectomy.
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