ArticleHead & neck2024
Predicting reoperation and readmission for head and neck free flap patients using machine learning.
Article in Head & neck, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic Accuracy of AI in Prediction and Assessment of Compromised Free Flaps: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Externally Validated Machine Learning Models for 30-/90-/180-Day Unplanned Readmission After Head and Neck Cancer Hospitalizations in the United States.JCO clinical cancer informatics · 2026Article
- Predicting Breast Reconstruction Readmission, Reoperation, and Prolonged Length of Stay: A Machine Learning Approach.Journal of surgical oncology · 2025Article
- Development and validation of a Nomogram to predict postoperative flap necrosis risk in breast cancer patients undergoing modified radical mastectomy.American journal of cancer research · 2025Article
- Perioperative laboratory profiles predict complications after extensive head and neck reconstruction: a proof-of-concept study.Frontiers in oncology · 2025Article
- Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
backgroundTo develop machine learning (ML) models predicting unplanned readmission and reoperation among patients undergoing free flap reconstruction for head and neck (HN) surgery.
methodsData were extracted from the 2012-2019 NSQIP database. eXtreme Gradient Boosting (XGBoost) was used to develop ML models predicting 30-day readmission and reoperation based on demographic and perioperative factors. Models were validated using 2019 data and evaluated.
resultsFour-hundred and sixty-six (10.7%) of 4333 included patients were readmitted within 30 days of initial surgery. The ML model demonstrated 82% accuracy, 63% sensitivity, 85% specificity, and AUC of 0.78. Nine-hundred and four (18.3%) of 4931 patients underwent reoperation within 30 days of index surgery. The ML model demonstrated 62% accuracy, 51% sensitivity, 64% specificity, and AUC of 0.58.
conclusionXGBoost was used to predict 30-day readmission and reoperation for HN free flap patients. Findings may be used to assist clinicians and patients in shared decision-making and improve data collection in future database iterations.
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Registered trials
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