Observational studySupportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer2024
Utilizing machine learning approaches to investigate the relationship between cystatin C and serious complications in esophageal cancer patients after esophagectomy.
Observational study in Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
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Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Predictive role of geriatric nutritional risk index for postoperative complications in operated esophageal cancer patients: a meta-analysis.Journal of cardiothoracic surgery · 2025Pooled it
- 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
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Authors and funding
9 authors.
Funding
Abstract
backgroundThe purpose of this study is to investigate the relationship between preoperative cystatin C levels and the risk of serious postoperative complications in esophageal cancer (EC) patients, utilizing advanced machine learning (ML) methodologies.
methodsWe conducted an observational cohort study, involving 524 EC patients from December 2014 to July 2022. ML models, including logistic regression (LR) and multilayer perceptron (MLP), were applied to investigate the relationship between cystatin C and the serious postoperative complications. The predictive value of cystatin C was evaluated using receiver operating characteristic (ROC) analysis. Based on a restricted cubic spline (RCS) method, the potential nonlinear association was scrutinized.
resultsThe morbidity of serious postoperative complications was 8.78%. Bleeding volume, operating time, NRS2002 score, PONS score, and cystatin C were significantly associated with serious postoperative complications. The MLP model demonstrated superior predictive accuracy (AUC = 0.775, 95% CI: 0.701-0.849) compared to the LR model (AUC = 0.714, 95% CI: 0.630-0.798) and cystatin C alone (AUC = 0.612, 95% CI: 0.526-0.699). High cystatin C level independently predicted serious postoperative complications in EC patients. A positive and linear association was found between cystatin C and serious complications.
conclusionThis research uncovers a notable correlation between cystatin C and the severe complications in EC patients after esophagectomy. Employing ML techniques offers a robust method for forecasting patient outcomes and emphasizes the potential of cystatin C as a predictive biomarker in medical practice.
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