ArticleAnnals of gastroenterological surgery2019
Development and validation of grade-based prediction models for postoperative morbidity in gastric cancer resection using a Japanese web-based nationwide registry.
Article in Annals of gastroenterological surgery, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Explainable machine learning for postoperative respiratory failure prediction in open-heart surgery patients - a study based on the MIMIC-IV database.BMC medical informatics and decision making · 2026Article
- Time-sequential prediction of postoperative complications after gastric cancer surgery using machine learning: a multicenter cohort study.Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association · 2025Article
- Development of gastroenterological surgery over the last decade in Japan: analysis of the National Clinical Database.Surgery today · 2021Review
- Clinical impact of Endoscopic Surgical Skill Qualification System (ESSQS) by Japan Society for Endoscopic Surgery (JSES) for laparoscopic distal gastrectomy and low anterior resection based on the National Clinical Database (NCD) registry.Annals of gastroenterological surgery · 2020Article
- The effect of the visceral fat area on the predictive accuracy of C-reactive protein for infectious complications after laparoscopy-assisted gastrectomy.Annals of gastroenterological surgery · 2020Article
- Development and validation of grade-based prediction models for postoperative morbidity in gastric cancer resection using a Japanese web-based nationwide registry.Annals of gastroenterological surgery · 2019Article
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9 authors.
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Abstract
aimGastric cancer is the second leading cause of cancer death worldwide. Surgery is the mainstay treatment for gastric cancer. There are no prediction models that examine the severity of postoperative morbidity. Herein, we constructed prediction models that analyze the risk for postoperative morbidity based on severity.
methodsPerioperative data were retrieved from the National Clinical Database in patients who underwent elective gastric cancer resection between 2011 and 2012 in Japan. Severity of postoperative complications was determined by Clavien-Dindo classification. Patients were randomly divided into two groups, the development set and the validation set. Logistic regression analysis was used to build prediction models. Calibration powers of the models were assessed by a calibration plot in which linearity between the observed and predicted event rates in 10 risk bands was assessed by the Pearson
resultsWe obtained 154 278 patients for the analysis. Prediction models were constructed for grade ≥2, grade ≥3, grade ≥4, and grade 5 in the development set (n = 77 423). Calibration plots of these models showed significant linearity in the validation set (n = 76 855):
conclusionPrediction models for postoperative morbidity based on grade will provide a comprehensive risk of surgery. These models may be useful for informed consent and surgical decision-making.
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