AI Article Synopsis

  • - The study aimed to develop a prediction rule for the risks of overall and major postoperative complications in gastric cancer patients undergoing surgery, acknowledging the impact of these complications on long-term outcomes.
  • - It involved a retrospective analysis of 1,223 patients treated between 1992 and 2016, identifying several independent predictors, such as age, chronic diseases, and surgical factors, linked to overall and major complications.
  • - The resulting prediction models and nomograms proved effective for identifying high-risk patients, facilitating better clinical decision-making by using a combination of clinical and surgical data.

Article Abstract

Background: Postoperative complications after gastric cancer resection vary in different series and they might have a significant impact in long-term outcomes. Our aim was to build a prediction rule on gastric cancer patients' overall and major morbidity risks.

Methods: This retrospective study included 1223 patients from a single center who were resected between 1992 and 2016. Overall and major morbidity predictors were identified through multiple logistic regression. Models' performances were assessed through discrimination, calibration, and cross-validation, and nomograms were constructed.

Results: The mean age was 61.3-year old and the male gender was more frequent (60%). The most common comorbidities were hypertension (HTN), diabetes, and chronic obstructive pulmonary disease (COPD). A D2-distal gastrectomy was the most frequent procedure and 87% of all lesions were located in the middle or distal third. Age, COPD, coronary heart disease, chronic liver disease, pancreatic resection, and operative time were independent predictors of overall and major morbidity. The extent of resection and splenectomy was associated with overall events and HTN with major ones. Both models were very effective in predicting events among patients at higher risk.

Conclusions: The overall and major morbidity models and nomograms included clinical- and surgical-related data that were very effective in predicting events, especially for high-risk patients.

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Source
http://dx.doi.org/10.1002/jso.25743DOI Listing

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