Predictors of 30-day mortality patients undergoing Colorecta
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According to this JAMA study, machine learning methods may be of additional value in analyzing quality indicators in colorectal cancer surgery, thereby providing directions to optimize case-mix corrections for benchmarking in clinical auditing.

The aim was to investigate the added value of machine learning to predict quality indicators for colorectal cancer surgery and identify previously unrecognized predictors of 30-day mortality based on a large, nationwide colorectal cancer registry that collected extensive data on comorbidities.

All patients who underwent resection for primary colorectal cancer registered in the Dutch ColoRectal Audit were included. Multiple machine learning models were made to predict quality indicators. Model performance was compared with conventionally used scores. Risk factors were identified by logistic regression analyses and Shapley additive explanations. This cohort study included 62501 records and had an American Society of Anesthesiology score of II.

--A 30-day mortality rate of 2.7% was found. The area under the curve of the best machine learning model for 30-day mortality was significantly higher than the American Society of Anesthesiology score, Charlson Comorbidity Index, and preoperative score to predict postoperative mortality.

--Hypertension, myocardial infarction, chronic obstructive pulmonary disease, and asthma were comorbidities with a high risk for increased mortality.

--Machine learning identified specific risk factors for a complicated course, intensive care unit admission, prolonged hospital stay, and readmission.

--Laparoscopic surgery was associated with a decreased risk for all adverse outcomes.

In conclusion, machine learning methods outperformed conventional scores to predict 30-day mortality after colorectal cancer surgery, identified specific patient groups at risk for adverse outcomes, and provided directions to optimize benchmarking in clinical audits.