Published online Dec 26, 2021. doi: 10.12998/wjcc.v9.i36.11255
Peer-review started: August 10, 2021
First decision: September 2, 2021
Revised: September 15, 2021
Accepted: November 3, 2021
Article in press: November 3, 2021
Published online: December 26, 2021
Recently, machine learning has proven helpful in the interpretation of medical results and has potential for helping guide diagnosis and treatment, ultimately improving patient outcomes.
Machine learning methods to predict acute kidney injury (AKI) events remain largely unexplored.
We aimed to develop prediction models for AKI after liver cancer resection based on machine learning techniques.
A total of 2450 patients who had undergone primary hepatocellular carcinoma resection at Changzheng Hospital, Shanghai City, China, from January 1, 2015 to August 31, 2020 were screened. Patients were randomly assigned to the training and the test sets at a ratio of 7:3. The training set was used for model development and optimization, while the test set was used for model validation and evaluation.
AKI events occurred in 296 patients (12.1%) after surgery. Among the original models based on machine learning techniques, the random forest (RF) algorithm had optimal discrimination with an area under the curve value of 0.92, compared to 0.87 for extreme gradient boosting, 0.90 for decision tree, 0.90 for support vector machine, and 0.85 for logistic regression. The RF algorithm also had the highest concordance-index (0.86) and the lowest Brier score (0.076). The variables that contributed the most in the RF algorithm were age, cholesterol, and surgery time.
Machine learning technology can accurately predict AKI after hepatectomy.
In the era of personalized medicine, our model based on machine learning can discriminate patients at high risk for AKI, thus helping guide clinical decisions and facilitating prospective interventions for high-risk individuals.