Published online Apr 26, 2022. doi: 10.12998/wjcc.v10.i12.3729
Peer-review started: December 14, 2021
First decision: January 26, 2022
Revised: February 11, 2022
Accepted: March 6, 2022
Article in press: March 6, 2022
Published online: April 26, 2022
Processing time: 128 Days and 3.1 Hours
Microvascular tissue reconstruction is a well-established technique used for the wide variety of tissue defects. However, still a risk of experiencing flap failure exist that eventually results in additional hospital stays, financial burden, and mental stress of the patients.
The application of the machine learning technique in flap failure events remains an underestimated area.
The objective of the current study was to develop machine learning-based predictive models for the flap failure to identify potential factors and screening the high-risk patients.
To establish machine learning classifiers, we used a data set with 945 consecutive patients who underwent microvascular tissue reconstruction. Model performances were evaluated by the indicators including area under the receiver operating characteristic curve, accuracy, precision, recall, and F1 score. A multivariable regression analysis was also performed for the essential variables in the random forest model.
The flap failure event occurred in 152 patients (1.9%) after the operation. The random forest classifier based on various preoperative and intraoperative variables performed the best, with an area under the curve score of 0.770 in the test set. The top variables in the random forest were age, body mass index, ischemia time, smoking, diabetes, experience, prior chemotherapy, hypertension, insulin, and obesity.
Machine learning models were successfully developed for identifying the potential factors and screening out the high-risk patients for the interesting outcome of flap failure.
In our study, the machine learning technique correctly predicted flap failure in the patients who followed microvascular tissue reconstruction. Results from our research will help the clinician in decision-making by better understanding the underlying pathologic mechanisms of the disease and improving the long-term outcome of patients.