Published online Oct 16, 2021. doi: 10.12998/wjcc.v9.i29.8729
Peer-review started: June 6, 2021
First decision: June 25, 2021
Revised: July 7, 2021
Accepted: July 22, 2021
Article in press: July 22, 2021
Published online: October 16, 2021
Processing time: 130 Days and 21.9 Hours
Hypotension, which most often occurs on the induction of anesthesia, is known to lead to the development of adverse events and poor outcomes in patients following surgery. However, risk scores based on conventional logistic regression analysis have a low ability to discriminate characteristics that influence the development of post-induction hypotension (PIH) events.
Recently, a model based on machine learning techniques was reported to effectively predict or actively monitor events of interest by using variables in medical records datasets.
We attempted to construct a random forest (RF) model for the prediction of PIH events by using electronic information in a patient records dataset.
Data were acquired from the electronic dataset of the Second Affiliated Hospital of Hainan Medical University. A RF model based on an up-to-date machine learning algorithm was used to predict post-anesthesia hypotension in patients during cardiac surgery.
Of the 3030 patients analyzed, 1578 (52.1%) experienced hypotensive events after anesthesia. The RF model had a high predictive performance, with an AUC of 0.843 (0.808-0.877). The most important variable attributing to the accuracy of hypotension prediction after anesthesia in the RF model was the mean blood pressure, followed by age and body mass index.
RF technology can accurately predict PIH in patients following cardiac surgery.
In the era of individualized medicine, precise machine learning modeling based on accessible patient information may offer anesthesiologists an opportunity for early intervention in PIH events.