Establishment of prediction model for risk of postoperative cognitive dysfunction after non-cardiac surgery based on different machine learning algorithms

To establish a risk model for predicting postoperative cognitive dysfunction（POCD）after non-cardiac surgery using preoperative indicators based on machine learning algorithm.

A case-control study was designed, and conducted on the POCD patients after non-cardiac surgery in the medical big data platform of our hospital from January 2014 to January 2019. Finally, 92 patients were included in the POCD group. According to surgical type and age matched of the POCD group, another 276 patients who did not develop POCD after surgery and discharged from the hospital during the same time period from the same big data platform were assigned into the non-POCD group at a ratio of 1∶3. At the same time, these 368 patients were randomly divided into modeling group（n=259）and validation group（n=109）at a ratio of 7∶3. After data preprocessing and feature selection of preoperative clinical indicators（general data, relevant scoring scales, surgical-related data and results of preoperative laboratory tests）, the risk prediction model of POCD for non-cardiac surgery was established based on 3 machine learning algorithms, that is, logistic regression（LR）, support vector machine（SVM）and Decision Tree. The model efficacy was evaluated by sensitivity, specificity, F1 score, and the area under the receiver operating characteristic curve（AUC）.

The SVM algorithm was the best model among the 3 machine learning algorithms to predict the risk of POCD after non-cardiac surgery. The AUC value of the model in the validation group was 0.957（95%CI: 0.905~1.000）, with a sensitivity and specificity of 92.6% and 98.8%, respectively.

A prediction model of POCD after non-cardiac surgery is successfully established based on machine learning algorithm. This model shows good predictive performance for POCD.