Risk prediction model for postoperative cognitive dysfunction after total knee replacement based on Bayesian network algorithm

To establish a prediction model of risk for postoperative cognitive dysfunction（POCD）after total knee replacement（TKR）by Bayesian network（BN）algorithm and investigate its predictive performance.

A case-control trial was conducted on 1 260 inpatients who underwent TKR from January 2017 to December 2021 in the Department of Joint Surgery of our hospital. Their main diagnosis of inclusion was severe osteoarthritis of left/right knee joint. They were 240 cases of male（19.0%）and 1 020 cases of female（81.0%）, at an average age of 66.73±8.46（23~79）years and a mean body mass index（BMI）of 25.08±5.08 kg/m². The POCD patients（n=71）after surgery（from the end of surgery to discharge）were randomly divided into A1 group and B1 group at a ratio of 7∶3, and those without POCD（1 189 cases）were also randomly divided into A2 group and B2 group at a same ratio. The patients from A1 and A2 groups were combined together as training set, and those out of B1 and B2 groups as test set. Thirty-six indexes related to perioperative anesthesia decision, disease outcome and length of stay in TKR were selected as nodes, and the probability distribution model diagram of each node was established by using BN algorithm to predict the probability of risk for POCD, so as to minimize the length of stay and promote the maximum recovery of patients.

The prediction model of risk for POCD after TKR was established based on BN algorithm. The area value under receiver operating characteristic curve（ROC-AUC）of the training set was 0.966 1（95% CI: 0.954 1~0.978 4）, and the ROC-AUC value of the test set was 0.897 4（95% CI: 0.867 2~0.928 5）, with an accuracy of 96.43%（95%CI: 0.951 1~0.976 4）and 93.44%（95% CI: 0.909 2~0.959 6）, respectively.

Our risk prediction model for POCD after TKR based on BN algorithm has good performance and high accuracy