Abstract:
Objective To explore the influencing factors of delirium after total hip arthroplasty and construct a predictive Nomogram model.
Methods Clinical materials of 320 patients with total hip arthroplasty from April 2018 to April 2023 were analyzed retrospectively, and they were divided into delirium group (n=72) and no delirium group (n=248) according to incidence of postoperative delirium. One-way ANOVA was used to analyze the general materials between two groups; the significant indicators were enrolled into a binary Logistic regression model, the relevant factors affecting delirium after total hip arthroplasty were analyzed, and a predictive model was established.
Results Binary Logistic regression analysis showed that age, concomitant chronic obstructive pulmonary disease, femoral neck fracture, operation time, intraoperative blood loss, level of C-reaction protein (CRP) at 4 hours after operation, postoperative hypoxemia, resuscitation time (≥1 h), postoperative pain score (≥ 4 points), concomitant dystrophia, concomitant sleep disorders and concomitant cognitive impairment were the risk factors affecting delirium after total hip arthroplasty (P < 0.05). The overall prediction accuracy of the prediction model constructed by incorporating the above factors was 99.7%, and the Hosmer-Lemeshow fit test analysis indicated that the model had good goodness of fit. The area under the curve (AUC) of the Bootstrap method for internal validation of prediction model was 0.992, with a sensitivity of 99.6% and a specificity of 93.1%.
Conclusion Age, concomitant chronic obstructive pulmonary disease, femoral neck fracture, operation time, intraoperative blood loss, level of CRP at 4 hours after operation, postoperative hypoxemia, resuscitation time (≥1 h), postoperative pain score (≥ 4 points), concomitant dystrophia, concomitant sleep disorders and concomitant cognitive impairment are the risk factors affecting delirium after total hip arthroplasty. The risk prediction model constructed based on the above relevant factors has good stability and predictability.