Association between cerebrovascular disease and perioperative neurocognitive disorders: a retrospective cohort study

Author:

Feng Lan123,Liu Yuanhui12,Li PengFei12,Wan Hengjun12,Deng Xiren12,Wang Tingting4,Fu Hong3,Duan Xiaoxia12

Affiliation:

1. Department of Anesthesiology

2. Anesthesiology and Critical Care Medicine Key Laboratory of Luzhou, Southwest Medical University, Luzhou, Sichuan Province

3. Department of Anesthesiology, Chongqing Emergency Medical Center, Chongqing University Central Hospital, School of Medicine, Chongqing University, Chongqing, People’s Republic of China

4. Department of Psychiatry, The Affiliated Hospital of Southwest Medical University

Abstract

Background: Previous studies have shown that patients with cerebrovascular disease (CVD) have a significantly increased risk of cognitive decline or dementia; however, the association between preoperative CVD and perioperative neurocognitive disorders (PNDs) remains unclear. This study aimed to explore the correlation between preoperative CVD and PNDs, as well as combine logistic regression and receiver operating characteristic (ROC) curves to construct a clinical prediction PND model. Materials and Methods: This retrospective cohort study evaluated 13 899 surgical patients of a large-scale comprehensive hospital between January 2021 and January 2022 to explore the association between preoperative CVD and PNDs, with follow-up to monitor postoperative survival until 28 February 2023, unless the patient died. The study participants comprised all inpatients from the Bone and Joint Surgery, Spine Surgery, Urology, Hepatobiliary Surgery, Gastrointestinal Surgery, and Thoracic Surgery departments. Patients were classified into two groups: the CVD group with a confirmed diagnosis and the noncerebrovascular disease group. The incidence of PNDs was measured, and potential associations between patient demographic information, preoperative comorbidities, and CVD, as well as the correlation between preoperative CVD and PNDs, were investigated by multivariate logistic regression analysis. Next, the authors constructed a clinical prediction PND model by drawing the ROC curve. The postoperative survival of all patients was tracked, and a survival curve was constructed and incorporated into the Cox proportional hazard regression model to analyze the relationship between preoperative CVD and the overall postoperative survival rate. Results: Of the included 13 899 patients, propensity score matching yielded 1006 patient pairs. Multivariate logistic regression analysis revealed that CVD was an independent risk factor for PNDs [odds ratio: 10.193; 95% CI: 7.454–13.938; P<0.001]. Subsequently, the authors developed a clinical prediction model for PNDs by multivariate logistic regression analysis. The area under the ROC curve was 0.798 (95% CI: 0.765–0.830). The survival of 11 702 patients was followed up. Multivariate Cox hazard ratio regression analysis revealed that CVD affected the overall postoperative survival rate (hazard ratio, 1.398; 95% CI: 1.112–1.758; P<0.001). Conclusion: CVD was an independent risk factor for PNDs and affected the overall postoperative survival rate of surgical patients with preoperative CVD.

Publisher

Ovid Technologies (Wolters Kluwer Health)

Subject

General Medicine,Surgery

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