Correlation between heart rate variability and perioperative neurocognitive disorders in patients undergoing non-cardiac surgery: A retrospective cohort study.

OBJECTIVE: With the improvement of medical level, the number of elderly patients is increasing, and the postoperative outcome of the patients cannot be ignored. However, there have been no studies on the relationship between preoperative heart rate variability (HRV) and Perioperative Neurocognitive Disorders (PND). The purpose of this study was to explore the correlation between (HRV) and (PND), postoperative intensive care unit (ICU), and hospital stay in patients undergoing non-cardiac surgery. METHOD: This retrospective analysis included 687 inpatients who underwent 24-hour dynamic electrocardiogram examination in our six departments from January 2021 to January 2022. Patients were divided into two groups based on heart rate variability (HRV): high and low. Possible risk factors of perioperative outcomes were screened using univariate analysis, and risk factors were included in multivariate logistic regression to screen for independent risk factors. The subgroup analysis was carried out to evaluate the robustness of the results. The nomogram of PND multi-factor logistic prediction model was constructed. The receiver operating characteristic (ROC) curve was drawn, and the calibration curve was drawn by bootstrap resampling 1000 times for internal verification to evaluate the prediction ability of nomogram. RESULT: A total of 687 eligible patients were included. The incidence of low HRV was 36.7% and the incidence of PND was 7.6%. The incidence of PND in the low HRV group was higher than that in the high HRV group (11.8% vs 5.2%), the postoperative ICU transfer rate was higher (15.9% than 9.3%P = 0.009), and the hospital stay was longer [15 (11, 19) vs (13), 0.015]. The multivariable logistic regression analysis showed that after adjusting for other factors, decreased low HRV was identified as an independent risk factor for the occurrence of PND (Adjusted Odds Ratio = 2.095; 95% Confidence Interval: 1.160-3.784; P = 0.014) and postoperative ICU admission (Adjusted Odds Ratio = 1.925; 95% Confidence Interval: 1.128-3.286; P = 0.016). This study drew a nomogram column chart for a multivariate logistic regression model, incorporating age and HRV. The calibration curve shows that the predicted value of the model for the occurrence of cardio-cerebrovascular events is in good agreement with the actual observed value, with C-index of 0.696 (95% CI: 0.626 ~ 0.766). Subgroup analysis showed that low HRV was an independent risk factor for PND in patients with gastrointestinal surgery and ASA Ⅲ, aged ≥ 65 years. CONCLUSION: In patients undergoing non-cardiac surgery, the low HRV was an independent risk factor for PND and postoperative transfer to the ICU, and the hospitalization time of patients with low HRV was prolonged. Through establishing a risk prediction model for the occurrence of PND, high-risk patients can be identified during the perioperative period for early intervention.

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

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.