Develop a Clinical Prediction Model for Postoperative Cognitive Dysfunction after Major Noncardiac Surgery in Elderly Patients: A Protocol for a Prospective Observational Study

医学 认知功能衰退 人口 神经认知 逻辑回归 入射(几何) 观察研究 外科 前瞻性队列研究 术后认知功能障碍 认知 痴呆 内科学 围手术期 物理 疾病 环境卫生 精神科 光学
作者
Yang Shen,Xianchen Li,Junyan Yao
出处
期刊:Gerontology [Karger Publishers]
卷期号:68 (5): 538-545 被引量:10
标识
DOI:10.1159/000517511
摘要

Perioperative neurocognitive disorders (PNDs) refer to cognitive decline identified in the preoperative or postoperative period. It has been reported that the incidence of postoperative neurocognitive impairment after noncardiac surgery in patients older than 65 at 1 week was 25.8∼41.4%, and at 3 months 9.9∼12.7%. PNDs will last months or even develop to permanent dementia, leading to prolonged hospital stays, reduced quality of life, and increased mortality within 1 year. Despite the high incidence and poor prognosis of PNDs in the aged population, no effective clinical prediction model has been established to predict postoperative cognitive decline preoperatively. To develop a clinical prediction model for postoperative neurocognitive dysfunction, a prospective observational study (Clinical trial registration number: ChiCTR2000036304) will be performed in the Shanghai General Hospital during January 2021 to October 2022. A sample size of 675 patients aged &#x3e;65 years old, male or female, and scheduled for elective major noncardiac surgery will be recruited. A battery of neuropsychological tests will be used to test the cognitive function of patients at 1 week, 1 month, and 3 months postoperatively. We will evaluate the associations of PNDs with a bunch of candidate predictors including general characteristics of patients, blood biomarkers, indices associated with anesthesia and surgery, retinal nerve-fiber layer thickness, and frailty index to develop the clinical prediction model by using multiple logistic regression analysis and least absolute shrinkage and the selection operator (LASSO) method. The <i>k</i>-fold cross-validation method will be utilized to validate the clinical prediction model. In conclusion, this study was aimed to develop a clinical prediction model for postoperative cognitive dysfunction of old patients. It is anticipated that the knowledge gained from this study will facilitate clinical decision-making for anesthetists and surgeons managing the aged patients undergoing noncardiac surgery.
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