Objective To explore the morbidity and risk factors of postoperative delirium in the patients undergoing coronary artery bypass grafting (CABG).Methods A total of 286 patients were continuously enrolled for CABG surgery from January to December 2013 in cardiac surgery ward in Beijing Anzhen Hospital.Delirium Rating Scale-Revised-98 was used as the diagnostic tool for the analysis of the morbidity and risk factors of postoperative delirium.Results (1) Delirium occurred in 24 patients of 286 patients and morbidity of postoperative delirium was 8.4％.Sixteen of 24 patients (66.7％) suffered from transient delirium (＜24 h).Eight of 24 patients (33.3％) suffered from continuous delirium (≥48 h).(2) The common clinical presentation of postoperative delirium included increased psychomotor activity, a disordered sleep-wake cycle and a reduced level of consciousness.(3) Univariate Logistic regression analysis results showed that delirium was associated with advanced age over 70 years old (OR =3.05, 95％ CI 2.55-72.94, P =0.023), previous cerebral infarction(OR =1.78, 95％ CI 1.07-2.96, P =0.026), previous brain heamorrhage (OR =1.99, 95 ％ CI 1.20-3.31, P =0.003), surgery time (OR =1.05, 95％ CI0.05-2.04, P =0.047), intensive care unit (ICU) staying time (OR =2.87, 95％ CI 1.43-5.72, P=0.003), use offentanyl (OR=2.78, 95％ CI 1.02-7.57, P=0.045).(4) The multiple Logistic stepwise regression analysis indicated that the perioperative risk factors of delirium included advanced age over 70 years old (OR =3.196, 95％ CI 1.574-6.488, P =0.001), cerebrovascular accident history(OR =2.610, 95％ CI 1.538-4.431, P =0.000) and ICU duration time(OR =1.480, 95 ％ CI 1.070-2.046, P =0.018).Conclusions The morbidity of postoperative delirium after CABG is 8.4％.Many predisposing and precipitating factors contribute to postoperative delirium.These factors include age ＞ 70 years, cerebral infarction and ICU staying time.Assessment and preventive strategies should be considered to reduce the incidence of delirium, particularly among those patients with high risks.

Extremely unbalanced data here refers to datasets where the values of independent or dependent variables exhibit severe unbalance in proportions, such as extremely unbalanced case-control ratio, very low incidence rate of disease, heavily censored time-to-event data, and low-frequency or rare variants. In such scenarios, the statistic derived from hypothesis test using the classical statistical method, e.g., logistic regression model and Cox proportional hazard regression model, might deviate from theoretical asymptotic distribution, resulting in inflation or deflation of type I error. With the increased availability and exploration of resources from large-scale population cohorts in genome-wide association study (GWAS), there is a growing demand for effective and accurate statistical approaches to handle extremely unbalanced data in independent and non-independent samples. Our study introduces classical statistical methods in genetic statistics firstly, then, summarizes the failure of classical statistical methods in dealing with extremely unbalanced data through simulation experiments to draw researchers' attention to the extremely unbalanced data in GWAS.