Alcohol consumption may be associated with postoperative delirium in the elderly: the PNDABLE study

Author:

Wu Xiaoyue,Zhang Ning,Zhou Bin,Liu Siyu,Wang Fei,Wang Jiahan,Tang Xinhui,Lin Xu,Wang Bin,Bi Yanlin

Abstract

Abstract Objectives This study aimed to reveal the relationship between alcohol consumption and Postoperative delirium (POD) in the elderly. Methods We selected 252 patients from the Perioperative Neurocognitive Disorder And Biomarker Lifestyle (PNDABLE ) study. Patients in the PNDABLE database have been measured for Alzheimer-related biomarkers in CSF (Aβ40, Aβ42, P-tau, and tau protein). Mini-Mental State Examination (MMSE) was used to assess the preoperative mental status of patients. POD was diagnosed using the Confusion Assessment Method (CAM) and assessed for severity using the Memorial Delirium Assessment Scale (MDAS). Logistic regression analysis was utilized to explore the association of alcohol consumption with POD. Linear regression analysis was used to study the relationship between alcohol consumption and CSF biomarkers. Mediation analyses with 10,000 bootstrapped iterations were used to explore the mediation effects. Finally, we constructed the receiver operating characteristic (ROC) curve and the nomogram model to evaluate the efficacy of alcohol consumption and CSF biomarkers in predicting POD.  Result The incidence of POD was 17.5%. Logistic regression showed that alcohol consumption (OR = 1.016, 95%CI 1.009–1.024, P < 0.001) is a risk factor for POD. What’s more, Aβ42 is a protective factor for POD (OR = 0.993, 95%CI 0.989–0.997, P < 0.05), and P-Tau was a risk factor for POD (OR = 1.093, 95%CI 1.022–1.168, P < 0.05). Linear regression analysis revealed that alcohol consumption was negatively associated with CSF Aβ42 (β = -0.638, P < 0.001) in POD patients. Mediation analyses showed that alcohol consumption is likely to partially mediate POD through Aβ42 (proportion:14.21%). ROC curve showed that alcohol consumption (AUC = 0.904; P < 0.001) exhibited a relatively better discriminatory ability in POD prediction compared to Aβ42 (AUC = 0.798; P < 0.001). The calibration curve indicated a good nomogram prediction (P = 0.797). Conclusion Alcohol consumption is a risk factor for POD (particularly for those with > 24 g a day on average) in the elderly, and contributes to POD through the mediation of Aβ42.

Publisher

Springer Science and Business Media LLC

Subject

Anesthesiology and Pain Medicine

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