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

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 m...

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Detalles Bibliográficos
Autores principales: Wu, Xiaoyue, Zhang, Ning, Zhou, Bin, Liu, Siyu, Wang, Fei, Wang, Jiahan, Tang, Xinhui, Lin, Xu, Wang, Bin, Bi, Yanlin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10290379/
https://www.ncbi.nlm.nih.gov/pubmed/37353780
http://dx.doi.org/10.1186/s12871-023-02178-x
Descripción
Sumario: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 (Aβ(40), Aβ(42), 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, Aβ(42) 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 Aβ(42) (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 Aβ(42) (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 Aβ(42).