Development of a Brief Cognitive Screening Tool for Predicting Postoperative Delirium in Patients with Parkinson’s Disease: A Secondary Analysis
Notice bibliographique
Résumé
Yongde Zhou,1,* Xiaoxiao Wang,2,3,* Zhengqian Li,2,4 Yu Ma,5 Cuiping Yu,1 Yao Chen,1 Jian Ding,1 Jianfeng Yu,1 Rongsong Zhou,5 Ning Yang,2,4 Taotao Liu,2,4 Xiangyang Guo,2,4 Ting Fan,1 Chengmei Shi2,4 1Department of Anesthesiology, Tsinghua University Yuquan Hospital, Beijing, 100040, People’s Republic of China; 2Department of Anesthesiology, Peking University Third Hospital, Beijing, 100191, People’s Republic of China; 3Research Center of Clinical Epidemiology, Peking University Third Hospital, Beijing, 100191, People’s Republic of China; 4Beijing Center of Quality Control and Improvement on Clinical Anesthesia, Beijing, 100191, People’s Republic of China; 5Department of Neurosurgery, Tsinghua University Yuquan Hospital, Beijing, 100040, People’s Republic of China*These authors contributed equally to this workCorrespondence: Chengmei Shi, Department of Anesthesiology, Peking University Third Hospital, Beijing, 100191, People’s Republic of China, Tel +86-13811592813, Email shichengmei@bjmu.edu.cn Ting Fan, Department of Anesthesiology, Tsinghua University Yuquan Hospital, Beijing, 100040, People’s Republic of China, Tel +86-13681100715, Email 13681100715@163.comBackground: A simple, rapid, and effective cognitive screening test appropriate for fast-paced settings with limited resources and staff is essential, especially preoperatively. This study aimed to develop and validate the short versions of Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) for predicting postoperative delirium (POD) in patients with Parkinson’s disease (PD) who were scheduled for surgery.Methods: The current study was a secondary analysis of data collected from 128 inpatients scheduled for deep brain stimulation of the subthalamic nuclei (STN-DBS) lasting > 60 min, at Tsinghua University Yuquan Hospital, China. Preoperative cognitive screening was performed during the preoperative visit using the MMSE and MoCA. The optimal MMSE and MoCA cut-off scores for detecting PD-MCI was 27 and 23 respectively. The POD was assessed twice a day on the first postoperative day until discharge by the confusion assessment method. The backward conditional logistic regression analysis was used to organize the reduced versions of the MMSE or MoCA. Also, the areas under the receiver operating characteristic curves (AUCs) were examined using the DeLong test.Results: 125/128 PD patients were included in the analysis, and 27 (21.6%) developed POD. The MMSE reduced version (orientation to time, attention and calculation, and comprehension) demonstrated performance similar to the original MMSE in predicting POD (z=0.820, p=0.412). The AUC of the original MoCA and the short MoCA (visuospatial and executive attention and orientation) were 0.808 and 0.826, respectively. There was no significantly difference in the AUC values between the tests (z=0.561, p=0.575).Conclusion: Our simplified MMSE and MoCA could be efficiently used to identify patients at risk for POD. Also, short cognitive tests could be considered while predicting POD in fast-paced preoperative settings with limited resources and staff.Plain Language Summary: Routine preoperative cognitive screening is very important in predicting postoperative delirium.MMSE and MoCA are the most commonly used cognitive testing method which normally takes a long time.The short variant of MMSE and MoCA show a predictive performance similar to the original tests.Keywords: Parkinson’s disease, preoperative cognitive impairment, mini-mental state examination, Montreal Cognitive Assessment, postoperative delirium
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Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
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score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
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