Dexmedetomidine in Prevention of Postoperative Delirium: A Systematic Review

Fondeur, Jack; Mendez, Lisbeth Escudero; Srinivasan, Mirra; Hamouda, Ranim K; Ambedkar, B. R.; Arzoun, Hadia; Sahib, Isra; Mohammed, Lubna

doi:10.7759/cureus.25639

Cited by 10 publications

(9 citation statements)

References 41 publications

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“… 13 , 14 Dexmedetomidine has dual effects of sedation and analgesia, which can also reduce the incidence of postoperative delirium. 15 Magnesium has been used as a multimodal analgesic agent in some studies. But we do not think it is applicable to cardiac multimodal analgesic regimen.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of the Effect of New Multimodal Analgesia Regimen for Cardiac Surgery: A Prospective, Randomized Controlled, Single-Center Clinical Study

Jia¹,

Liang²,

Yuan³

et al. 2023

DDDT

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Objective To investigate the feasibility of multimodal regimen by paracetamol, gabapentin, ketamine, lidocaine, dexmedetomidine and sufentanil among cardiac surgery patients, and compare the analgesia efficacy with conventional sufentanil-based regimen. Design A single-center, prospective, randomized, controlled clinical trial. Setting One participating center, the cardiovascular center of the major integrated teaching hospital. Participants A total of 115 patients were assessed for eligibility: 108 patients were randomized, 7 cases were excluded. Interventions The control group (group T) received conventional anesthesia management. Interventions in the multimodal group (group M) were as follows in addition to the standard of care: gabapentin and acetaminophen 1 hour before surgery; ketamine for induction and to maintain anesthesia with lidocaine and dexmedetomide. Ketamine, lidocaine, and dexmedetomidine were added to routine sedatives postoperatively in group M. Measurements and Main Results The incidence of moderate-to-severe pain on coughing made no significant difference (68.5% vs 64.8%, P =0.683). Group M had significantly less sufentanil use (135.72µg vs 94.85µg, P =0.000) and lower rescue analgesia rate (31.5% vs 57.4%, P =0.007). There was no significant difference in the incidence of chronic pain, PONV, dizziness, inflammation index, mechanical ventilation time, length of stay, and complications between the two groups. Conclusion Our multimodal regimen in cardiac surgery is feasible, but was not superior to traditional sufentanil-based regimen in the aspects of analgesia effects; however, it did reduce perioperative opioid consumption along with rescue analgesia rate. Moreover, it showed the same length of stay and the incidences of postoperative complications.

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Section: Discussionmentioning

confidence: 99%

Evaluation of the Effect of New Multimodal Analgesia Regimen for Cardiac Surgery: A Prospective, Randomized Controlled, Single-Center Clinical Study

Jia¹,

Liang²,

Yuan³

et al. 2023

DDDT

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“…these patients we may need to avoid exacerbation of preoperative phychotic symptoms and avoid using benzodiazepines [42,43]. Besides, dexmedetomidine might be considered to decrease the risk of POD [44,45].…”

Section: Discussionmentioning

confidence: 99%

Predicting postoperative delirium after hip arthroplasty for elderly patients using machine learning

Chen

Wang

et al. 2023

Aging Clin Exp Res

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Background: Postoperative delirium (POD) is a common and severe complication after hip arthroplasty for elderly patients. We aim to develop and validate a machine learning method that determines essential features related to postoperative delirium and predicts POD after hip arthroplasty for elderly patients.Methods: We reviewed preoperative and intraoperative clinical data and laboratory tests of hip arthroplasty elderly patients between January 2017 and April 2021 in Orthopedics of First A liated Hospital of Chongqing Medical University. The Confusion Assessment Method (CAM) was administered to the patients in their perioperative period. Machine learning algorithms were trained to predict the POD and determine leading features. The predictive performance was evaluated using the area under the curve (AUC), accuracy (ACC), sensitivity, speci city, and F1-score.Results: 476 arthroplasty elderly patients (POD = 86, non-POD = 390) with general anesthesia were included in this study, and the combination of feature selection method mutual information (MI) and linear binary classi er using logistic regression (LR) achieved an encouraging performance (AUC = 0.94, ACC = 0.88, sensitivity = 0.85, speci city = 0.90, F1-score = 0.87) on a balanced test dataset. Conclusion: The machine learning (ML) model could predict POD for arthroplasty elderly patients with satisfying accuracy and revealed the major risk factors of suffering POD such as age, Cystatin C, GFR, CHE, CRP, LDH, monocyte count (MONO), history of mental illness or psychotropic drug use and intraoperative blood loss. Proper preoperative interventions for these factors could assist clinicians in reducing the incidence of POD in arthroplasty elderly patients.

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Section: Discussionmentioning

confidence: 99%

Predicting postoperative delirium after hip arthroplasty for elderly patients using machine learning

Chen

Wang

et al. 2022

Preprint

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Background: Postoperative delirium (POD) is a common and severe complication after hip arthroplasty for elderly patients. We aim to develop and validate a machine learning method that determines essential features related to postoperative delirium and predicts POD after hip arthroplasty for elderly patients. Methods: We reviewed preoperative and intraoperative clinical data and laboratory tests of hip arthroplasty elderly patients between January 2017 and April 2021 in Orthopedics of First Affiliated Hospital of Chongqing Medical University. The Confusion Assessment Method (CAM) was administered to the patients in their perioperative period. Machine learning algorithms were trained to predict the POD and determine leading features. The predictive performance was evaluated using the area under the curve (AUC), accuracy (ACC), sensitivity, specificity, and F1-score. Results: 476 arthroplasty elderly patients (POD = 86, non-POD = 390) with general anesthesia were included in this study, and the combination of feature selection method mutual information (MI) and linear binary classifier using logistic regression (LR) achieved an encouraging performance (AUC = 0.94, ACC = 0.88, sensitivity = 0.85, specificity = 0.90, F1-score = 0.87) on a balanced test dataset. Conclusion: The machine learning (ML) model could predict POD for arthroplasty elderly patients with satisfying accuracy and revealed the major risk factors of suffering POD such as age, Cystatin C, GFR, CHE, CRP, LDH, monocyte count (MONO), history of mental illness or psychotropic drug use and intraoperative blood loss. Proper preoperative interventions for these factors could assist clinicians in reducing the incidence of POD in arthroplasty elderly patients.

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Dexmedetomidine in Prevention of Postoperative Delirium: A Systematic Review

Cited by 10 publications

References 41 publications

Evaluation of the Effect of New Multimodal Analgesia Regimen for Cardiac Surgery: A Prospective, Randomized Controlled, Single-Center Clinical Study

Evaluation of the Effect of New Multimodal Analgesia Regimen for Cardiac Surgery: A Prospective, Randomized Controlled, Single-Center Clinical Study

Predicting postoperative delirium after hip arthroplasty for elderly patients using machine learning

Predicting postoperative delirium after hip arthroplasty for elderly patients using machine learning

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