Development and validation of a delirium risk prediction preoperative model for cardiac surgery patients (DELIPRECAS): An observational multicentre study

Predicting postoperative delirium assessed by the Nursing Screening Delirium Scale in the recovery room for non-cardiac surgeries without craniotomy: A retrospective study using a machine learning approach

Postoperative delirium (POD) contributes to severe outcomes such as death or development of dementia. Thus, it is desirable to identify vulnerable patients in advance during the perioperative phase. Previous studies mainly investigated risk factors for delirium during hospitalization and further used a linear logistic regression (LR) approach with time-invariant data. Studies have not investigated patients' fluctuating conditions to support POD precautions. In this single-center study, we aimed to predict POD in a recovery room setting with a non-linear machine learning (ML) technique using pre-, intra-, and postoperative data. The target variable POD was defined with the Nursing Screening Delirium Scale (Nu-DESC) ≥ 1. Feature selection was conducted based on robust univariate test statistics and L1 regularization. Non-linear multi-layer perceptron (MLP) as well as tree-based models were trained and evaluated-with the receiver operating characteristics curve (AUROC), the area under precision recall curve (AUPRC), and additional metrics-against LR and published models on bootstrapped testing data. The prevalence of POD was 8.2% in a sample of 73,181 surgeries performed between 2017 and 2020. Significant univariate impact factors were the preoperative ASA status (American Society of Anesthesiologists physical status classification system), the intraoperative amount of given remifentanil, and the postoperative Aldrete score. The best model used pre-, intra-, and postoperative data. The non-linear boosted trees model achieved a mean AUROC of 0.854 and a mean AUPRC of 0.418 outperforming linear LR, well as best applied and retrained baseline models. Overall, non-linear machine learning models using data from multiple perioperative time phases were superior to traditional ones in predicting POD in the recovery room. Class imbalance was seen as a main impediment for model application in clinical practice.

Preoperative Delirium Risk Screening in Patients Undergoing a Cardiac Surgery: Results from the Prospective Observational FINDERI Study

OBJECTIVE

Postoperative delirium (POD) is a common complication of cardiac surgery that is associated with higher morbidity, longer hospital stay, cognitive decline, and mortality. Preoperative assessments may help to identify patients´ POD risk. However, a standardized screening assessment for POD risk has not been established.

DESIGN

Prospective observational FINd DElirium RIsk factors (FINDERI) study.

PARTICIPANTS

Patients aged ≥50 years undergoing cardiac surgery.

MEASUREMENTS

The primary aim was to analyze the predictive value of the Delirium Risk Screening Questionnaire (DRSQ) prior to cardiac surgery. Secondary aims are to investigate cognitive, frailty, and geriatric assessments, and to use data-driven machine learning (ML) in predicting POD. Predictive properties were assessed using receiver operating characteristics analysis and multivariate approaches (regularized LASSO regression and decision trees).

RESULTS

We analyzed a data set of 504 patients (68.3 ± 8.2 years, 21.4% women) who underwent cardiac surgery. The incidence of POD was 21%. The preoperatively administered DRSQ showed an area under the curve (AUC) of 0.68 (95% CI 0.62, 0.73), and the predictive OR was 1.25 (95% CI 1.15, 1.35, p <0.001). Using a ML approach, a three-rule decision tree prediction model including DRSQ (score>7), Trail Making Test B (time>118), and Montreal Cognitive Assessment (score ≤ 22) was identified. The AUC of the three-rule decision tree on the training set was 0.69 (95% CI 0.63, 0.75) and 0.62 (95% CI 0.51, 0.73) on the validation set.

CONCLUSION

Both the DRSQ and the three-rule decision tree might be helpful in predicting POD risk before cardiac surgery.

Development and validation of a new drug-focused predictive risk score for postoperative delirium in orthopaedic and trauma surgery patients

BACKGROUND

Postoperative delirium (POD) is the most common complication following surgery in elderly patients. During pharmacist-led medication reconciliation (PhMR), a predictive risk score considering delirium risk-increasing drugs and other available risk factors could help to identify risk patients.

METHODS

Orthopaedic and trauma surgery patients aged ≥ 18 years with PhMR were included in a retrospective observational single-centre study 03/2022-10/2022. The study cohort was randomly split into a development and a validation cohort (6:4 ratio). POD was assessed through the 4 A's test (4AT), delirium diagnosis, and chart review. Potential risk factors available at PhMR were tested via univariable analysis. Significant variables were added to a multivariable logistic regression model. Based on the regression coefficients, a risk score for POD including delirium risk-increasing drugs (DRD score) was established.

RESULTS

POD occurred in 42/328 (12.8%) and 30/218 (13.8%) patients in the development and validation cohorts, respectively. Of the seven evaluated risk factors, four were ultimately tested in a multivariable logistic regression model. The final DRD score included age (66-75 years, 2 points; > 75 years, 3 points), renal impairment (eGFR < 60 ml/min/1.73m2, 1 point), anticholinergic burden (ACB-score ≥ 3, 1 point), and delirium risk-increasing drugs (n ≥ 2; 2 points). Patients with ≥ 4 points were classified as having a high risk for POD. The areas under the receiver operating characteristic curve of the risk score model were 0.89 and 0.81 for the development and the validation cohorts, respectively.

CONCLUSION

The DRD score is a predictive risk score assessable during PhMR and can identify patients at risk for POD. Specific preventive measures concerning drug therapy safety and non-pharmacological actions should be implemented for identified risk patients.

The role of perioperative sedative anesthetics in preventing postoperative delirium: a systematic review and network-meta analysis including 6679 patients

OBJECTIVE

Postoperative delirium is a common and debilitating complication that significantly affects patients and their families. The purpose of this study is to investigate whether there is an effective sedative that can prevent postoperative delirium while also examining the safety of using sedatives during the perioperative period.

METHODS

The net-meta analysis was used to compare the incidence of postoperative delirium among four sedatives: sevoflurane, propofol, dexmedetomidine, and midazolam. Interventions were ranked according to their surface under the cumulative ranking curve (SUCRA).

RESULTS

A total of 41 RCT studies involving 6679 patients were analyzed. Dexmedetomidine can effectively reduce the incidence of postoperative delirium than propofol (OR 0.47 95% CI 0.25-0.90), midazolam (OR 0.42 95% CI 0.17-1.00), normal saline (OR 0.42 95% CI 0.33-0.54) and sevoflurane (OR 0.39 95% CI 0.18-0.82). The saline group showed a significantly lower incidence of bradycardia compared to the group receiving dexmedetomidine (OR 0.55 95% CI 0.37-0.80). In cardiac surgery, midazolam (OR 3.34 95%CI 2.04-5.48) and normal saline (OR 2.27 95%CI 1.17-4.39) had a higher rate of postoperative delirium than dexmedetomidine, while in non-cardiac surgery, normal saline (OR 1.98 95%CI 1.44-2.71) was more susceptible to postoperative delirium than dexmedetomidine.

CONCLUSION

Our analysis suggests that dexmedetomidine is an effective sedative in preventing postoperative delirium whether in cardiac surgery or non-cardiac surgery. The preventive effect of dexmedetomidine on postoperative delirium becomes more apparent with longer surgical and extubation times. However, it should be administered with caution as it was found to be associated with bradycardia.

Effect of remimazolam on electroencephalogram burst suppression in elderly patients undergoing cardiac surgery: Protocol for a randomized controlled noninferiority trial

Background

Postoperative delirium (POD) is a common complication following cardiac surgery and increases postoperative morbidity and mortality. Intraoperative electroencephalogram (EEG) burst suppression suggests excessively deep anesthesia and predicts POD. Use of remimazolam provides a stable hemodynamic status and an appropriate depth of anesthesia. We aim to assess remimazolam administered for anesthesia and sedation in elderly patients having cardiac surgery.

Methods

This is a randomized controlled clinical trial with noninferiority design. A total of 260 elderly patients aged equal to or greater than 60 years undergoing cardiac surgery will be randomly allocated to receive remimazolam or propofol (1:1) for general anesthesia and postoperative sedation until extubation. The primary outcome is the cumulative time with EEG burst suppression which is obtained from the SedLine system. The noninferiority margin is 2.0 min. The secondary outcomes include the POD occurrence within the first 5 days postoperatively and the duration of perioperative hypotension.

Discussion

This noninferiority trial is the first to evaluate the effect of perioperative remimazolam administration on EEG burst suppression, POD occurrence, and duration of hypotension in elderly patients who undergo cardiac surgery.

Trial registration

Chinese Clinical Trial Registry (ChiCTR2200056353).

Online interpretable dynamic prediction models for postoperative delirium after cardiac surgery under cardiopulmonary bypass developed based on machine learning algorithms: A retrospective cohort study

OBJECTIVE

Postoperative delirium (POD) is strongly associated with poor early and long-term prognosis in cardiac surgery patients with cardiopulmonary bypass (CPB). This study aimed to develop dynamic prediction models for POD after cardiac surgery under CPB using machine learning (ML) algorithms.

METHODS

From July 2021 to June 2022, clinical data were collected from patients undergoing cardiac surgery under CPB at Nanjing First Hospital. A dataset from the same center (October 2022 to November 2022) was also used for temporal external validation. We used ML and deep learning to build models in the training set, optimized parameters in the test set, and finally validated the best model in the validation set. The SHapley Additive exPlanations (SHAP) method was introduced to explain the best models.

RESULTS

Of the 885 patients enrolled, 221 (25.0%) developed POD. 22 (22.0%) of 100 validation cohort patients developed POD. The preoperative and postoperative artificial neural network (ANN) models exhibited optimal performance. The validation results demonstrated satisfactory predictive performance of the ANN model, with area under the receiver operator characteristic curve (AUROC) values of 0.776 and 0.684 for the preoperative and postoperative models, respectively. Based on the ANN algorithm, we constructed dynamic, highly accurate, and interpretable web risk calculators for POD.

CONCLUSIONS

We successfully developed online interpretable dynamic ANN models as clinical decision aids to identify patients at high risk of POD before and after cardiac surgery to facilitate early intervention or care.

Development of postoperative delirium prediction models in patients undergoing cardiovascular surgery using machine learning algorithms

Associations between delirium and postoperative adverse events in cardiovascular surgery have been reported and the preoperative identification of high-risk patients of delirium is needed to implement focused interventions. We aimed to develop and validate machine learning models to predict post-cardiovascular surgery delirium. Patients aged ≥ 40 years who underwent cardiovascular surgery at a single hospital were prospectively enrolled. Preoperative and intraoperative factors were assessed. Each patient was evaluated for postoperative delirium 7 days after surgery. We developed machine learning models using the Bernoulli naive Bayes, Support vector machine, Random forest, Extra-trees, and XGBoost algorithms. Stratified fivefold cross-validation was performed for each developed model. Of the 87 patients, 24 (27.6%) developed postoperative delirium. Age, use of psychotropic drugs, cognitive function (Mini-Cog < 4), index of activities of daily living (Barthel Index < 100), history of stroke or cerebral hemorrhage, and eGFR (estimated glomerular filtration rate) < 60 were selected to develop delirium prediction models. The Extra-trees model had the best area under the receiver operating characteristic curve (0.76 [standard deviation 0.11]; sensitivity: 0.63; specificity: 0.78). XGBoost showed the highest sensitivity (AUROC, 0.75 [0.07]; sensitivity: 0.67; specificity: 0.79). Machine learning algorithms could predict post-cardiovascular delirium using preoperative data.Trial registration: UMIN-CTR (ID; UMIN000049390).

The Potential Value of Systemic Inflammation Response Index on Delirium After Hip Arthroplasty Surgery in Older Patients: A Retrospective Study

Purpose

To explore the relationship between the systemic inflammation response index (SIRI) and postoperative delirium (POD) in older patients with hip arthroplasty surgery.

Patients and Methods

Older patients who underwent elective hip arthroplasty surgery were included in this retrospective study. SIRI, neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) were collected from blood routine examination at admission. Binary logistic regression analysis was performed to evaluate the association between SIRI levels and POD was analyzed.

Results

Ultimately, 116 older patients who met the inclusion criteria were assessed. Thirty-four (29%) of 116 patients diagnosed with POD were defined as the POD group, and the rest consisted of the Non-POD group. Compared with non-POD patients, POD patients showed significantly higher levels of SIRI (P < 0.001) and NLR (P = 0.002) at admission. There was no significance in the levels of PLR between two groups. SIRI was independently associated with the occurrence of POD in multivariate logistic regression analysis [odds ratio (OR) = 3.34, 95% confidence interval (95% CI) = 1.26-8.85, P = 0.016]. Receiver operating characteristic curve analysis indicated that SIRI with an optimal cutoff value of 0.987 predicted the POD with a sensitivity of 88.2% and specificity of 74.4%, and the area under the curve was 0.82 (95% CI, 0.74-0.90, P < 0.01).

Conclusion

Preoperative SIRI and NLR levels in the blood are associated with the occurrence of POD. Moreover, preoperative SIRI level is a useful candidate biomarker to identify delirium after elective hip arthroplasty surgery in older patients.

Inclusion of frailty improved performance of delirium prediction for elderly patients in the cardiac intensive care unit (D-FRAIL): A prospective derivation and external validation study

BACKGROUND

The elderly patients admitted to cardiac intensive care unit (CICU) are at relatively high risk for developing delirium. A simple and reliable predictive model can benefit them from early recognition of delirium followed by timely and appropriate preventive strategies.

OBJECTIVE

To explore the role of frailty in delirium prediction and develop and validate a delirium predictive model including frailty for elderly patients in CICU.

DESIGN

A prospective, observational cohort study.

SETTINGS

CICU at China-Japan Friendship Hospital from March 1, 2022 to August 25, 2022 (derivation cohort); CICU at Beijing Anzhen Hospital affiliated to Capital Medical University from March 14, 2023 to May 8, 2023 (external validation cohort).

PARTICIPANTS

A total of 236 and 90 participants were enrolled in the derivation and external validation cohorts, respectively. Participants in the derivation cohort were assigned into either the delirium (n = 70) or non-delirium group (n = 166) based on the occurrence of delirium.

METHODS

The simplified Chinese version of the Confusion Assessment Method for the Diagnosis of Delirium in the Intensive Care Unit was used to assess delirium twice a day at 8:00-10:00 and 18:00-20:00 until the onset of delirium or discharge from the CICU. Frailty was assessed using the FRAIL scale during the first 24 h in the CICU. Other possible risk factors were collected prospectively through patient interviews and medical records review. After processing missing data via multiple imputations, univariate analysis and bootstrapped forward stepwise logistic regression were performed to select optimal predictors and develop the models. The models were internally validated using bootstrapping and evaluated comprehensively via discrimination, calibration, and clinical utility in both the derivation and external validation cohorts.

RESULTS

The study developed D-FRAIL predictive model using FRAIL score, hearing impairment, Acute Physiology and Chronic Health Evaluation-II score, and fibrinogen. The area under the receiver operating characteristic curve (AUC) was 0.937 (95% confidence interval [CI]: 0.907-0.967) and 0.889 (95%CI: 0.840-0.938) even after bootstrapping in the derivation cohort. Inclusion of frailty was demonstrated to improve the model performance greatly with the AUC increased from 0.851 to 0.937 (p < 0.001). In the external validation cohort, the AUC of D-FRAIL model was 0.866 (95%CI: 0.782-0.907). Calibration plots and decision curve analysis suggested good calibration and clinical utility of the D-FRAIL model in both the derivation and external validation cohorts.

CONCLUSIONS

For elderly patients in the CICU, FRAIL score is an independent delirium predictor and the D-FRAIL model demonstrates superior performance in predicting delirium.

Development and Validation of a Delirium Risk Prediction Model for Elderly Patients Undergoing Elective Orthopedic Surgery

Purpose

This study aimed to develop and validate a post-operative delirium (POD) nomogram in a population of elderly patients undergoing elective orthopedic surgery.

Patients and Methods

A predictive model was developed based on a training dataset of 474 elderly patients undergoing elective orthopedic surgery from March 2021 to May 2022. POD was identified using the Confusion Assessment Methods (CAM). The least absolute shrinkage and selection operator (LASSO) method was used to screen risk factors, and prediction models were created by combining the outcomes with logistic regression analysis. We employ bootstrap validation for internal validation to examine the model's repeatability. The results were validated using a prospective study on 153 patients operated on from January 2022 to May 2022 at another institution.

Results

The predictors in the POD nomogram included age, the Mini-Mental State Examination(MMSE), sleep disorder, neurological disorders, preoperative serum creatinine (Pre-SCR), and ASA classification. The c-index of the model was 0.928 (95% confidence interval 0.898 ~ 0.957) and the bootstrap validation still achieved a high c-index of 0.912. The c-index of the external validation was 0.921. The calibration curve for the diagnostic probability showed good agreement between prediction by nomogram and actual observation.

Conclusion

By combining preoperative and intraoperative clinical risk factors, we created a POD risk nomogram to predict the probability of POD in elderly patients who undergo elective orthopedic surgery. It could be a tool for guiding individualized interventions.

Postoperative delirium: An independent risk factor for poorer quality of life with long-term cognitive and functional decline after cardiac surgery

STUDY OBJECTIVE

To evaluate the influence of delirium on the functional and cognitive capacity of patients included in the DELIPRECAS study, as well as on their quality of life, in the 3-4 years after cardiac surgery.

DESIGN

Prospective observational study.

SETTING

Assessment of cognitive and functional status from hospital discharge to the present, 3 years after cardiac surgery.

PATIENTS

313 patients undergoing cardiac surgery consecutively, aged 18 years or over.

MEASUREMENTS

The primary outcome measure was the cognitive and functional status of the patients 3 years after cardiac surgery, evaluated by telephone interview, and the possible influence on them of delirium diagnosed by the Confusion Assessment Method in Intensive Care Units (CAM-ICU) during their stay in the intensive care unit after cardiac surgery.

MAIN RESULTS

Postoperative delirium acts as an independent risk factor for the long-term development of memory problems (OR 6.11, 95% CI 2.54 to 14.68, p < 0.001), concentration (OR 11.20, 95% CI 3.58 to 35.09, p > 0.001), confusion/disorientation (OR 10.93, 95% CI 3.61 to 33.12, p > 0.001), sleep problems (OR 5.21, 95% CI 2 0.29 to 11.84, p < 0.001), nightmares (OR 8.99, 95% CI 1.98 to 40.90, p = 0.004), emotional problems (OR 4.30, 95% CI 1.87 to 9.91, p = 0.001) and poorer mobility after hospital discharge (OR 2.436, 95% CI 1.06 to 5.61, p = 0.037). The number of hospital readmissions was also significantly higher in those patients who developed delirium after cardiac surgery (27% vs 13.8%, p = 0.022).

CONCLUSION

Postoperative delirium is a risk factor for decreased quality of life in patients 3 years after heart surgery, being associated with greater cognitive and functional deterioration, as well as greater risk of hospital readmission. Therefore, emphasis should be placed on both prevention and early recognition and treatment of delirium to improve long-term outcomes for patients after cardiac surgery.

Predisposing and Precipitating Factors Associated With Delirium: A Systematic Review

IMPORTANCE

Despite discrete etiologies leading to delirium, it is treated as a common end point in hospital and in clinical trials, and delirium research may be hampered by the attempt to treat all instances of delirium similarly, leaving delirium management as an unmet need. An individualized approach based on unique patterns of delirium pathophysiology, as reflected in predisposing factors and precipitants, may be necessary, but there exists no accepted method of grouping delirium into distinct etiologic subgroups.

OBJECTIVE

To conduct a systematic review to identify potential predisposing and precipitating factors associated with delirium in adult patients agnostic to setting.

EVIDENCE REVIEW

A literature search was performed of PubMed, Embase, Web of Science, and PsycINFO from database inception to December 2021 using search Medical Subject Headings (MeSH) terms consciousness disorders, confusion, causality, and disease susceptibility, with constraints of cohort or case-control studies. Two reviewers selected studies that met the following criteria for inclusion: published in English, prospective cohort or case-control study, at least 50 participants, delirium assessment in person by a physician or trained research personnel using a reference standard, and results including a multivariable model to identify independent factors associated with delirium.

FINDINGS

A total of 315 studies were included with a mean (SD) Newcastle-Ottawa Scale score of 8.3 (0.8) out of 9. Across 101 144 patients (50 006 [50.0%] male and 49 766 [49.1%] female patients) represented (24 015 with delirium), studies reported 33 predisposing and 112 precipitating factors associated with delirium. There was a diversity of factors associated with delirium, with substantial physiological heterogeneity.

CONCLUSIONS AND RELEVANCE

In this systematic review, a comprehensive list of potential predisposing and precipitating factors associated with delirium was found across all clinical settings. These findings may be used to inform more precise study of delirium's heterogeneous pathophysiology and treatment.

Two-stage prediction model for postoperative delirium in patients in the intensive care unit after cardiac surgery

OBJECTIVES

Postoperative delirium is a common severe complication in patients in the intensive care unit after cardiac surgery. We developed a two-stage prediction model and quantified the risk of developing postoperative delirium to assist in early prevention before and after surgery.

METHODS

We conducted a prospective cohort study and consecutively recruited adult patients after cardiac surgery. The Confusion Assessment Method for patients in the intensive care unit was used to diagnose delirium 5 days postoperatively. The stage I model was constructed using patient demographics, health conditions and laboratory results obtained preoperatively, whereas the stage II model was built on both pre- and postoperative predictors. The model was validated internally using the bootstrap method and externally using data from an external cohort.

RESULTS

The two-stage model was developed with 654 patients and was externally validated with 214 patients undergoing cardiac surgery. The stage I model contained 6 predictors, whereas the stage II model included 10 predictors. The stage I model had an area under the receiver operating characteristic curve of 0.76 (95% confidence interval: 0.68-0.81), and the stage II model's area under the receiver operating characteristic curve increased to 0.85 [95% confidence interval (CI): 0.81-0.89]. The external validation resulted in an area under the curve of 0.76 (95% CI: 0.67-0.86) for the stage I model and 0.78 (95% CI: 0.69-0.86) for the stage II model.

CONCLUSIONS

The two-stage model assisted medical staff in identifying patients at high risk for postoperative delirium before and 24 h after cardiac surgery. This model showed good discriminative power and predictive accuracy and can be easily accessed in clinical settings.

TRIAL REGISTRATION

The study was registered with the US National Institutes of Health ClinicalTrials.gov (NCT03704324; registered 11 October 2018).

Forecasting Postoperative Delirium in Older Adult Patients with Fast-and-Frugal Decision Trees

Postoperative delirium (POD) is associated with increased complication and mortality rates, particularly among older adult patients. However, guideline recommendations for POD detection and management are poorly implemented. Fast-and-frugal trees (FFTrees), which are simple prediction algorithms, may be useful in this context. We compared the capacity of simple FFTrees with two more complex models—namely, unconstrained classification trees (UDTs) and logistic regression (LogReg)—for the prediction of POD among older surgical patients in the perioperative setting. Models were trained and tested on the European BioCog project clinical dataset. Based on the entire dataset, two different FFTrees were developed for the pre-operative and postoperative settings. Within the pre-operative setting, FFTrees outperformed the more complex UDT algorithm with respect to predictive balanced accuracy, nearing the prediction level of the logistic regression. Within the postoperative setting, FFTrees outperformed both complex models. Applying the best-performing algorithms to the full datasets, we proposed an FFTree using four cues (Charlson Comorbidity Index (CCI), site of surgery, physical status and frailty status) for the pre-operative setting and an FFTree containing only three cues (duration of anesthesia, age and CCI) for the postoperative setting. Given that both FFTrees contained considerably fewer criteria, which can be easily memorized and applied by health professionals in daily routine, FFTrees could help identify patients requiring intensified POD screening.

Outcomes of postoperative delirium in patients undergoing cardiac surgery: A systematic review and meta-analysis

Background

Postoperative delirium (POD) is an acute brain dysfunction that is frequently observed in patients undergoing cardiac surgery. Increasing evidence indicates POD is related to higher mortality among cardiac surgical patients, but the results remain controversial. Moreover, a quantitative evaluation of the influence of POD on hospital days, intensive care unit (ICU) time, and mechanical ventilation (MV) time has not been performed.

Objective

This study aimed to evaluate the correlation between POD and outcomes in patients undergoing cardiac surgery by a systematic review and meta-analysis.

Materials and methods

A total of 7 electronic databases (Cochrane Library, PubMed, EMBASE, CINAHL Complete, MEDLINE, Wan-fang database, and China National Knowledge Infrastructure) were searched from January 1980 to July 20, 2021, with language restrictions to English and Chinese, to estimate the impact of the POD on outcome in patients who underwent cardiac surgery. The meta-analysis was registered with PROSPERO (Registration: CRD42021228767).

Results

Forty-two eligible studies with 19785 patients were identified. 3368 (17.0%) patients were in the delirium group and 16417 (83%) were in the non-delirium group. The meta-analysis showed that compared to patients without POD, patients with POD had 2.77-fold higher mortality (OR = 2.77, 95% CI 1.86-4.11, P < 0.001), 5.70-fold higher MV (>24h) rate (OR = 5.70, 95% CI 2.93-11.09, P < 0.001); and longer MV time (SMD = 0.83, 95% CI 0.57-1.09, P < 0.001), ICU time (SMD = 0.91, 95% CI 0.60-1.22, P < 0.001), hospital days (SMD = 0.62, 95% CI 0.48-0.76, P < 0.001).

Conclusion

The synthesized evidence suggests that POD is causally related to the increased risk of mortality, prolonged length of ICU and hospital stay, and a longer duration of MV time. Future research should focus on the interventions for POD, to reduce the incidence.

Systematic review registration

[www.crd.york.ac.uk/PROSPERO], identifier [CRD42021228767].

Perioperative neurocognitive disorders: A narrative review focusing on diagnosis, prevention, and treatment

Perioperative neurocognitive disorders (NCDs) refer to neurocognitive abnormalities detected during the perioperative periods, including preexisting cognitive impairment, preoperative delirium, delirium occurring up to 7 days after surgery, delayed neurocognitive recovery, and postoperative NCD. The Diagnostic and Statistical Manual of Mental Disorders‐5th edition (DSM‐5) is the golden standard for diagnosing perioperative NCDs. Given the impracticality of using the DSM‐5 by non‐psychiatric practitioners, many diagnostic tools have been developed and validated for different clinical scenarios. The etiology of perioperative NCDs is multifactorial and includes predisposing and precipitating factors. Identifying these risk factors is conducive to preoperative risk stratification and perioperative risk reduction. Prevention for perioperative NCDs should include avoiding possible contributors and implementing nonpharmacologic and pharmacological interventions. The former generally includes avoiding benzodiazepines, anticholinergics, prolonged liquid fasting, deep anesthesia, cerebral oxygen desaturation, and intraoperative hypothermia. Nonpharmacologic measures include preoperative cognitive prehabilitation, comprehensive geriatric assessment, implementing fast‐track surgery, combined use of regional block, and sleep promotion. Pharmacological measures including dexmedetomidine, nonsteroidal anti‐inflammatory drugs, and acetaminophen are found to have beneficial effects. Nonpharmacological treatments are the first‐line measures for established perioperative NCDs. Pharmacological treatments are still limited to severely agitated or distressed patients.

Association of postoperative delirium with cognitive outcomes: A meta-analysis

STUDY OBJECTIVE

To determine the association between postoperative delirium (POD) and cognitive outcomes at least 1 month after surgery in elderly patients, and synthesize the dynamic risk trajectory of cognition impairment after POD.

DESIGN

Meta-analysis searching PubMed, Cochrane and EMBASE from inception to November 1, 2020. The terms postoperative delirium, delirium after surgery, postsurgical delirium, postoperative cogniti*, postoperative cognitive dysfunction, postoperative cognition decline, cognitive decline, cognitive impair* and dement* were searched alone or in combination.

MEASUREMENTS

Inclusion criteria were prospective cohort studies investigating the association between POD and cognitive outcomes in patients aged ≥60 years underwent surgery. The primary outcome was the association between POD and cognitive outcomes at 1 or more months after surgery. We considered cognitive outcomes measured up to 12 months after surgery as short-term and beyond 12 months as long-term. Two authors performed the study screening, data extraction and quality assessments. Effect sizes were calculated as Hedges g or Odds ratio (OR) based on random- and fixed-effects models. Meta-regression was conducted to analyze the role of potential contributors to heterogeneity.

MAIN RESULTS

Eighteen studies were included. Our result showed a significant and medium association between POD and cognitive outcomes after at least 1 month postoperatively (g = 0.61 95% CI 0.43-0.79; I² = 65.1%), indicating that patients with POD were associated with worse cognitive outcomes. The association of POD with short- and long-term cognitive impairment were also both significant (short-term: g = 0.46 95% CI 0.24-0.68; I² = 53.1%; and long-term: g = 0.82 95% CI 0.57-1.06; I² = 57.1%). A multivariate meta-regression suggested that age and measure of delirium were significant sources of heterogeneity. POD was also associated with the significant risk for dementia (OR = 6.08 95% CI 3.80-9.72; I² = 0) as well as attention (OR = 1.74 95% CI 1.13-2.68; I² = 0), executive (OR = 1.33 95% CI 1.00-1.80; I² = 0) and memory impairment (OR = 1.59 95% CI 1.20-2.10; I² = 43.0%). Additionally, our results showed that the risk trajectory for cognitive decline associated with POD within five years after surgery revealed exponential growth.

CONCLUSIONS

This is the first meta-analysis quantifying the association between POD and cognitive outcomes. Our results showed that POD was significantly associated with worse cognitive outcomes, including short- and long-term cognitive outcomes following surgery.

Development and validation of a risk score for predicting postoperative delirium after major abdominal surgery by incorporating preoperative risk factors and surgical Apgar score

STUDY OBJECTIVE

To develop and validate a simple delirium-predicting scoring system in patients undergoing major abdominal surgery by incorporating preoperative risk factors and intraoperative surgical Apgar score (SAS).

DESIGN

Observational retrospective cohort study.

SETTING

A tertiary general hospital in China.

PATIENTS

1055 patients who received major abdominal surgery from January 2015 to December 2019.

MEASUREMENTS

We collected data on preoperative and intraoperative variables, and postoperative delirium. A risk scoring system for postoperative delirium in patients after major open abdominal surgery was developed and validated based on traditional logistic regression model. The elastic net algorithm was further developed and evaluated.

MAIN RESULTS

The incidence of postoperative delirium was 17.8% (188/1055) in these patients. They were randomly divided into the development (n = 713) and validation (n = 342) cohorts. Both the logistic regression model and the elastic net regression model identified that advanced age, arrythmia, hypoalbuminemia, coagulation dysfunction, mental illness or cognitive impairments and low surgical Apgar score are related with increased risk of postoperative delirium. The elastic net algorithm has an area under the receiver operating characteristic curve (AUROC) of 0.842 and 0.822 in the development and validation cohorts, respectively. A prognostic score was calculated using the following formula: Prognostic score = Age classification (0 to 3 points) + arrythmia + 2 * hypoalbuminemia + 2 * coagulation dysfunction + 4 * mental illness or cognitive impairments + (10-surgical Apgar score). The 22-point risk scoring system had good discrimination and calibration with an AUROC of 0.823 and 0.834, and a non-significant Hosmer-Lemeshow test P = 0.317 and P = 0.853 in the development and validation cohorts, respectively. The bootstrapping internal verification method (R = 1000) yielded a C-index of 0.822 (95% CI: 0.759-0.857).

CONCLUSION

The prognostic scoring system, which used both preoperative risk factors and surgical Apgar score, serves as a good first step toward a clinically useful predictive model for postoperative delirium in patients undergoing major open abdominal surgery.