Risk Prediction Models for Postoperative Delirium: A Systematic Review and Meta-Analysis

Development and validation of a machine learning model to predict postoperative delirium using a nationwide database: A retrospective, observational study

STUDY OBJECTIVE

Postoperative delirium is a neuropsychological syndrome that typically occurs in surgical patients. Its onset can lead to prolonged hospitalization as well as increased morbidity and mortality. Therefore, it is important to promptly identify its signs. This study aimed to develop and validate a machine learning predictive model for postoperative delirium using extensive population data.

DESIGN

Retrospective observational study.

SETTING

Japanese Diagnosis Procedure Combination inpatient data. Data were used for internal (2016.4-2018.12) and temporal validation (2019.01-2019.10).

PATIENTS

Patients aged ≥65 years who underwent general anesthesia for surgical procedure.

MEASUREMENTS

The primary outcome was postoperative delirium, which was defined as a condition requiring newly prescribed antipsychotic drugs or assignment of the corresponding insurance claim code after the date of surgery. We trained and tuned the optimal machine-learning model through 10-fold cross-validation using the selected optimal area under the receiver operating characteristic curve (AUC) value. In the temporal validation, we measured the performance of our model.

MAIN RESULTS

The analysis included 557,990 patients. The light-gradient boosting machine models showed a higher AUC value (0.826 [95% confidence interval (CI): 0.822-0.829]) than the other models. Regarding performance, the model had a recall value of 0.124 (95% CI: 0.119-0.129) and precision value of 0.659 (95% CI: 0.641-0.677]). This performance was sustained in the temporal validation (AUC, 0.815 [95% CI: 0.811-0.818]). At a sensitivity of 0.80, the model achieved a specificity of 0.672 (95% CI: 0.670-0.674]), a negative predictive value of 0.975 (95% CI: 0.974-0.975), and a positive predictive value of 0.176 (95% CI: 0.176-0.179).

CONCLUSIONS

Using extensive Diagnostic Procedure Combination data, we successfully created and validated a machine learning model for predicting postoperative delirium. This model may facilitate prediction of postoperative delirium.

Contemporary Incidence of Cognitive Impairment or Dementia in Patients Undergoing Coronary Artery Bypass Grafting: A Systematic Review and Meta-Analysis

INTRODUCTION

Despite the high prevalence of cognitive impairment or dementia post-coronary artery bypass grafting (CABG), the incidence of cognitive impairment or dementia post-CABG in contemporary practice is currently unclear. Therefore, this paper aims to investigate the incidence and associated risk factors of cognitive impairment or dementia in patients' post-CABG.

METHODS

A systematic search across three databases (PubMed, SCOPUS, and Embase) was conducted for studies published in or after 2013 that reported cognitive impairment or dementia post-CABG. Subgroup analyses and meta-regression by risk factors were performed to determine their influence on the results.

RESULTS

This analysis included 23 studies with a total of 2,620 patients. The incidence of cognitive impairment or dementia less than 1 month, 2 to 6 months, and more than 12 months post-CABG was 35.96% (95% confidence interval [CI]: 28.22-44.51, I2 = 87%), 21.33% (95% CI: 13.44-32.15, I2 = 88%), and 39.13% (95% CI: 21.72-58.84, I2 = 84%), respectively. Meta-regression revealed that studies with more than 80% of the cohort diagnosed with hypertension were significantly associated with incidence of cognitive impairment or dementia less than 1 month post-CABG.

CONCLUSION

This meta-analysis demonstrates a high incidence of cognitive impairment or dementia in patients' post-CABG in contemporary practice, particularly less than 1 month post-CABG and more than 12 months post-CABG. We found that hypertension was a significant risk factor in the short-term (less than 1 month) follow-up period for cognitive impairment or dementia post-CABG. Future research should be done to assess strategies to reduce cognitive impairment post-CABG.

Retinal imaging with hand-held optical coherence tomography in older people with or without postoperative delirium after hip fracture surgery: A feasibility study

INTRODUCTION

Postoperative delirium in older people may result from the interaction between intrinsic brain vulnerability (e.g. neurodegeneration) and precipitating factors (e.g. surgery induced cytokines). Intrinsic brain vulnerability may be overt (e.g. Alzheimer's disease) or preclinical. In cognitively intact older people presenting for surgery, identification of preclinical neurodegeneration using bedside tools could aid postoperative delirium risk stratification. Thinning of the circumpapillary retinal nerve fibre layer thickness is associated with neurodegenerative disorders e.g. Alzheimer's disease. We propose that thinning of the retinal nerve fibre layer may be present some older people with postoperative delirium due to preclinical neurodegeneration, albeit to a lesser extent than in overt dementia.

OBJECTIVES

The primary objective: Feasibility of acquiring usable retinal images with the hand-held optical coherence device, at the bedside of older, hip fracture surgery patients. Secondary objective: Comparison of the circumpapillary retinal nerve fibre layer thickness between people who did/did not have postoperative delirium. Proportion of exclusions due to retinal pathology.

METHOD

Feasibility study involving 30, cognitively intact, older people recovering from hip fracture surgery. Retinal images were obtained using the hand-held optical coherence tomography device at the participants' bedside. Imaging was deferred in participants who had postoperative delirium.

RESULTS

Retinal images that could be assessed for circumpapillary retinal nerve fibre layer thickness were obtained in 26 participants (22 no postoperative delirium, 4 postoperative delirium). The mean circumpapillary retinal nerve fibre layer thickness was lower in the participants who had postoperative delirium compared to those who did not experience postoperative delirium (Mean (95% CI) of 76.50 (62.60-90.40) vs 89.19 (85.41-92.97) respectively).

CONCLUSION

Retinal imaging at the patient's bedside, using hand-held OCT is feasible. Our data suggests that the circumpapillary retinal nerve fibre layer may be thinner in older people who experience postoperative delirium compared to those who do not. Further studies are required.

Risk stratification and predictive modeling of postoperative delirium in chronic subdural hematoma

Background- Postoperative delirium is a common complication associated with the elderly, causing increased morbidity and prolonged hospital stay. However, its risk factors in chronic subdural hematoma patients have not been well studied. Methods- A total of 202 consecutive patients with chronic subdural hematoma at Peking University Third Hospital between January 2018 and January 2023 were enrolled. Various clinical indicators were analyzed to identify independent risk factors for postoperative delirium using univariate and multivariate regression analyses. Delirium risk prediction models were developed as a nomogram and a Markov chain. Results- Out of the 202 patients (age, 71 (IQR, 18); female-to-male ratio, 1:2.7) studied, 63 (31.2%) experienced postoperative delirium. Univariate analysis identified age (p < 0.001), gender (p = 0.014), restraint belt use (p < 0.001), electrolyte imbalance (p < 0.001), visual analog scale score (p < 0.001), hematoma thickness (p < 0.001), midline shift (p < 0.001), hematoma side (p = 0.013), hematoma location (p = 0.018), and urinal catheterization (p = 0.028) as significant factors. Multivariate regression analysis confirmed the significance of restraint belt use (B = 7.657, p < 0.001), electrolyte imbalance (B = -3.993, p = 0.001), visual analog scale score (B = 2.331, p = 0.016), and midline shift (B = 0.335, p = 0.007). Hematoma thickness and age had no significant impact. Conclusion- Increased midline shift and visual analog scale scores, alongside restraint belt use and electrolyte imbalance elevate delirium risk in chronic subdural hematoma surgery. Our prediction models may offer reference value in this context.

Predicting delirium and the effects of medications in hospitalized COVID-19 patients using machine learning: A retrospective study within the Korean Multidisciplinary Cohort for Delirium Prevention (KoMCoDe)

Objective

Delirium is commonly reported from the inpatients with Coronavirus disease 2019 (COVID-19) infection. As delirium is closely associated with adverse clinical outcomes, prediction and prevention of delirium is critical. We developed a machine learning (ML) model to predict delirium in hospitalized patients with COVID-19 and to identify modifiable factors to prevent delirium.

Methods

The data set (n = 878) from four medical centers was constructed. Total of 78 predictors were included such as demographic characteristics, vital signs, laboratory results and medication, and the primary outcome was delirium occurrence during hospitalization. For analysis, the extreme gradient boosting (XGBoost) algorithm was applied, and the most influential factors were selected by recursive feature elimination. Among the indicators of performance for ML model, the area under the curve of the receiver operating characteristic (AUROC) curve was selected as the evaluation metric.

Results

Regarding the performance of developed delirium prediction model, the accuracy, precision, recall, F1 score, and the AUROC were calculated (0.944, 0.581, 0.421, 0.485, 0.873, respectively). The influential factors of delirium in this model included were mechanical ventilation, medication (antipsychotics, sedatives, ambroxol, piperacillin/tazobactam, acetaminophen, ceftriaxone, and propacetamol), and sodium ion concentration (all p < 0.05).

Conclusions

We developed and internally validated an ML model to predict delirium in COVID-19 inpatients. The model identified modifiable factors associated with the development of delirium and could be clinically useful for the prediction and prevention of delirium in COVID-19 inpatients.

Temporal Generalizability of Machine Learning Models for Predicting Postoperative Delirium Using Electronic Health Record Data: Model Development and Validation Study

BACKGROUND

Although machine learning models demonstrate significant potential in predicting postoperative delirium, the advantages of their implementation in real-world settings remain unclear and require a comparison with conventional models in practical applications.

OBJECTIVE

The objective of this study was to validate the temporal generalizability of decision tree ensemble and sparse linear regression models for predicting delirium after surgery compared with that of the traditional logistic regression model.

METHODS

The health record data of patients hospitalized at an advanced emergency and critical care medical center in Kumamoto, Japan, were collected electronically. We developed a decision tree ensemble model using extreme gradient boosting (XGBoost) and a sparse linear regression model using least absolute shrinkage and selection operator (LASSO) regression. To evaluate the predictive performance of the model, we used the area under the receiver operating characteristic curve (AUROC) and the Matthews correlation coefficient (MCC) to measure discrimination and the slope and intercept of the regression between predicted and observed probabilities to measure calibration. The Brier score was evaluated as an overall performance metric. We included 11,863 consecutive patients who underwent surgery with general anesthesia between December 2017 and February 2022. The patients were divided into a derivation cohort before the COVID-19 pandemic and a validation cohort during the COVID-19 pandemic. Postoperative delirium was diagnosed according to the confusion assessment method.

RESULTS

A total of 6497 patients (68.5, SD 14.4 years, women n=2627, 40.4%) were included in the derivation cohort, and 5366 patients (67.8, SD 14.6 years, women n=2105, 39.2%) were included in the validation cohort. Regarding discrimination, the XGBoost model (AUROC 0.87-0.90 and MCC 0.34-0.44) did not significantly outperform the LASSO model (AUROC 0.86-0.89 and MCC 0.34-0.41). The logistic regression model (AUROC 0.84-0.88, MCC 0.33-0.40, slope 1.01-1.19, intercept -0.16 to 0.06, and Brier score 0.06-0.07), with 8 predictors (age, intensive care unit, neurosurgery, emergency admission, anesthesia time, BMI, blood loss during surgery, and use of an ambulance) achieved good predictive performance.

CONCLUSIONS

The XGBoost model did not significantly outperform the LASSO model in predicting postoperative delirium. Furthermore, a parsimonious logistic model with a few important predictors achieved comparable performance to machine learning models in predicting postoperative delirium.

Risk of bias in prognostic models of hospital-induced delirium for medical-surgical units: A systematic review

PURPOSE

The purpose of this systematic review was to assess risk of bias in existing prognostic models of hospital-induced delirium for medical-surgical units.

METHODS

APA PsycInfo, CINAHL, MEDLINE, and Web of Science Core Collection were searched on July 8, 2022, to identify original studies which developed and validated prognostic models of hospital-induced delirium for adult patients who were hospitalized in medical-surgical units. The Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies was used for data extraction. The Prediction Model Risk of Bias Assessment Tool was used to assess risk of bias. Risk of bias was assessed across four domains: participants, predictors, outcome, and analysis.

RESULTS

Thirteen studies were included in the qualitative synthesis, including ten model development and validation studies and three model validation only studies. The methods in all of the studies were rated to be at high overall risk of bias. The methods of statistical analysis were the greatest source of bias. External validity of models in the included studies was tested at low levels of transportability.

CONCLUSIONS

Our findings highlight the ongoing scientific challenge of developing a valid prognostic model of hospital-induced delirium for medical-surgical units to tailor preventive interventions to patients who are at high risk of this iatrogenic condition. With limited knowledge about generalizable prognosis of hospital-induced delirium in medical-surgical units, existing prognostic models should be used with caution when creating clinical practice policies. Future research protocols must include robust study designs which take into account the perspectives of clinicians to identify and validate risk factors of hospital-induced delirium for accurate and generalizable prognosis in medical-surgical units.

Independent External Validation of a Preoperative Prediction Model for Delirium After Cardiac Surgery: A Prospective Observational Cohort Study

OBJECTIVE

This investigation provided independent external validation of an existing preoperative risk prediction model.

DESIGN

A prospective observational cohort study of patients undergoing cardiac surgery covering the period between April 16, 2018 and January 18, 2022.

SETTING

Two academic hospitals in Switzerland.

PARTICIPANTS

Adult patients (≥60 years of age) who underwent elective cardiac surgery, including coronary artery bypass graft, mitral or aortic valve replacement or repair, and combined procedures.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

The primary outcome measure was the incidence of postoperative delirium (POD) in the intensive or intermediate care unit, diagnosed using the Intensive Care Delirium Screening Checklist. The prediction model contained 4 preoperative risk factors to which the following points were assigned: Mini-Mental State Examination (MMSE) score ≤23 received 2 points; MMSE 24-27, Geriatric Depression Scale (GDS) >4, prior stroke and/or transient ischemic attack (TIA), and abnormal serum albumin (≤3.5 or ≥4.5 g/dL) received 1 point each. The missing data were handled using multiple imputation. In total, 348 patients were included in the study. Sixty patients (17.4%) developed POD. For point levels in the prediction model of 0, 1, 2, and ≥3, the cumulative incidence of POD was 12.6%, 22.8%, 25.8%, and 35%, respectively. The validation resulted in a pooled area under the receiver operating characteristics curve of 0.60 (median CI, 0.525-0.679).

CONCLUSIONS

The evaluated predictive model for delirium after cardiac surgery in this patient cohort showed only poor discriminative capacity but fair calibration.

Does the SORG Orthopaedic Research Group Hip Fracture Delirium Algorithm Perform Well on an Independent Intercontinental Cohort of Patients With Hip Fractures Who Are 60 Years or Older?

BACKGROUND

Postoperative delirium in patients aged 60 years or older with hip fractures adversely affects clinical and functional outcomes. The economic cost of delirium is estimated to be as high as USD 25,000 per patient, with a total budgetary impact between USD 6.6 to USD 82.4 billion annually in the United States alone. Forty percent of delirium episodes are preventable, and accurate risk stratification can decrease the incidence and improve clinical outcomes in patients. A previously developed clinical prediction model (the SORG Orthopaedic Research Group hip fracture delirium machine-learning algorithm) is highly accurate on internal validation (in 28,207 patients with hip fractures aged 60 years or older in a US cohort) in identifying at-risk patients, and it can facilitate the best use of preventive interventions; however, it has not been tested in an independent population. For an algorithm to be useful in real life, it must be valid externally, meaning that it must perform well in a patient cohort different from the cohort used to "train" it. With many promising machine-learning prediction models and many promising delirium models, only few have also been externally validated, and even fewer are international validation studies.

QUESTION/PURPOSE

Does the SORG hip fracture delirium algorithm, initially trained on a database from the United States, perform well on external validation in patients aged 60 years or older in Australia and New Zealand?

METHODS

We previously developed a model in 2021 for assessing risk of delirium in hip fracture patients using records of 28,207 patients obtained from the American College of Surgeons National Surgical Quality Improvement Program. Variables included in the original model included age, American Society of Anesthesiologists (ASA) class, functional status (independent or partially or totally dependent for any activities of daily living), preoperative dementia, preoperative delirium, and preoperative need for a mobility aid. To assess whether this model could be applied elsewhere, we used records from an international hip fracture registry. Between June 2017 and December 2018, 6672 patients older than 60 years of age in Australia and New Zealand were treated surgically for a femoral neck, intertrochanteric hip, or subtrochanteric hip fracture and entered into the Australian & New Zealand Hip Fracture Registry. Patients were excluded if they had a pathological hip fracture or septic shock. Of all patients, 6% (402 of 6672) did not meet the inclusion criteria, leaving 94% (6270 of 6672) of patients available for inclusion in this retrospective analysis. Seventy-one percent (4249 of 5986) of patients were aged 80 years or older, after accounting for 5% (284 of 6270) of missing values; 68% (4292 of 6266) were female, after accounting for 0.06% (4 of 6270) of missing values, and 83% (4690 of 5661) of patients were classified as ASA III/IV, after accounting for 10% (609 of 6270) of missing values. Missing data were imputed using the missForest methodology. In total, 39% (2467 of 6270) of patients developed postoperative delirium. The performance of the SORG hip fracture delirium algorithm on the validation cohort was assessed by discrimination, calibration, Brier score, and a decision curve analysis. Discrimination, known as the area under the receiver operating characteristic curves (c-statistic), measures the model's ability to distinguish patients who achieved the outcomes from those who did not and ranges from 0.5 to 1.0, with 1.0 indicating the highest discrimination score and 0.50 the lowest. Calibration plots the predicted versus the observed probabilities, a perfect plot has an intercept of 0 and a slope of 1. The Brier score calculates a composite of discrimination and calibration, with 0 indicating perfect prediction and 1 the poorest.

RESULTS

The SORG hip fracture algorithm, when applied to an external patient cohort, distinguished between patients at low risk and patients at moderate to high risk of developing postoperative delirium. The SORG hip fracture algorithm performed with a c-statistic of 0.74 (95% confidence interval 0.73 to 0.76). The calibration plot showed high accuracy in the lower predicted probabilities (intercept -0.28, slope 0.52) and a Brier score of 0.22 (the null model Brier score was 0.24). The decision curve analysis showed that the model can be beneficial compared with no model or compared with characterizing all patients as at risk for developing delirium.

CONCLUSION

Algorithms developed with machine learning are a potential tool for refining treatment of at-risk patients. If high-risk patients can be reliably identified, resources can be appropriately directed toward their care. Although the current iteration of SORG should not be relied on for patient care, it suggests potential utility in assessing risk. Further assessment in different populations, made easier by international collaborations and standardization of registries, would be useful in the development of universally valid prediction models. The model can be freely accessed at: https://sorg-apps.shinyapps.io/hipfxdelirium/ .

LEVEL OF EVIDENCE

Level III, therapeutic study.

Risk factor stratification for postoperative delirium: A retrospective database study

METHODS

The EHR of 32734 patients >18 years of age undergoing surgery and had POD assessment were reviewed. Patient characteristics and study variables were summarized between delirium groups. We constructed univariate logistic regression models for POD using each study variable to estimate odds ratios (OR) and constructed a multivariable logistic regression model with stepwise variable selection. In order to create a clinically useful/implementable tool we created a nomogram to predict risk of delirium.

RESULTS

Overall, we found a rate of POD of 3.7% across our study population. The Model achieved an AUC of the ROC curve of 0.83 (95% CI 0.82-0.84). We found that age, increased American Society of Anesthesiologists (ASA) score (ASA 3-4 OR 2.81, CI 1.49-5.28, P < .001), depression (OR 1.28, CI 1.12-1.47, P < .001), postoperative benzodiazepine use (OR 3.52, CI 3.06-4.06, P < .001) and urgent cases (Urgent OR 3.51, CI 2.92-4.21, P < .001; Emergent OR 3.99, CI 3.21-4.96, P < .001; Critically Emergent OR 5.30, CI 3.53-7.96, P < .001) were associated with POD.

DISCUSSION

We were able to distinguish the contribution of individual risk factors to the development of POD. We created a clinically useful easy-to-use tool that has the potential to accurately identify those at high-risk of delirium, a first step to prevent POD.

Association between multidimensional prognostic index (MPI) and pre-operative delirium in older patients with hip fracture

Pre-operative delirium may cause delay in surgical intervention in older patients hospitalized for hip fracture. Also it has been associated with higher risk of post-surgical complications and worst functional outcomes. Aim of this retrospective cohort study was to evaluate whether the multidimensional prognostic index (MPI) at hospital admission was associated with pre-operative delirium in older individuals with hip fracture who are deemed to require surgical intervention. Consecutive older patients (≥ 65 years) with hip fracture underwent a comprehensive geriatric assessment to calculate the MPI at hospital admission. According to previously established cut-offs, MPI was expressed in three grades, i.e. MPI-1 (low-risk), MPI-2 (moderate-risk) and MPI-3 (high risk of mortality). Pre-operative delirium was assessed using the four 'A's Test. Out of 244 older patients who underwent surgery for hip fracture, 104 subjects (43%) received a diagnosis of delirium. Overall, the incidence of delirium before surgery was significantly higher in patients with more severe MPI score at admission. Higher MPI grade (MPI-3) was independently associated with higher risk of pre-operative delirium (OR 2.45, CI 1.21-4.96). Therefore, the MPI at hospital admission might help in early identification of older patients with hip fracture at risk for pre-operative delirium.

Structural integrity of the anterior mid-cingulate cortex contributes to resilience to delirium in SuperAging

Despite its devastating clinical and societal impact, approaches to treat delirium in older adults remain elusive, making it important to identify factors that may confer resilience to this syndrome. Here, we investigated a cohort of 93 cognitively normal older patients undergoing elective surgery recruited as part of the Successful Aging after Elective Surgery study. Each participant was classified either as a SuperAger (n = 19) or typically aging older adult (n = 74) based on neuropsychological criteria, where the former was defined as those older adults whose memory function rivals that of young adults. We compared these subgroups to examine the role of preoperative memory function in the incidence and severity of postoperative delirium. We additionally investigated the association between indices of postoperative delirium symptoms and cortical thickness in functional networks implicated in SuperAging based on structural magnetic resonance imaging data that were collected preoperatively. We found that SuperAging confers the real-world benefit of resilience to delirium, as shown by lower (i.e. zero) incidence of postoperative delirium and decreased severity scores compared with typical older adults. Furthermore, greater baseline cortical thickness of the anterior mid-cingulate cortex—a key node of the brain’s salience network that is also consistently implicated in SuperAging—predicted lower postoperative delirium severity scores in all patients. Taken together, these findings suggest that baseline memory function in older adults may be a useful predictor of postoperative delirium risk and severity and that superior memory function may contribute to resilience to delirium. In particular, the integrity of the anterior mid-cingulate cortex may be a potential biomarker of resilience to delirium, pointing to this region as a potential target for preventive or therapeutic interventions designed to mitigate the risk or consequences of developing this prevalent clinical syndrome.

Head-to-head comparison of 14 prediction models for postoperative delirium in elderly non-ICU patients: an external validation study

OBJECTIVES

Delirium is associated with increased morbidity, mortality, prolonged hospitalisation and increased healthcare costs. The number of clinical prediction models (CPM) to predict postoperative delirium has increased exponentially. Our goal is to perform a head-to-head comparison of CPMs predicting postoperative delirium in non-intensive care unit (non-ICU) elderly patients to identify the best performing models.

SETTING

Single-site university hospital.

DESIGN

Secondary analysis of prospective cohort study.

PARTICIPANTS AND INCLUSION

CPMs published within the timeframe of 1 January 1990 to 1 May 2020 were checked for eligibility (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). For the time period of 1 January 1990 to 1 January 2017, included CPMs were identified in systematic reviews based on prespecified inclusion and exclusion criteria. An extended literature search for original studies was performed independently by two authors, including CPMs published between 1 January 2017 and 1 May 2020. External validation was performed using a surgical cohort consisting of 292 elderly non-ICU patients.

PRIMARY OUTCOME MEASURES

Discrimination, calibration and clinical usefulness.

RESULTS

14 CPMs were eligible for analysis out of 366 full texts reviewed. External validation was previously published for 8/14 (57%) CPMs. C-indices ranged from 0.52 to 0.74, intercepts from -0.02 to 0.34, slopes from -0.74 to 1.96 and scaled Brier from -1.29 to 0.088. Based on predefined criteria, the two best performing models were those of Dai et al (c-index: 0.739; (95% CI: 0.664 to 0.813); intercept: -0.018; slope: 1.96; scaled Brier: 0.049) and Litaker et al (c-index: 0.706 (95% CI: 0.590 to 0.823); intercept: -0.015; slope: 0.995; scaled Brier: 0.088). For the remaining CPMs, model discrimination was considered poor with corresponding c-indices <0.70.

CONCLUSION

Our head-to-head analysis identified 2 out of 14 CPMs as best-performing models with a fair discrimination and acceptable calibration. Based on our findings, these models might assist physicians in postoperative delirium risk estimation and patient selection for preventive measures.

Early Ambulation to Prevent Delirium After Long-Time Head and Neck Cancer Surgery

Objective

Postoperative delirium is known to have various adverse effects on head and neck surgery patients. This study was designed to identify possible risk factors of delirium following long periods of head and neck cancer surgery and to help prevent postoperative delirium.

Methods

We enrolled 197 patients who underwent long-time (>6 h) head and neck surgery at the Asan Medical Center from January 2017 to December 2018 in this study. Clinical covariates that may be associated with delirium were analyzed retrospectively using univariate and multivariate analyses.

Results

Delirium occurred in 18 patients (9.1%). Within the first 7 days, 16 patients (88.9%) experienced delirium. Upon univariate analysis, delirium was associated with old age (≥75, p = 0.001), past neurological history (p = 0.019), time to ambulation (p = 0.014), and postoperative hospital day (p = 0.048). In multivariate analysis, old age (≥75, odds ratios (OR) 6.16, CI 2.00–19.00, p = 0.002), time to ambulation (OR 1.04, CI 1.01–1.07, p = 0.017), and past neurological history (OR 5.26, CI 1.09–25.37, p = 0.039) were significant risk factors associated with postoperative delirium.

Conclusions

Older patients or patients with neurologic history must be attended with care, especially early after surgery. Encouraging early ambulation might lower the incidence of postoperative delirium and, subsequently, reduce adverse effects. This result could benefit patients by helping them avoid undesirable outcomes.

Postoperative Delirium and Postoperative Cognitive Dysfunction in Patients with Elective Hip or Knee Arthroplasty: A Narrative Review of the Literature

Postoperative delirium (POD) and postoperative cognitive dysfunction (POCD) are common complications following total knee arthroplasty (TKA) and total hip arthroplasty (THA), affecting the length of hospital stay and increasing medical complications. Although many papers have been published on both conditions in this setting, no reviews have currently been written. Thus, the purpose of our study is to summarize the current literature and provide information about POD and POCD following elective THA or TKA. Our literature search was conducted in the electronic databases PubMed and the Cochrane library. We found that POD is a common complication following elective THA or TKA, with a median incidence of 14.8%. Major risk factors include older age, cognitive impairment, dementia, preoperative (pre-op) comorbidities, substance abuse, and surgery for fracture. Diagnosis can be achieved using tools such as the confusion assessment method (CAM), which is sensitive, specific, reliable, and easy to use, for the identification of POD. Treatment consists of risk stratification and the implementation of a multiple component prevention protocol. POCD has a median incidence of 19.3% at 1 week, and 10% at 3 months. Risk factors include older age, high BMI, and cognitive impairment. Treatment consists of reversing risk factors and implementing protocols in order to preserve physiological stability. POD and POCD are common and preventable complications following TKA and THA. Risk stratification and specific interventions can lower the incidence of both syndromes. Every physician involved in the care of such patients should be informed on every aspect of these conditions in order to provide the best care for their patients.

Current status of delirium assessment tools in the intensive care unit: a prospective multicenter observational survey

Delirium is a critical challenge in the intensive care unit (ICU) or high care unit (HCU) setting and is associated with poor outcomes. There is not much literature on how many patients in this setting are assessed for delirium and what tools are used. This study investigated the status of delirium assessment tools of patients in the ICU/HCU. We conducted a multicenter prospective observational study among 20 institutions. Data for patients who were admitted to and discharged from the ICU/HCU during a 1-month study period were collected from each institution using a survey sheet. The primary outcome was the usage rate of delirium assessment tools on an institution- and patient-basis. Secondary outcomes were the delirium prevalence assessed by each institution’s assessment tool, comparison of delirium prevalence between delirium assessment tools, delirium prevalence at the end of ICH/HCU stay, and the relationship between potential factors related to delirium and the development of delirium. Result showed that 95% of institutions used the Intensive Care Delirium Screening Checklist (ICDSC) or the Confusion Assessment Method for the ICU (CAM-ICU) to assess delirium in their ICU/HCU, and the remaining one used another assessment scale. The usage rate (at least once during the ICU/HCU stay) of the ICDSC and the CAM-ICU among individual patients were 64.5% and 25.1%, and only 8.2% of enrolled patients were not assessed by any delirium assessment tool. The prevalence of delirium during ICU/HCU stay was 17.9%, and the prevalence of delirium at the end of the ICU/HCU stay was 5.9%. In conclusion, all institutions used delirium assessment tools in the ICU/HCU, and most patients received delirium assessment. The prevalence of delirium was 17.9%, and two-thirds of patients had recovered at discharge from ICU/HCU.

Trial registration number: UMIN000037834.

Postoperative delirium prediction using machine learning models and preoperative electronic health record data

Background

Accurate, pragmatic risk stratification for postoperative delirium (POD) is necessary to target preventative resources toward high-risk patients. Machine learning (ML) offers a novel approach to leveraging electronic health record (EHR) data for POD prediction. We sought to develop and internally validate a ML-derived POD risk prediction model using preoperative risk features, and to compare its performance to models developed with traditional logistic regression.

Methods

This was a retrospective analysis of preoperative EHR data from 24,885 adults undergoing a procedure requiring anesthesia care, recovering in the main post-anesthesia care unit, and staying in the hospital at least overnight between December 2016 and December 2019 at either of two hospitals in a tertiary care health system. One hundred fifteen preoperative risk features including demographics, comorbidities, nursing assessments, surgery type, and other preoperative EHR data were used to predict postoperative delirium (POD), defined as any instance of Nursing Delirium Screening Scale ≥2 or positive Confusion Assessment Method for the Intensive Care Unit within the first 7 postoperative days. Two ML models (Neural Network and XGBoost), two traditional logistic regression models (“clinician-guided” and “ML hybrid”), and a previously described delirium risk stratification tool (AWOL-S) were evaluated using the area under the receiver operating characteristic curve (AUC-ROC), sensitivity, specificity, positive likelihood ratio, and positive predictive value. Model calibration was assessed with a calibration curve. Patients with no POD assessments charted or at least 20% of input variables missing were excluded.

Results

POD incidence was 5.3%. The AUC-ROC for Neural Net was 0.841 [95% CI 0. 816–0.863] and for XGBoost was 0.851 [95% CI 0.827–0.874], which was significantly better than the clinician-guided (AUC-ROC 0.763 [0.734–0.793], p < 0.001) and ML hybrid (AUC-ROC 0.824 [0.800–0.849], p < 0.001) regression models and AWOL-S (AUC-ROC 0.762 [95% CI 0.713–0.812], p < 0.001). Neural Net, XGBoost, and ML hybrid models demonstrated excellent calibration, while calibration of the clinician-guided and AWOL-S models was moderate; they tended to overestimate delirium risk in those already at highest risk.

Conclusion

Using pragmatically collected EHR data, two ML models predicted POD in a broad perioperative population with high discrimination. Optimal application of the models would provide automated, real-time delirium risk stratification to improve perioperative management of surgical patients at risk for POD.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12871-021-01543-y.

Identification of risk factors for postoperative delirium in elderly patients with hip fractures by a risk stratification index model: A retrospective study

INTRODUCTION

Postoperative delirium is one of the most common and dangerous psychiatric complications after hip surgery. The aim of this study was to investigate the incidence of postoperative delirium in elderly patients after hip fracture surgery and to identify risk factors for such, as part of developing a risk stratification index (RSI) system to predict a patient's risk of postoperative delirium.

METHODS

Elderly patients (aged 65 years or older) with hip fractures who had received surgical treatment in our hospital between March 2018 and December 2019 were retrospectively included. Clinical data were collected, and multivariate logistic regression analysis was performed to investigate the relevant risk factors of postoperative delirium. An RSI system was developed based on factors identified in the regression analysis.

RESULTS

Of 272 patients included, 52 (19.12%) experienced postoperative delirium. Drinking history (> 3/ week), the perioperative lactic acid level (Lac > 2 mmol/L), postoperative visual analog score (VAS) > 3, American Society of Anesthesiologists (ASA) physical status > II, application of the bispectral index, and preoperative diabetes were independent risk factors of postoperative delirium. When RSI ≥ 5, the rate of postoperative delirium significantly increased (p < .05).

CONCLUSION

The RSI system developed here can safely guide postoperative outcomes of elderly patients with hip fractures, and RSI ≥ 5 may be able to predict the onset of postoperative delirium.

The effect of the anticholinergic burden on duration and severity of delirium in older hip-surgery patients with and without haloperidol prophylaxis: A post hoc analysis

BACKGROUND

Anticholinergic acting drugs have been associated with delirium in older patients.

OBJECTIVE

To examine the association between the anticholinergic burden (ACB) and the duration and severity of delirium in older hip-surgery patients with or without haloperidol prophylaxis.

METHODS

Older patients with a postoperative delirium following hip surgery from a randomized controlled trial investigating the effects of haloperidol prophylaxis on delirium incidence were included in this study. The ACB was quantified using two different tools, the Anticholinergic Drug Scale and an Expert Panel. Using linear regression, the association between the ACB and delirium was analyzed.

RESULTS

Overall delirium duration and severity were not significantly associated with the ACB. Also, no statistically significant differences were found in delirium duration or severity between the placebo and haloperidol treatment groups for the ACB groups. The protective effect of haloperidol on delirium duration and severity however tended to be present in patients with no or a low ACB but not or to a lesser extent in patients with an intermediate to high ACB.

CONCLUSIONS

The ACB was not significantly associated with delirium duration or severity. Haloperidol prophylaxis tended to shorten delirium duration and decrease delirium severity in patients with no or a low ACB. To further explore the influence of anticholinergic acting drugs on delirium duration and severity and the effect of concomitant haloperidol use, additional research with a higher haloperidol dose, a larger study population, and ACB quantification taking drug exposure into account is warranted.

Related Factors and Treatment of Postoperative Delirium in Old Adult Patients: An Integrative Review

“Postoperative delirium” is defined as delirium occurring in the hospital up to one week after a procedure or before discharge (whichever occurs first) that meets the DSM-5 diagnostic criteria. Objectives: To describe the risk factors related to this pathology and identify effective non-pharmacological forms of treatment. An integrative review of the available literature was performed. The search results considered included all quantitative studies published between 2011 and 2019 in both English and Spanish. A total of 117 studies were selected. Advanced age was identified as the principal risk factor for postoperative delirium. Nursing interventions appear to be the key to preventing or reducing the seriousness of delirium after an anaesthetic episode. The aetiology of postoperative delirium remains unknown, and no treatment exists to eliminate this pathology. The role of nursing staff is fundamental in the prevention, diagnosis, and management of the pathology.

Risk factors for postoperative delirium: An umbrella review of systematic reviews

BACKGROUND

Postoperative delirium (POD) is associated with increased mortality, increased length of hospital stays and increased rates and severity of subsequent cognitive decline including dementia. A wide range of risk factors for POD have been suggested in the literature across multiple surgical specialities. However few are validated and no accurate prognostic models exist. We therefore aimed to map the existing evidence regarding risk factors for POD to help guide future research by undertaking an umbrella review of systematic reviews examining risk factors for POD in any context.

MATERIALS AND METHODS

We systematically searched multiple medical databases for systematic reviews examining the risk factors for POD in adults undergoing any surgery. We then selected relevant reviews with minimal overlap in primary studies and extracted information about individual risk factors.

RESULTS

Thirty-five relevant reviews were identified of which ten were in trauma and orthopaedic surgery patients (four exclusively examined hip fractures), five were in cardiac surgery patients, and four were in vascular surgery patients. Due to substantial overlap in reviews, eighteen reviews were analysed in detail finding the widely examined and consistent risk factors were increasing age, nursing home residency, pre-existing cognitive impairment, psychiatric disorders, cerebrovascular disease, end stage renal failure, low albumin, higher ASA score, and intra-operative blood transfusion. Many other risk factors were examined, but they were either not studied in multiple systematic reviews, or inconsistent either in results or in categorisation (which for many factors was heterogenous even within systematic reviews). There are also a large number of existing prognostic models, many of which remain unvalidated.

CONCLUSION

Given the wealth of existing literature, future research should avoid simple risk factor evaluation except for novel candidates, validate existing prognostic models where possible, and instead focus on interventional research.

A Machine Learning Approach for Investigating Delirium as a Multifactorial Syndrome

Delirium is a psycho-organic syndrome common in hospitalized patients, especially the elderly, and is associated with poor clinical outcomes. This study aims to identify the predictors that are mostly associated with the risk of delirium episodes using a machine learning technique (MLT). A random forest (RF) algorithm was used to evaluate the association between the subject’s characteristics and the 4AT (the 4 A’s test) score screening tool for delirium. RF algorithm was implemented using information based on demographic characteristics, comorbidities, drugs and procedures. Of the 78 patients enrolled in the study, 49 (63%) were at risk for delirium, 32 (41%) had at least one episode of delirium during the hospitalization (38% in orthopedics and 31% both in internal medicine and in the geriatric ward). The model explained 75.8% of the variability of the 4AT score with a root mean squared error of 3.29. Higher age, the presence of dementia, physical restraint, diabetes and a lower degree are the variables associated with an increase of the 4AT score. Random forest is a valid method for investigating the patients’ characteristics associated with delirium onset also in small case-series. The use of this model may allow for early detection of delirium onset to plan the proper adjustment in healthcare assistance.

Why should you stay one night? Prospective observational study of enhanced recovery in elderly patients

BACKGROUND

Delirium is a severe condition that can arise in many contexts during hospitalization. The aim of this research was to measure the incidence of postoperative delirium in patients aged 75 years or older, with the exclusion of those with preexisting neurocognitive disorders (NCD), who underwent fast-track, moderate surgery.

METHODS

We conducted a prospective cohort study with patients ≥ 75 years of age who were eligible for fast-track, moderate surgery, without severe dementia, with a planned hospitalization of 24 h and with a physical status varying from very fit to vulnerable. The 4-item confusion assessment method (CAM4) was used to measure delirium.

RESULTS

Of the 209 eligible patients, 195 subjects were enrolled in the study. The percentage of the population with a CAM4 score above 0 before surgery was 2.56%; after surgery, the percentage was 10.25%; and on the following day, the percentage was 4.61%. There was a statistically significant difference in the CAM4 scores between immediately after surgery and at 24 h after surgery (p = 0.0172).

CONCLUSION

The data from this study support an enhanced recovery approach for elderly patients, in which after a minor surgical procedure with anaesthesia, a recovery period of one night in the hospital can contribute to normalizing the CAM4 score and reducing the incidence of delirium.

Incidence and associated factors of delirium after orthopedic surgery in elderly patients: a systematic review and meta-analysis

BACKGROUND

A total of 4.5-41.2% of orthopedic surgery patients experience delirium. Until now, no formal systematic review or meta-analysis was performed to summarize the risk factors of delirium after orthopedic surgery.

AIMS

The present study aimed to comprehensively and quantitatively conclude the risk factors of delirium after orthopedic surgery in elderly patients.

METHODS

A search was applied to Medline, Chinese National Knowledge Infrastructure (CNKI), Embase, and Cochrane central database (all up to February 2020). All studies on the risk factors of delirium after orthopedic surgery in elderly patients without language restriction were reviewed, and qualities of included studies were assessed using the Newcastle-Ottawa Scale. Data were pooled and a meta-analysis was completed.

RESULTS

A total of 15 studies altogether included 10,053 patients with orthopedic surgery, 825 cases of delirium occurred after surgery, suggesting the accumulated incidence of 8.2%. Results of meta-analyses showed that age > 70 years (odds ratio (OR) 3.78, 95% confidence interval (CI) 2.97-4.80), advanced age (standardized mean difference 0.82, 95% CI 0.54-1.09), male sex (OR 1.78, 95% CI 1.13-2.79), medical comorbidities (OR 2.18, 95% CI 1.23-3.88), malnutrition (OR 3.10, 95% CI 2.19-4.38), preoperative and postoperative haemoglobin (SMD - 0.37, 95% CI - 0.54 to - 0.19; SMD - 0.33, 95% CI - 0.55 to - 0.11), postoperative sodium (SMD - 0.52, 95% CI - 0.74 to - 0.29) and longer hospitalization after surgery (SMD 0.27, 95% CI 0.11-0.43), hearing impairment (OR 2.78, 95% CI 1.98-3.90), multiple medications (OR 1.36, 95% CI 1.21-1.52), psychotic drugs(OR 4.27, 95% CI 1.37-13.24), morphine (OR 1.97, 95% CI 1.11-3.51), cognitive impairment (OR 2.72, 95% CI 1.96-3.78), length of stay (SMD 0.26, 95% CI 0.14-0.39) and hip surgery (OR 1.63, 95% CI 1.08-2.48) were more likely to sustain delirium after hip surgery in elderly patients. ASA I and II was less likely to develop delirium after orthopedic surgery (OR 0.52, 95% CI 0.34-0.79).

CONCLUSIONS

Related prophylaxis strategies should be implemented in the elderly involved with above-mentioned risk factors to prevent delirium after orthopedic surgery.

Association Between Postoperative Long-Term Heart Rate Variability and Postoperative Delirium in Elderly Patients Undergoing Orthopedic Surgery: A Prospective Cohort Study

Background

Postoperative delirium (POD) is a common complication after orthopedic surgery in elderly patients. The elderly may experience drastic changes in autonomic nervous system (ANS) activity and circadian rhythm disorders after surgery. Therefore, we intend to explore the relationship between postoperative long-term heart rate (HR) variability (HRV), as a measure of ANS activity and circadian rhythm, and occurrence of POD in elderly patients.

Methods

The study population of this cohort was elderly patients over 60 years of age who scheduled for orthopedic surgery under spinal anesthesia. Patients were screened for inclusion and exclusion criteria before surgery. Then, participants were invited to wear a Holter monitor on the first postoperative day to collect 24-h electrocardiographic (ECG) data. Parameters in the time domain [the standard deviation of the normal-to-normal (NN) intervals (SDNN), mean of the standard deviations of all the NN intervals for each 5-min segment of a 24-h HRV recording (SDNNI), and the root mean square of successive differences of the NN intervals (RMSSD)] and frequency domain [heart rate (HR), high frequency (HF), low frequency (LF), very low frequency (VLF), ultra low frequency (ULF), and total power (TP)] were calculated. Assessment of delirium was performed daily up to the seventh postoperative day using the Chinese version of the 3-Min Diagnostic Interview for CAM-defined Delirium (3D-CAM). The relationship between HRV and POD, as well as the association between HRV and duration of POD, was assessed.

Results

Of the 294 cases that finally completed the follow-up, 60 cases developed POD. Among the HRV parameters, SDNNI, VLF, and ULF were related to the occurrence of POD. After adjustment for potential confounders, the correlation between HRV indices and POD disappeared. Through stratified analysis, two significant negative correlations emerged: ULF in young-old participants and SDNNI, VLF, and ULF in male patients.

Conclusion

The lower HRV parameters may be related to the occurrence of POD, and this correlation is more significant in young-old and male patients. ANS disorders and rhythm abnormalities reflected by HRV changes may represent a possible mechanism that promotes POD.

Perioperative Vascular Biomarker Profiling in Elective Surgery Patients Developing Postoperative Delirium: A Prospective Cohort Study

Background: Postoperative delirium (POD) ranks among the most common complications in surgical patients. Blood-based biomarkers might help identify the patient at risk. This study aimed to assess how serum biomarkers with specificity for vascular and endothelial function and for inflammation are altered, prior to or following surgery in patients who subsequently develop POD. Methods: This was a study on a subcohort of consecutively recruited elective non-cardiac as well as cardiac surgery patients (age > 60 years) of the single-center PROPDESC trial at a German tertiary care hospital. Serum was sampled prior to and following surgery, and the samples were subjected to bead-based multiplex analysis of 17 serum proteins (IL-3, IL-8, IL-10, Cripto, CCL2, RAGE, Resistin, ANGPT2, TIE2, Thrombomodulin, Syndecan-1, E-Selectin, VCAM-1, ICAM-1, CXCL5, NSE, and uPAR). Development of POD was assessed during the first five days after surgery, using the Confusion Assessment Method for ICU (CAM-ICU), the CAM, the 4-‘A’s test (4AT), and the Delirium Observation Scale (DOS). Patients were considered positive if POD was detected at least once during the visitation period by any of the applied methods. Non-parametric testing, as well as propensity score matching were used for statistical analysis. Results: A total of 118 patients were included in the final analysis; 69% underwent non-cardiac surgery, median overall patient age was 71 years, and 59% of patients were male. In the whole cohort, incidence of POD was 28%. The male gender was significantly associated with the development of POD (p = 0.0004), as well as a higher ASA status III (p = 0.04). Incidence of POD was furthermore significantly increased in cardiac surgery patients (p = 0.002). Surgery induced highly significant changes in serum levels of almost all biomarkers except uPAR. In preoperative serum samples, none of the analyzed parameters was significantly altered in subsequent POD patients. In postoperative samples, CCL2 was significantly increased by a factor of 1.75 in POD patients (p = 0.03), as compared to the no-POD cohort. Following propensity score matching, CCL2 remained the only biomarker that showed significant differences in postoperative values (p = 0.01). In cardiac surgery patients, postoperative CCL2 serum levels were more than 3.5 times higher than those following non-cardiac surgery (p < 0.0001). Moreover, after cardiac surgery, Syndecan-1 serum levels were significantly increased in POD patients, as compared to no-POD cardiac surgery patients (p = 0.04). Conclusions: In a mixed cohort of elective non-cardiac as well as cardiac surgery patients, preoperative serum biomarker profiling with specificity for vascular dysfunction and for systemic inflammation was not indicative of subsequent POD development. Surgery-induced systemic inflammation—as evidenced by the significant increase in CCL2 release—was associated with POD, particularly following cardiac surgery. In those patients, postoperative glycocalyx injury might furthermore contribute to POD development.

Risk prediction of delirium in hospitalized patients using machine learning: An implementation and prospective evaluation study

OBJECTIVE

Machine learning models trained on electronic health records have achieved high prognostic accuracy in test datasets, but little is known about their embedding into clinical workflows. We implemented a random forest-based algorithm to identify hospitalized patients at high risk for delirium, and evaluated its performance in a clinical setting.

MATERIALS AND METHODS

Delirium was predicted at admission and recalculated on the evening of admission. The defined prediction outcome was a delirium coded for the recent hospital stay. During 7 months of prospective evaluation, 5530 predictions were analyzed. In addition, 119 predictions for internal medicine patients were compared with ratings of clinical experts in a blinded and nonblinded setting.

RESULTS

During clinical application, the algorithm achieved a sensitivity of 74.1% and a specificity of 82.2%. Discrimination on prospective data (area under the receiver-operating characteristic curve = 0.86) was as good as in the test dataset, but calibration was poor. The predictions correlated strongly with delirium risk perceived by experts in the blinded (r = 0.81) and nonblinded (r = 0.62) settings. A major advantage of our setting was the timely prediction without additional data entry.

DISCUSSION

The implemented machine learning algorithm achieved a stable performance predicting delirium in high agreement with expert ratings, but improvement of calibration is needed. Future research should evaluate the acceptance of implemented machine learning algorithms by health professionals.

CONCLUSIONS

Our study provides new insights into the implementation process of a machine learning algorithm into a clinical workflow and demonstrates its predictive power for delirium.

Preoperative anxiety predicted the incidence of postoperative delirium in patients undergoing total hip arthroplasty: a prospective cohort study

Background

Delirium was characterized with a series of symptoms of a sudden onset of disturbances in attention, a loss in memory loss and defects in other cognitive abilities that were also appeared in the syndrome of anxiety. Even though there are overlapped clinical symptoms existed in anxiety and delirium, the relationship between anxiety and delirium was still unclear. The propose of this study was to investigated the effect of preoperative anxiety on postoperative delirium.

Methods

Three hundred and seventy-two adults undergoing total hip arthroplasty were enrolled from October 2019 to May 2020 in the study. The preoperative anxiety was measured with the Hospital Anxiety and Depression Scale-Anxiety (HADS-A). The participants were allocated into anxiety group (HADS-A≧7) and non-anxiety group (HADS-A < 7). The primary outcome was the incidence of the postoperative delirium assessed with the Confusion Assessment Method (CAM). The secondary outcomes were the duration and the severity of delirium evaluated with the Memorial Delirium assessment Scale (MDAS). The risks of delirium were also evaluated with logistic regression analysis.

Results

There were 325 patients enrolled in the end, 95 of whom met the criteria for anxiety. The incidence of delirium was 17.8% in all participants. The patients with anxiety had a higher incidence of delirium than the non-anxiety patients (25.3% vs. 14.8%, odds ratio (OR) = 0.51, 95% confidence interval (CI) = 0.92–0.29, p = 0.025). However, no significant differences were found in the duration and the severity of the delirium between the above two groups. The age, alcohol abuse, history of stroke, scores of the HADS-A, and education level were considered to be predictors of delirium.

Conclusions

The preoperative anxiety predicted the incidence of the postoperative delirium in total hip arthroplasty patients. The related intervention may be a good point for delirium prophylaxis.

Trial registration

It was registered at Chinese Clinical Trial Registry (www.chictr.org.cn) with the name of “the effect of preoperative anxiety on the postoperative cognitive function” (ChiCTR1900026054) at September 19, 2019.

Prevalence of Cognitive Impairment in Individuals with Vascular Surgical Pathology: a Systematic Review and Meta-Analysis

OBJECTIVE

A significant proportion of vascular surgery patients may have undiagnosed cognitive impairment; however, its true prevalence and impact on outcomes are unknown. The aim of this review was to estimate the prevalence of cognitive impairment among individuals with clinically significant vascular surgical pathology and investigate its associations with post-operative outcomes in those undergoing vascular surgery.

METHODS

MEDLINE, EMBASE, EMCare, CINAHL, PsycINFO, and Scopus were searched for relevant studies. Included studies assessed cognitive function among individuals with either symptomatic vascular surgical pathology, or disease above threshold for intervention, using a validated cognitive assessment tool. The primary outcome measure was prevalence of cognitive impairment. Secondary outcomes included incidence of post-operative delirium (POD). Two reviewers independently extracted relevant study data and assessed risk of bias (ROBINS-E or RoB 2 tool). Prevalence (%) of cognitive impairment was calculated for individual studies and presented with 95% confidence intervals (CI). Prevalence data from comparable studies were pooled using the Mantel-Haenszel method (random effects model) for separate vascular disease types. Certainty of effect estimates was assessed using the GRADE criteria.

RESULTS

Twenty-four studies (2 564 participants) were included in the systematic review, and nine studies (1 310 participants) were included in the meta-analyses. The prevalence of cognitive impairment was 61% (95% CI 48 - 74; 391 participants; low certainty) in studies including multiple vascular surgical pathologies, 38% (95% CI 32 - 44; 278 participants; very low certainty) in carotid artery disease, and 19% (95% CI 10 - 33; 641 participants; low certainty) in those with intermittent claudication. Lower cognitive assessment scores were associated with POD (five studies; 841 participants), but data were not suitable for pooling.

CONCLUSION

Screening elective vascular surgery patients for cognitive impairment may be appropriate given its high prevalence, and the association of worse cognition with POD, among individuals with clinically significant vascular surgical pathology.

Machine Learning to Develop and Internally Validate a Predictive Model for Post-operative Delirium in a Prospective, Observational Clinical Cohort Study of Older Surgical Patients

BACKGROUND

Our objective was to assess the performance of machine learning methods to predict post-operative delirium using a prospective clinical cohort.

METHODS

We analyzed data from an observational cohort study of 560 older adults (≥ 70 years) without dementia undergoing major elective non-cardiac surgery. Post-operative delirium was determined by the Confusion Assessment Method supplemented by a medical chart review (N = 134, 24%). Five machine learning algorithms and a standard stepwise logistic regression model were developed in a training sample (80% of participants) and evaluated in the remaining hold-out testing sample. We evaluated three overlapping feature sets, restricted to variables that are readily available or minimally burdensome to collect in clinical settings, including interview and medical record data. A large feature set included 71 potential predictors. A smaller set of 18 features was selected by an expert panel using a consensus process, and this smaller feature set was considered with and without a measure of pre-operative mental status.

RESULTS

The area under the receiver operating characteristic curve (AUC) was higher in the large feature set conditions (range of AUC, 0.62-0.71 across algorithms) versus the selected feature set conditions (AUC range, 0.53-0.57). The restricted feature set with mental status had intermediate AUC values (range, 0.53-0.68). In the full feature set condition, algorithms such as gradient boosting, cross-validated logistic regression, and neural network (AUC = 0.71, 95% CI 0.58-0.83) were comparable with a model developed using traditional stepwise logistic regression (AUC = 0.69, 95% CI 0.57-0.82). Calibration for all models and feature sets was poor.

CONCLUSIONS

We developed machine learning prediction models for post-operative delirium that performed better than chance and are comparable with traditional stepwise logistic regression. Delirium proved to be a phenotype that was difficult to predict with appreciable accuracy.

Development of a Simple and Practical Delirium Screening Tool for Use in Surgical Wards

BACKGROUND

Delirium is an important and common medical condition, particularly in hospitalized patients, that is associated with adverse outcomes. The identification, prevention, and treatment of delirium are increasingly regarded as major public health priorities.

PURPOSE

The aim of this study was to create a simple-to-use screening tool for delirium in hospitalized patients using clinical manifestations of delirium regularly observed by nurses.

METHODS

This study was conducted using data on 2,168 patients who had been admitted to the surgical ward between January 2011 and December 2014. Data were collected retrospectively from medical records. Univariate and multivariate analyses were performed, and a logistic regression model was constructed for the development of a predictive screening tool. After constructing a new screening tool for delirium, a receiver operating characteristic curve was drawn, the most appropriate cutoff value was decided, and the area under the curve was obtained. Bootstrapping was used for the internal model validation.

RESULTS

A screening tool for delirium (Subjective Delirium Screening Scale by Nurse) with a total score of 5 points was constructed as follows: 2 points for disorientation and 1 point each for restlessness, somnolence, and hallucination. The area under the curve for the Subjective Delirium Screening Scale by Nurse was 81.9% (95% CI [77.9%, 85.8%]), and the most appropriate cutoff value was determined to be 2 (sensitivity of 61.0% and specificity of 96.7%). Bootstrapped validation beta coefficients of the predictive factors were similar to the original cohort beta coefficients.

CONCLUSIONS

We created a screening tool for delirium using factors that were regularly observed and recorded by nurses. This tool is simple and practical and has adequate diagnostic accuracy.

Derivation, Validation, Sustained Performance, and Clinical Impact of an Electronic Medical Record-Based Perioperative Delirium Risk Stratification Tool

BACKGROUND

Postoperative delirium is an important problem for surgical inpatients and was the target of a multidisciplinary quality improvement project at our institution. We developed and tested a semiautomated delirium risk stratification instrument, Age, WORLD backwards, Orientation, iLlness severity, Surgery-specific risk (AWOL-S), in 3 independent cohorts from our tertiary care hospital and describe its performance characteristics and impact on clinical care.

METHODS

The risk stratification instrument was derived with elective surgical patients who were admitted at least overnight and received at least 1 postoperative delirium screen (Nursing Delirium Screening Scale [NuDESC] or Confusion Assessment Method for the Intensive Care Unit [CAM-ICU]) and preoperative cognitive screening tests (orientation to place and ability to spell WORLD backward). Using data pragmatically collected between December 7, 2016, and June 15, 2017, we derived a logistic regression model predicting probability of delirium in the first 7 postoperative hospital days. A priori predictors included age, cognitive screening, illness severity or American Society of Anesthesiologists physical status, and surgical delirium risk. We applied model odds ratios to 2 subsequent cohorts ("validation" and "sustained performance") and assessed performance using area under the receiver operator characteristic curves (AUC-ROC). A post hoc sensitivity analysis assessed performance in emergency and preadmitted patients. Finally, we retrospectively evaluated the use of benzodiazepines and anticholinergic medications in patients who screened at high risk for delirium.

RESULTS

The logistic regression model used to derive odds ratios for the risk prediction tool included 2091 patients. Model AUC-ROC was 0.71 (0.67-0.75), compared with 0.65 (0.58-0.72) in the validation (n = 908) and 0.75 (0.71-0.78) in the sustained performance (n = 3168) cohorts. Sensitivity was approximately 75% in the derivation and sustained performance cohorts; specificity was approximately 59%. The AUC-ROC for emergency and preadmitted patients was 0.71 (0.67-0.75; n = 1301). After AWOL-S was implemented clinically, patients at high risk for delirium (n = 3630) had 21% (3%-36%) lower relative risk of receiving an anticholinergic medication perioperatively after controlling for secular trends.

CONCLUSIONS

The AWOL-S delirium risk stratification tool has moderate accuracy for delirium prediction in a cohort of elective surgical patients, and performance is largely unchanged in emergent/preadmitted surgical patients. Using AWOL-S risk stratification as a part of a multidisciplinary delirium reduction intervention was associated with significantly lower rates of perioperative anticholinergic but not benzodiazepine, medications in those at high risk for delirium. AWOL-S offers a feasible starting point for electronic medical record-based postoperative delirium risk stratification and may serve as a useful paradigm for other institutions.

ICU Delirium-Prediction Models: A Systematic Review

Supplemental Digital Content is available in the text.

Objective:

Summarize performance and development of ICU delirium-prediction models published within the past 5 years.

Data Sources:

Systematic electronic searches were conducted in April 2019 using PubMed, Embase, Cochrane Central, Web of Science, and Cumulative Index to Nursing and Allied Health Literature to identify peer-reviewed studies.

Study Selection:

Eligible studies were published in English during the past 5 years that specifically addressed the development, validation, or recalibration of delirium-prediction models in adult ICU populations.

Data Extraction:

Screened citations were extracted independently by three investigators with a 42% overlap to verify consistency using the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies.

Data Synthesis:

Eighteen studies featuring 23 distinct prediction models were included. Model performance varied greatly, as assessed by area under the receiver operating characteristic curve (0.62–0.94), specificity (0.50–0.97), and sensitivity (0.45–0.96). Most models used data collected from a single time point or window to predict the occurrence of delirium at any point during hospital or ICU admission, and lacked mechanisms for providing pragmatic, actionable predictions to clinicians.

Conclusions:

Although most ICU delirium-prediction models have relatively good performance, they have limited applicability to clinical practice. Most models were static, making predictions based on data collected at a single time-point, failing to account for fluctuating conditions during ICU admission. Further research is needed to create clinically relevant dynamic delirium-prediction models that can adapt to changes in individual patient physiology over time and deliver actionable predictions to clinicians.

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

STUDY OBJECTIVE

To develop and validate a delirium risk prediction preoperative model for patients undergoing cardiac surgery.

DESIGN

Observational prospective multicentre study.

SETTING

Six intensive care units in Spain.

PATIENTS

689 patients undergoing cardiac surgery consecutively, aged ≥18 years.

MEASUREMENTS

The primary outcome measure was the development of delirium, diagnosed using the Confusion Assessment Method in Intensive Care Units (CAM-ICU), during the stay in the intensive care unit after cardiac surgery.

MAIN RESULTS

The model was developed with 345 consecutive patients undergoing cardiac surgery at six hospitals and validated with another 344 patients from the same hospitals. The prediction model contained four preoperative risk factors: age over 65 years, Mini-Mental State Examination (MMSE) score of 25-26 points (possible impairment of cognitive function) or < 25 (impairment of cognitive function), insomnia needing medical treatment and low physical activity (walk less than 30 min a day). The model had an area under the receiver operating characteristics curve of 0.825 (95% confidence interval: 0.76-0.89). The validation resulted in an area under the curve of 0.79 (0.73-0.85) and the pooled area under the receiver operating characteristics curve (n = 689) was 0.81 (0.76-0.85). We stratified patients in groups of low (0%-20%), moderate (> 20%-40%), high (> 40%-60%) and very high (> 60%) risk of developing delirium, with a positive and negative predictive value for the very high risk group of 70.97% and 85.56%, respectively.

CONCLUSION

The DELIPRECAS model (DELIrium PREvention CArdiac Surgery), consisting of four well-defined clinical risk factors, can predict in the preoperative period the risk of developing postoperative delirium in patients undergoing cardiac surgery. An automatic version of the risk calculator is available.

Preoperative Nutritional Status and Risk for Subsyndromal Delirium in Older Adults Following Joint Replacement Surgery

BACKGROUND

Subsyndromal delirium following surgery in older adults is related to increased lengths of hospital stay and increased admissions to long-term care. Impaired nutrition increases risk for delirium, but its relationship to subsyndromal delirium remains unclear.

PURPOSE

This correlational study examined the relationship between nutritional status and subsyndromal delirium in older adults.

METHODS

Assessments for subsyndromal delirium in 53 adults 65 years or older were completed for three consecutive days following joint replacement surgery. Relationships between nutritional status and subsyndromal delirium were analyzed. Level of significance for all tests was set at p ≤ .05.

RESULTS

Participants' scores from the Mini Nutritional Assessment screen were significantly related (p = .05) to subsyndromal delirium severity after accounting for variability posed by age and cognition status.

CONCLUSION

When preoperative risk assessment of older adults indicates nutritional risk, preoperative optimization may improve effectiveness of delirium prevention efforts.

Risk Factors for Delirium and Cognitive Decline Following Coronary Artery Bypass Grafting Surgery: A Systematic Review and Meta-Analysis

Background

Coronary artery bypass grafting (CABG) is known to improve heart function and quality of life, while rates of surgery‐related mortality are low. However, delirium and cognitive decline are common complications. We sought to identify preoperative, intraoperative, and postoperative risk or protective factors associated with delirium and cognitive decline (across time) in patients undergoing CABG.

Methods and Results

We conducted a systematic search of Medline, PsycINFO, EMBASE, and Cochrane (March 26, 2019) for peer‐reviewed, English publications reporting post‐CABG delirium or cognitive decline data, for at least one risk factor. Random‐effects meta‐analyses estimated pooled odds ratio for categorical data and mean difference or standardized mean difference for continuous data. Ninety‐seven studies, comprising data from 60 479 patients who underwent CABG, were included. Moderate to large and statistically significant risk factors for delirium were as follows: (1) preoperative cognitive impairment, depression, stroke history, and higher European System for Cardiac Operative Risk Evaluation (EuroSCORE) score, (2) intraoperative increase in intubation time, and (3) postoperative presence of arrythmia and increased days in the intensive care unit; higher preoperative cognitive performance was protective for delirium. Moderate to large and statistically significant risk factors for acute cognitive decline were as follows: (1) preoperative depression and older age, (2) intraoperative increase in intubation time, and (3) postoperative presence of delirium and increased days in the intensive care unit. Presence of depression preoperatively was a moderate risk factor for midterm (1–6 months) post‐CABG cognitive decline.

Conclusions

This meta‐analysis identified several key risk factors for delirium and cognitive decline following CABG, most of which are nonmodifiable. Future research should target preoperative risk factors, such as depression or cognitive impairment, which are potentially modifiable.

Registration

URL: https://www.crd.york.ac.uk/prosp​ero/; Unique identifier: CRD42020149276.

Does postoperative delirium following elective noncardiac surgery predict long-term mortality?

OBJECTIVE

to determine whether incident postoperative delirium in elective older surgical patient was associated with increased risk for mortality, controlling for covariates of 5-year mortality.

DESIGN

secondary analysis of prospective cohort studies.

SETTING

academic Medical Center.

SUBJECTS

patients ≥65 years of age undergoing elective non-cardiac surgery.

OUTCOMES

postoperative assessments of delirium measured using the Confusion Assessment Method (CAM), mortality within 5 years of the index surgery was determined from National Death Index records.

RESULTS

postoperative delirium occurred in 332/1,315 patients (25%). Five years after surgery, 175 patients (13.3%) were deceased. Older age was associated with an increased odds of mortality [odds ratio (OR) 1.90, 95% confidence interval (CI) 1.20-2.70] for those aged 70-79 years compared to those aged <70 years, and OR 3.29, 95% CI 2.14-5.06 for those aged >80 years. Other variables associated with 5-year mortality on bi-variate analyses were white race, self-rated functional status, lower preoperative cognitive status, higher risk score as measured by the American Society of Anesthesiologists (ASA) classification, higher surgical risk score, history of congestive heart failure, myocardial infarction, renal disease, cancer, peripheral vascular disease and postoperative delirium. However, postoperative delirium was not associated with 5-year mortality on multi-variate logistic regression (OR 1.18, 95% CI 0.85-1.65).

CONCLUSIONS

our results showed that delirium was not associated with 5-year mortality in elective surgical patients after consideration of co-variates of mortality. Our results suggest the importance of accounting for known preoperative risks for mortality when investigating the relationship between delirium and long-term mortality.

Registration of attentional function as a predictor of incident delirium (the RAPID study)

INTRODUCTION

Older adults undergoing elective surgery have a high risk of developing postoperative delirium (POD). Validated models predicting POD are scarce. This study investigated whether preoperative impairment of attentional function predicts POD in older adults without previously diagnosed cognitive impairment.

METHODS

In this prospective cohort study we recruited patients aged ≥70 years preceding major elective surgery. Preoperatively a visual vigilance test was administered to determine intra-individual reaction-time variability. Postoperatively, presence of delirium was screened daily.

RESULTS

We recruited 152 patients, 25 (16.4%) developed POD. Intra-individual reaction-time variability was not significantly different between patients with or without POD (0.18 ± 0.08 ms vs 0.22 ± 0.11 ms; P = 0.087). Receiver operating characteristic analyses indicated a poor accuracy for POD (area under the curve 0.609 ± 0.63). Except for surgery duration, no clinically significant between-group differences were found for secondary outcome parameters.

DISCUSSION

Preoperative intra-individual reaction time variability does not predict the incidence of POD in older patients undergoing major elective surgery.

Predicting incident delirium diagnoses using data from primary-care electronic health records

IMPORTANCE

risk factors for delirium in hospital inpatients are well established, but less is known about whether delirium occurring in the community or during an emergency admission to hospital care might be predicted from routine primary-care records.

OBJECTIVES

identify risk factors in primary-care electronic health records (PC-EHR) predictive of delirium occurring in the community or recorded in the initial episode in emergency hospitalisation. Test predictive performance against the cumulative frailty index.

DESIGN

Stage 1: case-control; Stages 2 and 3: retrospective cohort.

SETTING

clinical practice research datalink: PC-EHR linked to hospital discharge data from England.

SUBJECTS

Stage 1: 17,286 patients with delirium aged ≥60 years plus 85,607 controls. Stages 2 and 3: patients ≥ 60 years (n = 429,548 in 2015), split into calibration and validation groups.

METHODS

Stage 1: logistic regression to identify associations of 110 candidate risk measures with delirium. Stage 2: calibrating risk factor weights. Stage 3: validation in independent sample using area under the curve (AUC) receiver operating characteristic.

RESULTS

fifty-five risk factors were predictive, in domains including: cognitive impairment or mental illness, psychoactive drugs, frailty, infection, hyponatraemia and anticholinergic drugs. The derived model predicted 1-year incident delirium (AUC = 0.867, 0.852:0.881) and mortality (AUC = 0.846, 0.842:0.853), outperforming the frailty index (AUC = 0.761, 0.740:0.782). Individuals with the highest 10% of predicted delirium risk accounted for 55% of incident delirium over 1 year.

CONCLUSIONS

a risk factor model for delirium using data in PC-EHR performed well, identifying individuals at risk of new onsets of delirium. This model has potential for supporting preventive interventions.

Development and validation of the delirium risk assessment score (DRAS)

PURPOSE

Development and validation of a delirium risk assessment score. Predisposing risk factors for delirium were used, which are easily assessed at hospital admission without additional clinical or laboratory testing.

METHODS

A systematic literature search identified ten risk factors: acute admission, alcohol use > 4 units/day, cognitive impairment, ADL impairment, age > 75 years, earlier delirium, hearing/vision problems, number of medication ≥ 5, number of morbidities > 2 and male. The DRAS was developed in a mixed patient population (N = 842) by the use of univariate and multivariate analyses and -2 log-likelihood calculation to weigh the risk factors. Based on the sensitivity and specificity, a cutoff score was calculated. The validation was performed in 3 cohorts (N = 408, N = 186, N = 365). In cohort 3, the DRAS was compared (AUC, sensitivity and specificity) to 3 instruments (Inouye, Kalisvaart, VMS rules).

RESULTS

The delirium incidence was 31.8%, 20.3%, 15.6% and 15.1%. All risk factors were independently predictive for delirium, except male. The multivariate analyses excluded morbidities. The final DRAS consists of 8 items; acute admission, cognitive impairment, alcohol use (3 points), ADLimpairment/mobilityproblems (2 points), higher age, earlier delirium, hearing/vision problems, and medication (1 point). The total score is 15 points and at a cut-of score of 5 or higher the patient is at risk of developing a delirium. The cutoff was at 5 or more points, AUC: 0.76 (95% CI 0.72-0.79), sensitivity 0.77, specificity 0.60. Validation cohorts AUC was 0.75 (95% CI 0.96-0.81), 0.76 (95% CI 0.70-0.83) and 0.78 (95% CI 0.70-0.87), sensitivity 0.71, 0.67 and 0.89 and specificity 0.70, 0.72 and 0.60. The comparison revealed the highest AUC for the DRAS.

CONCLUSION

Based on an admission interview, the delirium risk can be easily evaluated using the DRAS shortlist score of predisposing risk factors for delirium in older inpatients.

Risk Factors Associated With Postoperative Delirium in Patients Undergoing Head and Neck Free Flap Reconstruction

Importance

Postoperative delirium (POD) is associated with an increased rate of adverse events, higher health care costs, and longer hospital stays. At present, limited data are available regarding the risk factors for developing POD in patients undergoing head and neck free flap reconstruction. Identification of patients at high risk of developing POD will allow implementation of risk-mitigation strategies.

Objective

To determine the frequency of and risk factors associated with POD in patients undergoing free flap reconstruction secondary to head and neck disease.

Design, Setting, and Participants

This retrospective cohort study included 515 patients undergoing free flap reconstruction from January 1, 2006, through December 31, 2012, at the James Cancer Hospital and Solove Research Institute, The Ohio State University Comprehensive Care Center, a tertiary care cancer hospital. Preoperative, intraoperative, and postoperative data were collected retrospectively. Data from January 1, 2006, through December 31, 2012, were analyzed, and the final date of data analysis was January 8, 2018.

Interventions

Head and neck free flap reconstruction.

Main Outcomes and Measures

The primary outcome was the development of POD as defined by the Diagnostic and Statistical Manual of Mental Disorders (Fourth Edition). Univariable and multivariable logistic regression were used to identify risk factors associated with POD.

Results

Five hundred fifteen patients underwent free flap reconstruction during the study period (66.2% male; mean [SD] age, 60.1 [12.8] years). Of these, 56 patients (10.9%) developed POD. On multivariable analysis, risk factors associated with POD included increased age (odds ratio [OR], 1.06; 95% CI, 1.02-1.11), male sex (OR, 5.02; 95% CI, 1.47-17.20), increased operative time (OR for each 1-minute increase, 1.004 [95% CI, 1.001-1.006]; OR for each 1-hour increase, 1.26 [95% CI, 1.08-1.46]), advanced nodal disease (OR, 3.00; 95% CI, 1.39-6.46), and tobacco use (OR, 7.23; 95% CI, 1.43-36.60). Preoperative abstinence from alcohol was identified as a protective factor (OR, 0.24; 95% CI, 0.12-0.51).

Conclusions and Relevance

This study identified variables associated with a higher risk of developing POD. Although many of these risk factors are nonmodifiable, they provide a target population for quality improvement initiatives. Furthermore, preoperative alcohol abstinence may be useful in preventing POD.

CSF Beta-amyloid 1-42 Concentration Predicts Delirium Following Elective Arthroplasty Surgery in an Observational Cohort Study

OBJECTIVE

To test the hypothesis that APOE ε4 status and cerebrospinal fluid (CSF) Aβ42, T-tau and P-tau would independently predict the risk of postoperative delirium.

BACKGROUND

Delirium following surgery is common and associated with adverse outcomes. Age and cognitive impairment are consistent risk factors for postoperative delirium.

METHODS

This observational cohort study recruited 282 participants aged 65 years or older, without a diagnosis of dementia, admitted for primary elective hip or knee arthroplasty. Cognitive tests were undertaken preoperatively, blood and CSF were sampled at the time of spinal anesthesia, and participants were assessed daily postoperatively for delirium.

RESULTS

Increasing age (P = 0.04), preoperative comorbidity (P = 0.03), type of surgery (P = 0.05), intravenous opioid usage (P = 0.04), and low CSF Aβ42 (P < 0.01) were independent predictors of postoperative delirium.

CONCLUSIONS

This study is the first to show an independent association between CSF Aβ42 and delirium incidence in an elective surgical population, suggesting that postoperative delirium may indicate incipient Alzheimer disease.

Renal function and delirium in older fracture patients: different information from different formulas?

OBJECTIVES

the association between renal function and delirium has not been investigated in older fracture patients. Creatinine is frequently low in these subjects, which may influence the association between delirium and renal function as estimated with creatinine-based formulas. Cystatin C could be a more reliable filtration marker in these patients.

AIM

to confirm the association between renal function and delirium in older fracture patients comparing creatinine- and cystatin-based estimated glomerular filtration rate (eGFR) METHODS: patients aged 65+ requiring surgery for traumatic bone fractures were included. Six equations were used to calculate eGFR, based on serum creatinine and/or cystatin C obtained within 24 h of admission: Modification of Diet in Renal Disease (MDRD), Chronic Kidney Disease Epidemiology (CKD-EPI_cr, CKD-EPI_cys, CKD-EPI_cr-cys) and Berlin Initiative Study equations (BIS-1, BIS-2). Delirium was identified with a chart-based method.

RESULTS

571 patients (mean age 83) were enrolled. Delirium occurred in the 34% and was associated with a lower eGFR regardless of the equation used. In a multivariable model, the association between moderate renal impairment (eGFR 30-60 ml/min/1.73 m²) and delirium remained significant in patients aged 75-84 and only when estimated with cystatin-based or BIS-1 equations. Only dementia was significantly associated with delirium in subjects 85+.

CONCLUSIONS

in older fracture patients, moderate renal impairment was independently associated with delirium only among subjects aged 75-84, when eGFR was estimated with cystatin-based or BIS 1 equations, and not with the most commonly used equations (MDRD, CKD-EPI_cr).

Role of perceived stress in postoperative delirium: an investigation among elderly patients

Objectives: This study examined levels of perceived stress (PS), postoperative delirium (POD) and associated factors among Thai elderly patients undergoing elective noncardiac surgery.Background and aims: Preoperative PS and change after operation have not been widely studied. Moreover, psychological factors associated with PS and POD has been poorly investigated.Materials and Methods: In total, 429 elderly patients were recruited at a university hospital. The preoperative evaluation included sociodemographic data, health behaviors at risk, Perceived Stress Scale (PSS-10), Neuroticism Inventory (NI), Mental State Examination T10 (MSET10), Montreal Cognitive Assessment (MoCA) and Geriatric Depression Scale (GDS-15). Three-day postoperative evaluation included PSS-10 and Confusion Assessment Method Algorithm (CAM) or CAM-ICU for Delirium. Multiple regression and logistic regression analysis were performed to determine potential predictors.Results: Females were 58.97%, and the mean age was 69.93 ± 6.87 years. Mean pre- and postoperative PS were 12.77 ± 5.41 and 13.39 ± 5.26, respectively (P < 0.05). Multiple regression revealed that neuroticism, depression, and BMI predicted PS significantly. None of the independent variables was found to predict postoperative PS except for preoperative PS (p <.001). POD at the recovery room was predicted by preoperative PS (odds ratio = 1.181, 95% CI = 1.019-1.369), whereas overall POD was predicted by MoCA (odds ratio = .864, 95% CI = .771 -.968).Conclusion: Preoperative PS was significant in that it was associated with postoperative PS and POD. A careful assessment of preoperative PS as well as providing brief interventions for patients with high levels of this condition may reduce the risk of POD.

Impact of a delirium prevention project among older hospitalized patients who underwent orthopedic surgery: a retrospective cohort study

Background

Postoperative delirium (POD) is a common clinical syndrome with significant negative outcomes. Thus, we aimed to evaluate the feasibility and effectiveness of a delirium screening tool and multidisciplinary delirium prevention project.

Methods

A retrospective cohort study was conducted at a single teaching center in Korea. A cohort of patients who underwent a delirium prevention program using a simple delirium screening tool from December 2018 to February 2019 (intervention group, N = 275) was compared with the cohort from the year before implementation of the delirium prevention program (December 2017 to February 2018) (control group, N = 274). Patients aged ≥65 years who were admitted to orthopedic wards and underwent surgery were included. The incidence rates of delirium before and after implementation of the delirium prevention program, effectiveness of the delirium screening tool, change in the knowledge score of nurses, and length of hospital stay were assessed.

Results

The sensitivity and specificity of the screening tool for the incidence of POD were 94.1 and 72.7%, respectively. The incidence rates of POD were 10.2% (control group) and 6.2% (intervention group). The odds ratio for the risk reduction effect of the project related to the incidence of POD was 0.316 (95% confidence interval: 0.125–0.800, p = 0.015) after adjustment for possible confounders. The delirium knowledge test score increased from 40.52 to 43.24 out of 49 total points (p < 0.001). The median length of hospital stay in the intervention and control groups was 6.0 (interquartile range, 4–9) and 7.0 (interquartile range, 4–10) days, respectively (p = 0.062).

Conclusion

The screening tool successfully identified patients at a high risk of POD at admission. The POD prevention project was feasible to implement, effective in preventing delirium, and improved knowledge regarding delirium among the medical staff.

Trial registration

None.

Cerebrospinal Fluid Spermidine, Glutamine and Putrescine Predict Postoperative Delirium Following Elective Orthopaedic Surgery

Delirium is a marker of brain vulnerability, associated with increasing age, pre-existing cognitive impairment and, recently, cerebrospinal fluid (CSF) biomarkers of Alzheimer’s disease. This nested case-control study used a targeted quantitative metabolomic methodology to profile the preoperative CSF of patients (n = 54) who developed delirium following arthroplasty (n = 28) and those who did not (n = 26). The aim was to identify novel preoperative markers of delirium, and to assess potential correlations with clinical data. Participants without a diagnosis of dementia (≥65 years) undergoing elective primary hip or knee arthroplasty were postoperatively assessed for delirium once-daily for three days. Groups were compared using multivariate, univariate and receiving operator characteristic (ROC) methods. Multivariate modelling using Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA) of metabolomic data readily distinguished between delirium and control groups (R2 ≤ 0.56; Q2 ≤ 0.10). Three metabolites (spermidine, putrescine and glutamine) significantly differed between groups (P < 0.05; FDR < 0.07), and performed well as CSF biomarkers (ROC > 0.75). The biomarker performance of the two polyamines (spermidine/putrescine) was enhanced by ratio with CSF Aβ42 (ROC > 0.8), and spermidine significantly correlated with Aβ42 (pearson r = −0.32; P = 0.018). These findings suggest that spermidine and putrescine levels could be useful markers of postoperative delirium risk, particularly when combined with Aβ42, and this requires further investigation.