The relationship between body mass index and postoperative delirium

Preoperative cognitive function as a risk factor of postoperative delirium in cancer surgeries: A systematic review and meta-analysis

Postoperative delirium (POD) after cancer surgeries can be a result of chemo brain, anesthesia, surgery duration, and preoperative cognitive impairment. Although older age and preoperative cognitive dysfunction were reported to increase the risk of POD in noncardiac surgery, the role of preoperative cognitive function and age in the development of POD after all types of cancer surgeries is not clear. This study aimed to determine the relationship between preoperative cognitive function and likelihood of POD after cancer surgeries. This study used three main online databases and followed PRISMA guidelines. English language original articles that examined preoperative cognitive function before solid tumor cancer surgery and assessed patients for postoperative delirium were included. We employed the random effect meta-analysis method. The overall incidence of POD ranged from 8.7% to 50.9%. The confusion assessment method was the most common tool used to assess delirium. Mini-mental state evaluation (MMSE), Mini-cog, and Montreal cognitive assessment were the most common tools to assess cognitive function. The pooled (total observation = 4676) random effects SMD was estimated at -0.84 (95% confidence interval [CI]: -1.30 to -0.31), indicating that lower MMSE scores before surgery are associated with a higher risk of POD. The pooled (total observation = 2668) random effects OR was estimated at 5.17 (95% CI: 2.51 to -10.63), indicating preoperative cognitive dysfunction can significantly predict the occurrence of POD after cancer surgeries. In conclusion, preoperative cognitive function is an independent and significant predictor of POD after solid tumor cancer surgeries.

Mesial temporal atrophy in preoperative MRI rather than steep Trendelenburg position is associated with postoperative delirium in patients undergoing a major urologic surgery

PURPOSE

To investigate whether steep Trendelenburg in a major urologic surgery is associated with postoperative delirium, and to examine other potential clinical and radiologic factors predictive of postoperative delirium.

METHODS

182 patients who received a major urologic surgery and underwent a 3.0-T brain MRI scan within 1 year prior to the date of surgery were retrospectively enrolled. Preoperative brain MRIs were used to analyze features related to small vessel disease burden and mesial temporal atrophy. Presence of a significant mesial temporal atrophy was defined as Scheltens' scale ≥ 2. Patients' clinico-demographic data and MRI features were used to identify significant predictors of postoperative delirium using the logistic regression analysis. Independent predictors found significant in the univariate analysis were further evaluated in the multivariate analysis.

RESULTS

Incidence of postoperative delirium was 6.0%. Patients with postoperative delirium had lower body mass index (21.3 vs. 25.0 kg/m2, P = 0.003), prolonged duration of anesthesia (362.7 vs. 224.7 min, P < 0.001) and surgery (302.2 vs. 174.5 min, P < 0.001), and had more significant mesial temporal atrophy (64% vs. 30%, P = 0.046). In the univariate analysis, female sex, type of surgery (radical prostatectomy over cystectomy), prolonged duration of anesthesia (≥ 6 h), and presence of a significant mesial temporal atrophy were significant predictors (all P-values < 0.050), but only the presence of significant mesial temporal atrophy was significant in the multivariate analysis [odds ratio (OR), 3.69; 95% CI 0.99-13.75; P = 0.046].

CONCLUSION

Steep Trendelenburg was not associated with postoperative delirium. Significant mesial temporal atrophy (Scheltens' scale ≥ 2) in preoperative brain MRI was predictive of postoperative delirium.

TRIAL REGISTRATION

Not applicable.

Study on the causal relationship between educational attainment and delirium: A two-sample Mendelian randomization study

This study aimed to investigate whether there is a causal relationship between educational attainment and delirium at the genetic level using the Mendelian randomization method, and provide new evidence for studies in this field. We found a causal relationship between educational attainment and delirium at the genetic level after excluding confounders using Mendelian randomization. The inverse variance weighting method of random effects was the main analysis method. The weighted median and Mendelian Randomization-Egger methods, as well as simple, and weighted modes were used as supplementary analysis methods. Additionally, horizontal pleiotropy tests were conducted, including the Mendelian Randomization-Egger intercept test and Mendelian Randomization Pleiotropy RESidual Sum and Outlier. Cochran's Q statistic was used to assess the size of heterogeneity. We retrieved all second single nucleotide polymorphism features and performed multivariate Mendelian randomization to adjust for the effect of potential confounders on our results. The inverse variance weighting suggested a negative correlation between genetically predicted educational attainment and delirium (0.67[0.49-0.92], p = 0.013); Mendelian Randomization Pleiotropy RESidual Sum and Outlier (0.67[0.49-0.92], p = 0.013) and multivariate Mendelian randomization (0.52[0.33-0.82], p = 0.005) results were generally consistent with the inverse variance weighting method. The Mendelian Randomization-Egger, simple, and weighted mode results were consistent with the inverse variance weighting results. Our results were not affected by pleiotropy or heterogeneity (p > 0.05, for both pleiotropy and heterogeneity). In addition, the "leave-one-out" analysis showed that the results of our Mendelian randomization analysis were not influenced by individual single nucleotide polymorphisms. Studies have found a causal relationship between educational attainment and delirium at the genetic level; higher educational attainment may be a protective factor against delirium. Clinically, more attention should be paid to patients at a high risk of delirium with low educational attainment.

Association between body mass index and delirium incidence in critically ill patients: a retrospective cohort study based on the MIMIC-IV Database

OBJECTIVES

Delirium is a form of brain dysfunction with high incidence and is associated with many negative outcomes in the intensive care unit. However, few studies have been large enough to reliably examine the associations between body mass index (BMI) and delirium, especially in critically ill patients. The objective of this study was to investigate the association between BMI and delirium incidence in critically ill patients.

DESIGN

A retrospective cohort study.

SETTING

Data were collected from the Medical Information Mart for Intensive Care-IV V2.0 Database consisting of critically ill participants between 2008 and 2019 at the Beth Israel Deaconess Medical Center in Boston.

PARTICIPANTS

A total of 20 193 patients with BMI and delirium records were enrolled in this study and were divided into six groups.

PRIMARY OUTCOME MEASURE

Delirium incidence.

RESULTS

Generalised linear models and restricted cubic spline analysis were used to estimate the associations between BMI and delirium incidence. A total of 30.81% of the patients (6222 of 20 193) developed delirium in the total cohort. Compared with those in the healthy weight group, the patients in the different groups (underweight, overweight, obesity grade 1, obesity grade 2, obesity grade 3) had different relative risks (RRs): RR=1.10, 95% CI=1.02 to 1.19, p=0.011; RR=0.93, 95% CI=0.88 to 0.97, p=0.003; RR=0.88, 95% CI=0.83 to 0.94, p<0.001; RR=0.94, 95% CI=0.86 to 1.03, p=0.193; RR=1.14, 95% CI=1.03 to 1.25, p=0.010, respectively. For patients with or without adjustment variables, there was an obvious U-shaped relationship between BMI as a continuous variable and delirium incidence.

CONCLUSION

BMI was associated with the incidence of delirium. Our results suggested that a BMI higher or lower than obesity grade 1 rather than the healthy weight in critically ill patients increases the risk of delirium incidence.

Opioid use and subsequent delirium risk in patients with advanced cancer in palliative care: a multicenter registry study

The prevalent use of opioids for pain management in patients with advanced cancer underscores the need for research on their neuropsychiatric impacts, particularly delirium. Therefore, we aimed to investigate the potential association between opioid use and the risk of delirium in patients with advanced cancer admitted to the acute palliative care unit. We conducted a retrospective observational study utilizing a multicenter, patient-based registry cohort by collecting the data from January 1, 2019, to December 31, 2020, in South Korea. All data regarding exposures, outcomes, and covariates were obtained through retrospective chart reviews by a team of specialized medical professionals with expertise in oncology. Full unmatched and 1:1 propensity-score matched cohorts were formed, and stratification analysis was conducted. The primary outcome, delirium, was defined and diagnosed by the DSM-IV. Of the 2,066 patients with advanced cancer, we identified 42.8% (mean [SD] age, 64.4 [13.3] years; 60.8% male) non-opioid users and 57.2% (62.8 [12.5] years; 55.9% male) opioid users, respectively. Opioid use was significantly associated with an increased occurrence of delirium in patients with advanced cancer (OR, 2.02 [95% CI 1.22-3.35]). The risk of delirium in patients with advanced cancer showed increasing trends in a dose-dependent manner. High-dose opioid users showed an increased risk of delirium in patients with advanced cancer compared to non-opioid users (low-dose user: OR, 2.21 [95% CI 1.27-3.84]; high-dose user: OR, 5.75 [95% CI 2.81-11.77]; ratio of OR, 2.60 [95% CI 1.05-6.44]). Patients with old age, male sex, absence of chemotherapy during hospitalization, and non-obese status were more susceptible to increased risk of delirium in patients with cancer. In this multicenter patient-based registry cohort study, we found a significant, dose-dependent association between opioid use and increased risk of delirium in patients with advanced cancer. We also identified specific patient groups more susceptible to delirium. These findings highlight the importance of opioid prescription in these patients with advanced cancer, balancing effective doses for pain management and adverse dose-inducing delirium.

Associations between Alzheimer's disease biomarkers and postoperative delirium or cognitive dysfunction: A meta-analysis and trial sequential analysis of prospective clinical trials

BACKGROUND

The relationship between Alzheimer's disease biomarkers and postoperative complications, such as postoperative delirium (POD) and postoperative cognitive dysfunction (POCD), remains a subject of ongoing debate.

OBJECTIVE

This meta-analysis aimed to determine whether there is an association between perioperative Alzheimer's disease biomarkers and postoperative complications.

DESIGN

We conducted a meta-analysis of observational clinical studies that explored the correlation between Alzheimer's disease biomarkers and POD or POCD in patients who have undergone surgery, following PRISMA guidelines. The protocol was previously published (INPLASY: INPLASY202350001).

DATA SOURCES

A comprehensive search was conducted across PubMed, Embase, Web of Science, and Cochrane databases until March 2023.

ELIGIBILITY CRITERIA

Surgical patients aged at least 18 years, studies focusing on POD or POCD, research involving Alzheimer's disease biomarkers, including Aβ or tau in blood or cerebrospinal fluid (CSF), and availability of the full text.

RESULTS

Our meta-analysis included 15 studies: six focusing on POD and nine on POCD. The findings revealed a negative correlation between preoperative CSF β-amyloid 42 (Aβ42) levels and the onset of POD [mean difference -86.1, 95% confidence interval (CI), -114.15 to -58.05, I2 : 47%]; this association was strongly supported by trial sequential analysis (TSA). A similar negative correlation was discerned between preoperative CSF Aβ42 levels and the incidence of POCD (-165.01, 95% CI, -261.48 to -68.53, I2 : 95%). The TSA also provided robust evidence for this finding; however, the evidence remains insufficient to confirm a relationship between other Alzheimer's disease biomarkers [β-amyloid 40 (Aβ40), total tau (T-tau), phosphorylated tau (P-tau), and Aβ42/T-tau ratio] and POD or POCD.

CONCLUSION

The study results indicate a negative correlation between preoperative CSF Aβ42 levels and the occurrence of both POD and POCD. Future investigations are warranted to identify the predictive cutoff value of preoperative CSF Aβ42 for POD and POCD.

Relationship between preoperative neutrophil-to-lymphocyte ratio and postoperative delirium: The PNDABLE and the PNDRFAP cohort studies

OBJECTIVES

In this study, the relationship between preoperative neutrophil-to-lymphocyte ratio (NLR) and Alzheimer-related biomarkers in cerebrospinal fluid (CSF) was investigated to determine whether high NLR is a potential risk factor for postoperative delirium (POD) and to evaluate its predictive efficacy.

METHODS

We selected 1000 patients from the perioperative neurocognitive disorder risk factor and prognosis (PNDRFAP) database and 999 patients from the perioperative neurocognitive disorder and biomarker lifestyle (PNDABLE) database. Patients in the PNDABLE database have been measured for Alzheimer-related biomarkers in CSF (Aβ40 , Aβ42 , P-tau, and tau protein). Mini-mental state examination was used to assess the preoperative mental status of patients. POD was diagnosed using the confusion assessment method and assessed for severity using the memorial delirium assessment scale. Logistic regression analysis was utilized to explore the association of preoperative NLR with POD. What's more, we also performed sensitivity analysis by adding corrected confounders, and the results were almost unchanged. Spearman's rank correlation was used to determine the associations between NLR and Alzheimer-related biomarkers. Mediation analyses with 10,000 bootstrapped iterations were used to explore the mediation effects. Finally, we use decision curves and the nomogram model to evaluate the efficacy of preoperative NLR in predicting POD; we also performed external validation using data from Qilu Hospital.

RESULT

Logistic regression results showed that an elevated preoperative NLR was a risk factor for the development of POD in patients (PNDRFAP: OR = 1.067, 95% CI 1.020-1.116; PNDABLE: OR = 1.182, 95% CI 1.048-1.335, p < .05). Spearman's rank correlation analysis showed a positive but weak correlation between NLR and P-tau/T-tau (R = .065). The mediating effect results indicate that NLR likely mediates the occurrence of POD through elevated tau protein levels (proportion: 47.47%). The results of the box plots showed statistically significant NLR and CSF biomarkers between the POD and non-POD (NPOD) groups (p < .05), with higher NLR, P-tau, and T-tau in the POD group than in the NPOD group. In contrast, the NPOD group had higher Aβ42 levels compared to the POD group. In addition, we used R package to plot the decision curve and nomogram both suggesting a good predictive effect of preoperative NLR on the occurrence of POD.

CONCLUSION

Elevated preoperative NLR levels may be a risk factor for POD and likely mediate the development of POD through elevated P-tau/T-tau levels.

Metabolic syndrome and the risk of postoperative delirium and postoperative cognitive dysfunction: a multi-centre cohort study

BACKGROUND

Metabolic syndrome and its components are risk factors for cognitive impairment, but their contribution to perioperative neurocognitive disorders is unknown. We examined their associations with the risk of postoperative delirium (POD) and postoperative cognitive dysfunction (POCD) in older patients.

METHODS

In 765 male and female participants aged ≥65 years, we measured preoperative metabolic parameters and screened for POD for 7 days or until discharge. POCD was defined through comparison of cognitive change on six neuropsychological tests with non-surgical controls. Multiple logistic regression analyses examined the association of metabolic parameters with risk of POD and POCD with adjustment for age, sex, and surgery type.

RESULTS

A total of 149 patients (19.5% of 765) developed POD and 53 (10.1% of 520 attendees) had POCD at 3 months. Patients with metabolic syndrome were at 1.85-fold higher risk of POD (95% confidence interval [CI] 1.26-2.70). Each 1 mmol L^-1 higher high-density lipoprotein cholesterol (HDL-C) was associated with a 0.47-fold lower POD risk (95% CI 0.30-0.74). Each 1 kg m^-2 higher body mass index (BMI) was associated with a 1.09-fold higher POCD risk (95% CI 1.02- 1.16).

CONCLUSIONS

Older surgical patients with metabolic syndrome were at increased risk of POD. Only reduced HDL-C was significantly associated with POD. For POCD, a higher preoperative BMI was identified as a risk factor. These findings add to mounting evidence of a distinct epidemiology of POD and POCD. Screening programmes taking advantage of HDL-C and BMI measurements and of metabolic interventions in reducing perioperative neurocognitive disorders should be evaluated.

CLINICAL TRIAL REGISTRATION

NCT02265263.

Relationship between body mass index and risk of delirium in an intensive care unit

OBJECTIVE

Delirium in the intensive care unit (ICU) is a common and critical condition that leads to poor prognosis in older patients, but the association between body mass index (BMI) and the incidence of delirium remains unclear.

METHODS

We retrospectively analyzed 5,622 patients admitted to the ICU of a tertiary referral hospital between 2013 and 2022. We collected sociodemographic data, vital signs, laboratory results, and delirium scale scores. We subdivided the patients into four categories: underweight (<18.5 kg/m²), normal weight (18.5-22.9 kg/m²), overweight (23-24.9 kg/m²), and obese (>25 kg/m²). The primary outcome was the incidence of delirium according to the BMI categories. We performed multivariable logistic regression analysis, adjusted for sex, age, past smoking and alcohol history, benzodiazepine use, and laboratory abnormalities.

RESULTS

Among the 5,622 patients in the ICU (mean age, 72.9 years; male, 60.1%; mean BMI, 24.2 kg/m²⁾, the incidence of delirium was 19.0% (1,069 patients). The mean modified incidence of delirium was higher among underweight patients (odds ratio [OR]=1.51, confidence interval [CI]=1.07-2.12, p = 0.02) than among normal-weight patients. Overweight and obese status were not independently associated with delirium (OR=0.90, CI=0.70-1.17, p = 0.43; OR= 0.97; CI=0.77-1.21, p = 0.78, respectively). The area under the receiver-operating characteristic curve of the multivariable logistic regression model was 0.71 (95% CI=0.69-0.73).

CONCLUSIONS

Underweight status is an independent risk factor for delirium in the ICU. Additional caution is required when evaluating underweight patients for delirium. Obese or overweight status are not associated with delirium, providing evidence for the obesity paradox.

Early prediction of delirium upon intensive care unit admission: Model development, validation, and deployment

STUDY OBJECTIVE

To develop, validate, and deploy models for predicting delirium in critically ill adult patients as early as upon intensive care unit (ICU) admission.

DESIGN

Retrospective cohort study.

SETTING

Single university teaching hospital in Taipei, Taiwan.

PATIENTS

6238 critically ill patients from August 2020 to August 2021.

MEASUREMENTS

Data were extracted, pre-processed, and split into training and testing datasets based on the time period. Eligible variables included demographic characteristics, Glasgow Coma Scale, vital signs parameters, treatments, and laboratory data. The predicted outcome was delirium, defined as any positive result (a score ≥ 4) of the Intensive Care Delirium Screening Checklist that was assessed by primary care nurses in each 8-h shift within 48 h after ICU admission. We trained models to predict delirium upon ICU admission (ADM) and at 24 h (24H) after ICU admission by using logistic regression (LR), gradient boosted trees (GBT), and deep learning (DL) algorithms and compared the models' performance.

MAIN RESULTS

Eight features were extracted from the eligible features to train the ADM models, including age, body mass index, medical history of dementia, postoperative intensive monitoring, elective surgery, pre-ICU hospital stays, and GCS score and initial respiratory rate upon ICU admission. In the ADM testing dataset, the incidence of ICU delirium occurred within 24 h and 48 h was 32.9% and 36.2%, respectively. The area under the receiver operating characteristic curve (AUROC) (0.858, 95% CI 0.835-0.879) and area under the precision-recall curve (AUPRC) (0.814, 95% CI 0.780-0.844) for the ADM GBT model were the highest. The Brier scores of the ADM LR, GBT, and DL models were 0.149, 0.140, and 0.145, respectively. The AUROC (0.931, 95% CI 0.911-0.949) was the highest for the 24H DL model and the AUPRC (0.842, 95% CI 0.792-0.886) was the highest for the 24H LR model.

CONCLUSION

Our early prediction models based on data obtained upon ICU admission could achieve good performance in predicting delirium occurred within 48 h after ICU admission. Our 24-h models can improve delirium prediction for patients discharged >1 day after ICU admission.

Factors associated with intensive care unit delirium in patients with acute myocardial infarction

Some patients admitted to intensive care units (ICU) would develop delirium, which is associated with poor prognosis. The purpose of this retrospective study was to identify factors associated with ICU delirium in patients with acute myocardial infarction (AMI). We included 753 AMI and divided those into the ICU-delirium group (n = 110) and the non-ICU-delirium group (n = 643) according to the presence of ICU delirium. The ICU delirium was evaluated by confusion assessment method for the intensive care unit. Patient characteristics and clinical outcomes were compared between the 2 groups, and factors associated with ICU delirium were sought by multivariate analysis. The prevalence of female sex was significantly higher in the ICU-delirium group (43.6%) than in the non-ICU-delirium group (20.2%) (p < 0.001). The incidence of in-hospital death was significantly higher in the ICU-delirium group (17.3%) than in the non-ICU-delirium group (0.5%) (p < 0.001). The multivariate logistic regression analysis revealed that age [every 10 years increase: odds ratio (OR) 1.439, 95% confidence interval (CI) 1.127-1.837, p = 0.004], female sex (OR 2.237, 95%CI 1.300-3.849, p = 0.004), triple vessel disease (OR 2.317, 95%CI 1.365-3.932, p = 0.002), body mass index < 18.5 kg/m² (OR 2.910, 95%CI 1.410-6.008, p = 0.004), use of mechanical support (OR 2.812, 95%CI 1.500-5.270, p = 0.001), respiratory failure (OR 5.342, 95%CI 3.080-9.265, p < 0.001), and use of continuous renal replacement therapy (OR 5.901, 95%CI 2.520-13.819, p < 0.001) were significantly associated with ICU delirium. In conclusion, ICU delirium was associated with in-hospital death. Older age, female sex, triple vessel disease, leanness, use of mechanical support, respiratory failure, and continuous renal replacement therapy were significantly associated with the occurrence of ICU delirium.

Association of preoperative prognostic nutritional index with risk of postoperative delirium: A systematic review and meta-analysis

Study objective

To assess the association between prognostic nutritional index (PNI) and risk of postoperative delirium (POD) in adult patients.

Methods

MEDLINE, Google scholar, EMBASE, and Cochrane library databases were searched from inception till April 2022. The primary outcome was the association between PNI and the risk of POD, while the secondary outcomes were correlations of other prognostic factors with POD risk. The correlation between PNI and the incidence of POD was assessed with three approaches: Difference in preoperative PNI between POD and non-POD groups (Model 1) as well as the association of PNI as a continuous parameter (Model 2) or as a binary variable (i.e., low vs. high using a PNI cut-off value of 50) (Model 3) with POD risk.

Results

Analysis of nine observational studies published from 2010 to 2021 recruiting 3,743 patients showed a POD incidence of 6.4-35%. Our meta-analysis demonstrated a lower PNI among patients in the POD group (MD: -3.78, 95% CI: -4.85 to -2.71, p < 0.0001, I ² = 54.2%) compared to the non-POD group (Model 1). Pooled results revealed a negative association between PNI and POD risk for both Model 2 (OR: 0.91, 95% CI: 0.86-0.97, p = 0.002, I ² = 71%) and Model 3 (OR: 1.68, 95% CI: 1.26-2.23, p < 0.0001, I ² = 0%). Besides, while our results supported an age-dependent increase in POD risk, other factors including body-mass index, surgical time, health status, hypertension, diabetes mellitus, and male gender were non-significant predictors of POD.

Conclusion

Our results demonstrated a negative association between PNI and POD, which warrant further large-scale studies for validation.

Systematic review registration

https://www.crd.york.ac.uk/prospero/, identifier CRD42022323809.

Potential Value of Serum Uric Acid in the Identification of Postoperative Delirium in Geriatric Patients Undergoing Knee Replacement

Objectives

The relationship between preoperative serum uric acid (SUA) and cerebrospinal fluid (CSF) Alzheimer-related biomarkers were investigated to determine whether high SUA is a potential risk factor for postoperative delirium (POD) and to evaluate its predictive efficacy.

Methods

The participants were selected from the Perioperative Neurocognitive Disorder Risk Factor and Prognosis (PNDRFAP) study and the Perioperative Neurocognitive Disorder and Biomarker Lifestyle (PNDABLE) study. The logistic regression equation was used to analyze the risk factors and protective factors of POD. The interaction term (SUA × Sex) was introduced into the linear model to explore the potential modification effects of sex on the identified correlations. We analyzed the mediating effects of Alzheimer-related biomarkers. Finally, we constructed the receiver operating characteristic (ROC) curve and the nomogram model to evaluate the efficacy of SUA and Alzheimer-related biomarkers in predicting POD.

Results

Patients with POD had elevated SUA level (PNDRFAP: p = 0.002, PNDABLE: p < 0.001). Preoperative SUA level was positively correlated with CSF phosphorylated tau (P-tau) (p = 0.027) and β-amyloid42 (Aβ₄₂)/P-tau (p = 0.023). Interaction analysis did not find any modification effect of sex. The relationship between SUA and POD was partially mediated by CSF P-tau (15.3%). ROC curve showed that the model combining SUA and Alzheimer-related biomarkers had better performance in predicting POD [area under the curve (AUC) = 0.880; p < 0.001], and the predictive model is accurate.

Conclusions

High SUA may enhance CSF P-tau level, thus increasing the risk of POD, and the model combining SUA and Alzheimer-related biomarkers can accurately predict the occurrence of POD.