Associations of the metabolic syndrome and its components with cognitive impairment in older adults

BACKGROUND: The metabolic syndrome (MetS) is an established cardiovascular risk factor. Here, we investigated its role in cognitive impairment. METHODS: Baseline data from 202 participants (aged 65 to 87 years) of the BioCog study were used. All were free of clinical dementia (MMSE≥24/30). Cognitive impairment was defined as the lowest tertile of a cognitive summary score. Multiple logistic regression analyses examined associations of body mass index (BMI), triglycerides (TG), high-density lipoprotein (HDL-C), glucose and glycated hemoglobin A1c (HbA1c) levels with the odds of cognitive impairment. MetS was defined as ≥3 of its 5 components obesity (BMI ≥ 30 kg/m2), elevated TG (TG ≥1.7 mmol/L), reduced HDL-C (males:  0.05). Results for HDL-C were similar when HDL-C, glucose, BMI and TG were entered into a single model (OR 2.56 per 1 mmol/L reduction, 95% CI 1.09, 5.88, p = 0.031) and when cerebrovascular disease and coronary heart disease were additionally controlled for (OR 2.56 per 1 mmol/L reduction, 95% CI 1.06, 6.25, p = 0.036). Among the 5 MetS components, participants with elevated TG were at 2-fold increased odds of impairment (OR 2.09, 95% CI 1.08, 4.05, p = 0.028) including when the remaining 4 MetS components were entered (OR 2.23, 95% CI 1.07, 4.65, p = 0.033), but the finding was no longer statistically significant when cerebrovascular disease and coronary heart disease were additionally controlled for (p = 0.11). Presence of MetS and of obesity, reduced HDL-C, elevated glucose or elevated blood pressure were not significantly associated with impairment (all p > 0.05). CONCLUSION: Our findings support low HDL-C as an independent risk marker of cognitive impairment in older age. The need for research into mediatory and confounding factors, and re-evaluation of traditional cut-off points is highlighted. TRIAL REGISTRATION: The study was registered on 15th October 2014 at clinicaltrials.gov (NCT02265263)

Development and validation of PRE-DELIRIC (PREdiction of DELIRium in ICu patients) delirium prediction model for intensive care patients: observational multicentre study

Objectives To develop and validate a delirium prediction model for adult intensive care patients and determine its additional value compared with prediction by caregivers

Stability of neuropsychological test performance in older adults serving as normative controls for a study on postoperative cognitive dysfunction

OBJECTIVE: Studies of postoperative cognitive dysfunction (POCD) rely on repeat neuropsychological testing. The stability of the applied instruments, which are affected by natural variability in performance and measurement imprecision, is often unclear. We determined the stability of a neuropsychological test battery using a sample of older adults from the general population. Forty-five participants aged 65 to 89 years performed six computerized and non-computerized neuropsychological tests at baseline and again at 7 day and 3 months follow-up sessions. Mean scores on each test were compared across time points using repeated measures analyses of variance (ANOVA) with pairwise comparison. Two-way mixed effects, absolute agreement analyses of variance intra-class correlation coefficients (ICC) determined test-retest reliability. RESULTS: All tests had moderate to excellent test-retest reliability during 7-day (ICC range 0.63 to 0.94; all p < 0.01) and 3-month intervals (ICC range 0.60 to 0.92; all p < 0.01) though confidence intervals of ICC estimates were large throughout. Practice effects apparent at 7 days eased off by 3 months. No substantial differences between computerized and non-computerized tests were observed. We conclude that the present six-test neuropsychological test battery is appropriate for use in POCD research though small sample size of our study needs to be recognized as a limitation. Trial registration ClinicalTrials.gov Identifier NCT02265263 (15th October 2014)

The DSM-5 criteria, level of arousal and delirium diagnosis: Inclusiveness is safer

© 2014 European Delirium Association et al.; licensee BioMed Central Ltd. Background: Delirium is a common and serious problem among acutely unwell persons. Alhough linked to higher rates of mortality, institutionalisation and dementia, it remains underdiagnosed. Careful consideration of its phenomenology is warranted to improve detection and therefore mitigate some of its clinical impact. The publication of the fifth edition of the Diagnostic and Statistical Manual of the American Psychiatric Association (DSM-5) provides an opportunity to examine the constructs underlying delirium as a clinical entity.Discussion: Altered consciousness has been regarded as a core feature of delirium; the fact that consciousness itself should be physiologically disrupted due to acute illness attests to its clinical urgency. DSM-5 now operationalises 'consciousness' as 'changes in attention'. It should be recognised that attention relates to content of consciousness, but arousal corresponds to level of consciousness. Reduced arousal is also associated with adverse outcomes. Attention and arousal are hierarchically related; level of arousal must be sufficient before attention can be reasonably tested.Summary: Our conceptualisation of delirium must extend beyond what can be assessed through cognitive testing (attention) and accept that altered arousal is fundamental. Understanding the DSM-5 criteria explicitly in this way offers the most inclusive and clinically safe interpretation

Genes influencing coagulation and the risk of aneurysmal subarachnoid hemorrhage, and subsequent complications of secondary cerebral ischemia and rebleeding

We investigated whether genes influencing coagulation are associated with the occurrence of aneurysmal subarachnoid hemorrhage (SAH) and with secondary cerebral ischemia and rebleeding in patients with aneurysmal SAH. Genotyping for factor V Leiden (G1691A), prothrombin G20210A, methylenetetetrahydrofolate reductase (MTHFR) C677T, factor XIII subunit A Val34Leu, Tyr204Phe and Pro564Leu, and factor XIII subunit B His95Arg was performed in 208 patients with aneurysmal SAH and in 925 controls. Secondary cerebral ischemia occurred in 49 (24%) patients and rebleeding in 28 (14%) during their clinical course of 3 months after the aneurysmal SAH. The risk of aneurysmal SAH was assessed as odds ratio (OR) with 95% confidence interval (95% CI). The risk of secondary cerebral ischemia and rebleeding was assessed as hazard ratio (HR) with 95% CI using Cox regression. Carriers of the subunit B His95Arg factor XIII polymorphism had an increased risk of aneurysmal SAH with 23% of the patients homozygous or heterozygous for the variant allele compared to 17% of control subjects (OR 1.5, 95% CI 1.0-2.2). For the remaining genetic variants no effect on the risk of aneurysmal SAH could be demonstrated. A clear relation with the risk of secondary cerebral ischemia and of rebleeding could not be established for any of the genetic variants. We found that aneurysmal SAH patients are more often carriers of the subunit B His95Arg factor XIII polymorphism compared to controls. This suggests that carriers of the subunit B His95Arg factor XIII polymorphism have an increased risk of aneurysmal SAH. Larger studies should confirm our results. As aneurysmal SAH patients who died soon after admission could not be included in the present study, our results only apply to a population of patients who survived the initial hours after the hemorrhage. For the other studied genetic factors involved in coagulation, no association with the occurrence of aneurysmal SAH or with the occurrence of secondary cerebral ischemia or rebleeding after aneurysmal SAH could be demonstrated

Delirium prediction in the intensive care unit: comparison of two delirium prediction models

Background: Accurate prediction of delirium in the intensive care unit (ICU) may facilitate efficient use of early preventive strategies and stratification of ICU patients by delirium risk in clinical research, but the optimal delirium prediction model to use is unclear. We compared the predictive performance and user convenience of the prediction model for delirium (PRE-DELIRIC) and early prediction model for delirium (E-PRE-DELIRIC) in ICU patients and determined the value of a two-stage calculation. Methods: This 7-country, 11-hospital, prospective cohort study evaluated consecutive adults admitted to the ICU who could be reliably assessed for delirium using the Confusion Assessment Method-ICU or the Intensive Care Delirium Screening Checklist. The predictive performance of the models was measured using the area under the receiver operating characteristic curve. Calibration was assessed graphically. A physician questionnaire evaluated user convenience. For the two-stage calculation we used E-PRE-DELIRIC immediately after ICU admission and updated the prediction using PRE-DELIRIC after 24 h. Results: In total 2178 patients were included. The area under the receiver operating characteristic curve was significantly greater for PRE-DELIRIC (0.74 (95% confidence interval 0.71-0.76)) compared to E-PRE-DELIRIC (0.68 (95% confidence interval 0.66-0.71)) (z score of -2.73 (p < 0.01)). Both models were well-calibrated. The sensitivity improved when using the two-stage calculation in low-risk patients. Compared to PRE-DELIRIC, ICU physicians (n = 68) rated the E-PRE-DELIRIC model more feasible. Conclusions: While both ICU delirium prediction models have moderate-to-good performance, the PRE-DELIRIC model predicts delirium better. However, ICU physicians rated the user convenience of E-PRE-DELIRIC superior to PRE-DELIRIC. In low-risk patients the delirium prediction further improves after an update with the PRE-DELIRIC model after 24 h

Apolipoprotein E gene is related to mortality only in normal weight individuals: The Rotterdam study

Objective To investigate the relationship between the apolipoprotein E (APOE) gene and the risk of mortality in normal weight, overweight and obese individuals. Methods and Results In a population-based study of 7,983 individuals aged 55 years and older, we compared the risks of all-cause and coronary heart disease (CHD) mortality by APOE genotype, both overall and in subgroups defined by body mass index (BMI). We found significant evidence for interaction between APOE and BMI in relation to total cholesterol (p = 0.04) and HDL cholesterol (p < 0.001). Overall, APOE*2 carriers showed a decreased risk of all-cause mortality. Analyses within BMI strata showed a beneficial effect of APOE*2 only in normal weight persons (adjusted hazard ratio (HR) 0.7[95% CI 0.5–0.9]). APOE*2 was not associated with a lower risk of all-cause mortality in overweight or obese persons. The effect of APOE*2 in normal weight individuals tended to be due to the risk of CHD mortality (adjusted HR 0.5 [95% CI 0.2–1.2]). Conclusion The APOE*2 allele confers a lower risk of all-cause mortality only to normal weight individuals

Advancing specificity in delirium: The delirium subtyping initiative

BACKGROUND: Delirium, a common syndrome with heterogeneous etiologies and clinical presentations, is associated with poor long-term outcomes. Recording and analyzing all delirium equally could be hindering the field's understanding of pathophysiology and identification of targeted treatments. Current delirium subtyping methods reflect clinically evident features but likely do not account for underlying biology. METHODS: The Delirium Subtyping Initiative (DSI) held three sessions with an international panel of 25 experts. RESULTS: Meeting participants suggest further characterization of delirium features to complement the existing Diagnostic and Statistical Manual of Mental Disorders Fifth Edition Text Revision diagnostic criteria. These should span the range of delirium-spectrum syndromes and be measured consistently across studies. Clinical features should be recorded in conjunction with biospecimen collection, where feasible, in a standardized way, to determine temporal associations of biology coincident with clinical fluctuations. DISCUSSION: The DSI made recommendations spanning the breadth of delirium research including clinical features, study planning, data collection, and data analysis for characterization of candidate delirium subtypes. HIGHLIGHTS: Delirium features must be clearly defined, standardized, and operationalized. Large datasets incorporating both clinical and biomarker variables should be analyzed together. Delirium screening should incorporate communication and reasoning

The Delphi Delirium Management Algorithms. A practical tool for clinicians, the result of a modified Delphi expert consensus approach

Delirium is common in hospitalised patients, and there is currently no specific treatment. Identifying and treating underlying somatic causes of delirium is the first priority once delirium is diagnosed. Several international guidelines provide clinicians with an evidence-based approach to screening, diagnosis and symptomatic treatment. However, current guidelines do not offer a structured approach to identification of underlying causes. A panel of 37 internationally recognised delirium experts from diverse medical backgrounds worked together in a modified Delphi approach via an online platform. Consensus was reached after five voting rounds. The final product of this project is a set of three delirium management algorithms (the Delirium Delphi Algorithms), one for ward patients, one for patients after cardiac surgery and one for patients in the intensive care unit.</p

Multinational development and validation of an early prediction model for delirium in ICU patients

Rationale Delirium incidence in intensive care unit (ICU) patients is high and associated with poor outcome. Identification of high-risk patients may facilitate its prevention. Purpose To develop and validate a model based on data available at ICU admission to predict delirium development during a patient’s complete ICU stay and to determine the predictive value of this model in relation to the time of delirium development. Methods Prospective cohort study in 13 ICUs from seven countries. Multiple logistic regression analysis was used to develop the early prediction (E-PRE-DELIRIC) model on data of the first two-thirds and validated on data of the last one-third of the patients from every participating ICU. Results In total, 2914 patients were included. Delirium incidence was 23.6 %. The E-PRE-DELIRIC model consists of nine predictors assessed at ICU admission: age, history of cognitive impairment, history of alcohol abuse, blood urea nitrogen, admission category, urgent admission, mean arterial blood pressure, use of corticosteroids, and respiratory failure. The area under the receiver operating characteristic curve (AUROC) was 0.76 [95 % confidence interval (CI) 0.73–0.77] in the development dataset and 0.75 (95 % CI 0.71–0.79) in the validation dataset. The model was well calibrated. AUROC increased from 0.70 (95 % CI 0.67–0.74), for delirium that developed 6 days. Conclusion Patients’ delirium risk for the complete ICU length of stay can be predicted at admission using the E-PRE-DELIRIC model, allowing early preventive interventions aimed to reduce incidence and severity of ICU delirium