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Introduction: The interaction between delirium and dementia is complex. We examined if Alzheimer's disease (AD) biomarkers in patients without clinical dementia are associated with increased risk of postoperative delirium, and whether AD biomarkers demonstrate a graded association with delirium severity., Methods: Participants ( n  = 59) were free of clinical dementia, age ≥ 70 years, and scheduled for elective total knee or hip arthroplasties. Cerebrospinal fluid (CSF) was collected at the time of induction for spinal anesthesia. CSF AD biomarkers were measured by enzyme-linked immunosorbent assay (ELISA) (ADX/Euroimmun); cut points for amyloid, tau, and neurodegeneration (ATN) biomarker status were A  = amyloid beta (Aβ) 42  <175 pg/mL or Aβ 42/40 ratio <0.07; T  = p-tau >80 pg/mL; and N  = t-tau >700 pg/mL. Confusion Assessment Method (CAM) and CAM-Severity (CAM-S) were rated daily post-operatively for delirium and delirium severity, respectively., Results: Aβ 42 , tau, and p-tau mean pg/mL (SD) were 361.5 (326.1), 618.3 (237.1), and 97.1 (66.1), respectively, for those with delirium, and 550.4 (291.6), 518.3 (213.5), and 54.6 (34.5), respectively, for those without delirium. Thirteen participants (22%) were ATN positive. Delirium severity by peak CAM-S [mean difference (95% confidence interval)] was 1.48 points higher (0.29-2.67), P  = 0.02 among the ATN positive. Delirium in the ATN-positive group trended toward but did not reach statistical significance (23% vs. 7%, p = 0.10). Peak CAM-S [mean (SD)] in the delirium group was 7 (2.8) compared to no delirium group 2.5 (1.3), but when groups were further classified by ATN status, an incremental effect on delirium severity was observed, such that patients who were both ATN and delirium negative had the lowest mean (SD) peak CAM-S scores of 2.5 (1.3) points, whereas those who were ATN and delirium positive had CAM-S scores of 8.7 (2.3) points; other groups (either ATN or delirium positive) had intermediate CAM-S scores., Discussion: The presence of AD biomarkers adds important information in predicting delirium severity. Future studies are needed to confirm this relationship and to better understand the role of AD biomarkers, even in pre-clinical phase, in delirium., Competing Interests: The authors declare that there is no conflict of interest, (© 2020 The Authors. Alzheimer's & Dementia: Translational Research & Clinical Interventions published by Wiley Periodicals, Inc. on behalf of Alzheimer's Association.)

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.

Introduction: The Role of Inflammation after Surgery for Elders study correlates novel inflammatory markers measured in blood, cerebrospinal fluid (CSF) assays, and [ 11 C]-PBR28 positron-emission tomography imaging., Methods: This study involved a prospective cohort design with patients who underwent elective hip and knee arthroplasty under spinal anesthesia. Sixty-five adults participated with their family members. Inflammatory biomarker assays were measured preoperatively on day 1 and postoperatively at one month., Results: On average, participants were 75 years old, and 72% were female. 54% underwent total knee arthroplasty, and 46% underwent total hip arthroplasty. The mean Modified Mini-Mental State (3MS) Examination score was 89.3; four patients (6%) scored ≤77 points. Plasma assays were completed in 63 (97%) participants, cerebrospinal fluid assays in 61 (94%), and PET imaging in 44 (68%)., Discussion: This complex study presents an innovative effort to correlate peripheral and central inflammatory biomarkers before and after major surgery in older adults. Strengths include collecting concurrent blood, cerebrospinal fluid, and positron-emission tomography with detailed clinical characterization of delirium, cognition, and functional status., (© 2019 The Authors.)

Background/objectives: To describe the design, procedures, and cohort for the Better ASsessment of ILlness -(BASIL) study, which is conducted to develop and test new delirium severity measures, compare them with existing measures, and examine related clinical outcomes., Methods: Prospective cohort study with 1 year follow-up of study participants at a large teaching hospital in Boston, Massachusetts. After brief cognitive testing and the Delirium Symptom Interview, delirium and delirium severity were rated daily in the hospital using the Confusion Assessment Method (CAM) and CAM-Severity score, the Delirium Rating Scale-Revised-98 (DRS-R-98), and the Memorial Delirium Assessment Scale (MDAS). Other key study variables included comorbidity, physical function (basic and instrumental activities of daily living [ADL]), ratings of subjective health and well-being, and clinical outcomes (length of stay, 30 day rehospitalization, nursing home admission, healthcare utilization). Follow-up interviews occurred at 1- and 12-month with patients and families. In 42 patient interviews, inter-rater reliability for key variables was assessed., Results: Of 768 eligible patients approached, 469 were screened and 352 enrolled, yielding an overall study response rate of 67% for potentially eligible participants. The mean participant was 80.3 years old (SD 6.8) and 203 (58%) were female. The majority of patients were medically complex with Charlson Comorbidity Scores ≥2 (192 patients, 55%), and 102 (29%) met criteria for dementia. Inter-rater reliability assessments (n = 42 pairs) were high for overall ratings of presence or absence of delirium by CAM (κ = 1.0), delirium severity by DRS-R-98 and MDAS (weighted kappa, κ = 1.0 for each) and for ADL impairment (κ = 1.0). For eligible participants at each time point, 278 out of 308 (90%) completed the 1-month follow-up and 132 out of 256 (53%) have completed the 12-month follow-up to date, which is still in progress. Among those who completed interviews, there was only 1-3% missing data on most major outcomes (delirium, basic ADL, and readmission)., Conclusion: The BASIL study presents an innovative effort to advance the conceptualization and measurement of delirium severity. Unique strengths include the diverse cohort with complete high quality data and longitudinal follow-up, along with detailed collection of multiple delirium measures daily during hospitalization., (© 2018 S. Karger AG, Basel.)