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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. [ABSTRACT FROM AUTHOR]

BACKGROUND/OBJECTIVES: Delirium manifests clinically in varying ways across settings. More than 40 instruments currently exist for characterizing the different manifestations of delirium. We evaluated all delirium identification instruments according to their psychometric properties and frequency of citation in published research. DESIGN: We conducted the systematic review by searching Cumulative Index to Nursing and Allied Health Literature (CINAHL), Cochrane Library, Excerpta Medica Database (Embase), PsycINFO, PubMed, and Web of Science from January 1, 1974, to January 31, 2020, with the keywords "delirium" and "instruments," along with their known synonyms. We selected only systematic reviews, meta‐analyses, or narrative literature reviews including multiple delirium identification instruments. MEASUREMENTS: Two reviewers assessed the eligibility of articles and extracted data on all potential delirium identification instruments. Using the original publication on each instrument, the psychometric properties were examined using the Consensus‐based Standards for the Selection of Health Measurement Instruments (COSMIN) framework. RESULTS: Of 2,542 articles identified, 75 met eligibility criteria, yielding 30 different delirium identification instruments. A count of citations was determined using Scopus for the original publication for each instrument. Each instrument underwent methodological quality review of psychometric properties using COSMIN definitions. An expert panel categorized key domains for delirium identification based on criteria from the Diagnostic and Statistical Manual of Mental Disorders (DSM)‐III through DSM‐5. Four instruments were notable for having at least two of three of the following: citation count of 200 or more, strong validation methodology in their original publication, and fulfillment of DSM‐5 criteria. These were, alphabetically, Confusion Assessment Method, Delirium Observation Screening Scale, Delirium Rating Scale‐Revised‐98, and Memorial Delirium Assessment Scale. CONCLUSION: Four commonly used and well‐validated instruments can be recommended for clinical and research use. An important area for future investigation is to harmonize these measures to compare and combine studies on delirium. [ABSTRACT FROM AUTHOR]

Background: Delirium is a common and preventable geriatric syndrome. Moving beyond the binary classification of delirium present/absent, delirium severity represents a potentially important outcome for evaluating preventive and treatment interventions and tracking the course of patients. Although several delirium severity assessment tools currently exist, most have been developed in the absence of advanced measurement methodology and have not been evaluated with rigorous validation studies. Objective: We aimed to report our development of new delirium severity items and the results of item reduction and selection activities guided by psychometric analysis of data derived from a field study. Methods: Building on our literature review of delirium instruments and expert panel process to identify domains of delirium severity, we adapted items from existing delirium severity instruments and generated new items. We then fielded these items among a sample of 352 older hospitalized patients. Results: We used an expert panel process and psychometric data analysis techniques to narrow a set of 303 potential items to 17 items for use in a new delirium severity instrument. The 17-item set demonstrated good internal validity and favorable psychometric characteristics relative to comparator instruments, including the Confusion Assessment Method – Severity (CAM-S) score, the Delirium Rating Scale Revised 98, and the Memorial Delirium Assessment Scale. Conclusion: We more fully conceptualized delirium severity and identified characteristics of an ideal delirium severity instrument. These characteristics include an instrument that is relatively quick to administer, is easy to use by raters with minimal training, and provides a severity rating with good content validity, high internal consistency reliability, and broad domain coverage across delirium symptoms. We anticipate these characteristics to be represented in the subsequent development of our final delirium severity instrument. [ABSTRACT FROM AUTHOR]