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Abstract Background As a third of all community dwellers aged 65+ fall each year, falls are common reasons for older adults to present to an Emergency Department (ED). Although EDs should assess patients’ multifactorial fall risks to prevent future fall-related injuries, this frequently does not occur. We describe our protocol to determine the feasibility, acceptability, and safety of a pilot ED Virtual Observation Unit (VOU) Falls program. Methods To ensure standardized conduct and reporting, the Standard Protocol Items for Intervention Trials (SPIRIT) guidelines will be used. The VOU is a program where patients are sent home from the ED but are part of a virtual observation unit in that they can call on-call ED physicians while they are being treated for conditions such as cellulitis, congestive heart failure, or pneumonia. A paramedic conducts daily visits with the patient and facilitates a telemedicine consult with an ED physician. VOU nursing staff conduct daily assessments of patients via telemedicine. The ED VOU Falls program is one of the VOU pathways and is a multi-component fall prevention program for fall patients who present after an ED visit. The paramedic conducts a home safety evaluation, a Timed Up and Go Test (TUG). During the VOU visit, the ED physician conducts a telemedicine visit, while the paramedic is visiting the home, to review patients' fall-risk-increasing drugs and their TUG test. We will determine feasibility by calculating rates of patient enrollment refusal, and adherence to fall-risk prevention recommendations using information from 3-month follow-up telephone calls, as well as qualitative interviews with the paramedics. We will determine the acceptability of the ED VOU Falls program based on patient and provider surveys using a Likert scale. We will ask VOU nursing staff to report any safety issues encountered while the patient is in the ED VOU Falls program (e.g., tripping hazards). We will use the chi-square test or Fisher’s exact test for categorical variables, Student’s t-test for continuous variables, and Mann-Whitney for nonparametric data. We will review interview transcripts and generate codes. Codes will then be extracted and organized into concepts to generate an overall theme following grounded theory methods. This is a pilot study; hence, results cannot be extrapolated. However, a definite trial would be the next step in the future to determine if such a program could be implemented as part of fall prevention interventions. Discussion This study will provide insights into the feasibility and acceptability of a novel ED VOU Falls program with the aim of ultimately decreasing falls. In the future, such a program could be implemented as part of fall prevention interventions.

Introduction Surgical patients over 70 experience postoperative delirium (POD) complications in up to 50% of procedures. Sleep/circadian disruption has emerged as a potential risk factor for POD in epidemiological studies. This protocol presents a single-site, prospective observational study designed to examine the relationship between sleep/circadian regulation and POD and how this association could be moderated or mediated by Alzheimer’s disease (AD) pathology and genetic risk for AD.Methods and analysis Study staff members will screen for eligible patients (age ≥70) seeking joint replacement or spinal surgery at Massachusetts General Hospital (MGH). At the inclusion visit, patients will be asked a series of questionnaires related to sleep and cognition, conduct a four-lead ECG recording and be fitted for an actigraphy watch to wear for 7 days before surgery. Blood samples will be collected preoperatively and postoperatively and will be used to gather information about AD variant genes (APOE-ε4) and AD-related pathology (total and phosphorylated tau). Confusion Assessment Method-Scale and Montreal Cognitive Assessment will be completed twice daily for 3 days after surgery. Seven-day actigraphy assessments and Patient-Reported Outcomes Measurement Information System questionnaires will be performed 1, 3 and 12 months after surgery. Relevant patient clinical data will be monitored and recorded throughout the study.Ethics and dissemination This study is approved by the IRB at MGH, Boston, and it is registered with the US National Institutes of Health on ClinicalTrials.gov (NCT06052397). Plans for dissemination include conference presentations at a variety of scientific institutions. Results from this study are intended to be published in peer-reviewed journals. Relevant updates will be made available on ClinicalTrials.gov.Trial registration number NCT06052397.

Introduction Postoperative delirium in geriatric hip fracture patients adversely affects clinical and functional outcomes and increases costs. A preoperative prediction tool to identify high-risk patients may facilitate optimal use of preventive interventions. The purpose of this study was to develop a clinical prediction model using machine learning algorithms for preoperative prediction of postoperative delirium in geriatric hip fracture patients. Materials & Methods Geriatric patients undergoing operative hip fracture fixation were queried in the American College of Surgeons National Surgical Quality Improvement Program database (ACS NSQIP) from 2016 through 2019. A total of 28 207 patients were included, of which 8030 (28.5%) developed a postoperative delirium. First, the dataset was randomly split 80:20 into a training and testing subset. Then, a random forest (RF) algorithm was used to identify the variables predictive for a postoperative delirium. The machine learning-model was developed on the training set and the performance was assessed in the testing set. Performance was assessed by discrimination (c-statistic), calibration (slope and intercept), overall performance (Brier-score), and decision curve analysis. Results The included variables identified using RF algorithms were (1) age, (2) ASA class, (3) functional status, (4) preoperative dementia, (5) preoperative delirium, and (6) preoperative need for mobility-aid. The clinical prediction model reached good discrimination (c-statistic = .79), almost perfect calibration (intercept = −.01, slope = 1.02), and excellent overall model performance (Brier score = .15). The clinical prediction model was deployed as an open-access web-application: https://sorg-apps.shinyapps.io/hipfxdelirium/ . Discussion & Conclusions We developed a clinical prediction model that shows promise in estimating the risk of postoperative delirium in geriatric hip fracture patients. The clinical prediction model can play a beneficial role in decision-making for preventative measures for patients at risk of developing a delirium. If found to be externally valid, clinicians might use the available web-based application to help incorporate the model into clinical practice to aid decision-making and optimize preoperative prevention efforts.

Introduction: Persistent misconceptions of frailty and dementia in geriatric patients impact physician-patient communication and leave patients vulnerable to disempowerment. Physicians may inappropriately focus the discussion of treatment options to health care proxies instead of patients. Our study explores the consenting process in a decision-making capable orthogeriatric trauma patient population to determine if there is a relationship between increased patient age and surgical consent by health care proxy. Methods: Patients aged 65 and older who underwent operative orthopaedic fracture fixation between 1 of 2 Level 1 Trauma Centers were retrospectively reviewed. Decision-making capable status was defined as an absence of patient history of cognitive impairment and a negative patient pre-surgical Confusion Assessment Method (CAM) and Mini-Cog Assessment screen. Provider of surgical consent was the main outcome and was determined by signature on the consent form. Results: 510 patients were included, and 276 (54.1%) patients were deemed capable of consent. In 27 (9.8%) of 276 decision-capable patients, physicians obtained consent from health care proxies. 20 of these 27 patients (74.1%) were 80 years of age or older. However, in patients aged 70 to 79, only 7 health care proxies provided consent. (p = 0.07). For every unit increase in age, the log odds of proxy consent increased by .0008 (p < 0.001). Age (p < 0.001), income level (p = 0.03), and physical presence of proxy at consult (p < 0.001) were factors associated with significantly increased utilization of health care proxy provided consent. Language other than English was a significant predictor of proxy-provided consent (p = 0.035). 48 (22%) decision-making incapable patients provided their own surgical consent. Discussion: The positive linear association between age and health care proxy provided consent in cognitively intact geriatric orthopaedic patients indicates that increased patient age impacts the consenting process. Increased physician vigilance and adoption of institutional consenting guidelines can reinforce appropriate respect of geriatric patients’ consenting capacity.