Efficacy and safety of transcranial magnetic stimulation on cognition in mild cognitive impairment, Alzheimer's disease, Alzheimer's disease-related dementias, and other cognitive disorders: a systematic review and meta-analysis.

OBJECTIVE: We aim to analyze the efficacy and safety of TMS on cognition in mild cognitive impairment (MCI), Alzheimer's disease (AD), AD-related dementias, and nondementia conditions with comorbid cognitive impairment. DESIGN: Systematic review, Meta-Analysis. SETTING: We searched MEDLINE, Embase, Cochrane database, APA PsycINFO, Web of Science, and Scopus from January 1, 2000, to February 9, 2023. PARTICIPANTS AND INTERVENTIONS: RCTs, open-label, and case series studies reporting cognitive outcomes following TMS intervention were included. MEASUREMENT: Cognitive and safety outcomes were measured. Cochrane Risk of Bias for RCTs and MINORS (Methodological Index for Non-Randomized Studies) criteria were used to evaluate study quality. This study was registered with PROSPERO (CRD42022326423). RESULTS: The systematic review included 143 studies (n = 5,800 participants) worldwide, encompassing 94 RCTs, 43 open-label prospective, 3 open-label retrospective, and 3 case series. The meta-analysis included 25 RCTs in MCI and AD. Collectively, these studies provide evidence of improved global and specific cognitive measures with TMS across diagnostic groups. Only 2 studies (among 143) reported 4 adverse events of seizures: 3 were deemed TMS unrelated and another resolved with coil repositioning. Meta-analysis showed large effect sizes on global cognition (Mini-Mental State Examination (SMD = 0.80 [0.26, 1.33], p = 0.003), Montreal Cognitive Assessment (SMD = 0.85 [0.26, 1.44], p = 0.005), Alzheimer's Disease Assessment Scale-Cognitive Subscale (SMD = -0.96 [-1.32, -0.60], p < 0.001)) in MCI and AD, although with significant heterogeneity. CONCLUSION: The reviewed studies provide favorable evidence of improved cognition with TMS across all groups with cognitive impairment. TMS was safe and well tolerated with infrequent serious adverse events.

Postdischarge Video Visits for Adherence to Hospital Discharge Recommendations: A Randomized Clinical Trial.

Objective: To determine whether a postdischarge video visit with patients, conducted by hospital medicine advanced practice providers, improves adherence to hospital discharge recommendations. Patients and Methods: We conducted a single-institution 2-site randomized clinical trial with 1:1 assignment to intervention vs control, with enrollment from August 10, 2020, to June 23, 2022. Hospital medicine patients discharged home or to an assisted living facility were randomized to a video visit 2-5 days postdischarge in addition to usual care (intervention) vs usual care (control). During the video visit, advanced practice providers reviewed discharge recommendations. Both intervention and control groups received telephone follow-up 3-6 days postdischarge to ascertain the primary outcome of adherence to all discharge recommendations for new and chronic medication management, self-management and action plan, and home support. Results: Among 1190 participants (594 intervention; 596 control), the primary outcome was ascertained in 768 participants (314 intervention; 454 control). In intervention vs control, there was no difference in the proportion of participants with the primary outcome (76.7% vs 72.5%; P=.19) or in the individual domains of the primary outcome: new and chronic medication management (94.1% vs 92.8%; P=.50), self-management and action plan (76.5% vs 71.5%; P=.18), and home support (94.1% vs 94.3%; P=.94). Women receiving intervention vs control had higher adherence to recommendations (odds ratio, 1.77; 95% CI, 1.08-2.91). Conclusion: In hospital medicine patients, a postdischarge video visit did not improve adherence to discharge recommendations. Potential gender differences in adherence require further investigation.Clinicaltrials.gov number, NCT04547803.

The Macro Impact of Microtransitions in Post-acute and Long-Term Care.

The care transitions concept emerged in medical literature more than 40 years ago, with an exponential rise in publications dedicated to its exploration since that time. It is generally accepted that older patients are particularly vulnerable during care transitions because of complex medical comorbidity, frailty, cognitive dysfunction, and the fragmented nature of health care. A care transition is defined as the movement of patients from one health care setting to another as their care needs change during acute or chronic illness. Easily recognizable examples include the discharge of a patient from the hospital to a skilled nursing facility or an admission to the hospital after a patient is evaluated in the emergency department. These macrotransitions are marked by major changes in clinical condition and span days to weeks. This discussion examines a new term coined by the authors: microtransitions, which are care transitions characterized by movement of a patient between health care settings or within a given setting, usually over shorter periods (less than 24 hours) and accompanied by changes in clinical or custodial responsibility for a patient. Although often unrecognized as formal care transitions, these microtransitions, if not handled appropriately, can lead to poor outcomes, including clinical deterioration and the need for macrotransition. The authors propose formal recognition of microtransitions, standardization of processes related to them, and practical considerations for implementation.

Hospitalist perspective on pandemic related clinical and administrative changes: a cross sectional survey study.

OBJECTIVE: Hospitalists have played a leading role in caring for hospitalized COVID-19 patients. Many clinical and administrative changes occurred in hospitals to meet the varied pandemic needs. We surveyed hospitalists to understand their perspective on pandemic-related changes in technology, models of care, administration and leadership, impact on personal lives, and which of these changes should be continued versus reverting to pre-pandemic practices. METHODS: A 30-question survey was distributed to hospitalists working across the United States between 6 April 2022 to 16 May 2022. Baseline demographics were measured, and post-pandemic perspectives related to changes were analyzed. Perspectives were measured using a 5-point Likert scale and responses were categorized into 'agree' and 'did not agree' for analysis. Variation was assessed using Chi-square or Fisher exact tests. Open-ended questions were reported following qualitative content analysis organized into themes and reported as frequency. RESULTS: 177 respondents (39%) completed the survey. Nearly three-fourths favored hybrid meetings, and two-thirds preferred to continue new models of care. Nearly 90% desired more family and leisure time, continued wellness, and support services, and resumption of social gatherings. No major differences in perspectives were noted between hospitalists at teaching facilities and non-teaching facilities except for resuming protected time for non-clinical activities in those from teaching facilities (83.0% vs 62.5%). Respondents less than age 50 were more likely to prefer virtual meetings (59.0% vs 31.3%). Content analysis of open-ended questions resulted in different themes for each question. Respondents favored more work-life balance and less administrative and logistical work burden. CONCLUSIONS: Hospitalists preferred to continue the use of technology and new models of care even in the post-pandemic period and express a desire for more work-life balance and less administrative and logistical work burden.

An artificial intelligence approach for predicting death or organ failure after hospitalization for COVID-19: development of a novel risk prediction tool and comparisons with ISARIC-4C, CURB-65, qSOFA, and MEWS scoring systems.

BACKGROUND: We applied machine learning (ML) algorithms to generate a risk prediction tool [Collaboration for Risk Evaluation in COVID-19 (CORE-COVID-19)] for predicting the composite of 30-day endotracheal intubation, intravenous administration of vasopressors, or death after COVID-19 hospitalization and compared it with the existing risk scores. METHODS: This is a retrospective study of adults hospitalized with COVID-19 from March 2020 to February 2021. Patients, each with 92 variables, and one composite outcome underwent feature selection process to identify the most predictive variables. Selected variables were modeled to build four ML algorithms (artificial neural network, support vector machine, gradient boosting machine, and Logistic regression) and an ensemble model to generate a CORE-COVID-19 model to predict the composite outcome and compared with existing risk prediction scores. The net benefit for clinical use of each model was assessed by decision curve analysis. RESULTS: Of 1796 patients, 278 (15%) patients reached primary outcome. Six most predictive features were identified. Four ML algorithms achieved comparable discrimination (P > 0.827) with c-statistics ranged 0.849-0.856, calibration slopes 0.911-1.173, and Hosmer-Lemeshow P > 0.141 in validation dataset. These 6-variable fitted CORE-COVID-19 model revealed a c-statistic of 0.880, which was significantly (P < 0.04) higher than ISARIC-4C (0.751), CURB-65 (0.735), qSOFA (0.676), and MEWS (0.674) for outcome prediction. The net benefit of the CORE-COVID-19 model was greater than that of the existing risk scores. CONCLUSION: The CORE-COVID-19 model accurately assigned 88% of patients who potentially progressed to 30-day composite events and revealed improved performance over existing risk scores, indicating its potential utility in clinical practice.

Characteristics, Treatment Patterns, and Clinical Outcomes After Heart Failure Hospitalizations During the COVID-19 Pandemic, March to October 2020.

OBJECTIVE: To compare clinical characteristics, treatment patterns, and 30-day all-cause readmission and mortality between patients hospitalized for heart failure (HF) before and during the coronavirus disease 2019 (COVID-19) pandemic. PATIENTS AND METHODS: The study was conducted at 16 hospitals across 3 geographically dispersed US states. The study included 6769 adults (mean age, 74 years; 56% [5033 of 8989] men) with cumulative 8989 HF hospitalizations: 2341 hospitalizations during the COVID-19 pandemic (March 1 through October 30, 2020) and 6648 in the pre-COVID-19 (October 1, 2018, through February 28, 2020) comparator group. We used Poisson regression, Kaplan-Meier estimates, multivariable logistic, and Cox regression analysis to determine whether prespecified study outcomes varied by time frames. RESULTS: The adjusted 30-day readmission rate decreased from 13.1% (872 of 6648) in the pre-COVID-19 period to 10.0% (234 of 2341) in the COVID-19 pandemic period (relative risk reduction, 23%; hazard ratio, 0.77; 95% CI, 0.66 to 0.89). Conversely, all-cause mortality increased from 9.7% (645 of 6648) in the pre-COVID-19 period to 11.3% (264 of 2341) in the COVID-19 pandemic period (relative risk increase, 16%; number of admissions needed for one additional death, 62.5; hazard ratio, 1.19; 95% CI, 1.02 to 1.39). Despite significant differences in rates of index hospitalization, readmission, and mortality across the study time frames, the disease severity, HF subtypes, and treatment patterns remained unchanged (P>0.05). CONCLUSION: The findings of this large tristate multicenter cohort study of HF hospitalizations suggest lower rates of index hospitalizations and 30-day readmissions but higher incidence of 30-day mortality with broadly similar use of HF medication, surgical interventions, and devices during the COVID-19 pandemic compared with the pre-COVID-19 time frame.

Natural Language Processing CAM Algorithm Improves Delirium Detection Compared With Conventional Methods.

Delirium is known to be underdiagnosed and underdocumented. Delirium detection in retrospective studies occurs mostly by clinician diagnosis or nursing documentation. This study aims to assess the effectiveness of natural language processing-confusion assessment method (NLP-CAM) algorithm when compared to conventional modalities of delirium detection. A multicenter retrospective study analyzed 4351 COVID-19 hospitalized patient records to identify delirium occurrence utilizing three different delirium detection modalities namely clinician diagnosis, nursing documentation, and the NLP-CAM algorithm. Delirium detection by any of the 3 methods is considered positive for delirium occurrence as a comparison. NLP-CAM captured 80% of overall delirium, followed by clinician diagnosis at 55%, and nursing flowsheet documentation at 43%. Increase in age, Charlson comorbidity score, and length of hospitalization had increased delirium detection odds regardless of the detection method. Artificial intelligence-based NLP-CAM algorithm, compared to conventional methods, improved delirium detection from electronic health records and holds promise in delirium diagnostics.

Within-Person Blood Pressure Variability During Hospitalization and Clinical Outcomes Following First-Ever Acute Ischemic Stroke.

BACKGROUND: Uncertainty remains over the relationship between blood pressure (BP) variability (BPV), measured in hospital settings, and clinical outcomes following acute ischemic stroke (AIS). We examined the association between within-person systolic blood pressure (SBP) variability (SBPV) during hospitalization and readmission-free survival, all-cause readmission, or all-cause mortality 1 year after AIS. METHODS: In a cohort of 862 consecutive patients (age [mean ± SD] 75 ± 15 years, 55% women) with AIS (2005-2018, follow-up through 2019), we measured SBPV as quartiles of standard deviations (SD) and coefficient of variation (CV) from a median of 16 SBP readings obtained throughout hospitalization. RESULTS: In the cumulative cohort, the measured SD and CV of SBP in mmHg were 16 ± 6 and 10 ± 5, respectively. The hazard ratios (HR) for readmission-free survival between the highest vs. lowest quartiles were 1.44 (95% confidence interval [CI] 1.04-1.81) for SD and 1.29 (95% CI 0.94-1.78) for CV after adjustment for demographics and comorbidities. Similarly, incident readmission or mortality remained consistent between the highest vs. lowest quartiles of SD and CV (readmission: HR 1.29 [95% CI 0.90-1.78] for SD, HR 1.29 [95% CI 0.94-1.78] for CV; mortality: HR 1.15 [95% CI 0.71-1.87] for SD, HR 0.86 [95% CI 0.55-1.36] for CV). CONCULSIONS: In patients with first AIS, SBPV measured as quartiles of SD or CV based on multiple readings throughout hospitalization has no independent prognostic implications for the readmission-free survival, readmission, or mortality. This underscores the importance of overall patient care rather than a specific focus on BP parameters during hospitalization for AIS.

Post-discharge early assessment with remote video link (PEARL) initiative for patients discharged from hospital medicine services.

OBJECTIVES: The COVID-19 pandemic impacted the availability and accessibility of outpatient care following hospital discharge. Hospitalists (physicians) and hospital medicine advanced practice providers (HM-APPs) coordinate discharge care of hospitalized patients; however, it is unknown if they can deliver post-discharge virtual care and overcome barriers to outpatient care. The objective was to develop and provide post-discharge virtual care for patients discharged from hospital medicine services. METHODS: We developed the Post-discharge Early Assessment with Remote video Link (PEARL) initiative for HM-APPs to conduct a post-discharge video visit (to review recommendations) and telephone follow-up (to evaluate adherence) with patients 2-6 days following hospital discharge. Participants included patients discharged from hospital medicine services at an institution's hospitals in Rochester (May 2020-August 2020) and Austin (November 2020-February 2021) in Minnesota, US. HM-APPs also interviewed patients about their experience with the video visit and completed a survey on their experience with PEARL. RESULTS: Of 386 eligible patients, 61.4% were enrolled (n = 237/386) including 48.1% women (n = 114/237). In patients with complete video visit and telephone follow-up (n = 141/237), most were prescribed new medications (83.7%) and took them as prescribed (93.2%). Among five classes of chronic medications, patient-reported adherence ranged from 59.2% (narcotics) to 91.5% (anti-hypertensives). Patient-reported self-management of 12 discharge recommendations ranged from 40% (smoking cessation) to 100% (checking rashes). Patients reported benefit from the video visit (agree: 77.3%) with an equivocal preference for video visits over clinic visits. Among HM-APPs who responded to the survey (88.2%; n = 15/17), 73.3% reported benefit from visual contact with patients but were uncertain if video visits would reduce emergency department visits. CONCLUSION: In this novel initiative, HM-APPs used video visits to provide care beyond their hospital role, reinforce discharge recommendations for patients, and reduce barriers to outpatient care. The effect of this initiative is under evaluation in a randomized controlled trial.

Validation and Recalibration of Modified Mayo Delirium Prediction Tool in a Hospitalized Cohort.

BACKGROUND: Delirium prediction can augment and optimize care of older adults. Mayo Delirium Prediction (MDP) tool is a robust tool, developed from a large retrospective data set. The MDP tool predicts delirium risk for hospitalized older adults, within 24 hours of hospital admission, based on risk factor information available from electronic health record. OBJECTIVE: We intend to validate the prediction performance of this tool and optimize the tool for clinical use. METHODS: This is an observational cohort study conducted at Mayo Clinic Hospitals, Rochester, MN. All hospitalized older adults (age >50 years) from December 2019 to June 2020 were included. Patients with an admitting diagnosis of substance use disorder were excluded. The original MDP tool was modified to adjust for the fall risk variable as a binary variable that will facilitate broader applicability across different fall risk tools. The modified MDP tool was validated in the retrospective derivation and validation data set which yielded similar prediction capability (area under the receiver operating curve = 0.85 and 0.83, respectively). Diagnosis of delirium was captured by flowsheet diagnosis of delirium documented by nursing staff in the medical record. Predictive variable data were collected daily. RESULTS: A total of 8055 patients were included in the study (median age 71 y). Delirium prediction of the modified MDP tool compared to delirium occurrence was 4% in the low-risk group, 17.8% in the medium-risk group, and 45.3% in the high-risk group (area under receiver operating curve of 0.80). Recalibration of the tool was attempted to further optimize the tool which resulted in both simplification and increased performance (area under receiver operating curve 0.82). The simplified tool was able to predict delirium in hospitalized patients admitted to both medical and surgical services. CONCLUSIONS: Validation of the modified MDP tool revealed good prediction capabilities. Recalibration resulted in simplification with increased performance of the tool in both medical and surgical hospitalized patients.

A hybrid model to identify fall occurrence from electronic health records.

INTRODUCTION: Falls are a leading cause of unintentional injury in the elderly. Electronic health records (EHRs) offer the unique opportunity to develop models that can identify fall events. However, identifying fall events in clinical notes requires advanced natural language processing (NLP) to simultaneously address multiple issues because the word "fall" is a typical homonym. METHODS: We implemented a context-aware language model, Bidirectional Encoder Representations from Transformers (BERT) to identify falls from the EHR text and further fused the BERT model into a hybrid architecture coupled with post-hoc heuristic rules to enhance the performance. The models were evaluated on real world EHR data and were compared to conventional rule-based and deep learning models (CNN and Bi-LSTM). To better understand the ability of each approach to identify falls, we further categorize fall-related concepts (i.e., risk of fall, prevention of fall, homonym) and performed a detailed error analysis. RESULTS: The hybrid model achieved the highest f1-score on sentence (0.971), document (0.985), and patient (0.954) level. At the sentence level (basic data unit in the model), the hybrid model had 0.954, 1.000, 0.988, and 0.999 in sensitivity, specificity, positive predictive value, and negative predictive value, respectively. The error analysis showed that that machine learning-based approaches demonstrated higher performance than a rule-based approach in challenging cases that required contextual understanding. The context-aware language model (BERT) slightly outperformed the word embedding approach trained on Bi-LSTM. No single model yielded the best performance for all fall-related semantic categories. CONCLUSION: A context-aware language model (BERT) was able to identify challenging fall events that requires context understanding in EHR free text. The hybrid model combined with post-hoc rules allowed a custom fix on the BERT outcomes and further improved the performance of fall detection.

Ascertainment of Delirium Status Using Natural Language Processing From Electronic Health Records.

BACKGROUND: Delirium is underdiagnosed in clinical practice and is not routinely coded for billing. Manual chart review can be used to identify the occurrence of delirium; however, it is labor-intensive and impractical for large-scale studies. Natural language processing (NLP) has the capability to process raw text in electronic health records (EHRs) and determine the meaning of the information. We developed and validated NLP algorithms to automatically identify the occurrence of delirium from EHRs. METHODS: This study used a randomly selected cohort from the population-based Mayo Clinic Biobank (N = 300, age ≥65). We adopted the standardized evidence-based framework confusion assessment method (CAM) to develop and evaluate NLP algorithms to identify the occurrence of delirium using clinical notes in EHRs. Two NLP algorithms were developed based on CAM criteria: one based on the original CAM (NLP-CAM; delirium vs no delirium) and another based on our modified CAM (NLP-mCAM; definite, possible, and no delirium). The sensitivity, specificity, and accuracy were used for concordance in delirium status between NLP algorithms and manual chart review as the gold standard. The prevalence of delirium cases was examined using International Classification of Diseases, 9th Revision (ICD-9), NLP-CAM, and NLP-mCAM. RESULTS: NLP-CAM demonstrated a sensitivity, specificity, and accuracy of 0.919, 1.000, and 0.967, respectively. NLP-mCAM demonstrated sensitivity, specificity, and accuracy of 0.827, 0.913, and 0.827, respectively. The prevalence analysis of delirium showed that the NLP-CAM algorithm identified 12 651 (9.4%) delirium patients, the NLP-mCAM algorithm identified 20 611 (15.3%) definite delirium cases, and 10 762 (8.0%) possible cases. CONCLUSIONS: NLP algorithms based on the standardized evidence-based CAM framework demonstrated high performance in delineating delirium status in an expeditious and cost-effective manner.

Psychosis Management in Lewy Body Dementia: A Comprehensive Clinical Approach.

Lewy body dementia (LBD) is asynucleinopathy that results in clinical manifestation of motor and neuropsychiatric symptoms. The disease burden associated with psychosis in LBD patients is significantly higher compared to other types of dementia or even to LBD without psychosis. Effective care management processes should include consideration of de-prescribing any offending agents including anticholinergics and dopaminergic agents, followed by nonpharmacological and low risk pharmacological approach. If addition of pharmacological agents is required, consideration should be given to acetylcholinesterase inhibitors, pimavanserin and atypical antipsychotics such as quetiapine or clozapine. Side effects of these medications should be considered prior to selection and initiation of a medication regimen. Goals of care and functional assessment are a crucial part of the optimized care plan, given overall guarded prognosis, in the context of numerous complications observed in this population. Palliative care consultation could facilitate symptom control and timely enrollment into hospice if consistent with patient's goals.

Hospital medicine team perception of diversity and inclusion at work.

BACKGROUND: : Diversity and Inclusion concepts are crucial in healthcare as the patient population we encounter as hospitalist medicine team is diverse. A diverse and inclusive environment for healthcare employees can lead to improved job satisfaction and high-quality medical care of patients. However, hospitalist perspectives on diversity and inclusion in their work environment are not well studied and noted in literature. Understanding hospitalist perspectives of diversity and inclusion is important in promoting organizational culture. METHODS: We conducted an online survey of a large hospitalist group at Mayo Clinic, Rochester, from October-December 2019, as part of Hospital Internal Medicine (HIM) Diversity Council (HIM-DC) inception, to understand the perceptions of its staff about diversity and inclusion at work and facilitate the next best steps for the team. The responses to the survey questions were graded on a likert scale. Descriptive statistics were used to analyze and interpret the data. RESULTS: : Of the 135 team members, 78 responded (58%). Of the respondents, more than 80% never witnessed or experienced discrimination from a colleague, while more than 50% did witness or experience discrimination from a patient/visitor. More than 70% did not report this discrimination. Nearly 90% felt that it was an inclusive environment at work, across different personal attributes. Most of the respondents requested additional cultural education and social events. CONCLUSION: Unfortunately, a higher percentage of discrimination is perceived from patients/visitors. This highlights the need for institutional policies about visitor conduct. A high proportion of HIM staff felt inclusive at workplace. Committees such as HIM-DC can augment cultural education and social events to improve team's perception.

Predicting Delirium Risk Using an Automated Mayo Delirium Prediction Tool: Development and Validation of a Risk-Stratification Model.

OBJECTIVE: To develop a delirium risk-prediction tool that is applicable across different clinical patient populations and can predict the risk of delirium at admission to hospital. METHODS: This retrospective study included 120,764 patients admitted to Mayo Clinic between January 1, 2012, and December 31, 2017, with age 50 and greater. The study group was randomized into a derivation cohort (n=80,000) and a validation cohort (n=40,764). Different risk factors were extracted and analyzed using least absolute shrinkage and selection operator (LASSO) penalized logistic regression. RESULTS: The area under the receiver operating characteristic curve (AUROC) for Mayo Delirium Prediction (MDP) tool using derivation cohort was 0.85 (95% confidence interval [CI], .846 to .855). Using the regression coefficients obtained from the derivation cohort, predicted probability of delirium was calculated for each patient in the validation cohort. For the validation cohort, AUROC was 0.84 (95% CI, .834 to .847). Patients were classified into 1 of the 3 risk groups, based on their predicted probability of delirium: low (≤5%), moderate (6% to 29%), and high (≥30%). In the derivation cohort, observed incidence of delirium was 1.7%, 12.8%, and 44.8% (low, moderate, and high risk, respectively), which is similar to the incidence rates in the validation cohort of 1.9%, 12.7%, and 46.3%. CONCLUSION: The Mayo Delirium Prediction tool was developed from a large heterogeneous patient population with good validation results and appears to be a reliable automated tool for delirium risk prediction with hospitalization. Further prospective validation studies are required.

Older adults on involuntary hold status in the emergency department.

BACKGROUND: As the United States' population ages, the health care system will experience overall change. This study aims to identify factors in the older adult that may contribute to involuntary hold status in the ED. METHODS: This study is a retrospective review conducted at a suburban acute-care hospital ED of adult patients evaluated while on involuntary hold from January 1, 2014, through November 30, 2015. Older adults (patients born on or before 06/31/1964) were compared to younger adults (born on or after 07/01/1964) according to demographic and clinical variables including medical comorbidity, ED length of stay, reason for involuntary hold, psychiatric disorder, suicide attempt, substance use disorder, serum alcohol level, urine drug testing, medical comorbidity, violence in the ED, 30-day ED readmission, and 30-day mortality. RESULTS: Of 251 patients, 90 (35.9%) were older adults. The most common reason for involuntary hold in both cohorts was suicidal ideation. Medical comorbidities were more prevalent in older adults [60 (66.7%) vs. 64 (39.8%), P ≤.0001]. Older adults were less likely to report current drug abuse [31 (34.4%) vs. 77 (47.8%), P = .04]. The most commonly misused substance in both groups was alcohol; however, despite similar rates, blood alcohol levels (BAC) and urine drug screen (UDS) were performed less often in older adults. Cohorts were not significantly different with respect to sex, race, violence in the ED, psychiatric diagnosis, and ED LOS. CONCLUSIONS: Involuntary older adult patients present with medical comorbidities that impact mental health. In the ED, they are less likely report substance use, and drug screening may be underutilized. Medical needs make their care unique and may present challenges in transfer of care to inpatient psychiatric facilities.

Sex-specific biatrial volumetric measurements obtained with use of multidetector computed tomography in subjects with and without coronary artery disease.

Atrial volumetric measurement has proven clinical implications. Advances in cardiac imaging, notably the precision enabled by multidetector computed tomography (MDCT), herald the need for new criteria of what constitutes normal volumetric measurements. With use of 64-slice MDCT, we compared the atrial volumes in healthy individuals with those in individuals with coronary artery disease. By means of manual segmentation, we measured biatrial volume in 686 participants who underwent retrospective electrocardiographic-gated MDCT angiographic evaluation. The study population included a control group of 203 persons with no cardiac abnormalities, and a study group of 483 patients with obstructive coronary artery disease. All variables were compared between men and women and between the groups. We found a significant difference in left atrial end-systolic and end-diastolic volumes between men and women in the control group (P <0.05); however, right atrial volumes were similar. In comparison with the entire control group, the coronary artery disease group had significantly higher left atrial volume, significantly lower right atrial stroke volume, and significantly lower biatrial ejection fraction, except for left atrial ejection fraction in men. Right atrial volume and left atrial stroke volume were not significantly different. The results imply that a sex-specific reference value is necessary for left atrial volumetric evaluation, and that left atrial volume and biatrial ejection fraction (excluding left atrial ejection fraction in men) might be useful during diagnosis and prognosis in patients who have coronary artery disease.