Geriatric End-of-Life Screening Tool Prediction of 6-Month Mortality in Older Patients.

Importance: Emergency department (ED) visits by older adults with life-limiting illnesses are a critical opportunity to establish patient care end-of-life preferences, but little is known about the optimal screening criteria for resource-constrained EDs. Objectives: To externally validate the Geriatric End-of-Life Screening Tool (GEST) in an independent population and compare it with commonly used serious illness diagnostic criteria. Design, Setting, and Participants: This prognostic study assessed a cohort of patients aged 65 years and older who were treated in a tertiary care ED in Boston, Massachusetts, from 2017 to 2021. Patients arriving in cardiac arrest or who died within 1 day of ED arrival were excluded. Data analysis was performed from August 1, 2023, to March 27, 2024. Exposure: GEST, a logistic regression algorithm that uses commonly available electronic health record (EHR) datapoints and was developed and validated across 9 EDs, was compared with serious illness diagnoses as documented in the EHR. Serious illnesses included stroke/transient ischemic attack, liver disease, cancer, lung disease, and age greater than 80 years, among others. Main Outcomes and Measures: The primary outcome was 6-month mortality following an ED encounter. Statistical analyses included area under the receiver operating characteristic curve, calibration analyses, Kaplan-Meier survival curves, and decision curves. Results: This external validation included 82 371 ED encounters by 40 505 unique individuals (mean [SD] age, 76.8 [8.4] years; 54.3% women, 13.8% 6-month mortality rate). GEST had an external validation area under the receiver operating characteristic curve of 0.79 (95% CI, 0.78-0.79) that was stable across years and demographic subgroups. Of included encounters, 53.4% had a serious illness, with a sensitivity of 77.4% (95% CI, 76.6%-78.2%) and specificity of 50.5% (95% CI, 50.1%-50.8%). Varying GEST cutoffs from 5% to 30% increased specificity (5%: 49.1% [95% CI, 48.7%-49.5%]; 30%: 92.2% [95% CI, 92.0%-92.4%]) at the cost of sensitivity (5%: 89.3% [95% CI, 88.8-89.9]; 30%: 36.2% [95% CI, 35.3-37.1]). In a decision curve analysis, GEST outperformed serious illness criteria across all tested thresholds. When comparing patients referred to intervention by GEST with serious illness criteria, GEST reclassified 45.1% of patients with serious illness as having low risk of mortality with an observed mortality rate 8.1% and 2.6% of patients without serious illness as having high mortality risk with an observed mortality rate of 34.3% for a total reclassification rate of 25.3%. Conclusions and Relevance: The findings of this study suggest that both serious illness criteria and GEST identified older ED patients at risk for 6-month mortality, but GEST offered more useful screening characteristics. Future trials of serious illness interventions for high mortality risk in older adults may consider transitioning from diagnosis code criteria to GEST, an automatable EHR-based algorithm.

SOFA score performs worse than age for predicting mortality in patients with COVID-19.

The use of the Sequential Organ Failure Assessment (SOFA) score, originally developed to describe disease morbidity, is commonly used to predict in-hospital mortality. During the COVID-19 pandemic, many protocols for crisis standards of care used the SOFA score to select patients to be deprioritized due to a low likelihood of survival. A prior study found that age outperformed the SOFA score for mortality prediction in patients with COVID-19, but was limited to a small cohort of intensive care unit (ICU) patients and did not address whether their findings were unique to patients with COVID-19. Moreover, it is not known how well these measures perform across races. In this retrospective study, we compare the performance of age and SOFA score in predicting in-hospital mortality across two cohorts: a cohort of 2,648 consecutive adult patients diagnosed with COVID-19 who were admitted to a large academic health system in the northeastern United States over a 4-month period in 2020 and a cohort of 75,601 patients admitted to one of 335 ICUs in the eICU database between 2014 and 2015. We used age and the maximum SOFA score as predictor variables in separate univariate logistic regression models for in-hospital mortality and calculated area under the receiver operator characteristic curves (AU-ROCs) and area under precision-recall curves (AU-PRCs) for each predictor in both cohorts. Among the COVID-19 cohort, age (AU-ROC 0.795, 95% CI 0.762, 0.828) had a significantly better discrimination than SOFA score (AU-ROC 0.679, 95% CI 0.638, 0.721) for mortality prediction. Conversely, age (AU-ROC 0.628 95% CI 0.608, 0.628) underperformed compared to SOFA score (AU-ROC 0.735, 95% CI 0.726, 0.745) in non-COVID-19 ICU patients in the eICU database. There was no difference between Black and White COVID-19 patients in performance of either age or SOFA Score. Our findings bring into question the utility of SOFA score-based resource allocation in COVID-19 crisis standards of care.

Applications of Artificial Intelligence in Helicopter Emergency Medical Services: A Scoping Review.

OBJECTIVE: Recent systematic reviews of acute care medicine applications of artificial intelligence (AI) have focused on hospital and general prehospital uses. The purpose of this scoping review was to identify and describe the literature on AI use with a focus on applications in helicopter emergency medical services (HEMS). METHODS: A literature search was performed with specific inclusion and exclusion criteria. Articles were grouped by characteristics such as publication year and general subject matter with categoric and temporal trend analyses. RESULTS: We identified 21 records focused on the use of AI in HEMS. These applications included both clinical and triage uses and nonclinical uses. The earliest study appeared in 2006, but over one third of the identified studies have been published in 2021 or later. The passage of time has seen an increased likelihood of HEMS AI studies focusing on nonclinical issues; for each year, the likelihood of a nonclinical focus had an odds ratio of 1.3. CONCLUSION: This scoping review provides overview and hypothesis-generating information regarding AI applications specific to HEMS. HEMS AI may be ultimately deployed in nonclinical arenas as much as or more than for clinical decision support. Future studies will inform future decisions as to how AI may improve HEMS systems design, asset deployment, and clinical care.

Disparities Associated With Electronic Behavioral Alerts for Safety and Violence Concerns in the Emergency Department.

STUDY OBJECTIVE: Although electronic behavioral alerts are placed as an alert flag in the electronic health record to notify staff of previous behavioral and/or violent incidents in emergency departments (EDs), they have the potential to reinforce negative perceptions of patients and contribute to bias. We provide characterization of ED electronic behavioral alerts using electronic health record data across a large, regional health care system. METHODS: We conducted a retrospective cross-sectional study of adult patients presenting to 10 adult EDs within a Northeastern United States health care system from 2013 to 2022. Electronic behavioral alerts were manually screened for safety concerns and then categorized by the type of concern. In our patient-level analyses, we included patient data at the time of the first ED visit where an electronic behavioral alert was triggered or, if a patient had no electronic behavioral alerts, the earliest visit in the study period. We performed a mixed-effects regression analysis to identify patient-level risk factors associated with safety-related electronic behavioral alert deployment. RESULTS: Of the 2,932,870 ED visits, 6,775 (0.2%) had associated electronic behavioral alerts across 789 unique patients and 1,364 unique electronic behavioral alerts. Of the encounters with electronic behavioral alerts, 5,945 (88%) were adjudicated as having a safety concern involving 653 patients. In our patient-level analysis, the median age for patients with safety-related electronic behavioral alerts was 44 years (interquartile range 33 to 55 years), 66% were men, and 37% were Black. Visits with safety-related electronic behavioral alerts had higher rates of discontinuance of care (7.8% vs 1.5% with no alert; P<.001) as defined by the patient-directed discharge, left-without-being-seen, or elopement-type dispositions. The most common topics in the electronic behavioral alerts were physical (41%) or verbal (36%) incidents with staff or other patients. In the mixed-effects logistic analysis, Black non-Hispanic patients (vs White non-Hispanic patients: adjusted odds ratio 2.60; 95% confidence interval [CI] 2.13 to 3.17), aged younger than 45 (vs aged 45-64 years: adjusted odds ratio 1.41; 95% CI 1.17 to 1.70), male (vs female: adjusted odds ratio 2.09; 95% CI 1.76 to 2.49), and publicly insured patients (Medicaid: adjusted odds ratio 6.18; 95% CI 4.58 to 8.36; Medicare: adjusted odds ratio 5.63; 95% CI 3.96 to 8.00 vs commercial) were associated with a higher risk of a patient having at least 1 safety-related electronic behavioral alert deployment during the study period. CONCLUSION: In our analysis, younger, Black non-Hispanic, publicly insured, and male patients were at a higher risk of having an ED electronic behavioral alert. Although our study is not designed to reflect causality, electronic behavioral alerts may disproportionately affect care delivery and medical decisions for historically marginalized populations presenting to the ED, contribute to structural racism, and perpetuate systemic inequities.

Automating risk stratification for geriatric syndromes in the emergency department.

BACKGROUND: Geriatric emergency department (GED) guidelines endorse screening older patients for geriatric syndromes in the ED, but there have been significant barriers to widespread implementation. The majority of screening programs require engagement of a clinician, nurse, or social worker, adding to already significant workloads at a time of record-breaking ED patient volumes, staff shortages, and hospital boarding crises. Automated, electronic health record (EHR)-embedded risk stratification approaches may be an alternate solution for extending the reach of the GED mission by directing human actions to a smaller subset of higher risk patients. METHODS: We define the concept of automated risk stratification and screening using existing EHR data. We discuss progress made in three potential use cases in the ED: falls, cognitive impairment, and end-of-life and palliative care, emphasizing the importance of linking automated screening with systems of healthcare delivery. RESULTS: Research progress and operational deployment vary by use case, ranging from deployed solutions in falls screening to algorithmic validation in cognitive impairment and end-of-life care. CONCLUSIONS: Automated risk stratification offers a potential solution to one of the most pressing problems in geriatric emergency care: identifying high-risk populations of older adults most appropriate for specific GED care. Future work is needed to realize the promise of improved care with less provider burden by creating tools suitable for widespread deployment as well as best practices for their implementation and governance.

Dementia risk analysis using temporal event modeling on a large real-world dataset.

The objective of the study is to identify healthcare events leading to a diagnosis of dementia from a large real-world dataset. This study uses a data-driven approach to identify temporally ordered pairs and trajectories of healthcare codes in the electronic health record (EHR). This allows for discovery of novel temporal risk factors leading to an outcome of interest that may otherwise be unobvious. We identified several known (Down syndrome RR = 116.1, thiamine deficiency RR = 76.1, and Parkinson's disease RR = 41.1) and unknown (Brief psychotic disorder RR = 68.6, Toxic effect of metals RR = 40.4, and Schizoaffective disorders RR = 40.0) factors for a specific dementia diagnosis. The associations with the greatest risk for any dementia diagnosis were found to be primarily related to mental health (Brief psychotic disorder RR = 266.5, Dissociative and conversion disorders RR = 169.8), or neurologic conditions or procedures (Dystonia RR = 121.9, Lumbar Puncture RR = 119.0). Trajectory and clustering analysis identified factors related to cerebrovascular disorders, as well as diagnoses which increase the risk of toxic imbalances. The results of this study have the ability to provide valuable insights into potential patient progression towards dementia and improve recognition of patients at risk for developing dementia.

Automatable end-of-life screening for older adults in the emergency department using electronic health records.

BACKGROUND: Emergency department (ED) visits are common at the end-of-life, but the identification of patients with life-limiting illness remains a key challenge in providing timely and resource-sensitive advance care planning (ACP) and palliative care services. To date, there are no validated, automatable instruments for ED end-of-life screening. Here, we developed a novel electronic health record (EHR) prognostic model to screen older ED patients at high risk for 6-month mortality and compare its performance to validated comorbidity indices. METHODS: This was a retrospective, observational cohort study of ED visits from adults aged ≥65 years who visited any of 9 EDs across a large regional health system between 2014 and 2019. Multivariable logistic regression that included clinical and demographic variables, vital signs, and laboratory data was used to develop a 6-month mortality predictive model-the Geriatric End-of-life Screening Tool (GEST) using five-fold cross-validation on data from 8 EDs. Performance was compared to the Charlson and Elixhauser comorbidity indices using area under the receiver-operating characteristic curve (AUROC), calibration, and decision curve analyses. Reproducibility was tested against data from the remaining independent ED within the health system. We then used GEST to investigate rates of ACP documentation availability and code status orders in the EHR across risk strata. RESULTS: A total of 431,179 encounters by 123,128 adults were included in this study with a 6-month mortality rate of 12.2%. Charlson (AUROC (95% CI): 0.65 (0.64-0.69)) and Elixhauser indices (0.69 (0.68-0.70)) were outperformed by GEST (0.82 (0.82-0.83)). GEST displayed robust performance across demographic subgroups and in our independent validation site. Among patients with a greater than 30% mortality risk using GEST, only 5.0% had ACP documentation; 79.0% had a code status previously ordered, of which 70.7% were full code. In decision curve analysis, GEST provided greater net benefit than the Charlson and Elixhauser scores. CONCLUSIONS: Prognostic models using EHR data robustly identify high mortality risk older adults in the ED for whom code status, ACP, or palliative care interventions may be of benefit. Although all tested methods identified patients approaching the end-of-life, GEST was most performant. These tools may enable resource-sensitive end-of-life screening in the ED.

Establishment of SEP-1 national practice guidelines does not impact fluid administration for septic shock patients.

BACKGROUND: The Centers for Medicare and Medicaid Services introduced the Early Management Bundle, Severe Sepsis/Septic Shock (SEP-1) as a national quality measure in October 2015. The purpose of SEP-1 is to facilitate the efficient, effective, and timely delivery of high-quality care to patients presenting along the spectrum of sepsis severity. OBJECTIVES: The primary aim of this study was to investigate whether provider practice surrounding emergency department (ED) fluid management of suspected septic shock patients was impacted by SEP-1. METHODS: The study was a retrospective observational analysis of 470,558 patient encounters at an urban academic center over a five-year period. The sample of suspected septic shock patients was defined by the following: blood cultures collected, antibiotics administered, and vasopressors initiated. Participants were divided into two cohorts based on date of presentation (Pre-SEP-1: May 1, 2013, - August 30, 2015, and Post-SEP-1: November 1, 2015, - February 28, 2018). The primary outcome was classified as a dichotomous variable based on whether the total volume of fluids administered equaled or exceeded the calculated weight-based (≥30 cc/kg) goal. Segmented logistic regression analyses were used to assess the immediate impact of SEP-1 as well as to compare the long-term trend of fluid volume administered between Pre-SEP-1 and Post-SEP-1 cohorts. RESULTS: A total of 413 and 482 septic shock patients were included in the Pre-SEP-1 and Post-SEP-1 cohorts, respectively. There was no statistically significant change in weight-based fluid management between the cohorts. The odds of compliance with the weight-based goal decreased 22% immediately following dissemination of SEP-1, however, this was not statistically significant (log-odds = -0.25, p = 0.41). A positive trend in compliance was observed during both the Pre-SEP-1 and Post-SEP-1 periods with odds ratios increasing 0.005 and 0.018 each month, respectively, however, these findings were not statistically significant (log-odds = 0.005, p = 0.736, and log-odds = 0.018, p = 0.10, respectively). CONCLUSIONS: Overall, there were no clinically or statistically meaningful changes in fluid volume resuscitation strategies for suspected septic shock patients following SEP-1. Broad mandates may not be effective tools for promoting practice change in the ED setting. Further research investigating barrier to changes in practice patterns surrounding fluid administration and other SEP-1 bundle elements is warranted.

Development of a Novel Emergency Department Quality Measure to Reduce Very Low-Risk Syncope Hospitalizations.

STUDY OBJECTIVE: Emergency department (ED) evaluations for syncope are common, representing 1.3 million annual US visits and $2 billion in related hospitalizations. Despite evidence supporting risk stratification and outpatient management, variation in syncope hospitalization rates persist. We sought to develop a new quality measure for very low-risk adult ED patients with syncope that could be applied to administrative data. METHODS: We developed this quality measure in 2 phases. First, we used an existing prospective, observational ED patient data set to identify a very low-risk cohort with unexplained syncope using 2 variables: age less than 50 years and no history of heart disease. We then applied this to the 2019 Nationwide Emergency Department Sample (NEDS) to assess its potential effect, assessing for hospital-level factors associated with hospitalization variation. RESULTS: Of the 8,647 adult patients in the prospective cohort, 3,292 (38%) patients fulfilled these 2 criteria: age less than 50 years and no history of heart disease. Of these, 15 (0.46%) suffered serious adverse events within 30 days. In the NEDS, there were an estimated 566,031 patients meeting these 2 criteria, of whom 15,507 (2.7%; 95% confidence interval [CI] 2.48% to 3.00%) were hospitalized. We found substantial variation in the hospitalization rates for this very low-risk cohort, with a median rate of 1.7% (range 0% to 100%; interquartile range 0% to 3.9%). Factors associated with increased hospitalization rates included a yearly ED volume of more than 80,000 (odds ratio [OR] 3.14; 95% CI 2.02 to 4.89) and metropolitan teaching status (OR 1.5; 95% CI 1.24 to 1.81). CONCLUSION: In summary, our novel syncope quality measure can assess variation in low-value hospitalizations for unexplained syncope. The application of this measure could improve the value of syncope care.

Cluster Analysis of Primary Care Physician Phenotypes for Electronic Health Record Use: Retrospective Cohort Study.

BACKGROUND: Electronic health records (EHRs) have become ubiquitous in US office-based physician practices. However, the different ways in which users engage with EHRs remain poorly characterized. OBJECTIVE: The aim of this study is to explore EHR use phenotypes among ambulatory care physicians. METHODS: In this retrospective cohort analysis, we applied affinity propagation, an unsupervised clustering machine learning technique, to identify EHR user types among primary care physicians. RESULTS: We identified 4 distinct phenotype clusters generalized across internal medicine, family medicine, and pediatrics specialties. Total EHR use varied for physicians in 2 clusters with above-average ratios of work outside of scheduled hours. This finding suggested that one cluster of physicians may have worked outside of scheduled hours out of necessity, whereas the other preferred ad hoc work hours. The two remaining clusters represented physicians with below-average EHR time and physicians who spend the largest proportion of their EHR time on documentation. CONCLUSIONS: These findings demonstrate the utility of cluster analysis for exploring EHR use phenotypes and may offer opportunities for interventions to improve interface design to better support users' needs.

Automated multilabel diagnosis on electrocardiographic images and signals.

The application of artificial intelligence (AI) for automated diagnosis of electrocardiograms (ECGs) can improve care in remote settings but is limited by the reliance on infrequently available signal-based data. We report the development of a multilabel automated diagnosis model for electrocardiographic images, more suitable for broader use. A total of 2,228,236 12-lead ECGs signals from 811 municipalities in Brazil are transformed to ECG images in varying lead conformations to train a convolutional neural network (CNN) identifying 6 physician-defined clinical labels spanning rhythm and conduction disorders, and a hidden label for gender. The image-based model performs well on a distinct test set validated by at least two cardiologists (average AUROC 0.99, AUPRC 0.86), an external validation set of 21,785 ECGs from Germany (average AUROC 0.97, AUPRC 0.73), and printed ECGs, with performance superior to signal-based models, and learning clinically relevant cues based on Grad-CAM. The model allows the application of AI to ECGs across broad settings.

Chemoselective restoration of para-azido-phenylalanine at multiple sites in proteins.

The site-specific incorporation of nonstandard amino acids (nsAAs) during translation has expanded the chemistry and function of proteins. The nsAA para-azido-phenylalanine (pAzF) encodes a biorthogonal chemical moiety that facilitates "click" reactions to attach diverse chemical groups for protein functionalization. However, the azide moiety is unstable in physiological conditions and is reduced to para-amino-phenylalanine (pAF). Azide reduction decreases the yield of pAzF residues in proteins to 50%-60% per azide and limits protein functionalization by click reactions. Here, we describe the use of a pH-tunable diazotransfer reaction that converts pAF to pAzF at >95% efficiency in proteins. The method selectively restores pAzF at multiple sites per protein without introducing off-target modifications. This work addresses a key limitation in the production of pAzF-containing proteins by restoring azides for multi-site functionalization with diverse chemical moieties, setting the stage for the production of genetically encoded biomaterials with broad applications in biotherapeutics, materials science, and biotechnology.

Patterns of Care Partner Communication for Persons Living with Dementia in the Emergency Department.

Introduction: Nearly half of all persons living with dementia (PLwD) will visit the emergency department (ED) in any given year and ED visits by PLwD are associated with short-term adverse outcomes. Care partner engagement is critical in the care of PLwD, but little is known about their patterns of communication with ED clinicians. Methods: We performed a retrospective electronic health record (EHR) review of a random sampling of patients ≥ 65 years with a historical diagnosis code of dementia who visited an ED within a large regional health network between 1/2014 and 1/2022. ED notes within the EHRs were coded for documentation of care partner communication and presence of a care partner in the ED. Logistic regression was used to identify patient characteristics associated with the composite outcome of either care partner communication or care partner presence in the ED. Results: A total of 460 patients were included. The median age was 83.0 years, 59.3% were female, 11.3% were Black, and 7.6% Hispanic. A care partner was documented in the ED for 22.4% of the visits and care partner communication documented for 43.9% of visits. 54.8% of patients had no documentation of care partner communication nor evidence of a care partner at the bedside. In multivariate logistic regression, increasing age (OR, (95% CI): 1.06 (1.04-1.09)), altered mental status (OR: 2.26 (1.01-5.05)), and weakness (OR: 3.38 (1.49-7.65)) significantly increased the probability of having care partner communication documented or a care partner at the bedside. Conclusion: More than half of PLwD in our sample did not have clinician documentation of communication with a care partner or a care partner in the ED. Further studies are needed to use these insights to improve communication with care partners of PLwD in the ED.

Analysis of Health Trajectories Leading to Adverse Opioid-Related Events.

Identifying patient risk factors leading to adverse opioid-related events (AOEs) may enable targeted risk-based interventions, uncover potential causal mechanisms, and enhance prognosis. In this article, we aim to discover patient diagnosis, procedure, and medication event trajectories associated with AOEs using large-scale data mining methods. The individual temporally preceding factors associated with the highest relative risk (RR) for AOEs were opioid withdrawal therapy agents, toxic encephalopathy, problems related to housing and economic circumstances, and unspecified viral hepatitis, with RR of 33.4, 26.1, 19.9, and 18.7, respectively. Patient cohorts with a socioeconomic or mental health code had a larger RR for over 75% of all identified trajectories compared to the average population. By analyzing health trajectories leading to AOEs, we discover novel, temporally-connected combinations of diagnoses and health service events that significantly increase risk of AOEs, including natural histories marked by socioeconomic and mental health diagnoses.

Risk factor identification and predictive models for central line requirements for patients on vasopressors.

Vasopressors are ubiquitous in intensive care units. While central venous catheters are the preferred route of infusion, recent evidence suggests peripheral administration may be safe for short, single-agent courses. Here, we identify risk factors and develop a predictive model for patient central venous catheter requirement using the Medical Information Mart for Intensive Care, a single-centre dataset of patients admitted to an intensive care unit between 2008 and 2019. Using prior literature, a composite endpoint of prolonged single-agent courses (>24 hours) or multi-agent courses of any duration was used to identify likely central venous catheter requirement. From a cohort of 69,619 intensive care unit stays, there were 17,053 vasopressor courses involving one or more vasopressors that met study inclusion criteria. In total, 3807 (22.3%) vasopressor courses involved a single vasopressor for less than six hours, 7952 (46.6%) courses for less than 24 hours and 5757 (33.8%) involved multiple vasopressors of any duration. Of these, 3047 (80.0%) less than six-hour and 6423 (80.8%) less than 24-hour single vasopressor courses used a central venous catheter. Logistic regression models identified associations between the composite endpoint and intubation (odds ratio (OR) 2.36, 95% confidence intervals (CI) 2.16 to 2.58), cardiac diagnosis (OR 0.72, CI 0.65 to 0.80), renal impairment (OR 1.61, CI 1.50 to 1.74), older age (OR 1.002, Cl 1.000 to 1.005) and vital signs in the hour before initiation (heart rate, OR 1.006, CI 1.003 to 1.009; oxygen saturation, OR 0.996, CI 0.993 to 0.999). A logistic regression model predicting the composite endpoint had an area under the receiver operating characteristic curve (standard deviation) of 0.747 (0.013) and an accuracy of 0.691 (0.012). This retrospective study reveals a high prevalence of short vasopressor courses in intensive care unit settings, a majority of which were administered using central venous catheters. We identify several important risk factors that may help guide clinicians deciding between peripheral and central venous catheter administration, and present a predictive model that may inform future prospective trials.