Pneumonia is split into 3 diagnostic categories: community-acquired pneumonia (CAP), health care-associated pneumonia, and ventilator-associated pneumonia. This classification scheme is driven not ...only by the location of infection onset but also by the predominant associated causal microorganisms. Pneumonia is diagnosed in over 1.5 million US emergency department visits annually (1.2% of all visits), and most pneumonia diagnosed by emergency physicians is CAP.
Diagnosis of Elder Abuse in U.S. Emergency Departments Evans, Christopher S.; Hunold, Katherine M.; Rosen, Tony ...
Journal of the American Geriatrics Society (JAGS),
January 2017, Letnik:
65, Številka:
1
Journal Article
Recenzirano
Odprti dostop
Objectives
To estimate the proportion of visits to U.S. emergency departments (EDs) in which a diagnosis of elder abuse is reached using two nationally representative datasets.
Design
Retrospective ...cross‐sectional analysis.
Setting
U.S. ED visits recorded in the 2012 Nationwide Emergency Department Sample (NEDS) or the 2011 National Hospital Ambulatory Medical Care Survey (NHAMCS).
Participants
All ED visits of individuals aged 60 and older.
Measurements
The primary outcome was elder abuse defined according to International Classification of Diseases, Ninth Revision, Clinical Modification diagnosis codes. The proportion of visits with elder abuse was estimated using survey weights. Odds ratios (ORs) were calculated to identify demographic characteristics and common ED diagnoses associated with elder abuse.
Results
In 2012, NEDS contained information on 6,723,667 ED visits of older adults, representing an estimated 29,056,673 ED visits. Elder abuse was diagnosed in an estimated 3,846 visits, corresponding to a weighted diagnosis period prevalence of elder abuse in U.S. EDs of 0.013% (95% confidence interval (CI) = 0.012–0.015%). Neglect and physical abuse were the most common types diagnosed, accounting for 32.9% and 32.2% of cases, respectively. Multivariable analysis showed greater weighted odds of elder abuse diagnosis in women (odds ratio (OR) = 1.95, 95% CI = 1.68–2.26) and individuals with contusions (OR = 2.91, 95% CI = 2.36–3.57), urinary tract infection (OR = 2.21, 95% CI = 1.84–2.65), and septicemia (OR = 1.92, 95% CI = 1.44–2.55). In the 2011 NHAMCS dataset, no cases of elder abuse were recorded for the 5,965 older adult ED visits.
Conclusion
The proportion of U.S. ED visits by older adults receiving a diagnosis of elder abuse is at least two orders of magnitude lower than the estimated prevalence in the population. Efforts to improve the identification of elder abuse in EDs may be warranted.
The Institute for Healthcare Improvement's 4-Ms framework of care for older adults recommends a multidisciplinary assessment of a patient's Medications, Mentation, Mobility, and What Matters Most. ...Electronic health record (EHR) systems were developed prior to this emphasis on the 4-Ms. We sought to understand how healthcare providers across the healthcare system perceive their EHRs and to identify any current best practices and ideas for improvement regarding integration of the 4-Ms.
Anonymous survey of healthcare providers who care for older adults. The survey aimed to evaluate efficiency, error tolerance, and satisfaction (usefulness and likeability). The survey was distributed through organizational list serves that focus on the care of older adults and through social media.
Sixty-six respondents from all geographic segments of the U.S. (n = 62) and non-U.S. practices (n = 4) responded. Most (82%) were physicians. Respondents used a range of EHRs and 82% had >5 years of experience with their current EHR. Over half of respondents agreed that their EHR had easy to find contact information (56%) and advance directives. Finding a patient's prior cognitive status (26% agreement), goals of care (24%), functional status (14%), and multidisciplinary geriatric assessments (27%) was more difficult. Only 3% were satisfied with how their EHR handles geriatric syndromes. In free text responses, respondents (79%) described three areas that the EHR assists in the care of older adults: screening tied to actions or orders; advance care planning, and medication alerts or review. Common suggestions on how to improve the EHR included incorporating geriatric assessments in notes, establishing a unified place to review the 4-Ms, and creating age-specific best practice alerts.
The majority of healthcare providers were not satisfied with how their EHR handles multidisciplinary geriatric assessment and geriatric care. EHR modifications would aide in reporting, communicating, and tracking the 4-Ms in EHRs.
Release of quality data was approved by the hospital's Data Quality Review team (2/9/2024). The funders had no role in the study design, interpretation of data, and writing of the report. Appendix A ...Supplementary data Supplementary materialSupplementary data table comparing demographics, disposition, and consultations for the audited subset to the entire cohort during the months chosen for auditing.Supplementary material Appendix A Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.ajem.2024.04.030.
Sepsis is a life-threatening condition with high mortality rates and expensive treatment costs. Early prediction of sepsis improves survival in septic patients. In this paper, we report our ...top-performing method in the 2019 DII National Data Science Challenge to predict onset of sepsis 4 h before its diagnosis on electronic health records of over 100,000 unique patients in emergency departments. A long short-term memory (LSTM)-based model with event embedding and time encoding is leveraged to model clinical time series and boost prediction performance. Attention mechanism and global max pooling techniques are utilized to enable interpretation for the deep-learning model. Our model achieved an average area under the curve of 0.892 and was selected as one of the winners of the challenge for both prediction accuracy and clinical interpretability. This study paves the way for future intelligent clinical decision support, helping to deliver early, life-saving care to the bedside of septic patients.
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•We present benchmark results of sepsis-onset prediction in emergency department•An LSTM-based model captures irregular time intervals with time encodings•Our deep-learning model shows superior performance compared with existing methods•Model interpretation enables real-world clinical applications
Sepsis is the leading cause of death worldwide and has become a global epidemiological burden. Early prediction of sepsis enables early treatment and increases the likelihood of survival for septic patients. The broad adoption of electronic health records (EHRs) provides an opportunity for sepsis prediction. However, most existing prediction approaches do not consider irregular time intervals between neighboring clinical events in EHRs. Besides, many deep-learning models suffer from black-box problems and are not trusted in clinical settings. We propose a deep-learning model with time encodings, offering both high accuracy and high transparency as well as clinical interpretability. We have already made our code and its detailed documentations publicly available, enabling colleagues to apply it to their applications and eventually make clinical impacts.
Electronic health records contain valuable temporal information for sepsis prediction. However, irregular time intervals between neighboring events are typically neglected. Besides, transparency and interpretability of deep-learning models with increasing complexity and superior performance has become a barrier to the models' clinical adoption. To this end, we propose an interpretable deep-learning model that better captures time information and achieves promising performance on sepsis prediction in the emergency department.
Background
Little is known about the optimal use of shared decision making (SDM) to guide palliative and end‐of‐life decisions in the emergency department (ED).
Objective
The objective was to convene ...a working group to develop a set of research questions that, when answered, will substantially advance the ability of clinicians to use SDM to guide palliative and end‐of‐life care decisions in the ED.
Methods
Participants were identified based on expertise in emergency, palliative, or geriatrics care; policy or patient‐advocacy; and spanned physician, nursing, social work, legal, and patient perspectives. Input from the group was elicited using a time‐staggered Delphi process including three teleconferences, an open platform for asynchronous input, and an in‐person meeting to obtain a final round of input from all members and to identify and resolve or describe areas of disagreement.
Conclusion
Key research questions identified by the group related to which ED patients are likely to benefit from palliative care (PC), what interventions can most effectively promote PC in the ED, what outcomes are most appropriate to assess the impact of these interventions, what is the potential for initiating advance care planning in the ED to help patients define long‐term goals of care, and what policies influence palliative and end‐of‐life care decision making in the ED. Answers to these questions have the potential to substantially improve the quality of care for ED patients with advanced illness.
Abstract Background The Geriatric Emergency Department (ED) Guidelines recommend screening older patients for need for evaluation by geriatric medicine, physical therapy (PT), and occupational ...therapy (OT), but explicit evidence that geriatric screening changes care compared to physician gestalt is lacking. We assessed changes in multidisciplinary consultation after implementation of standardized geriatric screening in the ED. Methods Retrospective single‐site observational cohort of older adult ED patients from 2019 to 2023 with three time periods: (1) preimplementation, (2) implementation of geriatric screening, and (3) postimplementation. Geriatric, PT, and OT consultations/referrals were available during all time periods. Descriptive analysis was stratified by disposition: discharged, observation and discharged, observation and hospital admission, and hospital admission. The independent variable was completion of three geriatric screening tools by ED nurses. The dependent variable was consultation and/or referral to geriatrics, PT, and OT. Secondary outcomes were disposition, ED revisits, and 30‐day rehospitalizations. Results There were 57,775 qualifying ED visits of patients age ≥ 65 years during the time periods: implementation increased geriatric screening from 0.5% to 63.2%; postimplementation, discharge patients who received screening had more consultations/referrals to geriatrics (1.5% vs. 0.4%), PT (7.9% vs. 1.9%), and OT (6.5% vs. 1.2%) compared to unscreened patients. Patients observed and then discharged had more consultations/referrals to geriatrics (15.1% vs. 11.3%), PT (74.1% vs. 64.5%), and OT (65.7% vs. 56.5%). Admitted patients had no change in consultation rates. Geriatric screening was not associated with a change in 7‐day ED revisits for discharged patients but was associated with decreased revisits for patients discharged from observation (11.6% vs. 42.9%, p < 0.001). Conclusion Geriatric screening was associated with increased consultations/referrals to geriatrics, PT, and OT in the ED and ED observation unit. This suggests that geriatric screening changes ED care for older adults.
Aims
The aim of this study was to describe the 1‐year direct and indirect transition probabilities to premature discontinuation of statin therapy after concurrently initiating statins and ...CYP3A4‐inhibitor drugs.
Methods
A retrospective new‐user cohort study design was used to identify (N = 160 828) patients who concurrently initiated CYP3A4 inhibitors (diltiazem, ketoconazole, clarithromycin, others) and CYP3A4‐metabolized statins (statin DDI exposed, n = 104 774) vs. other statins (unexposed to statin DDI, n = 56 054) from the MarketScan commercial claims database (2012–2017). The statin DDI exposed and unexposed groups were matched (2:1) through propensity score matching techniques. We applied a multistate transition model to compare the 1‐year transition probabilities involving four distinct states (start, adverse drug events ADEs, discontinuation of CYP3A4‐inhibitor drugs, and discontinuation of statin therapy) between those exposed to statin DDIs vs. those unexposed. Statistically significant differences were assessed by comparing the 95% confidence intervals (CIs) of probabilities.
Results
After concurrently starting stains and CYP3A, patients exposed to statin DDIs, vs. unexposed, were significantly less likely to discontinue statin therapy (71.4% 95% CI: 71.1, 71.6 vs. 73.3% 95% CI: 72.9, 73.6) but more likely to experience an ADE (3.4% 95% CI: 3.3, 3.5 vs. 3.2% 95% CI: 3.1, 3.3) and discontinue with CYP3A4‐inhibitor therapy (21.0% 95% CI: 20.8, 21.3 vs. 19.5% 95% CI: 19.2, 19.8). ADEs did not change these associations because those exposed to statin DDIs, vs. unexposed, were still less likely to discontinue statin therapy but more likely to discontinue CYP3A4‐inhibitor therapy after experiencing an ADE.
Conclusion
We did not observe any meaningful clinical differences in the probability of premature statin discontinuation between statin users exposed to statin DDIs and those unexposed.
Objectives
To determine if nonspecific symptoms and fever affect the posttest probability of acute bacterial infection in older patients in the emergency department (ED).
Design
Preplanned, secondary ...analysis of a prospective observational study.
Setting
Tertiary care, academic ED.
Participants
A total of 424 patients in the ED, 65 years or older, including all chief complaints.
Measurements
We identified presence of altered mental status, malaise/lethargy, and fever, as reported by the patient, as documented in the chart, or both. Bacterial infection was adjudicated by agreement among two or more of three expert reviewers. Odds ratios were calculated using univariable logistic regression. Positive and negative likelihood ratios (PLR and NLR, respectively) were used to determine each symptom's effect on posttest probability of infection.
Results
Of 424 subjects, 77 (18%) had bacterial infection. Accounting for different reporting methods, presence of altered mental status (PLR range, 1.40‐2.53) or malaise/lethargy (PLR range, 1.25‐1.34) only slightly increased posttest probability of infection. Their absence did not assist with ruling out infection (NLR, greater than 0.50 for both). Fever of 38°C or higher either before or during the ED visit had moderate to large increases in probability of infection (PLR, 5.15‐18.10), with initial fever in the ED perfectly predictive, but absence of fever did not rule out infection (NLR, 0.79‐0.92). Results were similar when analyzing lower respiratory, gastrointestinal, and urinary tract infections (UTIs) individually. Of older adults diagnosed as having UTIs, 47% did not complain of UTI symptoms.
Conclusions
The presence of either altered mental status or malaise/lethargy does not substantially increase the probability of bacterial infection in older adults in the ED and should not be used alone to indicate infection in this population. Fever of 38°C or higher is associated with increased probability of infection. J Am Geriatr Soc 67:484–492, 2019.
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