Study objective: We identify predictors of 30-day serious events after syncope in older adults. Methods:We reviewed the medical records of older adults (age Ն60 years) who presented with syncope or near syncope to one of 3 emergency departments (EDs) between 2002 and 2005. Our primary outcome was occurrence of a predefined serious event within 30 days after ED evaluation. We used multivariable logistic regression to identify predictors of 30-day serious events.Results: Of 3,727 potentially eligible patients, 2,871 (77%) met all eligibility criteria. We excluded an additional 287 patients who received a diagnosis of a serious clinical condition while in the ED. In the final study cohort (nϭ2,584), we identified 173 (7%) patients who experienced a 30-day serious event. High-risk predictors included age greater than 90 years, male sex, history of an arrhythmia, triage systolic blood pressure greater than 160 mm Hg, abnormal ECG result, and abnormal troponin I level. A low-risk predictor was a complaint of near syncope rather than syncope. A risk score, generated by summing high-risk predictors and subtracting the low-risk predictor, can stratify patients into low-(event rate 2.5%; 95% confidence interval [CI] 1.4% to 3.6%), intermediate-(event rate 6.3%; 95% CI 5.1% to 7.5%), and high-risk (event rate 20%; 95% CI 15% to 25%) groups. Conclusion:We identified predictors of 30-day serious events after syncope in adults aged 60 years and greater. A simple score was able to stratify these patients into distinct risk groups and, if externally validated, might have the potential to aid ED decisionmaking.
Objective Hospitalizations that occur shortly after emergency department (ED) discharge may reveal opportunities to improve ED or follow-up care. There currently is limited, population-level information about such events. We identified hospital and visit-level predictors of bounce-back admissions, defined as 7-day unscheduled hospital admissions after ED discharge. Methods Using the California Office of Statewide Health Planning and Development (OSHPD) files, we conducted a retrospective cohort analysis of adult (age≥18 years) ED visits resulting in discharge in 2007. Candidate predictors included index hospital structural characteristics such as ownership, teaching affiliation, trauma status, and index ED size; along with index visit patient characteristics of demographic information, day of service, against medical advice or eloped disposition, insurance, and ED primary discharge diagnosis. We fit a multivariable, hierarchical logistic regression to account for clustering of ED visits by hospitals. Results The study cohort contained a total of 5,035,833 visits to 288 facilities in 2007. Bounce-back admission within 7 days occurred in 130,526 (2.6%) visits and was associated with Medicaid (OR 1.42, 95% CI 1.40–1.45) or Medicare insurance (OR 1.53, 95% CI1.50–1.55) and a disposition of leaving against medical advice (AMA) or before the evaluation was complete (OR 1.9, 95% CI 1.89–2.0). The three most common age-adjusted index ED discharge diagnoses associated with a bounce-back admission were chronic renal disease, not end stage (OR 3.3, 95% CI 2.8–3.8), end stage renal disease (OR 2.9, 95% CI 2.4–3.6), and congestive heart failure (OR 2.5, 95% CI 2.3–2.6). Hospital characteristics associated with a higher bounce–back admission rate were for-profit status (OR 1.2, 95% CI 1.1–1.3) and teaching affiliation (OR 1.2, 95% CI 1.0–1.3). Conclusion We found 2.6% of discharged patients from California EDs to have a bounce-back admission within 7 days. We identified vulnerable populations, such as the very old and the use of Medicaid Insurance, and chronic or end stage renal disease as being especially at risk. Our findings suggest that quality improvement efforts focus on high-risk individuals and that the disposition plan of patients consider vulnerable populations.
Objective The emergency department (ED) is an inherently high-risk setting. Early death after an ED evaluation is a rare and devastating outcome which the understanding of can potentially help improve patient care and outcomes. Using administrative data from an integrated health system, we describe characteristics and predictors of patients who experience 7-day death after ED discharge. Methods Administrative data from 12 hospitals were used to identify death after discharge in adults age 18 or older within 7 days of ED presentation from 1/1/07 to 12/31/08. Patients who were non members of the health system, in hospice care, or seen at out of network EDs were excluded. Predictors of 7-day post-discharge death were identified using multivariable logistic regression. Results The study cohort contained a total of 475,829 members with 728,312 discharges from Kaiser Permanente Southern California (KPSC) EDs in 2007 and 2008. Death within 7 days of discharge occurred in 357 cases (0.05%). Increasing age, male gender, and number of pre-existing co-morbidities were associated with increased risk of death. The top 3 primary discharge diagnoses predictive of 7-day death after discharge include non-infectious lung disease (OR 7.1, 95% CI 2.9-17.4), renal disease (OR 5.6, 95% CI 2.2-14.2), and ischemic heart disease (OR 3.8, 95%CI 1.0-13.6). Conclusions Our study suggests that 50 in 100,000 patients in the U.S. die within 7-days after discharge from an emergency department. Our study is the first to identify potentially “high risk” discharge diagnoses in patients who suffer a short-term death after discharge.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.