Predictors of Admission in Adult Unscheduled Return Visits to the Emergency Department

Machine learning models for predicting unscheduled return visits of patients with abdominal pain at emergency department and validation during COVID-19 pandemic: A retrospective cohort study

Machine learning (ML) models for predicting 72-hour unscheduled return visits (URVs) for patients with abdominal pain in the emergency department (ED) were developed in a previous study. This study refined the data to adjust previous prediction models and evaluated the model performance in future data validation during the COVID-19 era. We aimed to evaluate the practicality of the ML models and compare the URVs before and during the COVID-19 pandemic. We used electronic health records from Chang Gung Memorial Hospital from 2018 to 2019 as a training dataset, and various machine learning models, including logistic regression (LR), random forest (RF), extreme gradient boosting (XGB), and voting classifier (VC) were developed and subsequently used to validate against the 2020 to 2021 data. The models highlighted several determinants for 72-hour URVs, including patient age, prior ER visits, specific vital signs, and medical interventions. The LR, XGB, and VC models exhibited the same AUC of 0.71 in the testing set, whereas the VC model displayed a higher F1 score (0.21). The XGB model demonstrated the highest specificity (0.99) and precision (0.64) but the lowest sensitivity (0.01). Among these models, the VC model showed the most favorable, balanced, and comprehensive performance. Despite the promising results, the study illuminated challenges in predictive modeling, such as the unforeseen influences of global events, such as the COVID-19 pandemic. These findings not only highlight the significant potential of machine learning in augmenting emergency care but also underline the importance of iterative refinement in response to changing real-world conditions.

Machine learning models for predicting unscheduled return visits to an emergency department: a scoping review

BACKGROUND

Unscheduled return visits (URVs) to emergency departments (EDs) are used to assess the quality of care in EDs. Machine learning (ML) models can incorporate a wide range of complex predictors to identify high-risk patients and reduce errors to save time and cost. However, the accuracy and practicality of such models are questionable. This review compares the predictive power of multiple ML models and examines the effects of multiple research factors on these models' performance in predicting URVs to EDs.

METHODS

We conducted the present scoping review by searching eight databases for data from 2010 to 2023. The criteria focused on eligible articles that used ML to predict ED return visits. The primary outcome was the predictive performances of the ML models, and results were analyzed on the basis of intervals of return visits, patient population, and research scale.

RESULTS

A total of 582 articles were identified through the database search, with 14 articles selected for detailed analysis. Logistic regression was the most widely used method; however, eXtreme Gradient Boosting generally exhibited superior performance. Variations in visit interval, target group, and research scale did not significantly affect the predictive power of the models.

CONCLUSION

This is the first study to summarize the use of ML for predicting URVs in ED patients. The development of practical ML prediction models for ED URVs is feasible, but improving the accuracy of predicting ED URVs to beyond 0.75 remains a challenge. Including multiple data sources and dimensions is key for enabling ML models to achieve high accuracy; however, such inclusion could be challenging within a limited timeframe. The application of ML models for predicting ED URVs may improve patient safety and reduce medical costs by decreasing the frequency of URVs. Further research is necessary to explore the real-world efficacy of ML models.

The risk factors of the 72-h unscheduled return visit admission to emergency department in adults below 50 years old

BACKGROUND

An unscheduled return visit (URV) to the emergency department (ED) within 72-h is an indicator of ED performance. An unscheduled return revisit (URV) within 72-h was used to monitor adverse events and medical errors in a hospital quality improvement program. The study explores the potential factors that contribute to URV to the ED within 72-h and the unscheduled return revisit admission (URVA) in adults below 50 years old.

METHODS

The case-control study enrolled 9483 URV patients during 2015-2020 in National Cheng-Kung University Hospital. URVA and URV non-admission (URVNA) patients were analyzed. The Gini impurity index was calculated by decision tree (DT) to split the variables capable of partitioning the groups into URVA and URVNA. Logistic regression is applied to calculate the odds ratio (OR) of candidate variables. The α level was set at 0.05.

RESULTS

Among patients under the age of 50, the percentage of females in URVNA was 55.05%, while in URVA it was 53.25%. Furthermore, the average age of URVA patients was 38.20 ± 8.10, which is higher than the average age of 35.19 ± 8.65 observed in URVNA. The Charlson Comorbidity Index (CCI) of the URVA patients (1.59 ± 1.00) was significantly higher than that of the URVNA patients (1.22 ± 0.64). The diastolic blood pressure (DBP) of the URVA patients was 85.29 ± 16.22, which was lower than that of the URVNA (82.89 ± 17.29). Severe triage of URVA patients is 21.1%, which is higher than the 9.7% of URVNA patients. The decision tree suggests that the factors associated with URVA are "severe triage," "CCI higher than 2," "DBP less than 86.5 mmHg," and "age older than 34 years". These risk factors were verified by logistic regression and the OR of CCI was 2.42 (1.50-3.90), the OR of age was 1.84 (1.50-2.27), the OR of DBP less than 86.5 was 0.71 (0.58-0.86), and the OR of severe triage was 2.35 (1.83-3.03).

CONCLUSIONS

The results provide physicians with a reference for discharging patients and could help ED physicians reduce the cognitive burden associated with the diagnostic errors and stress.

Prehospital prediction of hospital admission for emergent acuity patients transported by paramedics: A population-based cohort study using machine learning

INTRODUCTION

The closest emergency department (ED) may not always be the optimal hospital for certain stable high acuity patients if further distanced ED's can provide specialized care or are less overcrowded. Machine learning (ML) predictions may support paramedic decision-making to transport a subgroup of emergent patients to a more suitable, albeit more distanced, ED if hospital admission is unlikely. We examined whether characteristics known to paramedics in the prehospital setting were predictive of hospital admission in emergent acuity patients.

MATERIALS AND METHODS

We conducted a population-level cohort study using four ML algorithms to analyze ED visits of the National Ambulatory Care Reporting System from January 1, 2018 to December 31, 2019 in Ontario, Canada. We included all adult patients (≥18 years) transported to the ED by paramedics with an emergent Canadian Triage Acuity Scale score. We included eight characteristic classes as model predictors that are recorded at ED triage. All ML algorithms were trained and assessed using 10-fold cross-validation to predict hospital admission from the ED. Predictive model performance was determined using the area under curve (AUC) with 95% confidence intervals and probabilistic accuracy using the Brier Scaled score. Variable importance scores were computed to determine the top 10 predictors of hospital admission.

RESULTS

All machine learning algorithms demonstrated acceptable accuracy in predicting hospital admission (AUC 0.77-0.78, Brier Scaled 0.22-0.24). The characteristics most predictive of admission were age between 65 to 105 years, referral source from a residential care facility, presenting with a respiratory complaint, and receiving home care.

DISCUSSION

Hospital admission was accurately predicted based on patient characteristics known prehospital to paramedics prior to arrival. Our results support consideration of policy modification to permit certain emergent acuity patients to be transported to a further distanced ED. Additionally, this study demonstrates the utility of ML in paramedic and prehospital research.

Predictors of 72-h unscheduled return visits with admission in patients presenting to the emergency department with abdominal pain

BACKGROUND

Unscheduled return visits (URVs) to the emergency department (ED) constitute a crucial indicator of patient care quality.

OBJECTIVE

We aimed to analyze the clinical characteristics of patients who visited the ED with abdominal pain and to identify the risk of URVs with admission (URVAs) from URVs without admission (URVNAs).

METHODS

This retrospective study included adult patients who visited the ED of Taipei Medical University Hospital because of abdominal pain and revisited in 72 h over a 5-year period (January 1, 2014, to December 31, 2018). Multivariable logistic regression analysis was employed to identify risk factors for URVAs and receiver operating characteristic (ROC) curve analysis was performed to determine the efficacy of variables predicting URVAs and the optimal cut-off points for the variables. In addition, a classification and regression tree (CART)-based scoring system was used for predicting risk of URVA.

RESULTS

Of 702 eligible patients with URVs related to abdominal pain, 249 had URVAs (35.5%). In multivariable analysis, risk factors for URVAs during the index visit included execution of laboratory tests (yes vs no: adjusted odds ratio [AOR], 4.32; 95% CI 2.99-6.23), older age (≥ 40 vs < 40 years: AOR, 2.10; 95% CI 1.10-1.34), Level 1-2 triage scores (Levels 1-2 vs Levels 3-5: AOR, 2.30; 95% CI 1.26-4.19), and use of ≥ 2 analgesics (≥ 2 vs < 2: AOR, 2.90; 95% CI 1.58-5.30). ROC curve analysis results revealed the combination of these 4 above variables resulted in acceptable performance (area under curve: 0.716). The above 4 variables were used in the CART model to evaluate URVA propensity.

CONCLUSIONS

Elder patients with abdominal pain who needed laboratory workup, had Level 1-2 triage scores, and received ≥ 2 doses of analgesics during their index visits to the ED had higher risk of URVAs.

Upstreamist leaders: how risk factors for unscheduled return visits (URV) to the emergency department can inform integrated healthcare

PURPOSE

This paper aims to report on research undertaken in an National Health Service (NHS) emergency department in the north of England, UK, to identify which patients, with which clinical conditions are returning to the emergency department with an unscheduled return visit (URV) within seven days. This paper analyses the data in relation to the newly introduced Integrated Care Boards (ICBs). The continued upward increase in demand for emergency care services requires a new type of "upstreamist", health system leader from the emergency department, who can report on URV data to influence the development of integrated care services to reduce further demand on the emergency department.

DESIGN/METHODOLOGY/APPROACH

Patients were identified through the emergency department symphony data base and included patients with at least one return visit to emergency department (ED) within seven days. A sample of 1,000 index visits between 1 January 2019-31 October 2019 was chosen by simple random sampling technique through Excel. Out of 1,000, only 761 entries had complete data in all variables. A statistical analysis was undertaken using Poisson regression using NCSS statistical software. A review of the literature on integrated health care and its relationship with health systems leadership was undertaken to conceptualise a new type of "upstreamist" system leadership to advance the integration of health care.

FINDINGS

Out of all 83 variables regressed with statistical analysis, only 12 variables were statistically significant on multi-variable regression. The most statistically important factor were patients presenting with gynaecological disorders, whose relative rate ratio (RR) for early-URV was 43% holding the other variables constant. Eye problems were also statistically highly significant (RR = 41%) however, clinically both accounted for just 1% and 2% of the URV, respectively. The URV data combined with "upstreamist" system leadership from the ED is required as a critical mechanism to identify gaps and inform a rationale for integrated care models to lessen further demand on emergency services in the ED.

RESEARCH LIMITATIONS/IMPLICATIONS

At a time of significant pressure for emergency departments, there needs to be a move towards more collaborative health system leadership with support from statistical analyses of the URV rate, which will continue to provide critical information to influence the development of integrated health and care services. This study identifies areas for further research, particularly for mixed methods studies to ascertain why patients with specific complaints return to the emergency department and if alternative pathways could be developed. The success of the Esther model in Sweden gives hope that patient-centred service development could create meaningful integrated health and care services.

PRACTICAL IMPLICATIONS

This research was a large-scale quantitative study drawing upon data from one hospital in the UK to identify risk factors for URV. This quality metric can generate important data to inform the development of integrated health and care services. Further research is required to review URV data for the whole of the NHS and with the new Integrated Health and Care Boards, there is a new impetus to push for this metric to provide robust data to prioritise the need to develop integrated services where there are gaps.

ORIGINALITY/VALUE

To the best of the authors' knowledge, this is the first large-scale study of its kind to generate whole hospital data on risk factors for URVs to the emergency department. The URV is an important global quality metric and will continue to generate important data on those patients with specific complaints who return back to the emergency department. This is a critical time for the NHS and at the same time an important opportunity to develop "Esther" patient-centred approaches in the design of integrated health and care services.

High-risk Return Visits to United States Emergency Departments, 2010–2018

Introduction: Although factors related to a return visit to the emergency department (ED) have been reported, only a few studies have examined “high-risk” ED revisits with serious adverse outcomes. In this study we aimed to describe the incidence and trend of high-risk ED revisits in United States EDs and to investigate factors associated with these revisits. Methods: We obtained data from the National Hospital Ambulatory Medical Care Survey (NHAMCS), 2010–2018. Adult ED revisits within 72 hours of a previous discharge were identified using a mark on the patient record form. We defined high-risk revisits as revisits with serious adverse outcomes, including intensive care unit admissions, emergency surgery, cardiac catheterization, or cardiopulmonary resuscitation (CPR) during the return visit. We performed analyses using descriptive statistics and multivariable logistic regression, accounting for NHAMCS’s complex survey design. Results: Over the nine-year study period, there were an estimated 37,700,000 revisits, and the proportion of revisits in the entire ED population decreased slightly from 5.1% in 2010 to 4.5% in 2018 (P for trend = 0.02). By contrast, there were an estimated 827,000 high-risk ED revisits, and the proportion of high-risk revisits in the entire ED population remained stable at approximately 0.1%. The mean age of these high-risk revisit patients was 57 years, and 43% were men. Approximately 6% of the patients were intubated, and 13% received CPR. Most of them were hospitalized, and 2% died in the ED. Multivariable analysis showed that older age (65+ years), Hispanic ethnicity, daytime visits, and arrival by ambulance during the revisit were independent predictors of high-risk revisits. Conclusion: High-risk revisits accounted for a relatively small fraction (0.1%) of ED visits. Over the period of the NHAMCS survey between 2010-2018, this fraction remained stable. We identified factors during the return visit that could be used to label high-risk revisits for timely intervention.

Comparison of outcomes in emergency department revisiting patients before and after coronavirus disease 2019 epidemic

BACKGROUND AND IMPORTANCE

The outbreak of COVID-19 challenged the global health system and specifically impacted the emergency departments (EDs). Studying the quality indicators of ED care under COVID-19 has been a necessary task, and ED revisits have been used as an indicator to monitor ED performance.

OBJECTIVES

The study investigated whether discrepancies existed among ED revisiting cases before and after COVID-19 and whether the COVID-19 epidemic was a predictor of poor outcomes of ED revisits.

DESIGN

Retrospective study.

SETTINGS AND PARTICIPANTS

We used electronic health records data from a tertiary medical center. Data of patients with 72-h ED revisit after the COVID-19 epidemic were collected from February 2020 to June 2020 and compared with those of patients before COVID-19, from February 2019 to June 2019.

OUTCOME MEASURES AND ANALYSIS

The investigated outcomes included hospital admission, ICU admission, out-of-hospital cardiac arrest, and subsequent inhospital mortality. Univariate and multivariate logistic regression models were used to identify independent predictors of 72-h ED revisit outcomes.

MAIN RESULTS

In total, 1786 patients were enrolled in our study - 765 in the COVID group and 1021 in the non-COVID group. Compared with the non-COVID group, patients in the COVID group were younger (53.9 vs. 56.1 years old; P = 0.002) and more often female (66.1% vs. 47.3%; P < 0.001) and had less escalation of triage level (11.6% vs. 15.0%; P = 0.041). The hospital admission and inhospital mortality rates in the COVID and non-COVID groups were 33.9% vs. 32.0% and 2.7% vs. 1.5%, respectively. In the logistic regression model, the COVID-19 period was significantly associated with inhospital mortality (adjusted odds ratio, 2.289; 95% confidence interval, 1.059-4.948; P = 0.035).

CONCLUSION

Patients with 72-h ED revisits showed distinct demographic and clinical patterns before and after the COVID-19 epidemic; the COVID-19 period was an independent predictor of increased inhospital mortality.

Development of a Novel Care Rehabilitation Pathway for Post-COVID Conditions (Long COVID) in a Provincial Health System in Alberta, Canada

OBJECTIVE

The purpose of this study was to describe the development and composition of a codesigned, multidisciplinary, integrated, systematic rehabilitation framework for post-COVID conditions (PCC) that spans the care continuum to streamline and standardize rehabilitation services to support persons with PCC in Alberta, Canada.

METHODS

A collaborative, consensus-based approach was used involving 2 iterative provincial taskforces in a Canadian provincial health system. The first taskforce (59 multidisciplinary stakeholders) sought to clarify the requisite facets of a sustainable, provincially coordinated rehabilitation approach for post-COVID rehabilitation needs based on available research evidence. The second taskforce (129 multidisciplinary stakeholders) translated that strategy and criteria into an operational framework for provincial implementation. Both taskforces sought to align with operational realities of the provincial health system.

RESULTS

The summation of this collaborative consensus approach resulted in the Provincial Post COVID-19 Rehabilitation Response Framework (PCRF). The PCRF includes 3 care pathways across the care continuum specifically targeting in-hospital care, continuing care, and community-based care with 3 key elements: (1) the use of specific symptom screening and assessment tools to systematically identify PCC symptoms and functional impairments, (2) pathways to determine patients' rehabilitation trajectory and guide their transition between care settings, and (3) self-management and education resources for patients and providers.

CONCLUSION

The PCRF aligns with international mandates for novel, codesigned, multidisciplinary approaches to systematically address PCC and its myriad manifestations across the care continuum. The PCRF allows for local adaptation and highlights equity considerations, allowing for further spread and scale provincially, nationally, and internationally.

IMPACT

The PCRF is a framework for health systems to ensure consistent identification, assessment, and management of the rehabilitation needs of postacute and chronic PCC. Rehabilitation providers and health systems can build from the PCRF for their local communities to reduce unmet needs and advance the standardization of access to rehabilitation services for persons with PCC.

Intravenous antibiotics at the index emergency department visit as an independent risk factor for hospital admission at the return visit within 72 hours

Introduction

Although infection was the most common symptom in patients returning to the ED, whether intravenous antibiotic administration at the index visit could serve as an indicator of patients with infectious diseases at high risk for hospital admission after returning to the ED within a short period of time remains unclear. The study aimed to investigate the potential risk factors for hospital admission in patients returning to the ED within 72 hours with a final diagnosis of infectious diseases.

Material and methods

This retrospective cohort study analyzed return visits to the ED from January to December 2019. Adult patients aged >20 years who had a return visit to the ED within 72 hours with an infectious disease were included herein. In total, 715 eligible patients were classified into the intravenous antibiotics and non-intravenous antibiotics group (reference group). The outcome studied was hospital admission to general ward and intensive care unit (ICU) at the return visits.

Results

Patients receiving intravenous antibiotics at index visits had significantly higher risk—approximately two times—for hospital admission at the return visits than those did not (adjusted odds ratio = 2.47, 95% CI = 1.34–4.57, p = 0.004). For every 10 years increase in age, the likelihood for hospital admission increased by 38%. Other factors included abnormal respiratory rate and high C-reactive protein levels.

Conclusions

Intravenous antibiotic administration at the index visit was an independent risk factor for hospital admission at return visits in patients with an infection disease. Physicians should consider carefully before discharging patients receiving intravenous antibiotics.

A Machine Learning Model for Predicting Unscheduled 72 h Return Visits to the Emergency Department by Patients with Abdominal Pain

Seventy-two-hour unscheduled return visits (URVs) by emergency department patients are a key clinical index for evaluating the quality of care in emergency departments (EDs). This study aimed to develop a machine learning model to predict 72 h URVs for ED patients with abdominal pain. Electronic health records data were collected from the Chang Gung Research Database (CGRD) for 25,151 ED visits by patients with abdominal pain and a total of 617 features were used for analysis. We used supervised machine learning models, namely logistic regression (LR), support vector machine (SVM), random forest (RF), extreme gradient boosting (XGB), and voting classifier (VC), to predict URVs. The VC model achieved more favorable overall performance than other models (AUROC: 0.74; 95% confidence interval (CI), 0.69–0.76; sensitivity, 0.39; specificity, 0.89; F1 score, 0.25). The reduced VC model achieved comparable performance (AUROC: 0.72; 95% CI, 0.69–0.74) to the full models using all clinical features. The VC model exhibited the most favorable performance in predicting 72 h URVs for patients with abdominal pain, both for all-features and reduced-features models. Application of the VC model in the clinical setting after validation may help physicians to make accurate decisions and decrease URVs.

Impact of underlying malignancy on emergency department utilization and outcomes

PURPOSE

Cancer patients frequently utilize the emergency department (ED) for a variety of diagnoses both related to and unrelated to their cancer, yet ED outcomes for cancer patients are not well documented. This study sought to define risks and identify predictors for inpatient admission and hospital mortality among cancer patients presenting to the ED.

PATIENTS AND METHODS

We utilized the National Emergency Department Sample to identify patients with and without a diagnosis of cancer presenting to the ED between January 2016 and December 2018. We used multivariable mixed-effects logistic regression models to assess the influence of cancer on outcomes of hospital admission after the ED visit and hospital mortality for the whole patient cohort and individual presenting diagnoses.

RESULTS

There were 340 million weighted ED visits, of which 8.3 million (2.3%) were associated with a cancer diagnosis. Compared to non-cancer patients, patients with cancer had an increased risk of inpatient admission (64.7% vs. 14.8%; p < 0.0001) and hospital mortality (4.6% vs. 0.5%; p < 0.0001). For each of the top 15 presenting diagnoses, cancer patients had increased risks of hospitalization (odds ratio [OR] range 2.0-13.2) or death (OR range 2.1-14.4). Although our dataset does not contain reliable estimation of stage, cancer site was the most robust individual predictor associated with the risk of hospitalization or death compared to other clinical or system-related factors.

CONCLUSIONS

Cancer patients in the ED have high risks for hospital admission and death when compared to patients without cancer. Cancer patients represent a distinct population and may benefit from cancer-specific risk stratification or focused interventions to improve outcomes.

Risk Factors for Early Return Visits to the Emergency Department in Patients Presenting with Nonspecific Abdominal Pain and the Use of Computed Tomography Scan

Over a quarter of patients presenting with abdominal pain at emergency departments (EDs) are diagnosed with nonspecific abdominal pain (NSAP) at discharge. This study investigated the risk factors associated with return ED visits in Taiwanese patients with NSAP after discharge. We divided patients into two groups: the study group comprising patients with ED revisits after the index ED visit, and the control group comprising patients without revisits. During the study period, 10,341 patients discharged with the impression of NSAP after ED management. A regression analysis found that older age (OR [95%CI]: 1.007 [1.003–1.011], p = 0.004), male sex (OR [95%CI]: 1.307 [1.036–1.650], p = 0.024), and use of NSAIDs (OR [95%CI]: 1.563 [1.219–2.003], p < 0.001) and opioids (OR [95%CI]: 2.213 [1.643–2.930], p < 0.001) during the index visit were associated with increased return ED visits. Computed tomography (CT) scans (OR [95%CI]: 0.605 [0.390–0.937], p = 0.021) were associated with decreased ED returns, especially for those who were older than 60, who had an underlying disease, or who required pain control during the index ED visit.

Inpatient Outcomes Following a Return Visit to the Emergency Department: A Nationwide Cohort Study

Introduction

Emergency department (ED) revisits are traditionally used to measure potential lapses in emergency care. However, recent studies on in-hospital outcomes following ED revisits have begun to challenge this notion. We aimed to examine inpatient outcomes and resource use among patients who were hospitalized following a return visit to the ED using a national database.

Methods

This was a retrospective cohort study using the National Health Insurance Research Database in Taiwan. One-third of ED visits from 2012–2013 were randomly selected and their subsequent hospitalizations included. We analyzed the inpatient outcomes (mortality and intensive care unit [ICU] admission) and resource use (length of stay [LOS] and costs). Comparisons were made between patients who were hospitalized after a return visit to the ED and those who were hospitalized during the index ED visit.

Results

Of the 3,019,416 index ED visits, 477,326 patients (16%) were directly admitted to the hospital. Among the 2,504,972 patients who were discharged during the index ED visit, 229,059 (9.1%) returned to the ED within three days. Of them, 37,118 (16%) were hospitalized. In multivariable analyses, the inpatient mortality rates and hospital LOS were similar between the two groups. Compared with the direct-admission group, the return-admission group had a lower ICU admission rate (adjusted odds ratio, 0.78; 95% confidence interval [CI], 0.72–0.84), and lower costs (adjusted difference, −5,198 New Taiwan dollars, 95% CI, −6,224 to −4,172).

Conclusion

Patients who were hospitalized after a return visit to the ED had a lower ICU admission rate and lower costs, compared to those who were directly admitted. Our findings suggest that ED revisits do not necessarily translate to poor initial care and that subsequent inpatient outcomes should also be considered for better assessment.

Factors for return to emergency department and hospitalization in elderly urinary tract infection patients

BACKGROUND

Appropriate decision of emergency department (ED) disposition is essential for improving the outcome of elderly urinary tract infection (UTI) patients. However, studies on early return visit (ERV) to the ED in elderly UTI patients are limited. Therefore, we aimed to identify factors for ERV and hospitalization after return visit (HRV) in this population.

METHODS

Elderly patients discharged from the ED with International Classification of diseases 10th Revision codes of UTI were selected from the registry for evaluation of ED revisit in 6 urban teaching hospitals. Retrospective data were extracted from the electronic medical records and ERV and hospitalization to scheduled revisit (SRV) were compared.

RESULT

Among a total of 419 patients found in the study period, 45 were ERV patients and 24 were HRV patients. Absence of UTI-specific symptoms (odds ratio [OR] 2.789; 95% confidence interval [CI] 1.368-5.687; P = 0.005), C-reactive protein (CRP) levels >30 mg/L (OR 2.436; 95% CI 1.017-3.9; P = 0.024), and body temperature ≥ 38 °C (OR 1.992; 95% CI 1.017-3.9; P = 0.044) were independent risk factors for ERV, and absence of UTI-specific symptoms (OR 3.832; 95% CI 1.455-10.088; P = 0.007), CRP levels >30 mg/L (OR 3.224; 95% CI 1.235-8.419; P = 0.017), and systolic blood pressure ≤ 100 mmHg (OR 3.795;95% CI 1.156-12.462; P = 0.028) were independent risk factors for HRV. However, there was no significant difference in empirical antibiotic resistance in ERV and HRV patients, compared to SRV patients.

CONCLUSION

The independent risk factors of ERV and HRV should be considered for ED disposition in elderly UTI patients; the resistance to empirical antibiotics was not found to affect ERV or HRV within 3 days.

In-Hospital Outcomes in Patients Admitted to the Intensive Care Unit after a Return Visit to the Emergency Department

Background: Intensive care unit (ICU) admission following a short-term emergency department (ED) revisit has been considered a particularly undesirable outcome among return-visit patients, although their in-hospital prognosis has not been discussed. We aimed to compare clinical outcomes between adult patients admitted to the ICU after unscheduled ED revisits and those admitted during index ED visits. Method: This retrospective study was conducted at two tertiary medical centers in Taiwan from 1 January 2016 to 31 December 2017. All adult non-trauma patients admitted to the ICU directly via the ED during the study period were included and divided into two comparison groups: patients admitted to the ICU during index ED visits and those admitted to the ICU during return ED visits. The outcomes of interest included in-hospital mortality, mechanical ventilation (MV) support, profound shock, hospital length of stay (HLOS), and total medical cost. Results: Altogether, 12,075 patients with a mean (standard deviation) age of 64.6 (15.7) years were included. Among these, 5.3% were admitted to the ICU following a return ED visit within 14 days and 3.1% were admitted following a return ED visit within 7 days. After adjusting for confounding factors for multivariate regression analysis, ICU admission following an ED revisit within 14 days was not associated with an increased mortality rate (adjusted odds ratio (aOR): 1.08, 95% confidence interval (CI): 0.89 to 1.32), MV support (aOR: 1.06, 95% CI: 0.89 to 1.26), profound shock (aOR: 0.99, 95% CI: 0.84 to 1.18), prolonged HLOS (difference: 0.04 days, 95% CI: −1.02 to 1.09), and increased total medical cost (difference: USD 361, 95% CI: −303 to 1025). Similar results were observed after the regression analysis in patients that had a 7-day return visit. Conclusion: ICU admission following a return ED visit was not associated with major in-hospital outcomes including mortality, MV support, shock, increased HLOS, or medical cost. Although ICU admissions following ED revisits are considered serious adverse events, they may not indicate poor prognosis in ED practice.

Risk factors of admission in 72-h return visits to emergency department

Objective:

Return visit to emergency department (ED) is a common phenomenon and has been a clinical indicator of quality of care in ED. Most of previous articles focused on the characteristics of the patients returning within 72 h after ED discharge, while those on subsequent admission are numbered. This study's purpose is to identify risk factors for admission among 72-h return visit in the ED adult population.

Materials and Methods:

This retrospective cohort study was conducted at a medical center in Eastern Taiwan. The study period was from January 1, 2013, to December 31, 2013. We excluded patients who left against medical advice or without being seen, who was admitted or transferred at the index ED visit, whose medical records were incomplete, and whose age was below 18 years old. Significant variables were selected based on univariate analysis and later entered into multivariate logistic regression analysis to identify risk factors for 72-h return admission.

Results:

We identified 1575 eligible visits, and there were 1,119 visits entering into the final analysis. Male gender (odds ratio [OR] = 1.44), ambulance-transport at return visit (OR = 3.68), senior staff (OR = 1.52), work-up (OR = 3.03), and longer length of stay (LOS) were associated with higher risks of admission among ED 72-h return visits. Age, comorbidity, mode of transport at index visit, consultation, triage, type of illness, outpatient department visit between ED visits, and interval between index and return visits were not significantly associated with return admission.

Conclusion:

Gender, mode of transportation, staff experience, check-up, and LOS are associated with ED return admission.

Discharge in Pandemic: Suspected Covid-19 patients returning to the Emergency Department within 72 hours for admission

INTRODUCTION

Coronavirus disease 2019 (Covid-19) has led to unprecedented healthcare demand. This study seeks to characterize Emergency Department (ED) discharges suspected of Covid-19 that are admitted within 72 h.

METHODS

We abstracted all adult discharges with suspected Covid-19 from five New York City EDs between March 2nd and April 15th. Those admitted within 72 h were then compared against those who were not using descriptive and regression analysis of background and clinical characteristics.

RESULTS

Discharged ED patients returning within 72 h were more often admitted if suspected of Covid-19 (32.9% vs 12.1%, p < .0001). Of 7433 suspected Covid-19 discharges, the 139 (1.9%) admitted within 72 h were older (55.4 vs. 45.6 years, OR 1.03) and more often male (1.32) or with a history of obstructive lung disease (2.77) or diabetes (1.58) than those who were not admitted (p < .05). Additional associations included non-English preference, cancer, heart failure, hypertension, renal disease, ambulance arrival, higher triage acuity, longer ED stay or time from symptom onset, fever, tachycardia, dyspnea, gastrointestinal symptoms, x-ray abnormalities, and decreased platelets and lymphocytes (p < .05 for all). On 72-h return, 91 (65.5%) subjects required oxygen, and 7 (5.0%) required mechanical ventilation in the ED. Twenty-two (15.8%) of the study group have since died.

CONCLUSION

Several factors emerge as associated with 72-h ED return admission in subjects suspected of Covid-19. These should be considered when assessing discharge risk in clinical practice.

Influence of Overcrowding in the Emergency Department on Return Visit within 72 Hours

This study was conducted to determine whether overcrowding in the emergency department (ED) affects the occurrence of a return visit (RV) within 72 h. The crowding indicator of index visit was the average number of total patients, patients under observation, and boarding patients during the first 1 and 4 h from ED arrival time and the last 1 h before ED departure. Logistic regression analysis was conducted to determine whether each indicator affects the occurrence of RV and post-RV admission. Of the 87,360 discharged patients, 3743 (4.3%) returned to the ED within 72 h. Of the crowding indicators pertaining to total patients, the last 1 h significantly affected decrease in RV (p = 0.0046). Boarding patients were found to increase RV occurrence during the first 1 h (p = 0.0146) and 4 h (p = 0.0326). Crowding indicators that increased the likelihood of admission post-RV were total number of patients during the first 1 h (p = 0.0166) and 4 h (p = 0.0335) and evaluating patients during the first 1 h (p = 0.0059). Overcrowding in the ED increased the incidence of RV and likelihood of post-RV admission. However, overcrowding at the time of ED departure was related to reduced RV.

Frailty screening in hospitalised older adults: How does the brief Dutch National Safety Management Program perform compared to a more extensive approach?

AIMS AND OBJECTIVES

To examine the predictive properties of the brief Dutch National Safety Management Program for the screening of frail hospitalised older patients (VMS) and to compare these with the more extensive Maastricht Frailty Screening Tool for Hospitalised Patients (MFST-HP).

BACKGROUND

Screening of older patients during admission may help to detect frailty and underlying geriatric conditions. The VMS screening assesses patients on four domains (i.e. functional decline, delirium risk, fall risk and nutrition). The 15-item MFST-HP assesses patients on three domains of frailty (physical, social and psychological).

DESIGN

Retrospective cohort study.

METHODS

Data of 2,573 hospitalised patients (70+) admitted in 2013 were included, and relative risks, sensitivity and specificity and area under the receiver operating characteristic (AUC) curve of the two tools were calculated for discharge destination, readmissions and mortality. The data were derived from the patients nursing files. A STARD checklist was completed.

RESULTS

Different proportions of frail patients were identified by means of both tools: 1,369 (53.2%) based on the VMS and 414 (16.1%) based on the MFST-HP. The specificity was low for the VMS, and the sensitivity was low for the MFST-HP. The overall AUC for the VMS varied from 0.50 to 0.76 and from 0.49 to 0.69 for the MFST-HP.

CONCLUSION

The predictive properties of the VMS and the more extended MFST-HP on the screening of frailty among older hospitalised patients are poor to moderate and not very promising.

RELEVANCE TO CLINICAL PRACTICE

The VMS labels a high proportion of older patients as potentially frail, while the MFST-HP labels over 80% as nonfrail. An extended tool did not increase the predictive ability of the VMS. However, information derived from the individual items of the screening tools may help nurses in daily practice to intervene on potential geriatric risks such as delirium risk or fall risk.

Characteristics and determinants of high-risk unscheduled return visits to the emergency department

Background

High-risk unscheduled return visits (HRURVs), defined as return visits within 72 hours that require admission or die in the emergency department (ED) on representation, are a key quality metric in the ED. The objective of this study was to determine the incidence and describe the characteristics and predictors of HRURVs to the ED.

Methods

Case–control study, conducted between 1 November 2014 and 31 October 2015. Cases included all HRURVs over the age of 18 that presented to the ED. Controls were selected from patients who were discharged from the ED during the study period and did not return in the next 72 hours. Controls were matched to cases based on gender, age (±5 years) and date of presentation.

Results

Out of 38 886 ED visits during the study period, 271 are HRURVs, giving an incidence of HRURV of 0.70% (95% CI 0.62% to 0.78%). Our final analysis includes 270 HRURV cases and 270 controls, with an in-ED mortality rate of 0.7%, intensive care unit admission of 11.1% and need for surgical intervention of 22.2%. After adjusting for other factors, HRURV cases are more likely to be discharged with a diagnosis related to digestive system or infectious disease (OR 1.64, 95% CI 1.02 to 2.65 and OR 2.81, 95% CI 1.05 to 7.51, respectively). Furthermore, presentation to the ED during off-hours is a significant predictor of HRURV (OR 1.64, 95% CI 1.11 to 2.43) as is the presence of a handover during the patient visit (OR 1.68, 95% CI 1.02 to 2.75).

Conclusion

HRURV is an important key quality outcome metric that reflects a subgroup of ED patients with specific characteristics and predictors. Efforts to reduce this HRURV rate should focus on interventions targeting patients discharged with digestive system, kidney and urinary tract and infectious diseases diagnosis as well as exploring the role of handover tools in reducing HRURVs.