Predictors Of Hospital Admission Research Articles

Predicting Hospital Admissions to Reduce Crowding in the Emergency Departments

Having an increasing number of patients in the emergency department constitutes an obstacle to the admissions process and hinders the emergency department (ED)’s ability to deal with the continuously arriving demand for new admissions. In addition, forecasting is an important aid in many areas of hospital management, including elective surgery scheduling, bed management, and staff resourcing. Therefore, this paper aims to develop a precise prediction model for admissions in the Integral Healthcare System for Public Use in Catalonia. These models assist in reducing overcrowding in emergency rooms and improve the quality of care offered to patients. Data from 60 EDs were analyzed to determine the likelihood of hospital admission based on information readily available at the time of arrival in the ED. The first part of the study targeted the obtention of models with high accuracy and area under the curve (AUC), while the second part targeted the obtention of models with a sensitivity higher than 0.975 and analyzed the possible benefits that could come from the application of such models. From the 3,189,204 ED visits included in the study, 11.02% ended in admission to the hospital. The gradient boosting machine method was used to predict a binary outcome of either admission or discharge.

Development of forecast models for COVID-19 hospital admissions using anonymized and aggregated mobile network data

Reliable forecast of COVID-19 hospital admissions in near-term horizons can help enable effective resource management which is vital in reducing pressure from healthcare services. The use of mobile network data has come to attention in response to COVID-19 pandemic leveraged on their ability in capturing people social behavior. Crucially, we show that there are latent features in irreversibly anonymized and aggregated mobile network data that carry useful information in relation to the spread of SARS-CoV-2 virus. We describe development of the forecast models using such features for prediction of COVID-19 hospital admissions in near-term horizons (21 days). In a case study, we verified the approach for two hospitals in Sweden, Sahlgrenska University Hospital and Södra Älvsborgs Hospital, working closely with the experts engaged in the hospital resource planning. Importantly, the results of the forecast models were used in year 2021 by logisticians at the hospitals as one of the main inputs for their decisions regarding resource management.

Heart failure symptoms as predictors of hospital admission, readmission and all-cause mortality

Heart failure symptoms as predictors of hospital admission, readmission and all-cause mortality

Prediction models for the prediction of unplanned hospital admissions in community-dwelling older adults: A systematic review.

Identification of community-dwelling older adults at risk of unplanned hospitalizations is of importance to facilitate preventive interventions. Our objective was to review and appraise the methodological quality and predictive performance of prediction models for predicting unplanned hospitalizations in community-dwelling older adults. We searched MEDLINE, EMBASE and CINAHL from August 2013 to January 2021. Additionally, we checked references of the identified articles for the inclusion of relevant publications and added studies from two previous reviews that fulfilled the eligibility criteria. We included prospective and retrospective studies with any follow-up period that recruited adults aged 65 and over and developed a prediction model predicting unplanned hospitalizations. We included models with at least one (internal or external) validation cohort. The models had to be intended to be used in a primary care setting. Two authors independently assessed studies for inclusion and undertook data extraction following recommendations of the CHARMS checklist, while quality assessment was performed using the PROBAST tool. A total of 19 studies met the inclusion criteria. Prediction horizon ranged from 4.5 months to 4 years. Most frequently included variables were specific medical diagnoses (n = 11), previous hospital admission (n = 11), age (n = 11), and sex or gender (n = 8). Predictive performance in terms of area under the curve ranged from 0.61 to 0.78. Models developed to predict potentially preventable hospitalizations tended to have better predictive performance than models predicting hospitalizations in general. Overall, risk of bias was high, predominantly in the analysis domain. Models developed to predict preventable hospitalizations tended to have better predictive performance than models to predict all-cause hospitalizations. There is however substantial room for improvement on the reporting and analysis of studies. We recommend better adherence to the TRIPOD guidelines.

Predicting hospital admission from emergency department triage data for patients presenting with fall-related fractures.

Predict in advance the need for hospitalization of adult patients for fall-related fractures based on information available at the time of triage to help decision-making at the emergency department (ED). We developed machine learning models using routinely collected triage data at a regional hospital chain in Pennsylvania to predict admission to an inpatient unit. We considered all patients presenting to the ED for fall-related fractures. Patients who were 18 years or younger, who left the ED against medical advice, left the ED waiting room without being seen by a provider, and left the ED after initial diagnostics were excluded from the analysis. We compared models obtained using triage data (pre-model) with models developed using additional data obtained after physicians' diagnoses (post-model). Our results show good discriminatory power on predicting hospital admissions. Neural network models performed the best (AUC: pre-model = 0.938 [CI 0.920-0.956], post-model = 0.983 [0.974-0.992]). The logistic regression analysis provides additional insights into the data and the relationships between the variables. Using limited data available at the time of triage, we developed four machine learning models aimed at predicting hospitalization for patients presenting to the ED for fall-related fractures. All the four models were robust and performed well. Neural network method, however, performed the best for both pre- and post-models. Simple, parsimonious machine learning models can provide high accuracy for predicting hospital admission.

Reducing Crowding in Emergency Departments With Early Prediction of Hospital Admission of Adult Patients Using Biomarkers Collected at Triage: Retrospective Cohort Study

Background Emergency department crowding continues to threaten patient safety and cause poor patient outcomes. Prior models designed to predict hospital admission have had biases. Predictive models that successfully estimate the probability of patient hospital admission would be useful in reducing or preventing emergency department “boarding” and hospital “exit block” and would reduce emergency department crowding by initiating earlier hospital admission and avoiding protracted bed procurement processes. Objective To develop a model to predict imminent adult patient hospital admission from the emergency department early in the patient visit by utilizing existing clinical descriptors (ie, patient biomarkers) that are routinely collected at triage and captured in the hospital’s electronic medical records. Biomarkers are advantageous for modeling due to their early and routine collection at triage; instantaneous availability; standardized definition, measurement, and interpretation; and their freedom from the confines of patient histories (ie, they are not affected by inaccurate patient reports on medical history, unavailable reports, or delayed report retrieval). Methods This retrospective cohort study evaluated 1 year of consecutive data events among adult patients admitted to the emergency department and developed an algorithm that predicted which patients would require imminent hospital admission. Eight predictor variables were evaluated for their roles in the outcome of the patient emergency department visit. Logistic regression was used to model the study data. Results The 8-predictor model included the following biomarkers: age, systolic blood pressure, diastolic blood pressure, heart rate, respiration rate, temperature, gender, and acuity level. The model used these biomarkers to identify emergency department patients who required hospital admission. Our model performed well, with good agreement between observed and predicted admissions, indicating a well-fitting and well-calibrated model that showed good ability to discriminate between patients who would and would not be admitted. Conclusions This prediction model based on primary data identified emergency department patients with an increased risk of hospital admission. This actionable information can be used to improve patient care and hospital operations, especially by reducing emergency department crowding by looking ahead to predict which patients are likely to be admitted following triage, thereby providing needed information to initiate the complex admission and bed assignment processes much earlier in the care continuum.

Using the alcohol use disorders identification test to predict hospital admission for alcohol-related conditions in the Danish general population: a record-linkage study.

Most studies validating the alcohol use disorders identification test (AUDIT) have either assessed its factor structure and/or test-retest reliability or used diagnostic interviews as validators of current alcohol use disorders. The aim of the present study was to determine whether AUDIT and AUDIT-Consumption (AUDIT-C) scores are associated with subsequent risk of hospital admission for alcohol-related disorders and diseases (ARDDs). We used a historical cohort study. Using national registers, survey respondents were tracked from 1 September 2011 to hospitalization for an ARDD, emigration, death, or 31 December 2018, whichever occurred first. Denmark. Respondents (n = 4522) from a Danish national survey conducted in autumn 2011. Outcome was incident ARDD admission recorded in the National Patient Register. Predictors were AUDIT and AUDIT-C scores, and covariates were age, gender, highest level of education and previous psychiatric disorder. During the study period, 56 respondents had a first-time ARDD admission. Respondents who scored above the 8-point AUDIT cut-off and respondents who scored above the 5-point AUDIT-C cut-off had a significantly increased risk of being admitted for an ARDD compared with respondents who scored below the cut-offs, (AUDIT: hazard ratio (HR), 4.72; 95% CI, 2.59-8.60; AUDIT-C: HR, 7.97; 95% CI, 3.66-17.31). Scores above alcohol use disorders identification test (AUDIT) and AUDIT-Consumption (AUDIT-C) cut-offs are associated with an increased risk of long-term alcohol-related hospital admissions. At widely used cut-offs, the AUDIT-C is a better predictor of alcohol-related hospitalizations among members of the general population than the full AUDIT.

Comparison of the Effectiveness of Shock Index, Modified Shock Index, and Age Shock Index in COPD Exacerbations.

To evaluate the effectiveness and practicality of shock index (SI), modified shock index (MSI), and age-shock index (Age-SI) in predicting the prognosis, mortality, ICU and service admission, and the need for intermittent mandatory ventilation (IMV) and nasal intermittent mandatory ventilation) (NIMV in the ED patients with chronic obstructive pulmonary disease (COPD) exacerbation. Retrospective study. Balikesir University Faculty of Medicine, Emergency Service, Balıkesir, Turkey, from January 2019 to May 2020. Adult patients, who were admitted to the Emergency Department with diagnosis of COPD exacerbation, were included. Patients with missing data were excluded. SI, MSI, and age-SI values were calculated by using the vital signs. ROC curve analysis was used to evaluate the diagnostic performances of SI, MSI, and age-SI. The study consisted of 201 patients, 152 (75.6%) were males. Six (3%) patients died, 26 (12%) were admitted to ICU, 112 (55.7%) were admitted to the service, 11 (5.5%) needed IMV, and 48 (23.9%) needed NIMV in ED. SI was superior to the MSI and age-SI in predicting mortality, and AUC values of 0.802, 0.727, and 0.704, respectively. SI was also superior to the MSI and age-SI in predicting hospital admissions (SI AUC=0.591, p=0.029; MSI AUC=0.572, p=0.059; and age-SI AUC=0.580, p=0.089). Respectively none of the three indices was independently sufficient in predicting IMV, NIMV, and the need for ICU. SI is a valuable parameter in discriminating the COPD exacerbation. It is superior to the MSI and age-SI in predicting mortality and hospital admissions. It will be useful to evaluate SI for the severity classification, follow-up, and management of the patients with COPD. Age shock index, Chronic obstructive pulmonary disease, Modified shock index, mortality, Shock index.

Imbalanced prediction of emergency department admission using natural language processing and deep neural network

The emergency department (ED) plays a very significant role in the hospital. Owing to the rising number of ED visits, medical service points, and ED market, overcrowding of EDs has become serious worldwide. Overcrowding has long been recognized as a vital issue that increases the risk to patients and negative emotions of medical personnel and impacts hospital cost management. For the past years, many researchers have been applying artificial intelligence to reduce crowding situations in the ED. Nevertheless, the datasets in ED hospital admission are naturally inherent with the high-class imbalance in the real world. Previous studies have not considered the imbalance of the datasets, particularly addressing the imbalance. This study purposes to develop a natural language processing model of a deep neural network with an attention mechanism to solve the imbalanced problem in ED admission. The proposed framework is used for predicting hospital admission so that the hospitals can arrange beds early and solve the problem of congestion in the ED. Furthermore, the study compares a variety of methods and obtains the best composition that has the best performance for forecasting hospitalization in ED. The study used the data from a specific hospital in Taiwan as an empirical study. The experimental result demonstrates that almost all imbalanced methods can improve the model’s performance. In addition, the natural language processing model of Bi-directional Long Short-Term Memory with attention mechanism has the best results in all-natural language processing methods.

Winter sport musculoskeletal injuries: epidemiology and factors predicting hospital admission.

Participation in winter sports such as skiing, snowboarding and snowmobiling is associated with risk of musculoskeletal injury. The purpose of our study was to describe and quantify emergency department encounters associated with these sports. The National Electronic Injury Surveillance System (NEISS) was queried for skiing-, snowboarding- and snowmobiling-related injuries from 2009 to 2018. Patient demographics and disposition data were collected from emergency department encounters. Descriptive statistics were utilized to describe the trends in injuries from each sport and factors associated with the sports-specific injuries. From 2009 to 2018, there were an estimated 156,353 injuries related to snowboarding, skiing, or snowmobiling. Estimated injury incidence per 100,000 people decreased over time for skiing (3.24-1.23), snowboarding (3.98-1.22,) and snowmobiling (0.71-0.22,). The most common injury location by sport was shoulder for skiing (29.6%), wrist for snowboarding (32.5%) and shoulder for snowmobiling (21.9%), with fractures being the most common diagnosis. Only 4.5% required admission to the hospital. Fracture or dislocation was associated with highest likelihood of hospital admission (OR 42.34; 95% CI 22.59-79.37). Snowmobiling injuries (OR 1.63; 95% CI 1.20-2.22) and white race (OR 1.42; 95% CI 1.17-1.72) were also both associated with increased risk of hospital admission. Upper extremity injuries, particularly those involving fractures, were more common than lower extremity injuries for all three sports, with the shoulder being the most common location of injury for skiing and snowmobiling. This study can serve as the foundation for future research in sports safety and health policy to continue the declining trend of musculoskeletal injuries in the future. III.

Physician gestalt for emergency department triage: A prospective videotaped study.

Appropriate triage in patients presenting to the emergency department (ED) is often challenging. Little is known about the role of physician gestalt in ED triage. We aimed to compare the accuracy of emergency physician gestalt against the currently used computerized triage process. We conducted a prospective observational study in the ED at an academic medical center. Adult patients aged ≥20 years were included and underwent a standard triage protocol. The patients underwent system-based triage using the computerized software the Taiwan Triage and Acuity Scale. The entire triage process was recorded, and triage data were collected. Five physician raters provided triage levels (physician-based) according to their perceived urgency after reviewing videos. The primary outcome was hospital admission. The secondary outcomes were ED length of stay (EDLOS) and charges. In total, 656 patients were recruited (mean age 52 years, 50% male). The median system-based triage level was 3. By contrast, the median physician-based triage level was 4. The physician raters tended to provide lower triage levels than the system, with an average difference of 1. There was modest concordance between the two triage methods (correlation coefficient 0.30), with a weighted kappa coefficient of 0.18. The area under the receiver operating curve for the system- and physician-based triage in predicting hospital admission were similar (0.635 vs. 0.631, p=0.896). Attending physicians appeared to have better performance than residents in predicting admission. The variation explained (R2 ) in EDLOS and charges were similar between the two triage methods (R2 = 3% for EDLOS, 7%-9% for charges). Emergency physician gestalt for triage showed similar performance to a computerized system; however, physicians redistributed patients to lower triage levels. Physician gestalt has advantages for identifying low-risk patients. This approach may avoid undue time pressure for health care providers and promote rapid discharge.

Machine learning for real-time aggregated prediction of hospital admission for emergency patients

Machine learning for hospital operations is under-studied. We present a prediction pipeline that uses live electronic health-records for patients in a UK teaching hospital’s emergency department (ED) to generate short-term, probabilistic forecasts of emergency admissions. A set of XGBoost classifiers applied to 109,465 ED visits yielded AUROCs from 0.82 to 0.90 depending on elapsed visit-time at the point of prediction. Patient-level probabilities of admission were aggregated to forecast the number of admissions among current ED patients and, incorporating patients yet to arrive, total emergency admissions within specified time-windows. The pipeline gave a mean absolute error (MAE) of 4.0 admissions (mean percentage error of 17%) versus 6.5 (32%) for a benchmark metric. Models developed with 104,504 later visits during the Covid-19 pandemic gave AUROCs of 0.68–0.90 and MAE of 4.2 (30%) versus a 4.9 (33%) benchmark. We discuss how we surmounted challenges of designing and implementing models for real-time use, including temporal framing, data preparation, and changing operational conditions.

Updated International Society of Geriatric Oncology COVID-19 working group recommendations on COVID-19 vaccination among older adults with cancer

Updated International Society of Geriatric Oncology COVID-19 working group recommendations on COVID-19 vaccination among older adults with cancer

Machine Learning Can Accurately Predict Overnight Stay, Readmission, and 30-Day Complications Following Anterior Cruciate Ligament Reconstruction

This study aimed to develop machine learning (ML) models to predict hospital admission (overnight stay) as well as short-term complications and readmission rates following anterior cruciate ligament reconstruction (ACLR). Furthermore, we sought to compare the ML models with logistic regression models in predicting ACLR outcomes. The American College of Surgeons National Surgical Quality Improvement Program database was queried for patients who underwent elective ACLR from 2012 to 2018. Artificial neural network ML and logistic regression models were developed to predict overnight stay, 30-day postoperative complications, and ACL-related readmission, and model performance was compared using the area under the receiver operating characteristic curve. Regression analyses were used to identify variables that were significantly associated with the predicted outcomes. A total of 21,636 elective ACLR cases met inclusion criteria. Variables associated with hospital admission included White race, obesity, hypertension, and American Society of Anesthesiologists classification 3 and greater, anesthesia other than general, prolonged operative time, and inpatient setting. The incidence of hospital admission (overnight stay) was 10.2%, 30-day complications was 1.3%, and 30-day readmission for ACLR-related causes was 0.9%. Compared with logistic regression models, artificial neural network models reported superior area under the receiver operating characteristic curve values in predicting overnight stay (0.835 vs 0.589), 30-day complications (0.742 vs 0.590), reoperation (0.842 vs 0.601), ACLR-related readmission (0.872 vs 0.606), deep-vein thrombosis (0.804 vs 0.608), and surgical-site infection (0.818 vs 0.596). The ML models developed in this study demonstrate an application of ML in which data from a national surgical patient registry was used to predict hospital admission and 30-day postoperative complications after elective ACLR. ML models developed performed well, outperforming regression models in predicting hospital admission and short-term complications following elective ACLR. ML models performed best when predicting ACLR-related readmissions and reoperations, followed by overnight stay. IV, retrospective comparative prognostic trial.

The lung ultrasound “Rule of 7” in the prognosis of COVID-19 patients: Results from a prospective multicentric study

PurposeThere is growing evidence regarding the imaging findings of coronavirus disease 2019 (COVID-19) in lung ultrasound (LUS), however the use of a combined prognostic and triage tool has yet to be explored.To determine the impact of the LUS in the prediction of the mortality of patients with highly suspected or confirmed COVID-19.The secondary outcome was to calculate a score with LUS findings with other variables to predict hospital admission and emergency department (ED) discharge. Material and methodsProspective study performed in the ED of three academic hospitals. Patients with highly suspected or confirmed COVID-19 underwent a LUS examination and laboratory tests. ResultsA total of 228 patients were enrolled between March and September 2020. The mean age was 61.9 years (Standard Deviation – SD 21.1). The most common findings in LUS was a right posteroinferior isolated irregular pleural line (53.9%, 123 patients). A logistic regression model was calculated, including age over 70 years, C-reactive protein (CRP) over 70mg/L and a lung score over 7 to predict mortality, hospital admission and discharge from the ED. We obtained a predictive model with a sensitivity of 56.8% and a specificity of 87.6%, with an AUC of 0.813 [p<0.001]. ConclusionsThe combination of LUS, clinical and laboratory findings in this easy to apply “rule of 7” showed excellent performance to predict hospital admission and mortality.

Performance of Machine Learning Algorithms for Predicting Adverse Outcomes in Community-Acquired Pneumonia

Background: The ability to assess adverse outcomes in patients with community-acquired pneumonia (CAP) could improve clinical decision-making to enhance clinical practice, but the studies remain insufficient, and similarly, few machine learning (ML) models have been developed. Objective: We aimed to explore the effectiveness of predicting adverse outcomes in CAP through ML models. Methods: A total of 2,302 adults with CAP who were prospectively recruited between January 2012 and March 2015 across three cities in South America were extracted from DryadData. After a 70:30 training set: test set split of the data, nine ML algorithms were executed and their diagnostic accuracy was measured mainly by the area under the curve (AUC). The nine ML algorithms included decision trees, random forests, extreme gradient boosting (XGBoost), support vector machines, Naïve Bayes, K-nearest neighbors, ridge regression, logistic regression without regularization, and neural networks. The adverse outcomes included hospital admission, mortality, ICU admission, and one-year post-enrollment status. Results: The XGBoost algorithm had the best performance in predicting hospital admission. Its AUC reached 0.921, and accuracy, precision, recall, and F1-score were better than those of other models. In the prediction of ICU admission, a model trained with the XGBoost algorithm showed the best performance with AUC 0.801. XGBoost algorithm also did a good job at predicting one-year post-enrollment status. The results of AUC, accuracy, precision, recall, and F1-score indicated the algorithm had high accuracy and precision. In addition, the best performance was seen by the neural network algorithm when predicting death (AUC 0.831). Conclusions: ML algorithms, particularly the XGBoost algorithm, were feasible and effective in predicting adverse outcomes of CAP patients. The ML models based on available common clinical features had great potential to guide individual treatment and subsequent clinical decisions.

Current state of artificial intelligence-based algorithms for hospital admission prediction in patients with heart failure: a scoping review.

Patients with congestive heart failure (HF) are prone to clinical deterioration leading to hospital admissions, burdening both patients and the healthcare system. Predicting hospital admission in this patient group could enable timely intervention, with subsequent reduction of these admissions. To date, hospital admission prediction remains challenging. Increasing amounts of acquired data and development of artificial intelligence (AI) technology allow for the creation of reliable hospital prediction algorithms for HF patients. This scoping review describes the current literature on strategies and performance of AI-based algorithms for prediction of hospital admission in patients with HF. PubMed, EMBASE, and the Web of Science were used to search for articles using machine learning (ML) and deep learning methods to predict hospitalization in patients with HF. After eligibility screening, 23 articles were included. Sixteen articles predicted 30-day hospital (re-)admission resulting in an area under the curve (AUC) ranging from 0.61 to 0.79. Six studies predicted hospital admission over longer time periods ranging from 6 months to 3 years, with AUC's ranging from 0.65 to 0.78. One study prospectively evaluated performance of a disposable sensory patch at home after hospitalization which resulted in an AUC of 0.89 for unplanned hospital admission prediction. AI has the potential to enable prediction of hospital admission in HF patients. Improvement of data management, adding new data sources such as telemonitoring data and ML models and prospective and external validation of current models must be performed before clinical applicability is possible.

Evolving spectrum but persistent high mortality of COVID-19 among patients on kidney replacement therapy in the vaccine era: the Spanish COVID-19 KRT Registry.

ABSTRACTBackgroundKidney replacement therapy (KRT) conferred a high risk for coronavirus disease 2019 (COVID-19) related mortality early in the pandemic. We evaluate the presentation, treatment and outcomes of COVID-19 in patients on KRT over time during the pandemic.MethodsThis registry-based study involved 6080 dialysis and kidney transplant (KT) patients with COVID-19, representing roughly 10% of total Spanish KRT patients. Epidemiology, comorbidity, infection, vaccine status and treatment data were recorded, and predictors of hospital admission, intensive care unit (ICU) admission and mortality were evaluated.ResultsVaccine introduction decreased the number of COVID-19 cases from 1747 to 280 per wave. Of 3856 (64%) COVID-19 KRT patients admitted to the hospital, 1481/3856 (38%) were admitted during the first of six waves. Independent predictors for admission included KT and the first wave. During follow-up, 1207 patients (21%) died, 500/1207 (41%) during the first wave. Among vaccinated patients, mortality was 19%, mostly affecting KT recipients. Overall, independent predictors for mortality were older age, disease severity (lymphopaenia, pneumonia) and ICU rejection. Among patient factors, older age, male sex, diabetes, KT and no angiotensin receptor blockers (ARB) were independent predictors of death. In KT recipients, individual immunosuppressants were independent predictors of death. Over time, patient characteristics evolved and in later pandemic waves, COVID-19 was mainly diagnosed in vaccinated KT recipients; in the few unvaccinated dialysis patients, ICU admissions increased and mortality decreased (28% for the first wave and 16–22% thereafter).ConclusionsThe clinical presentation and outcomes of COVID-19 during the first wave no longer represent COVID-19 in KRT patients, as the pandemic has become centred around vaccinated KT recipients. Vaccines lowered the incidence of diagnosed COVID-19 and mortality. However, mortality remains high despite increased access to ICU care.

Using Machine Learning Techniques to Predict Hospital Admission at the Emergency Department.

IntroductionOne of the most important tasks in the Emergency Department (ED) is to promptly identify the patients who will benefit from hospital admission. Machine Learning (ML) techniques show promise as diagnostic aids in healthcare.Aim of the studyOur objective was to find an algorithm using ML techniques to assist clinical decision-making in the emergency setting.Material and methodsWe assessed the following features seeking to investigate their performance in predicting hospital admission: serum levels of Urea, Creatinine, Lactate Dehydrogenase, Creatine Kinase, C-Reactive Protein, Complete Blood Count with differential, Activated Partial Thromboplastin Time, DDi-mer, International Normalized Ratio, age, gender, triage disposition to ED unit and ambulance utilization. A total of 3,204 ED visits were analyzed.ResultsThe proposed algorithms generated models which demonstrated acceptable performance in predicting hospital admission of ED patients. The range of F-measure and ROC Area values of all eight evaluated algorithms were [0.679-0.708] and [0.734-0.774], respectively. The main advantages of this tool include easy access, availability, yes/no result, and low cost. The clinical implications of our approach might facilitate a shift from traditional clinical decision-making to a more sophisticated model.ConclusionsDeveloping robust prognostic models with the utilization of common biomarkers is a project that might shape the future of emergency medicine. Our findings warrant confirmation with implementation in pragmatic ED trials.

Upper extremity firearm injuries: epidemiology and factors predicting hospital admission.

Civilian gun violence is a public health crisis in the USA that will be an economic burden reported to be as high as $17.7billion with over half coming from US taxpayers dollars through Medicaid-related costs. The purpose of this study is to review the epidemiology of upper extremity firearm injuries in the USA and the associated injury burden. The Inter-university Consortium for Political and Social Research's Firearm Injury Surveillance Study database, collected from the National Electronic Injury Surveillance System, was queried from 1993 to 2015. The following variables were reviewed: patient demographics, date of injury, diagnosis, injury location, firearm type (if provided), incident classification, and a descriptive narrative of the incident. We performed chi-square testing and complex descriptive statistics, and binomial logistic regression model to predict factors associated with hospital admission. From 1993 to 2015, an estimated 314,369 (95% CI: 291,528-337,750; 16,883 unweighted) nonfatal firearm upper extremity injuries with an average incidence rate of 4.76 per 100,000 persons (SD: 0.9; 03.77-7.49) occurred. The demographics most afflicted with nonfatal gunshot wound injuries were black adolescent and young adult males (ages 15-24years). Young adults aged 25-34 were the second largest estimate of injuries by age group. Hands were the most commonly injured upper extremity, (55,014; 95% CI: 75,973-89,667) followed by the shoulder, forearm, and upper arm. Patients who underwent amputation (OR: 28.65; 95% CI: 24.85-33.03) or with fractures (OR: 26.20; 95% CI: 23.27-29.50) experienced an increased likelihood for hospitalization. Patients with a shoulder injury were 5.5× more likely to be hospitalized than those with a finger injury (OR:5.57; 95% CI:5.35-5.80). The incidence of upper extremity firearm injuries has remained steady over the last decade ranging between 4 and 5 injuries per 100,000 persons. Patients with proximal injuries or injuries involving the bone were more likely to require hospital admission. This study should bring new information to the forefront for policy makers regarding gun violence.