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Rapid growth in emergency department (ED) triage literature has been accompanied by diversity in study design, methodology, and outcome assessment. We aim to synthesize existing ED triage literature by using a framework that enables performance comparisons and benchmarking across triage systems, with respect to clinical outcomes and reliability.PubMed, EMBASE, Scopus, and Web of Science were systematically searched for studies of adult ED triage systems through 2016. Studies evaluating triage systems with evidence of widespread adoption (Australian Triage Scale, Canadian Triage and Acuity Scale, Emergency Severity Index, Manchester Triage Scale, and South African Triage Scale) were cataloged and compared for performance in identifying patients at risk for mortality, critical illness and hospitalization, and interrater reliability. This study was performed and reported in adherence to Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines.A total of 6,160 publications were identified, with 182 meeting eligibility criteria and 50 with sufficient data for inclusion in comparative analysis. The Canadian Triage and Acuity Scale (32 studies), Emergency Severity Index (43), and Manchester Triage Scale (38) were the most frequently studied triage scales, and all demonstrated similar performance. Most studies (6 of 8) reported high sensitivity (90%) of triage scales for identifying patients with ED mortality as high acuity at triage. However, sensitivity was low (80%) for identification of patients who had critical illness outcomes and those who died within days of the ED visit or during the index hospitalization. Sensitivity varied by critical illness and was lower for severe sepsis (36% to 74%), pulmonary embolism (54%), and non-ST-segment elevation myocardial infarction (44% to 85%) compared with ST-segment elevation myocardial infarction (56% to 92%) and general outcomes of ICU admission (58% to 100%) and lifesaving intervention (77% to 98%). Some proportion of hospitalized patients (3% to 45%) were triaged to low acuity (level 4 to 5) in all studies. Reliability measures (κ) were variable across evaluations, with only a minority (11 of 42) reporting κ above 0.8.We found that a substantial proportion of ED patients who die postencounter or are critically ill are not designated as high acuity at triage. Opportunity to improve interrater reliability and triage performance in identifying patients at risk of adverse outcome exists.

Background: Inappropriate antibiotic prescription leads to increased Clostridiodes difficile infections, adverse effects including organ toxicity, and generation of antibiotic-resistant bacteria. Despite efforts to improve antibiotic use in acute-care settings, unnecessary and inappropriate prescription still occur in 30%–50% of patients. Objectives: We assessed factors associated with inappropriate antibiotic prescription at 2 time points: (1) initial, empiric therapy and (2) 3–5 days after therapy initiation. Methods: As part of a multicenter study investigating strategies to reduce antibiotic therapy after 3–5 days of use, antibiotic prescription data were collected from 11 adult and pediatric intensive care and general medical units at 6 hospitals in Maryland in 2014 and 2015. We performed a retrospective cohort study of all hospitalized patients who received any of 23 common antibiotics for at least 3 days. Each medical record was reviewed for demographics, admission and discharge dates, patient comorbidities, and antibiotic regimen by at least 1 infectious disease physician or pharmacist. Classification of antibiotic inappropriateness was based on each institution’s guidelines and standards. Bivariate analyses were performed using logistic regression for both initial therapy and therapy at days 3–5. Multivariable logistic regression was performed using covariates meeting the significance level of P < .05. Results: In total, 3,436 antibiotic courses were assessed at time of initial therapy, and 1541 regimens were continued and reviewed again at days 3–5 of therapy. For the initial therapy, 1,255 regimens (37%) were inappropriate; 45% of these were considered unnecessary and 41% were too broad in spectrum. In the multivariable regression, older age and antibiotic prescription during the summer were associated with the receipt of inappropriate antibiotics (Table 1). Having end-stage renal disease as a comorbid condition was protective against inappropriate use. At days 3–5 of therapy, 688 (45%) of the antibiotic courses were inappropriate. Reasons regimens were considered inappropriate included unnecessary antibiotic prescriptions (49%) and antibiotics being too broad (38%). Older age and receiving cefepime or piperacillin-tazobactam on day 3 of therapy were factors associated with inappropriate use (Table 2). Having undergone a transplant or a surgical procedure was protective of inappropriate antimicrobial use at days 3–5 of therapy. Conclusions: Older patients are more likely to receive inappropriate antibiotics at both initial regimen and 3–5 days later. Patients receiving cefepime or piperacillin-tazobactam are at greater risk of receiving inappropriate antibiotics at days 3–5 due to failure to de-escalate. Antibiotic stewardship strategies targeting these patient populations may limit inappropriate use.Funding: NoneDisclosures: None

BACKGROUND Patients hospitalized with heart failure suffer the highest rates of 30-day readmission among any clinically-defined patient populations in the United States. Investigation into the predictability of 30-day readmissions can lead to clinical decision-support tools and targeted interventions that can help care providers to improve individual patient care and reduce readmission risk. OBJECTIVE We developed a dynamic readmission risk prediction model that yields daily predictions for hospitalized heart failure patients toward identifying risk trajectories over time. We identified clinical predictors associated with different patterns in readmission risk trajectories. METHODS A two-stage predictive modeling approach combining logistic and beta regression was applied to electronic health record (EHR) data accumulated daily to predict 30-day readmission for a cohort of 534 heart failure patient encounters over 2,750 patient-days. Unsupervised clustering was performed on predictions to uncover time-dependent trends in readmission risk over the patient’s hospital stay. RESULTS Readmission occurred in 107 (20.0%) encounters. The out-of-sample AUC for the two-stage predictive model was 0.73 (±0.08). Dynamic clinical predictors capturing lab results and vital signs had the highest predictive value compared to demographic, administrative, medication and procedural data included. Unsupervised clustering identified four risk trajectory groups: decreasing risk (24.5% encounters), high risk (21.2%), moderate risk (33.1%), and low risk (21.2%). The decreasing risk group demonstrated change in average probability of readmission from admission (0.69) to discharge (0.30), while the high risk (0.75), moderate risk (0.61), and low risk (0.39) maintained consistency over the hospital course. Clinical predictors that discriminated groups included lab measures (hemoglobin, potassium, sodium), vital signs (diastolic blood pressure), and the number of prior hospitalizations. CONCLUSIONS Dynamically predicting readmission and quantifying trends over patients’ hospital stay illuminated differing risk trajectory groups. Identifying risk trajectory patterns and distinguishing predictors may shed new light on indicators of readmission and the isolated effects of the index hospitalization.

Background Hospital-onset C. difficile infection (HO-CDI) rates are publicly reported. However, patient-level risk factors are not included in the current risk adjustment methodology, and the knowledge as to which risk factors to include is incomplete. This study aimed to determine whether electronically-available comorbidities and laboratory indicators of severity of illness are risk factors for HO-CDI. Methods We performed a retrospective cohort study of all adult patients admitted to three hospitals (one academic, two community) in Baltimore, Maryland between January 1, 2016 and January 1, 2018. Information extracted from electronic medical records included demographics, ICD-10 codes, laboratory results within 24 hours of admission (i.e., hematocrit, hemoglobin, platelet count, leukocytes, BUN, CO2, creatinine, glucose, sodium, and potassium), medication administration (i.e., antibiotic and antacid use), and C. difficile test result. Comorbid conditions were assessed by the Elixhauser Comorbidity Index components. HO-CDI was defined by positive laboratory test > 3 days after admission. Potential risk factors for HO-CDI were assessed using bivariate log binomial regression. Multivariable log binomial regression was conducted using significant (P < 0.1) covariates. Results At hospital 1 (academic), 314 of the 48,057 (0.65%) eligible patient admissions had HO- CDI; 41 of the 8,791 (0.47%) and 75 of the 29,211 (0.26%) of patient admissions at community hospitals 2 and 3, respectively, had HO-CDI. In multivariable analysis, Elixhauser Score was a significant risk factor for HO-CDI at all hospitals when controlling for antibiotic and antacid use; for every one-point increase in Elixhauser Score, there was an increased risk of HO-CDI of 1.27 (95% CI: 1.21, 1.32) at hospital 1, 1.38 (95% CI: 1.24, 1.54) at hospital 2, and 1.28 (95% CI: 1.10, 1.31) at hospital 3. Table 1 shows significant risk factors for HO-CDI for each hospital. When individual comorbidities were assessed in the regression analysis, fluid and electrolyte disorders were a significant risk factor for HO-CDI for all hospitals. Conclusion Laboratory values upon admission and electronically available patient comorbidities are important risk factors for HO-CDI and should be considered for future risk adjustment. Disclosures All authors: No reported disclosures.

Background: Patients hospitalized with heart failure suffer the highest rates of 30-day readmission among other clinically defined patient populations in the United States. Investigation into the predictability of 30-day readmissions can lead to clinical decision support tools and targeted interventions that can help care providers to improve individual patient care and reduce readmission risk. Objective: This study aimed to develop a dynamic readmission risk prediction model that yields daily predictions for patients hospitalized with heart failure toward identifying risk trajectories over time and identifying clinical predictors associated with different patterns in readmission risk trajectories. Methods: A two-stage predictive modeling approach combining logistic and beta regression was applied to electronic health record data accumulated daily to predict 30-day readmission for 534 hospital encounters of patients with heart failure over 2750 patient days. Unsupervised clustering was performed on predictions to uncover time-dependent trends in readmission risk over the patient’s hospital stay. We used data collected between September 1, 2013, and August 31, 2015, from a community hospital in Maryland (United States) for patients with a primary diagnosis of heart failure. Patients who died during the hospital stay or were transferred to other acute care hospitals or hospice care were excluded. Results: Readmission occurred in 107 (107/534, 20.0%) encounters. The out-of-sample area under curve for the 2-stage predictive model was 0.73 (SD 0.08). Dynamic clinical predictors capturing laboratory results and vital signs had the highest predictive value compared with demographic, administrative, medical, and procedural data included. Unsupervised clustering identified four risk trajectory groups: decreasing risk (131/534, 24.5% encounters), high risk (113/534, 21.2%), moderate risk (177/534, 33.1%), and low risk (113/534, 21.2%). The decreasing risk group demonstrated change in average probability of readmission from admission (0.69) to discharge (0.30), whereas the high risk (0.75), moderate risk (0.61), and low risk (0.39) groups maintained consistency over the hospital course. A higher level of hemoglobin, larger decrease in potassium and diastolic blood pressure from admission to discharge, and smaller number of past hospitalizations are associated with decreasing readmission risk (P