Demographics and Outcomes of Pulmonary Hypertension Patients in United States Emergency Departments

Introduction: Pulmonary hypertension (PH) is a common, yet under-diagnosed, contributor to morbidity and mortality. Our objective was to characterize the prevalence of PH among adult patients presenting to United States (US) emergency departments (ED) and to identify demographic patterns and outcomes of PH patients in the ED.Methods: We analyzed the Nationwide Emergency Department Sample (NEDS) database, with a focus on ED patients aged 18 years and older, with any International Classification of Diseases, Clinical Modification (ICD)-9-CM or ICD-10-CM diagnosis code for PH from 2011 to 2015. The primary outcome was inpatient, all-cause mortality. The secondary outcomes were hospital admission rates and hospital length of stay (LOS).Results: From 2011 to 2015, in a sample of 121,503,743 ED visits, representing a weighted estimate of 545,500,486 US ED visits, patients with a diagnosis of PH accounted for 0.78% (95% confidence interval [CI], 0.75- 0.80%) of all US ED visits. Of the PH visits, 86.9% were admitted to the hospital, compared to 16.3% for all other ED visits (P <0.001). Likewise, hospital LOS and hospital-based mortality were higher in the PH group than for other ED patients (e.g., inpatient mortality 4.5% vs 2.6%, P < 0.001) with an adjusted odds ratio (aOR) of 1.34 (95% CI, 1.31–1.37). Age had the strongest association with mortality, with an aOR of 10.6 for PH patients over 80 years (95% CI, 10.06–11.22), compared to a reference of ages 18 to 30 years.Conclusion: In this nationally representative sample, presentations by patients with PH were relatively common, accounting for nearly 0.8% of US ED visits. Patients with PH were significantly more likely to be admitted to the hospital than all other patients, had longer hospital LOS, and increased risk of inpatient mortality

Emergency department triage prediction of clinical outcomes using machine learning models

Abstract Background Development of emergency department (ED) triage systems that accurately differentiate and prioritize critically ill from stable patients remains challenging. We used machine learning models to predict clinical outcomes, and then compared their performance with that of a conventional approach—the Emergency Severity Index (ESI). Methods Using National Hospital and Ambulatory Medical Care Survey (NHAMCS) ED data, from 2007 through 2015, we identified all adult patients (aged ≥ 18 years). In the randomly sampled training set (70%), using routinely available triage data as predictors (e.g., demographics, triage vital signs, chief complaints, comorbidities), we developed four machine learning models: Lasso regression, random forest, gradient boosted decision tree, and deep neural network. As the reference model, we constructed a logistic regression model using the five-level ESI data. The clinical outcomes were critical care (admission to intensive care unit or in-hospital death) and hospitalization (direct hospital admission or transfer). In the test set (the remaining 30%), we measured the predictive performance, including area under the receiver-operating-characteristics curve (AUC) and net benefit (decision curves) for each model. Results Of 135,470 eligible ED visits, 2.1% had critical care outcome and 16.2% had hospitalization outcome. In the critical care outcome prediction, all four machine learning models outperformed the reference model (e.g., AUC, 0.86 [95%CI 0.85–0.87] in the deep neural network vs 0.74 [95%CI 0.72–0.75] in the reference model), with less under-triaged patients in ESI triage levels 3 to 5 (urgent to non-urgent). Likewise, in the hospitalization outcome prediction, all machine learning models outperformed the reference model (e.g., AUC, 0.82 [95%CI 0.82–0.83] in the deep neural network vs 0.69 [95%CI 0.68–0.69] in the reference model) with less over-triages in ESI triage levels 1 to 3 (immediate to urgent). In the decision curve analysis, all machine learning models consistently achieved a greater net benefit—a larger number of appropriate triages considering a trade-off with over-triages—across the range of clinical thresholds. Conclusions Compared to the conventional approach, the machine learning models demonstrated a superior performance to predict critical care and hospitalization outcomes. The application of modern machine learning models may enhance clinicians’ triage decision making, thereby achieving better clinical care and optimal resource utilization

Variation in Risk‐Standardized Acute Admission Rates Among Patients With Heart Failure in Accountable Care Organizations: Implications for Quality Measurement

Background Accountable care organizations (ACOs) aim to improve health care quality and reduce costs, including among patients with heart failure (HF). However, variation across ACOs in admission rates for patients with HF and associated factors are not well described. Methods and Results We identified Medicare fee‐for‐service beneficiaries with HF who were assigned to a Medicare Shared Savings Program ACO in 2017 and survived ≥30 days into 2018. We calculated risk‐standardized acute admission rates across ACOs, assigned ACOs to 1 of 3 performance categories, and examined associations between ACO characteristics and performance categories. Among 1 232 222 beneficiaries with HF, 283 795 (mean age, 81 years; 54% women; 86% White; 78% urban) were assigned to 1 of 467 Medicare Shared Savings Program ACOs. Across ACOs, the median risk‐standardized acute admission rate was 87 admissions per 100 people, ranging from 61 (minimum) to 109 (maximum) admissions per 100 beneficiaries. Compared to the overall average, 13% of ACOs performed better on risk‐standardized acute admission rates, 72% were no different, and 14% performed worse. Most ACOs with better performance had fewer Black beneficiaries and were not hospital affiliated. Most ACOs that performed worse than average were large, located in the Northeast, had a hospital affiliation, and had a lower proportion of primary care providers. Conclusions Admissions are common among beneficiaries with HF in ACOs, and there is variation in risk‐standardized acute admission rates across ACOs. ACO performance was associated with certain ACO characteristics. Future studies should attempt to elucidate the relationship between ACO structure and characteristics and admission risk