The Relationship Between Governing Board Composition and Medicare Shared Savings Program Accountable Care Organizations Outcomes: an Observational Study.

BACKGROUND: Early studies of Medicare Shared Savings Program (MSSP) accountable care organizations (ACOs) suggested that physician leadership was an important driver of ACO success, but it is unknown whether the demographic and professional composition of current MSSP ACO governing boards is associated with ACOs' publicly reported outcomes. OBJECTIVE: To investigate whether governing boards with higher physician participation and greater female involvement have better outcomes. DESIGN: Cross-sectional observational study. PARTICIPANTS: All 2017 MSSP ACOs identified by the Center for Medicare and Medicaid Services ACO Public Use Files (PUF). MAIN MEASURES: We collected governing board composition from ACO websites in 2019. Outcome metrics included risk-standardized readmission and unplanned admissions rates. We used descriptive statistics and linear regression models to examine the association between board composition and outcomes. KEY RESULTS: Of the 339 ACOs that still existed in 2019 and had available data, 77% had physician-majority boards and 11.5% had no women on their boards. Eighty-nine percent reported a Medicare beneficiary on their board, of which about one-third had a woman representative. The average number of members on MSSP ACO boards was 12, with a mean of 67% physicians and 24% women. Board composition varied minimally by ACO characteristics, such as geographic region, number of beneficiaries, or type of participants. Higher levels of physician participation in ACO governing boards were associated with lower all-cause unplanned admission rates for patients with heart failure (p = - 0.26, p < 0.001) and for patients with multiple chronic conditions (p = - 0.28, p = 0.001). The number of women on the board was not associated with any outcome differences. CONCLUSIONS: MSSP ACO governing boards were predominately male and physician-led. Physician involvement may be important for achieving quality goals, while lack of female involvement showcases an opportunity to diversify boards.

Trends in US hospitalizations for anaphylaxis among infants and toddlers: 2006 to 2015.

BACKGROUND: Anaphylaxis is a potentially fatal acute allergic reaction. Its overall prevalence appears to be rising, but little is known about US hospitalization trends among infants and toddlers. OBJECTIVE: To identify the trends and predictors of hospitalization for anaphylaxis among infants and toddlers. METHODS: We used the nationally representative National Inpatient Sample (NIS), from 2006 to 2015, to perform an analysis of trends in US hospitalizations for anaphylaxis among infants and toddlers (age, <3 years) and other children (age, 3-18 years). For internal consistency, we identified patients with anaphylaxis by the International Classification of Diseases, Ninth Revision, Clinical Modification diagnosis code and excluded those with the International Classification of Diseases, Tenth Revision, Clinical Modification (late 2015). We calculated trends in anaphylaxis hospitalizations over time by age group and then used multivariable logistic regression to describe anaphylaxis hospitalizations among infants and toddlers. RESULTS: Among infants and toddlers, there was no significant change in anaphylaxis hospitalizations during the 10-year study period (Ptrend = .14). Anaphylaxis hospitalization among infants and toddlers was more likely in males, with private insurance, in the highest income quartile, with chronic pulmonary disease, who presented on a weekend day, to an urban teaching hospital, located in the Northeast. In contrast, anaphylaxis hospitalizations among older children (age, 3-<18 years) rose significantly during the study (Ptrend < .001). CONCLUSION: Anaphylaxis hospitalizations among infants and toddlers in the United States were stable from 2006 to 2015, whereas hospitalizations among older children were rising. Future research should focus on the trends in disease prevalence and health care utilization in the understudied population of infants and toddlers.

Association of obstructive sleep apnea with severity of patients hospitalized for acute asthma.

BACKGROUND: Studies suggest that obstructive sleep apnea (OSA) is associated with suboptimal disease control and worse chronic severity of asthma. However, little is known about the relations of OSA with acute asthma severity in hospitalized patients. OBJECTIVE: To investigate the association of OSA with acute asthma severity. METHODS: This is a retrospective cohort study (2010-2013) using State Inpatient Databases from 8 geographically diverse states in the United States. The outcomes were markers of acute severity, including mechanical ventilation use, hospital length of stay, and in-hospital mortality. To determine the association of interest, we fit multivariable logistic regression models, adjusting for age, sex, race/ethnicity, primary insurance, household income, patient residence, comorbidities, hospital state, and hospitalization year. We repeated the analysis for children aged 6 to 17 years. RESULTS: Among 73,408 adult patients hospitalized for acute asthma, 10.3% had OSA. Coexistent OSA was associated with a significantly higher risk of noninvasive positive pressure ventilation use (14.9% vs 3.1%; unadjusted odds ratio, 6.48; 95% CI, 5.88-7.13; adjusted odds ratio, 5.20; 95% CI, 4.65-5.80), whereas coexistent OSA was not significantly associated with the risk of invasive mechanical ventilation use. Patients with OSA had 37% longer hospital length of stay (unadjusted incidence rate ratio, 1.37; 95% CI, 1.33-1.40); this significant association persisted in the multivariable model (incidence rate ratio, 1.13; 95% CI, 1.10-1.17). The in-hospital mortality did not significantly differ between groups. These findings were consistent in both obesity and nonobesity groups and in 27,935 children. CONCLUSION: Among patients hospitalized for acute asthma, OSA was associated with a higher risk of noninvasive positive pressure ventilation use and longer length of stay compared with those without OSA.

Contribution of social factors to readmissions within 30 days after hospitalization for COPD exacerbation.

BACKGROUND: To investigate whether, in patients hospitalized for COPD, the addition of social factors improves the predictive ability for the risk of overall 30-day readmissions, early readmissions (within 7 days after discharge), and late readmissions (8-30 days after discharge). METHODS: Patients (aged ≥40 years) hospitalized for COPD were identified in the Medicare Current Beneficiary Survey from 2006 through 2012. With the use of 1000 bootstrap resampling from the original cohort (training-set), two prediction models were derived: 1) the reference model including age, comorbidities, and mechanical ventilation use, and 2) the optimized model including social factors (e.g., educational level, marital status) in addition to the covariates in the reference model. Prediction performance was examined separately for 30-day, early, and late readmissions. RESULTS: Following 905 index hospitalizations for COPD, 18.5% were readmitted within 30 days. In the test-set, for overall 30-day readmissions, the discrimination ability between reference and optimized models did not change materially (C-statistic, 0.57 vs. 0.58). By contrast, for early readmissions, the optimized model had significantly improved discrimination (C-statistic, 0.57 vs. 0.63; integrated discrimination improvement [IDI], 0.018 [95%CI, 0.003-0.032]) and reclassification (continuous net reclassification index [NRI], 0.298 [95%CI 0.060-0.537]). Likewise, for late readmissions, the optimized model also had significantly improved discrimination (C-statistic, 0.65 vs. 0.68; IDI, 0.026 [95%CI 0.009-0.042]) and reclassification (continuous NRI, 0.243 [95%CI 0.028-0.459]). CONCLUSIONS: In a nationally-representative sample of Medicare beneficiaries hospitalized for COPD, we found that the addition of social factors improved the predictive ability for readmissions when early and late readmissions were examined separately.

ICU Utilization for Patients With Acute Exacerbation of Chronic Obstructive Pulmonary Disease Receiving Noninvasive Ventilation.

OBJECTIVES: We investigated whether patients with chronic obstructive pulmonary disease could safely receive noninvasive ventilation outside of the ICU. DESIGN: Retrospective cohort study. SETTING: Twelve states with ICU utilization flag from the State Inpatient Database from 2014. PATIENTS: Patients greater than or equal to 18 years old with primary diagnosis of acute exacerbation of chronic obstructive pulmonary disease and secondary diagnosis of respiratory failure who received noninvasive ventilation. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Multilevel logistic regression models were used to obtain hospital-level ICU utilization rates. We risk-adjusted using both patient/hospital characteristics. The primary outcome was in-hospital mortality; secondary outcomes were invasive monitoring (arterial/central catheters), hospital length of stay, and cost. We examined 5,081 hospitalizations from 424 hospitals with ICU utilization ranging from 0.05 to 0.98. The overall median in-hospital mortality was 2.62% (interquartile range, 1.72-3.88%). ICU utilization was not significantly associated with in-hospital mortality (ß = 0.01; p = 0.05) or length of stay (ß = 0.18; p = 0.41), which was confirmed by Spearman correlation (ρ = 0.06; p = 0.20 and ρ = 0.02; p = 0.64, respectively). However, lower ICU utilization was associated with lower rates of invasive monitor placement by linear regression (ß = 0.05; p < 0.001) and Spearman correlation (ρ = 0.28; p < 0.001). Lower ICU utilization was also associated with significantly lower cost by linear regression (ß = 14.91; p = 0.02) but not by Spearman correlation (ρ = 0.09; p = 0.07). CONCLUSIONS: There is wide variability in the rate of ICU utilization for noninvasive ventilation across hospitals. Chronic obstructive pulmonary disease patients receiving noninvasive ventilation had similar in-hospital mortality across the ICU utilization spectrum but a lower rate of receiving invasive monitors and probably lower cost when treated in lower ICU-utilizing hospitals. Although the results suggest that noninvasive ventilation can be delivered safely outside of the ICU, we advocate for hospital-specific risk assessment if a hospital were considering changing its noninvasive ventilation delivery policy.

Emergency department triage prediction of clinical outcomes using machine learning models.

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.

Incidence of Acute Cardiovascular Event After Acute Exacerbation of COPD.

BACKGROUND: There is a lack of comprehensive view of the association between acute exacerbation of COPD (AECOPD) and the risk of acute cardiovascular events. OBJECTIVE: To determine the association of AECOPD with 30-day and 1-year incidences of acute cardiovascular event. DESIGN: Self-controlled case series analysis using population-based datasets from three US states from 2005 through 2011. PARTICIPANTS: Patients aged ≥ 40 years with AECOPD. MAIN MEASURES: The primary outcome was a composite of an ED visit or hospitalization for acute cardiovascular events, including acute myocardial infarction, heart failure, atrial fibrillation, pulmonary embolism, and stroke. We compared the incidence of each patient's acute cardiovascular event during the first 30-day period before the index AECOPD (30-day reference period) in comparison with that during the 30-day period after the index AECOPD. Likewise, with the 1-year period before the index AECOPD as reference, we also estimated incidence rate ratios (IRRs) for each patient's outcomes during 1-year period after the index AECOPD. KEY RESULTS: Overall, there were 362,867 patients with an ED visit or hospitalization for AECOPD. Compared with the 30-day reference period, the incidence of acute cardiovascular event in the 30-day period after the AECOPD was significantly higher (IRR, 1.34; 95%CI, 1.30-1.39; P < 0.001). Likewise, compared with the 1-year reference period, the incidence during the 1-year period after the AECOPD was also higher (IRR, 1.20; 95%CI, 1.18-1.22; P < 0.001). For each of acute cardiovascular conditions, the associations remained significant (all P < 0.05). CONCLUSIONS: AECOPD was associated with increased 30-day and 1-year incidences of acute cardiovascular event.

Machine learning approaches for predicting disposition of asthma and COPD exacerbations in the ED.

OBJECTIVE: The prediction of emergency department (ED) disposition at triage remains challenging. Machine learning approaches may enhance prediction. We compared the performance of several machine learning approaches for predicting two clinical outcomes (critical care and hospitalization) among ED patients with asthma or COPD exacerbation. METHODS: Using the 2007-2015 National Hospital and Ambulatory Medical Care Survey (NHAMCS) ED data, we identified adults with asthma or COPD exacerbation. In the training set (70% random sample), using routinely-available triage data as predictors (e.g., demographics, arrival mode, vital signs, chief complaint, comorbidities), we derived four machine learning-based models: Lasso regression, random forest, boosting, and deep neural network. In the test set (the remaining 30% of sample), we compared their prediction ability against traditional logistic regression with Emergency Severity Index (ESI, reference model). RESULTS: Of 3206 eligible ED visits, corresponding to weighted estimates of 13.9 million visits, 4% had critical care outcome and 26% had hospitalization outcome. For the critical care prediction, the best performing approach- boosting - achieved the highest discriminative ability (C-statistics 0.80 vs. 0.68), reclassification improvement (net reclassification improvement [NRI] 53%, P = 0.002), and sensitivity (0.79 vs. 0.53) over the reference model. For the hospitalization prediction, random forest provided the highest discriminative ability (C-statistics 0.83 vs. 0.64) reclassification improvement (NRI 92%, P < 0.001), and sensitivity (0.75 vs. 0.33). Results were generally consistent across the asthma and COPD subgroups. CONCLUSIONS: Based on nationally-representative ED data, machine learning approaches improved the ability to predict disposition of patients with asthma or COPD exacerbation.

Association of Bariatric Surgery With Risk of Infectious Diseases: A Self-Controlled Case Series Analysis.

Background: Although emerging data demonstrate that obesity is a risk factor for infectious diseases, no study has investigated the relationship of bariatric surgery with the risk of infectious diseases among obese adults. Methods: We conducted a self-controlled case series analysis using data from the State Emergency Department Database and State Inpatient Database of 3 US states (California, Florida, and Nebraska) from 2005 through 2011. We included obese adults who underwent bariatric surgery as an instrument of weight reduction. Primary outcomes were emergency department (ED) visit or hospitalization for skin and soft-tissue infection (SSTI), respiratory infection, intra-abdominal infection, or urinary tract infection (UTI). Results: Among 56277 obese adults who underwent bariatric surgery, compared to presurgery months 13-24 as the reference period, the risk of ED visit or hospitalization in the 0- to 12-month postsurgery period decreased significantly for SSTI (aOR, 0.85 [95% confidence interval {CI}, .76-.95]) and respiratory infection (aOR, 0.82 [95% CI, .75-.90]) and remained significantly low in the 13- to 24-month postsurgery period (aORs, 0.77 [95% CI, .68-.86] and 0.75 [95% CI, .68-.82], respectively). By contrast, the risk increased significantly in the 0- to 12-month postsurgery period for intra-abdominal infection (aOR, 2.09 [95% CI, 1.78-2.46]) and UTI (aOR, 1.93 [95% CI, 1.74-2.15]) and remained high in the 13- to 24-month postsurgery period (aORs, 1.29 [95% CI, 1.09-1.54] and 1.31 [95% CI, 1.17-1.47], respectively). Conclusions: We found a divergent risk pattern in the risk of 4 common infectious diseases after bariatric surgery. The risk of SSTI and respiratory infection decreased after bariatric surgery whereas that of intra-abdominal infection and UTI increased.

Cohort Study of Maternal Gestational Weight Gain, Gestational Diabetes, and Childhood Asthma.

Data on the association of maternal gestational weight gain (GWG) and gestational diabetes mellitus (GDM) with childhood asthma are limited and inconsistent. We aimed to investigate these associations in a U.S. pre-birth cohort. Analyses included 16,351 mother-child pairs enrolled in the Massachusetts General Hospital Maternal-Child Cohort (1998-2010). Data were obtained by linking electronic health records for prenatal visits/delivery to determine BMI, GWG, and GDM (National Diabetes Data Group criteria) and to determine asthma incidence and allergies (atopic dermatitis or allergic rhinitis) for children. The associations of prenatal exposures with asthma were evaluated using logistic regression adjusted for maternal characteristics. A total of 2306 children (14%) developed asthma by age 5 years. Overall, no association was found between GWG and asthma. GDM was positively associated with offspring asthma (OR 1.46, 95% CI 1.14-1.88). Associations between GDM and asthma were observed only among mothers with early pregnancy BMI between 20 and 24.9 kg/m2 (OR 2.31, CI 1.46-3.65, p-interaction 0.02). We report novel findings on the impact of prenatal exposures on asthma, including increased risk among mothers with GDM, particularly those with a normal BMI. These findings support the strengthening of interventions targeted toward a healthier pregnancy, which may also be helpful for childhood asthma prevention.

Pulmonary Rehabilitation and Readmission Rates for Medicare Beneficiaries with Acute Exacerbation of Chronic Obstructive Pulmonary Disease.

RATIONALE: Clinical trials outside of the United States have assessed whether pulmonary rehabilitation (PR) decreases readmission rates for chronic obstructive pulmonary disease (COPD). We investigated if PR was associated with lower readmission risk for Medicare patients hospitalized for COPD. METHODS: We identified adults enrolled in Medicare hospitalized for COPD exacerbation from a random sample of 5 million Medicare beneficiaries (2010-2012). Patients received PR if they attended ≥1 outpatient session. A cohort was identified to study non-elective, 30-day all-cause readmissions; a subcohort was identified to study 1-year all-cause readmissions. We used stabilized inverse probability weights to balance groups by patient demographics, comorbidities, frailty, smoking status, and long-term oxygen use. We performed cause-specific regression with death as a competing risk. RESULTS: Of 1,839,827 hospitalizations from 2011-2012, we identified 78,074 for COPD. The 30-day cohort contained 7825 COPD index hospitalizations, of which 235 (3%) received PR; the1-year cohort contained 3401, of which 108 (3%) received PR. The median number of PR sessions was 3 (interquartile range 1-11) for both cohorts. The hazard ratio for 30-day readmission was 0.47 (95% confidence interval [CI] 0.33-0.68, P<0.0001). The hazard ratio for 1-year readmission was 1.45 (95% CI 1.19-1.76, P<0.001). CONCLUSIONS: This is one of the first studies of PR and readmissions in Medicare patients. We found that PR was associated with a lower risk of 30-day all-cause readmissions but a higher risk of 1-year all-cause readmission.

Trends in Emergency Department Visits and Hospitalizations for Acute Allergic Reactions and Anaphylaxis Among US Older Adults: 2006-2014.

BACKGROUND: The US older adult population (age ≥65 years) is increasing and may be at increased risk for severe anaphylaxis. Little is known about the health care use for acute allergic reactions (AAR), including anaphylaxis, among older adults. OBJECTIVE: To characterize trends in emergency department (ED) visits and hospitalizations for AAR and anaphylaxis among US older adults from 2006 to 2014 and examine factors associated with severe anaphylaxis. METHODS: We performed cross-sectional analyses of trends in ED visits and hospitalizations among older adults using data from the Nationwide Emergency Department Sample and the National (Nationwide) Inpatient Sample in 2006 to 2014. We used International Classification of Diseases, Ninth Revision, Clinical Modification diagnostic codes to identify visits for AAR, including anaphylaxis. Multivariable logistic regression modeling was used to identify factors associated with severe anaphylaxis (cardiac arrest, intubation, and death). RESULTS: In 2006 to 2014, older adults experienced approximately 1,019,967 AAR-related ED visits, 173,844 AAR-related hospitalizations, 93,795 anaphylaxis-related ED visits, and 72,677 anaphylaxis-related hospitalizations. Whereas AAR-related ED visit and hospitalization rates remained stable (P = .28 and .16, respectively), anaphylaxis-related ED visit and hospitalization rates increased significantly over time (37 visits/100,000 in 2006 to 51 in 2014, P < .001; and from 13 hospitalizations/100,000 in 2006 to 23 in 2014, P < .001), especially hospitalization rates for drug-related anaphylaxis (47 hospitalizations/100,000 in 2006 to 85 in 2014; P < .001). Risk factors for anaphylaxis-related death included older age and drug-related trigger. CONCLUSIONS: In a nationally representative sample of US older adults, the rate of anaphylaxis-related ED visits and hospitalizations increased over time. Drug-related triggers represented a substantial portion of increased health care use and are a growing risk in this vulnerable population.

Risk of Acute Myocardial Infarction and Ischemic Stroke in Patients with Asthma Exacerbation: A Population-Based, Self-Controlled Case Series Study.

BACKGROUND: Patients with asthma have a high incidence of acute myocardial infarction and ischemic stroke. OBJECTIVE: To investigate the acute effect of asthma exacerbation on these cardiovascular events. METHODS: Using population-based inpatient data of 3 geographically diverse US states (Florida, Nebraska, and New York) during the period 2011 to 2014, we conducted a self-controlled case series study of adults (aged ≥40 years) hospitalized with asthma exacerbation. The primary outcome was a composite of acute myocardial infarction and ischemic stroke. We used conditional Poisson regression to compare each patient's incidence rate of the outcome during 3 sequential risk periods (1-7, 8-14, and 15-28 days after asthma exacerbation) with that of the reference period (ie, summed period before and after the 3 risk periods). RESULTS: We identified 4607 adults hospitalized for asthma exacerbation who had a first episode of acute myocardial infarction or ischemic stroke. During the reference period, the incidence rate of acute myocardial infarction or ischemic stroke was 25.0/100 person-years. Compared with the reference period, the incidence rate significantly increased during the first risk period (129.1/100 person-years), with a corresponding adjusted incidence rate ratio of 5.04 (95% CI, 4.29-5.88; P < .001). In the 2 subsequent risk periods, the incidence rate declined but remained high-50.1/100 person-years (adjusted incidence rate ratio, 1.96; 95% CI, 1.51-2.48; P < .001) and 38.0/100 person-years (adjusted incidence rate ratio, 1.48; 95% CI, 1.20-1.81; P < .001), respectively. The findings were similar when the 2 outcomes were examined separately. CONCLUSIONS: In this population-based study of adults with asthma, the risk of acute myocardial infarction and ischemic stroke increased significantly after asthma exacerbation.

Machine Learning-Based Prediction of Clinical Outcomes for Children During Emergency Department Triage.

Importance: While machine learning approaches may enhance prediction ability, little is known about their utility in emergency department (ED) triage. Objectives: To examine the performance of machine learning approaches to predict clinical outcomes and disposition in children in the ED and to compare their performance with conventional triage approaches. Design, Setting, and Participants: Prognostic study of ED data from the National Hospital Ambulatory Medical Care Survey from January 1, 2007, through December 31, 2015. A nationally representative sample of 52 037 children aged 18 years or younger who presented to the ED were included. Data analysis was performed in August 2018. Main Outcomes and Measures: The outcomes were critical care (admission to an intensive care unit and/or in-hospital death) and hospitalization (direct hospital admission or transfer). In the training set (70% random sample), using routinely available triage data as predictors (eg, demographic characteristics and vital signs), we derived 4 machine learning-based models: lasso regression, random forest, gradient-boosted decision tree, and deep neural network. In the test set (the remaining 30% of the sample), we measured the models' prediction performance by computing C statistics, prospective prediction results, and decision curves. These machine learning models were built for each outcome and compared with the reference model using the conventional triage classification information. Results: Of 52 037 eligible ED visits by children (median [interquartile range] age, 6 [2-14] years; 24 929 [48.0%] female), 163 (0.3%) had the critical care outcome and 2352 (4.5%) had the hospitalization outcome. For the critical care prediction, all machine learning approaches had higher discriminative ability compared with the reference model, although the difference was not statistically significant (eg, C statistics of 0.85 [95% CI, 0.78-0.92] for the deep neural network vs 0.78 [95% CI, 0.71-0.85] for the reference; P = .16), and lower number of undertriaged critically ill children in the conventional triage levels 3 to 5 (urgent to nonurgent). For the hospitalization prediction, all machine learning approaches had significantly higher discrimination ability (eg, C statistic, 0.80 [95% CI, 0.78-0.81] for the deep neural network vs 0.73 [95% CI, 0.71-0.75] for the reference; P < .001) and fewer overtriaged children who did not require inpatient management in the conventional triage levels 1 to 3 (immediate to urgent). The decision curve analysis demonstrated a greater net benefit of machine learning models over ranges of clinical thresholds. Conclusions and Relevance: Machine learning-based triage had better discrimination ability to predict clinical outcomes and disposition, with reduction in undertriaging critically ill children and overtriaging children who are less ill.

Near-universal hospitalization of US emergency department patients with cancer and febrile neutropenia.

IMPORTANCE: Febrile neutropenia (FN) is the most common oncologic emergency and is among the most deadly. Guidelines recommend risk stratification and outpatient management of both pediatric and adult FN patients deemed to be at low risk of complications or mortality, but our prior single-center research demonstrated that the vast majority (95%) are hospitalized. OBJECTIVE: From a nationwide perspective, to determine the proportion of cancer patients of all ages hospitalized after an emergency department (ED) visit for FN, and to analyze variability in hospitalization rates. Our a priori hypothesis was that >90% of US cancer-associated ED FN visits would end in hospitalization. DESIGN: Analysis of data from the Nationwide Emergency Department Sample, 2006-2014. SETTING: Stratified probability sample of all US ED visits. PARTICIPANTS: Inclusion criteria were: (1) Clinical Classification Software code indicating cancer, (2) diagnostic code indicating fever, and (3) diagnostic code indicating neutropenia. We excluded visits ending in transfer. EXPOSURE: The hospital at which the visit took place. MAIN OUTCOMES AND MEASURES: Our main outcome is the proportion of ED FN visits ending in hospitalization, with an a priori hypothesis of >90%. Our secondary outcomes are: (a) hospitalization rates among subsets, and (b) proportion of variability in the hospitalization rate attributable to which hospital the patient visited, as measured by the intra-class correlation coefficient (ICC). RESULTS: Of 348,868 visits selected to be representative of all US ED visits, 94% ended in hospitalization (95% Confidence Interval [CI] 93-94%). Each additional decade of age conferred 1.23x increased odds of hospitalization. Those with private (92%), self-pay (92%), and other (93%) insurance were less likely to be hospitalized than those with public insurance (95%, odds ratios [OR] 0.74-0.76). Hospitalization was least likely at non-metropolitan hospitals (84%, OR 0.15 relative to metropolitan teaching hospitals), and was also less likely at metropolitan non-teaching hospitals (94%, OR 0.64 relative to metropolitan teaching hospitals). The ICC adjusted for hospital random effects and patient and hospital characteristics was 26% (95%CI 23-29%), indicating that 26% of the variability in hospitalization rate was attributable to which hospital the patient visited. CONCLUSIONS AND RELEVANCE: Nearly all cancer-associated ED FN visits in the US end in hospitalization. Inter-hospital variation in hospitalization practices explains 26% of the limited variability in hospitalization decisions. Simple, objective tools are needed to improve risk stratification for ED FN patients.

Reduced Risk of Acute Exacerbation of COPD After Bariatric Surgery: A Self-Controlled Case Series Study.

BACKGROUND: Obesity is common among individuals with COPD and associated with increased COPD morbidities. However, little is known about the impact of weight reduction on COPD-related outcomes in patients who are obese. METHODS: Using the population-based ED and inpatient sample in three US states (California, Florida, and Nebraska), we performed a self-controlled case series study of 481 adults who were obese (40-65 years of age) with COPD who underwent bariatric surgery. The primary outcome was an ED visit or hospitalization for acute exacerbation of COPD (AECOPD) from 2005 through 2011. We compared each patient's risk of the outcome during sequential 12-month periods using presurgery months 13 through 24 as the reference period. RESULTS: During the 13 to 24 months before bariatric surgery (ie, reference period), 28% (95% CI, 24%-32%) of patients had an ED visit or hospitalization for AECOPD. In the subsequent 12-month presurgery period, the risk did not change materially (31%; 95% CI, 27%-35%), with an adjusted OR (aOR) of 1.16 (95% CI, 0.88-1.53; P = .29). By contrast, during the first 12 months after bariatric surgery, the risk declined significantly (12%; 95% CI, 9%-15%; aOR, 0.35; 95% CI, 0.25-0.49; P < .001). Likewise, in the subsequent period of 13 to 24 months after bariatric surgery, the risk remained significantly low (13%; 95% CI, 11%-17%; aOR, 0.39; 95% CI, 0.28-0.55; P < .001). CONCLUSIONS: The risk of an ED visit or hospitalization for AECOPD substantially decreased after bariatric surgery in patients who are obese. This observation suggests the effectiveness of substantial weight reduction on COPD morbidity.

Obesity and Severity of Acute Exacerbation of Chronic Obstructive Pulmonary Disease.

RATIONALE: Obesity is relatively common among individuals with chronic obstructive pulmonary disease (COPD). However, little is known about the association of obesity with severity of acute exacerbation of COPD and in-hospital mortality. OBJECTIVES: To examine the association of obesity with markers of severity of acute exacerbation of COPD and in-hospital mortality. METHODS: This is a population-based, retrospective cohort study using the 2012-2013 State Inpatient Databases of seven U.S. states (Arkansas, Florida, Iowa, Nebraska, New York, Utah, and Washington). We included adults (aged ≥40 yr) hospitalized for acute exacerbation of COPD. Obesity, use of noninvasive positive pressure ventilation (NIPPV), and use of invasive mechanical ventilation were determined by International Classification of Diseases, Ninth Revision codes. To examine associations between obesity and each outcome (NIPPV, invasive mechanical ventilation, hospital length of stay (LOS), and in-hospital mortality), we fit unadjusted and adjusted logistic regression models using generalized estimating equations to account for patient clustering within hospitals. We adjusted for age, sex, race/ethnicity, primary payer, median household income, patient residence, hospitalization year, chronic comorbidities, and hospital state. In the sensitivity analysis, we used stabilized inverse probability weighting to estimate the causal relation of obesity with outcomes in this observational study. RESULTS: Of 187,647 patients hospitalized for an acute exacerbation of COPD, 17% were obese. Obesity was associated with increased use of both NIPPV (12.0% vs. 6.5%; adjusted odds ratio [OR] = 1.86; 95% confidence interval [CI] = 1.77-1.95; P < 0.001) and invasive mechanical ventilation (3.5% vs. 2.8%; adjusted OR = 1.13; 95% CI = 1.04-1.22; P = 0.003). Similarly, obese patients were more likely to have a hospital LOS of 4 days or longer (57.9% vs. 50.3%; adjusted OR = 1.37; 95% CI = 1.33-1.41; P < 0.001). In contrast, obesity was associated with a lower in-hospital mortality (0.9% vs. 1.4%; unadjusted OR = 0.63; 95% CI = 0.56-0.72; P < 0.001). After adjusting for potential confounders, this association was no longer statistically significant (adjusted OR = 0.86; 95% CI = 0.75-1.00; P = 0.06). Results were similar in sensitivity analyses using stabilized inverse probability weighting. CONCLUSIONS: In this population-based study of adults hospitalized with an acute exacerbation of COPD, obesity was associated with increased use of noninvasive and invasive ventilation, increased hospital LOS, but was not associated with increased in-hospital mortality.

The association of aspirin use with severity of acute exacerbation of chronic obstructive pulmonary disease: a retrospective cohort study.

Little is known about the effect of long-term aspirin use on acute severity of COPD. We hypothesized that, in patients hospitalized for acute exacerbation of COPD (AECOPD), long-term aspirin use is associated with lower risks of disease severity (in-hospital death, mechanical ventilation use, and hospital length-of-stay). We conducted a retrospective cohort study using large population-based data from 2012 through 2013. Among 206,686 patients (aged ≥40 years) hospitalized for AECOPD, aspirin users had lower in-hospital mortality (1.0 vs. 1.4%; OR 0.60 [95% CI 0.50-0.72]; P < 0.001) and lower risk of invasive mechanical ventilation use (1.7 vs. 2.6%; OR 0.64 [95% CI 0.55-0.73]; P < 0.001) compared to non-users, while there was no significant difference in risks of non-invasive positive pressure ventilation use. Length-of-stay was shorter in aspirin users compared to non-users (P < 0.001). In sum, in patients with AECOPD, aspirin use was associated with lower rates of in-hospital mortality and invasive mechanical ventilation use, and shorter length-of-stay.