Underweight patients are at higher risk of death after acute myocardial infarction (AMI) than normal weight patients; however, it is unclear whether this relationship is explained by confounding due ...to cachexia or other factors associated with low body mass index (BMI). This study aimed to answer two questions: (1) does comprehensive risk adjustment for comorbid illness and frailty measures explain the higher mortality after AMI in underweight patients, and (2) is the relationship between underweight and mortality also observed in patients with AMI who are otherwise without significant chronic illness and are presumably free of cachexia?
We analyzed data from the Cooperative Cardiovascular Project, a cohort-based study of Medicare beneficiaries hospitalized for AMI between January 1994 and February 1996 with 17 y of follow-up and detailed clinical information to compare short- and long-term mortality in underweight and normal weight patients (n = 57,574). We used Cox proportional hazards regression to investigate the association of low BMI with 30-d, 1-y, 5-y, and 17-y mortality after AMI while adjusting for patient comorbidities, frailty measures, and laboratory markers of nutritional status. We also repeated the analyses in a subset of patients without significant comorbidity or frailty. Of the 57,574 patients with AMI included in this cohort, 5,678 (9.8%) were underweight and 51,896 (90.2%) were normal weight at baseline. Underweight patients were older, on average, than normal weight patients and had a higher prevalence of most comorbidities and measures of frailty. Crude mortality was significantly higher for underweight patients than normal weight patients at 30 d (25.2% versus 16.4%, p < 0.001), 1 y (51.3% versus 33.8%, p < 0.001), 5 y (79.2% versus 59.4%, p < 0.001), and 17 y (98.3% versus 94.0%, p < 0.001). After adjustment, underweight patients had a 13% higher risk of 30-d death and a 26% higher risk of 17-y death than normal weight patients (30-d hazard ratio HR 1.13, 95% CI 1.07-1.20; 17-y HR 1.26, 95% CI 1.23-1.30). Survival curves for underweight and normal weight patients separated early and remained separate over 17 y, suggesting that underweight patients remained at a significant survival disadvantage over time. Similar findings were observed among the subset of patients without comorbidity at baseline. Underweight patients without comorbidity had a 30-d adjusted mortality similar to that of normal weight patients but a 21% higher risk of death over the long term (30-d HR 1.08, 95% CI 0.93-1.26; 17-y HR 1.21, 95% CI 1.14-1.29). The adverse effects of low BMI were greatest in patients with very low BMIs. The major limitation of this study was the use of surrogate markers of frailty and comorbid conditions to identify patients at highest risk for cachexia rather than clear diagnostic criteria for cachexia.
Underweight BMI is an important risk factor for mortality after AMI, independent of confounding by comorbidities, frailty measures, and laboratory markers of nutritional status. Strategies to promote weight gain in underweight patients after AMI are worthy of testing.
The current ability to predict readmissions in patients with heart failure is modest at best. It is unclear whether machine learning techniques that address higher dimensional, nonlinear ...relationships among variables would enhance prediction. We sought to compare the effectiveness of several machine learning algorithms for predicting readmissions.
Using data from the Telemonitoring to Improve Heart Failure Outcomes trial, we compared the effectiveness of random forests, boosting, random forests combined hierarchically with support vector machines or logistic regression (LR), and Poisson regression against traditional LR to predict 30- and 180-day all-cause readmissions and readmissions because of heart failure. We randomly selected 50% of patients for a derivation set, and a validation set comprised the remaining patients, validated using 100 bootstrapped iterations. We compared C statistics for discrimination and distributions of observed outcomes in risk deciles for predictive range. In 30-day all-cause readmission prediction, the best performing machine learning model, random forests, provided a 17.8% improvement over LR (mean C statistics, 0.628 and 0.533, respectively). For readmissions because of heart failure, boosting improved the C statistic by 24.9% over LR (mean C statistic 0.678 and 0.543, respectively). For 30-day all-cause readmission, the observed readmission rates in the lowest and highest deciles of predicted risk with random forests (7.8% and 26.2%, respectively) showed a much wider separation than LR (14.2% and 16.4%, respectively).
Machine learning methods improved the prediction of readmission after hospitalization for heart failure compared with LR and provided the greatest predictive range in observed readmission rates.
Health reform and policy initiatives over the last 2 decades have led to significant changes in pediatric clinical practice. However, little is known about recent trends in pediatric hospitalizations ...and readmissions at a national level.
Data from the 2010-2016 Healthcare Cost and Utilization Project Nationwide Readmissions Database and National Inpatient Sample were analyzed to characterize patient-level and hospital-level trends in annual pediatric (ages 1-17 years) admissions and 30-day readmissions. Poisson regression was used to evaluate trends in pediatric readmissions over time.
From 2010 to 2016, the total number of index admissions decreased by 21.3%, but the percentage of admissions for children with complex chronic conditions increased by 5.7%. Unadjusted pediatric 30-day readmission rates increased over time from 6.26% in 2010 to 7.02% in 2016 with a corresponding increase in numbers of admissions for patients with complex chronic conditions. When stratified by complex or chronic conditions, readmission rates declined or remained stable across patient subgroups. Mean risk-adjusted hospital readmission rates increased over time overall (6.46% in 2010 to 7.14% in 2016) and in most hospital subgroups but decreased over time in metropolitan teaching hospitals.
Pediatric admissions declined from 2010 to 2016 as 30-day readmission rates increased. The increase in readmission rates was associated with greater numbers of admissions for children with chronic conditions. Hospitals serving pediatric patients need to account for the rising complexity of pediatric admissions and develop strategies for reducing readmissions in this high-risk population.
Familial hypercholesterolemia (FH) and other extreme elevations in low-density lipoprotein cholesterol significantly increase the risk of atherosclerotic cardiovascular disease; however, recent data ...suggest that prescription rates for statins remain low in these patients. National rates of screening, awareness, and treatment with statins among individuals with FH or severe dyslipidemia are unknown.
Data from the 1999 to 2014 National Health and Nutrition Examination Survey were used to estimate prevalence rates of self-reported screening, awareness, and statin therapy among US adults (n=42 471 weighted to represent 212 million US adults) with FH (defined using the Dutch Lipid Clinic criteria) and with severe dyslipidemia (defined as low-density lipoprotein cholesterol levels ≥190 mg/dL). Logistic regression was used to identify sociodemographic and clinical correlates of hypercholesterolemia awareness and statin therapy.
The estimated US prevalence of definite/probable FH was 0.47% (standard error, 0.03%) and of severe dyslipidemia was 6.6% (standard error, 0.2%). The frequency of cholesterol screening and awareness was high (>80%) among adults with definite/probable FH or severe dyslipidemia; however, statin use was uniformly low (52.3% standard error, 8.2% of adults with definite/probable FH and 37.6% standard error, 1.2% of adults with severe dyslipidemia). Only 30.3% of patients with definite/probable FH on statins were taking a high-intensity statin. The prevalence of statin use in adults with severe dyslipidemia increased over time (from 29.4% to 47.7%) but not faster than trends in the general population (from 5.7% to 17.6%). Older age, health insurance status, having a usual source of care, diabetes mellitus, hypertension, and having a personal history of early atherosclerotic cardiovascular disease were associated with higher statin use.
Despite the high prevalence of cholesterol screening and awareness, only ≈50% of adults with FH are on statin therapy, with even fewer prescribed a high-intensity statin; young and uninsured patients are at the highest risk for lack of screening and for undertreatment. This study highlights an imperative to improve the frequency of cholesterol screening and statin prescription rates to better identify and treat this high-risk population. Additional studies are needed to better understand how to close these gaps in screening and treatment.
We sought to evaluate trends in pediatric inpatient unit capacity and access and to measure pediatric inpatient unit closures across the United States.
We performed a retrospective study of 4720 US ...hospitals using the 2008-2018 American Hospital Association survey. We used linear regression to describe trends in pediatric inpatient unit and PICU capacity. We compared trends in pediatric inpatient days and bed counts by state. We examined changes in access to care by calculating distance to the nearest pediatric inpatient services by census block group. We analyzed hospital characteristics associated with pediatric inpatient unit closure in a survival model.
Pediatric inpatient units decreased by 19.1% (34 units per year; 95% confidence interval CI 31 to 37), and pediatric inpatient unit beds decreased by 11.8% (407 beds per year; 95% CI 347 to 468). PICU beds increased by 16.0% (66.9 beds per year; 95% CI 53 to 81), primarily at children's hospitals. Rural areas experienced steeper proportional declines in pediatric inpatient unit beds (-26.1% vs -10.0%). Most states experienced decreases in both pediatric inpatient unit beds (median state -18.5%) and pediatric inpatient days (median state -10.0%). Nearly one-quarter of US children experienced an increase in distance to their nearest pediatric inpatient unit. Low-volume pediatric units and those without an associated PICU were at highest risk of closing.
Pediatric inpatient unit capacity is decreasing in the United States. Access to inpatient care is declining for many children, particularly those in rural areas. PICU beds are increasing, primarily at large children's hospitals. Policy and surge planning improvements may be needed to mitigate the effects of these changes.
Abstract Background As U.S. legislators are urged to combat ghost networks in behavioral health and address the provider data quality issue, it becomes important to better characterize the variation ...in data quality of provider directories to understand root causes and devise solutions. Therefore, this manuscript examines consistency of address, phone number, and specialty information for physician entries from 5 national health plan provider directories by insurer, physician specialty, and state. Methods We included all physicians in the Medicare Provider Enrollment, Chain, and Ownership System (PECOS) found in ≥ 2 health insurer physician directories across 5 large national U.S. health insurers. We examined variation in consistency of address, phone number, and specialty information among physicians by insurer, physician specialty, and state. Results Of 634,914 unique physicians in the PECOS database, 449,282 were found in ≥ 2 directories and included in our sample. Across insurers, consistency of address information varied from 16.5 to 27.9%, consistency of phone number information varied from 16.0 to 27.4%, and consistency of specialty information varied from 64.2 to 68.0%. General practice, family medicine, plastic surgery, and dermatology physicians had the highest consistency of addresses (37-42%) and phone numbers (37-43%), whereas anesthesiology, nuclear medicine, radiology, and emergency medicine had the lowest consistency of addresses (11-21%) and phone numbers (9-14%) across health insurer directories. There was marked variation in consistency of address, phone number, and specialty information by state. Conclusions In evaluating a large national sample of U.S. physicians, we found minimal variation in provider directory consistency by insurer, suggesting that this is a systemic problem that insurers have not solved, and considerable variation by physician specialty with higher quality data among more patient-facing specialties, suggesting that physicians may respond to incentives to improve data quality. These data highlight the importance of novel policy solutions that leverage technology targeting data quality to centralize provider directories so as not to not reinforce existing data quality issues or policy solutions to create national and state-level standards that target both insurers and physician groups to maximize quality of provider information.
Background An “obesity paradox” has been described in patients with acute myocardial infarction (AMI), whereby obese and overweight patients have a lower risk of short-term mortality after AMI than ...normal-weight patients. However, the long-term association of obesity with mortality after AMI remains unknown. Methods We used data from the Cooperative Cardiovascular Project, a prospective medical record study of Medicare beneficiaries hospitalized with AMI with 17 years of follow-up (N = 124,981), to evaluate the association of higher body mass index (BMI) with short- and long-term survival after AMI. Cox proportional hazards models were used to estimate life expectancy after AMI and the years of potential life lost or gained attributable to excess weight. Results Approximately 41.5% of patients were classified as normal weight; 38.7%, as overweight; 14.3%, as obese; and 5.5%, as morbidly obese. Normal-weight patients had the highest crude mortality at all follow-up time points, whereas obese patients had the lowest. Adjustment for patient and treatment characteristics reduced this difference, but the survival benefit persisted in overweight and obese patients at all time points. Morbidly obese and normal-weight patients had a comparable risk of death at 17 years. Life expectancy estimates were generally lowest for morbidly obese patients and highest for overweight patients. Differences in life expectancy between BMI groups were most pronounced in younger patients. After adjustment, overweight and obesity were associated with greater life years at all ages; however, morbid obesity was only associated with better survival in patients ≥75 years of age at the time of AMI. Conclusions Overweight and obesity are associated with improved short- and long-term survival after AMI, which results in moderate gains in life expectancy relative to normal-weight patients. These findings suggest that higher BMI confers a protective advantage over the entire remaining lifespan in older patients with AMI.
Young adults with hyperlipidemia, hypertension, and diabetes are at increased risk of developing heart disease later in life. Despite emphasis on early screening, little is known about awareness of ...these risk factors in young adulthood.
Data from the nationally representative cross-sectional National Health and Nutrition Examination Survey 2011–2014 were analyzed in 2017 to estimate the prevalence of self-reported awareness of hypercholesterolemia, hypertension, and diabetes in U.S. young adults aged 18–39 years (n=11,083). Prevalence estimates were weighted to population estimates using survey procedures, and predictors of awareness were identified using weighted logistic regression.
Among U.S. young adults, the prevalence of hypercholesterolemia, hypertension, and diabetes was 8.8% (SE=0.4%); 7.3% (SE=0.3%); and 2.6% (SE=0.2%), respectively. The prevalence of borderline high cholesterol, blood pressure, and blood glucose were substantially higher (21.6% SE= 0.6%; 26.9% SE=0.7%; and 18.9% SE=0.6%, respectively). Awareness was low for hypercholesterolemia (56.9% SE=2.4%) and moderate for hypertension and diabetes (62.7% SE=2.4% and 70.0% SE=2.7%); <25% of young adults with borderline levels of these risk factors were aware of their risk. Correlates of risk factor awareness included older age, insurance status, family income above the poverty line, U.S. origin, having a usual source of health care, and the presence of comorbid conditions.
Despite the high prevalence of cardiovascular risk factors in U.S. young adults, awareness remains less than ideal. Interventions that target access may increase awareness and facilitate achieving treatment goals in young adults.
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