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Introduction Heart failure (HF) is a major contributor to cardiovascular morbidity and mortality in people with diabetes. In this study, we estimated trends in the incidence of HF inpatient admissions and emergency department (ED) visits by diabetes status.Research design and methods Population-based age-standardized HF rates in adults with and without diabetes were estimated from the 2006–2017 National Inpatient Sample, Nationwide ED Sample and year-matched National Health Interview Survey, and stratified by age and sex. Trends were assessed using Joinpoint.Results HF inpatient admissions did not change in adults with diabetes between 2006 and 2013 (from 53.9 to 50.4 per 1000 persons; annual percent change (APC): −0.3 (95% CI −2.5 to 1.9) but increased from 50.4 to 62.3 between 2013 and 2017 (APC: 4.8 (95% CI 0.3 to 9.6)). In adults without diabetes, inpatient admissions initially declined (from 14.8 in 2006 to 12.9 in 2014; APC −2.3 (95% CI −3.2 to –1.2)) and then plateaued. Patterns were similar in men and women, but relative increases were greatest in young adults with diabetes. HF-related ED visits increased overall, in men and women, and in all age groups, but increases were greater in adults with (vs without) diabetes.Conclusions Causes of increased HF rates in hospital settings are unknown, and more detailed data are needed to investigate the aetiology and determine prevention strategies, particularly among adults with diabetes and especially young adults with diabetes.

BackgroundEmergency department (ED) crowding is associated with negative health outcomes, patient dissatisfaction, and longer length of stay (LOS). The addition of advanced diagnostic imaging (ADI), namely CT, ultrasound (U/S), and MRI to ED encounter work up is a predictor of longer length of stay. Earlier and improved prediction of patients' need for advanced imaging may improve overall ED efficiency. The aim of the study was to detect the association between ADI utilization and the structured and unstructured information immediately available during ED triage, and to develop and validate models to predict utilization of ADI during an ED encounter.MethodsWe used the United States National Hospital Ambulatory Medical Care Survey data from 2009 to 2014 to examine which sociodemographic and clinical factors immediately available at ED triage were associated with the utilization of CT, U/S, MRI, and multiple ADI during a patient's ED stay. We used natural language processing (NLP) topic modeling to incorporate free-text reason for visit data available at time of ED triage in addition to other structured patient data to predict the use of ADI using multivariable logistic regression models.ResultsAmong the 139,150 adult ED visits from a national probability sample of hospitals across the U.S, 21.9% resulted in ADI use, including 16.8% who had a CT, 3.6% who had an ultrasound, 0.4% who had an MRI, and 1.2% of the population who had multiple types of ADI. The c-statistic of the predictive models was greater than or equal to 0.78 for all imaging outcomes, and the addition of text-based reason for visit information improved the accuracy of all predictive models.ConclusionsPatient information immediately available during ED triage can accurately predict the eventual use of advanced diagnostic imaging during an ED visit. Such models have the potential to be incorporated into the ED triage workflow in order to more rapidly identify patients who may require advanced imaging during their ED stay and assist with medical decision-making.

National estimates suggest that kidney failure incidence is declining in the US. However, whether this trend is evident in areas with socioeconomic disadvantage is unknown. We examined trends in kidney failure incidence by county-level poverty between 2000 and 2017 and divided the study period into period 1 (2000-05), period 2 (2006-11), and period 3 (2012-17). The magnitude of disparity in kidney failure incidence between high- and low-poverty counties increased from 42.8 more incident cases per million in high-poverty counties in period 1 to 100.1 more in period 3. Despite a national decline, kidney failure incidence increased in high-poverty counties, and disparities between high- and low-poverty counties widened from 2000 to 2017. Achieving the Department of Health and Human Services objective of reducing incident kidney failure cases by 25 percent by 2030 will require focused attention on preventing kidney failure in counties with higher poverty.

Before the 2014 implementation of a new kidney allocation system by the United Network for Organ Sharing, white patients were more likely than black or Hispanic patients to receive a kidney transplant. To determine the effect of the new allocation system on these disparities, we examined data for 179,071 transplant waiting list events in the period June 2013-September 2016, and we calculated monthly transplantation rates (34,133 patients actually received transplants). Implementation of the new system was associated with a narrowing of the disparities in the average monthly transplantation rates by 0.29 percentage point for blacks compared to whites and by 0.24 percentage point for Hispanics compared to whites, which resulted in both disparities becoming nonsignificant after implementation of the new system., (Project HOPE—The People-to-People Health Foundation, Inc.)

The use of predictive modeling for real-time clinical decision making is increasingly recognized as a way to achieve the Triple Aim of improving outcomes, enhancing patients' experiences, and reducing health care costs. The development and validation of predictive models for clinical practice is only the initial step in the journey toward mainstream implementation of real-time point-of-care predictions. Integrating electronic health care predictive analytics (e-HPA) into the clinical work flow, testing e-HPA in a patient population, and subsequently disseminating e-HPA across US health care systems on a broad scale require thoughtful planning. Input is needed from policy makers, health care executives, researchers, and practitioners as the field evolves. This article describes some of the considerations and challenges of implementing e-HPA, including the need to ensure patients' privacy, establish a health system monitoring team to oversee implementation, incorporate predictive analytics into medical education, and make sure that electronic systems do not replace or crowd out decision making by physicians and patients., (Project HOPE—The People-to-People Health Foundation, Inc.)