Models for Heart Failure Admissions and Admission Rates, 2016 through 2018.

Autor:	Kruse CS; Department of Health Administration, Texas State University, San Marcos, TX 78666, USA., Beauvais BM; Department of Health Administration, Texas State University, San Marcos, TX 78666, USA., Brooks MS; Department of Health Administration, Texas State University, San Marcos, TX 78666, USA., Mileski M; Department of Health Administration, Texas State University, San Marcos, TX 78666, USA., Fulton LV; Department of Health Administration, Texas State University, San Marcos, TX 78666, USA.
Jazyk:	angličtina
Zdroj:	Healthcare (Basel, Switzerland) [Healthcare (Basel)] 2020 Dec 27; Vol. 9 (1). Date of Electronic Publication: 2020 Dec 27.
DOI:	10.3390/healthcare9010022
Abstrakt:	Background: Approximately 6.5 to 6.9 million individuals in the United States have heart failure, and the disease costs approximately $43.6 billion in 2020. This research provides geographical incidence and cost models of this disease in the U.S. and explanatory models to account for hospitals' number of heart failure DRGs using technical, workload, financial, geographical, and time-related variables. Methods: The number of diagnoses is forecast using regression (constrained and unconstrained) and ensemble (random forests, extra trees regressor, gradient boosting, and bagging) techniques at the hospital unit of analysis. Descriptive maps of heart failure diagnostic-related groups (DRGs) depict areas of high incidence. State- and county-level spatial and non-spatial regression models of heart failure admission rates are performed. Expenditure forecasts are estimated. Results: The incidence of heart failure has increased over time with the highest intensities in the East and center of the country; however, several Northern states have seen large increases since 2016. The best predictive model for the number of diagnoses (hospital unit of analysis) was an extremely randomized tree ensemble (predictive R 2 = 0.86). The important variables in this model included workload metrics and hospital type. State-level spatial lag models using first-order Queen criteria were best at estimating heart failure admission rates (R 2 = 0.816). At the county level, OLS was preferred over any GIS model based on Moran's I and resultant R 2 ; however, none of the traditional models performed well (R 2 = 0.169 for the OLS). Gradient-boosted tree models predicted 36% of the total sum of squares; the most important factors were facility workload, mean cash on hand of the hospitals in the county, and mean equity of those hospitals. Online interactive maps at the state and county levels are provided. Conclusions: Heart failure and associated expenditures are increasing. Costs of DRGs in the study increased $61 billion from 2016 through 2018. The increase in the more expensive DRG 291 outpaced others with an associated increase of $92 billion. With the increase in demand and steady-state supply of cardiologists, the costs are likely to balloon over the next decade. Models such as the ones presented here are needed to inform healthcare leaders.
Databáze:	MEDLINE
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