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Models for Heart Failure Admissions and Admission Rates 2016 through 2018

机译:心力衰竭招生和入场费的模型2016到2018年

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摘要

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 R2 = 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 (R2 = 0.816). At the county level, OLS was preferred over any GIS model based on Moran’s I and resultant R2; however, none of the traditional models performed well (R2 = 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.
机译:背景:2020年,美国大约6.5%至690万人具有心力衰竭,疾病成本约为436亿美元。本研究提供了美国和解释模型的地理发病率和本病的成本模型,以解释为医院的医院数量心力衰竭DRG使用技术,工作量,财务,地理和时间相关变量。方法:预测医院分析单位的回归(约束和无约束)和集合(随机森林,额外的树木回归,梯度升压和装袋)技术的诊断次数。心力衰竭诊断相关群体(DRGS)的描述性地图描绘了高发病率的区域。进行了心力衰竭入学率的状态和县级空间和非空间回归模型。估计支出预测。结果:心力衰竭的发生率随着时间的推移而增加,国家的最高强度在国家/地区的中心;然而,自2016年以来,几个北方各国已经出现大幅增加。诊断数量的最佳预测模型(医院分析单位)是一个极其随机的树集合(预测R2 = 0.86)。此模型中的重要变量包括工作负载指标和医院类型。使用一阶女王标准的状态级空间滞后模型最佳估算心力衰竭入学率(R2 = 0.816)。在县级,OLS在基于Moran的I和结果R2的任何GIS模型中都是优选的;然而,没有一个传统的模型表现良好(R2 = 0.169用于OLS)。梯度升压树模型预测了总线总和的36%;最重要的因素是设施工作量,县内医院手的均值现金,以及这些医院的均值。提供了国家和县级的在线互动地图。结论。心力衰竭和相关支出正在增加。从2016年至2018年,研究中的DRG的成本增加了610亿美元。更昂贵的DRG 291的增加超过了其他920亿美元。随着需求和稳态供应心脏病学家的增加,成本可能会在未来十年内进行气球。诸如此处提供的型号可通知医疗保健领袖。

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