Effects of Neighborhood-level Data on Performance and Algorithmic Equity of a Model That Predicts 30-day Heart Failure Readmissions at an Urban Academic Medical Center.
Effects of Neighborhood-level Data on Performance and Algorithmic Equity of a Model That Predicts 30-day Heart Failure Readmissions at an Urban Academic Medical Center.
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邻里级别数据对模型的性能和算法平等的影响，该模型可以预测城市学术医学中心30天心力衰竭的恢复。
DOI：
10.1016/j.cardfail.2021.04.021
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发表时间：
2021-09
影响因子：
6
通讯作者：
Kangovi S
中科院分区：
文献类型：
--
作者：
Weissman GE;Teeple S;Eneanya ND;Hubbard RA;Kangovi S
社会经济学数据可以改善临床事件的预测。然而，由于结构性种族主义，算法可能无法在种族亚组中公平地执行。因此，我们试图比较心力衰竭再入院的常用预测变量的总体预测性能，并按种族亚组进行比较，有和没有区域痴呆指数（ADI），一个社区水平的社会经济指标。我们对2015年4月1日至2017年3月31日期间从宾夕法尼亚大学卫生系统出院的1，316名主要诊断为充血性心力衰竭的费城居民进行了回顾性队列研究。我们训练了一个回归模型，使用临床和人口统计学变量预测30天再入院的概率。第二个模型还包括ADI作为预测变量。我们用Brier评分（BS）在一个保留的测试集中测量了预测性能。基线模型的总体性能中等（BS 0.13，95% CI 0.13 - 0.14），在白色患者（BS 0.12，95% CI 0.12 - 0.13）和非白色患者（BS 0.13，95% CI 0.13 - 0.14）中也是如此。性能和算法公平性都没有随着ADI的增加而显著改变。包含邻域级数据可能无法可靠地提高性能或算法公平性。
Socioeconomic data may improve predictions of clinical events. However, due to structural racism, algorithms may not perform equitably across racial subgroups. Therefore, we sought to compare the predictive performance overall, and by racial subgroup, of commonly used predictor variables for heart failure readmission with and without the Area Deprivation Index (ADI), a neighborhood-level socioeconomic measure. We conducted a retrospective cohort study of 1,316 Philadelphia residents discharged with a primary diagnosis of congestive heart failure from the University of Pennsylvania Health System between April 1, 2015 and March 31, 2017. We trained a regression model to predict the probability of a 30-day readmission using clinical and demographic variables. A second model also included the ADI as a predictor variable. We measured predictive performance with the Brier Score (BS) in a held-out test set. The baseline model had moderate performance overall (BS 0.13, 95% CI 0.13 to 0.14), and among white (BS 0.12, 95% CI 0.12 to 0.13) and non-white (BS 0.13, 95% CI 0.13 to 0.14) patients. Neither performance nor algorithmic equity were significantly changed with the addition of the ADI. The inclusion of neighborhood-level data may not reliably improve performance or algorithmic equity.
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影响因子：
39.2
作者：
Kind AJ;Jencks S;Brock J;Yu M;Bartels C;Ehlenbach W;Greenberg C;Smith M
通讯作者：
Smith M
DOI：
10.1161/circoutcomes.108.831073
发表时间：
2010-03-01
影响因子：
6.9
作者：
Hurley, Laura P.;Dickinson, L. Miriam;Havranek, Edward P.
通讯作者：
Havranek, Edward P.
影响因子：
6
作者：
Freedland, Kenneth E.;Skala, Judith A.;Rich, Michael W.
通讯作者：
Rich, Michael W.
影响因子：
4.5
作者：
Friedman, JH
通讯作者：
Friedman, JH
影响因子：
5.8
作者：
Kuhn, Max
通讯作者：
Kuhn, Max