Generalizable prediction of medication adherence in heart failure

心力衰竭(HF)与高住院率和高死亡率有关。虽然有一些证据- 基础疗法已被证明可以改善心力衰竭患者的预后，这些患者中近一半是 没有定期服用他们的药物。虽然及时服药可以提高服药依从性 对于临床医生来说，准确地识别和预测药物不依从性是具有挑战性的 护理点。这一挑战之所以持续存在，部分原因是服药依从性是一个复杂的过程，受到 与患者、提供者、系统、社区和治疗相关的多种因素的相互作用。这一差距在 可以通过增加相关数据的可用性来识别有未服药风险的患者 来自电子健康记录(EHR)，这提供了做出准确、实时预测的潜力 在HF中的粘附性。特别是，最近电子病历和药房数据的联系创造了机会 将先前的药物填充到基于EHR的依从性预测模型中，该模型已更新 一直都在。对这样的数据使用机器学习(ML)技术允许合并大量的 相互关联的风险因素的数量及其在模型中的相互作用和适应连续的 当有新的信息可用时更新。我们的目标是建立一个基于ML的算法来预测 心衰患者的依从性。具体目标是：1)开发有监督的最大似然算法来预测 心力衰竭患者的用药依从性，使用EHR临床数据、关联的药房填充数据和位置- 基于来自大型城市医疗系统的社会决定因素数据，该系统照顾不同的患者群体；2) 通过评估交叉验证的预测和校准来评估所开发算法的公平性 基于社会和经济因素的患者分组，以确保理想的预测性能 对不同的组进行维护；以及3)通过在 第二，庞大的城市卫生系统，照顾不同的人口。我们的方法是创新和新颖的 有好几种方法。首先，我们将利用药房填写信息和电子健康记录之间的联系来 将药房数据合并到我们的模型中。其次，我们将患者地址的地理编码与 可公开获得的数据，以纳入社区层面的健康社会决定因素，这些因素是 在我们的模型中，遵守的最重要的预测因素。第三，我们将通过以下方式评估模型的公平性 对不同背景的患者进行预测性能和校正的评估。第四，我们将 通过在一个不同的医疗系统中开发和验证预测算法来确保预测算法的普适性 该算法在第二个不同的健康系统中。这些模型将被开发，以便它们可以用于 用于护理点依从性预测。我们的长期目标是能够将它们落实到电子健康记录中， 这一点可以被纳入干预措施，以解决药物依从性问题，并最终， 改善心衰患者的依从性和临床结局。

Heart failure (HF) is associated with high rates of hospitalization and mortality. While a number of evidence- based therapies have been shown to improve outcomes for patients with HF, nearly half of these patients are not regularly taking their medications. Although medication adherence can be improved through timely interventions, it is challenging for clinicians to accurately identify and predict medication non-adherence at the point of care. The challenge persists partly because medication adherence is a complex process influenced by an interplay of a multitude of patient-, provider-, system-, community-, and therapy-related factors. This gap in identifying patients at risk of non-adherence can be addressed through increasing availability of relevant data from electronic health records (EHRs), which affords the potential to make accurate, real time predictions of adherence in HF. In particular, recent linkages of EHR and pharmacy data has created opportunity for incorporation of prior medication fills into EHR-based adherence prediction models that are updated continuously. Using machine learning (ML) techniques with such data allows for incorporation of a large number of intercorrelated risk factors and their interactions into models and for accommodating continuous updates as new information becomes available. Our objective is to build a ML-based algorithm to predict adherence among patients with HF. The specific aims are: 1) to develop supervised ML algorithms to predict medication adherence among HF patients, using EHR clinical data, linked pharmacy fill data, and location- based social determinants data from a large, urban health system that cares for a diverse patient population; 2) to assess fairness of the developed algorithms by evaluating cross-validated prediction and calibration on patient subgroups based on social and economic factors, to ensure that the desirable prediction performance is maintained for the diverse groups; and 3) to assess generalizability of the algorithms through validation in a second large, urban health system caring for a diverse population. Our approach is innovative and novel in several ways. First, we will take advantage of linkages between pharmacy fill information and the EHR to incorporate pharmacy data in our models. Second, we utilize geocoding of patient addresses combined with publicly available data to incorporate neighborhood-level social determinants of health, which are among the most important predictors of adherence, into our models. Third, we will assess fairness of the model by evaluating the predictive performance and calibration on patients from diverse backgrounds. Fourth, we will ensure generalizability of the prediction algorithm by developing it in one diverse health system and validating the algorithm in a second diverse health system. These models will be developed such that they can be used for point-of-care adherence prediction. Our long term goal is to be able to implement them into the EHR, at which point they can be incorporated into interventions to address medication adherence and, ultimately, improve both adherence and clinical outcomes for patients with HF.