Generalizable prediction of medication adherence in heart failure

心力衰竭药物依从性的普遍预测

• 批准号: 10851226
• 负责人: Samrachana Adhikari
• 金额: $ 5.47万
• 依托单位: NEW YORK UNIVERSITY SCHOOL OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-03-15 至 2026-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10851226
• 关键词: Address; Adherence; Algorithms; Area; Award; Bayesian Modeling; Biometry; Biostatistical Methods; Calibration; Cardiovascular system; Characteristics; Chronic Disease; Clinical; Clinical Data; Communities; Complex; Counseling; Data; Development; Disparate; Disparity; Doctor of Philosophy; Economic Factors; Electronic Health Record; Ensure; Exposure to; Funding; Geography; Goals; Health; Health Professional; Health system; Healthcare Systems; Heart failure; Immunization; Inequity; Intervention; Lead; Link; Location; Machine Learning; Maps; Medicine; Mentorship; Methodology; Methods; Minority Groups; Modeling; Morbidity - disease rate; National Heart, Lung, and Blood Institute; Neighborhoods; New York City; Paper; Parents; Patient-Focused Outcomes; Patients; Performance; Pharmaceutical Preparations; Pharmacy facility; Physicians; Population Heterogeneity; Process; Provider; Research; Risk; Risk Factors; Services; System; Techniques; Time; Update; Urban Health; Validation; Visit; Visualization; Work; Writing; access disparities; burden of illness; career development; dashboard; doctoral student; electronic health data; evidence base; experience; health care availability; health data; hospitalization rates; improved; improved outcome; innovation; low socioeconomic status; machine learning algorithm; machine learning model; machine learning prediction; medical schools; medication compliance; medication nonadherence; mortality; novel; patient population; patient subsets; point of care; pre-doctoral; prediction algorithm; predictive modeling; professional students; prototype; screening; simulation; social determinants; social factors; social health determinants; spatiotemporal; supervised learning; tool; urban setting

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.

心力衰竭（HF）与住院和死亡率高有关。虽然有许多证据 - 已证明基于基于HF患者的疗法可改善这些疗法，其中近一半是 不定期服用药物。尽管可以通过及时提高药物依从性 干预措施，临床医生要准确地识别和预测药物不遵守是一项挑战 护理点。挑战持续的部分是因为药物依从性是一个受影响的复杂过程 许多患者，提供者，系统，社区和治疗相关因素的相互作用。这个差距 可以通过增加相关数据的可用性来识别有非依从性风险的患者 从电子健康记录（EHRS）中，它具有对准确，实时预测的潜力 HF的依从性。特别是，EHR和药房数据的最新联系为 将先前的药物填充纳入已更新的基于EHR的依从性预测模型 连续。使用机器学习（ML）技术与此类数据可以合并大型 相互关联的风险因素及其相互作用到模型中的相互作用和可容纳连续 随着新信息可用的更新。我们的目标是构建一种基于ML的算法来预测 HF患者的依从性。具体目的是：1）开发监督的ML算法以预测 使用EHR临床数据，链接的药房填充数据和位置 - 来自大型城市卫生系统的基于社会决定因素数据，该系统关心多样化的患者人群； 2） 通过评估跨验证的预测和校准来评估开发算法的公平性 基于社会和经济因素的患者亚组，以确保理想的预测表现 为不同的群体维护； 3）通过验证来评估算法的概括性 第二大城市卫生系统照顾多样化的人群。我们的方法是创新的和新颖的 几种方式。首先，我们将利用药房填充信息与EHR之间的联系 将药房数据纳入我们的模型。第二，我们利用患者地址的地理编码与 可公开可用的数据，以结合邻里层面的健康决定因素，这是 最重要的依从性预测指标，我们的模型。第三，我们将通过 评估来自不同背景患者的预测性能和校准。第四，我们会的 通过在一个不同的卫生系统中开发预测算法并验证预测算法的普遍性 第二种卫生系统中的算法。这些模型将被开发，以便可以使用它们 用于护理点依从性预测。我们的长期目标是能够将它们实施到EHR中 可以将它们纳入干预措施中以解决药物依从性，并最终 改善HF患者的依从性和临床结果。