PDF(Pubmed)
Abstract：
UNASSIGNED: Patients with heart failure and reduced ejection fraction (HFrEF) are consistently underprescribed guideline-directed medications. Although many barriers to prescribing are known, identification of these barriers has relied on traditional a priori hypotheses or qualitative methods. Machine learning can overcome many limitations of traditional methods to capture complex relationships in data and lead to a more comprehensive understanding of the underpinnings driving underprescribing. Here, we used machine learning methods and routinely available electronic health record data to identify predictors of prescribing.
UNASSIGNED: We evaluated the predictive performance of machine learning algorithms to predict prescription of four types of medications for adults with HFrEF: angiotensin converting enzyme inhibitor/angiotensin receptor blocker (ACE/ARB), angiotensin receptor-neprilysin inhibitor (ARNI), evidence-based beta blocker (BB), or mineralocorticoid receptor antagonist (MRA). The models with the best predictive performance were used to identify the top 20 characteristics associated with prescribing each medication type. Shapley values were used to provide insight into the importance and direction of the predictor relationships with medication prescribing.
UNASSIGNED: For 3,832 patients meeting the inclusion criteria, 70% were prescribed an ACE/ARB, 8% an ARNI, 75% a BB, and 40% an MRA. The best-predicting model for each medication type was a random forest (area under the curve: 0.788-0.821; Brier score: 0.063-0.185). Across all medications, top predictors of prescribing included prescription of other evidence-based medications and younger age. Unique to prescribing an ARNI, the top predictors included lack of diagnoses of chronic kidney disease, chronic obstructive pulmonary disease, or hypotension, as well as being in a relationship, nontobacco use, and alcohol use.
UNASSIGNED: We identified multiple predictors of prescribing for HFrEF medications that are being used to strategically design interventions to address barriers to prescribing and to inform further investigations. The machine learning approach used in this study to identify predictors of suboptimal prescribing can also be used by other health systems to identify and address locally relevant gaps and solutions to prescribing.
UNASSIGNED: We evaluated the predictive performance of machine learning algorithms to predict prescription of four types of medications for adults with HFrEF: angiotensin converting enzyme inhibitor/angiotensin receptor blocker (ACE/ARB), angiotensin receptor-neprilysin inhibitor (ARNI), evidence-based beta blocker (BB), or mineralocorticoid receptor antagonist (MRA). The models with the best predictive performance were used to identify the top 20 characteristics associated with prescribing each medication type. Shapley values were used to provide insight into the importance and direction of the predictor relationships with medication prescribing.
UNASSIGNED: For 3,832 patients meeting the inclusion criteria, 70% were prescribed an ACE/ARB, 8% an ARNI, 75% a BB, and 40% an MRA. The best-predicting model for each medication type was a random forest (area under the curve: 0.788-0.821; Brier score: 0.063-0.185). Across all medications, top predictors of prescribing included prescription of other evidence-based medications and younger age. Unique to prescribing an ARNI, the top predictors included lack of diagnoses of chronic kidney disease, chronic obstructive pulmonary disease, or hypotension, as well as being in a relationship, nontobacco use, and alcohol use.
UNASSIGNED: We identified multiple predictors of prescribing for HFrEF medications that are being used to strategically design interventions to address barriers to prescribing and to inform further investigations. The machine learning approach used in this study to identify predictors of suboptimal prescribing can also be used by other health systems to identify and address locally relevant gaps and solutions to prescribing.
摘要：
■患有心力衰竭和射血分数降低（HFrEF）的患者始终未按指南指导用药。虽然处方的许多障碍是已知的，对这些障碍的识别依赖于传统的先验假设或定性方法。机器学习可以克服传统方法的许多限制，以捕获数据中的复杂关系，并导致对驱动不足的基础的更全面的理解。这里,我们使用机器学习方法和常规可用的电子健康记录数据来确定处方的预测因素.
我们评估了机器学习算法的预测性能，以预测成人HFrEF的四种药物的处方：血管紧张素转换酶抑制剂/血管紧张素受体阻滞剂（ACE/ARB），血管紧张素受体-脑啡肽抑制剂（ARNI），循证β受体阻滞剂（BB），或盐皮质激素受体拮抗剂（MRA）。具有最佳预测性能的模型用于识别与开处方每种药物类型相关的前20个特征。Shapley值用于提供对预测药物处方关系的重要性和方向的洞察。
■对于3,832名符合纳入标准的患者，70%开了ACE/ARB，8%的ARNI，75%一BB，和40%的MRA。每种药物类型的最佳预测模型是随机森林(曲线下面积:0.788-0.821;Brier评分:0.063-0.185)。在所有药物中，处方的主要预测因素包括其他循证药物的处方和年龄较小.独特的处方ARNI，最重要的预测因素包括缺乏慢性肾脏病的诊断，慢性阻塞性肺疾病,或者低血压,以及在一段关系中，非烟草使用，酒精的使用。
我们确定了HFrEF药物处方的多个预测因素，这些预测因素被用于战略性地设计干预措施，以解决处方障碍并为进一步的调查提供信息。本研究中用于识别次优处方预测因素的机器学习方法也可以被其他卫生系统用来识别和解决局部相关的差距和处方解决方案。
我们评估了机器学习算法的预测性能，以预测成人HFrEF的四种药物的处方：血管紧张素转换酶抑制剂/血管紧张素受体阻滞剂（ACE/ARB），血管紧张素受体-脑啡肽抑制剂（ARNI），循证β受体阻滞剂（BB），或盐皮质激素受体拮抗剂（MRA）。具有最佳预测性能的模型用于识别与开处方每种药物类型相关的前20个特征。Shapley值用于提供对预测药物处方关系的重要性和方向的洞察。
■对于3,832名符合纳入标准的患者，70%开了ACE/ARB，8%的ARNI，75%一BB，和40%的MRA。每种药物类型的最佳预测模型是随机森林(曲线下面积:0.788-0.821;Brier评分:0.063-0.185)。在所有药物中，处方的主要预测因素包括其他循证药物的处方和年龄较小.独特的处方ARNI，最重要的预测因素包括缺乏慢性肾脏病的诊断，慢性阻塞性肺疾病,或者低血压,以及在一段关系中，非烟草使用，酒精的使用。
我们确定了HFrEF药物处方的多个预测因素，这些预测因素被用于战略性地设计干预措施，以解决处方障碍并为进一步的调查提供信息。本研究中用于识别次优处方预测因素的机器学习方法也可以被其他卫生系统用来识别和解决局部相关的差距和处方解决方案。
