PDF(Pubmed)
Abstract：
UNASSIGNED: Guideline-directed medical therapy (GDMT) optimization can improve outcomes in heart failure with reduced ejection fraction.
UNASSIGNED: The objective of this study was to determine if a novel computable algorithm appropriately recommended GDMT.
UNASSIGNED: Clinical trial data from the GUIDE-IT (Guiding Evidence-Based Therapy Using Biomarker Intensified Treatment in Heart Failure) and HF-ACTION (Heart Failure: A Controlled Trial Investigating Outcomes of Exercise Training) trials were evaluated with a computable medication optimization algorithm that outputs GDMT recommendations and a medication optimization score (MOS). Algorithm-based recommendations were compared to medication changes. A Cox proportional-hazards model was used to estimate the associations between MOS and the composite primary end point for both trials.
UNASSIGNED: The algorithm recommended initiation of angiotensin-converting enzyme inhibitor/angiotensin receptor blocker, beta-blockers, and mineralocorticoid receptor antagonists in 52.8%, 34.9%, and 68.1% of GUIDE-IT visits, respectively, when not prescribed the drug. Initiation only occurred in 20.8%, 56.9%, and 15.8% of subsequent visits. The algorithm also identified dose titration in 48.8% of visits for angiotensin-converting enzyme inhibitor/angiotensin receptor blockers and 39.4% of visits for beta-blockers. Those increases only occurred in 24.3% and 36.8% of subsequent visits. A higher baseline MOS was associated with a lower risk of cardiovascular death or heart failure hospitalization (HR: 0.41; 95% CI: 0.21-0.80; P = 0.009) in GUIDE-IT and all-cause death and hospitalization (HR: 0.61; 95% CI: 0.44-0.84; P = 0.003) in HF-ACTION.
UNASSIGNED: The algorithm accurately identified patients for GDMT optimization. Even in a clinical trial with robust protocols, GDMT could have been further optimized in a meaningful number of visits. The algorithm-generated MOS was associated with a lower risk of clinical outcomes. Implementation into clinical care may identify and address suboptimal GDMT in patients with heart failure with reduced ejection fraction.
UNASSIGNED: The objective of this study was to determine if a novel computable algorithm appropriately recommended GDMT.
UNASSIGNED: Clinical trial data from the GUIDE-IT (Guiding Evidence-Based Therapy Using Biomarker Intensified Treatment in Heart Failure) and HF-ACTION (Heart Failure: A Controlled Trial Investigating Outcomes of Exercise Training) trials were evaluated with a computable medication optimization algorithm that outputs GDMT recommendations and a medication optimization score (MOS). Algorithm-based recommendations were compared to medication changes. A Cox proportional-hazards model was used to estimate the associations between MOS and the composite primary end point for both trials.
UNASSIGNED: The algorithm recommended initiation of angiotensin-converting enzyme inhibitor/angiotensin receptor blocker, beta-blockers, and mineralocorticoid receptor antagonists in 52.8%, 34.9%, and 68.1% of GUIDE-IT visits, respectively, when not prescribed the drug. Initiation only occurred in 20.8%, 56.9%, and 15.8% of subsequent visits. The algorithm also identified dose titration in 48.8% of visits for angiotensin-converting enzyme inhibitor/angiotensin receptor blockers and 39.4% of visits for beta-blockers. Those increases only occurred in 24.3% and 36.8% of subsequent visits. A higher baseline MOS was associated with a lower risk of cardiovascular death or heart failure hospitalization (HR: 0.41; 95% CI: 0.21-0.80; P = 0.009) in GUIDE-IT and all-cause death and hospitalization (HR: 0.61; 95% CI: 0.44-0.84; P = 0.003) in HF-ACTION.
UNASSIGNED: The algorithm accurately identified patients for GDMT optimization. Even in a clinical trial with robust protocols, GDMT could have been further optimized in a meaningful number of visits. The algorithm-generated MOS was associated with a lower risk of clinical outcomes. Implementation into clinical care may identify and address suboptimal GDMT in patients with heart failure with reduced ejection fraction.
摘要：
■指南指导的药物治疗（GDMT）优化可以改善心力衰竭的预后，降低射血分数。
■本研究的目的是确定新的可计算算法是否适当地推荐GDMT。
■来自GUIDE-IT（指导使用生物标志物强化治疗心力衰竭的循证治疗）和HF-ACTION（心力衰竭：运动训练的对照试验研究结果）试验的临床试验数据使用可计算的药物优化算法进行评估，该算法输出GDMT建议和药物优化评分（MOS）。将基于算法的建议与药物变化进行比较。Cox比例风险模型用于评估两个试验的MOS与复合主要终点之间的关联。
■算法建议启动血管紧张素转换酶抑制剂/血管紧张素受体阻滞剂，β受体阻滞剂，盐皮质激素受体拮抗剂占52.8%，34.9%,和68.1%的GUIDE-IT访问，分别,当没有开处方的时候。启动仅发生在20.8%，56.9%,以及15.8%的后续访问量。该算法还确定了48.8％的血管紧张素转换酶抑制剂/血管紧张素受体阻滞剂和39.4％的β受体阻滞剂的剂量滴定。这些增长仅发生在随后访问的24.3％和36.8％中。在GUIDE-IT中，较高的基线MOS与较低的心血管死亡或心力衰竭住院风险（HR：0.41;95％CI：0.21-0.80;P=0.009）以及HF-ACTION中的全因死亡和住院风险（HR：0.61;95％CI：0.44-0.84;P=0.003）相关。
■该算法准确地识别了GDMT优化的患者。即使在具有强大协议的临床试验中，GDMT可以在有意义的访问次数中进一步优化。算法生成的MOS与较低的临床结果风险相关。实施临床护理可以识别和解决射血分数降低的心力衰竭患者的次优GDMT。
■本研究的目的是确定新的可计算算法是否适当地推荐GDMT。
■来自GUIDE-IT（指导使用生物标志物强化治疗心力衰竭的循证治疗）和HF-ACTION（心力衰竭：运动训练的对照试验研究结果）试验的临床试验数据使用可计算的药物优化算法进行评估，该算法输出GDMT建议和药物优化评分（MOS）。将基于算法的建议与药物变化进行比较。Cox比例风险模型用于评估两个试验的MOS与复合主要终点之间的关联。
■算法建议启动血管紧张素转换酶抑制剂/血管紧张素受体阻滞剂，β受体阻滞剂，盐皮质激素受体拮抗剂占52.8%，34.9%,和68.1%的GUIDE-IT访问，分别,当没有开处方的时候。启动仅发生在20.8%，56.9%,以及15.8%的后续访问量。该算法还确定了48.8％的血管紧张素转换酶抑制剂/血管紧张素受体阻滞剂和39.4％的β受体阻滞剂的剂量滴定。这些增长仅发生在随后访问的24.3％和36.8％中。在GUIDE-IT中，较高的基线MOS与较低的心血管死亡或心力衰竭住院风险（HR：0.41;95％CI：0.21-0.80;P=0.009）以及HF-ACTION中的全因死亡和住院风险（HR：0.61;95％CI：0.44-0.84;P=0.003）相关。
■该算法准确地识别了GDMT优化的患者。即使在具有强大协议的临床试验中，GDMT可以在有意义的访问次数中进一步优化。算法生成的MOS与较低的临床结果风险相关。实施临床护理可以识别和解决射血分数降低的心力衰竭患者的次优GDMT。
