CATCHING CHOLESTEROL CULPRITS: MACHINE-LEARNING ALGORITHM (MLA)-BASED APPROACH TO DETECTION OF UNDIAGNOSED FAMILIAL HYPERCHOLESTEROLEMIA (FH)

IF 5.9 Q1 CARDIAC & CARDIOVASCULAR SYSTEMS
David Kulp MSc , Benjamin Furman MPH (co-first author) , Kain Kim BA , Shivani Lam BS , Shoshana Bardach PhD , Laurence Sperling MD , Danny Eapen MD
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引用次数: 0

Abstract

Therapeutic Area

ASCVD /CVD Risk Reduction

Background

FH is a genetic disorder marked by elevated LDL-C and risk for premature ASCVD. Most patients remain undiagnosed. MLAs offer scalable approaches for identifying high-risk individuals using electronic health record data. The Flag-Identify-Network-Deliver™ Initiative (FIND-FH®) uses an MLA from the Family Heart Foundation to flag potential cases, followed by chart review and patient outreach. This study evaluates the impact of MLA-driven identification on FH diagnosis and management.

Methods

Patients flagged by the FIND-FH® MLA from January 2017-June 2022 at a single academic medical center (n=471) were reviewed. After excluding individuals with known FH, those with LDL-C >190mg/dL and family history of ASCVD were deemed “likely FH” (n=115). In August 2024, patients were contacted via MyChart messages and phone calls recommending FH evaluation; primary care physicians (PCP) were also notified. The primary outcome was new documentation of FH post-contact; secondary outcomes included response rates, changes in lipid-lowering therapy, and specialty of managing clinician, tracked via chart review.

Results

Of 115 identified patients, 113 were contacted; 2 had died. Forty-one (36.3%) patients responded; 43 (38.1%) viewed messages. PCP response rate was 53.2%. Seventeen (15%) patients received a new diagnosis of FH post-contact; 2 (1.8%) had a family history of FH. Post-outreach, 16 (14.2%) had new notes discussing FH, and 22 (19.5%) had changes (escalation or reduction) to lipid-lowering therapy, 6 were newly diagnosed with FH. Twenty-two (19.5%) remained untreated, 74 (65.5%) on 1 therapy, 14 (12.4%) on 2, and 3 (2.7%) on 3; most (78.8%) were on statins. Lipids were managed by PCPs (48.7%), university-based cardiologists (38.1%), outside/community cardiologists (6.2%), or others (7.1%). Patients with a new diagnosis of FH, clinical notes discussing FH, or changes to lipid-lowering therapy were deemed as having “action taken.” Mean LDL-C among patients with action taken (n=39) was 130.5mg/dL compared to 108.1mg/dL for patients without (n=69, p=0.047). Lp(a) testing occurred in 11.5%; CAC scoring in 17.7%.

Conclusions

MLA-driven outreach led to new FH documentation and enhanced care coordination for previously undiagnosed individuals. Results suggest that MLA-enhanced case finding may help address gaps in FH diagnosis and management. Future efforts include optimizing outreach and embedding realtime MLA tools into clinical workflows.
捕捉胆固醇的罪魁祸首:基于机器学习算法(mla)的方法检测未诊断的家族性高胆固醇血症(fh)
治疗领域ASCVD /CVD风险降低背景fh是一种以LDL-C升高和早发ASCVD风险为特征的遗传性疾病。大多数患者仍未得到诊断。mla为使用电子健康记录数据识别高风险个人提供了可扩展的方法。flag - identify - network - deliver™Initiative (FIND-FH®)使用家庭心脏基金会的MLA来标记潜在病例,然后进行图表审查和患者外展。本研究评估了mla驱动的识别对FH诊断和管理的影响。方法回顾2017年1月至2022年6月在单一学术医疗中心(n=471)被FIND-FH®MLA标记的患者。排除已知FH的个体后,LDL-C为190mg/dL且有ASCVD家族史的患者被认为“可能为FH”(n=115)。2024年8月,通过MyChart消息和电话联系患者,建议进行FH评估;还通知了初级保健医生(PCP)。主要结果是接触后新记录的传播感染;次要结局包括反应率、降脂治疗的变化和管理临床医生的专业,通过图表回顾进行跟踪。结果115例确诊患者中,联系了113例;2人死了。41例(36.3%)患者有反应;43人(38.1%)查看消息。PCP有效率为53.2%。17例(15%)患者在接触后被新诊断为FH;2例(1.8%)有FH家族史。外展后,16例(14.2%)有新的记录讨论FH, 22例(19.5%)对降脂治疗有改变(升级或减少),6例新诊断为FH。22人(19.5%)未接受治疗,74人(65.5%)接受1种治疗,14人(12.4%)接受2种治疗,3人(2.7%)接受3种治疗;大多数(78.8%)服用他汀类药物。脂质由pcp(48.7%)、大学心脏病专家(38.1%)、外部/社区心脏病专家(6.2%)或其他(7.1%)管理。新诊断为FH的患者、讨论FH的临床记录或改变降脂治疗被视为“已采取行动”。接受治疗的患者(n=39)的平均LDL-C为130.5mg/dL,而未接受治疗的患者为108.1mg/dL (n=69, p=0.047)。Lp(a)检测占11.5%;CAC评分为17.7%。smla驱动的外展导致了新的FH文件,并加强了对以前未确诊个体的护理协调。结果表明,mla增强的病例发现可能有助于解决FH诊断和管理方面的差距。未来的努力包括优化外展和嵌入实时MLA工具到临床工作流程。
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来源期刊
American journal of preventive cardiology
American journal of preventive cardiology Cardiology and Cardiovascular Medicine
CiteScore
6.60
自引率
0.00%
发文量
0
审稿时长
76 days
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