Machine learning to predict elevated lipoprotein(a).
Clinical Snapshot
PICO Framework
| P — Population | Not applicable - this is a narrative review, not a systematic review with defined PICO |
| I — Intervention | Not applicable - narrative review |
| C — Comparator | Not applicable - narrative review |
| O — Outcomes | Not applicable - narrative review |
Bottom Line
This narrative review summarizes machine learning approaches to improve Lp(a) screening efficiency, but lacks the systematic methodology required for evidence-based practice recommendations. While the concept of using clinical variables to identify patients most likely to have elevated Lp(a) is promising, the review provides insufficient detail about study quality, methodology, or clinical outcomes. The ARISE framework showing >50% reduction in number needed to test is encouraging, but without systematic evaluation of the evidence, clinicians cannot determine the reliability or generalizability of these findings. More rigorous systematic reviews and validation studies are needed before implementing these approaches in routine practice.
Key Findings
P Value: Not provided
Effect Size: ARISE framework reduced number needed to test by >50%
Primary Outcome: Machine learning models can identify elevated Lp(a) using clinical variables
Nnt Or Sensitivity: Number needed to test reduced by more than 50%
Confidence Interval: Not provided
Clinical Application
Potentially feasible in health systems with electronic health records and clinical decision support Could be relevant for Australian practice given similar cardiovascular risk factors, though validation in Australian populations would be needed. No specific PBS or TGA considerations mentioned. Patients requiring cardiovascular risk assessment where Lp(a) testing is indicated
Abstract
PURPOSE OF REVIEW: Lipoprotein(a), Lp(a), is a genetically determined, lifelong risk factor for atherosclerotic cardiovascular disease (ASCVD). Despite broad guideline support for universal one-time testing, Lp(a) measurement remains rare in clinical practice. This review summarizes recent advances in machine learning-based strategies that can enhance the efficiency, yield, and equity of Lp(a) screening. RECENT FINDINGS: To date, three studies have developed and validated machine learning models to identify individuals with elevated Lp(a) using routinely available clinical variables. The ARISE framework, derived from the UK Biobank and validated across multiple US cohorts, reduced the number needed to test by more than 50% while maintaining consistent discrimination across demographic subgroups. Additional studies have confirmed the feasibility of decision-tree and neural network models to improve case finding for elevated Lp(a) in both clinical and population-based settings. SUMMARY: Machine learning-based strategies provide a scalable means of operationalizing universal Lp(a) testing recommendations within health systems. When developed using unbiased data, externally validated, and assessed for fairness and interpretability, these models can support systematic identification of individuals with elevated Lp(a) and integration of Lp(a) measurement into routine cardiovascular risk assessment.
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