A laboratory-driven clinical decision support system algorithm for personalized and automated cardiovascular risk stratification in primary care patients.

Clinical Snapshot

Key Findings

• P Value: Not applicable (descriptive study)

• Effect Size: 171 patients (7.5%) classified as high risk, 29 patients (1.6%) as very high risk

• Primary Outcome: Cardiovascular risk stratification using automated CDSS

• Nnt Or Sensitivity: 119 individuals (6.8%) reclassified to higher risk category after Lp(a) adjustment

• Confidence Interval: Not reported

Clinical Application

Requires integration with laboratory information systems and standardised clinical data collection Could complement Australian CVD risk calculators and RACGP guidelines, but would need validation in Australian population and integration with existing pathology services Primary care patients requiring cardiovascular risk assessment, particularly those with intermediate risk profiles

Abstract

OBJECTIVE: To develop and evaluate an automated clinical decision support system (CDSS) capable of computing both categorical and exact-percentage cardiovascular risk (CVR) in routine clinical practice. METHODS: We developed and implemented an automated CDSS (AlinIQ®) for CVR assessment within the Clinical Laboratory Department of Hospital Universitari i Politècnic La Fe (València, Spain), applied to patients referred from Primary Care. The analytical profile-requested either via automatic trigger or proactive clinician order-included total cholesterol, HDL-C, LDL-C, non-HDL-C, triglycerides, lipoprotein(a) [Lp(a)], serum creatinine, and estimated glomerular filtration rate. Additional required inputs were smoking status, systolic blood pressure, country of origin, and age at diabetes onset. The CDSS automatically computed SCORE2, SCORE2-OP, SCORE2-Diabetes, and SCORE2 Asia-Pacific, generating both categorical strata and exact-percentage CVR. Lp(a)-adjusted CVR was derived using coefficients from the Spanish Atherosclerosis Society. Following the ESC 2025 Focused Update, patients meeting predefined clinical criteria for moderate, high, or very high CVR were directly assigned to the corresponding category without SCORE calculation. The system also incorporated modifying risk factors, generating standardized interpretive comments and personalized lipid targets. RESULTS: Over one month, 2289 screenings were requested; 189 (8.3%) were excluded. A total of 171 patients (7.5%) were classified as high risk based on score-based calculation (164 [95.9%] triggered automatically), and 29 (1.6%) as very high risk (23 [79.3%] triggered automatically). 119 individuals (6.8%) were reclassified to a higher risk category after Lp(a)-based adjustment. CONCLUSION: This CDSS provides a scalable and reproducible framework for laboratory-driven cardiovascular prevention.

References

1. 1.Tortosa-Carreres, J., Piqueras, M., Cátedra, C., Vañó-Bellver, A., Domingo-Valero, D., Claramunt-Carbonell, T., Martínez-Dolz, L., & Laiz-Marro, B. (2026). A laboratory-driven clinical decision support system algorithm for personalized and automated cardiovascular risk stratification in primary care patients. Clinica Chimica Acta, 120943. https://doi.org/10.1016/j.cca.2026.120943