Background: Individualized risk prediction represents a prerequisite for providing personalized medicine. Objectives: This study compared proteomics-enabled machine-learning (ML) algorithms with classical and clinical risk prediction methods for all-cause mortality in cohorts of patients with cardiovascular risk factors in the LIFE-Heart Study, followed by validation in the PLIC (Progressione della Lesione Intimale Carotidea) study. Methods: Using the OLINK-Cardiovascular-II panel, 92 proteins were measured in a cohort of 1,998 individuals from the LIFE-Heart Study (derivation) and 772 subjects from the PLIC cohort (external validation). We constructed protein-based mortality prediction models using eXtreme Gradient Boosting (XGBoost) and a neural network, comparing the prediction performance with classical clinical risk scores (Systemic Coronary Risk Evaluation, Framingham), logistic and Cox regression models. Results: All-cause mortality occurred in 156 (8%) patients in the internal validation and 68 (9%) patients in the external validation cohort, within a median follow-up of 10 and 11 years, respectively. On internal and external validation, the Framingham Risk Score achieved areas under the curve (AUCs) of 0.64 (95% CI: 0.59-0.68) and 0.65 (95% CI: 0.58-0.74), logistic regression AUCs of 0.65 (95% CI: 0.57-0.73) and 0.67 (95% CI: 0.59-0.74), Cox regression AUCs of 0.55 (95% CI: 0.51–0.59) and 0.65 (95% CI: 0.57-0.73), the XGBoost classifier AUCs of 0.83 (95% CI: 0.79-0.87) and 0.91 (95% CI: 0.86-0.95), the XGBoost survival estimator AUCs of 0.83 (95% CI: 0.79-0.87) and 0.93 (95% CI: 0.88-0.97), and the neural network AUCs of 0.87 (95% CI: 0.83-0.91) and 0.94 (95% CI: 0.90-0.98), respectively (modern vs classical ML: P < 0.001). Conclusions: ML-driven multiprotein risk models outperform classical regression models and clinical scores for prediction of all-cause mortality in patients at increased cardiovascular risk.
Proteomics-Enabled Deep Learning Machine Algorithms Can Enhance Prediction of Mortality / M. Unterhuber, K.P. Kresoja, K.P. Rommel, C. Besler, A. Baragetti, N. Kloting, U. Ceglarek, M. Bluher, M. Scholz, A.L. Catapano, H. Thiele, P. Lurz. - In: JOURNAL OF THE AMERICAN COLLEGE OF CARDIOLOGY. - ISSN 0735-1097. - 78:16(2021 Oct 19), pp. 1621-1631. [10.1016/j.jacc.2021.08.018]
Proteomics-Enabled Deep Learning Machine Algorithms Can Enhance Prediction of Mortality
A. BaragettiMethodology
;A.L. CatapanoMethodology
;
2021
Abstract
Background: Individualized risk prediction represents a prerequisite for providing personalized medicine. Objectives: This study compared proteomics-enabled machine-learning (ML) algorithms with classical and clinical risk prediction methods for all-cause mortality in cohorts of patients with cardiovascular risk factors in the LIFE-Heart Study, followed by validation in the PLIC (Progressione della Lesione Intimale Carotidea) study. Methods: Using the OLINK-Cardiovascular-II panel, 92 proteins were measured in a cohort of 1,998 individuals from the LIFE-Heart Study (derivation) and 772 subjects from the PLIC cohort (external validation). We constructed protein-based mortality prediction models using eXtreme Gradient Boosting (XGBoost) and a neural network, comparing the prediction performance with classical clinical risk scores (Systemic Coronary Risk Evaluation, Framingham), logistic and Cox regression models. Results: All-cause mortality occurred in 156 (8%) patients in the internal validation and 68 (9%) patients in the external validation cohort, within a median follow-up of 10 and 11 years, respectively. On internal and external validation, the Framingham Risk Score achieved areas under the curve (AUCs) of 0.64 (95% CI: 0.59-0.68) and 0.65 (95% CI: 0.58-0.74), logistic regression AUCs of 0.65 (95% CI: 0.57-0.73) and 0.67 (95% CI: 0.59-0.74), Cox regression AUCs of 0.55 (95% CI: 0.51–0.59) and 0.65 (95% CI: 0.57-0.73), the XGBoost classifier AUCs of 0.83 (95% CI: 0.79-0.87) and 0.91 (95% CI: 0.86-0.95), the XGBoost survival estimator AUCs of 0.83 (95% CI: 0.79-0.87) and 0.93 (95% CI: 0.88-0.97), and the neural network AUCs of 0.87 (95% CI: 0.83-0.91) and 0.94 (95% CI: 0.90-0.98), respectively (modern vs classical ML: P < 0.001). Conclusions: ML-driven multiprotein risk models outperform classical regression models and clinical scores for prediction of all-cause mortality in patients at increased cardiovascular risk.File | Dimensione | Formato | |
---|---|---|---|
Unterhuber M et al. J Am Coll Cardiol 2021.pdf
accesso riservato
Tipologia:
Publisher's version/PDF
Dimensione
1.27 MB
Formato
Adobe PDF
|
1.27 MB | Adobe PDF | Visualizza/Apri Richiedi una copia |
Pubblicazioni consigliate
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.