July 5, 2021Computational and Mathematical Methods in MedicineOpen Access

Clinical Feature-Based Machine Learning Model for 1-Year Mortality Risk Prediction of ST-Segment Elevation Myocardial Infarction in Patients with Hyperuricemia: A Retrospective Study

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Why the study?

There is no practical method to predict or monitor the prognosis of patients with STEMI complicated by hyperuricemia.

Do machine learning models improve the prediction of 1-year mortality compared to the GRACE risk score in STEMI patients with hyperuricemia?

Structured PICO

Do machine learning models improve the prediction of 1-year mortality compared to the GRACE risk score in STEMI patients with hyperuricemia?

Population

656 adults (>18 years) diagnosed with STEMI and hyperuricemia who underwent emergency percutaneous coronary intervention, average age 62.5 ± 13.6 years, 83.6% male, at a single center.

Intervention

Machine learning models (logistic regression, k-nearest neighbor, CatBoost, random forest, and XGBoost) for risk prediction

Comparator

Traditional global (GRACE) risk score

Outcome

1-year mortalityhard clinical

Machine learning algorithms, particularly CatBoost, can accurately predict 1-year mortality in STEMI patients with hyperuricemia, potentially outperforming traditional risk scores.

Implication

ML models may aid 1-year mortality prediction in STEMI with hyperuricemia; leaves open external validation before clinical use.

Abstract

Accurate risk assessment of high-risk patients is essential in clinical practice. However, there is no practical method to predict or monitor the prognosis of patients with ST-segment elevation myocardial infarction (STEMI) complicated by hyperuricemia. We aimed to evaluate the performance of different machine learning models for the prediction of 1-year mortality in STEMI patients with hyperuricemia. We compared five machine learning models (logistic regression, <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" id="M1"> <a:mi>k</a:mi> </a:math> -nearest neighbor, CatBoost, random forest, and XGBoost) with the traditional global (GRACE) risk score for acute coronary event registrations. We registered patients aged >18 years diagnosed with STEMI and hyperuricemia at the Affiliated Hospital of Zunyi Medical University between January 2016 and January 2020. Overall, 656 patients were enrolled (average age, <c:math xmlns:c="http://www.w3.org/1998/Math/MathML" id="M2"> <c:mn>62.5</c:mn> <c:mo>±</c:mo> <c:mn>13.6</c:mn> <c:mtext> </c:mtext> <c:mtext>years</c:mtext> </c:math> ; 83.6%, male). All patients underwent emergency percutaneous coronary intervention. We evaluated the performance of five machine learning classifiers and the GRACE risk model in predicting 1-year mortality. The area under the curve (AUC) of the six models, including the GRACE risk model, ranged from 0.75 to 0.88. Among all the models, CatBoost had the highest predictive accuracy (0.89), AUC (0.87), precision (0.84), and F1 value (0.44). After hybrid sampling technique optimization, CatBoost had the highest accuracy (0.96), AUC (0.99), precision (0.95), and F1 value (0.97). Machine learning algorithms, especially the CatBoost model, can accurately predict the mortality associated with STEMI complicated by hyperuricemia after a 1-year follow-up.