Machine Learning Improves the Identification of Individuals With Higher Morbidity and Avoidable Health Costs After Acute Coronary Syndromes

Dec 1, 2020, 00:00
10.1016/j.jval.2020.08.2091
https://www.valueinhealthjournal.com/article/S1098-3015(20)34348-5/fulltext
Title : Machine Learning Improves the Identification of Individuals With Higher Morbidity and Avoidable Health Costs After Acute Coronary Syndromes
Citation : https://www.valueinhealthjournal.com/action/showCitFormats?pii=S1098-3015(20)34348-5&doi=10.1016/j.jval.2020.08.2091
First page : 1570
Section Title : HEALTH POLICY ANALYSIS
Open access? : No
Section Order : 1570

Objectives

Traditional risk scores improved the definition of the initial therapeutic strategy in acute coronary syndrome (ACS), but they were not designed for predicting long-term individual risks and costs. In parallel, attempts to directly predict costs from clinical variables in ACS had limited success. Thus, novel approaches to predict cardiovascular risk and health expenditure are urgently needed. Our objectives were to predict the risk of major/minor adverse cardiovascular events (MACE) and estimate assistance-related costs.

Methods

We used a 2-step approach that: (1) predicted outcomes with a common pathophysiological substrate (MACE) by using machine learning (ML) or logistic regression (LR) and compared with existing risk scores; (2) derived costs associated with noncardiovascular deaths, dialysis, ambulatory-care-sensitive-hospitalizations (ACSH), strokes, and MACE. With consecutive ACS individuals (n = 1089) from 2 cohorts, we trained in 80% of the population and tested in 20% using a 4-fold cross-validation framework. The 29-variable model included socioeconomic, clinical/lab, and coronarography variables. Individual costs were estimated based on cause-specific hospitalization from the Brazilian Health Ministry perspective.

Results

After up to 12 years follow-up (mean = 3.3 ± 3.1; MACE = 169), the gradient-boosting machine model was superior to LR and reached an area under the curve (AUROC) of 0.891 [95% CI 0.846-0.921] (test set), outperforming the Syntax Score II (AUROC = 0.635 [95% CI 0.569-0.699]). Individuals classified as high risk (>90th percentile) presented increased HbA1c and LDL-C both at .00001) greater per capita costs compared with low-risk individuals, mostly owing to avoidable costs (ACSH). This 2-step approach was more successful for finding individuals incurring high costs than predicting costs directly from clinical variables.

Conclusion

ML methods predicted long-term risks and avoidable costs after ACS.

Categories :
  • Artificial Intelligence, Machine Learning, Predictive Analytics
  • Cardiovascular Disorders
  • Cost/Cost of Illness/Resource Use Studies
  • Economic Evaluation
  • Methodological & Statistical Research
  • Specific Diseases & Conditions
Tags :
  • acute coronary syndromes
  • artificial intelligence
  • machine learning
  • modifiable risk factors
  • population health management
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