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

Journal: Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research
PMID: 33248512

Abstract

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.

Authors

  • Luiz Sérgio Fernandes de Carvalho
    Clarity Healthcare Intelligence, Jundiaí, SP, Brazil; Cardiology Department, State University of Campinas (Unicamp), Campinas, SP, Brazil; Laboratory of Data for Quality of Care and Outcomes Research, Institute of Strategic Management in Healthcare Brasília, DF, Brazil; Escola Superior de Ciências da Saúde, Brasília, DF, Brazil. Electronic address: lsergio@clarityhealth.com.br.
  • Silvio Gioppato
    Cardiology Department, State University of Campinas (Unicamp), Campinas, SP, Brazil; Vera Cruz Hospital, Campinas, SP, Brazil.
  • Marta Duran Fernandez
    Clarity Healthcare Intelligence, Jundiaí, SP, Brazil; Faculty of Electrical Engineering and Computation, Unicamp, Campinas, SP, Brazil.
  • Bernardo Carvalho Trindade
    School of Civil and Environmental Engineering, Cornell Univ., Ithaca, NY, USA.
  • José Carlos Quinaglia E Silva
    Laboratory of Data for Quality of Care and Outcomes Research, Institute of Strategic Management in Healthcare Brasília, DF, Brazil; Escola Superior de Ciências da Saúde, Brasília, DF, Brazil.
  • Rebeca Gouget Sérgio Miranda
    Secretariat of Foreign Trade, Ministry of the Economy, Brasília, DF, Brazil.
  • José Roberto Matos de Souza
    Laboratory of Data for Quality of Care and Outcomes Research, Institute of Strategic Management in Healthcare Brasília, DF, Brazil.
  • Wilson Nadruz
    Laboratory of Data for Quality of Care and Outcomes Research, Institute of Strategic Management in Healthcare Brasília, DF, Brazil.
  • Sandra Eliza Fontes Avila
    Institute of Computing, Unicamp, Campinas, SP, Brazil.
  • Andrei Carvalho Sposito
    Cardiology Department, State University of Campinas (Unicamp), Campinas, SP, Brazil.

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