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Artificial Intelligence in the Early Prediction of Cardiogenic Shock in Acute Heart Failure or Myocardial Infarction Patients: A Systematic Review and Meta-Analysis.
Popat, Apurva; Yadav, Sweta; Patel, Sagar K; Baddevolu, Sasanka; Adusumilli, Susmitha; Rao Dasari, Nikitha; Sundarasetty, Manoj; Anand, Sunethra; Sankar, Jawahar; Jagtap, Yugandha G.
Afiliación
  • Popat A; Internal Medicine, Marshfield Clinic Health System, Marshfield, USA.
  • Yadav S; Internal Medicine, Gujarat Medical Education & Research Society (GMERS) Medical College, Ahmedabad, IND.
  • Patel SK; Internal Medicine, Gujarat Adani Institute of Medical Sciences, Bhuj, IND.
  • Baddevolu S; Internal Medicine, Kurnool Medical College, Kurnool, IND.
  • Adusumilli S; College of Medicine, Chongqing Medical University, Chongqing, CHN.
  • Rao Dasari N; College of Medicine, Kamineni Academy of Medical Sciences and Research Centre, Hyderabad, IND.
  • Sundarasetty M; Radiodiagnosis, Bhaskar Medical College and General Hospital, Hyderabad, IND.
  • Anand S; Internal Medicine, Chengalpattu Medical College and Hospital, Chennai, IND.
  • Sankar J; Internal Medicine, Chengalpattu Medical College and Hospital, Chennai, IND.
  • Jagtap YG; Paediatrics, General Medicine, Mahatma Gandhi Mission (MGM) Medical School, Mumbai, IND.
Cureus ; 15(12): e50395, 2023 Dec.
Article en En | MEDLINE | ID: mdl-38213372
ABSTRACT
Cardiogenic shock (CS) may have a negative impact on mortality in patients with heart failure (HF) or acute myocardial infarction (AMI). Early prediction of CS can result in improved survival. Artificial intelligence (AI) through machine learning (ML) models have shown promise in predictive medicine. Here, we conduct a systematic review and meta-analysis to assess the effectiveness of these models in the early prediction of CS. A thorough search of the PubMed, Web of Science, Cochrane, and Scopus databases was conducted from the time of inception until November 2, 2023, to find relevant studies. Our outcomes were area under the curve (AUC), the sensitivity and specificity of the ML model, the accuracy of the ML model, and the predictor variables that had the most impact in predicting CS. Comprehensive Meta-Analysis (CMA) Version 3.0 was used to conduct the meta-analysis. Six studies were considered in our study. The pooled mean AUC was 0.808 (95% confidence interval 0.727, 0.890). The AUC in the included studies ranged from 0.77 to 0.91. ML models performed well, with accuracy ranging from 0.88 to 0.93 and sensitivity and specificity of 58%-78% and 88%-93%, respectively. Age, blood pressure, heart rate, oxygen saturation, and blood glucose were the most significant variables required by ML models to acquire their outputs. In conclusion, AI has the potential for early prediction of CS, which may lead to a decrease in the high mortality rate associated with it. Future studies are needed to confirm the results.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies / Systematic_reviews Idioma: En Revista: Cureus Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies / Systematic_reviews Idioma: En Revista: Cureus Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos