Artificial Intelligence in the Early Prediction of Cardiogenic Shock in Acute Heart Failure or Myocardial Infarction Patients: A Systematic Review and Meta-Analysis

作者全名："Popat, Apurva; Yadav, Sweta; Patel, Sagar K.; Baddevolu, Sasanka; Adusumilli, Susmitha; Dasari, Nikitha Rao; Sundarasetty, Manoj; Anand, Sunethra; Sankar, Jawahar; Jagtap, Yugandha G."

作者地址："[Popat, Apurva] Marshfield Clin Hlth Syst, Internal Med, Marshfield, WI 54449 USA; [Yadav, Sweta] Gujarat Med Educ & Res Soc GMERS Med Coll, Internal Med, Ahmadabad, India; [Patel, Sagar K.] Gujarat Adani Inst Med Sci, Internal Med, Bhuj, India; [Baddevolu, Sasanka] Kurnool Med Coll, Internal Med, Kurnool, India; [Adusumilli, Susmitha] Chongqing Med Univ, Coll Med, Chongqing, Peoples R China; [Dasari, Nikitha Rao] Kamineni Acad Med Sci & Res Ctr, Coll Med, Hyderabad, India; [Sundarasetty, Manoj] Bhaskar Med Coll & Gen Hosp, Radiodiag, Hyderabad, India; [Anand, Sunethra; Sankar, Jawahar] Chengalpattu Med Coll & Hosp, Internal Med, Chennai, India; [Jagtap, Yugandha G.] Mahatma Gandhi Miss MGM, Paediat, Gen Med, Med Sch, Mumbai, India"

通信作者："Popat, A (通讯作者)，Marshfield Clin Hlth Syst, Internal Med, Marshfield, WI 54449 USA."

来源：CUREUS JOURNAL OF MEDICAL SCIENCE

ESI学科分类：　

WOS号：WOS:001127111600030

JCR分区：Q3

影响因子：1

年份：2023

卷号：15

期号：12

开始页：　

结束页：　

文献类型：Review

关键词：prediction model in medicine; artificial intelligence in medicine; machine learning in medicine; artificial intelligence in cardiology; cardiogenic shock

摘要："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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