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Related Experiment Video

Updated: May 31, 2026

Predicting Amputation using Local Circulating Mononuclear Progenitor Cells in Angioplasty-treated Patients with Critical Limb Ischemia
07:25

Predicting Amputation using Local Circulating Mononuclear Progenitor Cells in Angioplasty-treated Patients with Critical Limb Ischemia

Published on: September 22, 2020

Machine Learning-Based Periprocedural Prediction Model for No-Reflow Risk After Percutaneous Coronary Intervention in

Rensong Liu1, Wanxiang Zheng1, Haoran Qin1

  • 1Department of Cardiovascular Medicine, Southwest Hospital, Army Medical University, Chongqing, China; Department of Cardiology, Key Laboratory of Geriatric Cardiovascular and Cerebrovascular Disease, Ministry of Education of China, Chongqing, China; Department of Cardiology, Key Laboratory of Chronobiology and Cardiometabolic Disease, Chongqing Education Commission of China, Chongqing, China.

The American Journal of Cardiology
|May 28, 2026
PubMed
Summary

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Acute Coronary Syndrome IV: Interprofessional Care01:28

Acute Coronary Syndrome IV: Interprofessional Care

IntroductionThe management of Acute Coronary Syndrome (ACS) aims to minimize myocardial damage, preserve myocardial function, and prevent complications.Initial ManagementInpatient management involves continuous cardiac monitoring, preferably in an ICU, focusing on blood pressure, serum sodium, potassium, and creatinine levels, and urine output. Ongoing pharmacologic management is crucial for stabilizing the patient.Supplemental Oxygen: Administer supplemental oxygen if oxygen saturation is...

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A new machine learning model accurately predicts coronary no-reflow (NR) after percutaneous coronary intervention (PCI) in acute coronary syndrome (ACS) patients. This tool aids in assessing NR risk, improving patient outcomes after PCI.

Area of Science:

  • Cardiology
  • Machine Learning in Medicine
  • Interventional Cardiology

Background:

  • Coronary no-reflow (NR) following percutaneous coronary intervention (PCI) is a significant predictor of adverse outcomes in patients with acute coronary syndrome (ACS).
  • Accurate risk stratification for peri-procedural NR is crucial for optimizing patient management and improving prognoses after PCI in ACS patients.

Purpose of the Study:

  • To develop and validate a machine learning-based risk prediction model for peri-procedural coronary no-reflow (NR) in patients with acute coronary syndrome (ACS) undergoing PCI.
  • To identify key clinical and procedural predictors of NR to facilitate early risk assessment.

Main Methods:

  • A prospective, observational cohort study enrolled 989 ACS patients undergoing PCI, divided into training (n=692) and testing (n=297) sets.
Keywords:
acute coronary syndromemachine learningno-reflowpercutaneous coronary interventionprediction model

Related Experiment Videos

Last Updated: May 31, 2026

Predicting Amputation using Local Circulating Mononuclear Progenitor Cells in Angioplasty-treated Patients with Critical Limb Ischemia
07:25

Predicting Amputation using Local Circulating Mononuclear Progenitor Cells in Angioplasty-treated Patients with Critical Limb Ischemia

Published on: September 22, 2020

  • Lasso regression and Boruta algorithm identified predictive features, including cardiac troponin (cTn), AST, pre-PCI TIMI flow, stent number, hypotension, and ACS subtype.
  • Five machine learning models (LR, SVM, RF, XGBoost, LightGBM) were constructed and validated; logistic regression (LR) demonstrated optimal performance.

    • Main Results:

    • The study identified 14.7% of patients developed NR.
    • The logistic regression model achieved an AUC of 0.8799, with high accuracy (0.8822) and F1-score (0.6316).
    • A nonlinear relationship between aspartate aminotransferase (AST) levels and NR risk was observed (P-nonlinear < 0.001).

    Conclusions:

    • A concise machine learning-based model was successfully established for stratifying post-PCI NR risk in ACS patients.
    • The developed nomogram and online calculator offer efficient peri-procedural risk assessment tools.
    • This model can aid clinicians in predicting and potentially mitigating NR events, thereby improving patient prognosis.