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Related Concept Videos

Blood Studies for Cardiovascular System I: Cardiac Biomarkers01:20

Blood Studies for Cardiovascular System I: Cardiac Biomarkers

758
Cardiac biomarkers are enzymes, proteins, and hormones released into the blood when cardiac cells are injured. They are powerful tools for triaging.
The essential diagnostic tools for detecting myocardial necrosis and monitoring individuals suspected of having acute coronary syndrome (ACS) include:
Troponins
Troponins, particularly cardiac troponins I and T, are the most precise and sensitive markers of myocardial injury. They are detectable within 4-6 hours of myocardial injury and remain...
758

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Related Experiment Video

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Evaluation of a Reliable Biomarker in a Cecal Ligation and Puncture-Induced Mouse Model of Sepsis
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Construction and Validation of a Risk Prediction Model for Sepsis-Induced Myocardial Injury.

Yi Gou1, Yun Cong2, Zhen-Zhen Guo3

  • 1Emergency Trauma Center, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, People's Republic of China.

International Journal of General Medicine
|December 22, 2025
PubMed
Summary

This study developed a nomogram to predict sepsis-induced myocardial injury (SMCI). The model accurately identifies high-risk patients using key biomarkers, improving early diagnosis and prognosis for sepsis patients.

Keywords:
IL-6myocardial injuryprediction modelsepsis

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Area of Science:

  • Cardiology
  • Critical Care Medicine
  • Biomarker Research

Background:

  • Sepsis patients have a high risk of myocardial injury, a significant factor in mortality.
  • Early and accurate assessment of myocardial injury risk is vital for improving patient outcomes.

Purpose of the Study:

  • To develop and validate a predictive model for sepsis-induced myocardial injury (SMCI).
  • To construct a nomogram for rapid risk assessment of SMCI.

Main Methods:

  • Utilized Least Absolute Shrinkage and Selection Operator (LASSO) and logistic regression for predictor identification.
  • Developed a nomogram incorporating identified risk factors.
  • Validated the model's performance using AUC, Hosmer-Lemeshow tests, DCA, and CIC.

Main Results:

  • Identified three independent risk factors: Log myoglobin (Myo), Log B-type natriuretic peptide (BNP), and Log interleukin-6 (IL-6).
  • The nomogram demonstrated strong predictive accuracy (AUC 0.856 training, 0.853 validation).
  • The model showed good calibration and significant clinical applicability.

Conclusions:

  • The developed nomogram serves as a practical tool for early identification of high-risk SMCI patients.
  • Facilitates rapid calculation of SMCI risk, aiding clinical decision-making.