Nomogram Predicting In-Hospital Mortality in Patients with Myocardial Infarction Treated with Primary Coronary

Lukasz Gawinski1,2, Anna Milewska3, Michal Marczak4

  • 1Department of Management and Logistics in Healthcare, Medical University of Lodz, 90-419 Lodz, Poland.

Biomedicines
|March 28, 2025
PubMed

Insights

A new risk assessment tool for in-hospital death after myocardial infarction (MI) uses modern angiographic and logistic factors. This validated nomogram aids in predicting mortality risk in patients undergoing primary percutaneous coronary intervention (PCI).

Area of Science:

  • Cardiology
  • Medical Informatics
  • Clinical Risk Prediction

Background:

  • Existing risk assessment tools for in-hospital death in myocardial infarction (MI) primarily rely on angiographic, electrocardiographic, and laboratory data.
  • There is a lack of risk systems incorporating contemporary angiographic data and logistic variables.
  • This study addresses the need for a risk assessment system based on modern predictors for MI patients undergoing primary percutaneous coronary intervention (PCI).

Purpose of the Study:

  • To develop and validate a novel risk assessment system for in-hospital mortality in patients with myocardial infarction (MI).
  • To identify modern angiographic and logistic predictors of in-hospital death in MI patients treated with primary PCI.
  • To create a clinical nomogram for predicting in-hospital mortality after primary PCI across the full spectrum of MI.

Main Methods:

  • A multivariate logistic regression model was developed using a subgroup of 603 patients from a single-center MI registry treated with primary PCI (February 2019 - January 2020).
  • The patient cohort was divided into a 60% derivation set for model development and a 40% validation set for internal validation.
  • The model predicted in-hospital mortality based on logistic and angiographic variables.

Main Results:

  • Coronary angiography findings and suboptimal post-PCI flow were significant predictors of in-hospital mortality.
  • The timing of PCI and the patient's presentation mode were also identified as contributors to in-hospital mortality.
  • The model demonstrated good discrimination (C-statistic = 0.848) and calibration in the derivation set, with satisfactory discrimination (C-statistic = 0.6438) in the validation set.

Conclusions:

  • A novel clinical nomogram integrating four logistic and angiographic variables has been developed and validated.
  • This tool effectively predicts in-hospital mortality risk in a broad range of myocardial infarction patients undergoing primary PCI.
  • The nomogram provides a valuable method for assessing mortality risk in contemporary MI management.

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