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Development and Validation of Nomogram to Predict Long-Term Prognosis of Critically Ill Patients with Acute

Yiyang Tang1, Qin Chen1, Lihuang Zha1

  • 1Department of Cardiology, Xiangya Hospital, Central South University, Changsha, Hunan, People's Republic of China.

International Journal of General Medicine
|August 16, 2021
PubMed

Insights

This study developed a nomogram to predict long-term survival in critically ill patients with acute myocardial infarction (AMI). The tool accurately identifies key factors, aiding in prognosis assessment and treatment guidance for AMI patients.

Area of Science:

  • Cardiovascular Medicine
  • Medical Informatics
  • Biostatistics

Background:

  • Acute myocardial infarction (AMI) presents a significant global health challenge with considerable long-term mortality.
  • Predicting survival in critically ill AMI patients is crucial for effective clinical management and resource allocation.

Purpose of the Study:

  • To develop and validate a predictive nomogram for estimating the long-term survival of critically ill patients diagnosed with AMI.
  • To identify independent prognostic factors influencing survival in this patient cohort.

Main Methods:

  • Utilized clinical data from the MIMIC-III v1.4 database for 1202 AMI patients.
  • Employed Cox proportional hazards models to identify independent prognostic factors.
  • Developed and validated a nomogram using training (n=841) and validation (n=361) sets, assessing performance with concordance index (C-index) and calibration curves.

Main Results:

  • Identified age, blood urea nitrogen, respiratory rate, hemoglobin, pneumonia, cardiogenic shock, dialysis, and mechanical ventilation as independent predictors of AMI survival.
  • The nomogram demonstrated favorable predictive performance, with C-indices of 0.789 in the training set and 0.762 in the validation set for 4-year survival.

Conclusions:

  • The developed nomogram provides an accurate tool for predicting the long-term survival of critically ill patients with AMI.
  • This predictive model can assist clinicians in assessing disease severity and guiding therapeutic strategies to improve patient outcomes.
Abstract

Keywords:
MIMIC-IIIacute myocardial infarctionlong-term prognosisnomogramretrospective study

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