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Nomogram for Predicting in-Hospital Severe Complications in Patients with Acute Myocardial Infarction Admitted in
Yaqin Song1, Kongzhi Yang2, Yingjie Su1
1Department of Emergency Medicine, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, Hunan, People's Republic of China.
Insights
A new nomogram predicts severe complications in acute myocardial infarction (AMI) patients. This tool uses readily available data to forecast risks, aiding clinical decisions and improving patient care during hospitalization.
Area of Science:
- Cardiology
- Predictive Modeling
- Clinical Risk Assessment
Background:
- Lack of predictive models for severe complications in acute myocardial infarction (AMI) patients.
- Need for tools to forecast in-hospital severe complications in AMI.
Purpose of the Study:
- To develop and validate a nomogram for predicting the likelihood of in-hospital severe complications in AMI patients.
- To utilize accessible clinical and laboratory data for risk assessment.
Main Methods:
- Logistic regression analysis (univariate and multivariate) on data from 1024 AMI patients (717 modeling, 307 validation).
- Identification of independent risk factors for severe complications.
- Construction and validation of a nomogram using identified risk factors.
Main Results:
- Seven independent risk factors identified: age, heart rate, mean arterial pressure, diabetes, hypertension, triglycerides, and white blood cells.
- Nomogram demonstrated high predictive accuracy (AUC=0.793 modeling, 0.732 validation).
- Strong consistency between predicted and observed values; practical clinical utility confirmed by DCA analysis.
Conclusions:
- An intuitive nomogram was developed and validated for predicting severe complications in AMI patients.
- The nomogram uses easily obtainable clinical and laboratory data.
- This tool assists clinicians in evaluating patient risk during hospitalization.
Background:
There is lack of predictive models for the risk of severe complications during hospitalization in patients with acute myocardial infarction (AMI). In this study, we aimed to create a nomogram to forecast the likelihood of in-hospital severe complications in AMI.
Methods:
From August 2020 to January 2023, 1024 patients with AMI including the modeling group (n=717) and the validation group (n=307) admitted in Changsha Central Hospital's emergency department. Conduct logistic regression analysis, both univariate and multivariate, on the pertinent patient data from the modeling cohort at admission, identify independent risk factors, create a nomogram to forecast the likelihood of severe complications in patients with AMI, and assess the accuracy of the graph's predictions in the validation cohort.
Results:
Age, heart rate, mean arterial pressure, diabetes, hypertension, triglycerides and white blood cells were seven independent risk factors for serious complications in AMI patients. Based on these seven variables, the nomogram model was constructed. The nomogram has high predictive accuracy (AUC=0.793 for the modeling group and AUC=0.732 for the validation group). The calibration curve demonstrates strong consistency between the anticipated and observed values of the nomogram in the modeling and validation cohorts. Moreover, the DCA curve results show that the model has a wide threshold range (0.01-0.73) and has good practicality in clinical practice.
Conclusion:
This study developed and validated an intuitive nomogram to assist clinicians in evaluating the probability of severe complications in AMI patients using readily available clinical data and laboratory parameters.
