Comparison of Risk Models in the Prediction of 30-Day Mortality in Acute Myocardial Infarction-Associated Cardiogenic Shock.

Acute myocardial infarction cardiogenic shock Cardiogenic shock Mortality Risk calculator Risk prediction

Journal

Structural heart : the journal of the Heart Team
ISSN: 2474-8714
Titre abrégé: Struct Heart
Pays: United States
ID NLM: 101743256

Informations de publication

Date de publication:
Nov 2022
Historique:
received: 31 03 2022
revised: 18 09 2022
accepted: 22 09 2022
medline: 8 6 2023
pubmed: 8 6 2023
entrez: 8 6 2023
Statut: epublish

Résumé

There are numerous risk-prediction models applied to acute myocardial infarction-related cardiogenic shock (AMI-CS) patients to determine a more accurate prognosis and to assist in patient triage. There is wide heterogeneity among the risk models including the nature of predictors evaluated and their specific outcome measures. The aim of this analysis was to evaluate the performance of 20 risk-prediction models in AMI-CS patients. Patients included in our analysis were admitted to a tertiary care cardiac intensive care unit with AMI-CS. Twenty risk-prediction models were computed utilizing vitals assessments, laboratory investigations, hemodynamic markers, and vasopressor, inotropic and mechanical circulatory support available from within the first 24 ​hours of presentation. Receiver operating characteristic curves were used to assess the prediction of 30-day mortality. Calibration was assessed with a Hosmer-Lemeshow test. Seventy patients (median age 63 years, 67% male) were admitted between 2017 and 2021. The models' area under the curve (AUC) ranged from 0.49 to 0.79, with the Simplified Acute Physiology Score II score having the most optimal discrimination of 30-day mortality (AUC: 0.79, 95% confidence interval [CI]: 0.67-0.90), followed by the Acute Physiology and Chronic Health Evaluation-III score (AUC: 0.72, 95% CI: 0.59-0.84) and the Acute Physiology and Chronic Health Evaluation-II score (AUC: 0.67, 95% CI: 0.55-0.80). All 20 risk scores demonstrated adequate calibration ( Among the models tested in a data set of patients admitted with AMI-CS, the Simplified Acute Physiology Score II risk score model demonstrated the highest prognostic accuracy. Further investigations are required to improve the discriminative capabilities of these models or to establish new, more streamlined and accurate methods for mortality prognostication in AMI-CS.

Sections du résumé

Background UNASSIGNED
There are numerous risk-prediction models applied to acute myocardial infarction-related cardiogenic shock (AMI-CS) patients to determine a more accurate prognosis and to assist in patient triage. There is wide heterogeneity among the risk models including the nature of predictors evaluated and their specific outcome measures. The aim of this analysis was to evaluate the performance of 20 risk-prediction models in AMI-CS patients.
Methods UNASSIGNED
Patients included in our analysis were admitted to a tertiary care cardiac intensive care unit with AMI-CS. Twenty risk-prediction models were computed utilizing vitals assessments, laboratory investigations, hemodynamic markers, and vasopressor, inotropic and mechanical circulatory support available from within the first 24 ​hours of presentation. Receiver operating characteristic curves were used to assess the prediction of 30-day mortality. Calibration was assessed with a Hosmer-Lemeshow test.
Results UNASSIGNED
Seventy patients (median age 63 years, 67% male) were admitted between 2017 and 2021. The models' area under the curve (AUC) ranged from 0.49 to 0.79, with the Simplified Acute Physiology Score II score having the most optimal discrimination of 30-day mortality (AUC: 0.79, 95% confidence interval [CI]: 0.67-0.90), followed by the Acute Physiology and Chronic Health Evaluation-III score (AUC: 0.72, 95% CI: 0.59-0.84) and the Acute Physiology and Chronic Health Evaluation-II score (AUC: 0.67, 95% CI: 0.55-0.80). All 20 risk scores demonstrated adequate calibration (
Conclusions UNASSIGNED
Among the models tested in a data set of patients admitted with AMI-CS, the Simplified Acute Physiology Score II risk score model demonstrated the highest prognostic accuracy. Further investigations are required to improve the discriminative capabilities of these models or to establish new, more streamlined and accurate methods for mortality prognostication in AMI-CS.

Identifiants

pubmed: 37288128
doi: 10.1016/j.shj.2022.100116
pii: S2474-8706(22)01915-7
pmc: PMC10242577
doi:

Types de publication

Journal Article

Langues

eng

Pagination

100116

Informations de copyright

© 2022 The Author(s).

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Auteurs

Lauren S Ranard (LS)

Department of Cardiology, Columbia University Irving Medical Center, New York, New York, USA.

Kenneth Guber (K)

Department of Medicine, Columbia University Irving Medical Center, New York, New York, USA.

Justin Fried (J)

Department of Cardiology, Columbia University Irving Medical Center, New York, New York, USA.

Koji Takeda (K)

Department of Cardiology, Columbia University Irving Medical Center, New York, New York, USA.

Yuji Kaku (Y)

Department of Cardiology, Columbia University Irving Medical Center, New York, New York, USA.

Dimitrios Karmpaliotis (D)

Department of Cardiology, Columbia University Irving Medical Center, New York, New York, USA.
Gagnon Cardiovascular Institute, Morristown Medical Center, Morristown, New Jersey, USA.

Gabriel Sayer (G)

Department of Cardiology, Columbia University Irving Medical Center, New York, New York, USA.

Leroy Rabbani (L)

Department of Cardiology, Columbia University Irving Medical Center, New York, New York, USA.

Daniel Burkhoff (D)

Cardiovascular Research Foundation, New York, New York, USA.

Nir Uriel (N)

Department of Cardiology, Columbia University Irving Medical Center, New York, New York, USA.

Ajay J Kirtane (AJ)

Department of Cardiology, Columbia University Irving Medical Center, New York, New York, USA.
Cardiovascular Research Foundation, New York, New York, USA.

Amirali Masoumi (A)

Department of Cardiology, Columbia University Irving Medical Center, New York, New York, USA.
Gagnon Cardiovascular Institute, Morristown Medical Center, Morristown, New Jersey, USA.

Classifications MeSH