Rapid Classification and Treatment Algorithm of Cardiogenic Shock Complicating Acute Coronary Syndromes: The SAVE ACS Classification

Introduction. We aimed to identify the independent “frontline” predictors of 30-day mortality in patients with acute coronary syndromes (ACS) and propose a rapid cardiogenic shock (CS) classification and management pathway. Materials and Methods. From 2011 to 2019, a total of 11439 incident ACS pati...

Full description

Saved in:
Bibliographic Details
Main Authors: Vasileios Panoulas, Charles Ilsley
Format: Article
Language:English
Published: Wiley 2022-01-01
Series:Journal of Interventional Cardiology
Online Access:http://dx.doi.org/10.1155/2022/9948515
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1832549476440473600
author Vasileios Panoulas
Charles Ilsley
author_facet Vasileios Panoulas
Charles Ilsley
author_sort Vasileios Panoulas
collection DOAJ
description Introduction. We aimed to identify the independent “frontline” predictors of 30-day mortality in patients with acute coronary syndromes (ACS) and propose a rapid cardiogenic shock (CS) classification and management pathway. Materials and Methods. From 2011 to 2019, a total of 11439 incident ACS patients were treated in our institution. Forward conditional logistic regression analysis was performed to determine the “frontline” predictors of 30 day mortality. The C-statistic assessed the discriminatory power of the model. As a validation cohort, we used 431 incident ACS patients admitted from January 1, 2020, to July 20, 2020. Results. Independent predictors of 30-day mortality included age (OR 1.05; 95% CI 1.04 to 1.07, p<0.001), intubation (OR 7.4; 95% CI 4.3 to 12.74, p<0.001), LV systolic impairment (OR severe_vs_normal 1.98; 95% CI 1.14 to 3.42, p=0.015, OR moderate_vs_normal 1.84; 95% CI 1.09 to 3.1, p=0.022), serum lactate (OR 1.25; 95% CI 1.12 to 1.41, p<0.001), base excess (OR 1.1; 95% CI 1.04 to 1.07, p<0.001), and systolic blood pressure (OR 0.99; 95% CI 0.982 to 0.999, p=0.024). The model discrimination was excellent with an area under the curve (AUC) of 0.879 (0.851 to 0.908) (p<0.001). Based on these predictors, we created the SAVE (SBP, Arterial blood gas, and left Ventricular Ejection fraction) ACS classification, which showed good discrimination for 30-day AUC 0.814 (0.782 to 0.845) and long-term mortality plog−rank<0.001. A similar AUC was demonstrated in the validation cohort (AUC 0.815). Conclusions. In the current study, we introduce a rapid way of classifying CS using frontline parameters. The SAVE ACS classification could allow for future randomized studies to explore the benefit of mechanical circulatory support in different CS stages in ACS patients.
format Article
id doaj-art-d510384b1c274e11990ffbd71b6f42b1
institution Kabale University
issn 1540-8183
language English
publishDate 2022-01-01
publisher Wiley
record_format Article
series Journal of Interventional Cardiology
spelling doaj-art-d510384b1c274e11990ffbd71b6f42b12025-02-03T06:11:18ZengWileyJournal of Interventional Cardiology1540-81832022-01-01202210.1155/2022/9948515Rapid Classification and Treatment Algorithm of Cardiogenic Shock Complicating Acute Coronary Syndromes: The SAVE ACS ClassificationVasileios Panoulas0Charles Ilsley1Department of CardiologyDepartment of CardiologyIntroduction. We aimed to identify the independent “frontline” predictors of 30-day mortality in patients with acute coronary syndromes (ACS) and propose a rapid cardiogenic shock (CS) classification and management pathway. Materials and Methods. From 2011 to 2019, a total of 11439 incident ACS patients were treated in our institution. Forward conditional logistic regression analysis was performed to determine the “frontline” predictors of 30 day mortality. The C-statistic assessed the discriminatory power of the model. As a validation cohort, we used 431 incident ACS patients admitted from January 1, 2020, to July 20, 2020. Results. Independent predictors of 30-day mortality included age (OR 1.05; 95% CI 1.04 to 1.07, p<0.001), intubation (OR 7.4; 95% CI 4.3 to 12.74, p<0.001), LV systolic impairment (OR severe_vs_normal 1.98; 95% CI 1.14 to 3.42, p=0.015, OR moderate_vs_normal 1.84; 95% CI 1.09 to 3.1, p=0.022), serum lactate (OR 1.25; 95% CI 1.12 to 1.41, p<0.001), base excess (OR 1.1; 95% CI 1.04 to 1.07, p<0.001), and systolic blood pressure (OR 0.99; 95% CI 0.982 to 0.999, p=0.024). The model discrimination was excellent with an area under the curve (AUC) of 0.879 (0.851 to 0.908) (p<0.001). Based on these predictors, we created the SAVE (SBP, Arterial blood gas, and left Ventricular Ejection fraction) ACS classification, which showed good discrimination for 30-day AUC 0.814 (0.782 to 0.845) and long-term mortality plog−rank<0.001. A similar AUC was demonstrated in the validation cohort (AUC 0.815). Conclusions. In the current study, we introduce a rapid way of classifying CS using frontline parameters. The SAVE ACS classification could allow for future randomized studies to explore the benefit of mechanical circulatory support in different CS stages in ACS patients.http://dx.doi.org/10.1155/2022/9948515
spellingShingle Vasileios Panoulas
Charles Ilsley
Rapid Classification and Treatment Algorithm of Cardiogenic Shock Complicating Acute Coronary Syndromes: The SAVE ACS Classification
Journal of Interventional Cardiology
title Rapid Classification and Treatment Algorithm of Cardiogenic Shock Complicating Acute Coronary Syndromes: The SAVE ACS Classification
title_full Rapid Classification and Treatment Algorithm of Cardiogenic Shock Complicating Acute Coronary Syndromes: The SAVE ACS Classification
title_fullStr Rapid Classification and Treatment Algorithm of Cardiogenic Shock Complicating Acute Coronary Syndromes: The SAVE ACS Classification
title_full_unstemmed Rapid Classification and Treatment Algorithm of Cardiogenic Shock Complicating Acute Coronary Syndromes: The SAVE ACS Classification
title_short Rapid Classification and Treatment Algorithm of Cardiogenic Shock Complicating Acute Coronary Syndromes: The SAVE ACS Classification
title_sort rapid classification and treatment algorithm of cardiogenic shock complicating acute coronary syndromes the save acs classification
url http://dx.doi.org/10.1155/2022/9948515
work_keys_str_mv AT vasileiospanoulas rapidclassificationandtreatmentalgorithmofcardiogenicshockcomplicatingacutecoronarysyndromesthesaveacsclassification
AT charlesilsley rapidclassificationandtreatmentalgorithmofcardiogenicshockcomplicatingacutecoronarysyndromesthesaveacsclassification