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 patie...
| Publicado en: | Journal of Interventional Cardiology pp. 1 - 11 |
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| Autores principales: | , |
| Formato: | Journal Article |
| Publicado: |
Wiley-Blackwell
1/12/2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=154652650&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 154652650 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08964327 Q0V jtl: Journal of Interventional Cardiology issn: 08964327 maglogo: Y pubinfo: dt: 1/12/2022 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 154652650 154652650 NLM35095349 10.1155/2022/9948515 NLM35095349 154652650 ppf: 1 ppct: 10 formats: tig: atl: Rapid Classification and Treatment Algorithm of Cardiogenic Shock Complicating Acute Coronary Syndromes: The SAVE ACS Classification. aug: au: Panoulas, Vasileios Ilsley, Charles affil: Department of Cardiology, Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation Trust, Harefield, UK sug: subj: Acute Coronary Syndrome Complications Shock, Cardiogenic Therapy Shock, Cardiogenic Etiology Acute Coronary Syndrome Therapy Stroke Volume Ventricular Function, Left Algorithms Scales ab: 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. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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