Shock index and TIMI risk index as valuable prognostic tools in patients with acute coronary syndrome complicated by cardiogenic shock

Supel, Karolina; Kacprzak, Michał; Zielińska, Marzenna

doi:10.1371/journal.pone.0227374

Cited by 9 publications

(8 citation statements)

References 15 publications

(18 reference statements)

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“…Previous studies have used SI to predict mortality in the ACS population and have found that an increased SI can predict short-term mortality in patients with STEMI. A SI ≥0.7-0.8 on admission in patients with STEMI was associated with a mortality rate of 16-20%, and the lower the SI, the lower the risk of death (31)(32)(33)(34). This study showed that the traditional inflammatory indicators, WBC count and myocardial enzymes (LDH and AST), remain essential in the risk prediction of STEMI and confirmed that anemia might increase the risk among patients after PCI.…”

Section: Discussionsupporting

confidence: 58%

Development of a machine learning model to predict the risk of late cardiogenic shock in patients with ST-segment elevation myocardial infarction

Bai¹,

Huang²,

Wang³

et al. 2021

Ann Transl Med

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Background: The in-hospital mortality of patients with ST-segment elevation myocardial infarction (STEMI) increases to more than 50% following a cardiogenic shock (CS) event. This study highlights the need to consider the risk of delayed calculation in developing in-hospital CS risk models. This report compared the performances of multiple machine learning models and established a late-CS risk nomogram for STEMI patients. Methods: This study used logistic regression (LR) models, least absolute shrinkage and selection operator (LASSO), support vector regression (SVM), and tree-based ensemble machine learning models [light gradient boosting machine (LightGBM) and extreme gradient boosting (XGBoost)] to predict CS risk in STEMI patients. The models were developed based on 1,598 and 684 STEMI patients in the training and test datasets, respectively. The models were compared based on accuracy, the area under the curve (AUC), recall, precision, and Gini score, and the optimal model was used to develop a late CS risk nomogram. Discrimination, calibration, and the clinical usefulness of the predictive model were assessed using C-index, calibration plotd, and decision curve analyses.

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Section: Discussionsupporting

confidence: 58%

Development of a machine learning model to predict the risk of late cardiogenic shock in patients with ST-segment elevation myocardial infarction

Bai¹,

Huang²,

Wang³

et al. 2021

Ann Transl Med

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“…Prior studies have demonstrated the prognostic utility of SI in patients with individual cardiac pathologies, including ACS, heart failure, and cardiogenic shock [ 31 – 35 ]. SI alone was shown to perform as well as the GRACE risk score in patients with ACS, with the advantage of being easier to calculate [ 36 – 38 ]. Notably, both HR and SBP are included in the GRACE risk score [ 7 , 39 ].…”

Section: Discussionmentioning

confidence: 99%

Association between the shock index on admission and in-hospital mortality in the cardiac intensive care unit

Padkins,

Kashani,

Tabi

et al. 2024

PLoS ONE

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Background An elevated shock index (SI) predicts worse outcomes in multiple clinical arenas. We aimed to determine whether the SI can aid in mortality risk stratification in unselected cardiac intensive care unit patients. Methods We included admissions to the Mayo Clinic from 2007 to 2015 and stratified them based on admission SI. The primary outcome was in-hospital mortality, and predictors of in-hospital mortality were analyzed using multivariable logistic regression. Results We included 9,939 unique cardiac intensive care unit patients with available data for SI. Patients were grouped by SI as follows: < 0.6, 3,973 (40%); 0.6–0.99, 4,810 (48%); and ≥ 1.0, 1,156 (12%). After multivariable adjustment, both heart rate (adjusted OR 1.06 per 10 beats per minute higher; CI 1.02–1.10; p-value 0.005) and systolic blood pressure (adjusted OR 0.94 per 10 mmHg higher; CI 0.90–0.97; p-value < 0.001) remained associated with higher in-hospital mortality. As SI increased there was an incremental increase in in-hospital mortality (adjusted OR 1.07 per 0.1 beats per minute/mmHg higher, CI 1.04–1.10, p-Value < 0.001). A higher SI was associated with increased mortality across all examined admission diagnoses. Conclusion The SI is a simple and universally available bedside marker that can be used at the time of admission to predict in-hospital mortality in cardiac intensive care unit patients.

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“…Heart failure markedly increased mortality risk in ACS patients, as it reduced the cardiac output, diminished the oxygen perfusion into the lung circulatory and induced renin–angiotensin–aldosterone system activation 40. On the other hand, cardiogenic shock, as an indicator of end-organ hypoperfusion due to cardiac dysfunction, significantly increased the risk of mortality in ACS patients 41 42…”

Section: Discussionmentioning

confidence: 99%

Novel scoring system based on clinical examination for prediction of in-hospital mortality in acute coronary syndrome patients: a retrospective cohort study

Pramudyo

Bijaksana²,

Yahya³

et al. 2022

Open Heart

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BackgroundThis study aims to develop PADjadjaran Mortality in Acute coronary syndrome (PADMA) Score to predict in-hospital mortality in acute coronary syndrome (ACS) patients based on clinical examination only. Additionally, we also compared the predictive value of the PADMA Score with the Global Registry of Acute Coronary Events (GRACE), Canada Acute Coronary Syndrome (C-ACS), and The Portuguese Registry of Acute Coronary Syndromes (ProACS) risk scores.MethodsThis retrospective cohort study included all ACS patients aged≥18 years who were admitted to Dr. Hasan Sadikin Central General Hospital from January 2018 to January 2022. Patients’ demographic, comorbidities and clinical presentation data were collected and analysed using multivariate logistic regression to create two models of scoring system (probability and cut-off model) to predict in-hospital all-cause mortality. The area under the curve (AUC) among PADMA, GRACE, C-ACS and ProACS risk scores was compared using the fisher Z test.ResultsMultivariate regression analysis of 1359 patients showed that older age, history of cerebrovascular disease, tachycardia, high Shock Index and Killip class III and IV were independent mortality predictors and included in the PADMA Score. PADMA Score ranged from 0 to 20, with a score≥5 that can predict all-cause mortality with 82.78% sensitivity and 72.35% specificity. The difference in AUC between PADMA and GRACE scores was insignificant (p=0.126). Moreover, the AUC of the PADMA Score was significantly higher compared with the C-ACS (p=0.002) and ProACS risk scores (p<0.001).ConclusionPADMA Score is a simple scoring system to predict in-hospital mortality in ACS patients. PADMA Score≥5 showed an accurate discriminative capability to predict in-hospital mortality, comparable with the GRACE Score and superior to C-ACS and ProACS scores.

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Shock index and TIMI risk index as valuable prognostic tools in patients with acute coronary syndrome complicated by cardiogenic shock

Cited by 9 publications

References 15 publications

Development of a machine learning model to predict the risk of late cardiogenic shock in patients with ST-segment elevation myocardial infarction

Development of a machine learning model to predict the risk of late cardiogenic shock in patients with ST-segment elevation myocardial infarction

Association between the shock index on admission and in-hospital mortality in the cardiac intensive care unit

Novel scoring system based on clinical examination for prediction of in-hospital mortality in acute coronary syndrome patients: a retrospective cohort study

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