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

The predictive value of PRECISE-DAPT score for long-term mortality in patients with acute coronary syndrome complicated by cardiogenic shock

BACKGROUND

Besides its primary clinical utility in predicting bleeding risk in patients with acute coronary syndrome (ACS), the PRECISE-DAPT (Predicting Bleeding Complications in Patients Undergoing Stent Implantation and Subsequent Dual Anti-Platelet Therapy) score may also be useful for predicting long-term mortality in ACS patients presenting with cardiogenic shock (CS) since several studies have reported an association between the score and certain cardiovascular conditions or events. The aim of the present study was to evaluate the utility of the PRECISE-DAPT score for predicting the long-term all-cause mortality in patients (n = 293) with ACS presenting with CS.

METHODS

The PRECISE-DAPT score was calculated for each patient who survived in hospital, and the association with long-term mortality was studied. Median follow-up time was 2.7 years. The performance of the final model was determined with measurements of its discriminative power (Harrell's and Uno's C indices and time-dependent area under the receiver operating characteristic curve [AUC]) and predictive accuracy (coefficient of determination [R2] and likelihood ratio χ2). Hazard ratios (HRs) were used to assess the relationship between the variables of the model and long-term all-cause death.

RESULTS

All-cause death occurred in 197 patients (67%). There was a positive association between the PRECISE-DAPT score (change from 17 to 38 was associated with an HR of 2.42 [95% CI: 1.59-3.68], R2 = 0.209, time-dependent AUC = 0.69) and the risk of death such that in the adjusted survival curve, the risk of mortality increased as the PRECISE-DAPT score increased.

CONCLUSION

The PRECISE-DAPT score may be a useful easy-to-use tool for predicting long-term mortality in patients with ACS complicated by CS.

Age shock index as an early predictor of cardiovascular death in acute coronary syndrome patients

BACKGROUND

The shock index (SI), reflecting heart rate (HR) to SBP ratio, is established for predicting adverse outcomes in acute coronary syndrome (ACS) patients. Exploring the age shock index (ASI), obtained by multiplying SI with age, could offer further insights into ACS prognosis.

OBJECTIVES

Assess ASI's effectiveness in predicting in-hospital death in individuals with ACS.

METHODS

This study encompassed patients with acute myocardial infarction, drawn from a national registry spanning October 2010 to January 2022. The optimal ASI threshold was established using receiver operating characteristic (ROC) curve analysis. The primary outcome was in-hospital mortality.

RESULTS

A total of 27 312 patients were enrolled, exhibiting a mean age of 66 ± 13 years, with 72.3% being male and 47.5% having ST-elevation myocardial infarction. ROC analysis yielded an area under the curve (AUC) of 0.80, identifying the optimal ASI cutoff as 44. Multivariate regression analysis, adjusting for potential confounders, established ASI ≥ 44 as an independent predictor of in-hospital death [hazard ratio: 3.09, 95% confidence interval: 2.56-3.71, P  < 0.001]. Furthermore, ASI emerged as a notably superior predictor of in-hospital death compared to the SI (AUC ASI  = 0.80 vs. AUC SI  = 0.72, P  < 0.0001), though it did not outperform the Global Registry of Acute Coronary Events (GRACE) score (AUC ASI  = 0.80 vs. AUC GRACE  = 0.85, P  < 0.001) or thrombolysis in myocardial infarction (TIMI) risk index (AUC ASI  = 0.80 vs. AUC TIMI  = 0.84, P  < 0.001).

CONCLUSION

The ASI offers an expedient mean to promptly identify ACS patients at elevated risk of in-hospital death. Its simplicity and effectiveness could render it a valuable tool for early risk stratification in this population.

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

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.

Reverse shock index multiplied by simplified motor score as a predictor of clinical outcomes for patients with COVID-19

BACKGROUND

The reverse shock index (rSI) combined with the Simplified Motor Score (sMS), that is, the rSI-sMS, is a novel and efficient prehospital triage scoring system for patients with COVID-19. In this study, we evaluated the predictive accuracy of the rSI-sMS for general ward and intensive care unit (ICU) admission among patients with COVID-19 and compared it with that of other measures, including the shock index (SI), modified SI (mSI), rSI combined with the Glasgow Coma Scale (rSI-GCS), and rSI combined with the GCS motor subscale (rSI-GCSM).

METHODS

All patients who visited the emergency department of Taipei Tzu Chi Hospital between January 2021 and June 2022 were included in this retrospective cohort. A diagnosis of COVID-19 was confirmed through a SARS-CoV-2 reverse-transcription polymerase chain reaction test or SARS-CoV-2 rapid test with oropharyngeal or nasopharyngeal swabs and was double confirmed by checking International Classification of Diseases, Tenth Revision, Clinical Modification codes in electronic medical records. In-hospital mortality was regarded as the primary outcome, and sepsis, general ward or ICU admission, endotracheal intubation, and total hospital length of stay (LOS) were regarded as secondary outcomes. Multivariate logistic regression was used to determine the relationship between the scoring systems and the three major outcomes of patients with COVID-19, including. The discriminant ability of the predictive scoring systems was investigated using the area under the receiver operating characteristic curve, and the most favorable cutoff value of the rSI-sMS for each major outcome was determined using Youden's index.

RESULTS

After 74,183 patients younger than 20 years (n = 11,572) and without COVID-19 (n = 62,611) were excluded, 9,282 patients with COVID-19 (median age: 45 years, interquartile range: 33-60 years, 46.1% men) were identified as eligible for inclusion in the study. The rate of in-hospital mortality was determined to be 0.75%. The rSI-sMS scores were significantly lower in the patient groups with sepsis, hyperlactatemia, admission to a general ward, admission to the ICU, total length of stay ≥ 14 days, and mortality. Compared with the SI, mSI, and rSI-GCSM, the rSI-sMS exhibited a significantly higher accuracy for predicting general ward admission, ICU admission, and mortality but a similar accuracy to that of the rSI-GCS. The optimal cutoff values of the rSI-sMS for predicting general ward admission, ICU admission, and mortality were calculated to be 3.17, 3.45, and 3.15, respectively, with a predictive accuracy of 86.83%, 81.94%%, and 90.96%, respectively.

CONCLUSIONS

Compared with the SI, mSI, and rSI-GCSM, the rSI-sMS has a higher predictive accuracy for general ward admission, ICU admission, and mortality among patients with COVID-19.

Comparative analysis of four established risk scores for prediction of in-hospital mortality in patients undergoing primary percutaneous coronary intervention

OBJECTIVE

This study was conducted to compare the predictive power of Shock Index (SI), TIMI Risk Index (TRI), LASH Score, and ACEF Score for the prediction of in-hospital mortality in a contemporary cohort of ST-segment elevation myocardial infarction (STEMI) patients undergoing primary percutaneous coronary intervention (PCI) at a tertiary care cardiac center of a developing country.

METHODS

Consecutive patients diagnosed with STEMI and undergoing primary PCI were included in this study. SI, TRI, LASH, and ACEF were computed and their predictive power was assessed as the area under the curve (AUC) on the receiver operating characteristics (ROC) curve analysis for in-hospital mortality.

RESULTS

We included 977 patients, 780 (79.8%) of which were male, and the mean age was 55.6 ± 11.5 years. The in-hospital mortality rate was 4.3% (42). AUC for TRI was 0.669 (optimal cutoff: ≥17.5, sensitivity: 76.2%, specificity: 45.6%). AUC for SI was 0.595 (optimal cutoff: ≥0.9, sensitivity: 21.4%, specificity: 89.8%). AUC for LASH score was 0.745 (optimal cutoff: ≥0, sensitivity: 76.2%, specificity: 66.9%). AUC for the ACEF score was 0.786 (optimal cutoff: ≥1.66, sensitivity: 71.4%, specificity: 73.5%).

CONCLUSION

In conclusion, ACEF showed sufficiently high predictive power with good sensitivity and specificity compared to other three scores. These simplified indices based on readily available hemodynamic parameters can be reliable alternatives to the computational complex scoring systems for the risk stratification of STEMI patients.

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

BACKGROUND

This 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.

METHODS

This 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.

RESULTS

Multivariate 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).

CONCLUSION

PADMA 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.

Nomogram for the Prediction of Intrahospital Mortality Risk of Patients with ST-Segment Elevation Myocardial Infarction Complicated with Hyperuricemia: A Multicenter Retrospective Study

Purpose

This study aimed to establish an accurate and easy predictive model for ST-segment elevation myocardial infarction (STEMI) patients with hyperuricemia, using readily available features to estimate intrahospital mortality risk.

Patients and Methods

This was a multicenter retrospective study involving the development of risk prediction models for intrahospital mortality among all STEMI patients with hyperuricemia from Zunyi Medical University Chest Pain Center’s specialized alliance between January 1, 2016 and June 30, 2020. The primary outcome was intrahospital mortality. A total of 48 candidate variables were considered from demographic and clinical data. The least absolute shrinkage and selection operator (LASSO) was used to develop a nomogram. Concordance index values, decision curve analysis, the area under the curve (AUC), and clinical impact curves were examined. In this study, 489 patients with STEMI were included in the training dataset and an additional 209 patients from the 44 chest pain centers were included in the test cohort. B-type natriuretic peptides, α-hydroxybutyrate dehydrogenase (α-HBDH), cystatin C, out-of-hospital cardiac arrest (OHCA), shock index, and neutrophil-to-lymphocyte ratio were associated with intrahospital mortality and included in the nomogram.

Results

The model showed good discrimination power, and the AUC generated to predict survival in the training set was 0.875 (95% confidence interval, 0.825–0.925). In the validation set, the AUC of survival predictions was 0.87 (95% confidence interval, 0.792–0.947). Calibration plots and decision curve analysis showed good model performance in both datasets. A web-based calculator (https://bzxzmu.shinyapps.io/STEMI-with-Hyperuricemia-intrahospital-mortality/) was established based on the nomogram model, which was used to measure the levels of OHCA, neutrophil-to-lymphocyte ratio, shock index, α-HBDH, cystatin C, and B-type natriuretic peptides.

Conclusion

For practical applications, this model may prove clinically useful for personalized therapy management in patients with STEMI with hyperuricemia.

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

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.

Results

A total of 2282 STEMI patients recruited between January 1, 2016 and May 31, 2020, were included in the complete dataset. The linear models built using LASSO and LR showed the highest overall predictive power, with an average accuracy over 0.93 and an AUC above 0.82. With a C-index of 0.811 [95% confidence interval (CI): 0.769-0.853], the LASSO nomogram showed good differentiation and proper calibration. In internal validation tests, a high C-index value of 0.821 was achieved. Decision curve analysis (DCA) and clinical impact curve (CIC) examination showed that compared with the previous score-based models, the LASSO model showed superior clinical relevance.

Conclusions

In this study, five machine learning methods were developed for in-hospital CS prediction. The LASSO model showed the best predictive performance. This nomogram could provide an accurate prognostic prediction for CS risk in patients with STEMI.