Canada Acute Coronary Syndrome Risk Score: a new risk score for early prognostication in acute coronary syndromes

Prehospital stratification and prioritisation of non-ST-segment elevation acute coronary syndrome patients (NSTEACS): the MARIACHI scale

OBJECTIVE

The objective of this study was to develop and validate a risk scale (MARIACHI) for patients classified as non-ST-segment elevation acute coronary syndrome (NSTEACS) in a prehospital setting with the ability to identify patients at an increased risk of mortality at an early stage.

METHODS

A retrospective observational study conducted in Catalonia over two periods: 2015-2017 (development and internal validation cohort) and Aug 2018-Jan 2019 (external validation cohort). We included patients classified as prehospital NSTEACS, assisted by an advanced life support unit and requiring hospital admission. The primary outcome was in-hospital mortality. Cohorts were compared using logistic regression and a predictive model was created using bootstrapping techniques.

RESULTS

The development and internal validation cohort included 519 patients. The model is composed of five variables associated with hospital mortality: age, systolic blood pressure, heart rate > 95 bpm, Killip-Kimball III-IV and ST depression ≥ 0.5 mm. The model showed good overall performance (Brier = 0.043) and consistency in discrimination (AUC 0.88, 95% CI 0.83-0.92) and calibration (slope = 0.91; 95% CI 0.89-0.93). We included 1316 patients for the external validation sample. There was no difference in discrimination (AUC 0.83, 95% CI 0.78-0.87; DeLong Test p = 0.071), but there was in calibration (p < 0.001), so it was recalibrated. The finally model obtained was stratified and scored into three groups according to the predicted risk of patient in-hospital mortality: low risk: < 1% (-8 to 0 points), moderate risk: 1-5% (+ 1 to + 5 points) and high risk: > 5% (6-12 points).

CONCLUSION

The MARIACHI scale showed correct discrimination and calibration to predict high-risk NSTEACS. Identification of high-risk patients may help with treatment and low referral decisions at the prehospital level.

A motivational interview program for cardiac rehabilitation after acute myocardial infarction: study protocol of a randomized controlled trial in primary healthcare

Background

Cardiac rehabilitation after acute myocardial infarction permits recovery of the heart function and enables secondary prevention programs in which changes in lifestyle habits are crucial. Cardiac rehabilitation often takes place in hospitals without coordination with primary healthcare and is not focused on individual patient preferences and goals, which is the core of the motivational interview. The objective of this study was to evaluate the efficacy of a cardiac rehabilitation program with a motivational interview in patients discharged from hospital after acute myocardial infarction.

Methods/design

A randomized, non-pharmacological clinical trial in six primary healthcare centers in Barcelona (Spain) will assess whether a tailored cardiac rehabilitation program consisting of four motivational interviews and visits with family physicians, primary healthcare nurses and a cardiologist, coordinated with the reference hospital, results in better cardiac rehabilitation than standard care. A minimum sample of 284 participants requiring cardiac rehabilitation after acute myocardial infarction will be randomized to a cardiac rehabilitation group with a motivational interview program or to standard primary healthcare. The main outcome will be physical function measured by the six-minute walk test, and the secondary outcome will be the effectiveness of secondary prevention: a composite outcome comprising control of blood pressure, cholesterol, diabetes mellitus, smoking and body weight. Results will be evaluated at 1,3 and 6 months.

Discussion

This is the first clinical trial to study the impact of a new primary healthcare cardiac rehabilitation program with motivational interviews for patients discharged from hospital after myocardial infarction. Changes in lifestyles and habits after myocardial infarction are a core element of secondary prevention and require patient-centered care strategies such as motivational interviews. Therefore, this study could clarify the impact of this approach on health indicators, such as functional capacity.

Trial registration

ClinicalTriasl.gov NCT05285969 registered on March 18, 2022. 

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.

Diagnostic Model of In-Hospital Mortality in Patients with Acute ST-Segment Elevation Myocardial Infarction Used Artificial Intelligence Methods

Background

Preventing in-hospital mortality in patients with ST-segment elevation myocardial infarction (STEMI) is a crucial step.

Objectives

The objective of our research was to develop and externally validate the diagnostic model of in-hospital mortality in acute STEMI patients used artificial intelligence methods.

Methods

We divided nonrandomly the American population with acute STEMI into a training set, a test set, and a validation set. We converted the unbalanced data into balanced data. We used artificial intelligence methods to develop and externally validate several diagnostic models. We used confusion matrix combined with the area under the receiver operating characteristic curve (AUC) to evaluate the pros and cons of the above models.

Results

The strongest predictors of in-hospital mortality were age, gender, cardiogenic shock, atrial fibrillation (AF), ventricular fibrillation (VF), third degree atrioventricular block, in-hospital bleeding, underwent percutaneous coronary intervention (PCI) during hospitalization, underwent coronary artery bypass grafting (CABG) during hospitalization, hypertension history, diabetes history, and myocardial infarction history. The F2 score of logistic regression in the training set, the test set, and the validation dataset was 0.81, 0.6, and 0.59, respectively. The AUC of logistic regression in the training set, the test set, and the validation data set was 0.77, 0.78, and 0.8, respectively. The diagnostic model built by logistic regression was the best.

Conclusion

The strongest predictors of in-hospital mortality were age, gender, cardiogenic shock, AF, VF, third degree atrioventricular block, in-hospital bleeding, underwent PCI during hospitalization, underwent CABG during hospitalization, hypertension history, diabetes history, and myocardial infarction history. We had used artificial intelligence methods developed and externally validated several diagnostic models of in-hospital mortality in acute STEMI patients. The diagnostic model built by logistic regression was the best. We registered this study with the registration number ChiCTR1900027129 (the WHO International Clinical Trials Registry Platform (ICTRP) on 1 November 2019).

Exploration of Black Boxes of Supervised Machine Learning Models: A Demonstration on Development of Predictive Heart Risk Score

Machine learning (ML) often provides applicable high-performance models to facilitate decision-makers in various fields. However, this high performance is achieved at the expense of the interpretability of these models, which has been criticized by practitioners and has become a significant hindrance in their application. Therefore, in highly sensitive decisions, black boxes of ML models are not recommended. We proposed a novel methodology that uses complex supervised ML models and transforms them into simple, interpretable, transparent statistical models. This methodology is like stacking ensemble ML in which the best ML models are used as a base learner to compute relative feature weights. The index of these weights is further used as a single covariate in the simple logistic regression model to estimate the likelihood of an event. We tested this methodology on the primary dataset related to cardiovascular diseases (CVDs), the leading cause of mortalities in recent times. Therefore, early risk assessment is an important dimension that can potentially reduce the burden of CVDs and their related mortality through accurate but interpretable risk prediction models. We developed an artificial neural network and support vector machines based on ML models and transformed them into a simple statistical model and heart risk scores. These simplified models were found transparent, reliable, valid, interpretable, and approximate in predictions. The findings of this study suggest that complex supervised ML models can be efficiently transformed into simple statistical models that can also be validated.

A Novel Risk Score to Predict In-Hospital Mortality in Patients With Acute Myocardial Infarction: Results From a Prospective Observational Cohort

Objectives

The aim of this study was to develop and validate a novel risk score to predict in-hospital mortality in patients with acute myocardial infarction (AMI) using the Heart Failure after Acute Myocardial Infarction with Optimal Treatment (HAMIOT) cohort in China.

Methods

The HAMIOT cohort was a multicenter, prospective, observational cohort of consecutive patients with AMI in China. All participants were enrolled between December 2017 and December 2019. The cohort was randomly assigned (at a proportion of 7:3) to the training and validation cohorts. Logistic regression model was used to develop and validate a predictive model of in-hospital mortality. The performance of discrimination and calibration was evaluated using the Harrell’s c-statistic and the Hosmer-Lemeshow goodness-of-fit test, respectively. The new simplified risk score was validated in an external cohort that included independent patients with AMI between October 2019 and March 2021.

Results

A total of 12,179 patients with AMI participated in the HAMIOT cohort, and 136 patients were excluded. In-hospital mortality was 166 (1.38%). Ten predictors were found to be independently associated with in-hospital mortality: age, sex, history of percutaneous coronary intervention (PCI), history of stroke, presentation with ST-segment elevation, heart rate, systolic blood pressure, initial serum creatinine level, initial N-terminal pro-B-type natriuretic peptide level, and PCI treatment. The c-statistic of the novel simplified HAMIOT risk score was 0.88, with good calibration (Hosmer–Lemeshow test: P = 0.35). Compared with the Global Registry of Acute Coronary Events risk score, the HAMIOT score had better discrimination ability in the training (0.88 vs. 0.81) and validation (0.82 vs. 0.72) cohorts. The total simplified HAMIOT risk score ranged from 0 to 121. The observed mortality in the HAMIOT cohort increased across different risk groups, with 0.35% in the low risk group (score ≤ 50), 3.09% in the intermediate risk group (50 < score ≤ 74), and 14.29% in the high risk group (score > 74).

Conclusion

The novel HAMIOT risk score could predict in-hospital mortality and be a valid tool for prospective risk stratification of patients with AMI.

Clinical Trial Registration

[https://clinicaltrials.gov], Identifier: [NCT03297164].

Generalizability of Cardiovascular Disease Clinical Prediction Models: 158 Independent External Validations of 104 Unique Models

Background:

While clinical prediction models (CPMs) are used increasingly commonly to guide patient care, the performance and clinical utility of these CPMs in new patient cohorts is poorly understood.

Methods:

We performed 158 external validations of 104 unique CPMs across 3 domains of cardiovascular disease (primary prevention, acute coronary syndrome, and heart failure). Validations were performed in publicly available clinical trial cohorts and model performance was assessed using measures of discrimination, calibration, and net benefit. To explore potential reasons for poor model performance, CPM-clinical trial cohort pairs were stratified based on relatedness, a domain-specific set of characteristics to qualitatively grade the similarity of derivation and validation patient populations. We also examined the model-based C-statistic to assess whether changes in discrimination were because of differences in case-mix between the derivation and validation samples. The impact of model updating on model performance was also assessed.

Results:

Discrimination decreased significantly between model derivation (0.76 [interquartile range 0.73–0.78]) and validation (0.64 [interquartile range 0.60–0.67], P<0.001), but approximately half of this decrease was because of narrower case-mix in the validation samples. CPMs had better discrimination when tested in related compared with distantly related trial cohorts. Calibration slope was also significantly higher in related trial cohorts (0.77 [interquartile range, 0.59–0.90]) than distantly related cohorts (0.59 [interquartile range 0.43–0.73], P=0.001). When considering the full range of possible decision thresholds between half and twice the outcome incidence, 91% of models had a risk of harm (net benefit below default strategy) at some threshold; this risk could be reduced substantially via updating model intercept, calibration slope, or complete re-estimation.

Conclusions:

There are significant decreases in model performance when applying cardiovascular disease CPMs to new patient populations, resulting in substantial risk of harm. Model updating can mitigate these risks. Care should be taken when using CPMs to guide clinical decision-making.

Development of Acute Myocardial Infarction Mortality and Readmission Models for Public Reporting on Hospital Performance in Canada

Background

Given changes in the care and outcomes of acute myocardial infarction (AMI) patients over the past several decades, we sought to develop prediction models that could be used to generate accurate risk-adjusted mortality and readmission outcomes for hospitals in current practice across Canada.

Methods

A Canadian national expert panel was convened to define appropriate AMI patients for reporting and develop prediction models. Preliminary candidate variable evaluation was conducted using Ontario patients hospitalized with a most responsible diagnosis of AMI from April 1, 2015 to March 31, 2018. National data from the Canadian Institute for Health Information was used to develop AMI prediction models. The main outcomes were 30-day all-cause in-hospital mortality and 30-day urgent all-cause readmission. Discrimination of these models (measured by c-statistics) was compared with that of existing Canadian Institute for Health Information models in the same study cohort.

Results

The AMI mortality model was assessed in 54,240 Ontario AMI patients and 153,523 AMI patients across Canada. We observed a 30-day in-hospital mortality rate of 6.3%, and a 30-day all-cause urgent readmission rate of 10.7% in Canada. The final Canadian AMI mortality model included 12 variables and had a c-statistic of 0.834. For readmission, the model had 13 variables and a c-statistic of 0.679. Discrimination of the new AMI models had higher c-statistics compared with existing models (c-statistic 0.814 for mortality; 0.673 for readmission).

Conclusions

In this national collaboration, we developed mortality and readmission models that are suitable for profiling performance of hospitals treating AMI patients in Canada.

A prognostic nomogram for long-term major adverse cardiovascular events in patients with acute coronary syndrome after percutaneous coronary intervention

BACKGROUND

Accurate prediction of major adverse cardiovascular events (MACEs) is very important for the management of acute coronary syndrome (ACS) patients. We aimed to construct an effective prognostic nomogram for individualized risk estimates of MACEs for patients with ACS after percutaneous coronary intervention (PCI).

METHODS

This was a prospective study of patients with ACS after PCI from January 2013 to July 2019 (n = 2465). After removing patients with incomplete clinical information, a total of 1986 patients were randomly divided into evaluation (n = 1324) and validation (n = 662) groups. Predictors included in the nomogram were determined by a multivariate Cox proportional hazards regression model based on the training set. Receiver operating characteristic (ROC) curves and calibration curves were used to assess the discrimination and predictive accuracy of the nomogram, which were then compared with those of the classic models. The clinical utility of the nomogram was assessed by X-tile analysis and Kaplan-Meier curve analysis.

RESULTS

Independent prognostic factors, including lactate level, age, left anterior descending branch stenosis, right coronary artery stenosis, brain natriuretic peptide level, and left ventricular ejection fraction, were determined and contained in the nomogram. The nomogram achieved good areas under the ROC curve of 0.712-0.762 in the training set and 0.724-0.818 in the validation set and well-fitted calibration curves. In addition, participants could be divided into two risk groups (low and high) according to this model.

CONCLUSIONS

A simple-to-use nomogram incorporating lactate level effectively predicted 6-month, 1-year, and 4-year MACE incidence among patients with ACS after PCI.

Clinical risk prediction models for the prognosis and management of acute coronary syndromes

Patients with acute coronary syndromes (ACS), particularly non-ST-segment elevation ACS, represent a spectrum of patients at variable risk of short- and long-term adverse clinical outcomes. Accurate prognostic assessment in this population requires the simultaneous consideration of multiple clinical and laboratory variables which may be under-recognized by the treating physicians, leading to an observed risk-treatment paradox in the use of invasive and pharmacological therapies. The routine application of established clinical risk scores, such as the Global Registry of Acute Coronary Events risk score, is recommended by major international clinical practice guidelines for structured risk stratification at the time of presentation, but uptake remains inconsistent. This article discusses the methodology of designing, deriving, and validating clinical risk scores, reviews the major validated risk scores for assessing prognosis in ACS, and examines their role in guiding clinical decision-making in ACS management, especially the timing of invasive coronary angiography. We also discuss emerging data on the impact of the routine use of such risk scores on patient management and clinical outcomes, as well as future directions for investigation in this field.

Comparison of Prognostic Value Among 4 Risk Scores in Patients with Acute Coronary Syndrome: Findings from the Improving Care for Cardiovascular Disease in China-ACS (CCC-ACS) Project

Background

Accurate risk assessment and prospective stratification are of great importance for treatment of acute coronary syndrome (ACS). However, the optimal risk evaluation systems for predicting different type of ACS adverse events in Chinese population have not been established.

Material/Methods

Our data were derived from the Improving Care for Cardiovascular Disease in China-ACS (CCC-ACS) Project, a multicenter registry program. We incorporated data on 44 750 patients in the study. We compared the performance of the following 4 different risk score systems with regard to prediction of in-hospital adverse events: the Global Registry for Acute Coronary Events (GRACE) risk score system; the age, creatinine and ejection fraction (ACEF) risk score system, and its modified version (AGEF), and the Canada Acute Coronary Syndrome (C-ACS) risk assessment system.

Results

Admission AGEF risk score was a better prognosis index of potential for in-hospital mortality for patients with ST segment elevation myocardial infarction (STEMI) than GRACE risk score (AUC: 0.845 vs 0.819, P=0.012), ACEF (AUC: 0.845 vs 0.827, P=0.014), C-ACS (AUC: 0.845 vs 0.767, P<0.001). In patients with non-ST segment-elevation acute coronary syndrome (NSTE-ACS), there was no statistically significant difference between the GRACE risk scale and AGEF (AUC: 0.853 vs 0.832, P=0.140) for in-hospital death.

Conclusions

AGEF risk score showed a non-inferior utility compared with the other 3 scoring systems in estimating in-hospital mortality in ACS patients.

Validation and Comparison of Six Risk Scores for Infection in Patients With ST-Segment Elevation Myocardial Infarction Undergoing Percutaneous Coronary Intervention

Aims: Very few of the risk scores to predict infection in ST-segment elevation myocardial infarction (STEMI) patients undergoing percutaneous coronary intervention (PCI) have been validated, and reports on their differences. We aimed to validate and compare the discriminatory value of different risk scores for infection.

Methods: A total of 2,260 eligible patients with STEMI undergoing PCI from January 2010 to May 2018 were enrolled. Six risk scores were investigated: age, serum creatinine, or glomerular filtration rate, and ejection fraction (ACEF or AGEF) score; Canada Acute Coronary Syndrome (CACS) risk score; CHADS₂ score; Global Registry for Acute Coronary Events (GRACE) score; and Mehran score conceived for contrast induced nephropathy. The primary endpoint was infection during hospitalization.

Results: Except CHADS₂ score (AUC, 0.682; 95%CI, 0.652–0.712), the other risk scores showed good discrimination for predicting infection. All risk scores but CACS risk score (calibration slope, 0.77; 95%CI, 0.18–1.35) showed best calibration for infection. The risks scores also showed good discrimination for in-hospital major adverse clinical events (MACE) (AUC range, 0.700–0.786), except for CHADS₂ score. All six risk scores showed best calibration for in-hospital MACE. Subgroup analysis demonstrated similar results.

Conclusions: The ACEF, AGEF, CACS, GRACE, and Mehran scores showed a good discrimination and calibration for predicting infection and MACE.

A risk score to predict in-hospital mortality in patients with acute coronary syndrome at early medical contact: results from the Improving Care for Cardiovascular Disease in China-Acute Coronary Syndrome (CCC-ACS) Project

Background

A number of models have been built to evaluate risk in patients with acute coronary syndrome (ACS). However, accurate prediction of mortality at early medical contact is difficult. This study sought to develop and validate a risk score to predict in-hospital mortality among patients with ACS using variables available at early medical contact.

Methods

A total of 62,546 unselected ACS patients from 150 tertiary hospitals who were admitted between 2014 and 2017 and enrolled in the Improving Care for Cardiovascular Disease in China-Acute Coronary Syndrome (CCC-ACS) project, were randomly assigned (at a ratio of 7:3) to a training dataset (n=43,774) and a validation dataset (n=18,772). Based on the identified predictors which were available prior to any blood test, a new point-based risk score for in-hospital death, CCC-ACS score, was derived and validated. The CCC-ACS score was then compared with Global Registry of Acute Coronary Events (GRACE) risk score.

Results

The in-hospital mortality rate was 1.9% in both the training and validation datasets. The CCC-ACS score, a new point-based risk score, was developed to predict in-hospital mortality using 7 variables that were available before any blood test including age, systolic blood pressure, cardiac arrest, insulin-treated diabetes mellitus, history of heart failure, severe clinical conditions (acute heart failure or cardiogenic shock), and electrocardiographic ST-segment deviation. This new risk score had an area under the curve (AUC) of 0.84 (P=0.10 for Hosmer-Lemeshow goodness-of-fit test) in the training dataset and 0.85 (P=0.13 for Hosmer-Lemeshow goodness-of-fit test) in the validation dataset. The CCC-ACS score was comparable to the Global Registry of Acute Coronary Events (GRACE) score in the prediction of in-hospital death in the validation dataset.

Conclusions

The newly developed CCC-ACS score, which utilizes factors that are acquirable at early medical contact, may be able to stratify the risk of in-hospital death in patients with ACS.

Clinical trial registration

URL: http://www.clinicaltrials.gov. Unique identifier: NCT02306616.

Differential leukocyte counts and cardiovascular mortality in very old patients with acute myocardial infarction: a Chinese cohort study

Background

Total leukocyte and differential Leukocyte counts are prognostic indictors in patients with coronary artery disease (CAD). However, there is no data available regarding their prognostic utility in very old patients with acute myocardial infarction (AMI). The aim of this study is to investigate the potential role of different leukocyte parameters in predicting the mortality among very old patients with AMI.

Methods

A total of 523 patients aged over 80 years with AMI were consecutively enrolled into this study. Leukocyte and its subtypes were obtained at admission in each patient. The primary study endpoint was cardiovascular mortality. Patients were followed up for an average of 2.2 years and 153 patients died. The associations of leukocyte parameters with mortality were assessed using Cox regression analyses. The concordance index was calculated to test the model efficiency.

Results

In multivariable regression analysis, neutrophils-plus-monocytes-to-lymphocytes ratio (NMLR) and neutrophils-to-lymphocytes ratio (NLR) were two most significant predictors of mortality among all the leukocyte parameters (HR = 3.21, 95% CI 1.75–5.35; HR = 2.79, 95% CI 1.59–4.88, respectively, all p < 0.001, adjusted for age, male gender, body mass index, family history of CAD, smoking, hypertension, diabetes mellitus, high-density lipoprotein cholesterol (HDL-C), non-HDL-C, high sensitivity C-reactive protein, creatinine, left ventricular ejection fraction, troponin I, use of statin, angiotensin-converting enzyme inhibitors/angiotensin receptor blockers, and percutaneous coronary intervention). Furthermore, adding NMLR and NLR into the Cox model increased the C-statistic by 0.038 and 0.037 respectively, which were more significant than that of other leukocyte parameters. Besides, addition of NMLR and NLR to the Canada Acute Coronary Syndrome Risk Score model also increased the C-statistic by 0.079 and 0.077 respectively.

Conclusion

Our data firstly indicated that most leukocyte subtypes were independent markers for the mortality in very old patients with AMI, while NMLR and NLR appeared to be more effective.

A Practical Risk Score to Predict 24-Month Post-Discharge Mortality Risk in Patients With Non-ST-Segment Elevation Myocardial Infarction

BACKGROUND

Risk stratification of patients with non-ST-segment elevation myocardial infarction (NSTEMI) is important in terms of treatment strategy selection. Current efforts have focused on short-term risk prediction after discharge, but we aimed to establish a risk score to predict the 24-month mortality risk in survivors of NSTEMI.Methods and Results:A total of 5,509 patients diagnosed with NSTEMI between January 2013 and September 2014 were included. Primary endpoint was all-cause death at 24 months. A multivariable Cox regression model was used to establish a practical risk score based on independent risk factors of death. The risk score included 9 variables: age, body mass index, left ventricular ejection fraction, reperfusion therapy during hospitalization, Killip classification, prescription of diuretics at discharge, heart rate, and hemoglobin and creatinine levels. The C-statistics for the risk model were 0.83 (95% confidence interval [CI]: 0.81-0.85) and 0.83 (95% CI: 0.79-0.86) in the development and validation cohorts, respectively. Mortality risk increased significantly across groups: 1.34% in the low-risk group (score: 0-58), 5.40% in intermediate group (score: 59-93), and 23.87% in high-risk group (score: ≥94).

CONCLUSIONS

The current study established and validated a practical risk score based on 9 variables to predict 24-month mortality risk in patients who survive NSTEMI. This score could help identify patients who are at high risk for future adverse events who may benefit from good adherence to guideline-recommended secondary prevention treatment.

A Nomogram Based on Apelin-12 for the Prediction of Major Adverse Cardiovascular Events after Percutaneous Coronary Intervention among Patients with ST-Segment Elevation Myocardial Infarction

Objective

This study aimed to establish a clinical prognostic nomogram for predicting major adverse cardiovascular events (MACEs) after primary percutaneous coronary intervention (PCI) among patients with ST-segment elevation myocardial infarction (STEMI).

Methods

Information on 464 patients with STEMI who performed PCI procedures was included. After removing patients with incomplete clinical information, a total of 460 patients followed for 2.5 years were randomly divided into evaluation (n = 324) and validation (n = 324) and validation (

Results

Apelin-12 change rate, apelin-12 level, age, pathological Q wave, myocardial infarction history, anterior wall myocardial infarction, Killip's classification > I, uric acid, total cholesterol, cTnI, and the left atrial diameter were independently associated with MACEs (all P < 0.05). After incorporating these 11 factors, the nomogram achieved good concordance indexes of 0.758 (95%CI = 0.707–0.809) and 0.763 (95%CI = 0.689–0.837) in predicting MACEs in the evaluation and validation cohorts, respectively, and had well-fitted calibration curves. The decision curve analysis (DCA) revealed that the nomogram was clinically useful.

Conclusions

We established and validated a novel nomogram that can provide individual prediction of MACEs for patients with STEMI after PCI procedures in a Chinese population. This practical prognostic nomogram may help clinicians in decision making and enable a more accurate risk assessment.

Predictive value of the Canada Acute Coronary Syndrome risk score for post-acute myocardial infarction infection

BACKGROUND

Although rare, infection in patients with ST-elevation myocardial infarction (STEMI) undergoing percutaneous coronary intervention (PCI) significantly increases mortality. Therefore, it is important to identify patients at high risk of infection. We aimed to validate the value of the Canada Acute Coronary Syndrome (C-ACS) risk score for predicting infection in such patients.

METHODS

We conducted a prospective cohort study. Consecutive patients with STEMI undergoing PCI at our hospital from January 2010 to June 2016 were enrolled . C-ACS risk score was calculated based on the following clinical parameters (1 point for each): age ≥ 75 years, Killip class >1, systolic blood pressure <100 mmHg, and heart rate > 100 beats/min. The primary outcome was development of post-acute myocardial infarction (P-AMI) infection.

RESULTS

A total of 2198 patients were enrolled, of whom 424 (18.5%) developed infection. The incidence of infection, in-hospital mortality, and major adverse clinical events (MACE) were significantly higher in those with a C-ACS risk score ≥2. After adjusting for potential risk factors, C-ACS risk score remained a significant predictor of P-AMI infection (odds ratio [OR] = 2.27, 95% confidence interval [CI] = 1.92-2.67, p < 0.001), in-hospital mortality, and MACE. Receiver operating characteristic curves demonstrated the C-ACS risk score had good predictive value for P-AMI infection (area under the curve = 0.783, 95% CI = 0.759-0.806, P < 0.001), in-hospital mortality and MACE.

CONCLUSIONS

The C-ACS risk score was a good predictor of P-AMI infection, and other clinical outcomes.

Perioperative proADM-change is associated with the development of acute respiratory distress syndrome in critically ill cardiac surgery patients: a prospective cohort study

Aim: Biomarkers of acute respiratory distress syndrome (ARDS) after cardiac-surgery may help risk-stratification and management. Preoperative single-value proADM increases predictive capacity of scoring-system EuroSCORE. To include the impact of surgery, we aim to assess the predictive value of the perioperative proADM-change on development of ARDS in 40 cardiac-surgery patients. Materials & methods: ProADM was measured in nine sequential blood samples. The Berlin definition of ARDS was used. For data-analyses, a multivariate model of EuroSCORE and perioperative proADM-change, linear mixed models and logistic regression were used. Results: Perioperative proADM-change was associated with ARDS after cardiac-surgery, and it was superior to EuroSCORE. A perioperative proADM-change >1.5 nmol/l could predict ARDS. Conclusion: Predicting post-surgery ARDS with perioperative proADM-change enables clinicians to intensify lung-protective interventions and individualized fluid therapy to minimize secondary injury.

Simple risk score based on the China Acute Myocardial Infarction registry for predicting in-hospital mortality among patients with non-ST-segment elevation myocardial infarction: results of a prospective observational cohort study

OBJECTIVES

To simplify our previous risk score for predicting the in-hospital mortality risk in patients with non-ST-segment elevation myocardial infarction (NSTEMI) by dropping laboratory data.

DESIGN

Prospective cohort.

SETTING

Multicentre, 108 hospitals across three levels in China.

PARTICIPANTS

A total of 5775 patients with NSTEMI enrolled in the China Acute Myocardial Infarction (CAMI) registry.

PRIMARY OUTCOME MEASURES

In-hospital mortality.

RESULTS

The simplified CAMI-NSTEMI (SCAMI-NSTEMI) score includes the following nine variables: age, body mass index, systolic blood pressure, Killip classification, cardiac arrest, ST-segment depression on ECG, smoking status, previous angina and previous percutaneous coronary intervention. Within both the derivation and validation cohorts, the SCAMI-NSTEMI score showed a good discrimination ability (C-statistics: 0.76 and 0.83, respectively); further, the SCAMI-NSTEMI score had a diagnostic performance superior to that of the Global Registry of Acute Coronary Events risk score (C-statistics: 0.78 and 0.73, respectively; p<0.0001 for comparison). The in-hospital mortality increased significantly across the different risk groups.

CONCLUSIONS

The SCAMI-NSTEMI score can serve as a useful tool facilitating rapid risk assessment among a broader spectrum of patients admitted owing to NSTEMI.

TRIAL REGISTRATION NUMBER

NCT01874691.

Application of Risks Scores in Acute Coronary Syndromes. How Does ProACS Hold Up Against Other Risks Scores?

BACKGROUND

Multiple risk scores (RS) are approved in the prediction of worse prognosis in acute coronary syndromes (ACS). Recently, the Portuguese Journal of Cardiology has proposed the ProACS RS.

OBJECTIVE

Application of several validated RS, as well as ProACS in patients, admitted for ACS. Evaluation of each RS's performance in predicting in-hospital mortality and the occurrence of all-cause mortality or non-fatal ACS at one-year follow-up and compare them to the ProACS RS.

METHODS

A retrospective study of ACS was performed. The following RS were applied: GRACE, ACTION Registry-GWTG, PURSUIT, TIMI, EMMACE, SRI, CHA2DS2-VASc-HS, C-ACS and ProACS. ROC Curves were created to determine the predictive power for each RS and then were directly compared to ProACS.

RESULTS

The ProACS, ACTION Registry-GWTG and GRACE showed a c-statistics of 0.908, 0.904 and 0.890 for predicting in-hospital mortality, respectively, performing better in ST-segment elevation myocardial infarction patients. The other RS performed satisfactorily, with c-statistics over 0.750, apart from the CHA2DS2-VASc-HS and C-ACS which underperformed. All RS underperformed in predicting worse long-term prognosis revealing c-statistics under 0.700.

CONCLUSION

ProACS is an easily obtained risk score for early stratification of in-hospital mortality. When evaluating all RS, the ProACS, ACTION Registry-GWTG and GRACE RS showed the best performance, demonstrating high capability of predicting a worse prognosis. ProACS was able to demonstrate statistically significant superiority when compared to almost all RS. Thus, the ProACS has showed that it is able to combine simplicity in the calculation of the score with good performance in predicting a worse prognosis.

Risk Scoring System for Prognosis Estimation of Multivessel Disease Among Patients with ST-Segment Elevation Myocardial Infarction

Multivessel disease (MVD) is an independent risk factor for poor prognosis in acute myocardial infarction patients. Although several global risk scoring systems (RSS) are in use in clinical practice, there is no dedicated RSS for MVD in ST-segment elevation myocardial infarction (STEMI). The primary objective of this study is to develop a novel RSS to estimate the prognosis of patients with MVD in STEMI.We used the Korean Acute Myocardial Infarction Registry (KAMIR) to identify 2,030 STEMI patients with MVD who underwent appropriate percutaneous coronary intervention (PCI). Their data were analyzed to develop a new RSS. The prognostic power of this RSS was validated with 2,556 STEMI patients with MVD in the Korean Working Group on Myocardial Infarction Registry (KORMI).Six prognostic factors related to all-cause death in STEMI patients with MVD were age, serum creatinine, Killip Class, lower body weight, decrease in left ventricular ejection fraction, and history of cerebrovascular disease. The RSS for all-cause death was constructed using these risk factors and their statistical weight. The RSS had appropriate performance (c-index: 0.72) in the KORMI validation cohort.We developed a novel RSS that estimates all-cause death in the year following discharge for patients with MVD in STEMI appropriately treated by PCI. This novel RSS was transformed into a simple linear risk score to yield a simplified estimate prognosis of MVD among STEMI patients.

Atherothrombotic risk stratification after acute myocardial infarction: The Thrombolysis in Myocardial Infarction Risk Score for Secondary Prevention in the light of the French Registry of Acute ST Elevation or non-ST Elevation Myocardial Infarction registries

BACKGROUND

Guidelines recommend using risk stratification tools in acute myocardial infarction (AMI) to assist decision-making. The Thrombolysis in Myocardial Infarction Risk Score for Secondary Prevention (TRS-2P) has been recently developed to characterize long-term risk in patients with MI.

HYPOTHESIS

We aimed to assess the TRS-2P in the French Registry of Acute ST Elevation or non-ST elevation MI registries.

METHODS

We used data from three 1-month French registries, conducted 5 years apart, from 2005 to 2015, including 13 130 patients with AMI (52% ST-elevation myocardial infarction [STEMI]). Atherothrombotic risk stratification was performed using the TRS-2P score. Patients were divided in to three categories: G1 (low-risk, TRS-2P = 0/1); G2 (intermediate-risk, TRS-2P = 2); and G3 (high-risk, TRS-2P ≥ 3). Baseline characteristics and outcomes were analyzed according to TRS-2P categories.

RESULTS

A total of 12 715 patients (in whom TRS-2P was available) were included. Prevalence of G1, G2, and G3 was 43%, 24%, and 33% respectively. Clinical characteristics and management significantly differed according to TRS-2P categories. TRS-2P successfully defined residual risk of death at 1 year (C-statistic 0.78): 1-year survival was 98% in G1, 94% in G2, and 78.5% in G3 (P < 0.001). Using Cox multivariate analysis, G3 was independently associated with higher risk of death at 1 year (hazard ratio [HR] 4.61; 95% confidence interval [CI]: 3.61-5.89), as G2 (HR 2.08; 95% CI: 1.62-2.65) compared with G1. The score appeared robust and correlated well with mortality in STEMI and NSTEMI populations, as well as in each cohort separately.

CONCLUSIONS

The TRS-2P appears to be a robust risk score, identifying patients at high risk after AMI irrespective of the type of MI and historical period.

Appropriate secondary prevention and clinical outcomes after acute myocardial infarction according to atherothrombotic risk stratification: The FAST-MI 2010 registry

BACKGROUND

Full secondary prevention medication regimen is often under-prescribed after acute myocardial infarction.

DESIGN

The purpose of this study was to analyse the relationship between prescription of appropriate secondary prevention treatment at discharge and long-term clinical outcomes according to risk level defined by the Thrombolysis In Myocardial Infarction (TIMI) Risk Score for Secondary Prevention (TRS-2P) after acute myocardial infarction.

METHODS

We used data from the 2010 French Registry of Acute ST-Elevation or non-ST-elevation Myocardial Infarction (FAST-MI) registry, including 4169 consecutive acute myocardial infarction patients admitted to cardiac intensive care units in France. Level of risk was stratified in three groups using the TRS-2P score: group 1 (low-risk; TRS-2P=0/1); group 2 (intermediate-risk; TRS-2P=2); and group 3 (high-risk; TRS-2P≥3). Appropriate secondary prevention treatment was defined according to the latest guidelines (dual antiplatelet therapy and moderate/high dose statins for all; new-P2Y12 inhibitors, angiotensin-converting-enzyme inhibitor/angiotensin-receptor-blockers and beta-blockers as indicated).

RESULTS

Prevalence of groups 1, 2 and 3 was 46%, 25% and 29% respectively. Appropriate secondary prevention treatment at discharge was used in 39.5%, 37% and 28% of each group, respectively. After multivariate adjustment, evidence-based treatments at discharge were associated with lower rates of major adverse cardiovascular events (death, re-myocardial infarction or stroke) at five years especially in high-risk patients: hazard ratio = 0.82 (95% confidence interval: 0.59-1.12, p = 0.21) in group 1, 0.74 (0.54-1.01; p = 0.06) in group 2, and 0.64 (0.52-0.79, p < 0.001) in group 3.

CONCLUSIONS

Use of appropriate secondary prevention treatment at discharge was inversely correlated with patient risk. The increased hazard related to lack of prescription of recommended medications was much larger in high-risk patients. Specific efforts should be directed at better prescription of recommended treatment, particularly in high-risk patients.

Recurrent Cardiovascular Events in Survivors of Myocardial Infarction With ST-Segment Elevation (from the AMI-QUEBEC Study)

The characteristics and predictors of long-term recurrent ischemic cardiovascular events (RICEs) after myocardial infarction with ST-segment elevation (STEMI) have not yet been clarified. We aimed to characterize the 10-year incidence, types, and predictors of RICE. We obtained 10-year follow-up of STEMI survivors at 17 Quebec hospitals in Canada (the AMI-QUEBEC Study) in 2003. There were 858 patients; mean age was 60 years and 73% were male. The majority of patients receive reperfusion therapy; 53.3% and 39.2% of patients received primary percutaneous coronary intervention (PCI) and fibrinolytic therapy, respectively. Seventy-five percent of patients underwent in-hospital PCI (elective, rescue, and primary). At 10 years, 42% of patients suffered a RICE, with most RICEs (88%) caused by recurrent cardiac ischemia. The risk of RICE was the highest during the first year (23.5 per patient-year). At 10 years, the all-cause mortality was 19.3%, with 1/3 of deaths being RICE-related. Previous cardiovascular event, heart failure during the index STEMI hospitalization, discharge prescription of calcium blocker increased the risk of RICE by almost twofold. Each point increase in TIMI (Thrombolysis In Myocardial Infarction) score augmented the risk of RICE by 6%, whereas discharge prescription of dual antiplatelets reduced the risk of RICE by 23%. Our findings suggested that survivors of STEMI remain at high long-term risk of RICE despite high rate of reperfusion therapy and in-hospital PCI. Patients with previous cardiovascular event, in-hospital heart failure, and high TIMI score were particularly susceptible to RICE. Future studies are needed to confirm the impacts of calcium blocker and dual antiplatelets on long-term risk of RICE.

ProACS risk score: An early and simple score for risk stratification of patients with acute coronary syndromes

INTRODUCTION

There are barriers to proper implementation of risk stratification scores in patients with acute coronary syndromes (ACS), including their complexity. Our objective was to develop a simple score for risk stratification of all-cause in-hospital mortality in a population of patients with ACS.

METHODS

The score was developed from a nationwide ACS registry. The development and internal validation cohorts were obtained from the first 31829 patients, randomly separated (60% and 40%, respectively). The external validation cohort consisted of the last 8586 patients included in the registry. This cohort is significantly different from the other cohorts in terms of baseline characteristics, treatment and mortality. Multivariate logistic regression analysis was used to select four variables with the highest predictive potential. A score was allocated to each parameter based on the regression coefficient of each variable in the logistic regression model: 1 point for systolic blood pressure ≤116 mmHg, Killip class 2 or 3, and ST-segment elevation; 2 points for age ≥72 years; and 3 points for Killip class 4.

RESULTS

The new score had good discriminative ability in the development cohort (area under the curve [AUC] 0.796), and it was similar in the internal validation cohort (AUC 0.785, p=0.333). In the external validation cohort, there was also excellent discriminative ability (AUC 0.815), with an adequate fit.

CONCLUSIONS

The ProACS risk score enables easy and simple risk stratification of patients with ACS for in-hospital mortality that can be used at the first medical contact, with excellent predictive ability in a contemporary population.

Canada Acute Coronary Syndrome Score: A Preprocedural Risk Score for Contrast-Induced Nephropathy After Primary Percutaneous Coronary Intervention

In patients with ST-segment elevation myocardial infarction (STEMI) undergoing primary percutaneous coronary intervention, contrast-induced nephropathy (CIN) is a serious complication associated with poor outcomes. We assessed the predictive value of the Canada Acute Coronary Syndrome (C-ACS) score for CIN in these patients. A total of 394 consecutive patients with STEMI were enrolled and divided into 3 groups according to their C-ACS scores—group 1, score 0; group 2, score 1; and group 3, score ≥2. The clinical outcomes were CIN and major adverse clinical events (MACEs) during hospital and follow-up; 8.4% of patients developed CIN. Patients with high C-ACS scores were more likely to develop CIN, in-hospital death, and MACEs ( P < .001). The C-ACS score was an independent predictor of CIN (odds ratio = 2.87; 95% confidence interval = 1.78-4.63; P < .001) and risk factor for long-term MACEs. The C-ACS score had good predictive values for CIN, in-hospital morality, MACEs, and long-term mortality. Patients with high C-ACS risk scores exhibited a worse survival rate than those with low scores (death, P = .02; MACEs, P = .006). In conclusion, in patients with STEMI, the C-ACS could predict CIN and clinical outcomes.

N-terminal pro-brain natriuretic peptide improves the C-ACS risk score prediction of clinical outcomes in patients with ST-elevation myocardial infarction

Background

It remained unclear whether the combination of the Canada Acute Coronary Syndrome Risk Score (CACS-RS) and N-terminal pro-brain natriuretic peptide (NT-pro-BNP) could have a better performance in predicting clinical outcomes in acute ST-elevation myocardial infarction (STEMI) patients with primary percutaneous coronary intervention.

Methods

A total of 589 consecutive STEMI patients were enrolled. The potential additional predictive value of NT-pro-BNP with the CACS-RS was estimated. Primary endpoint was in-hospital mortality and long-term poor outcomes.

Results

The incidence of in-hospital death was 3.1%. Patients with higher NT-pro-BNP and CACS-RS had a greater incidence of in hospital death. After adjustment for the CACS-RS, elevated NT-pro-BNP (defined as the best cutoff point based on the Youden’s index) was significantly associated with in hospital death (odd ratio = 4.55, 95%CI = 1.52–13.65, p = 0.007). Elevated NT-pro-BNP added to CACS-RS significantly improved the C-statistics for in-hospital death, as compared with the original score (0.762 vs. 0.683, p = 0.032). Furthermore, the addition of NT-pro-BNP to CACS-RS enhanced net reclassification improvement (0.901, p < 0.001) and integrated discrimination improvement (0.021, p = 0.033), suggesting effective discrimination and reclassification. In addition, the similar result was also demonstrated for in-hospital major adverse clinical events (C-statistics: 0.736 vs. 0.695, p = 0.017) or 3-year mortality (0.699 vs. 0.604, p = 0.004).

Conclusions

Both NT-pro-BNP and CACS-RS are risk predictors for in hospital poor outcomes in patients with STEMI. A combination of them could derive a more accurate prediction for clinical outcome s in these patients.

External validation of the ProACS score for risk stratification of patients with acute coronary syndromes

INTRODUCTION

The ProACS risk score is an early and simple risk stratification score developed for all-cause in-hospital mortality in acute coronary syndromes (ACS) from a Portuguese nationwide ACS registry. Our center only recently participated in the registry and was not included in the cohort used for developing the score. Our objective was to perform an external validation of this risk score for short- and long-term follow-up.

METHODS

Consecutive patients admitted to our center with ACS were included. Demographic and admission characteristics, as well as treatment and outcome data were collected. The ProACS risk score variables are age (≥72 years), systolic blood pressure (≤116 mmHg), Killip class (2/3 or 4) and ST-segment elevation. We calculated ProACS, Global Registry of Acute Coronary Events (GRACE) and Canada Acute Coronary Syndrome risk score (C-ACS) risk scores for each patient.

RESULTS

A total of 3170 patients were included, with a mean age of 64±13 years, 62% with ST-segment elevation myocardial infarction. All-cause in-hospital mortality was 5.7% and 10.3% at one-year follow-up. The ProACS risk score showed good discriminative ability for all considered outcomes (area under the receiver operating characteristic curve >0.75) and a good fit, similar to C-ACS, but lower than the GRACE risk score and slightly lower than in the original development cohort. The ProACS risk score provided good differentiation between patients at low, intermediate and high mortality risk in both short- and long-term follow-up (p<0.001 for all comparisons).

CONCLUSIONS

The ProACS score is valid in external cohorts for risk stratification for ACS. It can be applied very early, at the first medical contact, but should subsequently be complemented by the GRACE risk score.

Canada acute coronary syndrome score was a stronger baseline predictor than age ≥75 years of in-hospital mortality in acute coronary syndrome patients in western Romania

BACKGROUND

Several risk scores were developed for acute coronary syndrome (ACS) patients, but their use is limited by their complexity.

PURPOSE

The purpose of this study was to identify predictors at admission for in-hospital mortality in ACS patients in western Romania, using a simple risk-assessment tool - the new Canada acute coronary syndrome (C-ACS) risk score.

PATIENTS AND METHODS

The baseline risk of patients admitted with ACS was retrospectively assessed using the C-ACS risk score. The score ranged from 0 to 4; 1 point was assigned for the presence of each of the following parameters: age ≥75 years, Killip class >1, systolic blood pressure <100 mmHg, and heart rate >100 bpm.

RESULTS

A total of 960 patients with ACS were included, 409 (43%) with ST-segment elevation myocardial infarction (STEMI) and 551 (57%) with non-ST-segment elevation acute coronary syndrome (NSTE-ACS). The C-ACS score predicted in-hospital mortality in all ACS patients with a C-statistic of 0.95 (95% CI: 0.93-0.96), in STEMI patients with a C-statistic of 0.92 (95% confidence interval [CI]: 0.89-0.94), and in NSTE-ACS patients with a C-statistic of 0.97 (95% CI: 0.95-0.98). Of the 960 patients, 218 (22.7%) were aged ≥75 years. The proportion of patients aged ≥75 years was 21.7% in the STEMI subgroup and 23.4% in the NSTE-ACS subgroup (P>0.05). Age ≥75 years was significantly associated with in-hospital mortality in ACS patients (odds ratio [OR]: 3.25, 95% CI: 1.24-8.25) and in the STEMI subgroup (OR >3.99, 95% CI: 1.28-12.44). Female sex was strongly associated with mortality in the NSTE-ACS subgroup (OR: 27.72, 95% CI: 1.83-39.99).

CONCLUSION

We conclude that C-ACS score was the strongest predictor of in-hospital mortality in all ACS patients while age ≥75 years predicted the mortality well in the STEMI subgroup.

Validation of the Canada Acute Coronary Syndrome Risk Score for Hospital Mortality in the Gulf Registry of Acute Coronary Events-2

BACKGROUND

Several risk scores have been developed for acute coronary syndrome (ACS) patients, but their use is limited by their complexity. The new Canada Acute Coronary Syndrome (C-ACS) risk score is a simple risk-assessment tool for ACS patients. This study assessed the performance of the C-ACS risk score in predicting hospital mortality in a contemporary Middle Eastern ACS cohort.

HYPOTHESIS

The C-ACS score accurately predicts hospital mortality in ACS patients.

METHODS

The baseline risk of 7929 patients from 6 Arab countries who were enrolled in the Gulf RACE-2 registry was assessed using the C-ACS risk score. The score ranged from 0 to 4, with 1 point assigned for the presence of each of the following variables: age ≥75 years, Killip class >1, systolic blood pressure <100 mm Hg, and heart rate >100 bpm. The discriminative ability and calibration of the score were assessed using C statistics and goodness-of-fit tests, respectively.

RESULTS

The C-ACS score demonstrated good predictive values for hospital mortality in all ACS patients with a C statistic of 0.77 (95% confidence interval [CI]: 0.74-0.80) and in ST-segment elevation myocardial infarction and non-ST-segment elevation acute coronary syndrome patients (C statistic: 0.76, 95% CI: 0.73-0.79; and C statistic: 0.80, 95% CI: 0.75-0.84, respectively). The discriminative ability of the score was moderate regardless of age category, nationality, and diabetic status. Overall, calibration was optimal in all subgroups.

CONCLUSIONS

The new C-ACS score performed well in predicting hospital mortality in a contemporary ACS population outside North America.