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Identifying Patients at Risk for Prehospital Sudden Cardiac Arrest at the Early Phase of Myocardial Infarction

The e-MUST Study (Evaluation en Médecine d’Urgence des Stratégies Thérapeutiques des infarctus du myocarde)
and For the e-MUST Study Investigators
Originally publishedhttps://doi.org/10.1161/CIRCULATIONAHA.116.022954Circulation. 2016;134:2074–2083

Abstract

Background:

In-hospital mortality of ST-segment–elevation myocardial infarction (STEMI) has decreased drastically. In contrast, prehospital mortality from sudden cardiac arrest (SCA) remains high and difficult to reduce. Identification of the patients with STEMI at higher risk for prehospital SCA could facilitate rapid triage and intervention in the field.

Methods:

Using a prospective, population-based study evaluating all patients with STEMI managed by emergency medical services in the greater Paris area (11.7 million inhabitants) between 2006 and 2010, we identified characteristics associated with an increased risk of prehospital SCA and used these variables to build an SCA prediction score, which we validated internally and externally.

Results:

In the overall STEMI population (n=8112; median age, 60 years; 78% male), SCA occurred in 452 patients (5.6%). In multivariate analysis, younger age, absence of obesity, absence of diabetes mellitus, shortness of breath, and a short delay between pain onset and call to emergency medical services were the main predictors of SCA. A score built from these variables predicted SCA, with the risk increasing 2-fold in patients with a score between 10 and 19, 4-fold in those with a score between 20 and 29, and >18-fold in patients with a score ≥30 compared with those with scores <10. The SCA rate was 28.9% in patients with a score ≥30 compared with 1.6% in patients with a score ≤9 (P for trend <0.001). The area under the curve values were 0.7033 in the internal validation sample and 0.6031 in the external validation sample. Sensitivity and specificity varied between 96.9% and 10.5% for scores ≥10 and between 18.0% and 97.6% for scores ≥30, with scores between 20 and 29 achieving the best sensitivity and specificity (65.4% and 62.6%, respectively).

Conclusions:

At the early phase of STEMI, the risk of prehospital SCA can be determined through a simple score of 5 routinely assessed predictors. This score might help optimize the dispatching and management of patients with STEMI by emergency medical services.

Introduction

Overall mortality rates of ST-segment–elevation myocardial infarction (STEMI) have decreased drastically during the past decades, mainly as a result of a tremendous reduction in in-hospital mortality. In contrast, out-of-hospital mortality remains high.1 To further reduce STEMI mortality, efforts should now focus on the prehospital phase of STEMI management, during which sudden cardiac arrest (SCA) is the major cause of death.1,2

Appropriate response and management in the early minutes of SCA play a crucial role in improving survival.35 Identifying patients at increased risk of prehospital SCA could allow implementation of early, rapid measures by emergency medical services (EMS) and bystanders. It could also help EMS make decisions on the optimal management and triage of patients with STEMI. Studies describing the characteristics of patients who present SCA at the early phase of STEMI have been conducted on databases of catheterization laboratories and therefore included only a subset of the STEMI population who survived until hospital admission and underwent coronary angiography.68 A comprehensive assessment of all patients with STEMI who had early SCA before hospital admission has therefore never been performed, and a real prediction of SCA risk during STEMI cannot be done accurately with the currently available data.

Using the e-MUST (Evaluation en Médecine d’Urgence des Stratégies Thérapeutiques des infarctus du myocarde) registry, which includes all out-of-hospital STEMI managed by EMS in the greater Paris area, we identified the characteristics associated with an increased risk of SCA in the field and used these variables to build an SCA prediction score that was tested by internal and external validation.

Methods

Study Setting

The e-MUST study was initiated in 2003 and planned to enroll all out-of-hospital STEMI (according to current definition2) managed within 24 hours of pain onset in the greater Paris area, the most populated region of France (11.7 million inhabitants, 20% of the French population). The study was funded by a governmental agency, the Agence Régionale de Santé d’Ile de France,9 and coordinated by its registry department and by a scientific committee from the 8 branches of the Service d’Aide Medicale Urgente (SAMU) in the greater Paris area. Data were analyzed in the Cardiovascular Epidemiology Unit (U970) of the French Institute of Health and Medical Research. The study complied with the Declaration of Helsinki, and the data file of the e-MUST study was declared to and authorized by the French data protection committee.

Patients were included in the study if they were managed primarily by the EMS, they were alive at EMS arrival, they presented typical angina that started within the past 24 hours and lasted >20 minutes despite administration of nitrates, and the ECG showed ST-segment elevation ≥2 mm in 2 adjacent precordial leads (or 1 mm in standard leads) or a new or presumed new left bundle-branch block according to the current definition of STEMI.2 A standardized questionnaire has been used by all EMS since 2003, with new variables related to cardiovascular risk factors added on January 1, 2006. We therefore analyzed data over 5 years starting from 2006.

The French EMS-SAMU is formed by 2 components working in close collaboration: dispatch centers where an emergency physician receives and manages urgent medical calls through a dedicated phone number (15) and mobile intensive care units coordinated by the dispatch center and distributed homogeneously throughout each department. These units include at least 1 emergency physician, 1 nurse, and 1 ambulance driver and are sent to the field in cases of suspected STEMI. They manage patients in a standardized way via common predefined protocols.

Data Collection and Definitions

Data for the e-MUST registry are first collected on the phone by the EMS dispatcher and then completed by the EMS physician present in the field using the standardized questionnaire. Data are then entered into a computerized database and sent every 4 months to the registry department of the Agence Régionale de Santé d’Ile de France. An independent external random audit is held yearly by the registry department on 200 files (11.5% of the cases) to ensure completeness and reliability of the data. The ratio of cases included in the e-MUST registry over the total number of cases in the region is 92%.10

Collected data include the patient’s demographic and clinical data such as age, sex, risk factors, medical history, and current symptoms, including chest pain and dyspnea at rest. Times of pain onset, EMS call, EMS arrival, and hospital arrival are all systematically recorded, allowing the calculation of time delays between each of these steps. Time delay from EMS call to first medical contact is defined as the time interval between the call to the dispatch center and the EMS physician’s arrival at the patient’s location. The patient’s status on EMS arrival (Killip stage, STEMI location), EMS management strategy, and STEMI outcomes are also specified. Prehospital SCA is defined as a sudden and unexpected pulseless event without obvious extracardiac cause occurring before hospital arrival and witnessed by EMS.11 Patients who presented SCA before EMS arrival are not included because the diagnosis of STEMI can not be confirmed in the field even after successful resuscitation because of the poor predictive value of ECGs performed after SCA.12

Statistical Analysis

Continuous variables are reported as medians (interquartile range [IQR]), and categorical variables are given as proportions. Baseline characteristics of patients who experienced SCA and those who did not were compared by use of the χ2 test for categorical variables and the Student t test or Kruskal-Wallis test for continuous variables when appropriate. For the present analyses, age was classified as ≤40, 41 to 50, 51 to 60, 61 to 70, or >70 years. Time delay from chest pain onset to EMS call was classified as ≤30, 31 to 60, 61 to 120, or >120 minutes.

A multivariate regression model predictive of SCA in the setting of STEMI was developed to identify characteristics associated with an increased risk of SCA. Patients with complete data were therefore randomly assigned to either a derivation or a validation sample:

  • In the e-MUST derivation sample (two thirds of the population), univariate logistic regression was used to identify predictors of SCA. Nominally significant predictors (P<0.20) were then jointly entered into a single multivariate analysis. Backward selection was then used to select the most predictive parameters. Two-way interaction was tested in the final model with the Wald score test.

  • In the e-MUST validation sample (one third of the population), discrimination was estimated by means of the area under the receiver-operating characteristic curve, and the 95% confidence interval was obtained after 1000 bootstrapping with replications. Calibration, that is, the concordance between predicted and observed events by decile of estimated risk, was evaluated by the Hosmer-Lemeshow goodness-of-fit test; a value of P>0.20 indicates adequate calibration.

An external validation was also performed on an independent sample of patients with STEMI from another French region (Haute Savoie) in which similar data were prospectively collected between January 1, 2005, and January 31, 2012.

A point score system was then developed that was based on the results of the multivariate model. A weight was assigned to each independent predictor of SCA by multiplying its multivariate regression coefficient by 10 and rounding to the nearest whole number. The individual score was obtained by summing those weights for each patient. The median scores of subjects with and without SCA were compared by use of the Kruskal-Wallis test in the entire e-MUST population. Four groups of scores were created by 10 points of score (0–9, 10–19, 20–29, and ≥30), and within each group, the rate of observed SCA and the estimated rate ratio of SCA were determined, with the first group (score of 0–9) as the reference category. Score performances (area under the receiver-operating characteristic curve values, sensitivity, specificity, and likelihood ratio) were then calculated for each 10 points of score.

All statistical tests were 2 tailed. Statistical analyses were performed with STATA version 11.0 (STATA Corp, College Station, TX). The authors had full access to data and designed the statistical analysis, had final responsibility for the decision to submit the manuscript for publication, and vouch for the accuracy and completeness of the data and the analyses.

Results

Characteristics of Patients With STEMI and Incidence of SCA

Between January 2006 and December 2010, 8112 patients presenting with acute STEMI were managed by the EMS in the greater Paris area (Figure 1).

Figure 1.

Figure 1. Study flowchart. SCA indicates sudden cardiac arrest; and STEMI, ST-segment–elevation myocardial infarction.

In the overall STEMI population, median age was 60 years (IQR, 51–73 years), and 78% were male. Personal and family history of coronary artery disease was present in 19% and 18.5%, respectively. Smoking was the most common risk factor and was present in 52.8% of the patients; 15.1% had diabetes mellitus; 40.0% had hypertension; 35.7% had hyperlipidemia; and 24.8% were obese. Shortness of breath was reported by 3.6% of the patients. Median time between pain onset and call to the dispatch center was 60 minutes (IQR, 26–165 minutes; Table 1).

Table 1. Characteristics of the e-MUST Patients

nAll Patients(n =8 112)Without SCA(n =7 660)With SCA(n =452)P Value*
Age8070
 Median (IQR), y60 (51–73)60 (51–73)57 (48–68)<0.0001
 ≤40 y, n (%)414 (5.1)379 (5.0)35 (7.8)<0.0001
 41–50 y, n (%)1530 (19.0)1427 (18.7)103 (22.9)
 51–60 y, n (%)2258 (28.0)2125 (27.9)133 (29.6)
 61–70 y, n (%)1568 (19.4)1487 (19.5)81 (18.0)
 >70 y, n (%)2300 (28.5)2203 (28.9)97 (21.6)
Male, n (%)80956322 (78.1)5964 (78.0)358 (79.4)0.5
Risk factors, n (%)7924
 History of CAD1505 (19.0)1429 (19.0)76 (17.0)0.3
 Family history of CAD1470 (18.5)1398 (18.7)72 (16.1)0.2
 Current smoking4188 (52.8)3960 (53.0)228 (51.1)0.4
 Diabetes mellitus1195 (15.1)1154 (15.4)41 (9.2)<0.0001
 Hypertension3166 (40.0)3031 (40.5)135 (30.3)<0.0001
 Dyslipidemia2826 (35.7)2684 (35.9)142 (31.8)0.08
 Obesity1963 (24.8)1889 (25.3)74 (16.6)<0.0001
 Shortness of breath7752281 (3.6)202 (2.7)79 (19.4)<0.0001
Chest pain onset–to–call delay7924
 Median (IQR), min60 (26–165)63 (27–170)34 (12–78)<0.0001
 ≤30 min, n (%)2,409 (30.4)2204 (29.4)205 (47.7)<0.0001
 31–60 min, n (%)1561 (19.7)1472 (19.6)89 (20.7)
 61–120 min, n (%)1410 (17.8)1351 (18.0)59 (13.7)
 >120 min, n (%)2544 (32.1)2467 (32.9)77 (17.9)
Call to EMS arrival delay, min7959
 Median (IQR)20 (14–28)20 (14–28)18 (13–28)0.03

CAD indicates coronary artery disease; EMS, emergency medical service; e-MUST, Evaluation en Médecine d’Urgence des Stratégies Thérapeutiques des infarctus du myocarde; IQR, interquartile range; and SCA, sudden cardiac arrest.

*Comparison between patients with and without SCA was performed with the Student, Kruskal-Wallis, or χ2 test when appropriate.

EMS-witnessed prehospital SCA occurred in 452 patients (5.6%), with ventricular tachycardia and ventricular fibrillation being the initial rhythm in most patients (348 patients, 76.9%). Defibrillation was performed within 1 minute of SCA onset when indicated; thus, no-flow delay was <1 minute in such cases. The survival rate at hospital discharge was significantly lower among patients who presented SCA compared with those who did not (63.9% versus 95.9%; P<0.001).

Predicting SCA During STEMI

Compared with the patients who did not present SCA, the median age was lower in the group who presented SCA: 60 years (IQR, 51–73 years) versus 57 years (IQR, 48–68 years; P<0.001). Diabetes mellitus, hypertension, and obesity were less frequent in the SCA group (9.2% versus 15.4%, 30.3% versus 40.5%, and 16.6% versus 25.3%, respectively; all P<0.001), whereas shortness of breath was more often present before SCA (19.4% versus 2.7%; P<0.001). The median time delay between chest pain onset and call to the dispatch center was shorter in the SCA group: 34 minutes (IQR, 12–78 minutes) versus 63 minutes (IQR, 27–170 minutes; P<0.001). There was no significant difference in the frequency of hyperlipidemia, smoking, and personal or family history of coronary artery disease between the 2 groups (Table 1).

The derivation sample included 5353 patients, of whom 297 (5.5%) presented out-of-hospital SCA, and the validation sample included 2759 patients, of whom 155 patients (5.6%) presented SCA. In the final model, after the exclusion of patients with missing data, the derivation sample included 4902 patients and the validation sample had 2520 patients (Table I in the online-only Data Supplement). Therefore, the score was built on 7422 of the 8112 patients because risk factors were missing for age (n=42, 0.5%), family history or traditional risk factors (n=188, 2.3%), shortness of breath (n=360, 4.4%), and delays (n=188, 2.3%). The 690 patients with missing values had significantly more out-of-hospital SCA than the 7422 without missing values (9.9% versus 5.6%, respectively; P<0.001).

There was no significant difference between the baseline characteristics of patients in the e-MUST derivation and validation samples (Table II in the online-only Data Supplement). On univariate analysis, variables associated with SCA included young age, absence of diabetes mellitus, absence of hypertension, absence of obesity, shortness of breath, and short delay between pain onset and call to the dispatch center (Table III in the online-only Data Supplement).

Multivariate analysis showed that younger age, absence of diabetes mellitus, absence of obesity, shortness of breath, and a short delay between pain onset and call to the dispatch center were independent predictors of SCA (Table 2). No interaction between individual predictors was identified. When applied to the e-MUST derivation sample, the area under the receiver-operating characteristic curve was 0.72 (95% confidence interval, 0.68–0.77), indicating fairly good discrimination. As shown in Figure I in the online-only Data Supplement, the model was accurately calibrated in the validation sample with a close agreement between the number of predicted and observed SCAs in each decile of estimated risk (Pfor the Hosmer-Lemeshow test=0.91).

Table 2. Independent Predictors of SCA at the Acute Phase of STEMI in the Derivation Sample (n=4902): Multivariate Analysis

OR95% CIP Valueβ CoefficientScore
Age, y
 >7010
 61–701.40.9–2.10.10.33
 51–601.51.0–2.20.040.44
 41–502.11.4–3.1<0.00010.77
 ≤402.51.5–4.4<0.00010.99
Diabetes mellitus
 Yes10
 No1.61.0–2.60.030.55
Obesity
 Yes10
 No1.71.2–2.30.0030.55
Shortness of breath
 Absent10
 Present10.57.1–15.4<0.00012.323
Time from the chest pain onset to EMS call, min
 >12010
 61–1201.71.1–2.70.020.55
 31–602.31.5–3.4<0.00010.88
 ≤302.81.9–4.0<0.00011.010

CI indicates confidence interval; EMS, emergency medical services; OR, odds ratio; SCA, sudden cardiac arrest; and STEMI, ST-segment–elevation myocardial infarction. ORs and 95% CIs were estimated from backward multivariate logistic regression in two-thirds of the cohort (n=4902). We assigned to each independent predictor of SCA a weight by multiplying each β coefficient by 10.

Risk Score

From this model, we established for each patient a point score predictive of SCA. The median score was 18 (IQR, 13–23) in the overall e-MUST population, 23 (IQR, 18–28) for those with SCA, and 18 (IQR, 13–23) for those without SCA (P<0.001). The rate of SCA increased with score levels from 1.6% in the lowest group to 28.9% in the highest group (P for trend <0.001; Figure 2). The rate ratio of SCA increased 2-fold in patients with a score between 10 and 19 (n=3793 patients), 4-fold in those with a score between 20 and 29 (n=2642 patients), and 18-fold in patients with a score of ≥30 (n=239 patients) compared with patients presenting a score <9 (n=748). Score performances for predicting SCA at the acute phase of STEMI in the whole e-MUST population are summarized in Figure 2. Sensitivity and specificity were 96.9% and 10.5% for scores ≥10, 65.4% and 62.6% for scores ≥20, and 18.0% and 97.6% for scores ≥30.

Figure 2.

Figure 2. Rate ratios of sudden cardiac arrest (SCA) and score performances by increasing level of score. SCA indicates sudden cardiac arrest.The validity of the score was tested in the whole e-MUST (Evaluation en Médecine d’Urgence des Stratégies Thérapeutiques des infarctus du myocarde) population. The patients were grouped by 10 points of score. Analysis was performed on 7422 of the 8112 patients because risk factors were missing for age (n=42, 0.5%), family history or traditional risk factors (n=188, 2.3%), delays (n=188, 2.3%), and shortness of breath (n=360, 4.4%). n Indicates number of patients by level of predictive score.

The external validation cohort included 606 patients with STEMI, in whom baseline characteristics were similar to those of the e-MUST study (Table IV in the online-only Data Supplement). SCA witnessed by the EMS physician occurred in 39 patients (6.4% compared with 5.6% in the e-MUST study). With the use of the score developed from the e-MUST study, the incidence of SCA increased from 2% in patients with a score <10 to 5.2% in patients with a score between 10 and 19 and 9.1% in patients with a score ≥20 (compared with 1.6%, 3.2%, and 8.7%, respectively, in the e-MUST study).

The area under the curve values were 0.7033 in the internal validation sample and 0.6031 in the external validation sample.

Discussion

In this prospective study of 8112 patients with STEMI, 5.6% presented prehospital SCA. We identified 5 simple predictors for the occurrence of out-of-hospital SCA at the early phase of STEMI using the characteristics routinely assessed by EMS dispatchers on the phone in cases of chest pain: younger age, shortness of breath, absence of diabetes mellitus, absence of obesity, and short delay between pain onset and call to EMS. We then used these predictors to create a simple risk score for SCA during STEMI. In the group with the highest risk score, almost one third of the patients presented SCA.

The lower survival rate among patients who presented prehospital SCA highlights the unmet need for optimizing their management. Identifying patients at risk for SCA could improve the understanding of the mechanisms behind SCA occurrence and could be of potential aid by allowing the development of targeted, rapid response protocols.

Advanced age, diabetes mellitus, and obesity are well-known risk factors for coronary artery disease. In our study, they were less frequent in the SCA group. This finding might be explained by a progressive development of collateral circulation in chronic coronary artery disease, which alleviates the consequences of an abrupt coronary artery occlusion in patients with multiple risk factors, whereas patients presenting fewer risk factors and therefore probably more recent coronary disease most probably have less well-developed collaterals, leading to a higher rate of SCA.13,14 Other parameters have been evaluated for the identification of patients with STEMI at higher SCA risk. Genetic studies have allowed the identification of gene loci associated with an increased risk, mainly 21q21.15 Viral hypotheses have also been investigated, and a potential association between viral infections (mainly adenovirus and coxsackievirus) and rhythmic vulnerability has been described.16,17 The role of thrombus per se has also been analyzed, and the risk of SCA has been shown to vary according to the composition of the thrombus, with higher concentrations of endothelium-derived microparticles being associated with a higher risk.18,19 However, these tools are not adequate for daily clinical practice in which a quick and simple assessment of SCA risk is needed. Several studies assessed clinical characteristics of patients who presented prehospital SCA during STEMI, but they were restricted to patients who arrived alive to catheterization laboratories and therefore excluded patients who died of SCA before hospital admission.68,20 Last, there is no published risk score for SCA in STEMI.

In the setting of STEMI, the recommended time delay between first medical contact and reperfusion with primary percutaneous intervention is 90 minutes (60 minutes for patients presenting within 120 minutes of pain onset).2,21,22 Patients should be routed to a primary percutaneous intervention–capable facility when this time delay can be achieved; otherwise, thrombolysis should be administered. Because STEMI-related SCA most often occurs very early after chest pain onset, adding the risk of early SCA to the decision process could help in the selection of the most appropriate management strategy.2 Prehospital thrombolysis could be preferred in high-risk patients and when transportation delays are expected to be long, even if within the recommended time delay. In addition, hospital admission of such patients could be optimized by alerting an anesthesiology and a cardiac surgery team before arrival because tracheal intubation and mechanical ventilation or extracorporeal membrane oxygenation could be required. Last, when several primary percutaneous intervention–capable facilities are available within similar time delays, a tertiary center providing mechanical cardiovascular support could be preferred. Obviously, the value of all the aforementioned strategies needs to be tested in randomized, prospective trials.

Another potential use of this score will be possible in the near future. Recent advances in technology led to the development of smartphone applications allowing the monitoring of heart rhythm and the diagnosis of atrial fibrillation. Those applications have evolved and are now able to monitor QT length and, more important, ST-segment changes.2325 Their spread will allow the diagnosis of STEMI as soon as the patient calls EMS. This advance in technology opens the door to a new era in STEMI management, and a new issue has now to be solved: Which patients with STEMI can be left unattended and unmonitored for 25 to 60 minutes, the usual EMS arrival delays in STEMI, and which patients are at high risk for SCA and deserve a more rapid EMS intervention? With the use of this score, the current EMS management strategy can still be used in patients at low risk for SCA. As for those at high risk, a more aggressive management could be planned, with rapid deployment of interventions in the field, that could precede EMS arrival such as advising bystanders to locate the nearest automated external defibrillator and sending firefighters on site before EMS arrival.

The simplicity of this score that is based solely on the routine questionnaire performed by EMS dispatchers on the phone in cases of chest pain makes its use easy and not time-consuming. The threshold for classifying a patient at high SCA risk is a delicate issue. Ideally, the aim would be to obtain a score that can identify patients at higher risk for SCA (high sensitivity) but with high specificity to avoid unnecessary alerts. In this study, a score ≥30 had a very good specificity (97%) but a low sensitivity (18%); therefore, a large number of patients at high risk for SCA would have been missed. A score ≥20 was associated with acceptable specificity (62%) and sensitivity (65%). In this latter group, 1 of 15 patients with STEMI presents SCA compared with 1 of 4 in patients with a score ≥30. Given the dramatic consequences of SCA, it seems reasonable to use a threshold of 20 to keep a good sensitivity.

Our study is therefore the first to establish an easy-to-use score to estimate the risk of SCA at the early phase of STEMI based on the entire STEMI population. However, we acknowledge some limitations. First, our aim was to develop a tool for assessing SCA risk in patients with ECG-confirmed STEMI. Patients who presented SCA before EMS arrival were not included in the study because the diagnosis of STEMI could not be confirmed in the field even after successful resuscitation as a result of the poor predictive value of ECGs performed after SCA.12 However, this score is not intended for patients who present SCA early after symptom onset or for those who die before EMS arrival because they would not benefit from it. As for understanding the potential predictors of SCA, the large number of patients who presented EMS-witnessed SCA could allow identification of potential predictors. With the improvement in technologies allowing an earlier diagnosis of STEMI (mobile applications), a larger number of patients will benefit from this score. Second, we performed an internal and external validation of the score, but the extent to which similar findings would be obtained in populations from other countries with different emergency organization remains to be evaluated. The external validation sample was relatively small. However, it had the advantage of having the same selection criteria and assessing the same variables as e-MUST besides systematically recording out-of-hospital SCA complicating STEMI. Third, 8% of the patients had incomplete data, with 1 or 2 missing variables. These patients could not be included in the multivariate analysis but were still included in the comparison between the SCA and non-SCA groups and in the univariate analysis. They presented a higher rate of SCA, probably explaining the incomplete files, with EMS more focused on managing the SCA than on completing the files. Compared with patients with complete data, they presented the same sex ratio, median age, call–to–EMS arrival delays, and rates of shortness of breath. They differed only slightly in the rate of specific risk factors that would not potentially modify the final conclusions. Last, other items more closely related to the risk of SCA could potentially improve the predictive model (eg, syncope and family history of SCA26), but these are not routine questions during STEMI and would prolong and complicate the calculation of the score during the emergency call.

Conclusions

At the early phase of STEMI, the risk of prehospital SCA can be determined easily through a simple score using 5 predictors that can be obtained during an emergency call to the EMS dispatch center or immediately on EMS arrival at a STEMI location. This tool might help EMS make decisions on the dispatching and management of patients with STEMI according to their risk of SCA without adding delay to the evaluation phase.

Appendix

The following is a list of e-MUST collaborators. CH d’Arpajon (Arpajon): Dr Bensalem, Dr Rivoal, Dr Touitou, Dr Rodriguez; Dr Durand; CH d’Orsay (Orsay): Dr Alayrac, Dr Hellio, Dr Nunes; Dr Manet; CH de Coulommiers (Coulommiers): Dr Compagnon, Dr Toumani; Dr Echard; CH de Fontainebleau (Fontainebleau): Dr Fossay, Dr Grippon; Dr Bicharzon; CH de Gonesse (Gonesse): Dr Birlouez, Dr Sebbah, Dr Thevenin; Dr Hakim; CH de Juvisy sur Orge (Juvisy sur Orge): Dr Aubert, Dr Clavie, Dr Ducommun, Dr Faggianelli, Dr Schvahn; Dr Pillant; CH de Longjumeau (Longjumeau): Dr Coudray, Dr Hautefeuille, Dr Parpet, Dr Rousseau, Dr Ta; Dr Solvignon; CH de Marne la Vallée (Jossigny): Dr Mathieu, Dr Porcher, Dr Stibbe; Dr Echard; CH de Meaux (Meaux): Dr Bouvet, Dr Goes, Dr Limoges, Dr Thomas; Dr Echard; CH de Melun (Melun): Dr Dolveck, Dr Letarnec, Dr Pires, Dr Rebillard, Dr Tazarourte; Dr Luquet; CH de Montereau (Montereau): Dr Amokrane, Dr Cadot; Dr Derosin; CH de Nemours (Nemours): Dr Coletta, Dr Demiere; Dr Narcisse; CH de Pontoise (Pontoise): Dr Boukacem, Dr Dupas, Dr Giroud, Dr Ricard-Hibon; Dr Decup; CH de Provins (Provins): Dr Cineux, Dr D’Araujo, Dr Roy, Dr Tarlier; Dr Drevillon; CH de Rambouillet (Rambouillet): Dr Chevrier, Dr Clero; Dr Lederlin; CH de Versailles (Le Chesnay): Dr Boutot, Dr Lambert, Dr Moro, Dr Richard, Dr Sammut; Dr Jourdan; CH d’Etampes (Etampes): Dr Benaicha, Dr Gaffinel, Dr Jeufraux, Dr Legendre, Dr Nguyen, Dr Pone; Dr Mirolo; CH du Dr Delafontaine (Saint-Denis): Dr Hennequin; Dr Heurte; CH François Quesnay (Mantes la Jolie): Dr Goldman, Dr Hazan, Dr Hoffman, Dr Pasquereau, Dr Viso; Dr Meyer; CH Robert Ballanger (Aulnay sous Bois): Dr Biens, Dr Charestan, Dr Mezard, Dr Raphaël; Dr Benaceur, Dr Guerout, Dr Bonnaire; CH Simone Veil (Eaubonne): Dr Belotte, Dr Lefevre, Dr Monfroy, Dr Pouradier; Dr Peyron-Fourcade; CH Sud Francilien (Corbeil-Essonnes): Dr Briole, Dr Capitani, Dr Desclefs, Dr Laborne, Dr Pouges, Dr Roignant; Dr Cabo, Dr Lairy; CH Victor Dupouy (Argenteuil): Dr Cuvier, Dr Munoz; Dr Chevalier; CHI de Villeneuve Saint Georges (Villeneuve Saint Georges): Dr Auger, Dr Bergeron, Dr Meinadier, Dr Tshisumbule; Dr Casciani; CHI des Portes de l’Oise (Beaumont): Dr Binda, Dr Le Foll-Llanas, Dr Rakotonirina; Dr Bertiaux; CHI Le Raincy-Montfermeil (Montfermeil): Dr Beruben, Dr Cavagna, Dr Kergueno; Dr Douali, Dr Hennion, Dr Lesgourgues; CHI Poissy-Saint Germain en Laye (Poissy): Dr Getti, Dr Lefevre, Dr Ramaut, Dr Ruiz; Dr Lellouch, Dr Razafimamonjy; Hôpital Avicenne (APHP) (Bobigny): Dr Lenoir, Pr Adnet, Pr Lapostolle; Dr Duclos; Hôpital Beaujon (APHP) (Clichy): Dr Devaud, Dr Duchateau, Pr Mantz; Dr Bendersky; Hôpital Henri Mondor (APHP) (Créteil): Dr Aurore, Dr Bertrand, Dr Boche, Dr Goldstein, Dr Jacob, Dr Ladka, Dr Penet, Pr Marty; Dr Hemery; Hôpital Hôtel Dieu (APHP) (Paris): Dr Benmayouf, Dr Boizat, Dr Bourgeois, Dr Dahan, Dr Eche, Dr Kierzek, Dr Sahakian, Pr Pourriat; Dr Bouam; Hôpital Lariboisière (APHP) (Paris) Dr Chaplain, Dr Gueye, Dr Pereira, Pr Payen; Dr Brechat, Dr Lebrun, Dr Segouin; Hôpital Necker (APHP) (Paris): Dr Greffet, Dr Jaffry, Dr Lamhaut, Pr Carli; Dr Le Bihan-Benjamin; Hôpital Pitié-Salpétrière (APHP) (Paris): Dr Boon, Dr Delay, Dr Ecollan, Dr Kergueno; Dr Rufat, Pr Baudelou; Hôpital Raymond poincaré (APHP) (Garches): Dr Baer, Dr Cahun-Giraud, Dr Goddet, Dr Le Bail; Dr Maillard; Hôpital Saint-Antoine (APHP) (Paris): Dr Bourquard, Dr Brard; Dr Boule, Dr Marty; BSPP G1 Montreuil (Montreuil): Dr Courtiol, Dr Ramanidi, Dr Violin; BSPP G2 Vitry (Vitry): Dr Allonneau, Dr Ernouf, Dr Klein, Dr Lefort, Dr Mlynski; BSPP G3 Plessis-Clamart (Clamart): Dr Bon, Dr Culoma, Dr Gonzva, Dr Rivet, Dr Yavari-Sartakhti; BSPP Service Médical d’Urgence (Paris): Dr Bignand, Dr Domanski, Dr Jost, Pr Tourtier; Center Cardiologique d’Evecquemont (Evecquemont): Dr Herman; CH André Grégoire (Montreuil): Dr Menguy; Clinique Alleray Labrouste (Paris): Dr Herman, Dr Pioger, Dr El Farouki; Clinique Ambroise Paré (Neuilly sur Seine): Dr Bucquoit, Dr Eymeri; Clinique Bizet (Paris): Dr Housni, Dr Rousseau; Clinique Cardiologique du Nord (Saint-Denis): Dr Carradot; Clinique les Fontaines (Melun): Dr Grandcoin; Clinique Turin (Paris): Dr Gueyouche, Dr Leminou, Dr Morange; CMC Foch (Suresnes): Dr Leclerc; CMC Marie Lannelongue (Le Plessis Robinson): Dr Vallet; CMC Parly 2 (Le Chesnay): Dr Baget, Dr Debris, Dr de Livron, E. Chomette; Hôpital Ambroise Paré (Boulogne Billancourt): Dr Lot; Hôpital Américain de Paris (Neuilly sur Seine): Dr Richard, Dr Mathieu-Chakroun; Hôpital Bichat (APHP) (Paris): Dr Buzzi, Dr Deoliveira, Dr Gardesse, Dr Lê-leplat; Hôpital Cochin (APHP) (Paris): Dr Dreau, Dr Frenkiel; Hôpital d’Instruction des Armées du Val de Grace (Paris): Dr Romary; Hôpital Européen de Paris–La Roseraie (Aubervilliers): Dr Allouch, Dr Lebovisci; Hôpital Européen Georges Pompidou (APHP) (Paris): Dr Heudes, Dr Karafilovic, Pr Chatellier; Hôpital Privé d’Antony (Antony): Dr Gedin, Dr Villega; Hôpital Saint-Joseph (Paris): Dr Gaillard, Dr Rejasse; Hôpital Tenon (APHP) (Paris): Dr Lukacs; Institut Cardiovasculaire Paris Sud–Claude Galien (Quincy sous Sénart): Dr Servigne, Dr Vollaire; Institut Cardiovasculaire Paris Sud–Jacques Cartier (Massy): Dr Cohen-Attia, Dr Gedin, Dr Lequeu, Dr Vollaire; Institut Mutualiste Montsouris (Paris): Dr Gayer, Dr Germain, Dr Aminot.

Footnotes

*Drs Jouven and Lambert contributed equally.

Sources of Funding, see page 2082

The online-only Data Supplement is available with this article at http://circ.ahajournals.org/lookup/suppl/doi:10.1161/CIRCULATIONAHA.116.022954/-/DC1.

Circulation is available at http://circ.ahajournals.org.

Correspondence to: Nicole Karam, MD, MPH, Cardiology Department, European Georges Pompidou Hospital, 20-40 Rue Leblanc, 75908 Paris Cedex 15, France. E-mail

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Clinical Perspective

What Is New?

  • In-hospital mortality of ST-segment–elevation myocardial infarction (STEMI) has decreased drastically, whereas prehospital mortality from sudden cardiac arrest (SCA) remains high and requires better understanding.

  • This prospective study of 8112 patients with STEMI identified 5 predictors for the occurrence of prehospital SCA at the early phase of STEMI: younger age, shortness of breath, absence of diabetes mellitus, absence of obesity, and short delay between pain onset and call to emergency medical services.

  • Those predictors were used to create a simple risk score for SCA during STEMI. In the group with the highest score, almost one third of the patients presented SCA.

What Are the Clinical Implications?

  • Identification of factors associated with an increased risk of SCA might improve the understanding of potential mechanisms and factors associated with prehospital SCA, which has became the leading cause of death during STEMI.

  • The simplicity of the score that is based solely on the routine questionnaire performed by emergency medical services dispatchers on the phone in case of chest pain makes its use easy and not time-consuming. Its use may help emergency medical services make decisions about the optimal dispatching and management of patients with STEMI according to their risk of SCA and select patients most likely to need tertiary centers providing mechanical cardiovascular support.

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