Patient, health service factors and variation in mortality following resuscitated out-of-hospital cardiac arrest in acute coronary syndrome: Analysis of the Myocardial Ischaemia National Audit Project

Open AccessPublished:January 05, 2018DOI:https://doi.org/10.1016/j.resuscitation.2018.01.011

      Abstract

      Aims

      To determine patient and health service factors associated with variation in hospital mortality among resuscitated cases of out-of-hospital cardiac arrest (OHCA) with acute coronary syndrome (ACS).

      Methods

      In this cohort study, we used the Myocardial Ischaemia National Audit Project database to study outcomes in patients hospitalised with resuscitated OHCA due to ACS between 2003 and 2015 in the United Kingdom. We analysed variation in inter-hospital mortality and used hierarchical multivariable regression models to examine the association between patient and health service factors with hospital mortality.

      Results

      We included 17604 patients across 239 hospitals. Overall hospital mortality was 28.7%. In 94 hospitals that contributed at least 60 cases, mortality by hospital ranged from 10.7% to 66.3% (median 28.6%, IQR 23.2% to 39.1%)). Patient and health service factors explained 36.1% of this variation.
      After adjustment for covariates, factors associated with higher hospital mortality included increasing serum glucose, ST-Elevation myocardial infarction (STEMI) diagnosis, and initial admission to a primary percutaneous coronary intervention (pPCI) capable hospital. Hospital OHCA volume was not associated with mortality. The key modifiable factor associated with lower mortality was early reperfusion therapy in STEMI patients.

      Conclusion

      There was wide variation in inter-hospital mortality following resuscitated OHCA due to ACS that was only partially explained by patient and health service factors. Hospital OHCA volume and pPCI capability were not associated with lower mortality. Early reperfusion therapy was associated with lower mortality in STEMI patients.

      Keywords

      Introduction

      Across Europe, the annual incidence of treated out-of-hospital cardiac arrest (OHCA) is 49 cases per 100,000 population [
      • Gräsner J.-T.
      • Lefering R.
      • Koster R.W.
      • Masterson S.
      • Böttiger B.W.
      • Herlitz J.
      • et al.
      EuReCa ONE—27 nations, ONE, Europe, ONE registry.
      ]. Acute coronary syndrome (ACS) is a common cause of OHCA; where OHCA cause is recorded, approximately 76% of cases are attributed to cardiac aetiology [
      • Hawkes C.
      • Booth S.
      • Ji C.
      • Brace-McDonnell S.J.
      • Whittington A.
      • Mapstone J.
      • et al.
      Epidemiology and outcomes from out-of-hospital cardiac arrests in England.
      ]. Variation in OHCA mortality has been described between countries, Emergency Medical Service (EMS) systems and admitting hospitals [
      • Gräsner J.-T.
      • Lefering R.
      • Koster R.W.
      • Masterson S.
      • Böttiger B.W.
      • Herlitz J.
      • et al.
      EuReCa ONE—27 nations, ONE, Europe, ONE registry.
      ,
      • Hawkes C.
      • Booth S.
      • Ji C.
      • Brace-McDonnell S.J.
      • Whittington A.
      • Mapstone J.
      • et al.
      Epidemiology and outcomes from out-of-hospital cardiac arrests in England.
      ,
      • Carr B.G.
      • Kahn J.M.
      • Merchant R.M.
      • Kramer A.A.
      • Neumar R.W.
      Inter-hospital variability in post-cardiac arrest mortality.
      ].
      Regional cardiac arrest centres have been proposed as a strategy to reduce inter-hospital variation in OHCA mortality, but the quality of evidence supporting the concept is low [
      • Finn J.C.
      • Bhanji F.
      • Lockey A.
      • Monsieurs K.
      • Frengley R.
      • Iwami T.
      • et al.
      Part 8: education, implementation, and teams: 2015 international consensus on cardiopulmonary resuscitation and emergency cardiovascular care science with treatment recommendations.
      ,
      • OHCA steering group
      Resuscitation to recovery: a national framework to improve care of people with out-of-hospital cardiac arrest (OHCA) in England.
      ]. Regionalised care systems are based on the premise that the benefit of immediate admission to a hospital with specialist facilities and expertise outweighs any risk associated with a potentially increased transport time. Such systems are already established in major trauma and stroke [
      • Pickering A.
      • Cooper K.
      • Harnan S.
      • Sutton A.
      • Mason S.
      • Nicholl J.
      Impact of prehospital transfer strategies in major trauma and head injury: systematic review, meta-analysis, and recommendations for study design.
      ,
      • Pickering A.
      • Harnan S.
      • Cooper K.
      • Sutton A.
      • Mason S.
      • Nicholl J.
      Acute ischaemic stroke patients – direct admission to a specialist centre or initial treatment in a local hospital? A systematic review.
      ].
      In OHCA, improved understanding of inter-hospital variation in mortality is essential to improve understanding of the potential value of regionalised care systems. The availability in England and Wales of the only nation-wide ACS registry (Myocardial Ischaemia National Audit Project, MINAP) provides a unique opportunity to better understand these factors. Our study objective was to identify if there was evidence of inter-hospital variation in mortality among resuscitated cases of OHCA caused by ACS in the UK, and to identify the patient and health service factors that might contribute to any variation.

      Methods

       Data source

      MINAP is a national registry of patients admitted to hospital with acute coronary syndromes. Established in 1998, it provides a mechanism for participating hospitals to benchmark performance against national standards [
      • Birkhead J.S.
      Responding to the requirements of the National Service Framework for coronary disease: a core data set for myocardial infarction.
      ]. MINAP participation is mandatory, with all acute hospitals in England and Wales participating since 2003. Detailed care quality and clinical outcome data are collected at the hospital level, with entry validated through real-time checks and an annual hospital data validation review. This study linked MINAP to UK Office of National Statistics (ONS) data to provide information on patient social deprivation and enrich mortality data. MINAP identifies patients using their unique NHS number, which is pseudo-anonymised in the database. Patient identifiers (for example, date of birth) are encrypted prior to transfer to the central database, and are not released to researchers.

       Patient eligibility

      In this study, we included adult patients in the MINAP dataset where the initial cardiac arrest event occurred in the pre-hospital setting and where initial resuscitation attempts were successful leading to hospital admission. We excluded non-index (second or subsequent) cardiac arrests, events where the initial cardiac arrest event occurred in the in-hospital setting, and patients where the primary outcome was unknown.

       Data definitions

      For hospital-level data (volume, primary percutaneous coronary intervention (pPCI) capability, EMS distance), patients were categorised by the hospital to which they were first admitted. For hospital volume, the number of OHCA cases in each year at each hospital was calculated. Each patient was allocated to a volume category (low: 1–10 cases; medium: 11–24 cases; high: ≥25 cases) based on the hospital and year in which they were treated. We categorised patients as being treated in a pPCI capable hospital if it performed at least 100 pPCI procedures across all patients in the MINAP dataset in the year that the patient was admitted, as per UK guidance [
      • Banning A.P.
      • Baumbach A.
      • Blackman D.
      • Curzen N.
      • Devadathan S.
      • Fraser D.
      • et al.
      Percutaneous coronary intervention in the UK: recommendations for good practice.
      ]. EMS distance was calculated as the Euclidian distance between the patient’s home address and hospital. This assumed the OHCA event occurred at the patient’s home, which is true for over 80% of UK OHCAs [
      • Hawkes C.
      • Booth S.
      • Ji C.
      • Brace-McDonnell S.J.
      • Whittington A.
      • Mapstone J.
      • et al.
      Epidemiology and outcomes from out-of-hospital cardiac arrests in England.
      ].
      Reperfusion treatment was categorised as early or late. Thrombolysis was classified as early if call-to-needle time was up to 60 min, based on UK national standards [
      • Department of Health
      National service framework for coronary heart disease.
      ]. PPCI was classified as early if door-to-balloon time was up to 90 min, based on the MINAP benchmark [
      • Myocardial Ischaemia National Audit Project
      Myocardial Ischaemia National Audit Project: annual public report: april 2013–march 2014.
      ].
      For sub-group analyses, we categorised patients, based on the MINAP variable ‘ECG determining treatment,’ as having STEMI (ST-elevation acute myocardial infarction or presumed new left bundle branch block (LBBB)) or NSTEACS (non-ST Elevation Acute Coronary Syndrome, which included all patients that did not meet the STEMI definition including unstable angina patients).

       Outcome measures

      The primary outcome was all-cause hospital mortality, as recorded in the MINAP dataset or, where this was incomplete, cross-referencing with ONS mortality data.

       Sample size

      Preliminary data supplied by MINAP led to a projected sample size of 14,310 eligible OHCA cases with a projected hospital mortality of 24%. Based on this, we calculated a 4% difference in mortality between categories within a predictor variable could be detected reliably with at least 90% power and a significance level of 0.05.

       Statistical analysis

      Multiple imputation using chained equations was used to reduce the bias associated with missing data in predictor variables (Supplementary Data Table S1), based on the approach used in previous MINAP analyses [
      • Buuren S.
      Mice: multivariate imputation by chained equations in R.
      ,
      • Cattle B.A.
      • Baxter P.D.
      • Greenwood D.C.
      • Gale C.P.
      • West R.M.
      Multiple imputation for completion of a national clinical audit dataset.
      ]. Case identification and sub-group allocation was undertaken prior to imputation. Twenty-five imputed datasets were generated.
      After imputation, an unadjusted random effects logistic regression model was fitted to predict hospital mortality and obtain the estimate for the log of the odds ratio and the standard error for each imputed dataset. The inclusion of a random effects term for the hospital enabled variation between hospitals to be modelled. Estimates from each of the 25 imputed datasets were combined using the Rubin’s rules to get an overall odds ratio estimate of mortality, 95% confidence interval and p-value [
      • Rubin D.B.
      Multiple imputation for nonresponse in surveys.
      ]. We adopted a similar approach for the adjusted analysis. The model included all clinically relevant predictor variables, unless there was evidence of multi-collinearity due to two predictors being highly correlated or a variable was clearly confounded by an unmeasured variable.
      Alongside data from the whole cohort, we report data from STEMI and NSTEACS sub-groups, and sensitivity analyses (complete case; admission between 2003 and 2008; admission between 2009 and 2015). This sensitivity analysis cut-off reflects the year (2009) that pPCI became the most commonly recorded reperfusion treatment in MINAP [
      • Myocardial Ischaemia National Audit Project
      Myocardial Ischaemia National Audit Project: annual public report: april 2013–march 2014.
      ].
      Data processing and descriptive analyses pre-imputation were performed using SPSS version 22 (IBM Corp, Armonk, NY, USA). The R statistical program (R: A language and environment for statistical computing, R development core team; R Foundation for Statistical Computing, Vienna, Austria) and associated packages (MICE and gamm4 packages) were used for multiple imputation, descriptive analysis after multiple imputation, and fitting models for hospital mortality.

       Ethics/approvals

      The study was undertaken in accordance with the Declaration of Helsinki. The University of Warwick Biomedical Research Ethics Committee approved the study. MINAP, as part of the National Institute for Cardiovascular Outcomes Research, is approved under UK legislation to hold patient identifiable data without consent.

      Results

       Study population

      There were 1,127,140 patient datasets collected by MINAP between January 2003 and June 2015 (Fig. 1). Of these, 73,875 (6.6%) were identified as having had a cardiac arrest. Sequential application of study exclusion criteria led to the exclusion of 56,271 patients, most of whom had sustained an in-hospital cardiac arrest (N = 50,836, 90.3%). The study sample included data from 17,604 patients across 239 hospitals. The median number of cases reported per hospital over the study period was 46 (range 1–517). Neurological outcome data were available for 15,286 patients.
      Fig. 1
      Fig. 1Flow chart of case identification process.
      The number of cases included annually increased over the study period, with a peak of 2129 cases in 2012 (Supplementary Data Fig. S1).

       Patient characteristics (whole cohort)

      Patients were predominantly male (n = 13,188, 75.1%) with a mean age of 65.3 years (Table 1). The most common co-morbidity was hypertension (n = 6389, 41.0%). OHCA events typically occurred prior to EMS arrival (n = 10,533, 60.1%) with a shockable presenting rhythm (n = 14,778, 89.6%). Most were classified as STEMI (n = 12,220, 71.9%), and were admitted to the coronary care (N = 8872, 51.0%) or intensive care (N = 6154, 35.4%) unit. Most patients received reperfusion therapy (n = 9540, 62.9%), of which the majority received pPCI (n = 6160, 64.6%). Reperfusion therapy use increased over time (2003: 20.8%; 2015: 64.1%), with increases in pPCI use mirrored by a decline in thrombolysis use across all patients, and in STEMI/NSTEACS sub-groups (Fig. 2). Over the study period, the percentage of patients admitted to pPCI capable hospitals increased (2003: 0%; 2014: 81%) and the percentage admitted to low-volume OHCA hospitals decreased (Fig. 3; Supplementary Data Fig. S2).
      Table 1Patient characteristics across all cases, STEMI cases and NSTEACS cases.
      All cases (n = 17,604)
      The n for each variable is the total group size minus the number of missing cases (see Supplementary information). 612 cases were missing STEMI status, so not included in the sub-groups.
      STEMI (n = 12,220)
      The n for each variable is the total group size minus the number of missing cases (see Supplementary information). 612 cases were missing STEMI status, so not included in the sub-groups.
      NSTEACS (n = 4772)
      The n for each variable is the total group size minus the number of missing cases (see Supplementary information). 612 cases were missing STEMI status, so not included in the sub-groups.
      Demographic variables
      Age (Years)- Mean (SD)65.3 (13.15)63.9 (13.06)68.3 (12.73)
      Gender (Female)- n (%)4370 (24.9)3034 (24.9)1155 (24.2)
      Ethnicity, n (%)
       White14,343 (93.7)9927 (93.4)3904 (94.3)
       Asian531 (3.5)386 (3.6)135 (3.3)
       Black131 (0.9)88 (0.8)40 (1.0)
       Other303 (2.0)230 (2.2)63 (1.5)
      Index of multiple deprivation score- Mean (SD)22.31 (15.91)22.27 (15.92)22.42 (15.94)
      Medical history variables- n (%)
      Smoking status- Ever smoked8883 (63.5)6510 (65.8)2157 (57.9)
      Diabetes status- Diabetic2158 (13.7)1283 (11.8)795 (18.2)
      Hypercholesterolaemia- Yes3906 (25.9)2600 (24.9)1197 (28.4)
      Heart failure- Yes760 (5.0)356 (3.4)359 (8.4)
      Cerebrovascular disease- Yes1071 (7.0)609 (5.7)419 (9.8)
      Previous MI- Yes3092 (19.7)1701 (15.7)1243 (28.5)
      Asthma or COPD- Yes1814 (11.9)1161 (11.0)569 (13.3)
      Chronic renal failure- Yes555 (3.6)272 (2.6)252 (5.9)
      Peripheral vascular disease- Yes587 (3.9)358 (3.4)208 (4.9)
      Previous Angina- Yes2758 (17.8)1501 (14.0)1133 (26.3)
      Previous PCI- Yes1061 (6.9)670 (6.3)357 (8.3)
      Previous CABG- Yes790 (5.1)374 (3.5)395 (9.1)
      Hypertension- Yes6389 (41.0)4186 (38.8)2007 (46.3)
      OHCA presenting characteristics
      Time point of cardiac arrest, n (%)
       Before ambulance arrival10,533 (60.1)6371 (52.3)3747 (78.7)
       After ambulance arrival7004 (39.9)5811 (47.7)1013 (21.3)
      Cardiac arrest rhythm, n (%)
       Asystole885 (5.4)422 (3.7)388 (8.8)
       PEA837 (5.1)444 (3.8)353 (8.0)
       VF/VT14,778 (89.6)10,691 (92.5)3665 (83.2)
      Serum glucose (mmol/L)- Mean (SD)10.94 (5.00)10.92 (4.91)10.99 (5.23)
      Creatinine (micromol/L)- Mean (SD)108.12 (55.72)104.12 (49.86)117.80 (67.17)
      Left Ventricular Ejection Fraction, n (%)
       Good2783 (36.4)1873 (34.3)858 (41.4)
       Moderate3131 (40.9)2347 (43.0)744 (35.9)
       Poor1736 (22.7)1233 (22.6)469 (22.6)
      Haemoglobin (g/dL)- Mean (SD)13.57 (2.03)13.69 (2.01)13.27 (2.05)
      Serum cholesterol (mmol/L)- Mean (SD)4.80 (1.51)4.91 (1.47)4.46 (1.64)
      Admission diagnosis, n (%)
       Definite MI – anterior infarction3897 (27.0)3809 (40.6)78 (1.7)
       Definite MI – other infarction site3639 (25.2)3463 (36.9)159 (3.5)
       Other initial diagnosis6883 (47.7)2105 (22.4)4306 (94.8)
      Admission systolic BP (mmHg)- Mean (SD)125.69 (29.17)124.95 (28.75)127.27 (29.86)
      ECG that determined treatment, n (%)
       ST elevation or LBBB12,220 (71.9)12,220 (100.0%)0 (0%)
       ST depression/T wave changes only2325 (13.7)0 (0%)2325 (48.7%)
       Other change/No acute changes2447 (14.4)0 (0%)2447 (51.3%)
      Admission heart rate (/minute)- Mean (SD)89.22 (24.79)88.55 (24.10)90.54 (26.12)
      Daytime hospital admission (8am to <8pm)- n (%)11,741 (66.7%)8083 (66.1%)3272 (68.6%)
      Killip Class, n (%)
       Basal crepitations and/or elevated venous pressure796 (13.8)517 (12.1)269 (18.5)
       Pulmonary oedema317 (5.5)228 (5.3)89 (6.1)
       Cardiogenic shock1029 (17.9)853 (20.0)169 (11.6)
       No evidence of heart failure3612 (62.8)2665 (62.5)927 (63.8)
      Mini-Grace score- Mean (SD)173 (28.37)174 (27.92)172 (29.38)
      Care pathway variables
      Hospital volume (OHCA cases per year)- n (%)
       1 to 10 cases7984 (45.4)4900 (40.1)2673 (56.0)
       11 to 24 cases6516 (37.0)4799 (39.3)1565 (32.8)
       25 to 82 cases3104 (17.6)2521 (20.6)534 (11.2)
      Hospital pPCI capability- n (%)
       pPCI capable7800 (44.3)6514 (53.3)1205 (25.3)
       pPCI incapable9804 (55.7)5706 (46.7)3567 (74.7)
      EMS response time (Minutes)- Mean (SD)11.53 (11.82)12.03 (12.42)10.29 (10.11)
      EMS travel distance (Kilometres)- Mean (SD)11.24 (10.08)11.96 (10.50)9.67 (8.88)
      Admitting consultant, n (%)-
       Cardiologist10,680 (61.8)8480 (70.6)2008 (42.8)
       Other consultant6603 (37.5)3534 (29.4)2689 (57.2)
      Cardiological care during admission- yes- n (%)11,960 (90.7)8797 (93.3)2975 (85.0)
      Admission Ward, n (%)
       CCU8872 (51.0)6984 (57.9)1683 (35.4)
       Cardiac ward − non CCU500 (2.9)366 (3.0)123 (2.6)
       Intensive therapy unit6154 (35.4)3666 (30.4)2290 (48.2)
       General medical ward or Other1534 (8.8)868 (7.2)558 (11.8)
       Died in Emergency Department340 (1.9)176 (1.5)94 (2.0)
      Place where ECG performed, n (%)
       Pre-hospital11,053 (75.7)8253 (79.2)2659 (67.0)
       In hospital3551 (24.3)2162 (20.8)1311 (33.7)
      Reperfusion treatment and timing, n (%)
       None5633 (37.1)2048 (18.3)2350 (90.1)
       Thrombolysis (early)1080 (7.1)1053 (9.4)20 (0.5)
       Thrombolysis (late)1930 (12.7)1832 (16.4)85 (2.3)
       Thrombolysis (time missing)370 (2.4)314 (2.8)40 (1.1)
       pPCI (early)4424 (29.2)4293 (38.5)122 (3.3)
       pPCI (late)1063 (7.0)994 (8.9)65 (1.7)
       pPCI (time missing)673 (4.4)631 (5.7)36 (1.0)
      Discharge care variables
      Discharge Diagnosis, n (%)
       Acute coronary syndrome16,476 (95.1)11,710 (97.5)4232 (89.8)
       Other diagnosis843 (4.9)304 (2.5)483 (10.2)
      Echocardiography- yes/planned- n (%)11,140 (73.8)7902 (75.2)2990 (72.6)
      Outcomes
      Survival to hospital discharge-n (%)12,557 (71.3)9049 (74.1)3184 (66.7)
      Discharged without neurological deficit- n (%)
      Neurological outcome data available for 15,286 patients.
      9041 (59.1)6736 (62.9)2081 (51.4)
      CABG- Coronary Artery Bypass Graft; CCU- Cardiac Care Unit; COPD- Chronic Obstructive Pulmonary Disease; ECG- Electrocardiogram; EMS- Emergency Medical Service; LBBB- Left Bundle Branch Block; MI- Myocardial Infraction; OHCA- Out-of-Hospital Cardiac Arrest; (p)PCI- (primary) Percutaneous Coronary Intervention; PEA- Pulseless Electrical Activity; VF- Ventricular Fibrillation; VT- Ventricular Tachycardia.
      a The n for each variable is the total group size minus the number of missing cases (see Supplementary information). 612 cases were missing STEMI status, so not included in the sub-groups.
      Neurological outcome data available for 15,286 patients.
      Fig. 2
      Fig. 2Reperfusion rates by year across the whole cohort, STEMI cases, and NSTEACS cases.
      Fig. 3
      Fig. 3Percentage of patients admitted to pPCI capable centres by year.

       Length of stay and patient outcomes

      Overall hospital mortality was 28.7% (n = 5047) and 40.9% (n = 6245) died or were discharged with neurological deficit. In non-survivors, median time to death was 2 days (IQR 1–5, range 0–96) (Supplementary Data Fig. S3). For survivors, median length of hospital stay was 7 days (IQR 3–14, range 0–372) (Supplementary Data Fig. S4).

       Variation in inter-hospital mortality

      In the 94 hospitals that contributed at least 60 cases over the study period, hospital mortality by hospital ranged from 10.7% to 66.3% (median 28.6%, IQR 23.2% to 39.1%) (Supplementary Data Fig. S5).
      Demographic and medical history variables explained little variation, with age (R2 = 0.060) having the highest R2 value (Supplementary Data Table S2). A greater degree of variation was explained by some OHCA presenting characteristic and care pathway variables, such as OHCA rhythm (R2 = 0.104), serum glucose (R2 = 0.083), and admission ward (R2 = 0.178). Little variation was explained by OHCA hospital volume (R2 = 0.006), hospital pPCI capability (R2 = 0.003) and reperfusion therapy (R2 = 0.042). The adjusted analysis explained 36.1% (R2 = 0.361) of the variation across the dataset (Table 2).
      Table 2Multiviariate analysis across all cases, STEMI cases and NSTEACS cases.
      Odds ratio of in-hospital mortality (95% confidence intervals), p-value
      Values describe odds ratio (95% confidence interval), p value unless stated.
      All cases (n = 17,604)STEMI (n = 12,220)NSTEACS (n = 4772)
      Demographic variables
      Age (Years)
      Per whole unit increase.
      1.046 (1.042, 1.051), <0.0011.048 (1.043, 1.054), <0.0011.048 (1.040, 1.056), <0.001
      GenderMale0.877 (0.786, 0.979), 0.0190.921 (0.806, 1.052), 0.2260.758 (0.621, 0.925), 0.006
      Female
      Reference category (Medical History variables compared with absence of condition).
      EthnicityAsian1.022 (0.804, 1.299), 0.8600.961 (0.725, 1.275), 0.7831.167 (0.702, 1.939), 0.551
      Black0.939 (0.602, 1.464), 0.7800.833 (0.509, 1.364), 0.4681.059 (0.511, 2.198), 0.877
      Other0.991 (0.723, 1.358), 0.9561.023 (0.726, 1.440), 0.8980.871 (0.417, 1.819), 0.713
      White
      Reference category (Medical History variables compared with absence of condition).
      Index of multiple deprivation score
      Per whole unit increase.
      1.005 (1.002, 1.008), 0.0031.002 (0.998, 1.006), 0.3231.010 (1.004, 1.016), 0.002
      Medical history variables
      Reference category (Medical History variables compared with absence of condition).
      Smoking status- ever smoked0.903 (0.812, 1.004), 0.0590.875 (0.765, 1.000), 0.0501.009 (0.830, 1.226), 0.927
      Diabetes status- Diabetic1.125 (0.981, 1.290), 0.0921.162 (0.976, 1.384), 0.0911.123 (0.890, 1.416), 0.329
      Hypercholesterolaemia0.692 (0.615, 0.779), <0.0010.669 (0.577, 0.776), <0.0010.684 (0.551, 0.850), 0.001
      Heart failure1.318 (1.074, 1.618), 0.0081.584 (1.178, 2.128), 0.0021.192 (0.874, 1.624), 0.267
      Cerebrovascular disease1.299 (1.097, 1.537), 0.0021.075 (0.858, 1.348), 0.5291.703 (1.294, 2.241), <0.001
      Previous MI1.028 (0.900, 1.173), 0.6850.997 (0.836, 1.188), 0.9711.118 (0.899, 1.39), 0.327
      Asthma or COPD1.247 (1.087, 1.431), 0.0021.228 (1.030, 1.463), 0.0221.246 (0.975, 1.591), 0.079
      Chronic renal failure1.065 (0.841, 1.350), 0.6010.864 (0.613, 1.219), 0.4061.390 (0.969, 1.995), 0.073
      Peripheral vascular disease1.517 (1.208, 1.904), <0.0011.723 (1.286, 2.309), <0.0011.338 (0.903, 1.981), 0.146
      Previous angina1.011 (0.885, 1.156), 0.8671.037 (0.869, 1.239), 0.6840.926 (0.743, 1.154), 0.494
      Previous PCI1.025 (0.840, 1.251), 0.8061.051 (0.815, 1.356), 0.7000.923 (0.652, 1.305), 0.649
      Previous CABG0.996 (0.811, 1.222), 0.9661.189 (0.894, 1.583), 0.2350.830 (0.607, 1.135), 0.244
      Hypertension0.865 (0.784, 0.955), 0.0040.849 (0.752, 0.960), 0.0090.866 (0.723, 1.038), 0.120
      OHCA presenting characteristics
      Time point of cardiac arrestAfter EMS arrive0.492 (0.441, 0.548), <0.0010.483 (0.425, 0.548), <0.0010.424 (0.331, 0.544), <0.001
      Before EMS arrival
      Reference category (Medical History variables compared with absence of condition).
      Cardiac arrest rhythmPEA0.847 (0.658, 1.088), 0.1940.730 (0.507, 1.051), 0.0910.846 (0.573, 1.248), 0.399
      VF/VT0.217 (0.180, 0.262), <0.0010.189 (0.145, 0.247), <0.0010.231 (0.172, 0.310), <0.001
      Asystole
      Reference category (Medical History variables compared with absence of condition).
      Serum glucose
      Per whole unit increase.
      1.109 (1.096, 1.122), <0.0011.113 (1.097, 1.130), <0.0011.103 (1.081, 1.126), <0.001
      Haemoglobin
      Per whole unit increase.
      0.912 (0.878, 0.946), <0.0010.920 (0.884, 0.958), <0.0010.892 (0.842, 0.945), <0.001
      Serum cholesterol
      Per whole unit increase.
      0.956 (0.906, 1.010), 0.1080.953 (0.892, 1.017), 0.1500.980 (0.910, 1.056), 0.595
      Admission diagnosisOther diagnosis0.876 (0.750, 1.024), 0.0971.005 (0.850, 1.188), 0.9560.586 (0.329, 1.043), 0.069
      Definite MI – other infarct site1.022 (0.890, 1.173), 0.7621.029 (0.898, 1.179), 0.6791.192 (0.603, 2.358), 0.614
      Definite MI – anterior infarct
      Reference category (Medical History variables compared with absence of condition).
      Admission systolic blood pressureLinear term
      Estimates on the logarithmic scale.
      -42.15 (-48.35, −35.96), <0.001-33.76 (-40.3, −27.3), <0.001-23.45 (-29.6, −17.3), <0.001
      Quadratic term
      Estimates on the logarithmic scale.
      17.68 (11.79, 23.57), <0.00116.96 (11.00, 22.92), <0.0016.42 (0.46, 12.38), 0.035
      ECG that determined treatmentST elevation or LBBB1.592 (1.364, 1.858), <0.001Only ST elevation/LBBB patients included in this analysisData not included
      ST depression/T wave changes only0.907 (0.775, 1.062), 0.2270.859 (0.728, 1.014), 0.073
      Other change/No acute changes
      Reference category (Medical History variables compared with absence of condition).
      Admission heart rate
      Per whole unit increase.
      1.005 (1.004, 1.007), <0.0011.006 (1.004, 1.008), <0.0011.005 (1.002, 1.008), 0.004
      Time of the day of admission8pm to <8am (night)1.091 (0.994, 1.196), 0.0661.037 (0.926, 1.163), 0.5281.203 (1.010, 1.433), 0.038
      8am to <8pm (day)
      Reference category (Medical History variables compared with absence of condition).
      Admission year
      Per whole unit increase.
      Slope (2003–2008)0.947 (0.895, 1.002), 0.0570.996 (0.931, 1.066), 0.9160.885 (0.810, 0.968), 0.008
      Slope (2009–2015)1.044 (1.009, 1.079), 0.0121.038 (0.998, 1.081), 0.0651.016 (0.953, 1.082), 0.632
      Care pathway variables
      Hospital volume (OHCA cases per year)0–10 cases1.033 (0.723, 1.474), 0.8601.229 (0.904, 1.670), 0.1890.688 (0.386, 1.229), 0.207
      11–24 cases1.259 (0.877, 1.808), 0.2111.242 (0.926, 1.667), 0.1480.948 (0.534, 1.681), 0.854
      25 to 82 cases
      Reference category (Medical History variables compared with absence of condition).
      Hospital pPCI capabilitypPCI capable

      pPCI incapable
      Reference category (Medical History variables compared with absence of condition).
      1.262 (1.043, 1.527), 0.0171.584 (1.261, 1.989), <0.0010.849 (0.605, 1.190), 0.342
      EMS response time (Mins)
      Per whole unit increase.
      0.999 (0.995, 1.004), 0.7761.000 (0.995, 1.005), 0.9960.997 (0.987, 1.007), 0.589
      EMS travel distance (Km)
      Per whole unit increase.
      0.994 (0.989, 0.999), 0.0240.992 (0.986, 0.998), 0.0120.997 (0.987, 1.008), 0.612
      Admitting consultantCardiologist0.725 (0.641, 0.822), <0.0010.794 (0.680, 0.927), 0.0030.615 (0.494, 0.766), <0.001
      Other consultant
      Reference category (Medical History variables compared with absence of condition).
      Admission wardIntensive therapy unit3.741 (3.331, 4.202), <0.0013.267 (2.852, 3.742), <0.0015.239 (4.107, 6.685), <0.001
      Died in EDNot estimableNot estimableNot estimable
      General ward or other3.452 (2.941, 4.051), <0.0013.549 (2.884, 4.368), <0.0013.575 (2.642, 4.838), <0.001
      Cardiac ward − non CCU1.212 (0.841, 1.748), 0.3021.148 (0.728, 1.810), 0.5521.588 (0.861, 2.929), 0.138
      CCU
      Reference category (Medical History variables compared with absence of condition).
      Place where ECG performedIn hospital1.125 (0.970, 1.304), 0.1201.127 (0.956, 1.329), 0.1541.088 (0.878, 1.348), 0.439
      Pre hospitala
      Reference category (Medical History variables compared with absence of condition).
      Reperfusion treatment and timingThrombolysis (early)0.672 (0.523, 0.863), 0.0020.714 (0.550, 0.926), 0.0110.501 (0.121, 2.071), 0.340
      Thrombolysis (late)0.860 (0.723, 1.023), 0.0880.893 (0.741, 1.075), 0.2310.939 (0.480, 1.837), 0.854
      Thrombolysis (time missing)0.954 (0.702, 1.298), 0.7660.940 (0.672, 1.315), 0.7171.248 (0.500, 3.117), 0.635
      pPCI (early)0.704 (0.600, 0.826), <0.0010.618 (0.518, 0.737), <0.0010.802 (0.430, 1.498), 0.490
      pPCI (late)0.941 (0.773, 1.145), 0.5420.836 (0.675, 1.035), 0.1000.967 (0.479, 1.952), 0.926
      pPCI (time missing)0.690 (0.532, 0.893), 0.0050.610 (0.463, 0.802), <0.0011.496 (0.593, 3.773), 0.393
      None
      Reference category (Medical History variables compared with absence of condition).
      Random Effects estimate (R squared, Akaike Information Criterion)0.215
      Median from 25 datasets.
      (0.361
      Median from 25 datasets.
      , 14134
      Median from 25 datasets.
      )
      0.118
      Median from 25 datasets.
      (0.354
      Median from 25 datasets.
      , 9471
      Median from 25 datasets.
      )
      0.378
      Median from 25 datasets.
      (0.365
      Median from 25 datasets.
      , 4133
      Median from 25 datasets.
      )
      CABG- Coronary Artery Bypass Graft; CCU- Cardiac Care Unit; COPD- Chronic Obstructive Pulmonary Disease; ECG- Electrocardiogram; ED- Emergency Department; EMS- Emergency Medical Service; LBBB- Left Bundle Branch Block; MI- Myocardial Infraction; OHCA- Out-of-Hospital Cardiac Arrest; (p)PCI- (primary) Percutaneous Coronary Intervention; PEA- Pulseless Electrical Activity; VF- Ventricular Fibrillation; VT- Ventricular Tachycardia.
      * Values describe odds ratio (95% confidence interval), p value unless stated.
      ** Per whole unit increase.
      *** Estimates on the logarithmic scale.
      a Reference category (Medical History variables compared with absence of condition).
      b Median from 25 datasets.

       Factors influencing mortality

      Across the whole cohort, after co-variate adjustment, demographic factors associated with increased mortality included increasing age and social deprivation (Table 2). Other factors associated with higher mortality included female gender, history of heart failure, increasing blood glucose, OHCA prior to EMS arrival, and STEMI or LBBB on the initial ECG. Hypertension, hypercholesterolaemia, and a shockable OHCA rhythm were associated with lower mortality.
      Health service factors that did not influence mortality include time of day of admission, hospital OHCA volume, EMS response time, and first ECG location. Admission to a pPCI capable hospital was associated with higher mortality (OR 1.26, 95% CI 1.05–1.53). A supplementary analysis identified case-mix differences between patients treated in pPCI capable and non-pPCI capable hospitals. More patients initially admitted to a pPCI capable hospital presented in a shockable OHCA rhythm (92.8% v 86.9%), had a STEMI diagnosis, (84.4% v 61.5%), and were in cardiogenic shock (21.9% v 5.7%) (Supplementary Data Table S3).
      Hospital admission under a cardiologist (OR 0.73, 95% CI 0.64–0.82) was associated with lower mortality. Early reperfusion treatment was the key modifiable health service factor associated with reduced mortality (early thrombolysis 0.67, 95% CI 0.52–0.86; early pPCI OR 0.70, 95% CI 0.60–0.83).

       STEMI and NSTEACS cohorts

      Adjusted models restricted to STEMI and NSTEACS cohorts explained a similar degree of variation as the primary analysis (STEMI: R2 = 0.354; NSTEMI R2 = 0.365) (Table 2). Compared to STEMI patients, NSTEACS patients were older (mean age 68.3 v 63.9 years), had more co-morbidities, and were more likely to present in a non-shockable OHCA rhythm (Table 1). NSTEACS patients were less likely to be admitted to a high-volume OHCA (11.2% v 20.6%) or pPCI capable (25.3% v 53.3%) hospital, and less likely to receive reperfusion therapy (9.9% v 81.7%).
      Admission to pPCI hospital was associated with higher mortality in STEMI (OR 1.58, 95% CI 1.26–1.99) but not in NSTEACS (OR 0.85, 95% CI 0.61–1.19) patients. Reperfusion therapy was associated with lower mortality in STEMI patients, but not in NSTEACS patients (e.g. early pPCI STEMI OR 0.62, 95% CI 0.52–0.74; NSTEACS OR 0.80, 95% CI 0.43–1.50). Compared to daytime, overnight admission was associated with higher mortality in the NSTEACS (OR 1.20, 95% CI 1.01–1.43), but not the STEMI (OR 1.04, 95% CI 0.93–1.16) group.

       Sensitivity analyses

      Sensitivity analyses for the complete case (n = 2284, 13.0%), 2003–2008 (n = 6075, 34.5%), and 2009–2015 (n = 11,529, 65.5%) cohorts, explained a similar degree of variation to the primary analysis (Supplementary Data Table S4). Findings of these analyses were generally consistent with the primary analysis, albeit confidence intervals were typically wider. Point estimates for most reperfusion treatments in the complete case cohort indicated higher mortality, which may reflect a selection bias inasmuch as a complete dataset is likely easier to collect in patients that die.

      Discussion

      In this analysis of 17,604 OHCA patients with ACS, admitted alive to 239 UK hospitals, just under three in ten died in hospital. Across the 94 hospitals contributing at least 60 cases, we identified wide variation in inter-hospital mortality. Modelling explained approximately one third of this variation. Over the 12-year study period, we observed changes in clinical practice, including increased admission of patients to pPCI capable hospitals and high-volume OHCA hospitals, and increased use of reperfusion treatment. The key modifiable factor associated with lower hospital mortality in STEMI patients was early reperfusion treatment.
      Previous studies of OHCA have been inconsistent as to the association between hospital facilities, OHCA volume and outcome, which may be partly reflect variability in how these concepts are defined across the literature [
      • Tranberg T.
      • Lippert F.K.
      • Christensen E.F.
      • Stengaard C.
      • Hjort J.
      • Lassen J.F.
      • et al.
      Distance to invasive heart centre, performance of acute coronary angiography, and angioplasty and associated outcome in out-of-hospital cardiac arrest: a nationwide study.
      ,
      • Chocron R.
      • Bougouin W.
      • Beganton F.
      • Juvin P.
      • Loeb T.
      • Adnet F.
      • et al.
      Are characteristics of hospitals associated with outcome after cardiac arrest? Insights from the Great Paris registry.
      ,
      • Cudnik M.T.
      • Sasson C.
      • Rea T.D.
      • Sayre M.R.
      • Zhang J.
      • Bobrow B.J.
      • et al.
      Increasing hospital volume is not associated with improved survival in out of hospital cardiac arrest of cardiac etiology.
      ,
      • Schober A.
      • Sterz F.
      • Laggner A.N.
      • Poppe M.
      • Sulzgruber P.
      • Lobmeyr E.
      • et al.
      Admission of out-of-hospital cardiac arrest victims to a high volume cardiac arrest center is linked to improved outcome.
      ]. In contrast to the findings of a recent analysis of the American Cardiac Arrest Registry to Enhance Survival dataset, we unexpectedly observed an association between admission to a pPCI capable hospital and higher mortality [
      • Kragholm K.
      • Malta Hansen C.
      • Dupre M.E.
      • Xian Y.
      • Strauss B.
      • Tyson C.
      • et al.
      Direct transport to a percutaneous cardiac intervention center and outcomes in patients with out-of-hospital cardiac arrest.
      ]. This finding may be partly explained by case-mix differences between patients treated in pPCI and non-pPCI capable hospitals. In particular, a higher proportion of patients in cardiogenic shock were admitted to pPCI capable hospitals, although the degree of missingness within this variable precluded its imputation and modelling.
      The decisions by paramedics as to the most appropriate hospital to which to transfer a patient for ongoing treatment may be influenced by patient condition, hospital facilities, patient preference, local care pathways, and transfer time. Increased transfer time may increase the risk of clinical adverse events, but, in keeping with previous studies, we observed no harm associated with increasing transport distance [
      • Tranberg T.
      • Lippert F.K.
      • Christensen E.F.
      • Stengaard C.
      • Hjort J.
      • Lassen J.F.
      • et al.
      Distance to invasive heart centre, performance of acute coronary angiography, and angioplasty and associated outcome in out-of-hospital cardiac arrest: a nationwide study.
      ,
      • Geri G.
      • Gilgan J.
      • Wu W.
      • Vijendira S.
      • Ziegler C.
      • Drennan I.R.
      • et al.
      Does transport time of out-of-hospital cardiac arrest patients matter? A systematic review and meta-analysis.
      ].
      In line with previous ACS studies, we observed an association between increasing admission blood glucose and higher mortality [
      • Svensson A.-M.
      • McGuire D.K.
      • Abrahamsson P.
      • Dellborg M.
      Association between hyper- and hypoglycaemia and 2 year all-cause mortality risk in diabetic patients with acute coronary events.
      ,
      • Squire I.B.
      • Nelson C.P.
      • Ng L.L.
      • Jones D.R.
      • Woods K.L.
      • Lambert P.C.
      Prognostic value of admission blood glucose concentration and diabetes diagnosis on survival after acute myocardial infarction: results from 4702 index cases in routine practice.
      ]. Active management of hyperglycaemia in ACS has been associated with improved outcome [
      • Malmberg K.
      • Rydén L.
      • Efendic S.
      • Herlitz J.
      • Nicol P.
      • Waldenstrom A.
      • et al.
      Randomized trial of insulin-glucose infusion followed by subcutaneous insulin treatment in diabetic patients with acute myocardial infarction (DIGAMI study): effects on mortality at 1 year.
      ], and is recommended in international guidelines [
      • Roffi M.
      • Patrono C.
      • Collet J.P.
      • Mueller C.
      • Valgimigli M.
      • Andreotti F.
      • et al.
      2015 ESC guidelines for the management of acute coronary syndromes in patients presenting without persistent ST-segment elevation: task force for the management of acute coronary syndromes in patients presenting without persistent ST-segment elevation of the European Society of Cardiology (ESC).
      ,
      • Steg P.G.
      • James S.K.
      • Atar D.
      • Badano L.P.
      • Lundqvist C.B.
      • Borger M.A.
      • et al.
      ESC guidelines for the management of acute myocardial infarction in patients presenting with ST-segment elevation.
      ]. Our study did not analyse the medical management of hyperglycaemia, but our findings highlight a need for further research on this potentially modifiable clinical parameter.
      Our findings indicate the widespread implementation of evidence-based guidelines for the immediate management of myocardial infarction following OHCA [
      • Roffi M.
      • Patrono C.
      • Collet J.P.
      • Mueller C.
      • Valgimigli M.
      • Andreotti F.
      • et al.
      2015 ESC guidelines for the management of acute coronary syndromes in patients presenting without persistent ST-segment elevation: task force for the management of acute coronary syndromes in patients presenting without persistent ST-segment elevation of the European Society of Cardiology (ESC).
      ,
      • Steg P.G.
      • James S.K.
      • Atar D.
      • Badano L.P.
      • Lundqvist C.B.
      • Borger M.A.
      • et al.
      ESC guidelines for the management of acute myocardial infarction in patients presenting with ST-segment elevation.
      ]. Most STEMI patients (81.7%) and some NSTEACS patients (9.9%) received reperfusion therapy. In keeping with clinical trial data, early reperfusion treatment was associated with lower mortality in STEMI patients [
      • ISIS-2 (Second International Study of Infarct Survival) Collaborative Group
      Randomised trial of intravenous streptokinase, oral aspirin, both, or neither among 17, 187 cases of suspected acute myocardial infarction: ISIS-2.
      ,
      • Keeley E.C.
      • Boura J.A.
      • Grines C.L.
      Primary angioplasty versus intravenous thrombolytic therapy for acute myocardial infarction: a quantitative review of 23 randomised trials.
      ].
      We used the MINAP dataset to analyse outcomes in OHCA due to ACS. The key advantages to this dataset are national coverage and longevity. For the purpose of this study, its key limitation was that it does not capture key variables relevant to OHCA such as location (public v private) and bystander CPR. Future studies may consider enriching MINAP data through linkage with other relevant UK datasets, such as the OHCA outcomes project and intensive care case mix programme [
      • Hawkes C.
      • Booth S.
      • Ji C.
      • Brace-McDonnell S.J.
      • Whittington A.
      • Mapstone J.
      • et al.
      Epidemiology and outcomes from out-of-hospital cardiac arrests in England.
      ,
      • Harrison D.
      • Brady A.
      • Rowan K.
      Case mix, outcome and length of stay for admissions to adult, general critical care units in England, Wales and Northern Ireland: the intensive care national audit & research centre case mix programme database.
      ]. The OHCA outcomes project could provide key data on OHCA characteristics, but was established only in 2013 thereby limiting the opportunity for linkage. The intensive care case mix programme was established in the 1990s, but only approximately one in three patients in this study was admitted to the intensive care unit and the case mix programme does not directly collect provision of targeted temperature management although this may be derived from other variables [
      • Nolan J.P.
      • Ferrando P.
      • Soar J.
      • Benger J.
      • Thomas M.
      • Harrison D.A.
      • et al.
      Increasing survival after admission to UK critical care units following cardiopulmonary resuscitation.
      ].
      Our study has the limitations inherent in all observational studies. In particular, despite its large size and use of complex statistical analyses, our findings may be affected by unmeasured residual confounders. A key challenge in analysing audit datasets such as MINAP is the management of missing data [
      • Cattle B.A.
      • Baxter P.D.
      • Greenwood D.C.
      • Gale C.P.
      • West R.M.
      Multiple imputation for completion of a national clinical audit dataset.
      ]. Whilst we used sophisticated techniques to impute data, the degree of missingness in some important variables, such as Killip class, precluded this approach. Finally, there is known inter-hospital variation in methods used to identify patients for reporting to MINAP, particularly in NSTEACS patients, which may lead to selection bias [
      • Myocardial Ischaemia National Audit Project
      Myocardial Ischaemia National Audit Project: annual public report: april 2013–march 2014.
      ].

      Conclusions

      This large cohort study of patients with OHCA due to ACS found evidence of marked variation in mortality between hospitals, which was not fully explained by modelled patient and health service factors. Whilst we observed no association between cardiac arrest centre characteristics (volume, pPCI capability) and lower mortality, the early use of reperfusion treatment, which is likely to be available only in such centres, was associated with lower mortality in STEMI patients.

      Funding

      This project was funded by the National Institute for Health Research HS&DR programme (project number 11/2004/30).
      The views and opinions expressed therein are those of the authors and do not necessarily reflect those of the National Institute for Health Research HS&DR programme, NIHR, NHS or the Department of Health. The funder had no involvement in: study design; collection, analysis, and interpretation of data; writing of the manuscript; or the decision to submit the manuscript for publication.

      Conflict of interest

      KC, PKK, CPG, TQ, IBS, AM, JJMB, MWC, GDP report research grants from the NIHR. KC is supported as an NIHR Post-Doctoral Research Fellow . GDP and TQ are members of the NHS England Community Resuscitation Group and contributed to the national OHCA framework. GDP is an NIHR senior investigator, director of the national OHCA registry (funded by British Heart Foundation and Resuscitation Council (UK) ), is a panel member of NIHR HSDR, and is editor of Resuscitation journal. Bob Ewings and JL report personal fees from the NIHR for time spent as study PPI representatives.

      Acknowledgements

      We acknowledge the support of Professor John Deanfield and Dr Mark de Belder of NICOR during the course of this research. We would also like to thank Dr Marlous Hall and Dr Paul Norman of the University of Leeds for specialist advice on data analysis.

      Appendix A. Supplementary data

      The following is Supplementary data to this article:

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