Association of multimorbidity and changes in health-related quality of life following myocardial infarction: a UK multicentre longitudinal patient-reported outcomes study.

BACKGROUND: Multimorbidity is prevalent for people with myocardial infarction (MI), yet previous studies investigated single-health conditions in isolation. We identified patterns of multimorbidity in MI survivors and their associations with changes in HRQoL. METHODS: In this national longitudinal cohort study, we analysed data from 9566 admissions with MI from 77 National Health Service hospitals in England between 2011 and 2015. HRQoL was measured using EuroQol 5 dimension (EQ5D) instrument and visual analogue scale (EQVAS) at hospitalisation, 6, and 12 months following MI. Latent class analysis (LCA) of pre-existing long-term health conditions at baseline was used to identify clusters of multimorbidity and associations with changes in HRQoL quantified using mixed effects regression analysis. RESULTS: Of 9566 admissions with MI (mean age of 64.1 years [SD 11.9], 7154 [75%] men), over half (5119 [53.5%] had multimorbidities. LCA identified 3 multimorbidity clusters which were severe multimorbidity (591; 6.5%) with low HRQoL at baseline (EQVAS 59.39 and EQ5D 0.62) which did not improve significantly at 6 months (EQVAS 59.92, EQ5D 0.60); moderate multimorbidity (4301; 47.6%) with medium HRQoL at baseline (EQVAS 63.08, EQ5D 0.71) and who improved at 6 months (EQVAS 71.38, EQ5D 0.76); and mild multimorbidity (4147, 45.9%) at baseline (EQVAS 64.57, EQ5D 0.75) and improved at 6 months (EQVAS 76.39, EQ5D 0.82). Patients in the severe and moderate groups were more likely to be older, women, and presented with NSTEMI. Compared with the mild group, increased multimorbidity was associated with lower EQ-VAS scores (adjusted coefficient: -5.12 [95% CI -7.04 to -3.19] and -0.98 [-1.93 to -0.04] for severe and moderate multimorbidity, respectively. The severe class was more likely than the mild class to report problems in mobility, OR 9.62 (95% confidence interval: 6.44 to 14.36), self-care 7.87 (4.78 to 12.97), activities 2.41 (1.79 to 3.26), pain 2.04 (1.50 to 2.77), and anxiety/depression 1.97 (1.42 to 2.74). CONCLUSIONS: Among MI survivors, multimorbidity clustered into three distinct patterns and was inversely associated with HRQoL. The identified multimorbidity patterns and HRQoL domains that are mostly affected may help to identify patients at risk of poor HRQoL for which clinical interventions could be beneficial to improve the HRQoL of MI survivors. TRIAL REGISTRATION: ClinicalTrials.gov NCT01808027 and NCT01819103.

Long-term excess mortality associated with diabetes following acute myocardial infarction: a population-based cohort study.

BACKGROUND: The long-term excess risk of death associated with diabetes following acute myocardial infarction is unknown. We determined the excess risk of death associated with diabetes among patients with ST-elevation myocardial infarction (STEMI) and non-STEMI (NSTEMI) after adjustment for comorbidity, risk factors and cardiovascular treatments. METHODS: Nationwide population-based cohort (STEMI n=281 259 and NSTEMI n=422 661) using data from the UK acute myocardial infarction registry, MINAP, between 1 January 2003 and 30 June 2013. Age, sex, calendar year and country-specific mortality rates for the populace of England and Wales (n=56.9 million) were matched to cases of STEMI and NSTEMI. Flexible parametric survival models were used to calculate excess mortality rate ratios (EMRR) after multivariable adjustment. This study is registered at ClinicalTrials.gov (NCT02591576). RESULTS: Over 1.94 million person-years follow-up including 120 568 (17.1%) patients with diabetes, there were 187 875 (26.7%) deaths. Overall, unadjusted (all cause) mortality was higher among patients with than without diabetes (35.8% vs 25.3%). After adjustment for age, sex and year of acute myocardial infarction, diabetes was associated with a 72% and 67% excess risk of death following STEMI (EMRR 1.72, 95% CI 1.66 to 1.79) and NSTEMI (1.67, 1.63 to 1.71). Diabetes remained significantly associated with substantial excess mortality despite cumulative adjustment for comorbidity (EMRR 1.52, 95% CI 1.46 to 1.58 vs 1.45, 1.42 to 1.49), risk factors (1.50, 1.44 to 1.57 vs 1.33, 1.30 to 1.36) and cardiovascular treatments (1.56, 1.49 to 1.63 vs 1.39, 1.36 to 1.43). CONCLUSIONS: At index acute myocardial infarction, diabetes was common and associated with significant long-term excess mortality, over and above the effects of comorbidities, risk factors and cardiovascular treatments.

Geographic variation in the treatment of non-ST-segment myocardial infarction in the English National Health Service: a cohort study.

OBJECTIVES: To investigate geographic variation in guideline-indicated treatments for non-ST-elevation myocardial infarction (NSTEMI) in the English National Health Service (NHS). DESIGN: Cohort study using registry data from the Myocardial Ischaemia National Audit Project. SETTING: All Clinical Commissioning Groups (CCGs) (n=211) in the English NHS. PARTICIPANTS: 357 228 patients with NSTEMI between 1 January 2003 and 30 June 2013. MAIN OUTCOME MEASURE: Proportion of eligible NSTEMI who received all eligible guideline-indicated treatments (optimal care) according to the date of guideline publication. RESULTS: The proportion of NSTEMI who received optimal care was low (48 257/357 228; 13.5%) and varied between CCGs (median 12.8%, IQR 0.7-18.1%). The greatest geographic variation was for aldosterone antagonists (16.7%, 0.0-40.0%) and least for use of an ECG (96.7%, 92.5-98.7%). The highest rates of care were for acute aspirin (median 92.8%, IQR 88.6-97.1%), and aspirin (90.1%, 85.1-93.3%) and statins (86.4%, 82.3-91.2%) at hospital discharge. The lowest rates were for smoking cessation advice (median 11.6%, IQR 8.7-16.6%), dietary advice (32.4%, 23.9-41.7%) and the prescription of P2Y12 inhibitors (39.7%, 32.4-46.9%). After adjustment for case mix, nearly all (99.6%) of the variation was due to between-hospital differences (median 64.7%, IQR 57.4-70.0%; between-hospital variance: 1.92, 95% CI 1.51 to 2.44; interclass correlation 0.996, 95% CI 0.976 to 0.999). CONCLUSIONS: Across the English NHS, the optimal use of guideline-indicated treatments for NSTEMI was low. Variation in the use of specific treatments for NSTEMI was mostly explained by between-hospital differences in care. Performance-based commissioning may increase the use of NSTEMI treatments and, therefore, reduce premature cardiovascular deaths. TRIAL REGISTRATION NUMBER: NCT02436187.

Determinants of excess mortality following unprotected left main stem percutaneous coronary intervention.

OBJECTIVE: For percutaneous coronary intervention (PCI) to the unprotected left main stem (UPLMS), there are limited long-term outcome data. We evaluated 5-year survival for UPLMS PCI cases taking into account background population mortality. METHODS: A population-based registry of 10 682 cases of chronic stable angina (CSA), non-ST-segment elevation acute coronary syndrome (NSTEACS), ST-segment elevation myocardial infarction with (STEMI+CS) and without cardiogenic shock (STEMI-CS) who received UPLMS PCI from 2005 to 2014 were matched by age, sex, year of procedure and country to death data for the UK populace of 56.6 million people. Relative survival and excess mortality were estimated. RESULTS: Over 26 105 person-years follow-up, crude 5-year relative survival was 93.8% for CSA, 73.1% for NSTEACS, 77.5% for STEMI-CS and 28.5% for STEMI+CS. The strongest predictor of excess mortality among CSA was renal failure (EMRR 6.73, 95% CI 4.06 to 11.15), and for NSTEACS and STEMI-CS was preprocedural ventilation (6.25, 5.05 to 7.75 and 6.92, 4.25 to 11.26, respectively). For STEMI+CS, the strongest predictor of excess mortality was preprocedural thrombolysis in myocardial infarction (TIMI) 0 flow (2.78, 1.87 to 4.13), whereas multivessel PCI was associated with improved survival (0.74, 0.61 to 0.90). CONCLUSIONS: Long-term survival following UPLMS PCI for CSA was high, approached that of the background populace and was significantly predicted by co-morbidity. For NSTEACS and STEMI-CS, the requirement for preprocedural ventilation was the strongest determinant of excess mortality. By contrast, among STEMI+CS, in whom survival was poor, the strongest determinant was preprocedural TIMI flow. Future cardiovascular cohort studies of long-term mortality should consider the impact of non-cardiovascular deaths.

Patient and hospital determinants of primary percutaneous coronary intervention in England, 2003-2013.

OBJECTIVE: Primary percutaneous coronary intervention (PPCI) for ST-elevation myocardial infarction (STEMI) is insufficiently implemented in many countries. We investigated patient and hospital characteristics associated with PPCI utilisation. METHODS: Whole country registry data (MINAP, Myocardial Ischaemia National Audit Project) comprising PPCI-capable National Health Service trusts in England (84 hospital trusts; 92 350 hospitalisations; 90 489 patients), 2003-2013. Multilevel Poisson regression modelled the relationship between incidence rate ratios (IRR) of PPCI and patient and trust-level factors. RESULTS: Overall, standardised rates of PPCI increased from 0.01% to 86.3% (2003-2013). While, on average, there was a yearly increase in PPCI utilisation of 30% (adjusted IRR 1.30, 95% CI 1.23 to 1.36), it varied substantially between trusts. PPCI rates were lower for patients with previous myocardial infarction (0.95, 0.93 to 0.98), heart failure (0.86, 0.81 to 0.92), angina (0.96, 0.94 to 0.98), diabetes (0.97, 0.95 to 0.99), chronic renal failure (0.89, 0.85 to 0.90), cerebrovascular disease (0.96, 0.93 to 0.99), age >80 years (0.87, 0.85 to 0.90), and travel distances >30 km (0.95, 0.93 to 0.98). PPCI rates were higher for patients with previous percutaneous coronary intervention (1.09, 1.05 to 1.12) and among trusts with >5 interventional cardiologists (1.30, 1.25 to 1.34), more visiting interventional cardiologists (1-5: 1.31, 1.26 to 1.36; ≥6: 1.42, 1.35 to 1.49), and a 24 h, 7-days-a-week PPCI service (2.69, 2.58 to 2.81). Half of the unexplained variation in PPCI rates was due to between-trust differences. CONCLUSIONS: Following an 8 year implementation phase, PPCI utilisation rates stabilised at 85%. However, older and sicker patients were less likely to receive PPCI and there remained between-trust variation in PPCI rates not attributable to differences in staffing levels. Compliance with clinical pathways for STEMI is needed to ensure more equitable quality of care.