Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

High quality process of care increases one-year survival after acute myocardial infarction (AMI): A cohort study in Italy

  • Martina Ventura ,

    Contributed equally to this work with: Martina Ventura, Valeria Belleudi

    Roles Conceptualization, Data curation, Formal analysis, Methodology, Writing – original draft, Writing – review & editing

    Affiliation Department of Epidemiology of Lazio Regional Health Service, Rome, Italy

  • Valeria Belleudi ,

    Contributed equally to this work with: Martina Ventura, Valeria Belleudi

    Roles Conceptualization, Data curation, Formal analysis, Methodology, Supervision, Writing – original draft, Writing – review & editing

    Affiliation Department of Epidemiology of Lazio Regional Health Service, Rome, Italy

  • Paolo Sciattella,

    Roles Methodology, Writing – review & editing

    Affiliation Department of Statistical Sciences, “Sapienza” University of Rome, Rome, Italy

  • Riccardo Di Domenicantonio,

    Roles Writing – review & editing

    Affiliation Department of Epidemiology of Lazio Regional Health Service, Rome, Italy

  • Mirko Di Martino,

    Roles Methodology, Writing – review & editing

    Affiliation Department of Epidemiology of Lazio Regional Health Service, Rome, Italy

  • Nera Agabiti ,

    Roles Conceptualization, Writing – review & editing

    n.agabiti@deplazio.it

    Affiliation Department of Epidemiology of Lazio Regional Health Service, Rome, Italy

  • Marina Davoli,

    Roles Writing – review & editing

    Affiliation Department of Epidemiology of Lazio Regional Health Service, Rome, Italy

  • Danilo Fusco

    Roles Conceptualization, Supervision, Validation, Writing – review & editing

    Affiliation Department of Epidemiology of Lazio Regional Health Service, Rome, Italy

High quality process of care increases one-year survival after acute myocardial infarction (AMI): A cohort study in Italy

  • Martina Ventura, 
  • Valeria Belleudi, 
  • Paolo Sciattella, 
  • Riccardo Di Domenicantonio, 
  • Mirko Di Martino, 
  • Nera Agabiti, 
  • Marina Davoli, 
  • Danilo Fusco
PLOS
x

Abstract

Background

The relationship between guideline adherence and outcomes in patients with acute myocardial infarction (AMI) has been widely investigated considering the emergency, acute, post-acute phases separately, but the effectiveness of the whole care process is not known.

Aim

The study aim was to evaluate the effect of the multicomponent continuum of care on 1-year survival after AMI.

Methods

We conducted a cohort study selecting all incident cases of AMI from health information systems during 2011–2014 in the Lazio region. Patients’ clinical history was defined by retrieving previous hospitalizations and drugs prescriptions. For each subject the probability to reach the hospital and the conditional probabilities to survive to 30 days from admission and to 31–365 days post discharge were estimated through multivariate logistic models. The 1-year survival probability was calculated as the product of the three probabilities. Quality of care indicators were identified in terms of emergency timeliness (time between residence and the nearest hospital), hospital performance in treatment of acute phase (number/timeliness of PCI on STEMI) and drug therapy in post-acute phase (number of drugs among antiplatelet, β-blockers, ACE inhibitors/ARBs, statins). The 1-year survival Probability Ratio (PR) and its Bootstrap Confidence Intervals (BCI) between who were exposed to the highest level of quality of care (timeliness<10', hospitalization in high performance hospital, complete drug therapy) and who exposed to the worst (timeliness≥10', hospitalization in low performance hospital, suboptimal drug therapy) were calculated for a mean-severity patient and varying gender and age. PRs for patients with diabetes and COPD were also evaluated.

Results

We identified 38,517 incident cases of AMI. The out-of-hospital mortality was 27.6%. Among the people arrived in hospital, 42.9% had a hospitalization for STEMI with 11.1% of mortality in acute phase and 5.4% in post-acute phase. For a mean-severity patient the PR was 1.19 (BCI 1.14–1.24). The ratio did not change by gender, while it moved from 1.06 (BCI 1.05–1.08) for age<65 years to 1.62 (BCI 1.45–1.80) for age >85 years. For patients with diabetes and COPD a slight increase in PRs was also observed.

Conclusions

The 1-year survival probability post AMI depends strongly on the quality of the whole multicomponent continuum of care. Improving the performance in the different phases, taking into account the relationship among these, can lead to considerable saving of lives, in particular for the elderly and for subjects with chronic diseases.

Introduction

According to the Global Burden of Diseases estimates, ischemic heart diseases still remains the leading cause of premature death worldwide and in Italy [1]. In particular, each year more than 7 million people experience myocardial infarction with one-year mortality rates in the range of 10%, varying with patient characteristics [23].

The management of acute myocardial infarction (AMI) is built on clinical evidence drawn from many studies undertaken over the past three decades. The evolution in clinical practice has substantially reduced mortality and morbidity associated with the condition. [3]

The first few hours after symptom onset are the most critical in AMI and over 50% of cardiac deaths occur within the first 30 minutes, when the patient has not yet reached the hospital [4]. Several studies focused on the effect the overall system delay on adverse outcomes, including all components of delay modifiable by the health care system, both pre-hospital and door-to-balloon time [58]. Thus, reducing the interval between symptom onset and the hospital arrival (emergency phase) as well as receiving a timely and appropriate in-hospital medical treatment, are crucial factors in the survival of these patients [9, 10]. In particular, for patients with ST-elevation myocardial infarctions (STEMI), the European Society of Cardiology and the American College of Cardiology/American Heart Association recommend primary percutaneous coronary intervention (PCI) within 90 minutes of first medical contact and a total ischemic time, from symptom onset to reperfusion, within 120 minutes [11,12].

The relationship between timely PCI with respect to both short-term and long-term mortality has been widely studied [1318]. However, a recent study showed that the protective effect of a timely reperfusion of STEMI is reduced with increasing travel time from home to hospital [8], suggesting a strong effect of the quality of care on survival. Regarding long-term survival after AMI, several aspects following the initial acute phase should be considered as life behaviors and the adherence to drug therapy [19]. Platelet aggregation inhibitors (antiplatelets), β-blocking agents (β-blockers), agents acting on the renin-angiotensin system (ACEI angiotensin receptor blockers) and 3-hydroxy-3-methylglutaryl-coenzyme A (HMG-CoA) reductase inhibitors (statins) are well established therapeutic strategies for post-AMI secondary prevention [20, 21]. There is large evidence of suboptimal adherence to evidence based polytherapy in the post-acute phase for AMI patients [2224], despite the beneficial effect on prognosis has been clearly shown [2527].

In our Region in the last ten years on behalf of the Outcomes Evaluation Program in Lazio Region a number of “process of care indicators” related to the management and treatment of AMI are calculated, on the basis of a standardized methodology, periodically diffused on a website and used in audit and feedback multidisciplinary programs to improve clinical practice [28,29]. According to both clinical guidelines and regional AMI committee recommendations, these process indicators identify well recognized critical points of the AMI care, starting from the emergency to the post-acute phase [11,12,2831].

They include the proportion of PCIs performed on STEMI patients, the proportion of PCI executed within 90 minutes among all reperfusion procedures executed within 12 hours, and the adherence to evidence-based drug treatment in the 30 days after discharge. Moreover, a shorter travel time to hospital has been shown to improve the outcomes of AMI care in our Region [8], as observed in other contests [32].

In recent years, the efforts of health care systems have been focused separately on primary prevention programs, on reducing the delay in the management of the patient, and on improving the adherence to EB therapy. Actually, the care of AMI patients is a continuous lifelong process in which all the phases should not be considered in isolation but integrated. Exploring the whole multicomponent process of care (MPC) and understanding its effect on patient survival should be useful for health planning purposes. Moreover, the effect of the MPC is influenced by patient’s clinical profile [3335]; therefore, the identification of subgroups of patients with particular needs could contribute to better target the interventions.

We hypothesized that AMI patients exposed to a high quality process of care along the complex multicomponent continuum of care would experience a better long-term prognosis. The aims of this study were to evaluate the effect of the whole process of care on 1-year survival and to analyze the role of gender, age and chronic diseases on the relationship between the quality of care and survival.

Materials and methods

Data sources

Data were collected using the health information systems (HIS) of the Lazio region of Italy. Information on hospitalizations, emergency visits, drug prescriptions and cause-specific mortality is available for each individual registered in the Health Care Assistance Registry (approximately 97% of residents), and have been integrated using a deterministic record-linkage procedure based on anonymous identification codes. In this way, we created a chronological, demographical, residential, clinical, health-related patient profile. We derived additional information on comorbidities and time to surgery through the regional Admission and Discharge Information System.

Ethics

This study was carried out in full compliance with the current privacy laws. The Department of Epidemiology is legitimised by the Lazio Regional Committee in managing and analysing data from the regional health information systems for epidemiological purposes.

Study cohort

The present retrospective cohort study was based on the population aged 18–100 years, living in the Lazio region of Italy and registered in the regional health system. Lazio is the central region of Italy with about 5,5 million inhabitants, corresponding to 10% of the Italian population.

AMI cohort was identified selecting all hospital admissions with a main diagnosis of AMI or main diagnosis of an AMI-related condition along with a secondary diagnosis [28], and deaths for ischemic heart disease (ICD-9 410–414) [8] between 1 January 2011 and 31 December 2014. Only incident cases were considered (index event): patients with a hospital admission for AMI in the previous 3 years were excluded from the analysis.

Patient characteristics

For each patient, demographic and clinical characteristics were recorded in the index event. Moreover, patient clinical history was defined by retrieving specific conditions and procedures during hospitalizations or emergency visits in the previous 2 years, such as cancer, hypertension, hearth failure, arrhythmias, previous coronary angioplasty, other forms of ischemic heart diseases, chronic nephropathies [28]. The presence of diabetes and chronic obstructive pulmonary disease (COPD) was assessed in order to identify patients with a more complex clinical picture, using a validated algorithm based on ticket exemption, drug prescriptions and hospital admissions [3638].

To better define the clinical profiles of the patients, the use of drugs in the 6 months prior to the index event was also evaluated: cardiac therapies, antiplatelet therapies, anticoagulants, antihypertensive drugs, diuretics, beta-blocking agents, calcium channel blockers, angiotensin-converting-enzyme inhibitors, and angiotensin II antagonists or statins.

The multicomponent continuum of care process

The AMI care was considered as the process from the onset of symptoms to the secondary prevention post hospital discharge. Three phases were analyzed: the emergency phase, from the onset of symptoms to the arrival in an appropriate facility; the acute phase, from the arrival in hospital to the discharge; the post-acute phase, from the hospital discharge up to 1 year.

In each phase, we identified an exposure that measured the actual quality of care and took into account the specific level of intervention (individual or hospital) to be considered in order to promote the improvement of health assistance.

For the emergency phase, the travel time from home to the nearest emergency service was considered as exposure. After standardizing and geocoding patients and facilities addresses, the nearest hospital was assessed using a road network-based route analysis, that allowed to calculate the minimum driving travel time (in minutes) from patient address to hospital [8].

We classified exposure considering the median of the travel time’s empirical distribution (i.e. lower or higher than ten minutes).

In the acute phase, we considered patients with STEMI. At this stage, only hospital records were analyzed, making it possible to identify STEMIs through the reported ICD9-CM diagnoses. The exposure was based on the appropriateness and timeliness of the treatment and defined at hospital level. The hospital performance was described as the combination of two variables: the proportion of PCI performed on STEMI patients and the proportion of PCI executed within 90 minutes among all reperfusion procedures executed within 12 hours. Hospitals in the highest quartile of the distribution of both variables were classified as “high performance” and those in the lowest quartiles as “low performance”. All other facilities were classified as “medium performance”.

The exposure of the post-acute phase was based on the adherence to the EB drug treatment in the 30 days after discharge (intention to treat approach). Information about prescriptions of antiplatelets (ATC: B01AC04, B01AC05, B01AC06), β-blockers (ATC: C07), ACEI/ARBs (ATC: C09) and statins (ATC: C10AA) were retrieved for all patients. The number of EB drugs was evaluated (at most 2, 3 or 4).

Patients who died within 30 days after discharge or those with a hospital stay longer than 28 days were excluded in order to reduce misclassification and heterogeneity of the exposure in the post-acute phase.

By combining the phase-specific exposures, we obtained 18 different possible MPC scenarios. In particular, the best and the worst scenarios were respectively defined as:

  • travel time lower than 10 minutes, admission in a high performance hospital, complete EB drug therapy;
  • travel time of 10 minutes or more, admission in a low performance hospital, less than three EB drugs.

Outcomes

Three different outcomes post AMI were considered: the probability of reaching the nearest hospital alive; the probability of 30 days survival, given the survival to the emergency phase; the probability of 31–365 days survival, given the survival to the acute phase. The overall 1-year survival was defined as a combination of three outcomes.

Statistical analysis

As described in detail previously [39], we used multivariate logistic regression models to assess the effect of MPC on the three outcomes, accounting for demographic characteristics, comorbidities and co-medications of the patients. Among all factors potentially associated with the outcome, age and gender were considered to be a priori risk factors, the other factors were selected using a stepwise bootstrap procedure. Using this approach, 1000 replicated bootstrap samples were selected from the original cohort. A bootstrap sample is a sample of the same size as the original dataset chosen with replacement. Thus, a given subject in the original cohort may be selected multiple times, only once, or not at all in a specific bootstrap sample. A stepwise procedure, using thresholds of p  =  0.05 for variable selection and elimination, was applied to each replicated sample, and only the factors selected in at least 50% of the procedures were included in the final models.

In order to take into account the variability of survival between hospitals, a multilevel approach was applied in the acute and post-acute phase considering hospital of admission as a second level unit.

For each phase of the MPC the probability of survival was estimated considering a mean-severity patient, i.e. a patient with the same distribution of age, sex and mortality risk factors as observed in the cohort. Because the survival in each phase is conditioned to the survival in the previous phases, the overall 1-year survival probability was calculated as product of the three probabilities (Fig 1).

To assess the effectiveness of the MPC, the 1-year probability ratio (PR) between the best and the worst scenarios was calculated, and its Bootstrap Confidence Intervals (95% BCI) were estimated.

Furthermore, to estimate the potential saving of lives due to the MPC, for each phase the survival probability estimated for the best MPC was applied to the patients who experienced the worst MPC.

Finally, in order to evaluate the potential effect of demographic or clinical factors on the relationship among care-pathways and survival, the PRs were calculated by gender, age classes and considering patients with chronic conditions.

Sensitivity analysis

To assess the robustness of our results, some sensitivities analyses were performed. To evaluate the quality of exposure in the emergency phase the association between travel time and instantaneous survival was estimated in a subgroup of patients aged 75 years or more. The association between drug therapy and survival was examined using a Cox regression model with mixed effects—frailty model—in order to take into account the length of survival in the post-acute phase. Lastly, to evaluate the quality of exposure in the post-acute phase, we replicated the main analysis on the subgroup of new drug users.

Results

Between 2011 and 2014, 38,517 incident cases of AMI were identified in Lazio region. They were mainly men (61.8%) and older than 65 years (mean age 73 years). Diabetes and COPD were found in 29% and 21% of the cohort, respectively.

During the emergency phase, 27.6% of AMI died before reaching a hospital. Among those who survived 11,394 were admitted in a regional hospital with a STEMI diagnosis (42%) and of those, 11.1% died within 30 days of admission. In the post-acute phase 9,620 patients were analyzed and 5.4% died within 31–365 days post discharge (Fig 2).

As observed in Fig 2, eighteen different MPCs were identified by combining the different exposures. About half of the study population could reach a hospital with hemodynamic in less than ten minutes. In the acute phase, 21.6% (of which 10.4% with travel time lower than 10 minutes) and 24.7% was admitted respectively in high and low performance hospital. Considering the patients survived to the acute phase, 49.9% followed a 4-drugs EB therapy whilst at most 2 drugs were found in 18.0% of the cases. Thus, we found that patients experienced the best MPC in 5.2% of cases, whereas they followed the worst one in 2.3%.

Table 1 shows the determinants of survival in each phase of the AMI care In all the phases, no difference was found for gender, while a decreasing survival was observed with an increase in age. Patients with diabetes had a higher probability of survival to the emergency phase (OR = 1.44 p = < .0001), but a lower survival was found for these patients in the acute and post-acute phases (OR = 0.71 p = < .0001 and OR = 0.69 p = < .0001, respectively). For COPD patients a lower survival was observed in all the phases. Having a travel time of ten minutes or more was associated with a lower probability of reaching the hospital alive (OR = 0.90 p = < .0001). The effect of the hospital performance was found in the acute and post-acute phases: patients hospitalized in high performance facilities had a higher 30 days survival (OR = 1.49 p = 0.018), whereas those admitted in low performance hospitals had a lower long-term survival (OR = 0.62 p = 0.002). Lastly, to be adherent to the EB pharmacological treatment in the post-acute phase was associated with an increasing survival: patients undergoing a complete therapy (4 drugs) had a more than twofold probability of 1-year survival than those with at most 2 drugs (OR = 2.62 p = < .0001).

Considering a mean-severity patient, the overall 1-year survival PR following the best MPC was 1.19 (BCI 1.14–1.24) as compared with the worst scenario. Applying the survival probabilities estimated for the best MPC to the patients who experienced the worst MPC we calculated a potential saving of about 400 lives. The PRs did not change by gender, whereas a strong increase was observed by age: it moved from 1.06 (BCI 1.05–1.08) for the youngest patients, to 1.62 (BCI 1.45–1.80) for age >85 years. For patients with diabetes and COPD a slight increase in PRs was found (Fig 3).

thumbnail
Fig 3. Probability ratios (PRs) between patients undertaking the best and the worst process of care–mean-severity patient, age, gender and chronic conditions.

https://doi.org/10.1371/journal.pone.0212398.g003

The sensitivity analysis confirmed the main results. In particular, in the emergency phase, patients aged at least 75 years with a travel time of ten minutes or more had a lower probability of reaching the hospital alive (OR = 0.94 p = 0.044). The higher survival associated with a complete EB pharmacological treatment in the post-acute phase was observed both when we took into account the length of survival, and when we considered the subgroup of new drug users.

Discussion

In this study, we found that survival after AMI strongly depends on the quality of the whole multicomponent process of care: patients who experienced the best care, in terms of timeliness in the emergency, of admission in a high-performance facility, and of adherence to EB drug treatment, had a higher 1-year survival. If all patients followed the best care a considerable saving in lives would be observed.

As previously observed, in ST-elevation myocardial infarctions (STEMI) the majority of deaths occur during the first hours after symptom onset, and the beneficial effects of reperfusion treatment are critically time dependent [6, 40, 41]. De Luca et al., found that every minute of delay in treatment of patient with STEMI does affect 1-year mortality, with an increase of 7.5% for each 30-minute delay [42].

Several important time intervals need to be considered in the treatment of STEMI patients [43]. In the emergency phase, we focused on the relation between the time from home to the nearest emergency service and out of hospital mortality, and found that a higher travel time decreased the probability of reaching the hospital alive. Wei et al. reported a similar result for patients with their first AMI in Scotland, suggesting that distance from home to hospital may predict mortality outcome [44]. Moreover, in Lazio region, it has been demonstrated that travel time is a reliable proxy of pre-hospital system delay and a strong predictor of 30 days mortality after timely execution of PCI for STEMI [8].

The second time interval that we considered was the in-hospital time for patients with STEMI who reached the hospital alive. Health care systems are expected to treat those patients with PCI within 90 or 120 minutes from the first medical contact depending on whether they arrive in a PCI-capable or in a non-PCI-capable facility, respectively [11]. The relevant prognostic role of reperfusion delays in STEMI has been demonstrated and both door-to-balloon and total ischemic time have been linked to increasing mortality [42, 45, 46]. In our study the hospital performance, evaluated in terms of appropriateness and timeliness of treatment, was found to be related with both short-term and long-term survival. This result suggests that the adherence to guidelines affects the specific phase but also the subsequent phases, pointing out the need to consider the care of the patient with AMI as an integrated pathway.

Regarding secondary prevention, our results were consistent with other studies where the effectiveness of the EB polytherapy in reducing morbidity and mortality of patients after AMI was demonstrated [27, 47, 48]. In particular, when drugs are prescribed together incremental synergistic benefits were observed [49, 50]. Despite well-established benefits, these effective secondary prevention therapies remain underutilized [51].

To date, health care systems have addressed their efforts to improve adherence to guidelines, tackling separately the single phases of the complex care process. The care of AMI patients should be considered as an integrated process that follows the patient journey [1], from the onset of symptoms to the return to a normal life. For this purpose, we identified in each phase the specific level of intervention to promote improvement in the quality of care, and we evaluated the whole process of care using a synthetic value of probability to survive that combines information from all the phases.

This approach was previously used to evaluate the care-pathway in patients with stroke [39].

Then, combining information from all the phases, we identified 18 possible care scenario that patients could have followed. When comparing people exposed to the the best and the worst process of care, we found that an optimal quality of care had a positive effect on patients’ survival. It has been shown that quality of care of patients with AMI varies with age, sex, race, geographic location, physician specialty, and hospital teaching status [6, 52, 53].

Our results did not show a gender difference in PRs, while a strong increase of PRs was observed by age classes, suggesting a higher effect of the process of care for the oldest patients. When we evaluated the subgroups of patients with chronic conditions, a slightly increase of PRs was observed for both conditions. The higher effect of the optimal continuum of care in diabetic patients was mainly due to the lower survival observed for this subgroup of patients in the acute and post-acute phases. This agrees with previous observations in other countries [54, 55]. In fact, it has been recognized that mid or long-term outcomes of AMI are poorer in diabetic than in non-diabetic patient, whereas conflicting results were found on short-term outcomes [5659]. Moreover, our results showed a higher survival of the diabetics in the emergency phase. A possible explanation could lie in a greater responsiveness at the onset of symptoms, or a greater familiarity with the emergency systems, of patients with a more complex and severe clinical profile.

In patients with COPD, we found a lower survival in the emergency and post-acute phases, whilst no significant differences where observed in the acute phase. Although studies have reported higher long-term mortality in patients with COPD after MI, there is controversy regarding its impact on in-hospital mortality [60, 61]. Rothnie et al., showed that patients with COPD were more likely to have a delay in the diagnosis of MI and a longer time to reperfusion after STEMI, compared with non-COPD. The authors also suggested that the delay in diagnosis of MI in patients with COPD might be due to an incorrect attribution of symptoms to COPD rather than MI [62]. Furthermore, prior research has shown that β-blockers and other effective cardiac therapies are underused in patients with AMI and COPD [63].

Potential alternative explanations for our results need to be considered. In our study, we defined a synthetic measure of the multiphase quality of care by choosing and calculating indicators for the four major critical points in the continuum of care process. However, many other factors may play a role in the good prognosis in AMI patients, among them time for ambulance to arrive to patient’s house as well as pre-hospital delay times [64], referral and timely access to cardiac rehabilitation [65], healthcare provider recommendations for health behaviour change (e.g. weight management, smoking cessation) [66], unfortunately we were not able to measure factors like them, and it may represent a major limitation in the interpretation of our results.

Some other limitations of this study need to be considered. First, this study was based on data from the HIS. Despite some important advantages in collecting administrative data [67], as the large number of patients involved and the opportunity to integrate many sources of data to define and analyse MPCs, there remains questions about its accuracy, completeness and possible gaps in clinical information [68]. Secondly, in the emergency phase, the travel time was estimated from home to the nearest appropriate facility, resulting in a possible misclassification of exposure, since definitely not all patients might have been at their place of residence when STEMI occurs. However, our sensitivity analysis on the subgroups of patients aged at least 75 years, confirmed the main results.

Moreover, drug use data from our pharmaceutical database refer to the prescribed agents, but the actual levels of intake cannot be evaluated, and consequently a possible misclassification of drug utilization may have occurred. We acknowledge that prescriptions are not a measure of adherence, according to the findings and interpretation of the majority of pharmacological studies based on drug administrative registers [69].

Lastly, although several covariates were included in the models to adjust for differences in patient characteristics, we cannot exclude a residual confounding due to unmeasurable or unmeasured covariates that might affect outcome and exposure measures.

Conclusions

In conclusion, this study demonstrated that AMI patients exposed to optimal quality of care along the continuum of the complex multiphase care management have a better 1-year survival.

Health care systems should address their efforts to improve the effectiveness of EB interventions both in the specific phases of care, and taking into account the relationships among them. Careful monitoring is required in the management of subgroups of patients with particular needs, as the oldest or those with chronic diseases.

Acknowledgments

An abstract of this study was submitted at the “10th European Public Health Conference” held in Stockholm, Sweden, on 1–4 November 2017 and it was accepted as oral communication.

References

  1. 1. Institute for Health Metrics and Evaluation (IHME). GBD Compare Data Visualization. Seattle, WA: IHME, University of Washington, 2017. Available from http://vizhub.healthdata.org/gbd-compare. (Accessed [2018 May 10])
  2. 2. Piepoli MF, Corrà U, Dendale P, Frederix I, Prescott E, Schmid JP, et al. Challenges in secondary prevention after acute myocardial infarction: A call for action. Eur J Prev Cardiol. 2016 Dec;23(18):1994–2006. Epub 2016 Sep 27. pmid:27600690.
  3. 3. White HD1, Chew DP. Acute myocardial infarction. Lancet. 2008 Aug 16;372(9638):570–84. pmid:18707987
  4. 4. Saberi F, Adib-Hajbaghery M, Zohrehea J. Predictors of Prehospital Delay in Patients With Acute Myocardial Infarction in Kashan City. Nursing and Midwifery Studies. 2014;3(4):e24238. pmid:25741517
  5. 5. Terkelsen CJ, Sørensen JT, Maeng M, Jensen LO, Tilsted HH, Trautner S, et al. System delay and mortality among patients with STEMI treated with primary percutaneous coronary intervention. JAMA. 2010 Aug 18;304(7):763–71. pmid:20716739.
  6. 6. Henriksson C, Larsson M, Herlitz J, Karlsson JE, Wernroth L, Lindahl B. Influence of health-related quality of life on time from symptom onset to hospital arrival and the risk of readmission in patients with myocardial infarction. Open Heart. 2014 Dec 13;1(1):e000051. eCollection 2014. pmid:25525504; PubMed Central PMCID: PMC4267108.
  7. 7. Bates ER, Jacobs AK. Time to treatment in patients with STEMI. N Engl J Med. 2013 Sep 5;369(10):889–92. pmid:24004114.
  8. 8. Di Domenicantonio R, Cappai G, Sciattella P, Belleudi V, Di Martino M, Agabiti N, et al. The Tradeoff between Travel Time from Home to Hospital and Door to Balloon Time in Determining Mortality among STEMI Patients Undergoing PCI. PLoS One. 2016 Jun 23;11(6):e0158336. eCollection 2016. pmid:27336859; PubMed Central PMCID: PMC4918978.
  9. 9. Sullivan AL, Beshansky JR, Ruthazer R, Murman DH, Mader TJ, Selker HP. Factors associated with longer time to treatment for patients with suspected acute coronary syndromes: a cohort study. Circ Cardiovasc Qual Outcomes. 2014;7(1):86–94. pmid:24425697
  10. 10. De Luca G, Suryapranata H, Zijlstra F, van 't Hof AW, Hoorntje JC, Gosselink AT, et al. Symptom-onset-to-balloon time and mortality in patients with acute myocardial infarction treated by primary angioplasty. J Am Coll Cardiol. 2003 Sep 17;42(6):991–7. pmid:13678918.
  11. 11. Ibanez B, James S, Agewall S, Antunes MJ, Bucciarelli-Ducci C, Bueno H, et. ESC Scientific Document Group. 2017 ESC Guidelines for the management of acute myocardial infarction in patients presenting with ST-segment elevation: The Task Force for the management of acute myocardial infarction in patients presenting with ST-segment elevation of the European Society of Cardiology (ESC). Eur Heart J. 2018 Jan 7;39(2):119–177. pmid:28886621.
  12. 12. O'Gara PT, Kushner FG, Ascheim DD, Casey DE Jr, Chung MK, de Lemos JA, et al.2013 ACCF/AHA guideline for the management of ST-elevation myocardial infarction: executive summary: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol. 2013 Jan 29;61(4):485–510. Epub 2012 Dec 17. pmid:23256913
  13. 13. McNamara RL, Wang Y, Herrin J, Curtis JP, Bradley EH, Magid DJ, et al. Effect of door-to-balloon time on mortality in patients with ST-segment elevation myocardial infarction. J Am Coll Cardiol. 2006 Jun 6;47(11):2180–6. Epub 2006 May 15. pmid:16750682.
  14. 14. Sorita A, Ahmed A, Starr SR, Thompson KM, Reed DA, Prokop L, et al. Off-hour presentation and outcomes in patients with acute myocardial infarction: systematic review and meta-analysis. BMJ. 2014 Jan 21;348:f7393. Review. pmid:24452368; PubMed Central PMCID: PMC3898160.
  15. 15. Magid DJ, Wang Y, Herrin J, McNamara RL, Bradley EH, Curtis JP, et al. Relationship between time of day, day of week, timeliness of reperfusion, and in-hospital mortality for patients with acute ST-segment elevation myocardial infarction. JAMA. 2005 Aug 17;294(7):803–12. pmid:16106005.
  16. 16. OECD/EU (2016). “Mortality following acute myocardial infarction (AMI)”, in Health at a Glance: Europe 2016: State of Health in the EU Cycle, OECD Publishing, Paris. http://dx.doi.org/10.1787/health_glance_eur-2016-41-en
  17. 17. Rathore SS, Curtis JP, Nallamothu BK, Wang Y, Foody JM, Kosiborod M, et al. Association of door-to-balloon time and mortality in patients > or = 65 years with ST-elevation myocardial infarction undergoing primary percutaneous coronary intervention. Am J Cardiol. 2009 Nov 1;104(9):1198–203. pmid:19840562; PubMed Central PMCID: PMC2790921.
  18. 18. Yudi MB, Ramchand J, Farouque O, Andrianopoulos N, Chan W, Duffy SJ, et al. Impact of door-to-balloon time on long-term mortality in high- and low-risk patients with ST-elevation myocardial infarction. Int J Cardiol. 2016 Dec 1;224:72–78. Epub 2016 Sep 5. pmid:27631718.
  19. 19. Di Martino M, Alagna M, Cappai G, Mataloni F, Lallo A, Perucci C A, et al. Adherence to evidence-based drug therapies after myocardial infarction: is geographic variation related to hospital of discharge or primary care providers? A cross-classified multilevel design. BMJ Open. 2016; 6(4):e010926. pmid:27044584
  20. 20. National Clinical Guideline Centre (UK). MI—Secondary Prevention: Secondary Prevention in Primary and Secondary Care for Patients Following a Myocardial Infarction: Partial Update of NICE CG48 [Internet]. London: Royal College of Physicians (UK); 2013 Nov. Available from http://www.ncbi.nlm.nih.gov/books/NBK247688/ pmid:25340229.
  21. 21. Smith SC Jr, Benjamin EJ, Bonow RO, Braun LT, Creager MA, et al. AHA/ACCF Secondary Prevention and Risk Reduction Therapy for Patients with Coronary and other Atherosclerotic Vascular Disease: 2011 update: a guideline from the American Heart Association and American College of Cardiology Foundation. Circulation. 2011 Nov 29;124(22):2458–73. Epub 2011 Nov 3. Erratum in: Circulation. 2015 Apr 14;131(15):e408. pmid:22052934.
  22. 22. Lee JH, Yang DH, Park HS, Cho Y, Jeong MH, Kim YJ, et al. Suboptimal use of evidence-based medical therapy in patients with acute myocardial infarction from the Korea Acute Myocardial Infarction Registry: prescription rate, predictors, and prognostic value. Am Heart J. 2010 Jun;159(6):1012–9. pmid:20569714.
  23. 23. Yan AT, Yan RT, Tan M, Huynh T, Soghrati K, Brunner LJ, et al. Optimal medical therapy at discharge in patients with acute coronary syndromes: temporal changes, characteristics, and 1-year outcome. Am Heart J. 2007 Dec;154(6):1108–15. Epub 2007 Sep 14. pmid:18035083.
  24. 24. Lenzi J, Rucci P, Castaldini I, Protonotari A, Di Pasquale G, Di Martino M, et al. Does age modify the relationship between adherence to secondary prevention medications and mortality after acute myocardial infarction? A nested case-control study. Eur J Clin Pharmacol. 2015 Feb;71(2):243–50 pmid:25529226
  25. 25. Kirchmayer U, Di Martino M, Agabiti N, Bauleo L, Fusco D, Belleudi V, et al. Effect of evidence-based drug therapy on long-term outcomes in patients discharged after myocardial infarction: a nested case–control study in Italy. Pharmacoepidemiol Drug Saf. 2013 Jun;22(6):649–57. pmid:23529919; PubMed Central PMCID: PMC3746119.
  26. 26. Briffa T, Hickling S, Knuiman M, Hobbs M, Hung J, Sanfilippo FM, et al. Long term survival after evidence based treatment of acute myocardial infarction and revascularisation: follow-up of population based Perth MONICA cohort, 1984–2005. BMJ. 2009 Jan 26;338:b36. pmid:19171564; PubMed Central PMCID: PMC2769031.4.
  27. 27. Gouya G, Reichardt B, Ohrenberger G, Wolzt M. Survival of patients discharged after acute myocardial infarction and evidence-based drug therapy. Eur J Epidemiol. 2007;22(3):145–9. Epub 2007 Mar 14. pmid:17356927.
  28. 28. Fusco D, Barone AP, Sorge C, D'Ovidio M, Stafoggia M, Lallo A, et al. P.Re.Val.E.: outcome research program for the evaluation of health care quality in Lazio, Italy. BMC Health Serv Res. 2012. January 27;12:25 pmid:22283880
  29. 29. The Lazio Regional Outcome Evaluation Program. 2017. [Accessed 2018 Nov11] Available from: http://http://95.110.213.190/prevale2017/index.php.
  30. 30. De Luca L, Colivicchi F, Gulizia MM, Pugliese FR, Ruggieri MP, Musumeci G, Cibinel GA, Romeo F. Clinical pathways and management of antithrombotic therapy in patients with acute coronary syndrome (ACS): a Consensus Document from the Italian Association of Hospital Cardiologists (ANMCO), Italian Society of Cardiology (SIC), Italian Society of Emergency Medicine (SIMEU) and Italian Society of Interventional Cardiology (SICI-GISE). Eur Heart J Suppl. 2017 May;19 (Suppl D):D130–D150. Epub 2017 May 2. pmid:28751840
  31. 31. Renzi C, Asta F, Fusco D, Agabiti N, Davoli M, Perucci CA. Does public reporting improve the quality of hospital care for acute myocardial infarction? Results from a regional outcome evaluation program in Italy. Int J Qual Health Care. 2014 Jun;26(3):223–30. Epub 2014 Apr 15. pmid:24737832
  32. 32. Jäger B, Farhan S, Rohla M, Christ G, Podczeck-Schweighofer A, Schreiber W, et al. Clinical predictors of patient related delay in the VIENNA ST-elevation myocardial infarction network and impact on long-term mortality. Eur Heart J Acute Cardiovasc Care. 2017 Apr;6(3):254–261. pmid:26888787
  33. 33. Pilgrim T, Heg D, Tal K, Erne P, Radovanovic D, Windecker S, et al. Age- and Gender-related Disparities in Primary Percutaneous Coronary Interventions for Acute ST-segment elevation Myocardial Infarction. PLoSOne. 2015 Sep 9;10(9):e0137047. eCollection 2015. pmid:26352574; PubMed Central PMCID: PMC4564139.
  34. 34. Lundberg V, Stegmayr B, Asplund K, Eliasson M, Huhtasaari F. Diabetes as a risk factor for myocardial infarction: population and gender perspectives. J Intern Med. 1997 Jun;241(6):485–92. pmid:10497624.
  35. 35. Stefan MS, Bannuru RR, Lessard D, Gore JM, Lindenauer PK, Goldberg RJ. The Impact of COPD on Management and Outcomes of Patients Hospitalized With Acute Myocardial Infarction: A 10-Year Retrospective Observational Study. Chest. 2012;141(6):1441–1448. pmid:22207679
  36. 36. Department of Epidemiology Regional Health Service Lazio—Identification of people with chronic and acute disease using data from Heath Information Systems. Technical report—Edition 2017, [Accessed 2017 Oct 30]. Available from: http://www.deplazio.net/it/stato-di-salute.
  37. 37. Faustini A, Canova C, Cascini S, Baldo V, Bonora K, De Girolamo G, et al. The reliability of hospital and pharmaceutical data to assess prevalent cases of chronic obstructive pulmonary disease. COPD. 2012 Apr;9(2):184–96. Epub 2012 Mar 12. pmid:22409483.
  38. 38. Belleudi V, Agabiti N, Kirchmayer U, Cascini S, Bauleo L, Berardini L, et al. Definition and validation of a predictive model to identify patients with chronic obstructive pulmonary disease (COPD) from administrative databases]. Epidemiol Prev. 2012 May-Aug;36(3–4):162–71. Italian. pmid:22828229.
  39. 39. Belleudi V, Sciattella P, Agabiti N, Di Martino M, Di Domenicantonio R, Davoli M, et al. Socioeconomic differences in one-year survival after ischemic stroke: the effect of acute and post-acute care-pathways in a cohort study. 2016 May 16;16:408. pmid:27184959; PubMed Central PMCID: PMC4868039.
  40. 40. Newby LK, Rutsch WR, Califf RM, Simoons ML, Aylward PE, Armstrong PW, et al. Time from symptom onset to treatment and outcomes after thrombolytic therapy. GUSTO-1 Investigators. J Am Coll Cardiol. 1996 Jun;27(7):1646–55. pmid:8636549.
  41. 41. Juliard JM, Feldman LJ, Golmard JL, Himbert D, Benamer H, Haghighat T, et al. Relation of mortality of primary angioplasty during acute myocardial infarction to door-to-Thrombolysis In Myocardial Infarction (TIMI) time. Am J Cardiol. 2003 Jun 15;91(12):1401–5. pmid:12804723.
  42. 42. De Luca G, Suryapranata H, Ottervanger JP, Antman EM. Time delay to treatment and mortality in primary angioplasty for acute myocardial infarction: every minute of delay counts. Circulation. 2004 Mar 16;109(10):1223–5. Epub 2004 Mar 8. pmid:15007008.
  43. 43. Denktas AE, Anderson HV, McCarthy J, Smalling RW. Total ischemic time: the correct focus of attention for optimal ST-segment elevation myocardial infarction care. JACC Cardiovasc Interv. 2011 Jun;4(6):599–604. Review. pmid:21700244.
  44. 44. Wei L, Lang CC, Sullivan FM, Boyle P, Wang J, Pringle SD, et al. Impact on mortality following first acute myocardial infarction of distance between home and hospital: cohort study. Heart, 2008, 94(9), 1141–1146. http://doi.org/10.1136/hrt.2007.123612 pmid:17984217
  45. 45. Estévez-Loureiro R, López-Sainz Á, Pérez de Prado A, Cuellas C, Calviño Santos R, Alonso-Orcajo N, et al. Timely reperfusion for ST-segment elevation myocardial infarction: Effect of direct transfer to primary angioplasty on time delays and clinical outcomes. World Journal of Cardiology, 2014, 6(6), 424–433. http://doi.org/10.4330/wjc.v6.i6.424 pmid:24976914
  46. 46. Cannon CP, Gibson CM, Lambrew CT, Shoultz DA, Levy D, French WJ, et al. Relationship of symptom-onset-to-balloon time and door-to-balloon time with mortality in patients undergoing angioplasty for acute myocardial infarction. JAMA. 2000 Jun 14;283(22):2941–7. pmid:10865271.
  47. 47. Milonas C, Jernberg T, Lindbäck J, Agewall S, Wallentin L, Stenestrand U; RIKS-HIA Group. Effect of Angiotensin-converting enzyme inhibition on one-year mortality and frequency of repeat acute myocardial infarction in patients with acute myocardial infarction. Am J Cardiol. 2010 May 1;105(9):1229–34. Epub 2010 Mar 11. pmid:20403471.
  48. 48. Amann U, Kirchberger I, Heier M, Zirngibl A, von Scheidt W, Kuch B, et al. Effect of renin-angiotensin system inhibitors on long-term survival in patients treated with beta blockers and antiplatelet agents after acute myocardial infarction (from the MONICA/KORA Myocardial Infarction Registry). Am J Cardiol. 2014 Aug 1;114(3):329–35. Epub 2014 May 15. pmid:24927969.
  49. 49. Mukherjee D, Fang J, Chetcuti S, Moscucci M, KlineRogers E, Eagle KA. Impact of combination evidencebased medical therapy on mortality in patients with acute coronary syndromes. Circulation 2004; 109: 745–749. pmid:14970110
  50. 50. Amann U, Kirchberger I, Heier M, Golüke H, von Scheidt W, Kuch B, et al. Long-term survival in patients with different combinations of evidence-based medications after incident acute myocardial infarction: results from the MONICA/KORA Myocardial Infarction Registry. Clin Res Cardiol. 2014 Aug;103(8):655–64. Epub 2014 Mar 7. pmid:24604524.
  51. 51. Yan AT, Tan M, Fitchett D, Chow CM, Fowlis RA, McAvinue TG, et al. One-year outcome of patients after acute coronary syndromes (from the Canadian Acute Coronary Syndromes Registry). Am J Cardiol. 2004 Jul 1;94(1):25–9. Erratum in: Am J Cardiol. 2005 Feb 1;95(3):438. pmid:15219503.
  52. 52. Mehta RH, Montoye CK, Gallogly M, Baker P, Blount A, Faul J, et al.; GAP Steering Committee of the American College of Cardiology. Improving quality of care for acute myocardial infarction: The Guidelines Applied in Practice (GAP) Initiative. JAMA. 2002 Mar 13;287(10):1269–76. pmid:11886318.
  53. 53. Nguyen HL, Saczynski JS, Gore JM, Goldberg RJ. Age and Sex Differences in Duration of Pre-hospital Delay in Patients with Acute Myocardial Infarction: A Systematic Review. Circulation Cardiovascular quality and outcomes. 2010;3(1):82–92. pmid:20123674
  54. 54. Lundberg V, Stegmayr B, Asplund K, Eliasson M, Huhtasaari F. Diabetes as a risk factor for myocardial infarction: population and gender perspectives. J Intern Med. 1997 Jun;241(6):485–92. pmid:10497624.
  55. 55. Sprafka JM, Burke GL, Folsom AR, McGovern PG, Hahn LP. Trends in prevalence of diabetes mellitus in patients with myocardial infarction and effect of diabetes on survival. The Minnesota Heart Survey. Diabetes Care. 1991 Jul;14(7):537–43. pmid:1914792.
  56. 56. Lee MG, Jeong MH, Ahn Y, Chae SC, Hur SH, Hong TJ, et al. Comparison of clinical outcomes following acute myocardial infarctions in hypertensive patients with or without diabetes. Korean Circ J. 2009 Jun;39(6):243–50. Epub 2009 Jun 30. pmid:19949630; PubMed Central PMCID: PMC2771834.
  57. 57. Kim HL, Kang SH, Yoon CH, Cho YS, Youn TJ, Cho GY, et al. Differential prognostic impacts of diabetes over time course after acute myocardial infarction. J Korean Med Sci. 2013 Dec;28(12):1749–55. Epub 2013 Nov 26. pmid:24339704; PubMed Central PMCID: PMC3857370.
  58. 58. Koek HL, Soedamah-Muthu SS, Kardaun JWPF, Gevers E., de Bruin A, Reitsma JB, et al. Short- and long-term mortality after acute myocardial infarction: comparison of patients with and without diabetes mellitus. European Journal of Epidemiology, 2007, 22(12), 883–888. http://doi.org/10.1007/s10654-007-9191-5 pmid:17926133
  59. 59. Timmer JR, Ottervanger JP, Thomas K, Hoorntje JC, de Boer MJ, Suryapranata H, et al. Long-term, cause-specific mortality after myocardial infarction in diabetes. Eur Heart J. 2004 Jun;25(11):926–31. pmid:15172464.
  60. 60. Agarwal M, Agrawal S, Garg L, Garg A, Bhatia N, Kadaria D, et al. Effect of Chronic Obstructive Pulmonary Disease on In-Hospital Mortality and Clinical Outcomes After ST-Segment Elevation Myocardial Infarction. Am J Cardiol. 2017 May 15;119(10):1555–1559. Epub 2017 Mar 1. pmid:28390680; PubMed Central PMCID: PMC5599153.
  61. 61. Rothnie KJ, Yan R, Smeeth L, Quint JK. Risk of myocardial infarction (MI) and death following MI in people with chronic obstructive pulmonary disease (COPD): a systematic review and meta-analysis. BMJ Open. 2015 Sep 11;5(9):e007824. Review. pmid:26362660; PubMed Central PMCID:PMC4567661.
  62. 62. Rothnie KJ, Smeeth L, Herrett E, Pearce N, Hemingway H, Wedzicha J, et al. Closing the mortality gap after a myocardial infarction in people with and without chronic obstructive pulmonary disease. Heart. 2015 Jul;101(14):1103–10. Epub 2015 Mar 12. pmid:25765553; PubMed Central PMCID: PMC4516011.
  63. 63. Stefan MS, Bannuru RR, Lessard D, Gore JM, Lindenauer PK, Goldberg RJ. The impact of COPD on management and outcomes of patients hospitalized with acute myocardial infarction: a 10-year retrospective observational study. Chest. 2012 Jun;141(6):1441–1448. Epub 2011 Dec 29. pmid:22207679; PubMed Central PMCID: PMC3367486.
  64. 64. Sederholm Lawesson S, Isaksson RM, Ericsson M, Ängerud K, Thylén I; SymTime Study Group. Gender disparities in first medical contact and delay in ST-elevation myocardial infarction: a prospective multicentre Swedish survey study. BMJ Open. 2018 May 3;8(5):e020211 pmid:29724738
  65. 65. Marzolini S, Blanchard C, Alter DA, Grace SL, Oh PI. Delays in Referral and Enrolment Are Associated With Mitigated Benefits of Cardiac Rehabilitation After Coronary Artery Bypass Surgery. Circ Cardiovasc Qual Outcomes. 2015 Nov;8(6):608–20. pmid:26555125
  66. 66. Ribas N, García-García C, Meroño O, Recasens L, Pérez-Fernández S, Bazán V, et al. Secondary prevention strategies after an acute ST-segment elevation myocardial infarction in the AMI code era: beyond myocardial mechanical reperfusion. BMC Cardiovasc Disord. 2017 Feb 7;17(1):54. pmid:28173757
  67. 67. Aylin P, Bottle A, Majeed A. Use of administrative data or clinical databases as predictors of risk of death in hospital: comparison of models. BMJ 2007, 334(7602):1044. pmid:17452389
  68. 68. Iezzoni LI. Assessing quality using administrative data. Ann Intern Med 1997;127:666–674. pmid:9382378
  69. 69. Belleudi V, Fusco D, Kirchmayer U, Agabiti N, Di Martino M, Narduzzi S, et al. Definition of patients treated with evidence based drugs in absence of prescribed daily doses: the example of acute myocardial infarction. Pharmacoepidemiol Drug Saf. 2011 Feb;20(2):169–76. pmid:21254288