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Objectives In ST-segment elevation myocardial infarction (STEMI), time delay between symptom onset and treatment is critical to improve outcome. The expected transport delay between patient location and percutaneous coronary intervention (PCI) centre is paramount for choosing the adequate reperfusion therapy. The “Centro” region of Portugal has heterogeneity in PCI assess due to geographical reasons. We aimed to explore time delays between regions using process mining tools. Methods Retrospective observational analysis of patients with STEMI from the Portuguese Registry of Acute Coronary Syndromes. We collected information on geographical area of symptom onset, reperfusion option, and in-hospital mortality. We built a national and a regional patient's flow models by using a process mining methodology based on parallel activity-based log inference algorithm. Results Totally, 8956 patients (75% male, 48% from 51 to 70 years) were included in the national model. Most patients (73%) had primary PCI, with the median time between admission and treatment <120 minutes in every region; “Centro” had the longest delay. In the regional model corresponding to the “Centro” region of Portugal divided by districts, only 61% had primary PCI, with “Guarda” (05:04) and “Castelo Branco” (06:50) showing longer delays between diagnosis and reperfusion than “Coimbra” (01:19). For both models, in-hospital mortality was higher for those without reperfusion therapy compared to PCI and fibrinolysis. Conclusion Process mining tools help to understand referencing networks visually, easily highlighting its inefficiencies and potential needs for improvement. A new PCI centre in the “Centro” region is critical to offer timely first-line treatment to their population.

Introduction The expected delay of transport between patient location and percutaneous coronary intervention (PCI) centre is paramount for choosing the adequate reperfusion therapy in ST-segment elevation myocardial infarction (STEMI). The central region of Portugal has heterogeneity in PCI assess due to geographical reasons. However, this data is usually presented numerically without providing a visual distribution of patients. Purpose We aimed to analyse the impact of distance to PCI centres on mortality in patients with STEMI through visual maps of patients' flow by using an experimental process mining tool, integrated in EIT Health's project PATHWAYS. Methods Using the Portuguese Registry of Acute Coronary Syndromes (ProACS), we retrospectively assessed patients with an established diagnosis of STEMI, geographical presentation specified, reperfusion option identified (PCI, fibrinolysis or no reperfusion), short-term outcomes defined as discharge or in-hospital death. With the 2 317 patients that fulfilled the criteria, we used a process mining tool to build national and regional models that represent the flow of patients in a healthcare system, enhancing differences between groups. Results Colour gradient in nodes and arrows changes from green to red, with green representing a lower number of patients as opposed to red. In the national model, most patients from all regions had PCI. Mortality was similar between PCI and fibrinolysis groups (4%) but higher in those without reperfusion (9%). In the central region model, one third of the patients were more than 120 minutes away from a PCI centre. Despite that, almost one third of these patients had PCI instead of fibrinolysis. In this model, fibrinolytic therapy had higher in-hospital survival rate than PCI (98% vs. 94%). Overall mortality was higher in the central model compared with the national model (6.92% vs. 5%). Central region had less PCI (53% vs. 73%), more fibrinolysis (15% vs. 7%) and more patients with no reperfusion (32% vs. 20%). Conclusion In the ProACS registry, mortality was higher in the central region compared with national data. Even though global interpretation of these findings is limited by underrepresentation from certain central areas, process mining offers an easily understandable view of patients flow. With its statistical upgrade and continuous development, this tool will facilitate the analysis of big data and comparison between groups. Funding Acknowledgement Type of funding source: Public grant(s) – EU funding. Main funding source(s): EIT Health

Funding Acknowledgements Type of funding sources: None. Background Elevated plasma lipoprotein(a) [Lp(a)] concentrations are associated with an increased risk of atherosclerotic cardiovascular disease and its role in risk categorizing was recognized in the new ESC guidelines for the management of dyslipidaemias. We investigated 1) the association between baseline Lp(a) levels and incident long-term cardiovascular (CV) events and 2) its relationship with type 2 diabetes mellitus (T2DM) in a Southern European population. Methods We retrospectively assessed baseline Lp(a) concentrations in a total of 499 patients of a primary prevention cohort followed at the Lipidology Clinic of our hospital, with a median follow-up time of 15 (IQR 12-17) years. Lp(a) was analysed as a continuous variable, as a categorical variable with a 180mg/dL cut-off and by quartiles. We collected data on major CV events (CV death, myocardial infarction, stroke) as a composite outcome. Cox proportional hazard regression analyses were used to estimate hazard ratios (HR) and 95% confidence interval (CI). Results Mean age was 48.30 ± 14.41 years and 61.70% were male (n = 499). Median Lp(a) was 36.60 (IQR 0-396) mg/dL and 12.4% of patients had very high Lp(a) (≥180mg/dL); T2DM prevalence was 13.60%. The composite outcome incidence was 10%. At the baseline, individuals with T2DM had lower Lp(a) levels (11.85 IQR 3-330 mg/dL vs. 46.40 IQR 0-396, p &lt; 0.01 mg/dL). There was a moderate inverse correlation between Lp(a) and HbA1c (r = -0.67, p &lt; 0.01) but no significant correlations with lipid profile (total, LDL or HDL), risk scores (SCORE or the ACC pooled cohort equation), age nor gender. We found no relationship between baseline Lp(a) quartiles and composite outcome’s incidence (age-, sex-, and diabetes-adjusted HR: 1.15, 95%CI: 0.71-1.87, p = 0.57) (Figure 1), neither with the individual CV endpoints. Exploratory analysis showed that patients on aspirin had lower Lp(a) levels (29.55 IQR 0-264 mg/dL vs. 63.60 IQR 1-396 mg/dL, p &lt; 0.01). Conclusion In a single centre cohort of a primary prevention southern European population, we did not find an association between Lp(a) levels and incident CV events in a 15-year median follow-up time.

Funding Acknowledgements Type of funding sources: None. Introduction The blood urea nitrogen-to-creatinine ratio (BUN/SCr) has been proposed as a prognostic marker in heart failure (HF). We aimed to evaluate whether BUN/SCr predicts mortality outcomes in a real-world Southern European population with decompensated chronic HF. Methods We retrospectively studied 1057 patients with chronic HF admitted to our emergency department between November 2016 and December 2017 with acute decompensation. We excluded patients with a GFR &lt;15mL/min/m2 or on dialysis. The incidence of cardiovascular (CV) and all-cause death was evaluated through multivariable logistic regression models and by Kaplan-Meyer survival curves. Results 1025 patients were included, median age 80 years (IQR 73-85), 52.4% male, mean LVEF 42.8 ± 12.7%, and mean GFR 57.2 ± 23.9 mL/min/m2. Mean BUN/SCr was 24.9 ± 8.2 and mean SBP was 139 ± 29mmHg (r=-0.17, p &lt; 0.001). After a median follow-up of 5 months (IQR 3-11 months), CV and all-cause death occurred in 8.0% and 21.6%, respectively. Mean BUN/SCr was higher in patients with fatal outcomes both for CV (31.3 vs. 24.3, p &lt; 0.001) and all-cause death (28.6 vs. 23.8, p &lt; 0.001). BUN/Scr was grouped by terciles: T1 (&lt;20.78), T2 (20.78-27.15), T3 (&gt;27.15). In the T3 group, the multivariable-adjusted  OR for CV and all-cause death was 5.43 (95% CI 2.20-13.37) and 2.72 (95% CI 1.66-4.46), respectively, compared to the T1 group. No significant differences between T1 and T2 groups. Conclusions BUN/SCr at admission predicts CV and all-cause death in patients with chronic HF after an episode of decompensation. BUN/SCr, as an easy-to-use tool, helps to identify those patients who benefit from tight monitoring both during hospitalization and after discharge. Abstract Figure_1