Abstract
Background
Acute myocardial infarction (AMI) remains a leading cause of mortality in low- and middle-income countries, where delayed reperfusion worsens outcomes. Evidence on the long-term operation of multidisciplinary AMI programs in these settings is limited. We aimed to describe the five-year performance of a multidisciplinary AMI program in quality-of-care indicators and clinical outcomes.
Methods
Prospective cohort of adults with type 1 AMI treated between 2021 and 2025 in an internationally multidisciplinary AMI Center of Excellence, integrating standardized reperfusion pathways, multidisciplinary inpatient care, cardiac rehabilitation, and structured post-discharge follow-up. Indicators were compared across years; multivariable logistic regression identified factors associated with in-hospital mortality.
Results
Of 3581 patients, 131 (3.7%) died in hospital. Non-survivors more often had STEMI (54.2% vs 42.2%; P = 0.007), Killip class III–IV (52.2% vs 7.9%), and higher TIMI and GRACE scores (all P < 0.001). Killip class was independently associated with mortality, rising stepwise from class II (aOR 3.81; 95%CI 2.00–7.25) to class IV (aOR 35.81; 95%CI 19.54–65.65); AUC 0.789. Compared with 2021, adjusted odds of in-hospital mortality were lower in 2025 (aOR 0.34; 95%CI 0.14–0.84; P = 0.019). Nursing education adherence (92.0% to 98.9%) and timely phase I cardiac rehabilitation (74.6% to 96.7%) were higher in later years, and ECG acquisition time was shorter (all P < 0.001). Unadjusted in-hospital mortality did not differ significantly across years (P = 0.287), and door-to-balloon time remained stable (P = 0.118).
Conclusion
The program sustained high adherence to quality indicators and stable reperfusion metrics, whereas post-discharge follow-up completion declined. These findings do not establish causal effects.
Keywords: Myocardial infarction, ST elevation myocardial infarction, Non-ST elevated myocardial infarction, Percutaneous coronary intervention, Continuity of patient care
1. Introduction
Acute myocardial infarction (AMI) remains a leading cause of morbidity and mortality worldwide and accounts for a substantial proportion of cardiovascular deaths and healthcare utilization. Despite advances in pharmacological treatment and coronary revascularization, AMI continues to impose a disproportionate burden on low- and middle-income countries, where diagnostic delays, limited access to timely reperfusion, and fragmented healthcare systems remain major barriers to optimal care delivery [[1], [2], [3]]. In patients with ST-segment elevation myocardial infarction (STEMI), early restoration of coronary blood flow through primary percutaneous coronary intervention (PCI) is associated with lower risks of mortality, heart failure, and major cardiovascular complications, making timely reperfusion a key determinant of prognosis [4,5].
Contemporary international guidelines emphasize the development of integrated regional systems of care to facilitate rapid diagnosis, early activation of reperfusion pathways, and timely access to PCI within recommended door-to-device intervals [6,7]. In this context, specialized AMI centers and coordinated STEMI networks have emerged as organizational models intended to optimize patient flow, reduce treatment delays, improve adherence to evidence-based therapies, and strengthen care delivery across the cardiovascular care continuum [8,9].
Beyond reperfusion, contemporary AMI management increasingly relies on multidisciplinary, patient-centered models that integrate emergency medicine, interventional cardiology, intensive care, cardiovascular surgery, cardiac rehabilitation, nursing, quality-improvement initiatives, and longitudinal follow-up. Performance measures such as door-to-balloon time, timely reperfusion, adherence to secondary prevention therapies, and continuity-of-care indicators are commonly used to assess the operation and quality of AMI systems of care [10]. Previous studies have reported that coordinated care pathways may contribute to improved treatment delivery, fewer complications, greater healthcare efficiency, and better adherence to quality indicators throughout the patient journey [4,11].
However, despite the expansion of AMI systems of care worldwide, evidence on the implementation and long-term performance of multidisciplinary AMI programs in Latin America remains limited, particularly in middle-income settings characterized by heterogeneous access to cardiovascular services Therefore, this study aimed to describe the longitudinal performance of a multidisciplinary Acute Myocardial Infarction program regarding quality-of-care indicators and clinical outcomes over a five-year period.
2. Methods
2.1. Study design and setting
We conducted a prospective cohort study including consecutive adult patients (≥18 years) diagnosed with type 1 acute myocardial infarction (AMI) between January 2021 and December 2025 at a tertiary cardiovascular referral center in Latin America. The study was conducted within the Acute Myocardial Infarction Center of Excellence (AMICE), an internationally certified multidisciplinary program established in 2021 to standardize AMI management through evidence-based protocols and integrated cardiovascular care pathways. No preimplementation cohort or concurrent comparison group was included; therefore, the analyses were intended to describe program performance rather than estimate its causal or comparative effectiveness.
2.2. Acute myocardial infarction Center of Excellence
The AMICE program was structured as a multidisciplinary model. The center operates through coordinated clinical pathways designed to facilitate early diagnosis, timely reperfusion strategies, inpatient management, discharge planning, cardiovascular rehabilitation, and longitudinal follow-up after hospital discharge.
The care pathway begins at hospital admission through the emergency department, or direct transfer to the hemodynamics department for patients with STEMI eligible for primary PCI. During hospitalization, patients undergo standardized risk stratification, specialist evaluation, early angiography when indicated, guideline-directed medical therapy, structured education, and multidisciplinary inpatient management. After discharge, all patients are integrated into a structured longitudinal follow-up program including telephone assessments at 72 h, 2 months, 6 months, and 1 year, as well as an in-person cardiology visit during the first month after discharge. A detailed overview of the AMICE cycle of care is presented in Supplementary Fig. 1.
Supplementary Fig. 2 shows the multidisciplinary structure and integrated care pathway of the AMICE. Following AMI identification, the program activates coordinated care involving cardiology, nursing, psychology, social work, nutrition, physiotherapy/cardiac rehabilitation, hemodynamics, and geriatrics. Through daily multidisciplinary coordination, the program aims to optimize reperfusion times, improve adherence to evidence-based therapies, strengthen continuity of care, and enhance clinical outcomes.
2.3. Study population
Patients with a confirmed diagnosis of type 1 AMI, including STEMI and NSTEMI, were eligible for inclusion. AMI diagnosis was established according to contemporary international guideline criteria based on clinical presentation, electrocardiographic findings, and cardiac biomarker elevation [12].
2.4. Data collection and follow-up
Clinical information was prospectively collected during hospitalization and outpatient follow-up using a structured REDCap-based registry integrated with the institutional electronic health record system [13,14]. Automated extraction and synchronization processes allowed the incorporation of relevant clinical, laboratory, procedural, and administrative data into the longitudinal registry, while trained research nurses performed prospective validation and completion of follow-up information.
Structured follow-up was conducted by the nursing team through standardized telephone assessments at 72 h, 2 months, 6 months, and 12 months after hospital discharge. Additionally, all patients were scheduled for an in-person cardiology follow-up visit one month after the index event as part of the integrated care pathway.
2.5. Variables and outcomes
Sociodemographic variables included age, sex, educational level, marital status, and area of residence. Clinical variables included cardiovascular risk factors, smoking status, body mass index, AMI subtype (STEMI/NSTEMI), Killip classification, GRACE and TIMI risk scores, reperfusion strategy, coronary intervention type, coronary artery bypass grafting (CABG), and in-hospital length of stay. Operational and longitudinal care indicators related to the multidisciplinary AMI program were also evaluated throughout follow-up.
Timely phase I cardiac rehabilitation was defined as the initiation of the inpatient rehabilitation assessment or intervention within 72 h after the index cardiovascular procedure. Time was calculated from surgical myocardial revascularization when performed, from PCI when surgical revascularization was not performed, or from hospital admission when neither procedure was applicable.
To characterize the regional referral network of the multidisciplinary AMI program, the municipalities from which patients were referred were identified, and referral distances to the tertiary cardiovascular center were estimated using geographic routing data obtained from Google Maps based on the municipality of origin and the location of the referral institution.
2.6. Data management and quality assurance
The registry incorporated automated validation processes, standardized data entry procedures, and periodic quality control assessments to ensure data completeness and consistency. Integration with institutional clinical information systems enabled continuous updating of clinical and operational indicators for monitoring and quality improvement purposes.
2.7. Statistical analysis
Continuous variables were described as medians and interquartile ranges (IQRs), after assessment of distribution using the Shapiro–Wilk test. Categorical variables were summarized as absolute frequencies and percentages. Baseline sociodemographic, clinical, and cardiovascular characteristics were compared according to in-hospital mortality status. The Mann–Whitney U test was used for continuous variables, and the chi-square test or Fisher's exact test was used for categorical variables, as appropriate.
A multivariable logistic regression model was fitted to identify factors independently associated with in-hospital mortality. Covariates were selected a priori based on clinical relevance and availability at presentation and included age, sex, AMI type (STEMI vs. NSTEMI), diabetes mellitus, calendar year and Killip class. Adjusted odds ratios (aORs) with 95% confidence intervals (CIs) were reported. The analysis was performed using complete cases. Model goodness of fit was assessed using the Hosmer–Lemeshow test, and discrimination was evaluated using the area under the receiver operating characteristic curve (AUC).
An exploratory geographic analysis was conducted among patients with STEMI admitted directly through the emergency department, treated with PCI, and without previous thrombolysis. Clinical characteristics, reperfusion intervals, and in-hospital mortality were compared according to residence within or outside the Bucaramanga metropolitan area. The metropolitan area included Bucaramanga, Floridablanca, Girón, and Piedecuesta. Continuous variables were compared using the Mann–Whitney U test, and categorical variables using Fisher's exact test. Analyses of reperfusion intervals were restricted to patients with documented balloon time.
Longitudinal changes in quality-of-care indicators and operational performance metrics across calendar years were evaluated from 2021 to 2025. Categorical indicators were compared using chi-square tests for trend or Fisher's exact test when expected cell counts were small. Continuous annual indicators, including ECG acquisition time, door-to-balloon time, and length of stay, were compared using the Kruskal–Wallis test. For post-discharge follow-up call completion indicators, percentages were calculated using patients discharged alive as the denominator. Missing or non-applicable data were not included in the denominator for each specific indicator and were reported when relevant. All tests were two-sided, and a p value < 0.05 was considered statistically significant. Statistical analyses were performed using Stata version 16.0 (StataCorp, College Station, TX, USA).
2.8. Ethical considerations
The study was approved by the Institutional Research Ethics Committee (approval number CEI-2024-07461-45) and conducted in accordance with the ethical principles of the Declaration of Helsinki, as well as national and international regulations governing research involving human subjects and the protection of personal data confidentiality.
3. Results
A total of 3581 patients with acute myocardial infarction were included (Supplementary Fig. 3), of whom 131 (3.7%) died during hospitalization. The median age was 66 years (IQR 58–74), and patients who died were significantly older than survivors (70 [63–77] vs. 66 [58–74] years; P < 0.001). Most patients were male (72.0%), had a stable partner (63.2%), and resided in urban areas (80.7%). Hypertension was the most prevalent comorbidity (65.1%), followed by diabetes mellitus (32.1%) and dyslipidemia (31.4%) (Table 1).
Table 1.
Baseline sociodemographic, clinical, and cardiovascular characteristics according to in-hospital mortality in patients with acute myocardial infarction.
| Variable | Total n = 3581 | Death n = 131 | Alive n = 3450 | P value |
|---|---|---|---|---|
| Age, median (IQR) | 66 (58–74) | 70 (63–77) | 66 (58–74) | <0.001 |
| Sex, n (%) | 0.143 | |||
| Female | 1001 (28.0) | 44 (33.6) | 957 (27.7) | |
| Male | 2580 (72.0) | 87 (66.4) | 2493 (72.3) | |
| Marital status, n (%) | 0.303 | |||
| Without stable partner | 1323 (37.0) | 54 (41.2) | 1269 (36.8) | |
| With stable partner | 2257 (63.0) | 77 (58.8) | 2180 (63.2) | |
| Unknown | 7 (0.2) | 0 (0.0) | 7 (0.2) | |
| Area of residence | 0.771 | |||
| Rural | 691 (19.3) | 24 (18.3) | 667 (19.3) | |
| Urban | 2888 (80.7) | 107 (81.7) | 2781 (80.7) | |
| Educational level | 0.233 | |||
| No education/Preschool | 487 (13.6) | 25 (19.1) | 462 (13.4) | |
| Primary | 1953 (54.5) | 68 (51.9) | 1885 (54.6) | |
| Secondary/High school | 768 (21.5) | 23 (17.6) | 745 (21.6) | |
| Higher education | 373 (10.4) | 15 (11.5) | 358 (10.4) | |
| Medical History, n (%) | ||||
| Hypertension | 2334 (65.1) | 80 (61.0) | 2254 (65.3) | 0.315 |
| Diabetes mellitus | 1152 (32.1) | 52 (39.7) | 1100 (31.8) | 0.060 |
| Dyslipidemia | 1126 (31.4) | 30 (22.9) | 1096 (31.7) | 0.032 |
| Coronary artery disease | 253 (7.0) | 5 (3.8) | 248 (7.2) | 0.139 |
| Tobacco use | 448 (12.51) | 13 (9.92) | 435 (12.6) | 0.362 |
| Alcohol consumption | 292 (8.15) | 4 (3.05) | 288 (8.4) | 0.030 |
| BMI category | 0.001 | |||
| Underweight | 363 (10.1) | 9 (6.9) | 354 (10.3) | |
| Normal weight | 1501 (41.9) | 75 (57.3) | 1426 (41.3) | |
| Overweight | 1193 (33.3) | 33 (25.2) | 1160 (33.6) | |
| Obesity I | 420 (11.7) | 9 (6.9) | 411 (11.9) | |
| Obesity II | 80 (2.2) | 2 (1.5) | 78 (2.3) | |
| Obesity III | 23 (0.6) | 3 (2.3) | 20 (0.6) | |
| Type of myocardial infarction | 0.007 | |||
| STEMI | 1529 (42.7) | 71 (54.2) | 1458 (42.2) | |
| NSTEMI | 2052 (57.3) | 60 (45.8) | 1992 (57.7) | |
| Killip classification | <0.001 | |||
| Class I | 2152 (79.1) | 28 (31.1) | 2124 (80.7) | |
| Class II | 313 (11.5) | 15 (16.6) | 298 (11.3) | |
| Class III | 172 (6.3) | 19 (21.1) | 153 (5.8) | |
| Class IV | 83 (3.0) | 28 (31.1) | 55 (2.1) | |
| Missing | 861 (24.0) | 41 (31.3) | 820 (23.8) | |
| Percutaneous coronary intervention (PCI) | 2653 (74.1) | 98 (74.8) | 2555 (74.0) | 0.847 |
| Coronary artery bypass grafting (CABG) | 735 (20.5) | 10 (7.6) | 725 (21.0) | <0.001 |
| Cardiac rehabilitation | 3259 (91.0) | 58 (44.3) | 3201 (92.8) | <0.001 |
| No | 179 (5.0) | 38 (29.0) | 141 (4.1) | |
| Unknown | 143 (4.0) | 35 (26.7) | 108 (3.1) | |
| TIMI score | 3 (2–4) | 4 (3–5) | 3 (2–4) | <0.001 |
| GRACE score | 117 (98–136) | 153 (124–193) | 116 (97–135) | <0.001 |
Values are presented as median (interquartile range [IQR]) for continuous variables and as number (percentage) for categorical variables. Percentages for the death and alive groups were calculated according to the total number of patients within each subgroup. Comparisons between groups were performed using the chi-square or Fisher's exact test for categorical variables and the Mann–Whitney U test for continuous variables, as appropriate. AMI, acute myocardial infarction; BMI, body mass index; CABG, coronary artery bypass grafting; GRACE, Global Registry of Acute Coronary Events; IQR, interquartile range; NSTEMI, non-ST-segment elevation myocardial infarction; PCI, percutaneous coronary intervention; STEMI, ST-segment elevation myocardial infarction; TIMI, Thrombolysis in Myocardial Infarction.
Compared with survivors, patients who died more frequently presented with STEMI (54.2% vs. 42.2%; P = 0.007), higher Killip class III–IV (52.2% vs. 7.9%; P < 0.001), and higher TIMI and GRACE risk scores (both P < 0.001). Dyslipidemia and alcohol consumption were less frequent among patients who died (P = 0.032 and P = 0.03, respectively). Most patients underwent percutaneous coronary intervention (74.1%), while coronary artery bypass grafting was less frequent among non-survivors (7.6% vs. 21.0%; P < 0.001). Cardiac rehabilitation was documented in 91.0% of patients, including 92.8% of survivors and 44.3% of non-survivors (P < 0.001) (Table 1).
Most patients originated from Bucaramanga (28.9%). Referrals were also received from distant regions including Ocaña, Arauca, Bogotá D.C., Villavicencio, and Cúcuta, with estimated transfer distances exceeding 400 km and transfer times of up to 12 h in some cases Table 2.
Table 2.
Characterization of the regional referral network of the acute myocardial infarction center of excellence according to municipality of origin.
| Municipality | Department | Referred Patients (%) | Approximate Distance to Healthcare center | Estimated Transfer Time |
|---|---|---|---|---|
| Bucaramanga | Santander | 28.9 | 0–10 km | 10–30 min |
| Ocaña | Norte de Santander | 5.6 | ∼230 km | 4–5 h |
| Barrancabermeja | Santander | 4.3 | ∼120 km | 2–3 h |
| San Gil | Santander | 3.7 | ∼95 km | 2–3 h |
| Girón | Santander | 2.7 | 10–15 km | 20–35 min |
| Floridablanca | Santander | 2.3 | 5–10 km | 15–30 min |
| Socorro | Santander | 2.3 | ∼120 km | 2–3 h |
| Bogotá D.C. | Bogotá | 2.1 | ∼400 km | 8–10 h |
| Piedecuesta | Santander | 2.1 | 15–20 km | 25–40 min |
| Cúcuta | Norte de Santander | 2.0 | ∼250 km | 5–6 h |
| Aguachica | Cesar | 1.9 | ∼300 km | 4–6 h |
| Arauca | Arauca | 1.7 | ∼410 km | 8–12 h |
| Lebrija | Santander | 1.4 | ∼20 km | 30–40 min |
| Vélez | Santander | 1.3 | ∼200 km | 4–5 h |
| Cimitarra | Santander | 1.0 | ∼200 km | 4–5 h |
| Tunja | Boyacá | 1.0 | ∼280 km | 5–6 h |
| Málaga | Santander | 0.9 | ∼150 km | 4–5 h |
| Villavicencio | Meta | 0.6 | ∼430 km | 10–12 h |
| Other municipalities | - | 34.2 | - | - |
km, kilometers; min, minutes; h, hours; AMI, acute myocardial infarction. Distribution of the main municipalities of origin of patients with acute myocardial infarction referred. Approximate distances and estimated transfer times were calculated using standard ground transportation routes through Google Maps®, considering the usual road travel from the urban center of each municipality to the tertiary referral cardiovascular center under average traffic and road accessibility conditions.
In an exploratory analysis restricted to patients with STEMI admitted directly through the emergency department and treated with PCI without previous thrombolysis, 87 patients resided within the metropolitan area and 18 outside it. Age, GRACE score, Killip class III–IV, and in-hospital mortality were similar between the two groups. Among patients with documented reperfusion intervals, symptom-to-door time was numerically longer among those residing outside the metropolitan area, whereas door-to-balloon and symptom-to-balloon times did not differ significantly between groups (Table S1).
NSTEMI remained the predominant clinical presentation throughout the study period, with no significant temporal changes in AMI subtype distribution (P = 0.214). In-hospital mortality remained relatively stable over the five years, ranging from 2.8% to 4.3% (P = 0.287). Prescription rates of evidence-based discharge medications remained consistently high throughout the study period. In contrast, completion of scheduled follow-up calls varied across calendar years. Among years with available data, follow-up completion was generally lower in 2024–2025 than in earlier periods, particularly for the 6- and 12-month assessments. (Table 3).
Table 3.
Longitudinal quality-of-care indicators and operational performance metrics of the multidisciplinary Acute Myocardial Infarction Center of Excellence (2021–2025).
| Variable | 2021 (n = 540) | 2022 (n = 779) | 2023 (n = 805) | 2024 (n = 757) | 2025 (n = 700) | P |
|---|---|---|---|---|---|---|
| Type AMI | ||||||
| NSTEMI, n (%) | 316 (58.5) | 467 (60.0) | 462 (57.4) | 410 (54.2) | 397 (56.7) | 0.214 |
| STEMI, n (%) | 224 (41.5) | 312 (40.1) | 343 (42.6) | 347 (45.8) | 303 (43.3) | |
| In-hospital mortality, n (%) | 21 (3.9) | 33 (4.2) | 35 (4.3) | 21 (2.8) | 21 (3.0) | 0.287 |
| Nursing education adherence, n (%) | 497 (92.0) | 729 (93.6) | 786 (97.6) | 734 (97.0) | 692 (98.9) | <0.001 |
| Smoking cessation counseling adherence, n (%) | 521 (96.4) | 745 (95.7) | 796 (98.9) | 757 (100) | 670 (95.7) | 0.002 |
| Timely initiation of phase I cardiac rehabilitation, n (%) | 403 (74.6) | 706 (90.7) | 744 (92.4) | 674 (89.0) | 677 (96.7) | <0.001 |
| ECG acquisition time, median (IQR), min | 3.5 (0–26.5) | 9.5 (0.5–21) | 9 (0–22) | 5.5 (1–16) | 1 (0–6) | <0.001 |
| Door-to-balloon time, median (IQR), min | 88 (76–136) | 82 (56–157) | 82.5 (57–96) | 94 (75–152) | 94 (58–183) | 0.118 |
| Discharge medications, n (%) | ||||||
| ACE inhibitors/ARBs | 526 (97.4) | 722 (92.7) | 774 (96.2) | 745 (98.4) | 694 (99.1) | <0.001 |
| Beta-blockers | 531 (98.4) | 770 (98.9) | 802 (99.6) | 743 (98.1) | 693 (99.0) | 0.241 |
| Aspirin | 535 (99.0) | 776 (99.6) | 803 (99.7) | 754 (99.6) | 700 (100) | 0.356 |
| P2Y12 inhibitors | 533 (98.7) | 772 (99.1) | 802 (99.6) | 746 (98.5) | 700 (100) | 0.487 |
| Statins | 537 (99.4) | 772 (99.1) | 797 (99.0) | 748 (98.8) | 700 (100) | 0.611 |
| Length of stay, median (IQR), days | 4 (2–9.5) | 4 (2–10) | 4 (2–10) | 4 (2–11) | 4 (2–12) | 0.729 |
| 30-day readmission, n (%) | 24 (4.4) | 62 (8.0) | 27 (3.4) | 13 (1.7) | 21 (3.0) | <0.001 |
| 72-h follow-up call completion, n (%) | — | — | 707 (91.8) | 589 (80.0) | 529 (77.9) | <0.001 |
| 2-month follow-up call completion, n (%) | — | 664 (89.0) | 737 (95.7) | 596 (81.0) | 550 (81.0) | <0.001 |
| 6-months follow-up call completion, n (%) | — | 624 (83.6) | 621 (80.6) | 448 (60.9) | 462 (68.0) | <0.001 |
| 1-year follow-up call completion, n (%) | — | 605 (81.1) | 605 (78.6) | 457 (62.1) | 477 (70.3) | <0.001 |
| Smoking cessation during follow-up, n (%) | — | — | 634 (78.8) | 466 (61.5) | 439 (62.7) | <0.001 |
Values are presented as median (interquartile range [IQR]) for continuous variables and as number (percentage) for categorical variables. Comparisons across study years were performed using the chi-square test for categorical variables and the Kruskal–Wallis test for continuous variables. For post-discharge follow-up call completion indicators, percentages were calculated using patients discharged alive as the denominator; the corresponding annual denominators were 519 in 2021, 746 in 2022, 770 in 2023, 736 in 2024, and 679 in 2025. ACE, angiotensin-converting enzyme; AMI, acute myocardial infarction; ARBs, angiotensin receptor blockers; ECG, electrocardiogram; IQR, interquartile range; NSTEMI, non-ST-segment elevation myocardial infarction; STEMI, ST-segment elevation myocardial infarction. Timely initiation was defined as phase I cardiac rehabilitation performed within 72 h after the index cardiovascular procedure or, when no procedure was performed, within 72 h after hospital admission.
Longitudinal changes were observed in several quality-of-care indicators across the study period (Table 3 and Fig. 1). Nursing education adherence and timely initiation of phase I cardiac rehabilitation were higher in later years, while ECG acquisition times were shorter. In contrast, post-discharge follow-up completion varied across years and time points. These comparisons were unadjusted and should be interpreted as descriptive temporal patterns. As shown in Table 3, ECG acquisition time showed an overall reduction over the study period, reaching a median of 1 min in 2025, whereas median door-to-balloon time remained below 95 min in each study year, without significant temporal variation (P = 0.118). Fig. 1 displays the annual trajectories of nursing education adherence, smoking cessation counseling, timely cardiac rehabilitation referral, and 30-day readmission, with readmission rates being lower during the later years of the study period.
Fig. 1.
Longitudinal trends in quality-of-care indicators of the multidisciplinary Acute Myocardial Infarction Center of Excellence (AMICE), 2021–2025.
Temporal evolution of selected quality-of-care indicators during AMICE implementation over the five-year study period. Separate panels display nursing education adherence (A), smoking cessation counseling adherence (B), timely initiation of phase I cardiac rehabilitation (C), and 30-day readmission (D). Because these indicators have different observed ranges, they are shown in separate panels with panel-specific y-axis scales to improve interpretability. P values indicate comparisons across calendar years.
In the multivariable logistic regression analysis, Killip class remained independently associated with in-hospital mortality. Compared with Killip class I, the adjusted odds of in-hospital death were higher for Killip class II (aOR 3.81, 95% CI 2.00–7.25; P < 0.001), Killip class III (aOR 8.70, 95% CI 4.70–16.10; P < 0.001), and Killip class IV (aOR 35.81, 95% CI 19.54–65.65; P < 0.001). Compared with 2021, the adjusted odds of mortality were lower in 2025 (aOR 0.34, 95% CI 0.14–0.84; P = 0.019), while no significant differences were observed for 2022–2024. The model showed acceptable goodness of fit (Hosmer–Lemeshow P = 0.749) and moderate-to-good discrimination (AUC = 0.789) Table 4.
Table 4.
Multivariable logistic regression analysis of factors associated with in-hospital mortality.
| Variable | Adjusted OR | 95% CI | P value |
|---|---|---|---|
| 2021 | Reference | — | — |
| 2022 vs 2021 | 0.86 | 0.44–1.69 | 0.669 |
| 2023 vs 2021 | 0.90 | 0.45–1.79 | 0.756 |
| 2024 vs 2021 | 0.59 | 0.26–1.34 | 0.207 |
| 2025 vs 2021 | 0.34 | 0.14–0.84 | 0.019 |
| Age, per year | 1.00 | 1.00–1.01 | 0.257 |
| Male sex | 0.75 | 0.46–1.22 | 0.242 |
| NSTEMI vs STEMI | 0.64 | 0.40–1.02 | 0.060 |
| Diabetes mellitus | 1.28 | 0.80–2.03 | 0.300 |
| Killip I | Reference | — | — |
| Killip II | 3.81 | 2.00–7.25 | <0.001 |
| Killip III | 8.70 | 4.70–16.10 | <0.001 |
| Killip IV | 35.81 | 19.54–65.65 | <0.001 |
Values are presented as adjusted odds ratios (aORs) with 95% confidence intervals (CIs). Killip class I and STEMI were used as reference categories. AUC, area under the receiver operating characteristic curve; AMI, acute myocardial infarction; NSTEMI, non-ST-segment elevation myocardial infarction; STEMI, ST-segment elevation myocardial infarction.
4. Discussion
In this five-year descriptive prospective cohort, patients managed within a multidisciplinary Acute Myocardial Infarction Center of Excellence showed high annual adherence to several evidence-based quality indicators and access to structured continuity-of-care strategies. Nursing education adherence, cardiac rehabilitation referral, and ECG acquisition time differed across calendar years, whereas in-hospital mortality and door-to-balloon time remained relatively stable. However, completion of scheduled post-discharge follow-up calls was lower in the later study years, particularly for the 6- and 12-month assessments, indicating that the availability of a structured follow-up pathway did not ensure sustained completion over time. The available data did not allow us to determine the reasons for this decline. These findings characterize the longitudinal operation of an integrated AMI care pathway in a middle-income setting but do not establish that the program produced the observed clinical outcomes.
Our results are consistent with previous international reports showing that coordinated AMI systems of care improve adherence to guideline-directed therapies, optimize reperfusion times, and strengthen healthcare quality indicators. Contemporary cardiovascular guidelines emphasize the importance of regionalized STEMI networks, rapid diagnostic pathways, and multidisciplinary coordination to reduce delays in reperfusion and improve outcomes after AMI. In our cohort, median door-to-balloon times remained below 95 min throughout the study period, while ECG acquisition times were shorter in later years. These findings document changes in recorded operational indicators over time; however, they cannot be attributed specifically to AMICE because secular trends, changes in case mix, data completeness, institutional processes, or other concurrent interventions may also have contributed [15,16].
Importantly, evidence describing the longitudinal implementation and operational performance of multidisciplinary AMI programs in Latin America remains limited. Most available data originate from high-income healthcare systems with greater organizational resources and more homogeneous access to cardiovascular care. In contrast, our study provides real-world evidence from a tertiary referral center in a middle-income country, where healthcare fragmentation, referral barriers, and regional disparities frequently challenge timely AMI management. The integrated model evaluated in this study incorporated not only acute reperfusion strategies, but also psychosocial support, rehabilitation, nursing-led education, and longitudinal follow-up, thereby expanding the focus of care beyond the acute hospitalization period [17,18].
The exploratory geographic analysis showed a numerically longer symptom-to-door interval among patients residing outside the metropolitan area, although the difference was not statistically significant. In contrast, door-to-balloon time after arrival at the PCI-capable center was similar between geographic groups. These findings may suggest that relevant delays occurred predominantly before hospital arrival; however, the small number of patients residing outside the metropolitan area and the absence of actual transport and interhospital transfer times limit this interpretation. Municipality of residence should therefore be considered a geographic proxy rather than a direct measure of referral distance or transfer duration.
One relevant observation was the higher frequency of timely cardiac rehabilitation referral in later years, while adherence to evidence-based discharge medications remained consistently above 92% annually. Participation in rehabilitation, secondary prevention therapies, and structured follow-up have been associated with favorable cardiovascular outcomes in previous studies. In the present cohort, however, these variables were evaluated as program performance indicators, and their independent effects on mortality, readmission, functional recovery, or long-term cardiovascular risk were not assessed. Smoking cessation counseling and nursing-led follow-up were components of the care pathway, but the current design does not allow their effects on subsequent outcomes to be determined [19].
In contrast to the consistently high adherence observed for several in-hospital indicators, post-discharge follow-up performance was heterogeneous. Completion rates for scheduled calls were lower in later years at several follow-up time points, although partial recovery was observed in 2025 for the 6- and 12-month assessments. These patterns may be related to changes in patient volume, operational capacity, registry completeness, or documentation practices; however, these potential explanations were not directly evaluated. Therefore, the follow-up findings should be interpreted as operational performance measures rather than evidence of sustained continuity or program effectiveness.
Although in-hospital mortality remained relatively stable during the study period, mortality rates observed in this cohort were comparatively low considering the high proportion of STEMI patients and the complexity of a tertiary cardiovascular referral population. Non-survivors presented with higher Killip class, higher TIMI and GRACE scores, and a greater frequency of STEMI, findings that are consistent with well-established predictors of poor prognosis after AMI [20,21]. In the adjusted analysis, Killip class remained the strongest factor independently associated with in-hospital mortality, with a marked stepwise increase in risk from Killip II to Killip IV. This finding reinforces the continued prognostic relevance of bedside hemodynamic assessment in contemporary AMI care [22]. The relatively stable mortality observed throughout the study period occurred in the context of multidisciplinary coordination, standardized evidence-based treatment protocols, and access to invasive management strategies; however, the contribution of these components cannot be determined from the present study design. The observational design of the study precludes establishing causal relationships between the multidisciplinary program and mortality reduction [18,23].
This study has several strengths. First, it included a large prospective cohort with five years of prospective data collection within a structured institutional registry integrated with electronic health records. Second, the study evaluated not only traditional reperfusion metrics but also operational quality indicators and longitudinal continuity-of-care measures rarely reported in Latin American AMI studies. Third, the multidisciplinary design of the program allowed the integration of cardiology, nursing, psychology, nutrition, physiotherapy, social work, and rehabilitation into a coordinated patient-centered model of care.
Some limitations should be acknowledged. This was a single-center observational study conducted in a tertiary referral institution, which may limit the external generalizability of the findings. Most importantly, the absence of a preimplementation baseline group or a concurrent comparator prevents estimation of the effect of AMICE on mortality, readmission, reperfusion metrics, or other clinical outcomes. Differences across calendar years may reflect secular trends, changes in case mix, documentation practices, data completeness, institutional resources, or interventions occurring concurrently with the program. Some longitudinal indicators were unavailable during the initial years because several follow-up strategies were progressively incorporated into the registry. Residual confounding also cannot be excluded, and the study was not designed to evaluate long-term cardiovascular outcomes beyond the structured follow-up period. Therefore, the results should be interpreted as a description of program operation and performance rather than evidence of comparative or causal effectiveness. Future multicenter controlled studies are needed to evaluate the effects of multidisciplinary AMI programs on mortality, recurrent cardiovascular events, healthcare costs, and patient-reported outcomes.
5. Conclusion
Over five years, the multidisciplinary Acute Myocardial Infarction Center of Excellence showed consistently high adherence to evidence-based quality indicators, stable in-hospital mortality and door-to-balloon times and shorter ECG acquisition times in later years. The program provided a structured post-discharge follow-up pathway; however, completion of scheduled follow-up calls was lower during the later study years, particularly at the 6- and 12-month assessments. These findings describe the longitudinal performance of an integrated AMI care program in a tertiary cardiovascular center in a middle-income country and may inform similar quality-improvement initiatives in Latin America.
CRediT authorship contribution statement
Beatriz Mantilla Pérez: Writing – review & editing, Writing – original draft, Investigation, Data curation, Conceptualization. Alexandra Hurtado-Ortiz: Writing – review & editing, Writing – original draft, Methodology, Formal analysis, Data curation. Paula Andrea Gualteros Cely: Writing – review & editing, Writing – original draft, Investigation. Juliet López Serrano: Writing – review & editing, Writing – original draft, Investigation, Data curation, Conceptualization. Anderson Bermon Angarita: Writing – review & editing, Writing – original draft, Data curation. Edgar Fabián Manrique-Hernández: Writing – review & editing, Writing – original draft, Methodology, Formal analysis. Maricel Licht-Ardila: Writing – review & editing, Writing – original draft, Methodology, Formal analysis. José Federico Saiibi: Writing – review & editing, Writing – original draft, Data curation.
Data availability
The data underlying this article will be shared on reasonable request to the corresponding author, subject to institutional ethical approval.
Declaration of generative AI and AI-assisted technologies in the manuscript preparation process
During the preparation of this work the author(s) used ChatGPT (OpenAI, San Francisco, CA, USA) in order to support language refinement and improve manuscript readability. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the published article.
Funding
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Footnotes
Supplementary data to this article can be found online at https://doi.org/10.1016/j.ijcrp.2026.200711.
Contributor Information
Beatriz Mantilla Pérez, Email: beatrizmantilla@fcv.org.
Alexandra Hurtado-Ortiz, Email: alexandrajhop@gmail.com, alexandrahurtado@fcv.org.
Paula Andrea Gualteros Cely, Email: pgualterosc@unbosque.edu.co.
Juliet López Serrano, Email: Julietvanessa14@gmail.com.
Anderson Bermon Angarita, Email: andebermon@gmail.com.
Edgar Fabián Manrique-Hernández, Email: fabianmh1993@gmail.com.
Maricel Licht-Ardila, Email: mlichtar@gmail.com.
José Federico Saiibi, Email: josesaaibi@fcv.org.
Appendix A. Supplementary data
The following are the Supplementary data to this article:
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Supplementary Materials
Data Availability Statement
The data underlying this article will be shared on reasonable request to the corresponding author, subject to institutional ethical approval.