European Journal of Cardiovascular Medicine

The clinical manifestations of coronary artery disease, including unstable angina pectoris, ST-elevation myocardial infarction (STEMI), and non-ST-elevation myocardial infarction (NSTEMI), are collectively referred to as acute coronary syndrome (ACS).[1] Cardiovascular disease remains the world's leading cause of mortality, with ischemic heart disease accounting for nearly half of these deaths.[2] With over seven million deaths and 129 million disability-adjusted life years annually, ischemic heart disease is the primary cause of death and disability-adjusted life year loss globally.[3]The age-standardized mortality rates from ischemic heart disease are higher in low- and middle-income countries than in high-income countries, indicating that more people are dying at younger ages, contributing to the ongoing global burden of ACS.[3] Despite major advancements in diagnosis and therapy, ACS remains associated with high morbidity and mortality rates.[4] Patients with ACS account for approximately 1.5 million hospital discharges annually, and 18% of men and 23% of women over 40 die within a year of their first documented myocardial infarction.[4]

Because of the varied presentation and prognosis of ACS, efficient risk stratification techniques are essential to guide clinical decision-making and patient outcomes. Clinicians must accurately anticipate in-hospital mortality in ACS patients to choose the best treatment course, manage resources efficiently, and inform patients and families about prognosis. Risk stratification enables identification of high-risk patients who might benefit from more active interventions, as well as low-risk patients who could be appropriate for early discharge or less rigorous monitoring. The most frequently used and well-validated risk scoring systems are the Global Registry of Acute Coronary Events (GRACE) and Thrombolysis in Myocardial Infarction (TIMI) scores, with GRACE outperforming the others.[5]GRACE and TIMI risk scores are frequently used to predict prognosis in patients with ACS; in older patients, GRACE ratings are more accurate than TIMI scores.[6]The GRACE score, derived from a global registry, combines factors like age, heart rate, systolic blood pressure, creatinine, Killip class, cardiac arrest on admission, ST-segment deviation, and elevated cardiac biomarkers to predict 6-month mortality. With a C-statistic of 0.91, the GRACE score outperformed the TIMI score with a C-statistic of 0.69.[7]

However, even well-known scoring systems have drawbacks that could limit their usefulness, especially in resource-limited settings. The GRACE score necessitates numerous laboratory tests, such as cardiac biomarkers and electrocardiography, which might not be readily accessible in all medical facilities or could delay risk assessment.[8] Clinical examination-based scoring systems that can be quickly computed at the bedside without the need for additional investigations have been developed in response to the need for easier, more practical risk assessment instruments. Among these, hemodynamic metrics like the modified shock index (MSI) and shock index (SI) have shown promise as mortality predictors in ACS patients.[8] The shock index, calculated as the ratio of heart rate to systolic blood pressure, reflects the balance between cardiac output and vascular resistance and can predict severe adverse cardiac events and death in ACS.[9] The PADjadjaran Mortality in Acute Coronary Syndrome (PADMA) scoring system was created by Pramudyo and associates in response to the demand for practical risk assessment instruments.[1,2] Because it only requires patient history and clinical examination upon admission, this scoring method is simple and comparable to the GRACE scoring system, and it can be used during the first medical encounter in any healthcare facility. The initial PADMA score, created using multivariate regression analysis, included five independent mortality predictors: age, history of cerebrovascular illness, heart rate, shock index, and Killip class. A score of ≥5 on the PADMA scale, which ranged from 0 to 20, could predict all-cause death with 72.35% specificity and 82.78% sensitivity.[2] The area under the curve of the PADMA Score was significantly greater than the risk scores for Canada Acute Coronary Syndrome and Portuguese Registry of Acute Coronary Syndromes, whereas the difference between the PADMA and GRACE scores was negligible.[2]

Study design and patient selection

This cross-sectional observational study was conducted in the Department of Medicine at B.L.D.E. (Deemed to be University) Shri B.M. Patil Medical College Hospital and Research Centre, Vijayapura, Karnataka-586103. The research study spanned 18 months from March 2024 to December 2025. Based on anticipated sensitivity and specificity of the PADMA Score in ACS patients of 82% and 72% respectively, considering the prevalence at 7%, with a precision of 1% and 95% confidence interval, the required sample size was calculated to be 84 patients. All acute coronary syndrome patients aged >18 years hospitalized at the institution were included, while patients with age <18 years, valvular heart disease, congenital heart disease, and cardiomyopathy were excluded.

Eligible patients who satisfied the inclusion and exclusion criteria were enrolled following acquisition of informed consent from the patients or their legal guardians. Data were collected systematically using a pre-made proforma that recorded specific information regarding patient medical history, clinical examination results, and pertinent investigations. All enrolled patients were scored using the PADMA scoring system according to their clinical presentation and history at admission.

Age (Years):

• <65 years: 0 points (reference)
• 65-75 years: 4 points
• >75 years: 5 points

History of Cerebrovascular Disease: 2 points

Heart Rate (Beats per minute):

• ≤100 bpm: 0 points (reference)
• >100 bpm: 2 points

Shock Index:

• ≤0.70: 0 points (reference)
• 0.71-1.00: 3 points
• 1.00: 4 points

Killip Class:

• Class 1: 0 points (reference)
• Class 2: 1 point
• Class 3: 4 points
• Class 4: 7 points

The overall PADMA score ranged from 0 to 20, with accompanying mortality risk stratification classified as low risk (score 0, probability <3.0%), intermediate risk (scores 1-4, probability 3.0-6.8%), and high risk (scores 5-20, probability >6.8%).

As part of routine clinical care, all patients received standardized testing including electrocardiography, 2D echocardiography, complete blood counts, liver and renal function tests, erythrocyte sedimentation rate, and Troponin-I measures. The primary outcome measure was in-hospital mortality, defined as death occurring during the index hospitalization for acute coronary syndrome. Patient outcomes were monitored throughout their hospital stay until discharge or death, with every death documented and confirmed using death certificates and hospital records.

Statistical analyses

Data were entered into a Microsoft Excel spreadsheet and analyzed using Statistical Package for Social Sciences (SPSS) Version 26. Results were displayed as means (medians) together with relevant diagrams, counts, percentages, and standard deviations. The ANOVA test was used to compare regularly distributed continuous variables between groups, whereas the Kruskal-Wallis test was used for non-normally distributed variables. The Chi-square test was used to compare categorical variables. Logistic regression analysis was employed to find independent factors and assess statistical significance. Receiver Operating Characteristic (ROC) analysis was used to compute the Area Under the Curve (AUC) for outcome prediction. The PADMA scoring system's diagnostic performance was assessed by calculating sensitivity, specificity, positive predictive value, and negative predictive value. All statistical tests were two-tailed, and a p-value of less than 0.05 was deemed statistically significant. Ethical clearance was acquired from the Medical Research Ethics Committee , and written informed consent was obtained from each participant or their legal guardian prior to participation

The present study included 84 ACS patients to evaluate in-hospital mortality using the PADMA scoring system. The study population comprised 53 males (63.1%) and 31 females (36.9%) with a mean age of 63.63 ± 12.944 years. Age distribution showed 16 patients (19%) in the 30-50 years category, 42 patients (50%) in the 51-70 years category, and 26 patients (31%) in the 71-90 years category. NSTEMI was slightly more common (54.8%, n=46) than STEMI (45.2%, n=38). Regarding comorbidities, 22 patients (26.2%) had hypertension and 21 patients (25%) had diabetes mellitus. Only 2 patients (2.4%) had a history of cerebrovascular disease. The overall in-hospital mortality rate was 14.3% (n=12), with 72 patients (85.7%) surviving to hospital discharge.

Table 1 presents the baseline demographics and clinical characteristics of the study population, demonstrating that the cohort had a mean age of 63.63 years with male predominance and slightly higher prevalence of NSTEMI compared to STEMI. The overall mortality rate of 14.3% is comparable to mortality rates reported in other South Asian populations.

Table 1: Baseline Demographics and Clinical Characteristics (n=84)

Table 2 describes the hemodynamic parameters and risk stratification distribution, showing that the majority of patients (69%) fell into the high-risk category based on PADMA scoring. The distribution of shock index categories and Killip classification indicates a substantial proportion of patients with hemodynamic compromise and clinical heart failure at presentation.

Table 2: Hemodynamic Parameters and Risk Stratification (n=84)

Table 3 provides a comparison of various risk scoring systems applied to the same population, demonstrating that 40.5% of patients were classified as high-risk by GRACE score, while the majority fell into intermediate risk categories by C-ACS and ProACS scores. This table facilitates direct comparison of different scoring methodologies in the same cohort.

Table 3: Comparison of Risk Scoring Systems (n=84)

Table 4 presents the critical clinical predictors of in-hospital mortality and ROC curve analysis results. Female gender showed significant association with mortality (Table 4), with 66.7% of non-survivors being female despite females comprising only 36.9% of the total cohort (p=0.021), suggesting gender-based disparities in outcomes. Killip class III-IV demonstrated strong predictive value (Table 4), with 83.3% of non-survivors presenting in advanced heart failure classes compared to 37.5% of survivors (p=0.001). The mean shock index was significantly elevated in non-survivors (Table 4), indicating the importance of hemodynamic parameters in prognostication.

Table 4: Clinical Predictors of In-Hospital Mortality

The ROC analysis(Table 5) demonstrates that PADMA score achieved an AUC of 0.825 with high sensitivity (91.7%) but moderate specificity (66.7%), performing comparably to GRACE score (AUC 0.837) and superior to C-ACS (AUC 0.751) and ProACS (AUC 0.694) scores.

Table 5 : ROC curve analysis

Graph 1 : ROC curve analysis

Risk stratification of patients with acute coronary syndrome remains a major challenge in modern cardiovascular medicine, particularly in resource-limited settings where rapid and accurate prognostic evaluation can significantly impact clinical decision-making and resource allocation. Our study assessed the performance of the PADMA scoring system for predicting in-hospital mortality in ACS patients at our institution, demonstrating its utility as a simple bedside clinical tool that does not require laboratory testing or complex calculations. Our findings showed that the PADMA score demonstrated good discriminatory power with an area under the curve of 0.825, sensitivity of 91.7%, and specificity of 66.7% at the optimal cutoff of 8.5, performing comparably to more established risk scores including GRACE (AUC 0.837), C-ACS (AUC 0.751), and ProACS (AUC 0.694). The study population's demographics closely matched those observed in other ACS registries from Southeast Asian and Indian subcontinent communities, with a mean age of 63.63 years and male predominance of 63.1%, noticeably younger than Western cohorts where mean age typically exceeds 70 years.[10] Our study revealed significant associations between mortality and female gender (66.7% of non-survivors were female, p=0.021), consistent with numerous studies showing worse outcomes for women presenting with ACS.[11] The PADMA score proved to be a reliable indicator of in-hospital mortality, with mean scores of 11.67 ± 3.80 for non-survivors versus 6.57 ± 3.90 for survivors (p<0.001), showing remarkable agreement with initial validation studies by Bernardus et al. who achieved 67.92% sensitivity and 84.01% specificity for predicting all-cause death in 1504 ACS patients.[12] The simplicity of the PADMA score and its equivalency to the GRACE scoring system were highlighted in the original study by Pramudyo et al., who noted the score's special usefulness in situations where rapid laboratory results are not available.[13]

The GRACE score, regarded as the gold standard for ACS risk stratification, performed slightly better in our analysis with an AUC of 0.837 compared to PADMA's 0.825, though this difference was not statistically significant. Our results are consistent with several GRACE score validation studies conducted on various populations.[14] The mean GRACE score for our non-survivors was 166.75 ± 28.71, while survivors had a mean score of 126.25 ± 28.68 (p<0.001), with 75% of non-survivors falling into the high-risk category (score >140). These findings align with international data, as a meta-analysis examining GRACE score performance across multiple studies demonstrated strong discrimination with pooled C-statistics of 0.86 for in-hospital mortality and 0.80 for 6-month mortality.[15] However, the GRACE score's bedside applicability is limited by its complexity, including eight factors with laboratory data and requiring a computer or calculator for score calculation. The shock index proved to be a strong predictor of mortality in our investigation, with non-survivors having significantly higher values (1.06 ± 0.41) compared to survivors (0.76 ± 0.22, p<0.001), consistent with mounting evidence that shock index is a simple yet reliable prognostic indicator for ACS.[16] The Killip classification showed substantial correlation with mortality in our cohort, with 83.3% of non-survivors presenting in Killip classes III-IV compared to 37.5% of survivors (p=0.001), demonstrating the significance of clinical heart failure at presentation for prognosis.[17] The C-ACS score performed moderately in our group with an AUC of 0.751, sensitivity of 50%, and specificity of 91.7%, somewhat lower than initial derivation and validation studies which reported excellent negative predictive values.[18] The ProACS score had the lowest discriminatory performance with an AUC of 0.694, possibly due to differences in Portuguese and Indian healthcare systems, patient demographics, and treatment practices.[19]

Our study's substantial correlation between female gender and mortality (p=0.021) warrants thorough analysis, with women comprising 66.7% of non-survivors despite making up only 36.9% of the entire cohort, potentially indicating undertreatment, delayed presentation, or inherent biological disparities in ACS pathophysiology and treatment response.[20] The PADMA score's implementation may enhance ACS care delivery in several ways: first, it allows instantaneous risk classification at initial medical encounter whether in emergency room, ambulance, or general care setting; second, it assists in determining which patients need immediate critical care versus those better suited for step-down or regular ward management; third, its simplicity facilitates use by healthcare professionals with varying levels of experience, crucial in situations with specialist shortages.[21]

Study limitations

It includes the single-center design limiting generalizability, relatively small sample size with only 12 mortality events restricting statistical power, lack of long-term follow-up preventing assessment of post-discharge outcomes, and absence of evaluation for newer imaging modalities or biomarkers that could improve risk prediction.[22]

Study strengths

It includes  prospective data collection ensuring accuracy and completeness, head-to-head comparison of multiple risk scores in the same population eliminating confounding from population differences, and focus on a South Asian population where validated risk prediction tools are especially needed.[23]

Future research directions include multicenter studies to validate PADMA score performance in diverse healthcare settings, development of population-specific modifications to current scores, integration of point-of-care biomarkers when available, implementation studies assessing systematic PADMA score use impacts on clinical outcomes and resource utilization, development of mobile applications or electronic health record integration, and long-term follow-up studies evaluating capacity to forecast post-discharge events.[24]

The present study demonstrates that the PADMA scoring system can accurately predict in-hospital mortality in  acute coronary syndrome patients. The PADMA score exhibited robust discriminatory performance with an area under the curve of 0.825, comparable to the internationally validated GRACE score (AUC 0.837), while requiring only clinical examination parameters without laboratory investigations or complex calculations. The high sensitivity of 91.7% at the recommended cutoff of 8.5 makes the PADMA score a powerful screening tool for identifying high-risk patients requiring immediate intensive therapy, potentially preventing unfavorable consequences through timely intervention. While the GRACE score performed slightly better than existing risk ratings, the PADMA score has practical advantages, especially in resource-limited situations where laboratory findings may not be immediately accessible. Major associations between mortality and critical clinical indicators such as female gender, higher Killip class, and raised shock index provide useful insights into mechanisms causing unfavorable outcomes in our patient population and suggest targeted interventions. The PADMA score could improve ACS patient triage and management in Indian emergency rooms and other healthcare settings, with its simplicity enabling paramedics, emergency physicians, and cardiologists to utilize it for standardized risk stratification.

Funding: Nil

Conflict of interest : Nil

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