European Journal of Cardiovascular Medicine

Coronary artery disease (CAD) remains one of the leading causes of morbidity and mortality globally, with a rising burden in low- and middle-income countries like India. Early diagnosis and accurate assessment of the severity of CAD are crucial for timely intervention and reducing long-term cardiovascular risk. Among the diagnostic tools available, the Treadmill Test (TMT) and Coronary Angiography (CAG) are widely used to assess myocardial ischemia and coronary artery obstruction, respectively.

TMT is a non-invasive, cost-effective, and easily accessible diagnostic modality. It is commonly employed to evaluate the presence of inducible ischemia in individuals presenting with chest pain or other anginal equivalents. By analyzing ST-segment changes during exercise, clinicians can estimate the likelihood of significant coronary obstruction. However, the sensitivity and specificity of TMT vary across populations and risk profiles. A large-scale study involving 132 patients found that although TMT detected abnormalities in a majority of patients, only 47.2% had actual coronary artery disease on angiography, indicating limitations in its diagnostic accuracy ^[1].

Coronary Angiography, in contrast, is the gold standard for defining the anatomical extent and severity of CAD. It provides direct visualization of coronary arteries and allows for stratification into single, double, or triple vessel disease. However, it is invasive, costly, and not always accessible in resource-limited settings. Consequently, correlating non-invasive TMT findings with angiographic results has gained clinical relevance for predicting significant coronary disease and guiding patient management strategies.

Several Indian and regional studies have evaluated this correlation. In a study from Nepal involving 100 patients with positive TMT, 33% showed significant coronary disease on angiography, while 45% had normal coronaries. This emphasizes the variable predictive value of TMT, influenced by patient risk factors and the interpretation criteria used ^[2].

Further, subgroup analysis reveals differences between sexes. A study comparing male and female patients showed that TMT had a higher positive predictive value (PPV) in males (83%) compared to females (53%). Factors such as age and diabetes were significantly associated with increased probability of CAD among patients with a high-probability TMT ^[3].

In females specifically, the correlation between TMT and CAG is more complex. A study from Kerala analyzing 100 women undergoing both TMT and CAG found that the sensitivity and specificity of TMT were 61% and 69%, respectively. Despite moderate diagnostic power, the presence of diabetes and hypertension increased the likelihood of CAD among TMT-positive women ^[4].

A more targeted diagnostic clue comes from examining ST-segment elevation in lead aVR during TMT. One study highlighted that patients with >1 mm elevation in aVR had a 63% incidence of left main coronary artery (LMCA) disease on angiography, suggesting a highly specific predictive marker for severe CAD ^[5]. Similarly, another study found that ST-elevation in aVR correlated significantly with LMCA disease and triple vessel disease, further enhancing the prognostic value of this underutilized ECG lead ^[6].

Another dimension of diagnostic refinement is the use of Duke Treadmill Score (DTS) and Rate Pressure Product (RPP), which integrate exercise duration, ST-segment deviation, and hemodynamic response. A study conducted in Kerala found a strong negative correlation between DTS and angiographic severity as measured by SYNTAX score (r = -0.702), reinforcing that TMT-derived indices can quantify disease burden beyond binary outcomes ^[7].

Moreover, advanced imaging techniques such as two-dimensional speckle tracking echocardiography (2DSTE) have shown promise when combined with TMT. One Indian study revealed that the global longitudinal strain (GLS) measured via 2DSTE correlated significantly with angiographic lesion severity in TMT-positive females, with 94% sensitivity and 76% specificity, indicating its value as a non-invasive adjunct ^[8].

It is also important to recognize confounding factors. A case study reported that severe hypokalemia mimicked ischemic TMT changes, leading to a false positive result and unnecessary intervention. This highlights the necessity of clinical correlation and biochemical evaluation before drawing conclusions from a positive TMT ^[9].

Finally, there is emerging evidence for digital innovations. A novel Indian mobile application, “TMT Predict,” using machine learning on six clinical variables, showed up to 84% accuracy in predicting TMT outcomes and correlated reasonably well with coronary angiogram results. This could serve as an initial screening tool in resource-limited environments ^[10].

While TMT remains a valuable screening tool for CAD due to its affordability and accessibility, its correlation with angiographic findings varies significantly by patient profile, test interpretation, and associated clinical parameters. Strengthening its predictive power through scoring systems, lead aVR analysis, and modern adjuncts like strain imaging or machine learning models can improve diagnostic accuracy and guide appropriate use of invasive angiography.

The aim of this study was to assess the diagnostic value of treadmill test (TMT) in predicting coronary artery disease (CAD) by correlating its findings with coronary angiography (CAG) to determine its accuracy as a cost-effective screening tool.

1. Study Design

This was a cross-sectional observational study designed to assess the correlation between treadmill test (TMT) results and coronary angiography (CAG) findings in patients with suspected coronary artery disease (CAD). It aimed to evaluate the diagnostic accuracy of TMT in predicting the severity of CAD confirmed by CAG, without introducing any new interventions.

2. Study Setting

The study was conducted in the Department of Cardiology, J L N Medical College Ajmer, a tertiary care teaching hospital in India, equipped with facilities for both TMT and CAG. All diagnostic tests were performed by trained personnel under standard clinical protocols to ensure consistency and reliability.

3. Study Duration

The study was carried out over 12 months, from October 2024 to September 2025. This duration allowed for adequate patient recruitment and seasonal representation, and ensured completion of data collection, analysis, and interpretation.

4. Participants - Inclusion and Exclusion Criteria

Patients aged 30 years or above with suspected CAD and positive or borderline TMT who underwent CAG were included. Exclusion criteria were prior MI, revascularization, valvular disease, incomplete records, pregnancy, and serious systemic illnesses. TMT positive: >1mm ST depression below baseline or slow upsloping ST depression or ST elevation on exertion. TMT inconclusive: patients who failed to achieve 6 METs or who failed to achieve 85% of age-predicted maximum heart rate without ischemic responses in ECG. TMT negative: patient who completed their protocol, achieved target heart rate, without symptoms and ECG changes of is chaemia. ECG changes with rapid upsloping changes or ST depression < 1 mm was also considered as negative. Written informed consent was mandatory for all participants.

5. Study Sampling

Purposive sampling was used. Eligible patients undergoing both TMT and CAG during the study period were enrolled consecutively. This approach ensured practical feasibility and inclusion of clinically relevant cases for diagnostic correlation.

6. Study Sample Size

The study included 120 patients based on feasibility and prior similar studies. Only those with complete TMT and CAG data were considered. This sample size was adequate for assessing diagnostic correlation and statistical significance.

7. Study Groups

Participants were grouped based on CAG findings: Group A (normal coronaries), Group B (single or double vessel disease), and Group C (triple vessel or left main disease). TMT positivity levels were compared across these groups.

8. Study Parameters

Key parameters included TMT results, CAG findings, Duke Treadmill Score, and clinical risk factors such as age, gender, diabetes, hypertension, and smoking. These were used to assess correlation and diagnostic value.

9. Study Procedure

All patients underwent TMT using the Bruce protocol. Results were interpreted using Selzer’s criteria. CAG was done via standard techniques, and findings were classified based on the number and severity of vessel involvement.

10. Study Data Collection

Data were collected using a structured proforma. Clinical records, TMT reports, and CAG findings were reviewed and recorded. All data were anonymized and verified for completeness before analysis.

11. Data Analysis

Data were analyzed using SPSS version 25. Chi-square test and correlation coefficients were used. Sensitivity, specificity, positive and negative predictive values of TMT were calculated. A p-value <0.05 was considered statistically significant.

1. Demographic Profile of Study Participants

Most participants were middle-aged males, with high rates of diabetes and hypertension (Table 1).

Table 1: Demographic Profile of Study Participants (n=120)

2. Clinical Presentation at Time of Testing

Typical angina was the predominant presentation, followed by atypical chest pain (Table 2).

 Table 2: Clinical Presentation at Time of Testing (n=120)

3. TMT Positivity by Selzer’s Criteria

Most patients had moderate to strong TMT positivity, indicating probable ischemia (Table 3).

Table 3: TMT Positivity by Selzer’s Criteria (n=120)

4. Coronary Angiography Results

63.3% of patients had angiographically proven obstructive CAD (Table 4).

Table 4: Coronary Angiography Results (n=120)

5. Correlation between TMT Severity and Angiographic CAD

CAD detection increased with TMT positivity strength, especially in the strong group (Table 5).

Table 5: Correlation between TMT Severity and Angiographic CAD (n=120)

Chi-square test; significant correlation between increasing TMT positivity and angiographically proven CAD (p = 0.002).

6. Gender-wise Diagnostic Accuracy of TMT

TMT showed higher diagnostic accuracy in males compared to females (Table 6).

Table 6: Gender-wise Diagnostic Accuracy of TMT (n=120)

Chi-square test; TMT diagnostic accuracy significantly higher in males (p = 0.01).

7. Association of Risk Factors with Severity of CAD

CAD severity was notably higher in diabetic patients than non-diabetics (Table 7).

Table 7: Association of Risk Factors with Severity of CAD (n=120)

Chi-square test; diabetes significantly associated with higher CAD severity (p = 0.004).

8. Duke Treadmill Score vs Angiographic CAD Severity

Lower Duke Treadmill Scores were strongly associated with more severe CAD (Table 8).

Table 8: Duke Treadmill Score vs Angiographic CAD Severity (n=120)

ANOVA and Pearson correlation showed strong negative correlation between DTS and SYNTAX score (p = 0.001, r = −0.71).

9. Diagnostic Performance of TMT Compared to CAG

TMT showed good sensitivity and moderate specificity for detecting CAD (Table 9).

Table 9: Diagnostic Performance of TMT Compared to CAG (n=120)

McNemar test; overall diagnostic performance significant (p = 0.03).

Graph 1: Diagnostic Performance of TMT Compared to CAG

10. Coronary Artery Involvement by TMT Positivity

LAD was the most frequently involved artery, especially in strong TMT positives (Table 10).

Table 10: Coronary Artery Involvement by TMT Positivity (n=120)

Chi-square test; strong TMT positivity significantly associated with LAD and multivessel involvement (p = 0.009).

Graph 10: Coronary Artery Involvement by TMT Positivity

The present study aimed to evaluate the correlation between treadmill test (TMT) results and coronary angiography (CAG) findings in patients suspected of coronary artery disease (CAD). Among 120 patients, 63.3% had angiographically confirmed CAD, with a higher detection rate in those with moderate to strong TMT positivity. This supports the diagnostic utility of TMT as a non-invasive screening tool.

In our study, strongly positive TMT results correlated with obstructive CAD in 76.7% of cases, aligning with findings by Ameta et al. (2020), who reported that TMT positivity correlated with CAD severity, particularly when ST-depression exceeded 2 mm ^[11]. Similarly, Gunasekaran and Kannan (2017) observed that diabetic patients with strong TMT positivity had higher angiographic severity, which is consistent with our finding that diabetics had higher rates of double and triple vessel disease ^[12].

Regarding gender differences, our study found that TMT had better diagnostic accuracy in males (70.6%) compared to females (40%). This echoes findings by Harikrishnan et al. (2025), where females had a higher rate of false-positive TMT, possibly due to hormonal influences, microvascular dysfunction, and exercise ECG interpretation challenges in women ^[13].

The Duke Treadmill Score (DTS) in our study showed a strong inverse correlation with CAD severity. Patients with high-risk DTS had a mean SYNTAX score of 18.9 and 87.5% CAD prevalence. This trend is comparable to that reported by Madhavan et al. (2019), who demonstrated that lower DTS values predicted higher coronary lesion complexity ^[8].

Our study’s diagnostic performance of TMT (sensitivity 79.2%, specificity 61.4%) is in line with prior literature reporting variable sensitivity (68-85%) and specificity (50-70%). This moderate specificity suggests that while TMT is effective for initial screening, its results should be interpreted cautiously, especially in low-risk patients or women.

Overall, our findings reinforce the clinical value of TMT in identifying patients who warrant invasive evaluation, especially in resource-constrained settings. However, limitations such as gender variability, risk factor clustering, and borderline test results should be addressed through comprehensive risk assessment tools and adjunctive imaging modalities when required.

This study demonstrated that treadmill testing (TMT) has significant diagnostic value in predicting coronary artery disease (CAD), particularly in patients with moderate to strong positivity. The correlation between TMT results and coronary angiography (CAG) underscores its utility as a cost-effective, non-invasive screening tool. However, gender differences, moderate specificity, and influence of comorbidities such as diabetes highlight the need for cautious interpretation. Incorporating Duke Treadmill Score and risk factor profiling enhances predictive accuracy, guiding appropriate use of invasive angiography in clinical practice.

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DOI:10.70034/ijmedph.2025.3.420.