ABSTRACT
Background
Electrocardiographic parameters have emerged as valuable noninvasive markers for risk stratification in acute coronary syndrome (ACS).
Aims
This study aimed to evaluate the correlation between R wave peak time (RWPT) and coronary artery disease (CAD) severity, and to assess its prognostic value for in‐hospital major adverse cardiac events (MACE) in ACS patients.
Methods
We retrospectively analyzed 183 ACS patients who underwent coronary angiography at our hospital between January 2020 and December 2023. RWPT, QRS duration, and P wave peak time were measured from admission electrocardiograms. CAD severity was quantified using the Gensini score, with patients classified into mild (score < 25, n = 81) and moderate‐severe (score ≥ 25, n = 102) groups. In‐hospital MACE was systematically recorded during hospitalization.
Results
RWPT demonstrated a strong positive correlation with the Gensini score (r = 0.7, p < 0.001). Patients with moderate‐severe CAD exhibited significantly prolonged RWPT compared to those with mild disease (43.2 ± 6.3 vs. 36.8 ± 5.3 ms, p < 0.001). Similarly, patients who experienced MACE had significantly longer RWPT than those without complications (44.7 ± 6.2 vs. 37.1 ± 5.5 ms, p < 0.001). In‐hospital MACE occurred in 25 patients (13.7%). Multivariate logistic regression analysis identified both RWPT (OR: 1.10, 95% CI: 1.05−1.15, p < 0.001) and diabetes mellitus (OR: 3.02, 95% CI: 1.16−7.93, p = 0.024) as independent risk factors of in‐hospital MACE.
Conclusion
RWPT measured on admission electrocardiograms correlates significantly with CAD severity and independently predicts in‐hospital MACE in ACS patients. As a simple, readily available parameter, RWPT may enhance risk stratification in ACS, particularly in resource‐limited settings where advanced imaging or biomarker testing access is constrained.
Keywords: acute coronary syndrome, electrocardiography, major adverse cardiac events, prognosis, R wave peak time
1. Introduction
Acute coronary syndrome (ACS) represents a significant global health burden, accounting for substantial morbidity and mortality despite advances in interventional techniques and pharmacological therapies [1]. Approximately 1.4 million hospitalizations for ACS occur annually in the United States alone, with similar prevalence in other developed countries [2]. The spectrum of ACS spans from unstable angina to non‐ST‐segment elevation myocardial infarction (NSTEMI) and ST‐segment elevation myocardial infarction (STEMI), all sharing a common pathophysiological basis in coronary plaque disruption and thrombosis [1]. Early risk stratification remains paramount in guiding therapeutic decisions and improving clinical outcomes in ACS patients. Current stratification methods rely primarily on clinical scores (e.g., GRACE [Global Registry of Acute Coronary Events], TIMI [Thrombolysis In Myocardial Infarction]), biomarkers (particularly cardiac troponins), and imaging techniques [3]. However, these approaches have inherent limitations, including delayed biomarker elevation, limited accessibility of advanced imaging in many healthcare settings, and suboptimal predictive accuracy in certain patient subgroups [4]. Electrocardiography (ECG) maintains its position as the most accessible initial diagnostic tool in ACS evaluation, with established markers such as ST‐segment deviation and T‐wave changes guiding immediate management decisions [5]. Nevertheless, in patients with NSTEMI and unstable angina, conventional ECG parameters demonstrate only modest sensitivity and specificity for detecting coronary artery disease (CAD) severity and predicting adverse outcomes [6].
The search for novel, readily available ECG markers to enhance risk stratification in ACS has intensified in recent years. Beyond traditional parameters, researchers have explored QRS complex morphology characteristics, including QRS duration, fragmented QRS, and terminal QRS distortion, which have shown promising associations with myocardial damage extent and clinical outcomes [7, 8]. Among these emerging markers, R wave peak time (RWPT)—defined as the interval from the onset of the QRS complex to the peak of the R wave—has garnered increasing attention [9]. RWPT represents the time required for the electrical impulse to propagate from the endocardium to the epicardium, reflecting intraventricular conduction dynamics. This parameter can be measured from standard 12‐lead ECGs without specialized equipment, making it particularly attractive for widespread clinical application. The pathophysiological basis for RWPT prolongation in ACS involves ischemia‐induced conduction abnormalities within the Purkinje fibers and ventricular myocytes [10]. Myocardial ischemia disrupts the normal sequence of ventricular depolarization, potentially resulting in delayed R wave peak formation. Several studies have demonstrated associations between prolonged RWPT and impaired myocardial perfusion in various cardiac conditions, suggesting its potential utility as a marker of coronary microcirculation status [9, 11, 12].
Initial investigations have explored the potential role of RWPT in specific ACS subsets. Çağdaş et al. reported that prolonged RWPT correlated with the no‐reflow phenomenon after primary percutaneous coronary intervention in STEMI patients, suggesting its value in predicting reperfusion outcomes [13]. Rencüzoğulları et al. observed an association between RWPT and CAD complexity as assessed by the SYNTAX (Synergy Between PCI With Taxus and Cardiac Surgery) score in stable CAD patients [14]. However, comprehensive data regarding RWPT's relationship with angiographic coronary lesion severity across the ACS spectrum remain limited. Furthermore, the comparative prognostic value of RWPT relative to other ECG parameters, such as QRS duration and P wave peak time (PWPT), has not been thoroughly evaluated in ACS populations [15]. Additionally, most existing studies have focused on long‐term outcomes rather than in‐hospital events, which represent a critical phase for potential therapeutic interventions [16]. These knowledge gaps highlight the need for further investigation into RWPT's utility as a prognostic marker in ACS patients, particularly regarding its relationship with coronary disease severity and short‐term clinical outcomes.
In this study, we hypothesized that RWPT on admission electrocardiograms could serve as a novel marker for predicting CAD severity and in‐hospital major adverse cardiac events (MACE) in ACS patients. We aimed to investigate the correlation between RWPT and angiographic CAD as quantified by the Gensini score, a validated method for assessing coronary lesion severity that incorporates both stenosis degree and lesion location functional significance [17]. Additionally, we sought to evaluate whether RWPT could function as an independent predictor of in‐hospital MACE in this patient population. By examining the prognostic utility of this readily available ECG parameter, our findings may contribute to improved risk stratification strategies in ACS and help identify high‐risk patients who might benefit from more aggressive therapeutic approaches and closer monitoring during hospitalization.
2. Materials and Methods
2.1. Study Population
We conducted a retrospective study on patients diagnosed with ACS admitted to our institution between January 2020 and December 2023. The study protocol was approved by the ethics committee of the General Hospital of the Yangtze River Shipping, and the requirement for individual informed consent was waived due to the retrospective nature of the investigation. Patients were eligible for inclusion if they were (1) diagnosed with ACS based on clinical symptoms, electrocardiographic changes, and/or elevated cardiac biomarkers [1]; and (2) had undergone coronary angiography during the index hospitalization. Patients were excluded if they had (1) comorbid cardiac conditions including cardiomyopathy, valvular heart disease, congenital heart disease, pulmonary heart disease, or hypertensive heart disease; (2) history of prior myocardial infarction or revascularization procedures; (3) severe trauma, surgery, or infection within 3 months before admission; (4) severe cardiac conduction disturbances, atrial fibrillation, ventricular fibrillation, or atrial flutter; (5) abnormal liver or kidney function, malignant tumors, hematological diseases, or autoimmune diseases; or (6) incomplete clinical data. All data were extracted from the hospital's electronic medical record system. A total of 183 ACS patients were included in this retrospective observational study.
2.2. Electrocardiographic Analysis
Standard 12‐lead ECGs recorded on admission (25 mm/s paper speed, 10 mm/mV voltage calibration, 0.05−150 Hz filter range) were retrieved from the electronic medical record system. All ECG measurements were performed by two independent experienced cardiologists who were blinded to the patients' clinical and angiographic data. In case of disagreement, a consensus was reached by mutual discussion.
The QRS duration was defined as the interval from the onset of the QRS complex to the J point. PWPT was measured from the onset of the P wave to its peak amplitude. RWPT was defined as the interval from the onset of the QRS complex to the peak of the R wave. For optimal measurement accuracy, all electrocardiographic parameters were measured in the lead demonstrating the most prominent and clearly defined waveform morphology. Specifically, RWPT and QRS duration were measured in the lead showing the highest R‐wave amplitude and sharpest QRS morphology, while PWPT was measured in the lead with the most distinct P‐wave deflection (typically leads II or V1). This standardized approach ensures reproducible measurements by selecting leads where specific waveform components are most clearly delineated. All parameters were measured in the lead with the most prominent deflection, and values were expressed in milliseconds. For each parameter, the mean value of three consecutive beats was calculated.
2.3. Coronary Angiography and Gensini Score Calculation
Coronary angiography records were reviewed for all included patients. All procedures had been performed via the femoral or radial approach during the index hospitalization, following standard techniques. Coronary angiograms were retrospectively analyzed by two experienced interventional cardiologists who were blinded to the ECG findings. The severity of CAD was quantified using the Gensini score, which considers both the degree of luminal narrowing and the functional significance of the location of the stenosis [17]. For each lesion, a severity score was assigned: 1 for 1%−25% stenosis, 2 for 26%−50%, 4 for 51%−75%, 8 for 76%−90%, 16 for 91%−99%, and 32 for total occlusion. This score was then multiplied by a factor representing the functional significance of the lesion location: left main (5), proximal left anterior descending (LAD) or proximal left circumflex (2.5), mid‐LAD (1.5), distal LAD, first diagonal, proximal right coronary artery (RCA), distal left circumflex, obtuse marginal, or posterior descending artery (1), and others (0.5). The total Gensini score was calculated as the sum of all segment scores.
Based on the Gensini score, patients were classified into two groups: mild CAD (Gensini score < 25, n = 81) and moderate‐to‐severe CAD (Gensini score ≥ 25, n = 102) [18].
2.4. Definition of MACE
In‐hospital MACE was defined as a composite of cardiac death, recurrent myocardial infarction, cardiogenic shock, malignant ventricular arrhythmias requiring intervention, or urgent revascularization during the index hospitalization. All events were identified through a comprehensive review of electronic medical records, including daily progress notes, cardiac monitoring records, procedure reports, and discharge summaries.
2.5. Statistical Analysis
Statistical analyses were performed using SPSS version 26.0 (IBM Corporation, Armonk, NY, USA). Continuous variables were expressed as mean ± standard deviation or median (interquartile range) based on the distribution normality as assessed by the Shapiro−Wilk test. Categorical variables were presented as numbers and percentages. Differences between groups were compared using the Student's t‐test or Mann−Whitney U test for continuous variables and the chi‐square test or Fisher's exact test for categorical variables, as appropriate. Pearson's correlation coefficient was calculated to evaluate the relationship between electrocardiographic parameters and the Gensini score. Univariate and multivariate logistic regression analyses were performed to identify independent risk factors of in‐hospital MACE. Variables with a p < 0.05 in the univariate analysis were included in the multivariate model. Odds ratios (OR) and 95% confidence intervals (CI) were calculated. A post hoc sample size calculation was performed using G*Power 3.1 software. For our logistic regression analysis with eight potential predictors, a minimum sample size of 96 participants was required to detect an OR of 1.10 with 80% power at a 5% significance level, assuming a baseline MACE incidence of 13.7%. Our sample size of 183 patients exceeds this requirement, ensuring adequate statistical power for the primary analysis. A two‐sided p < 0.05 was considered statistically significant for all analyses.
3. Results
3.1. Baseline Characteristics
As shown in Table 1, patients with moderate‐severe CAD were significantly older (64.8 ± 10.1 vs. 61.2 ± 9.4 years, p = 0.017) and had a higher prevalence of diabetes mellitus (43.1% vs. 22.2%, p = 0.003) compared to the mild disease group. Importantly, all electrocardiographic parameters were significantly prolonged in the moderate‐severe group, including QRS duration (96.9 ± 12.6 vs. 89.3 ± 10.7 ms, p = 0.015), PWPT (114.7 ± 15.8 vs. 108.3 ± 12.7 ms, p = 0.004), and most notably RWPT (43.2 ± 6.3 vs. 36.8 ± 5.3 ms, p < 0.001).
Table 1.
Baseline characteristics between the two groups.
| Parameter | Mild group (n = 81) | Moderate‐severe group (n = 102) | p value |
|---|---|---|---|
| Age (years) | 61.2 ± 9.4 | 64.8 ± 10.1 | 0.017 |
| Gender (female), n (%) | 30 (37.0%) | 34 (33.3%) | 0.609 |
| Smoking, n (%) | 33 (40.7%) | 50 (49.0%) | 0.264 |
| Drinking, n (%) | 27 (33.3%) | 43 (42.2%) | 0.223 |
| Hypertension, n (%) | 49 (60.5%) | 75 (73.5%) | 0.061 |
| Diabetes mellitus, n (%) | 18 (22.2%) | 44 (43.1%) | 0.003 |
| Hyperlipidemia, n (%) | 28 (34.6%) | 49 (48.0%) | 0.067 |
| WBC (×109/L) | 6.4 ± 2.3 | 6.8 ± 2.1 | 0.371 |
| Hemoglobin (g/L) | 134.7 ± 15.8 | 132.1 ± 16.7 | 0.411 |
| Platelets (×109/L) | 223.5 ± 45.1 | 228.7 ± 49.3 | 0.131 |
| Creatinine (μmol/L) | 85 ± 19 | 89 ± 22 | 0.201 |
| Calcium (mmol/L) | 2.3 ± 0.1 | 2.2 ± 0.1 | 0.503 |
| HDL (mmol/L) | 1.7 ± 0.3 | 1.7 ± 0.3 | 0.065 |
| LDL (mmol/L) | 2.6 ± 0.7 | 2.7 ± 0.8 | 0.198 |
| Triglyceride (mmol/L) | 1.4 ± 0.5 | 1.7 ± 0.6 | 0.103 |
| QRS duration (ms) | 89.3 ± 10.7 | 96.9 ± 12.6 | 0.015 |
| P wave peak time (ms) | 108.3 ± 12.7 | 114.7 ± 15.8 | 0.004 |
| R wave peak time (ms) | 36.8 ± 5.3 | 43.2 ± 6.3 | < 0.001 |
Abbreviations: HDL, high‐density lipoprotein; LDL, low‐density lipoprotein; WBC, white blood cell count.
3.2. Correlation Analysis of Electrocardiographic Parameters With CAD Severity
The correlation analysis revealed a significant positive relationship between Gensini score and key electrocardiographic parameters (Figure 1). While age showed no significant correlation with Gensini score (r = 0.079, p = 0.286), QRS duration (r = 0.239, p = 0.001), PWPT (r = 0.166, p = 0.024), and especially RWPT (r = 0.7, p < 0.001) demonstrated statistically significant positive correlations with CAD severity.
Figure 1.
Correlation analysis of electrocardiographic parameters with CAD severity. [Color figure can be viewed at wileyonlinelibrary.com]
3.3. Comparison of Characteristics Between MACE and Non‐MACE Groups
Twenty‐five patients (13.7%) experienced in‐hospital MACE. The composite endpoint included cardiac death (n = 3, 1.6%), recurrent myocardial infarction (n = 8, 4.4%), cardiogenic shock (n = 6, 3.3%), malignant ventricular arrhythmias requiring intervention (n = 4, 2.2%), and urgent revascularization (n = 4, 2.2%). The median time to MACE occurrence was 3.2 days (interquartile range: 1.8−5.1 days) from admission.
Table 2 demonstrates that patients who experienced MACE were significantly older (67.2 ± 8.9 vs. 62.8 ± 9.7 years, p = 0.029) and had a higher prevalence of diabetes mellitus (52.0% vs. 31.0%, p = 0.039) compared to those without MACE. The MACE group also exhibited unfavorable lipid profiles with lower HDL (1.23 ± 0.27 vs. 1.37 ± 0.31 mmol/L, p = 0.035), higher LDL (2.78 ± 0.88 vs. 2.39 ± 0.71 mmol/L, p = 0.017), and higher triglyceride levels (1.74 ± 0.66 vs. 1.49 ± 0.56 mmol/L, p = 0.045). All electrocardiographic parameters were significantly prolonged in the MACE group, including QRS duration (97.8 ± 12.9 vs. 90.2 ± 11.3 ms, p = 0.011), PWPT (116.2 ± 17.5 vs. 109.4 ± 13.9 ms, p = 0.026), and RWPT (44.7 ± 6.2 vs. 37.1 ± 5.5 ms, p < 0.001).
Table 2.
Comparison of characteristics between MACE group and non‐MACE group.
| Parameter | MACE group (n = 25) | Non‐MACE group (n = 158) | p value |
|---|---|---|---|
| Age (years) | 67.2 ± 8.9 | 62.8 ± 9.7 | 0.029 |
| Gender (female), n (%) | 8 (32.0%) | 56 (35.4%) | 0.737 |
| Smoking, n (%) | 15 (60.0%) | 68 (43.0%) | 0.113 |
| Drinking, n (%) | 10 (40.0%) | 60 (37.9%) | 0.847 |
| Hypertension, n (%) | 20 (80.0%) | 104 (63.3%) | 0.159 |
| Diabetes mellitus, n (%) | 13 (52.0%) | 49 (31.0%) | 0.039 |
| Hyperlipidemia, n (%) | 14 (56.0%) | 63 (39.9%) | 0.129 |
| WBC (×109/L) | 7.1 ± 2.5 | 6.9 ± 2.2 | 0.679 |
| Hemoglobin (g/L) | 130.3 ± 17.2 | 133.1 ± 15.9 | 0.411 |
| Platelets (×109/L) | 237.6 ± 53.1 | 219.8 ± 46.3 | 0.072 |
| Creatinine (μmol/L) | 91.2 ± 24.2 | 86.9 ± 20.5 | 0.357 |
| Calcium (mmol/L) | 2.2 ± 0.1 | 2.2 ± 0.1 | 0.405 |
| HDL (mmol/L) | 1.2 ± 0.3 | 1.4 ± 0.3 | 0.035 |
| LDL (mmol/L) | 2.8 ± 0.9 | 2.4 ± 0.7 | 0.017 |
| Triglyceride (mmol/L) | 1.7 ± 0.7 | 1.5 ± 0.6 | 0.045 |
| QRS duration (ms) | 97.8 ± 12.9 | 90.2 ± 11.3 | 0.011 |
| P wave peak time (ms) | 116.2 ± 17.5 | 109.4 ± 13.9 | 0.026 |
| R wave peak time (ms) | 44.7 ± 6.2 | 37.1 ± 5.5 | < 0.001 |
Abbreviations: HDL, high‐density lipoprotein; LDL, low‐density lipoprotein; WBC, white blood cell count.
3.4. Logistic Regression Analysis of Risk Factors for MACE
In univariate logistic regression analysis, age (OR: 1.06, 95% CI: 1.01−1.11, p = 0.018), diabetes mellitus (OR: 3.54, 95% CI: 1.42−8.84, p = 0.007), QRS duration (OR: 1.03, 95% CI: 1.00−1.06, p = 0.045), and RWPT (OR: 1.09, 95% CI: 1.05−1.13, p < 0.001) were significantly associated with MACE; while PWPT (OR: 1.04, 95% CI: 0.99−1.09, p = 0.094) showed only marginal association. In the multivariate logistic regression analysis, only diabetes mellitus (OR: 3.02, 95% CI: 1.16−7.93, p = 0.024) and RWPT (OR: 1.10, 95% CI: 1.05−1.15, p < 0.001) remained independent risk factors of MACE (Table 3).
Table 3.
Logistic regression analysis of risk factors for MACE.
| Parameter | Univariate analysis | Multivariate analysis | ||
|---|---|---|---|---|
| OR (95% CI) | p value | OR (95% CI) | p value | |
| Age | 1.06 (1.01–1.11) | 0.018 | 1.04 (0.99–1.12) | 0.105 |
| Diabetes mellitus | 3.54 (1.42–8.84) | 0.007 | 3.02 (1.16–7.93) | 0.024 |
| QRS duration | 1.03 (1.00–1.06) | 0.045 | 1.02 (0.98–1.06) | 0.275 |
| P wave peak time | 1.04 (0.99–1.09) | 0.094 | 1.03 (0.98–1.07) | 0.236 |
| R wave peak time | 1.09 (1.05–1.13) | < 0.001 | 1.10 (1.05–1.15) | < 0.001 |
Abbreviations: CI, confidence intervals; OR, odds ratio.
4. Discussion
The present study was designed to investigate the relationship between electrocardiographic RWPT and both the severity of CAD and prognosis in patients with ACS. Our findings demonstrated a significant positive correlation between RWPT and the Gensini score. Furthermore, we observed that RWPT was significantly prolonged in patients with moderate‐to‐severe CAD patients compared to those with mild disease. Importantly, multivariate logistic regression analysis identified RWPT as an independent risk factor of in‐hospital MACE.
The pathophysiological mechanisms underlying the association between prolonged RWPT and CAD severity can be explained by ischemia‐induced alterations in ventricular conduction. In ACS, myocardial ischemia causes conduction disturbances within the Purkinje fibers and ventricular myocytes, leading to delayed electrical impulse propagation from the endocardium to the epicardium [10, 19]. This delay manifests as prolonged RWPT on surface electrocardiograms. The severity of these conduction abnormalities likely reflects the extent of myocardial ischemia, which in turn correlates with the anatomical complexity and functional significance of coronary lesions [3]. Additionally, prolonged RWPT may be associated with microvascular dysfunction in ACS, as impaired coronary microcirculation can exacerbate myocardial ischemia and further disrupt intraventricular conduction [20, 21]. When compared with other electrocardiographic parameters, RWPT appears to be more sensitive to early ischemic changes [9]. While QRS duration represents the total ventricular depolarization time, RWPT specifically reflects the initial phase of ventricular activation, which may be more vulnerable to ischemic insult [22]. Similarly, PWPT primarily reflects atrial conduction, which may be less directly affected by acute myocardial ischemia than ventricular conduction parameters [15].
While established risk stratification tools such as the GRACE and TIMI scores have demonstrated robust prognostic value in ACS patients, these scoring systems require compilation of multiple clinical variables including age, heart rate, blood pressure, creatinine levels, and cardiac biomarkers [23, 24]. In contrast, RWPT provides immediate prognostic information from the admission ECG without requiring laboratory results or hemodynamic measurements. This immediate availability represents a significant practical advantage, particularly in emergency settings where rapid risk assessment is crucial for management decisions. Our findings suggest that RWPT could serve as a complementary tool to traditional risk scores, potentially enhancing risk stratification when used in combination with established clinical predictors.
Our findings are consistent with previous research on electrocardiographic parameters in ACS. Rencüzoğulları et al. [14] demonstrated that prolonged RWPT was associated with higher SYNTAX scores in patients with non‐ST elevation ACS, reporting that RWPT ≥ 43.8 ms predicted a high SYNTAX score with 60% sensitivity and 75.6% specificity. Similarly, Çağdaş et al. [13] reported that prolonged RWPT was associated with the no‐reflow phenomenon in ST‐elevation myocardial infarction patients undergoing primary percutaneous coronary intervention, suggesting its value in predicting reperfusion outcomes. While their study focused on a specific complication in STEMI patients, our investigation broadens the application of RWPT to encompass the entire spectrum of ACS and demonstrates its utility in predicting overall in‐hospital outcomes. In comparison with Bayam et al.'s [15] research on PWPT in non‐ST elevation ACS, which found PWPT to be associated with CAD severity but not superior to RWPT, our study provides further evidence for the preferential prognostic value of RWPT. Collectively, these studies and our current findings establish RWPT as a valuable electrocardiographic marker that offers incremental prognostic information beyond traditional ECG parameters in the risk stratification of ACS patients [15, 16, 25, 26]. Based on our findings and previous research, RWPT values exceeding 44 ms appear to identify patients at higher risk for adverse outcomes. However, the establishment of definitive diagnostic thresholds requires validation in larger, prospective cohorts across diverse patient populations. Future studies should focus on receiver operating characteristic curve analysis to determine optimal cutoff values with appropriate sensitivity and specificity for clinical decision‐making. Until such validation studies are completed, clinicians should interpret RWPT values in conjunction with other clinical parameters rather than relying on absolute thresholds.
The clinical implications of our findings are substantial, as RWPT represents a simple, readily available, and noninvasive marker that could enhance prognostic assessment in ACS patients [5]. The measurement of RWPT requires no specialized equipment beyond a standard 12‐lead ECG, which is universally performed in patients presenting with suspected ACS [3]. By incorporating RWPT analysis into routine ECG evaluation, clinicians could obtain additional prognostic information to guide management decisions, including the timing of invasive strategies and intensity of monitoring [27]. The implementation of automated RWPT measurement in modern ECG machines would further facilitate its clinical application. The prognostic value of RWPT may be particularly beneficial in settings with limited access to advanced imaging techniques or biomarker testing, such as in resource‐constrained healthcare environments or during the initial evaluation in emergency departments [28]. Recent research by Yusuf et al. [29] demonstrated that baseline RWPT is a significant predictor of no‐reflow in STEMI patients undergoing primary PCI, and that persistently increased RWPT following primary PCI represents a highly sensitive and specific ECG marker of no‐reflow persistence, which is associated with adverse short‐term clinical outcomes. Another study by Kalçık et al. [30] showed that RWPT is significantly prolonged in patients with end‐stage renal disease, suggesting its utility extends beyond ACS to other clinical scenarios involving myocardial injury or conduction abnormalities. From a practical implementation perspective, RWPT measurement can be readily integrated into routine ECG interpretation workflows. Modern ECG machines could be programmed to automatically calculate and display RWPT values, similar to current automated measurements of QRS duration and QT intervals. Training cardiology staff and emergency physicians to recognize and interpret RWPT values would require minimal additional education, as the measurement technique follows standard electrocardiographic principles. The implementation could begin in high‐volume cardiac care units and emergency departments where ACS patients are frequently evaluated, with gradual expansion to other clinical settings as familiarity with the parameter increases.
The strengths of our study include the comprehensive assessment of both angiographic CAD severity and clinical outcomes in a well‐characterized cohort of ACS patients. The use of the Gensini scoring system provided an objective and validated method for quantifying CAD severity, taking into account both the degree of luminal narrowing and the functional significance of the affected coronary segments [17, 31]. We carefully controlled for potential confounding factors in our multivariate analyses, including demographic characteristics, cardiovascular risk factors, and other electrocardiographic parameters. Our focus on in‐hospital events represents a critical period for intervention, as the identification of high‐risk patients during the index hospitalization could enable prompt implementation of aggressive therapeutic strategies and closer monitoring, potentially improving outcomes [32, 33]. The consistency of our findings with previous studies on RWPT in different cardiovascular contexts strengthens the validity of RWPT as a prognostic marker and suggests its broad applicability across the spectrum of ischemic heart disease [9, 13, 14].
Several limitations of our study should be acknowledged. First, the retrospective single‐center design introduces potential selection bias and limits the generalizability of our findings. Second, the relatively modest sample size may have reduced the statistical power for detecting associations with less common outcomes or performing more detailed subgroup analyses. Third, the absence of long‐term follow‐up data precludes assessment of the relationship between RWPT and long‐term prognosis beyond the in‐hospital period. Fourth, the manual measurement of electrocardiographic parameters, although performed by experienced cardiologists blinded to clinical data, may introduce a degree of interobserver variability. Fourth, the manual measurement of electrocardiographic parameters, although performed by experienced cardiologists blinded to clinical data, may introduce a degree of interobserver variability. This limitation highlights the need for automated RWPT measurement algorithms in future studies and clinical applications. Manual measurement dependency could potentially limit the reproducibility and widespread adoption of RWPT as a clinical tool. The development of standardized automated measurement protocols would enhance consistency, reduce operator‐dependent variability, and facilitate the integration of RWPT assessment into routine clinical practice. Until such automated systems become available, institutions implementing RWPT measurement should establish standardized measurement protocols and interobserver reliability assessments. Future research should include prospective multicenter studies with larger sample sizes to validate our findings. The prognostic value of RWPT should be investigated in specific ACS subpopulations, such as elderly patients, those with diabetes mellitus, or patients with prior myocardial infarction, to determine whether its predictive accuracy varies across different clinical scenarios. Additionally, studies examining the combination of RWPT with other established risk markers, such as cardiac biomarkers, echocardiographic parameters, or clinical risk scores, could provide insights into the incremental prognostic value of this electrocardiographic parameter.
5. Conclusion
In conclusion, our study demonstrates that RWPT on admission electrocardiograms is significantly correlated with CAD severity as assessed by the Gensini score and serves as an independent risk factor of in‐hospital MACE in patients with ACS. As a readily available and easily measured electrocardiographic parameter, RWPT could enhance risk stratification in ACS patients, particularly in settings where advanced imaging or biomarker testing may not be immediately accessible. By identifying high‐risk individuals who might benefit from more aggressive management strategies and closer monitoring, the integration of RWPT assessment into clinical practice could potentially improve outcomes in this vulnerable patient population.
Ethics Statement
This study was approved by the Medical Ethics Committee of the General Hospital of the Yangtze River Shipping.
Consent
The authors have nothing to report.
Conflicts of Interest
The authors declare no conflicts of interest.
Acknowledgments
This study was supported by the effect of the NOD1/RIP2 signaling pathway on foam cell formation derived from THP‐1 cells (grant no WX20Q10).
Data Availability Statement
The data sets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data sets used and/or analyzed during the current study are available from the corresponding author on reasonable request.