Beyond Linear Risk: A U-Shaped Association Between Platelet Reactivity and Mortality in Coronary Artery Disease
by
, , , , , and
Sholpan Zhangelova
1
,
Orazbek Sakhov
1,
Lyazat Abisheva
2,
Dmitriy Polyakov
3,†,
Farida Rustamova
1,†,
Aizhan Almukhanova
1,
Galiya Umenova
2,
Gulzada Nurgaliyeva
1,
Aigyul Izhanova
1,
Dana Akhmentayeva
1,
Dina Kapsultanova
1,*
and
Friba Nurmukhammad
4,*
1
Faculty of Postgraduate Education, Asfendiyarov Kazakh National Medical University, Almaty 050012, Kazakhstan
2
Department of cardiology, City Cardiology Center, Almaty 050000, Kazakhstan
3
Federal Stae Budgetary Educational Institution, Higher Education «Privolzhsky Research Medical University», Nizhny Novgorod 603005, Russia
4
Faculty of Postgraduate Medical Education, Hodja Ahmed Yasawi International Kazakh-Turkish University, Turkestan 161200, Kazakhstan
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Diseases 2026, 14(6), 194; https://doi.org/10.3390/diseases14060194
Submission received: 3 May 2026
/
Revised: 25 May 2026
/
Accepted: 27 May 2026
/
Published: 29 May 2026
(This article belongs to the Special Issue Insights into the Management of Cardiovascular Disease Risk Factors)
Abstract
Background: Optimal platelet inhibition is essential for minimizing both thrombotic and hemorrhagic complications in patients with coronary artery disease (CAD). Although high on-treatment platelet reactivity (HPR) has been consistently associated with adverse clinical outcomes, the relationship between platelet reactivity—measured as P2Y12 reaction units (PRU)—and cardiovascular mortality remains incompletely characterized. In particular, potential non-linear associations have not been adequately explored. Objective: We aimed to investigate the association between PRU and cardiovascular mortality in patients with CAD, with a specific focus on identifying potential non-linear relationships. Methods: We conducted a retrospective observational cohort study including 1000 patients with angiographically confirmed CAD treated at a tertiary cardiology center in Almaty, Kazakhstan, between 2024 and 2025. Platelet reactivity was assessed using the VerifyNow P2Y12 assay. Multivariable logistic regression models were used to identify independent predictors of cardiovascular mortality. To assess potential non-linear associations between PRU and mortality, restricted cubic spline regression was applied with predefined knot placement. Model performance was evaluated in terms of discrimination (C-statistic) and calibration (Hosmer-Lemeshow goodness-of-fit test). Results: In conventional linear regression models, PRU was not independently associated with cardiovascular mortality (odds ratio [OR] ~1.00; p > 0.05). However, spline-based analyses demonstrated a statistically significant non-linear (U-shaped) relationship between PRU and mortality risk (p for non-linearity = X). Both low and high PRU values were associated with increased mortality, whereas intermediate PRU levels corresponded to the lowest observed risk. Additional independent predictors of mortality included advanced age, diabetes mellitus, and elevated inflammatory markers. Conclusions: Our findings reveal a significant non-linear association between platelet reactivity and cardiovascular mortality in patients with CAD. Both insufficient and excessive platelet inhibition appear to confer increased risk, suggesting that optimal PRU targets may lie within an intermediate therapeutic range. These results support a paradigm shift toward more individualized antiplatelet therapy strategies guided by platelet function testing.
1. Introduction
Platelets are central mediators of arterial thrombosis, and their excessive activation following atherosclerotic plaque rupture drives acute coronary syndrome (ACS) and ischemic complications after percutaneous coronary intervention (PCI) [1]. Conversely, overly aggressive platelet inhibition increases bleeding risk, a complication with its own mortality burden. Accordingly, characterizing the full spectrum of platelet reactivity and its relationship with clinical outcomes is of paramount therapeutic importance [2,3]. Residual high on-treatment platelet reactivity (HRPR), defined as PRU ≥ 208 by the VerifyNow assay, has been associated with a two- to ninefold increase in stent thrombosis risk and recurrent ischemic events [4,5,6]. Multiple patient-level factors contribute to HRPR, including advanced age, diabetes mellitus, chronic kidney disease (CKD), obesity, and CYP2C19 loss-of-function polymorphisms [7,8,9]. Importantly, patients over 70 years of age are two to three times more likely to exhibit HRPR compared with younger counterparts [10,11]. Despite this established risk, the prognostic utility of PRU as a continuous variable remains controversial [12]. Prior studies have predominantly examined high platelet reactivity thresholds, with limited attention to the potential harms of excessive platelet inhibition. Emerging evidence suggests that very low PRU values may also confer risk, implying the existence of an optimal therapeutic window, yet this has not been systematically evaluated across antiplatelet therapy strategies in a real-world cohort. The primary aim of this study was to identify independent predictors of all-cause mortality in patients with CAD receiving antiplatelet therapy and, secondarily, to characterize the shape of the association between PRU and mortality across the full range of platelet reactivity values [13,14].
Importantly, while high on-treatment platelet reactivity (HRPR) is established as a risk factor for ischemic events, excessively low PRU values—reflecting over-inhibition of platelet function—have been independently associated with increased bleeding risk, including gastrointestinal and intracranial hemorrhage, which carries its own mortality burden [15]. The existence of this dual risk implies that the optimal clinical target may lie within an intermediate therapeutic window rather than at maximal suppression. Despite this, the full PRU continuum—including very low values—has rarely been simultaneously evaluated in a single real-world cohort [15,16].
Objective: We aimed to evaluate the association between PRU and cardiovascular mortality in patients with CAD and to explore potential non-linear relationships.
2. Materials and Methods
2.1. Study Design and Setting
A retrospective cohort study was conducted using medical records of 1000 adult patients (≥18 years) with established coronary artery disease who received care at a cardiology center in Almaty, Kazakhstan, between June 2024 and December 2025. Patients were enrolled if they had ischemic heart disease and were receiving single or dual antiplatelet therapy. The exclusion criteria included atrial fibrillation with anticoagulant use (rivaroxaban, apixaban, or dabigatran), coagulopathy, and high bleeding risk. Nine patients with high bleeding risk (PRU < 95) were excluded from the primary analysis per a pre-specified study protocol, based on their clinical designation as high-bleeding-risk according to consensus VerifyNow P2Y12 thresholds (Figure 1).
As a sensitivity analysis, all primary analyses were repeated in the full cohort of 1000 patients, including the 9 patients with PRU < 95. The results were consistent with the primary analysis and are presented in Table A1. The exclusion of these patients from the primary analysis was a pre-specified protocol decision based on their clinical designation as high-bleeding-risk per institutional guidelines, not a post hoc statistical decision.
The primary outcome was cardiovascular mortality, defined as death attributable to a cardiovascular cause occurring during the observation period. Cause of death was ascertained from electronic hospital records, discharge summaries, and, where applicable, death certificates obtained from the regional civil registry. All deaths were reviewed and classified by two independent cardiologists who were blinded to the patients’ PRU values. Cardiovascular deaths included fatal acute myocardial infarction, sudden cardiac death, fatal heart failure, and fatal stroke. Deaths attributable to non-cardiovascular causes (infection, malignancy, or trauma) were excluded from the primary outcome. Deaths from major bleeding events were classified as hemorrhagic deaths and not counted under the primary cardiovascular mortality endpoint. No independent adjudication committee was established, which we acknowledge as a limitation. However, dual independent physician review was performed for all mortality events, and inter-rater disagreements were resolved by consensus.
2.2. Platelet Reactivity Assessment
Residual platelet reactivity (RPR) was evaluated using the VerifyNow P2Y12 assay (Instrumentation Laboratory, Bedford, MA, USA), a point-of-care system validated for quantifying P2Y12 receptor-mediated platelet inhibition. PRU thresholds were applied according to international consensus guidelines: HRPR was defined as PRU ≥ 208, therapeutic window as PRU 95-208, and high bleeding risk as PRU < 95.
2.3. Clinical and Laboratory Data Collection
Baseline demographic, clinical, and pharmacological data were extracted from electronic medical records. The variables systematically evaluated included lipid profile (total cholesterol, LDL-C, HDL-C, and triglycerides); renal function (serum creatinine and eGFR); hemoglobin (with anemia defined by WHO criteria); left ventricular ejection fraction (LVEF) by transthoracic echocardiography; body mass index (BMI); inflammatory and coagulation markers (CRP, fibrinogen, D-dimer, and troponin I); and cardiovascular comorbidities (heart failure, diabetes mellitus, prior myocardial infarction, and obstructive CAD).
2.4. Ethical Consideration
This study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki. Written informed consent was obtained from all participants prior to inclusion. The study protocol was reviewed and approved by the institutional ethics committee of Hodja Ahmed Yasawi International Kazakh-Turkish University (Protocol No. 53; dated 17 June 2024).
2.5. Statistical Analysis
Continuous variables are presented as medians and interquartile ranges [IQRs], as normality assumptions were not met based on the Shapiro-Wilk test. Between-group comparisons were performed using the Mann-Whitney U test for continuous variables and the chi-square test or Fisher’s exact test, as appropriate, for categorical variables. To identify independent predictors of cardiovascular mortality, multivariable logistic regression analysis was performed. Variables with a p-value of <0.10 in univariable analyses were considered for inclusion in the multivariable model. The results are reported as odds ratios (ORs) with corresponding 95% confidence intervals (CIs). To explore potential non-linear associations between platelet reactivity and mortality, PRU values were initially categorized into quartiles. Additionally, restricted cubic spline regression models were constructed with 3–5 knots placed at prespecified percentiles to flexibly model the relationship between PRU and mortality risk. Non-linearity was formally tested using likelihood ratio tests. The probability density distributions of PRU were examined across quartiles within subgroups defined by antiplatelet therapy to further characterize distributional patterns. Model performance was evaluated in terms of discrimination and calibration using the C-statistic and the Hosmer-Lemeshow goodness-of-fit test, respectively. All statistical tests were two-sided, and a p-value < 0.05 was considered statistically significant. Statistical analyses were performed using R software (version 4.x).
The use of logistic regression was dictated by the structure of the available data; individual time-to-event variables—specifically, the exact date of death for each patient—were not available in the source database. Cox regression requires a precise time-to-event variable for each observation, including censored cases (patients alive at the study end). Since this information was not captured during retrospective data extraction from electronic medical records, multivariable logistic regression was the only feasible method of multivariable analysis for a binary outcome, given the available data structure. Future prospective studies with systematic recording of exact event dates should employ Cox proportional hazards regression and Cox spline models. For the analysis of nonlinearity, logistic regression with restricted cubic splines was applied, consistent with the binary nature of the outcome; the results are qualitatively consistent with supplementary analyses performed using quartile and rank-based approaches.
3. Results
A total of 1000 patients were included in the analysis; 856 (85.6%) survived, and 144 (14.4%) died during the observation period due to cardiovascular events. Non-survivors were significantly older than survivors (median: 74.0 [67.0–79.0] vs. 65.0 [59.0–72.0] years; OR: 1.09 (95% CI: 1.07–1.12); p < 0.001) and were more frequently female (58.3% vs. 43.6%; OR: 0.55 for males (95% CI: 0.38–0.79); p = 0.001). Non-survivors demonstrated significantly impaired renal function (eGFR: 68.0 [55.0–84.0] vs. 77.0 [65.0–91.0] mL/min/1.73 m2; p < 0.001), lower hemoglobin (134 [123–146] vs. 141 [130–151] g/L; p < 0.001), and reduced LVEF (56.0 [45.0–62.2] vs. 59.0 [51.0–64.0]%; p = 0.004). Inflammatory markers—CRP (4.40 vs. 3.50 mg/L; p = 0.030) and fibrinogen (3.38 vs. 3.17 g/L; p = 0.021)—were elevated in non-survivors. Obstructive CAD was more prevalent in non-survivors (62.5% vs. 51.3%; OR: 1.58 (95% CI: 1.10–2.29); p = 0.013), as was a history of prior myocardial infarction (37.5% vs. 29.1%; OR: 1.46 (95% CI: 1.01–2.11); p = 0.046). A statistically significant but modest difference in PRU was observed between groups (120 [111–190] vs. 155 [120–200]; p = 0.046) (Table 1 and Table A2).
3.1. Multivariable Logistic Regression Analysis
Multivariable logistic regression was performed in 986 patients (Tjur R2 = 0.141). Advanced age remained the strongest independent predictor of mortality, with each additional year associated with an 8% increase in the odds of death (OR: 1.08 (95% CI: 1.06–1.11); p < 0.001). Male sex was independently associated with a lower risk of mortality (OR: 0.53 (95% CI: 0.33–0.86); p = 0.011). Reduced LVEF was an independent predictor of adverse outcome (OR: 0.98 per unit increase (95% CI: 0.96–1.00); p = 0.016). PRU reached statistical significance in the multivariable model (OR: 1.00 (95% CI: 0.99–1.00); p = 0.033), indicating that its effect is not captured by a simple linear term, consistent with the non-linear relationship described below. Obstructive CAD demonstrated borderline significance (OR: 1.46 (95% CI: 0.93–2.31); p = 0.098) (Table 2).
3.2. PRU Subgroup Analysis: Therapeutic Window vs. HRPR
After excluding nine patients with high bleeding risk, 991 patients were analyzed: 883 (89.1%) within the therapeutic window and 108 (10.9%) with HRPR. Baseline characteristics—including age, sex, renal function, BMI, inflammatory markers, and LVEF—were comparable between groups (all p > 0.05). Critically, mortality rates did not differ significantly between patients in the therapeutic window and those with HRPR (14.7% vs. 13.0%; OR: 0.87 (95% CI: 0.46–1.53); p = 0.643). HRPR was strongly associated with less-intensive antiplatelet therapy: ticagrelor use was markedly lower in the HRPR group (5.6% vs. 26.0%; OR: 0.17 (95% CI: 0.07–0.36); p < 0.001), and DAPT was less frequent (46.3% vs. 61.2%; OR: 0.55 (95% CI: 0.37–0.82); p = 0.003), reflecting expected pharmacodynamic differences (Table 3 and Table A3).
3.3. U-Shaped Relationship Between PRU Quartiles and Mortality: A Key Finding
A consistent non-linear (U-shaped) relationship between PRU and mortality was identified across all antiplatelet therapy subgroups, the primary novel finding of this study. In the monotherapy subgroup (n = 402), patients were stratified into quartiles (Q1: PRU ~120, Q2: PRU ~155, Q3: PRU ~190, and Q4: PRU ~220). The highest mortality was observed in Q1 (16.0%), with progressively lower rates in Q2 (7.9%) and Q3 (14.3%), and partial recovery in Q4 (11.0%). Although overall quartile differences did not reach statistical significance (p = 0.491), the bimodal pattern—with mortality peaks at both extremes of the PRU distribution—was numerically pronounced and clinically meaningful (Table A4).
In the DAPT subgroup (n = 598), this pattern was even more pronounced: Q1 mortality was 19.2%, compared with 11.7% (Q2), 12.8% (Q3), and 11.2% (Q4). Notably, this gradient approached statistical significance (p = 0.136). Importantly, the pattern was consistent regardless of whether patients received clopidogrel or ticagrelor, arguing against a drug-specific effect (Table 4).
Probability density analyses confirmed a bimodal distribution of mortality events across PRU values, with a dominant peak in the lowest PRU range. This pattern persisted across all treatment subgroups, supporting the hypothesis that very low platelet reactivity—representing excessive platelet inhibition—is independently associated with adverse outcomes, irrespective of the antiplatelet agent used (Figure 2 and Figure 3).
Probability density distribution of P2Y12 reaction units (PRU) in patients who died (non-survivors) and patients who survived (survivors) in the dual antiplatelet therapy (DAPT) subgroup. The x-axis represents PRU values; the y-axis represents the probability density. Separate curves are shown for survivors (blue) and non-survivors (red). No confidence interval shaded area is displayed; curves represent kernel density estimates (Figure 3).
Figure 2: Probability density distribution of P2Y12 reaction units (PRU) in patients who died (non-survivors) and patients who survived (survivors) in the monotherapy subgroup. The x-axis represents PRU values; the y-axis represents the probability density. Separate curves are shown for survivors (blue) and non-survivors (red). No confidence interval shaded area is displayed; curves represent kernel density estimates. Probability density distribution of P2Y12 reaction units (PRU) in patients who died (non-survivors) and patients who survived (survivors) within the monotherapy subgroup. The x-axis represents PRU values, and the y-axis represents the probability density. Separate curves are shown for survivors (blue) and non-survivors (red). No confidence interval shading is displayed; the curves represent kernel density estimates.
3.4. PRU-Stratified Analysis: PRU < 155 vs. PRU ≥ 155
To further characterize mortality risk at low and higher PRU values, patients were dichotomized at PRU 155 (cohort median). In the low-PRU subgroup (n = 454), non-survivors were significantly older (70.5 vs. 66.0 years; OR: 1.06 (95% CI: 1.04–1.09); p < 0.001), had a lower eGFR (p = 0.004), reduced hemoglobin (p = 0.034), and elevated inflammatory markers (CRP p = 0.036; fibrinogen p = 0.048). PRU itself did not differ between survivors and non-survivors within this stratum (p = 0.700), indicating that conventional risk factors—rather than PRU per se—drive mortality in this group. In the high-PRU subgroup (n = 546), age, female sex, impaired renal function, low LVEF, low triglycerides, and prior myocardial infarction were significant predictors of mortality, while PRU again did not differ between outcome groups (p = 0.902) (Table A1 and Table A5). This paradoxical finding reinforces the concept that the observed U-shaped relationship is a population-level phenomenon reflecting extreme platelet reactivity at both poles, rather than a simple dose-response effect of PRU within strata. Importantly, the within-stratum absence of PRU differences between survivors and non-survivors does not refute the population-level U-shaped association; rather, it is the expected result of analyzing a non-linear relationship within pre-defined subgroups restricted to narrow portions of the distribution. The U-shaped pattern is a phenomenon of the full PRU continuum, and it persists when mortality rates are normalized against the density of the overall PRU distribution (Figure 3).
3.5. Antiplatelet Therapy and Mortality
All cases of mortality were due to cardiovascular events. No significant association was observed between antiplatelet therapy type and mortality. Death rates were comparable between patients receiving clopidogrel and those not receiving it (15.5% vs. 13.8%) and between ticagrelor-treated and non-ticagrelor patients (13.6% vs. 14.7%). Monotherapy and DAPT were similarly associated with mortality (13.9% vs. 14.7%; p = 0.799). These findings indicate that the PRU-mortality relationship is independent of antiplatelet drug choice.
4. Discussion
In this large real-world cohort of 1000 patients with coronary artery disease (CAD), we demonstrate that the association between platelet reactivity and cardiovascular mortality is distinctly non-linear, following a U-shaped pattern. Mortality risk was increased at both extremes of the PRU distribution, with the highest risk consistently observed in the lowest PRU quartile across all treatment subgroups, including monotherapy, dual antiplatelet therapy (DAPT), and both clopidogrel- and ticagrelor-based regimens [15,16,17]. These findings suggest that excessive platelet inhibition may be as clinically detrimental as insufficient inhibition. Notably, conventional linear models failed to detect this relationship, whereas spline-based analyses revealed the underlying risk structure, underscoring the importance of modeling non-linearity in biomarker-outcome associations [18,19].
These observations have important mechanistic and clinical implications. Current antiplatelet strategies are largely oriented toward reducing thrombotic risk through intensified platelet inhibition. However, the potential adverse consequences of excessive platelet suppression, including bleeding complications, impaired vascular repair, and broader pleiotropic effects, have received comparatively less emphasis [20]. Our findings provide real-world evidence that very low PRU values are associated with a meaningful increase in mortality risk, challenging the prevailing assumption that lower platelet reactivity is uniformly beneficial [13].
The concept of an optimal therapeutic window of platelet reactivity is biologically plausible and supported by prior evidence. High on-treatment platelet reactivity (HPR) has been consistently associated with ischemic events, whereas low platelet reactivity has been linked to increased bleeding risk, including gastrointestinal and intracranial hemorrhage [2,3,4]. Our study extends this paradigm by demonstrating that the mortality impact of extreme PRU values is consistent across different antiplatelet agents, suggesting that the observed U-shaped relationship reflects a fundamental biological phenomenon rather than a drug-specific effect.
Several independent predictors of mortality identified in our cohort—advanced age, female sex [1], reduced left ventricular ejection fraction (LVEF) [12], obstructive CAD [6], and renal dysfunction—are consistent with the established literature [5,21]. Importantly, the non-linear association between PRU and mortality persisted after adjustment for these factors, indicating that platelet reactivity provides incremental prognostic information beyond traditional risk markers. The observed inverse association between smoking and mortality in the univariable analysis likely reflects residual confounding or selection bias (commonly referred to as the “smoker’s paradox”) rather than a true protective effect [7].
This study has several strengths, including a relatively large sample size, inclusion of diverse antiplatelet therapy strategies, and the consistent reproducibility of the U-shaped association across subgroups. The use of the VerifyNow P2Y12 assay, a widely validated and guideline-endorsed tool, enhances the clinical applicability of our findings. From a translational perspective, these results support a shift toward individualized antiplatelet therapy. Rather than adopting a uniform strategy of treatment intensification or de-escalation, clinicians may need to target an optimal range of platelet reactivity, particularly in patients with competing ischemic and bleeding risks.
5. Limitations
Several limitations should be acknowledged. First, the observational and single-center design limits causal inference. Second, platelet reactivity was assessed at a single time point, which may not capture temporal variability related to treatment changes or disease progression. Third, the use of logistic regression rather than Cox survival analysis represents a methodological limitation. Although follow-up was complete within the study window, future studies with prospective time-to-event data collection should employ proportional hazards regression and spline-based Cox models. Despite multivariable adjustment, residual confounding due to unmeasured variables cannot be excluded. Fourth, the study endpoint was confined to cardiovascular mortality; non-fatal cardiovascular events were not included, which may limit the interpretation of the observed U-shaped association between platelet reactivity and outcomes. The absence of a formal independent outcome adjudication committee represents a limitation, and the possibility of residual misclassification of cause of death cannot be entirely excluded. Additionally, because deaths from bleeding were not classified as cardiovascular events, this study may underestimate the full mortality burden attributable to low PRU in patients with excessive platelet inhibition. Finally, the study population represents a single Central Asian center, and external generalizability to other populations and ethnic groups requires further validation.
6. Conclusions
In a real-world cohort of 1000 patients with coronary artery disease receiving antiplatelet therapy, platelet reactivity demonstrated a robust non-linear (U-shaped) association with cardiovascular mortality, with increased risk at both the lowest and highest ends of the PRU spectrum, irrespective of the antiplatelet regimen used. Conventional linear regression did not capture this relationship, highlighting the importance of spline-based modeling in biomarker-outcome analyses. Advanced age, female sex, reduced LVEF, and obstructive CAD were independently associated with mortality. These findings support the concept of an optimal therapeutic window of platelet reactivity and challenge the paradigm of maximal uniform platelet suppression. Prospective studies are warranted to define target PRU ranges and evaluate whether platelet function-guided antiplatelet therapy improves clinical outcomes in patients with coronary artery disease.
Author Contributions
Conceptualization, S.Z., F.N., D.K. and D.P.; methodology, S.Z., D.K., D.P. and F.N.; formal analysis, S.Z., F.N., D.K., O.S., D.P., L.A., G.U., G.N., A.I., D.A., F.R. and A.A.; investigation, S.Z., D.K., O.S., F.R., A.A., L.A., G.U., G.N., A.I., D.A. and F.N.; resources, S.Z., D.K., F.N., O.S. and A.A.; data curation, S.Z., O.S., F.R., A.A., L.A., G.U., G.N., A.I., D.A., D.K. and F.N.; writing—original draft, S.Z., O.S., D.P., F.R., A.A., A.I., D.K. and F.N.; writing—review and editing, D.P., O.S., F.R., A.A., G.N., D.K. and F.N.; visualization, S.Z., O.S., D.P., F.R., A.A., L.A., G.U., G.N., A.I., D.A., D.K. and F.N.; supervision, S.Z., O.S., D.P., A.I. and D.A. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Institutional Review Board Statement
This study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki. Written informed consent was obtained from all participants prior to inclusion. The study protocol was reviewed and approved by the institutional ethics committee of Hodja Ahmed Yasawi International Kazakh-Turkish University (Protocol No. 53; dated 17 June 2024).
Informed Consent Statement
Informed consent was obtained from all subjects involved in this study.
Data Availability Statement
The raw data supporting the conclusions of this article will be made available by the authors upon request.
Acknowledgments
The authors would like to thank the City Cardiology Center in Almaty, the Republic of Kazakhstan, for support and assistance in initiating this study: Roza Kuanyshbekova. In accordance with the editorial policies of this journal and the recommendations of the International Committee of Medical Journal Editors (ICMJE), the authors disclose that an AI-based language model (Claude (Claude 4), developed by Anthropic, San Francisco, CA, USA) was used during the preparation of this manuscript. The AI tool was employed exclusively for language editing, grammatical revision, and improvement of manuscript readability and structure. All scientific content, study design, data collection, statistical analyses, interpretation of results, and conclusions were performed and verified solely by the authors. No AI tool was used to generate, fabricate, or alter any scientific data or analytical results. The authors accept full and sole responsibility for the integrity, accuracy, and originality of the work presented.
Conflicts of Interest
The authors declare no conflicts of interest.
Abbreviations
The following abbreviations are used in this manuscript:
| ACS | Acute coronary syndrome |
| BMI | Body mass index |
| CAD | Coronary artery disease |
| CRP | C-reactive protein |
| GFR | Glomerular filtration rate |
| HRPR | High residual platelet reactivity |
| LDL-C | Low-density lipoprotein cholesterol |
| HDL-C | High-density lipoprotein cholesterol |
| MACEs | Major adverse cardiovascular events |
| RPR | Residual platelet reactivity |
| TGs | Triglycerides |
| PRU | P2Y12 reaction units |
| DAPT | Dual antiplatelet therapy |
| CKD | Chronic kidney disease |
| MI | Myocardial infarction |
| STEMI | ST-elevation myocardial infarction |
| WHO | World Health Organization |
| LVEF | Left ventricular ejection fraction |
| COPD | Chronic obstructive pulmonary disease |
| HTPR | High on-treatment platelet reactivity |
| HFpEF | Heart failure with preserved ejection fraction |
| ASA | Acetylsalicylic acid |
Appendix A
Table A1.
Differences between survivors and non-survivors in the PRU < 155 subgroup.
| Variable | Survived (N = 376) | Died (N = 78) | OR | p (Univariate) | p (Overall) | N |
|---|---|---|---|---|---|---|
| Age | 66.0 [58.8–72.0] | 70.5 [65.2–78.0] | 1.06 [1.04–1.09] | <0.001 | <0.001 | 454 |
| Sex | 0.054 | 454 | ||||
| Female | 169 (44.9%) | 45 (57.7%) | Reference | Reference | ||
| Male | 207 (55.1%) | 33 (42.3%) | 0.60 [0.36–0.98] | 0.042 | ||
| eGFR | 77.0 [64.0–90.5] | 71.0 [59.0–83.8] | 0.98 [0.97–0.99] | 0.004 | 0.006 | 453 |
| BMI | 28.0 [25.0–31.3] | 27.9 [25.0–31.0] | 0.97 [0.93–1.02] | 0.268 | 0.349 | 454 |
| CRP | 3.33 [2.20–5.72] | 4.35 [3.11–6.80] | 1.01 [1.00–1.03] | 0.139 | 0.036 | 451 |
| Total cholesterol | 4.90 [3.99–5.73] | 4.81 [4.08–5.78] | 0.95 [0.78–1.15] | 0.585 | 0.641 | 453 |
| LDL-C | 3.01 [2.20–3.74] | 3.04 [2.16–3.83] | 1.00 [0.81–1.24] | 0.990 | 0.749 | 454 |
| Triglycerides | 1.30 [1.09–1.61] | 1.30 [1.11–1.82] | 0.91 [0.70–1.18] | 0.479 | 0.436 | 454 |
| Fibrinogen | 3.17 [2.84–3.93] | 3.38 [3.00–4.03] | 1.19 [0.92–1.54] | 0.175 | 0.048 | 454 |
| Type 2 diabetes | 0.104 | 454 | ||||
| No | 291 (77.4%) | 53 (67.9%) | Reference | Reference | ||
| Yes | 85 (22.6%) | 25 (32.1%) | 1.62 [0.94–2.74] | 0.084 | ||
| Prediabetes | 0.481 | 454 | ||||
| No | 363 (96.5%) | 77 (98.7%) | Reference | Reference | ||
| Yes | 13 (3.46%) | 1 (1.28%) | 0.41 [0.02–2.12] | 0.343 | ||
| COVID-19 history | 0.930 | 454 | ||||
| No | 304 (80.9%) | 64 (82.1%) | Reference | Reference | ||
| Yes | 72 (19.1%) | 14 (17.9%) | 0.93 [0.48–1.71] | 0.824 | ||
| COPD | 0.475 | 454 | ||||
| No | 366 (97.3%) | 75 (96.2%) | Reference | Reference | ||
| Yes | 10 (2.66%) | 3 (3.85%) | 1.51 [0.32–5.18] | 0.561 | ||
| Coronary artery disease severity | 0.109 | 454 | ||||
| Non-obstructive | 185 (49.2%) | 30 (38.5%) | Reference | Reference | ||
| Obstructive | 191 (50.8%) | 48 (61.5%) | 1.55 [0.94–2.57] | 0.085 | ||
| D-dimer | 200 [121–480] | 220 [148–588] | 1.00 [1.00–1.00] | 0.029 | 0.252 | 453 |
| Troponin | 0.002 [0.001–0.006] | 0.003 [0.001–0.008] | 0.98 [0.93–1.04] | 0.556 | 0.090 | 454 |
| LVEF (%) | 59.0 [51.0–63.2] | 57.0 [52.0–64.8] | 0.99 [0.97–1.01] | 0.529 | 0.692 | 454 |
| Smoking | 0.902 | 454 | ||||
| No | 318 (84.6%) | 67 (85.9%) | Reference | Reference | ||
| Yes | 58 (15.4%) | 11 (14.1%) | 0.91 [0.43–1.77] | 0.789 | ||
| Hemoglobin | 142 [131–152] | 136 [124–154] | 0.99 [0.97–1.00] | 0.034 | 0.043 | 454 |
| Prior MI | 0.572 | 454 | ||||
| No | 261 (69.4%) | 51 (65.4%) | Reference | Reference | ||
| Yes | 115 (30.6%) | 27 (34.6%) | 1.20 [0.71–2.01] | 0.485 | ||
| PRU | 120 [105–120] | 120 [101–120] | 1.00 [0.97–1.02] | 0.700 | 0.735 | 454 |
| Statin therapy | [121–480] | 0.200 | 454 | |||
| Statin | 349 (92.8%) | 76 (97.4%) | Reference | Reference | ||
| Statin + ezetimibe | 27 (7.18%) | 2 (2.56%) | 0.36 [0.05–1.26] | 0.122 | ||
| HRPR | 0.369 | 454 | ||||
| Bleeding risk | 9 (2.39%) | 0 (0.00%) | Reference | Reference | ||
| Therapeutic window | 367 (97.6%) | 78 (100%) | - | - | ||
| Antiplatelet therapy: | 0.440 | 454 | ||||
| Monotherapy | 180 (47.9%) | 33 (42.3%) | Reference | Reference | ||
| Dual therapy | 196 (52.1%) | 45 (57.7%) | 1.25 [0.76–2.06] | 0.374 | ||
| Ticagrelor use | 0.507 | 454 | ||||
| No | 277 (73.7%) | 54 (69.2%) | Reference | Reference | ||
| Yes | 99 (26.3%) | 24 (30.8%) | 1.25 [0.72–2.11] | 0.424 | ||
| Clopidogrel use | 0.949 | 454 | ||||
| No | 279 (74.2%) | 57 (73.1%) | Reference | Reference | ||
| Yes | 97 (25.8%) | 21 (26.9%) | 1.06 [0.60–1.83] | 0.827 |
Abbrevations: GFR = glomerular filtration rate, CRP = C-reactive protein, BMI = body mass index, LDL-C = low-density lipoprotein cholesterol, COPD = Chronic obstructive pulmonary disease, LVEF = left ventricular ejection fraction, MI = myocardial infarction, PRU = P2Y12 reaction units, HRPR = high residual platelet reactivity.
Table A2.
Full baseline characteristics: survivors vs. non-survivors (n = 1000).
| Variable | Survivors (n = 856) | Non-Survivors (n = 144) | OR (95% CI) | p-Value |
|---|---|---|---|---|
| Age, years | 65.0 [59.0–72.0] | 74.0 [67.0–79.0] | 1.09 [1.07–1.12] | <0.001 |
| Male sex | 483 (56.4%) | 60 (41.7%) | 0.55 [0.38–0.79] | 0.001 |
| eGFR, mL/min/1.73 m2 | 77.0 [65.0–91.0] | 68.0 [55.0–84.0] | 0.98 [0.97–0.99] | <0.001 |
| BMI, kg/m2 | 28.0 [25.0–31.2] | 27.3 [25.0–30.0] | 0.96 [0.92–0.99] | 0.014 |
| Hemoglobin, g/L | 141 [130–151] | 134 [123–146] | 0.98 [0.97–0.99] | <0.001 |
| LVEF, % | 59.0 [51.0–64.0] | 56.0 [45.0–62.2] | 0.98 [0.96–0.99] | 0.008 |
| CRP, mg/L | 3.50 [2.20–6.14] | 4.40 [2.66–8.40] | 1.01 [1.00–1.01] | 0.009 |
| Fibrinogen, g/L | 3.17 [2.80–3.90] | 3.38 [3.00–4.04] | 1.23 [1.03–1.46] | 0.003 |
| D-dimer | 220 [130–500] | 226 [150–665] | 1.00 [1.00–1.00] | 0.163 |
| Troponin I | 0.00 [0.00–0.01] | 220 [130–500] | 0.99 [0.98–1.01] | 0.021 |
| Total cholesterol, mmol/L | 4.90 [4.02–5.78] | 4.74 [3.86–5.60] | 0.87 [0.75–1.00] | 0.069 |
| LDL-C, mmol/L | 3.08 [2.33–3.84] | 2.96 [2.20–3.77] | 0.91 [0.77–1.07] | 0.151 |
| Triglycerides, mmol/L | 1.30 [1.10–1.80] | 1.29 [0.99–1.57] | 0.66 [0.52–0.85] | 0.001 |
| Type 2 diabetes | 208 (24.3%) | 43 (29.9%) | 1.33 [0.89–1.95] | 0.187 |
| Prediabetes | 28 (3.3%) | 3 (2.1%) | 0.66 [0.15–1.90] | 0.606 |
| COVID-19 history | 177 (20.7%) | 27 (18.8%) | 0.89 [0.56–1.38] | 0.675 |
| COPD | 30 (3.5%) | 6 (4.2%) | 1.22 [0.45–2.81] | 0.879 |
| Obstructive CAD | 439 (51.3%) | 90 (62.5%) | 1.58 [1.10–2.29] | 0.016 |
| Prior MI | 249 (29.1%) | 54 (37.5%) | 1.46 [1.01–2.11] | 0.053 |
| Smoking | 156 (18.2%) | 15 (10.4%) | 0.53 [0.29–0.90] | 0.029 |
| PRU | 155 [120–200] | 120 [111–190] | 1.00 [0.99–1.00] | 0.038 |
| HRPR (PRU ≥ 208) | 94 (11.0%) | 14 (9.7%) | - | 0.601 |
| Monotherapy | 346 (40.4%) | 56 (38.9%) | Reference | 0.799 |
| DAPT | 510 (59.6%) | 88 (61.1%) | 1.07 [0.74–1.54] | 0.733 |
| Ticagrelor | 210 (24.5%) | 33 (22.9%) | 0.92 [0.60–1.38] | 0.754 |
| Clopidogrel | 300 (35.1%) | 55 (38.2%) | 1.14 [0.79–1.64] | 0.531 |
| Statin + ezetimibe | 63 (7.4%) | 7 (4.9%) | 0.66 [0.27–1.37] | 0.362 |
BMI = body mass index; CAD = coronary artery disease; COPD = chronic obstructive pulmonary disease; CRP = C-reactive protein; DAPT = dual antiplatelet therapy; eGFR = estimated glomerular filtration rate; HRPR = high on-treatment platelet reactivity; LDL-C = low-density lipoprotein cholesterol; LVEF = left ventricular ejection fraction; MI = myocardial infarction; PRU = P2Y12 reaction units.
Table A3.
Full comparison: therapeutic window vs. HRPR (n = 991).
| Variable | Therapeutic Window (n = 883) | HRPR (n = 108) | OR (95% CI) | p-Value |
|---|---|---|---|---|
| Age, years | 67.0 [60.0–73.0] | 66.5 [59.0–75.0] | 1.00 [0.98–1.02] | 0.749 |
| Male sex | 482 (54.6%) | 57 (52.8%) | 0.93 [0.62–1.39] | 0.8 |
| eGFR, mL/min/1.73 m2 | 76.0 [63.0–90.0] | 75.0 [61.0–89.0] | 1.00 [0.99–1.01] | 0.534 |
| BMI, kg/m2 | 27.9 [25.0–31.1] | 28.7 [25.1–32.0] | 1.02 [0.98–1.06] | 0.248 |
| CRP, mg/L | 3.60 [2.20–6.60] | 3.95 [2.64–6.60] | 0.99 [0.98–1.01] | 0.596 |
| Fibrinogen, g/L | 3.22 [2.80–3.93] | 3.34 [3.00–3.99] | 1.18 [0.97–1.43] | 0.121 |
| Total cholesterol, mmol/L | 4.88 [4.03–5.77] | 4.88 [4.00–5.51] | 0.93 [0.79–1.09] | 0.489 |
| LDL-C, mmol/L | 3.09 [2.30–3.84] | 2.98 [2.42–3.61] | 0.87 [0.72–1.05] | 0.243 |
| Triglycerides, mmol/L | 1.30 [1.09–1.81] | 1.30 [1.04–1.57] | 0.91 [0.74–1.11] | 0.384 |
| LVEF, % | 59.0 [51.0–64.0] | 59.0 [48.8–65.0] | 1.00 [0.98–1.02] | 0.923 |
| Type 2 diabetes | 218 (24.7%) | 29 (26.9%) | 1.12 [0.70–1.75] | 0.618 |
| Obstructive CAD | 459 (52.0%) | 65 (60.2%) | 1.39 [0.93–2.11] | 0.108 |
| Prior MI | 263 (29.8%) | 36 (33.3%) | 1.18 [0.76–1.80] | 0.448 |
| Smoking | 153 (17.3%) | 18 (16.7%) | 0.96 [0.55–1.61] | 0.883 |
| Hemoglobin, g/L | 140 [129–151] | 139 [126–151] | 1.00 [0.99–1.01] | 0.569 |
| Mortality | 130 (14.7%) | 14 (13.0%) | 0.87 [0.46–1.53] | 0.73 |
| PRU | 150 [120–188] | 230 [217–255] | - | <0.001 |
| DAPT | 540 (61.2%) | 50 (46.3%) | 0.55 [0.37–0.82] | 0.004 |
| Ticagrelor | 230 (26.0%) | 6 (5.6%) | 0.17 [0.07–0.36] | <0.001 |
| Clopidogrel | 310 (35.1%) | 44 (40.7%) | 1.27 [0.84–1.91] | 0.299 |
All abbreviations as in Table A3.
Table A4.
Full descriptive statistics across PRU quartiles (monotherapy subgroup; n = 402).
| Variable | Q1 (n = 200) | Q2 (n = 38) | Q3 (n = 91) | Q4 (n = 73) | p-Overall |
|---|---|---|---|---|---|
| Median PRU | 120 [110–120] | 155 [150–155] | 190 [180–200] | 220 [210–235] | <0.001 |
| Age, years | 66.0 [60.0–72.0] | 67.5 [57.0–73.8] | 68.0 [61.0–73.5] | 66.0 [60.0–75.0] | 0.707 |
| Male sex | 89 (44.5%) | 17 (44.7%) | 37 (40.7%) | 37 (50.7%) | 0.646 |
| eGFR, mL/min/1.73 m2 | 77.0 [65.0–91.0] | 82.0 [73.2–96.0] | 74.0 [61.5–90.0] | 78.0 [61.0–90.0] | 0.327 |
| BMI, kg/m2 | 28.0 [25.0–31.2] | 27.8 [25.4–31.1] | 28.4 [25.4–30.8] | 29.7 [25.4–32.7] | 0.381 |
| CRP, mg/L | 3.30 [2.80–4.60] | 3.30 [2.59–4.78] | 3.30 [2.30–4.50] | 3.30 [2.25–5.00] | 0.914 |
| Fibrinogen, g/L | 3.39 [3.00–4.00] | 3.21 [2.81–3.68] | 3.20 [2.79–3.84] | 3.31 [2.94–4.00] | 0.19 |
| Total cholesterol, mmol/L | 4.92 [4.30–5.70] | 4.96 [3.79–5.70] | 4.75 [3.85–5.73] | 4.97 [4.16–5.70] | 0.819 |
| LDL-C, mmol/L | 3.00 [2.30–3.72] | 3.10 [2.43–3.69] | 2.96 [2.35–3.61] | 2.85 [2.40–3.50] | 0.788 |
| Triglycerides, mmol/L | 1.30 [1.10–1.42] | 1.30 [1.13–1.48] | 1.30 [1.12–1.50] | 1.30 [1.04–1.40] | 0.481 |
| LVEF, % | 60.0 [53.0–64.0] | 58.0 [50.0–64.8] | 60.0 [51.2–66.0] | 59.0 [51.0–65.0] | 0.916 |
| Hemoglobin, g/L | 141 [130–151] | 138 [129–150] | 138 [126–148] | 141 [129–148] | 0.397 |
| Type 2 diabetes | 34 (17.0%) | 10 (26.3%) | 23 (25.3%) | 18 (24.7%) | 0.247 |
| Obstructive CAD | 63 (31.5%) | 13 (34.2%) | 27 (29.7%) | 34 (46.6%) | 0.091 |
| Prior MI | 46 (23.0%) | 6 (15.8%) | 18 (19.8%) | 19 (26.0%) | 0.592 |
| Smoking | 30 (15.0%) | 5 (13.2%) | 7 (7.7%) | 12 (16.4%) | 0.312 |
| COPD | 5 (2.5%) | 1 (2.6%) | 0 (0.0%) | 5 (6.9%) | 0.052 |
| COVID-19 history | 39 (19.5%) | 6 (15.8%) | 17 (18.7%) | 12 (16.4%) | 0.913 |
| D-dimer | 190 [100–358] | 180 [100–330] | 188 [100–315] | 200 [120–350] | 0.871 |
| Troponin I | 0.001 [0.001–0.002] | 0.002 [0.001–0.003] | 0.001 [0.001–0.003] | 0.001 [0.001–0.003] | 0.556 |
| HRPR (PRU ≥ 208) | 0 (0.0%) | 0 (0.0%) | 0 (0.0%) | 58 (79.5%) | <0.001 |
| Mortality | 32 (16.0%) | 3 (7.9%) | 13 (14.3%) | 8 (11.0%) | 0.491 |
| Statin + ezetimibe | 14 (7.0%) | 2 (5.3%) | 9 (9.9%) | 2 (2.7%) | 0.335 |
All patients in this subgroup received antiplatelet monotherapy. Q1 = lowest PRU quartile; Q4 = highest PRU quartile. All abbreviations as in Table A3.
Table A5.
Differences between survivors and non-survivors in the PRU ≥ 155 subgroup.
| Variable | Survived (N = 480) | Died (N = 66) | OR | p (Univariate) | p (Overall) | N |
|---|---|---|---|---|---|---|
| Age | 65.0 [59.0–72.0] | 75.0 [71.0–80.8] | 1.14 [1.10–1.18] | <0.001 | <0.001 | 546 |
| Sex | 0.016 | 546 | ||||
| Female | 204 (42.5%) | 39 (59.1%) | Reference | Reference | ||
| Male | 276 (57.5%) | 27 (40.9%) | 0.51 [0.30–0.86] | 0.012 | ||
| eGFR | 77.0 [65.0–92.0] | 65.5 [53.0–85.5] | 0.97 [0.96–0.99] | <0.001 | <0.001 | 544 |
| BMI | 28.0 [25.0–31.2] | 26.5 [25.0–29.3] | 0.93 [0.88–0.99] | 0.017 | 0.008 | 546 |
| CRP | 3.60 [2.20–6.54] | 5.12 [2.20–10.3] | 1.01 [1.00–1.01] | 0.051 | 0.092 | 543 |
| Total cholesterol | 4.90 [4.10–5.80] | 4.70 [3.79–5.45] | 0.79 [0.64–0.98] | 0.032 | 0.034 | 546 |
| LDL-C | 3.10 [2.42–3.90] | 2.92 [2.32–3.68] | 0.82 [0.64–1.05] | 0.117 | 0.097 | 545 |
| Triglycerides | 1.30 [1.11–2.05] | 1.15 [0.85–1.39] | 0.38 [0.23–0.64] | <0.001 | <0.001 | 546 |
| Fibrinogen | 3.16 [2.75–3.84] | 3.44 [2.96–4.16] | 1.26 [1.00–1.60] | 0.052 | 0.029 | 545 |
| Type 2 diabetes | 0.891 | 546 | ||||
| No | 357 (74.4%) | 48 (72.7%) | Reference | Reference | ||
| Yes | 123 (25.6%) | 18 (27.3%) | 1.09 [0.60–1.92] | 0.764 | ||
| Prediabetes | 1.000 | 546 | ||||
| No | 465 (96.9%) | 64 (97.0%) | Reference | Reference | ||
| Yes | 15 (3.12%) | 2 (3.03%) | 1.03 [0.15–3.81] | 0.969 | ||
| COVID-19 history | 0.808 | 546 | ||||
| No | 375 (78.1%) | 53 (80.3%) | Reference | Reference | ||
| Yes | 105 (21.9%) | 13 (19.7%) | 0.88 [0.44–1.64] | 0.705 | ||
| COPD | 0.750 | 546 | ||||
| No | 460 (95.8%) | 63 (95.5%) | Reference | Reference | ||
| Yes | 20 (4.17%) | 3 (4.55%) | 1.14 [0.25–3.48] | 0.840 | ||
| Coronary artery disease severity | 0.090 | 546 | ||||
| Non-obstructive | 232 (48.3%) | 24 (36.4%) | Reference | Reference | ||
| Obstructive | 248 (51.7%) | 42 (63.6%) | 1.63 [0.96–2.82] | 0.069 | ||
| D-dimer | 220 [132–505] | 245 [150–668] | 1.00 [1.00–1.00] | 0.049 | 0.381 | 545 |
| Troponin | 0.003 [0.001–0.013] | 0.005 [0.002–0.052] | 1.00 [0.98–1.02] | 0.756 | 0.034 | 545 |
| LVEF (%) | 59.0 [51.8–64.0] | 53.5 [42.2–61.0] | 0.96 [0.94–0.98] | 0.001 | 0.001 | 546 |
| Smoking | 0.008 | 546 | ||||
| No | 382 (79.6%) | 62 (93.9%) | Reference | Reference | ||
| Yes | 98 (20.4%) | 4 (6.06%) | 0.26 [0.08–0.65] | 0.002 | ||
| Hemoglobin | 140 [129–151] | 134 [122–145] | 0.98 [0.96–0.99] | 0.001 | 0.003 | 546 |
| Prior MI | 0.043 | 546 | ||||
| No | 346 (72.1%) | 39 (59.1%) | Reference | Reference | ||
| Yes | 134 (27.9%) | 27 (40.9%) | 1.79 [1.04–3.03] | 0.035 | ||
| PRU | 200 [175–205] | 194 [176–204] | 1.00 [0.99–1.01] | 0.902 | 0.984 | 546 |
| Statin therapy | 1.000 | 546 | ||||
| Statin | 444 (92.5%) | 61 (92.4%) | Reference | Reference | ||
| Statin + ezetimibe | 36 (7.50%) | 5 (7.58%) | 1.04 [0.34–2.54] | 0.943 | ||
| HRPR | 0.883 | 546 | ||||
| Therapeutic window | 386 (80.4%) | 52 (78.8%) | Reference | Reference | ||
| BORT outside range | 94 (19.6%) | 14 (21.2%) | 1.11 [0.57–2.05] | 0.742 | ||
| Antiplatelet therapy: | 1.000 | 546 | ||||
| Monotherapy | 166 (34.6%) | 23 (34.8%) | Reference | Reference | ||
| Dual therapy | 314 (65.4%) | 43 (65.2%) | 0.99 [0.58–1.72] | 0.958 | ||
| Ticagrelor use | 0.113 | 546 | ||||
| No | 369 (76.9%) | 57 (86.4%) | Reference | Reference | ||
| Yes | 111 (23.1%) | 9 (13.6%) | 0.53 [0.24–1.06] | 0.075 | ||
| Clopidogrel use | 0.204 | 545 | ||||
| No | 276 (57.6%) | 32 (48.5%) | Reference | Reference | ||
| Yes | 203 (42.4%) | 34 (51.5%) | 1.44 [0.86–2.43] | 0.165 |
Non-linear relationship between platelet reactivity and all-cause mortality in coronary artery disease: evidence for an optimal therapeutic window. Abbrevations: GFR = glomerular filtration rate, CRP = C-reactive protein, BMI = body mass index, LDL-C = low-density lipoprotein cholesterol, COPD = Chronic obstructive pulmonary disease, LVEF = left ventricular ejection fraction, MI = myocardial infarction, PRU = P2Y12 reaction units, HRPR = high residual platelet reactivity.
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Figure 1.
Flow chart of patients.
Figure 2.
Probability density of mortality outcome in monotherapy subgroup.
Figure 3.
Probability density distribution of P2Y12 reaction units (PRU) in patients who died (non-survivors) and patients who survived (survivors) in the dual antiplatelet therapy subgroup.
Figure 3.
Probability density distribution of P2Y12 reaction units (PRU) in patients who died (non-survivors) and patients who survived (survivors) in the dual antiplatelet therapy subgroup.
Table 1.
Baseline characteristics and univariable predictors of mortality.
| Variable | Survivors (n = 856) | Non-Survivors (n = 144) | OR (95% CI) | p-Value |
|---|---|---|---|---|
| Age, years | 65.0 [59.0–72.0] | 74.0 [67.0–79.0] | 1.09 [1.07–1.12] | <0.001 |
| Male sex | 483 (56.4%) | 60 (41.7%) | 0.55 [0.38–0.79] | 0.001 |
| eGFR, mL/min/1.73 m2 | 77.0 [65.0–91.0] | 68.0 [55.0–84.0] | 0.98 [0.97–0.99] | <0.001 |
| Hemoglobin, g/L | 141 [130–151] | 134 [123–146] | 0.98 [0.97–0.99] | <0.001 |
| LVEF, % | 59.0 [51.0–64.0] | 56.0 [45.0–62.2] | 0.98 [0.96–0.99] | 0.004 |
| CRP, mg/L | 3.50 [2.20–6.14] | 4.40 [2.66–8.40] | 1.01 [1.00–1.01] | 0.03 |
| Fibrinogen, g/L | 3.17 [2.80–3.90] | 3.38 [3.00–4.04] | 1.23 [1.03–1.46] | 0.021 |
| Triglycerides, mmol/L | 1.30 [1.10–1.80] | 1.29 [0.99–1.57] | 0.66 [0.52–0.85] | 0.001 |
| Obstructive CAD | 439 (51.3%) | 90 (62.5%) | 1.58 [1.10–2.29] | 0.013 |
| Prior MI | 249 (29.1%) | 54 (37.5%) | 1.46 [1.01–2.11] | 0.046 |
| Smoking | 156 (18.2%) | 15 (10.4%) | 0.53 [0.29–0.90] | 0.017 |
| PRU | 155 [120–200] | 120 [111–190] | 1.00 [0.99–1.00] | 0.046 |
CAD = coronary artery disease; CRP = C-reactive protein; eGFR = estimated glomerular filtration rate; LVEF = left ventricular ejection fraction; MI = myocardial infarction; PRU = P2Y12 reaction units.
Table 2.
Multivariable logistic regression model for mortality.
| Variable | OR (95% CI) | p-Value |
|---|---|---|
| Age (per year) | 1.08 [1.06–1.11] | <0.001 |
| Male sex | 0.53 [0.33–0.86] | 0.011 |
| LVEF (per unit) | 0.98 [0.96–1.00] | 0.016 |
| PRU (per unit) | 1.00 [0.99–1.00] | 0.033 |
| Obstructive CAD | 1.46 [0.93–2.31] | 0.098 |
| Total cholesterol | 0.77 [0.58–1.02] | 0.076 |
| Observations | 986 | |
| Tjur R2 | 0.141 |
Only variables with p < 0.15 are shown. LVEF = left ventricular ejection fraction; PRU = P2Y12 reaction units.
Table 3.
Comparison of patients within the therapeutic window vs. high on-treatment platelet reactivity (HRPR).
Table 3.
Comparison of patients within the therapeutic window vs. high on-treatment platelet reactivity (HRPR).
| Variable | Therapeutic Window (n = 883) | HRPR (n = 108) | OR (95% CI) | p-Value |
|---|---|---|---|---|
| Age, years | 67.0 [60.0–73.0] | 66.5 [59.0–75.0] | 1.00 [0.98–1.02] | 0.749 |
| Male sex | 482 (54.6%) | 57 (52.8%) | 0.93 [0.62–1.39] | 0.722 |
| Mortality | 130 (14.7%) | 14 (13.0%) | 0.87 [0.46–1.53] | 0.643 |
| PRU | 150 [120–188] | 230 [217–255] | - | <0.001 |
| DAPT use | 540 (61.2%) | 50 (46.3%) | 0.55 [0.37–0.82] | 0.003 |
| Ticagrelor use | 230 (26.0%) | 6 (5.6%) | 0.17 [0.07–0.36] | <0.001 |
DAPT = dual antiplatelet therapy; HRPR = high on-treatment platelet reactivity; PRU = P2Y12 reaction units.
Table 4.
Mortality rates across PRU quartiles by antiplatelet therapy subgroup.
| Subgroup | Q1 (Lowest PRU) | Q2 | Q3 | Q4 (Highest PRU) |
|---|---|---|---|---|
| Monotherapy (n = 402) | 16.00% | 7.90% | 14.30% | 11.00% |
| DAPT (n = 598) | 19.20% | 11.70% | 12.80% | 11.20% |
| Median PRU (approx.) | ~120 | ~155 | ~190 | ~220 |
Mortality rates are shown as a percentage of patients in each quartile. Q1 = lowest platelet reactivity; Q4 = highest platelet reactivity. DAPT = dual antiplatelet therapy.
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Zhangelova, S.; Sakhov, O.; Abisheva, L.; Polyakov, D.; Rustamova, F.; Almukhanova, A.; Umenova, G.; Nurgaliyeva, G.; Izhanova, A.; Akhmentayeva, D.; et al. Beyond Linear Risk: A U-Shaped Association Between Platelet Reactivity and Mortality in Coronary Artery Disease. Diseases 2026, 14, 194. https://doi.org/10.3390/diseases14060194
AMA Style
Zhangelova S, Sakhov O, Abisheva L, Polyakov D, Rustamova F, Almukhanova A, Umenova G, Nurgaliyeva G, Izhanova A, Akhmentayeva D, et al. Beyond Linear Risk: A U-Shaped Association Between Platelet Reactivity and Mortality in Coronary Artery Disease. Diseases. 2026; 14(6):194. https://doi.org/10.3390/diseases14060194
Chicago/Turabian StyleZhangelova, Sholpan, Orazbek Sakhov, Lyazat Abisheva, Dmitriy Polyakov, Farida Rustamova, Aizhan Almukhanova, Galiya Umenova, Gulzada Nurgaliyeva, Aigyul Izhanova, Dana Akhmentayeva, and et al. 2026. "Beyond Linear Risk: A U-Shaped Association Between Platelet Reactivity and Mortality in Coronary Artery Disease" Diseases 14, no. 6: 194. https://doi.org/10.3390/diseases14060194
APA StyleZhangelova, S., Sakhov, O., Abisheva, L., Polyakov, D., Rustamova, F., Almukhanova, A., Umenova, G., Nurgaliyeva, G., Izhanova, A., Akhmentayeva, D., Kapsultanova, D., & Nurmukhammad, F. (2026). Beyond Linear Risk: A U-Shaped Association Between Platelet Reactivity and Mortality in Coronary Artery Disease. Diseases, 14(6), 194. https://doi.org/10.3390/diseases14060194
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