Introduction

Acute coronary syndrome (ACS) remains a major global health burden and is particularly crucial in the UAE, where cardiovascular disease (CVD) contributes to morbidity and mortality rates. Following coronary intervention (PCI), the selection of dual antiplatelet therapy (DAPT) duration require balance to minimize ischemic risk and manage, the risk of bleeding. [1] Innovation in drug-eluting stents (DES) and risk scoring based on personalized risk factors have assisted in clotting prevention, but the ability to balance clotting prevention and minimize bleeding still remains complicated. [2,3] This problem is particularly relevant in the UAE where a diverse population are highly heterogenous by genetic, metabolic, and lifestyle variations. [4] Although the period of DAPT should be individualized due to competing risks, there is no current practice of individualization, and the widely used protocols or fixed risk scores are PRECISE-DAPT and DAPT. These models are limited by their inability to dynamically adapt to evolve patient profiles and their multifaceted interactions among risk factors. [5] This research gap highlight the need for improved risk stratification approaches capable of integrating multidimensional medical information to support personalized DAPT decision-making beyond one-size-fits-all paradigms. [6]

Although widely used, PRECISE-DAPT and DAPT scores offer, limited predictive accuracy for individual patients. Machine learning methods capture complex nonlinear relationships among clinical variables and has the potential to improve risk prediction beyond conventional risk scores. [7] Similar observations have been reported in several East Asian population, where patients often emphasize the necessity of flexible treatment, demonstrate lower thrombotic risk but are highly bleeding susceptible during the antiplatelet therapy. [8] Therefore, fixed-duration DAPT regimens may be poorly suited to such heterogeneous populations, highlighting the importance of enhanced individualized risk stratification. [9] Risk prediction models based on machine learning can incorporate diverse patient data to estimate stent restenosis and other morbidities with higher accuracy. [10] This study focuses on clinical, procedural, and laboratory predictors rather than imaging-based or multi-omics inputs, which remain an important area for future investigation. Such approaches may be particularly useful in regions such as the UAE and Oman, where different populations and genetic conditions require a more delicate approach to DAPT strategies to improve the risk stratification and support adherence to guideline-based therapy decisions. [11]

Recent machine learning models have shown improved discrimination for predicting ischemic and bleeding events after PCI compared with traditional risk scores. [12] These models offer the potential to support dynamic post-PCI risk stratification by accounting for evolving comorbidities and treatment-related factors. [13] In addition, the potential clinical and economic implications of risk-guided strategies remain insufficiently explored. [14,15] This evidence gap is particularly relevant in the Arabian Gulf, where DAPT strategies derived from guidelines may not fully reflect population-specific risk profiles, contributing to variability in clinical decision-making and outcomes. Therefore, multicohort validation across distinct healthcare systems, combined with economic evaluation is essential to assess the generalizability and practical relevance of risk-guided DAPT personalization. [16]

Existing AI models for cardiac event prediction, such as those developed for the Malaysian population, demonstrate the capability of machine learning frameworks to create generalizable risk assessments for guiding revascularization strategies and improving patient safety. [17] Consequently, the development of robust AI models for DAPT duration personalization necessitates multicenter data from diverse cohorts to ensure broad applicability and equitable performance across different ethnic and genetic backgrounds prevalent in the UAE. [18] The economic burden of CVD adverse events cases in the United Arab Emirates, further supports the need of cost-efficient and tailored dual antiplatelet therapy (DAPT) interventions that could inform clinical-decision making. [19] Although prior studies have demonstrated higher discrimination performance of AI models for PCI-related outcomes, including bleeding risk, restenosis, and mortality, downstream clinical and economic implications remain insufficiently characterized. Genetic variability affecting platelet reactivity and antiplatelet metabolism adds further complexity, reinforcing the need for cautious, exploratory evaluation of AI-driven strategies and continued validation across diverse populations. Moreover, the predictive properties of traditional models based on nonlinear relations and higher-order interactions between various risk factors can be difficult to capture, thus restricting their personal-level predictive validity and epidemiological significance. Moreover, changes in platelet reactivity and the dynamic nature of the current knowledge regarding the relationship between genetics and the effect of DAPT require a constant revision of models and validation of this relationship using prospective and real-life data in different populations. [20–22]

Nevertheless, the integration of AI models in the clinical setting of cardiovascular care continues to be complicated by several issues such as ethics, model interpretability, and cost-effectiveness of the mass application. [23] These considerations are particularly relevant in heterogeneous healthcare environments, such as the UAE and neighboring Gulf countries, where robust external validation and careful evaluation of clinical utility are required due to population diversity and practice variability. [24] Accordingly, this study aimed to develop and externally validate a machine learning-based prediction model for ischemic and major bleeding events following PCI using routinely available clinical, procedural, and laboratory variables. The model was developed using a UAE PCI cohort and externally validated using the MIMIC-IV database. Model performance was compared with established risk scores, and exploratory analyses were conducted to evaluate potential differences in risk-guided management strategies using simulation-based projections. This work is intended as a proof-of-concept evaluation to inform future prospective studies, rather than to provide recommendations for practice-changing recommendations.

Method

Result

Study population and cohort derivation

A multicenter UAE PCI registry and a data set of a MIMIC-IV critical care were used as independent data sources to identify a cohort of consecutive patients receiving PCI and dual antiplatelet therapy (DAPT). The final analytic cohorts based on the use of prespecified inclusion, exclusion criteria and the imputation of missing data included a UAE development cohort to train and internally validate the models and an external validation cohort based on MIMIC-IV. Fig 1 shows the cohort derivation process, including exclusions related to the missing key covariates and insufficient follow-up. S5 Fig provides further details and Table 1 summarizes the baseline demographic, clinical, and procedural data of both cohorts. Additional baseline characteristics stratified according to ischemic and bleeding event status are presented in Supplementary S8 Table. The UAE development cohort and external validation cohort differed in several baseline characteristics representing anticipated heterogeneity across health care systems and populations. Baseline differences included age distribution, prevalence of cardiovascular comorbidities, severity of illness, and event rates, reflecting the ICU-enriched nature of the MIMIC-IV cohort relative to the UAE PCI registry (Table 1). Despite these differences, both cohorts included various ischemic and bleeding risk profiles, thereby contributing to the evaluation of model transportability and generalizability.

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Table 1. Baseline demographic, clinical, and procedural characteristics of patients undergoing percutaneous coronary intervention (PCI) in the UAE development cohort and the external validation cohort derived from the MIMIC-IV database.

https://doi.org/10.1371/journal.pone.0353066.t001

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Fig 1. Study design and analytical workflow.

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Clinical outcomes and event rates

Prespecified ischemic and bleeding outcomes were consistently defined and applied across both cohorts. Composite ischemic outcomes included myocardial infarction, ischemic stroke, stent thrombosis, and cardiovascular death. Major bleeding outcomes were defined according to Bleeding Academic Research Consortium (BARC) type 3 or type 5 criteria were harmonized across development and validation cohorts using standardized outcome definitions. Table 2 presents the observed cumulative event rates individual and composite ischemic and bleeding endpoints. As expected, ischemic and bleeding events occurred at clinically meaningful frequencies in both cohorts, with moderately higher event rates observed in the external validation cohort, consistent with its critical care-enriched population. These observed outcome distributions provide an appropriate empirical basis for model development, internal validation, and independent external evaluation.

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Table 2. Clinical outcome definitions and observed event rates for ischemic and bleeding endpoints in the UAE development cohort and the external validation cohort. Composite outcomes were prespecified and applied uniformly across datasets to ensure consistency in model development and validation.

https://doi.org/10.1371/journal.pone.0353066.t002

Predictive performance of the primary artificial intelligence model

Table 3 summarizes the discrimination and classification performance of the primary weighted LightGBM model for ischemic and bleeding risk prediction. Model training was conducted exclusively within the UAE development cohort, with cross-validation for internal validation. External validation was performed without recalibration in the independent MIMIC-IV cohort. LightGBM model demonstrated an AUROC of 0.87 (95 percent confidence interval 0.840.89) in ischemic events and 0.85 (95% CI 0.82–0.88) for bleeding events in internal validation. External validation demonstrated AUROC values of 0.84 (95% CI 0.82–0.86) for ischemic outcomes and 0.82 (95% CI 0.79–0.85) for bleeding outcomes. The AI model achieved AUROC values of 0.87 and 0.85 for ischemic and bleeding outcomes in internal validation respectively. The AUPRC values and AUROC area are consistent across cohorts, with expected decreased attenuation during external validation. Specificity, sensitivity, and F1-scores demonstrated equitable classification performance at prespecified thresholds. The time-window-specific performance metrics measured consistent discrimination across early, mid, and late post-PCI periods, thereby supporting the AI-based approach’s temporal robustness (S5 Table).

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Table 3. Predictive performance of the primary AI model across outcomes and validation cohorts.

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Comparison with the conventional DAPT risk scores

Table 4 and Fig 2 shows the Comparative discrimination performance between the AI model and established clinical risk scores. In both the internal and external validation cohorts, the weighted LightGBM model consistently demonstrated higher AUROC compared with PRECISE-DAPT and DAPT scores in predicting ischemic and bleeding events. Receiver operating characteristic curves in external validation demonstrated higher discrimination performance of the AI model across clinically relevant risk thresholds (Fig 2A-B). Fig 2 presents standard receiver operating characteristic (ROC) curves comparing sensitivity and specificity across classification thresholds for the weighted LightGBM model, logistic regression, and conventional DAPT risk scores. Conventional risk scores showed acceptable but lower performance, consistent with their static, rule-based design. These findings support that the AI-based approach provides better classification metrics relative to existing guideline-based instruments rather than replacing established risk assessment frameworks. Logistic regression, developed as a benchmark linear model using the same predictor set as the machine learning approaches, demonstrated lower discrimination for ischemic outcomes compared with the weighted LightGBM model in both internal and external validation cohorts. For bleeding outcomes, logistic regression showed comparatively better but still inferior performance relative to the primary AI model. Random forest and neural network models demonstrated intermediate performance compared with the weighted LightGBM model. (S4 Fig).

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Table 4. Comparative discrimination performance of the AI model versus conventional DAPT risk scores.

https://doi.org/10.1371/journal.pone.0353066.t004

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Fig 2. Receiver operating characteristic (ROC) curves comparing discrimination performance of the weighted LightGBM model, logistic regression, PRECISE-DAPT score, and DAPT score in the external validation cohort.

(A) Prediction of composite ischemic events. (B) Prediction of major bleeding events. Curves depict sensitivity versus 1-specificity across all classification thresholds. The diagonal reference line represents no discrimination. AUROC values are shown for each model.

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Model explainability and calibration

Fig 3 presents the explainability analyses using SHAP-based feature attribution. Age, diabetes mellitus, multivessel coronary disease, and procedural complexity emerged as the dominant contributors to ischemic risk predictions, whereas renal dysfunction, anticoagulant exposure, baseline hemoglobin, and prior bleeding history were the most influential factors for bleeding risk estimation. The orientation and the relative strength of the effects of the features were clinically plausible and consistent with the cardiovascular risk factors previously reported. The global SHAP feature importance rankings are provided in S6 Fig. The calibration performance of the primary AI model is shown in Fig 4A. Calibration assessment demonstrated good agreement between predicted and observed event probabilities in external validation. Calibration intercepts remained close to zero and calibration slopes remained close to one across major outcomes, indicating limited systematic overprediction. Minor deviations were observed at the highest predicted risk levels. Additional calibration analyses across dynamic post-PCI time windows are presented in S2 Fig. Decision curve analysis demonstrated a consistent net clinical benefit of the AI-guided strategy across a range of clinically relevant risk thresholds compared with conventional risk scores and treat-all or treat-none approaches (Fig 4B).

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Fig 3. Explainability of the primary AI model using SHAP-based feature attribution.

(A) displays SHAP summary plots for prediction of ischemic events. (B) displays SHAP summary plots for prediction of bleeding events. Features are ranked by their mean absolute contribution to model predictions, with each point representing an individual patient instance. Positive SHAP values indicate increased predicted risk, while negative values indicate reduced predicted risk. The direction and magnitude of feature effects demonstrate clinical plausibility and consistency with established cardiovascular risk factors.

https://doi.org/10.1371/journal.pone.0353066.g003

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Fig 4. Calibration and clinical utility of the primary AI model.

(A) shows calibration of the weighted LightGBM model, comparing predicted risk with observed event rates across deciles of predicted probability. The dashed line represents ideal calibration. (B) presents decision curve analysis comparing the net clinical benefit of the AI model with the PRECISE-DAPT and DAPT scores, as well as treat-all and treat-none strategies, across clinically relevant risk thresholds. Results demonstrate good calibration and consistent net benefit of the AI-based approach in external validation.

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Model-based clinical impact projections

Table 5 and Fig 5 summarize the model-based projections of clinical outcomes under AI-guided DAPT personalization. Across predefined follow-up windows, model-based simulations suggested lower projected ischemic and bleeding event rates under hypothetical AI-guided risk stratification compared with fixed-duration DAPT strategies. Estimated differences between simulated strategies were uncertain but directionally consistent across follow-up periods, without evidence of disproportionate trade-offs between ischemic and bleeding outcomes. Kaplan-Meier-based projections established separation of predicted event-free survival curves between AI-guided and guideline-based strategies (Fig 5A). Subgroup analyses derived from model projections showed consistent hazard ratios across key clinical strata, with no observed heterogeneity (Fig 5B).

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Table 5. Model-based clinical impact of AI-guided DAPT personalization on ischemic and bleeding outcomes.

https://doi.org/10.1371/journal.pone.0353066.t005

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Fig 5. Time-to-event analysis and subgroup consistency of AI-guided DAPT personalization.

(A) Displays Kaplan-Meier curves comparing event-free survival between AI-guided and guideline-based DAPT strategies. Kaplan-Meier curves comparing projected event-free survival under AI-guided versus guideline-based strategies. (B) Forest plot showing projected hazard ratios across predefined clinical subgroups derived from model-based simulations.

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Cost-effectiveness analysis

Table 6 shows the cost-effectiveness results comparing model-based projections of AI-guided versus guideline-based DAPT strategies. The model-based AI-guided strategy was associated with lower projected cumulative costs and higher projected quality-adjusted life-years within the assumptions of the decision-analytic model. Probabilistic sensitivity analyses suggested that the AI-guided strategy may be cost-effective at the conventionally applicable willingness-to-pay thresholds (Supplementary Fig 3). The strength of these results was also supported by one-way sensitivity analysis applied despite reasonable changes in the major cost and event-rate parameters (Supplementary Table 7).

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Table 6. Cost-Effectiveness of AI-Guided Versus Guideline-Based DAPT Strategies.

https://doi.org/10.1371/journal.pone.0353066.t006

Discussion

Our findings indicate that the proposed AI-driven model provides higher AUROC performance for individualized ischemic and bleeding risk stratification compared with conventional guideline-based risk scores, potentially supporting improved risk prediction for post-PCI DAPT decision-making instead of replacing existing clinical frameworks. By leveraging nonlinear relationships among routinely accessible clinical, procedural, and laboratory variables, the model enabled more refined risk assessment than static scoring systems. Importantly, these findings are derived from predictive modeling and simulation-based projections rather than observed treatment effects and should be interpreted as exploratory. [26] Although AUROC values exceeded 0.80 in most analyses, a slight reduction was noted during external validation, emphasizing the expected differences in health care environments and population characteristics. These findings support the potential utility of machine learning approaches for risk prediction in cardiovascular medicine and for supporting clinical decision-making, while not replacing clinical decision or guideline-based care. [27] Importantly, the objective of external validation was not to demonstrate equivalence between cohorts but rather to assess model transportability across clinically distinct populations with differing baseline risk profiles. These findings align with prior machine learning on cardiovascular risk models that show better discrimination relative to conventional tools. Importantly, employing SHapley Additive exPlanations improved transparency by providing clinically interpretable insights into patient-level risk drivers, supporting the model outputs plausibility and reliability.

The observed higher discrimination suggests the model’s ability to identify intricate interactions among procedural complexity, comorbidities, and laboratory markers that are not represented in traditional rule-based scores. [28] Instead, of predefining “implementation criteria,” the noted performance metrics (e.g., AUROC ≥0.80) ought to be considered as exploratory indicators of predictive validity, warranting further prospective validation rather than immediate clinical use. [29] Previous AI-based ischemic and bleeding risk prediction studies have reported similar performance ranges, reinforcing the consistency of these findings within the broader literature. Time-dependent analyses highlighted the dynamic nature of ischemic and bleeding risk following PCI, supporting the conceptual advantage of models capable of updating risk estimates across clinically relevant post-procedural windows. [15] These analyses did not involve meta-analysis or multivariate regression beyond the modeling framework described in the Method section but rather reflected model-based time-stratified performance assessments. The balanced sensitivity and specificity observed across time horizons support stable model performance across follow-up periods. [30] Our findings align with and extend prior work in this field, including the PATH-PCI trial, which prospectively evaluated AI-guided DAPT strategies against guideline-based approaches in a separate randomized setting. While PATH-PCI demonstrated clinical feasibility in a randomized setting, the present study differs by focusing on multi-cohort external validation, explainability, and economic modeling rather than interventional outcomes. [31] Recent developments in antithrombotic therapy have increasingly emphasized individualized treatment strategies after PCI. Modern reviews have highlighted the limitations of fixed-duration DAPT approaches and the growing need for risk stratification tools capable of balancing ischemic protection against bleeding risk across heterogeneous patient populations. Simultaneously, studies evaluating P2Y12 inhibitor monotherapy as both an early post-PCI strategy and a long-term secondary prevention approach have demonstrated the feasibility of treatment de-escalation in selected patients while maintaining ischemic protection. These evolving therapeutic paradigms support the clinical rationale for predictive models that can assist individualized antithrombotic decision-making and provide important context for future prospective evaluation of AI-guided DAPT management strategies. [32–34]

Similarly, genetic and pharmacogenomic studies have highlighted the influence of CYP2C19 and platelet reactivity on antiplatelet response, however such approaches often rely on specialized testing that is not commonly available. In contrast, our model integrates readily obtainable clinical and procedural variables, with genetic features treated as exploratory rather than required inputs. [35] Another trial Amarrise et al. utilized whole-exome sequencing of 50,000 subjects to correlate genotypic data with clinical phenotypes and platelet functional tests, thereby advancing the personalized medicine approach by identifying genetic variants influencing antiplatelet treatment efficacy. [36,37] Massmann et al. further elucidated the potential of machine learning in stratifying patient risk for stent restenosis, demonstrating its effectiveness in minimizing unnecessary follow-ups and optimizing treatment for individuals at high risk. [38] Population-specific considerations are relevant in the UAE, where cardiovascular risk profiles are influenced by high prevalence of diabetes, ethnic heterogeneity, and differing susceptibility of bleeding variable. In these contexts, risk scores derived from Western populations may exhibit restricted transportability underscoring the necessity for localized or adaptable predictive frameworks. [39,40] Rather than proposing race-specific thresholds, the present model demonstrates the feasibility of individualized risk estimation that implicitly accounts for population heterogeneity through data-driven learning on DAPT. [41] The projected clinical impact analyses suggested strategies derived from model predictions were associated with lower projected ischemic and bleeding event rates under model-based assumptions compared with fixed-duration approaches. The decision curve results suggest that use of individualized risk predictions could support decision-making across clinically relevant threshold probabilities by identifying patients more likely to benefit from prolonged or shortened DAPT while avoiding unnecessary treatment exposure.

Currently, the clinical practices in the UAE often follow guidelines based on the Western populations, which might not reflect the unique risk profiles of the local patients, which can lead to inadequate DAPT periods and an increase in adverse events. [42] The limited transportability of Western-centric risk scores underscores the need for adaptable predictive frameworks, particularly given evidence that ischemic and bleeding risk thresholds differ across global populations, including Western and East Asian cohorts. Such differences highlight the limitations of fixed, population-derived thresholds and support the use of individualized, data-driven risk estimation rather than race-specific scoring systems, especially in settings such as UAE where bleeding risk may coexist with high thrombotic burden. [43] However, the present model could be interpreted as a risk prediction tool rather than a clinical decision-making system, and also prospective trials are required to determine whether ML-guided strategies translate into improved patient outcomes. All variables required by the model are routinely available from electronic health records shortly after PCI, which may facilitate future implementation studies if prospective validation confirms clinical utility. Since ischemic and bleeding risks evolve over time, future implementations of risk prediction models may permit periodic reassessment of individualized DAPT risk profiles. Risk stratification is paramount because DAPT benefits must be precisely balanced against increased bleeding risks, particularly hemorrhagic stroke, especially in patients classified as “strong responders”. [44] Moreover, decision curves offer a valuable methodological framework for evaluating the clinical utility of such AI models, contrasting their net benefit against traditional risk assessment strategies across a spectrum of threshold probabilities for both ischemic and bleeding events. In future work, our AI model could be prospectively compared with established scores, such as the DAPT score, HAS-BLED, and ACUITY, to more fully characterize relative predictive performance and clinical utility in the UAE population.

A simplified decision-analytic model was used to explore potential economic implications under theoretical risk-guided management scenarios. The economic implications of the suboptimal duration of DAPT, including increased healthcare costs associated with adverse events highlight the potential value of improved risk prediction approaches, although prospective validation remains necessary. The proposed AI model may support risk-guided strategies for DAPT duration, which could potentially influence the healthcare resource utilization. However, economic implications observed in this study are associated with unnecessary readmissions and costly interventions for complications aligning with the ADAPT-DM registry that require future validation. [45] In economic modeling analyses, the simulated risk-guided strategy was associated with lower projected per-patient costs and higher projected quality-adjusted life-years compared with fixed-duration approaches (ICER: −451,315 AED/QALY; cost difference: −17,150 AED per patient). Instead of serving as evidence of economic advantage, the simulated findings suggest that dynamic, risk prediction models may support exploration of personalized DAPT strategies, although these projections require confirmation in prospective studies. [46] Nevertheless, the uncertainty in individual-level predictive accuracy, especially for infrequent yet catastrophic outcomes, necessitates further research to refine, validate, and contextualize these estimates. Policy considerations, including regulatory oversight, ethical governance, and equitable access, will be crucial policy for facilitating the responsible adoption of AI-driven clinical decision tools. Multi-cohort validation and prospective trials will be essential to strengthen the evidence based on AI-driven DAPT personalization, particularly considering the dynamic changes in patient risk profiles and evolving treatment paradigms. External validation across various populations beyond the UAE will be essential to determine the generalizability and effectiveness of the suggested AI model among diverse healthcare systems and demographic groups.

Several limitations of this study should be acknowledged. Importantly, the projected clinical and economic outcomes were derived from simulation modeling rather than observed treatment allocation, and should be interpreted as exploratory rather than confirmatory findings. First, the study was retrospective in design, which inherently limits causal inference and may introduce unmeasured confounding despite the use of multicohort validation. Clinician-estimated probabilities of ischemic and bleeding events were not available within the source datasets; therefore, direct comparison between model predictions and physician judgment could not be performed. Future prospective studies may evaluate the calibration and clinical utility of AI-based predictions relative to clinician-assigned risk assessments. Additionally, AI models should possess a high degree of explainability, as only in this way clinicians understand the logic of DAPT recommendations and thus build trust and become part of daily clinical practice. The most important aspect of the development and rigorous validation of AI models in DAPT personalization is the use of a well-designed methodological framework, which would involve detailed data collection, statistical data analysis, and model performance metrics. However, the implementation of such AI tools in a clinical setting requires continuous research and monitoring to improve the recommendations and changes in patient-centered care strategies. In the context of the present work, limitations include its retrospective design, reliance on two healthcare systems, and incomplete availability of pharmacogenomic and treatment-intensity data, which may restrict generalizability despite multicohort validation. [47] Second, outcome identification depends on administrative diagnostic codes and electronic health record phenotyping approaches, which introduce misclassification bias despite the use of standardized ICD-based definitions. Data heterogeneity across diverse datasets, including variations in diagnostic criteria, treatment protocols, and patient demographics, poses a significant challenge for the development of universally applicable AI models. Confounding variables, which are inherently not observed within existing datasets, could significantly influence model predictions and limit their clinical utility by failing to account for all factors affecting DAPT response and adverse events. Third, the external validation dataset included patients derived from MIMIC-IV critical care database, which primarily represents ICU-based populations and may not fully reflect broader PCI populations treated in routine cardiovascular practice. Genetic availability, including pharmacogenomic markers known to influence DAPT metabolism and efficacy, remains a significant limitation in existing datasets, delaying the development of truly personalized AI models. [48] Furthermore, economic assumptions affecting long-term cost effectiveness and social impact of AI-driven DAPT optimization often rely on extrapolations and estimations that may not fully capture the dynamic interplay of healthcare resource utilization and patient quality of life.

Future research should include prospective randomized controlled trials to evaluate whether risk prediction-guided DAPT strategies improve clinical outcomes, safety, and cost-effectiveness across diverse healthcare systems. Future studies may also compare model-generated risk estimates with clinician-assigned probabilities to better understand the incremental value of AI-assisted risk prediction in routine practice. Such trials would address important evidence gaps, including early DAPT de-escalation strategies and their relative effectiveness compared with P2Y12 monotherapy in these areas which are currently weak or conditional recommendations due to limited randomized evidence. Successful clinical translation will require seamless integration with electronic health record (EHR) platforms, including standard data formats, interoperability frameworks, and governance structures that enable secure. Large-scale leveraging registries that gather real-world data, including automated risk score derivation that could enable adaptive or pragmatic trial designs to improve DAPT duration guidelines after post-acute coronary syndrome. Second, incorporating registries, including the ANZACS-Q1, to create risk scores for clinicians to develop adaptive clinical trials to optimize the duration of DAPT use following acute coronary syndrome. Third, advanced methodological techniques, such as offline reinforcement learning and adaptive decision-support systems, have the potential to optimize complex longitudinal treatment approaches in coronary artery disease. However, the interpretation, safety monitoring, and clinical plausibility represent the issues that needs to be resolved in the future to ensure such approaches are safe to be implemented. These guidelines necessitate ethically accepted, clinically combined research platforms that may result in AI-driven accuracy and methodological excellence for cardiovascular care. [49]

Conclusion

We developed externally validated an explainable, artificial intelligence-based risk stratification framework for post-percutaneous coronary intervention in patients receiving dual antiplatelet therapy, balancing ischemic and bleeding events. The proposed model demonstrated consistent discrimination, acceptable calibration, and clinical feature attribution with routinely available clinical and procedural variables across a regionally representative UAE cohort and an independent external validation cohort. The AI-based risk stratification model provided higher AUROC compared with conventional risk scores, rather than “superior individual risk stratification,” and the explainability analysis supported clinical interpretability consistent with established cardiovascular risk profiles. External validation without recalibration implied reasonable transportability, although performance attenuation highlighted expected variability across population and healthcare settings. Based on models, simulation-based clinical utility and economic analyses suggested possible cost-effectiveness under modeled assumptions, rather than demonstrating direct clinical benefit. For potential clinical implementation, the model would be integrated into electronic health record systems where routinely collected post-PCI variables are automatically used to generate individualized ischemic and bleeding risk estimates at the point of care. These outputs could then be displayed within clinical dashboards or decision-support modules as supportive information rather than prescriptive recommendations to assist clinicians in comparing individualized risk profiles against guideline-based thresholds; however, prospective workflow studies are required before clinical deployment. Overall, this study indicates the potential viability and usefulness of explainable machine learning solutions as an adjunctive DAPT-personalization tool after PCI. Lastly, prospective validation, wider external testing, and real-world implementation studies could establish clinical impact, safety, and economic implications of introducing AI-driven decision support into daily practice of cardiovascular care.

Supporting information

Acknowledgments

The authors acknowledge the MIMIC-IV database and the PhysioNet platform for providing access to a large-scale, de-identified critical-care dataset, which supported the development and external validation of the AI-based risk prediction models evaluated in this study. The authors also acknowledge the Bayanat Open Data Portal (https://bayanat.ae) for providing access to publicly available UAE population-level datasets, including the dataset entitled “Prevalence of Obesity in the UAE.” These publicly available data contributed to regional epidemiological contextualization and interpretation of the study findings. The authors further recognize the efforts of the data contributors and curators whose work enabled responsible data sharing and supported reproducible research.