Predicting target lesion failure following percutaneous coronary intervention through machine learning risk assessment models
Ontology highlight
ABSTRACT:
SUBMITTER:
PROVIDER: S-EPMC10689920 | biostudies-literature | 2023 Aug
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:An accurate prediction of major adverse events after percutaneous coronary intervention (PCI) improves clinical decisions and specific interventions. To determine whether machine learning (ML) techniques predict peri-PCI adverse events [acute kidney injury (AKI), bleeding, and in-hospital mortality] with better discrimination or calibration than the National Cardiovascular Data Registry (NCDR-CathPCI) risk scores, we developed logistic regression and gradient descent boosting (XGBoost) models for each outcome using data from a prospective, all-comer, multicenter registry that enrolled consecutive coronary artery disease patients undergoing PCI in Japan between 2008 and 2020. The NCDR-CathPCI risk scores demonstrated good discrimination for each outcome (C-statistics of 0.82, 0.76, and 0.95 for AKI, bleeding, and in-hospital mortality) with considerable calibration. Compared with the NCDR-CathPCI risk scores, the XGBoost models modestly improved discrimination for AKI and bleeding (C-statistics of 0.84 in AKI, and 0.79 in bleeding) but not for in-hospital mortality (C-statistics of 0.96). The calibration plot demonstrated that the XGBoost model overestimated the risk for in-hospital mortality in low-risk patients. All of the original NCDR-CathPCI risk scores for adverse periprocedural events showed adequate discrimination and calibration within our cohort. When using the ML-based technique, however, the improvement in the overall risk prediction was minimal.
| S-EPMC9012739 | biostudies-literature
Project description:Fontan procedure is known to increase the risk of thromboembolic events. However, coronary artery thrombotic occlusion is rarely reported in patients with Fontan procedure. We present a case of a 10-year-old boy with hypoplastic left heart syndrome palliated with a Fontan procedure who presented with myocardial infarction secondary to thrombotic occlusion of the left circumflex coronary artery. He underwent successful percutaneous coronary intervention with thrombus aspiration, balloon angioplasty, and stent placement, highlighting the necessity of collaboration between congenital and adult cardiologists to treat acute coronary syndrome among this challenging young population.
| S-EPMC7152654 | biostudies-literature
Project description:BackgroundRecent studies have shown potential in introducing machine learning (ML) algorithms to predict outcomes post-percutaneous coronary intervention (PCI).AimsWe aimed to critically appraise current ML models' effectiveness as clinical tools to predict outcomes post-PCI.MethodsSearches of four databases were conducted for articles published from the database inception date to 29 May 2021. Studies using ML to predict outcomes post-PCI were included. For individual post-PCI outcomes, measures of diagnostic accuracy were extracted. An adapted checklist comprising existing frameworks for new risk markers, diagnostic accuracy, prognostic tools and ML was used to critically appraise the included studies along the stages of the translational pathway: development, validation, and impact. Quality of training data and methods of dealing with missing data were evaluated.ResultsTwelve cohorts from 11 studies were included with a total of 4,943,425 patients. ML models performed with high diagnostic accuracy. However, there are concerns over the development of the ML models. Methods of dealing with missing data were problematic. Four studies did not discuss how missing data were handled. One study removed patients if any of the predictor variable data points were missing. Moreover, at the validation stage, only three studies externally validated the models presented. There could be concerns over the applicability of these models. None of the studies discussed the cost-effectiveness of implementing the models.ConclusionsML models show promise as a useful clinical adjunct to traditional risk stratification scores in predicting outcomes post-PCI. However, significant challenges need to be addressed before ML can be integrated into clinical practice.
| S-EPMC11413637 | biostudies-literature
Project description:ObjectivesTo construct a model to predict long-term bleeding events following percutaneous coronary intervention (PCI).BackgroundTreatment with dual antiplatelet therapy following PCI involves balancing the benefits of preventing ischemic events with the risks of bleeding. There are no models to predict long-term bleeding events after PCI.MethodsWe analyzed 1-year bleeding outcomes from 3,128 PCI procedures in the Patient Risk Information Services Manager (PRISM) observational study. Patient-reported bleeding events were categorized according to Bleeding Academic Research Consortium (BARC) definitions. Logistic regression analysis was used to develop a model predicting BARC ≥ 1 bleeding.ResultsBARC 0, 1, 2 or 3 bleeding was observed in 574 (18.4%); 2382 (76.2%); 114 (3.6%); and 58 (1.8%) patients, respectively. Compared to patients who had no bleeding, patients with BARC ≥ 1 bleeding were more often female (30 vs. 23%), Caucasian (94 vs. 83%), had a higher incidence of drug eluting stent (DES) implantation (83 vs. 76%) and warfarin therapy (7.4 vs. 3.9%), and a lower incidence of diabetes (31 vs. 45%; P-value <0.01 for all comparisons). A 27-variable model had moderate discrimination (c-statistic of 0.674), and good calibration, as did a parsimonious model with 10 variables (c-statistic = 0.667). This model performed well in predicting BARC ≥ 2 bleeding events as well (c-statistic = 0.653).ConclusionsBleeding is common in the first year after PCI, and can be predicted by pre-procedural patient characteristics and use of DES. Objective estimates of bleeding risk may help support shared decision-making with respect to stent selection and duration of antiplatelet therapy following PCI. © 2016 Wiley Periodicals, Inc.
| S-EPMC5813282 | biostudies-literature
Project description:Dual antiplatelet therapy has long been the standard of care in preventing coronary and cerebrovascular thrombotic events in patients with chronic coronary syndrome and acute coronary syndrome undergoing percutaneous coronary intervention, but choosing the optimal treatment duration and composition has become a major challenge. Numerous studies have shown that certain patients benefit from either shortened or extended treatment duration. Furthermore, trials evaluating novel antithrombotic strategies, such as P2Y12 inhibitor monotherapy, low-dose factor Xa inhibitors on top of antiplatelet therapy, and platelet function- or genotype-guided (de-)escalation of treatment, have shown promising results. Current guidelines recommend risk stratification for tailoring treatment duration and composition. Although several risk stratification methods evaluating ischaemic and bleeding risk are available to clinicians, such as the use of risk scores, platelet function testing , and genotyping, risk stratification has not been broadly adopted in clinical practice. Multiple risk scores have been developed to determine the optimal treatment duration, but external validation studies have yielded conflicting results in terms of calibration and discrimination and there is limited evidence that their adoption improves clinical outcomes. Likewise, platelet function testing and genotyping can provide useful prognostic insights, but trials evaluating treatment strategies guided by these stratification methods have produced mixed results. This review critically appraises the currently available antithrombotic strategies and provides a viewpoint on the use of different risk stratification methods alongside clinical judgement in current clinical practice.
| S-EPMC8244639 | biostudies-literature
Project description:ObjectiveTo investigate the relation of annual household income to antiplatelet adherence following PCI.BackgroundTreatment with 6-12 months of dual antiplatelet therapy (DAPT) following percutaneous coronary intervention (PCI) is a Class I recommendation. Adherence to these medications is essential to reduce risk of stent thrombosis and recurrent ischemic events. Social risk factors like household income modify how patients access and adhere to essential pharmacologic therapies such as antiplatelet agents.MethodsWe identified individuals presenting with PCI in an administrative claims database of commercially insured and Medicare Advantage beneficiaries from 2017 to 2019. We collected data on age, sex, race, ethnicity, educational attainment, and covariates (prevalent coronary disease, medications, healthcare visits, insurance type, copay, antiplatelet medications, and Elixhauser Comorbidity Index conditions). We related annual household income, categorized as <$40,000; $40-49,999; $50-59,999; $60-74,999; $75-99,999; and ≥$100 K, to proportion of days covered (PDC) in multivariable-adjusted regression models. We defined non-adherence as PDC <80%.ResultsOur dataset included 90,163 individuals (age 69.0 ± 10.9 years, 33.1% women, 25.1% non-White race) who underwent PCI. We observed graded, decreased antiplatelet adherence across income categories: rates of PDC≥80% decreased with successively lower income. Individuals with annual income <$40,000 had 1.5-fold higher odds of non-adherence (95% CI, 1.40-1.56) compared to those with income ≥$100,000 after multivariable adjustment.ConclusionsIn a claims-based analysis, we determined that lower income is associated with decreased likelihood of adherence to antiplatelet agents following PCI. Our results indicate the importance of considering social risk factors in the evaluation of barriers to antiplatelet adherence following PCI.
| S-EPMC9434414 | biostudies-literature
Project description:PurposeTo determine the location of coronary atherosclerosis distribution observed in patients with chronic kidney disease (CKD).MethodsA cross-sectional study was conducted using the database of cardiovascular medicine data from Saitama Sekishinkai Hospital to clarify the association between renal function and angiographic characteristics of coronary atherosclerosis. In total, 3268 patients who underwent percutaneous coronary intervention were included. Propensity score matching revised the total to 1772. The association of renal function with the location and/or distribution of coronary atherosclerosis lesions was then examined.ResultsOverall, coronary lesion was observed in the left anterior descending coronary artery (LAD) in 56% patients, whereas 28% and 22% were in the right coronary artery (RCA) and left circumflex coronary artery (LCX), respectively. LAD was most affected and observed in 57% patients with stage 1 CKD. RCA was second-most affected, at 26% CKD stage 1, but it increased to 31%, 38%, and 59% in CKD 3, 4, and 5, respectively. In CKD 5 patients, the RCA was the most affected artery (59%), with 41% LAD lesions. Logistic regression analysis after propensity score matching showed that the odds ratios for an RCA lesion was 3.658 in CKD 5 (p = .025) compared with CKD 1 after adjusting for traditional risk factors.ConclusionThe prevalence of RCA lesions, but not LAD or LCX lesions, increased with increasing CKD stage. The pathophysiology of coronary atherosclerosis may differ by lesion location. Deterioration of renal function may affect progression of atherosclerosis more in the RCA than in the LAD or LCX.
| S-EPMC9272943 | biostudies-literature
Project description:Stratifying prognosis following coronary bifurcation percutaneous coronary intervention (PCI) is an unmet clinical need that may be fulfilled through the adoption of machine learning (ML) algorithms to refine outcome predictions. We sought to develop an ML-based risk stratification model built on clinical, anatomical, and procedural features to predict all-cause mortality following contemporary bifurcation PCI. Multiple ML models to predict all-cause mortality were tested on a cohort of 2393 patients (training, n = 1795; internal validation, n = 598) undergoing bifurcation PCI with contemporary stents from the real-world RAIN registry. Twenty-five commonly available patient-/lesion-related features were selected to train ML models. The best model was validated in an external cohort of 1701 patients undergoing bifurcation PCI from the DUTCH PEERS and BIO-RESORT trial cohorts. At ROC curves, the AUC for the prediction of 2-year mortality was 0.79 (0.74-0.83) in the overall population, 0.74 (0.62-0.85) at internal validation and 0.71 (0.62-0.79) at external validation. Performance at risk ranking analysis, k-center cross-validation, and continual learning confirmed the generalizability of the models, also available as an online interface. The RAIN-ML prediction model represents the first tool combining clinical, anatomical, and procedural features to predict all-cause mortality among patients undergoing contemporary bifurcation PCI with reliable performance.
| S-EPMC9224705 | biostudies-literature
Project description:BackgroundThe relationship between remnant-like particle cholesterol (RLP-C) and cardiovascular disease risk and prognosis has been established, but its effect on the prognosis of ischemic heart failure (IHF) patients undergoing percutaneous coronary intervention (PCI) remains uncertain.MethodIn this study, 2036 patients with IHF who underwent PCI were included. Patients were categorized into tertiles based on their RLP-C levels. The primary outcome was major adverse cardiovascular events (MACE). Kaplan-Meier survival analysis was used to assess the incidence of MACE and other outcomes. Multivariate Cox regression models were employed to investigate the correlation between RLP-C and the studied outcomes. The nonlinear relationship between RLP-C and MACE was examined through the restricted cubic spline (RCS). Subgroup analyses were performed and interactions were assessed.ResultThe study results showed a clear association between higher RLP-C levels and an increased incidence of MACE in the participants. This association was validated by Kaplan-Meier analyses. The multivariate Cox regression demonstrated RLP-C was an independent risk factor for MACE, whether assessed as a continuous variable[hazard ratio (HR), 95% confidence interval (CI): 1.50, 1.15-1.98, p = 0.003] or categorized into tertiles[HR, 95% CI: 2.57, 2.03-3.26, p < 0.001, tertile 3 vs tertile 1]. A nonlinear relationship between RLP-C and MACE was observed, indicating that the risk of MACE increased with higher RLP-C levels(Nonlinear p < 0.001). This association remained consistent across various subgroups, as no significant interactions were found.ConclusionThere was an independent and positive correlation between RLP-C and MACE in patients with IHF who underwent PCI.
| S-EPMC11792126 | biostudies-literature
Project description:ObjectiveWe devised a percutaneous coronary intervention (PCI) scoring system based on angiographic lesion complexity and assessed its association with in-hospital complications.BackgroundAlthough PCI is finding increasing application in patients with coronary artery disease, lesion complexity can lead to in-hospital complications.MethodsData from 3692 PCI patients were scored based on lesion complexity, defined by bifurcation, chronic total occlusion, type C, and left main lesion, along with acute thrombus in the presence of ST-segment elevation myocardial infarction (1 point assigned for each variable).ResultsThe patients' mean age was 67.5 +/- 10.8 years; 79.8% were male. About half of the patients (50.3%) presented with an acute coronary syndrome, and 2218 (60.1%) underwent PCI for at least one complex lesion. The patients in the higher-risk score groups were older (p < 0.001) and had present or previous heart failure (p = 0.02 and p = 0.01, respectively). Higher-risk score groups had significantly higher in-hospital event rates for death, heart failure, and cardiogenic shock (from 0 to 4 risk score; 1.7%, 4.5%, 6.3%, 7.1%, 40%, p < 0.001); bleeding with a hemoglobin decrease of >3.0 g/dL (3.1%, 11.0%, 13.1%, 10.3%, 28.6%, p < 0.001); and postoperative myocardial infarction (1.5%, 3.1%, 3.8%, 3.8%, 10%, p = 0.004), respectively. The association with adverse outcomes persisted after adjustment for known clinical predictors (odds ratio 1.72, p < 0.001).ConclusionThe complexity score was cumulatively associated with in-hospital mortality and complication rate and could be used for event prediction in PCI patients.
| S-EPMC4487684 | biostudies-literature