Dataset Information

Machine Learning Identifies New Predictors on Restenosis Risk after Coronary Artery Stenting in 10,004 Patients with Surveillance Angiography.

ABSTRACT:

Objective

Machine learning (ML) approaches have the potential to uncover regular patterns in multi-layered data. Here we applied self-organizing maps (SOMs) to detect such patterns with the aim to better predict in-stent restenosis (ISR) at surveillance angiography 6 to 8 months after percutaneous coronary intervention with stenting.

Methods

In prospectively collected data from 10,004 patients receiving percutaneous coronary intervention (PCI) for 15,004 lesions, we applied SOMs to predict ISR angiographically 6-8 months after index procedure. SOM findings were compared with results of conventional uni- and multivariate analyses. The predictive value of both approaches was assessed after random splitting of patients into training and test sets (50:50).

Results

Conventional multivariate analyses revealed 10, mostly known, predictors for restenosis after coronary stenting: balloon-to-vessel ratio, complex lesion morphology, diabetes mellitus, left main stenting, stent type (bare metal vs. first vs. second generation drug eluting stent), stent length, stenosis severity, vessel size reduction, and prior bypass surgery. The SOM approach identified all these and nine further predictors, including chronic vessel occlusion, lesion length, and prior PCI. Moreover, the SOM-based model performed well in predicting ISR (AUC under ROC: 0.728); however, there was no meaningful advantage in predicting ISR at surveillance angiography in comparison with the conventional multivariable model (0.726, p = 0.3).

Conclusions

The agnostic SOM-based approach identified-without clinical knowledge-even more contributors to restenosis risk. In fact, SOMs applied to a large prospectively sampled cohort identified several novel predictors of restenosis after PCI. However, as compared with established covariates, ML technologies did not improve identification of patients at high risk for restenosis after PCI in a clinically relevant fashion.

SUBMITTER: Guldener U

PROVIDER: S-EPMC10142067 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature

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Publications

Machine Learning Identifies New Predictors on Restenosis Risk after Coronary Artery Stenting in 10,004 Patients with Surveillance Angiography.

Güldener Ulrich U Kessler Thorsten T von Scheidt Moritz M Hawe Johann S JS Gerhard Beatrix B Maier Dieter D Lachmann Mark M Laugwitz Karl-Ludwig KL Cassese Salvatore S Schömig Albert W AW Kastrati Adnan A Schunkert Heribert H

Journal of clinical medicine 20230418 8

<h4>Objective</h4>Machine learning (ML) approaches have the potential to uncover regular patterns in multi-layered data. Here we applied self-organizing maps (SOMs) to detect such patterns with the aim to better predict in-stent restenosis (ISR) at surveillance angiography 6 to 8 months after percutaneous coronary intervention with stenting.<h4>Methods</h4>In prospectively collected data from 10,004 patients receiving percutaneous coronary intervention (PCI) for 15,004 lesions, we applied SOMs t ...[more]

PMID: 37109283

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Project description:BackgroundTransfemoral carotid artery stenting (TFCAS) carries important perioperative risks. Outcome prediction tools may help guide clinical decision-making but remain limited. We developed machine learning algorithms that predict 1-year stroke or death following TFCAS.Methods and resultsThe VQI (Vascular Quality Initiative) database was used to identify patients who underwent TFCAS for carotid artery stenosis between 2005 and 2024. We identified 112 features from the index hospitalization (82 preoperative [demographic/clinical], 13 intraoperative [procedural], and 17 postoperative [in-hospital course/complications]). The primary outcome was 1-year postprocedural stroke or death. The data were divided into training (70%) and test (30%) sets. Six machine learning models were trained using preoperative features with 10-fold cross-validation. The primary model evaluation metric was area under the receiver operating characteristic curve. The algorithm with the best performance was further trained using intra- and postoperative features. Model robustness was assessed using calibration plots and Brier scores. Overall, 35 214 patients underwent TFCAS during the study period and 3257 (9.2%) developed 1-year stroke or death. The best preoperative prediction model was extreme gradient boosting, achieving an area under the receiver operating characteristic curve of 0.94 (95% CI, 0.93-0.95). In comparison, logistic regression had an AUROC of 0.65 (95% CI, 0.63-0.67). The extreme gradient boosting model maintained excellent performance at the intra- and postoperative stages, with area under the receiver operating characteristic curve values of 0.94 (95% CI, 0.93-0.95) and 0.98 (95% CI, 0.97-0.99), respectively. Calibration plots showed good agreement between predicted/observed event probabilities with Brier scores of 0.11 (preoperative), 0.11 (intraoperative), and 0.09 (postoperative).ConclusionsMachine learning can accurately predict 1-year stroke or death following TFCAS, performing better than logistic regression.

Dataset Information

Machine Learning Identifies New Predictors on Restenosis Risk after Coronary Artery Stenting in 10,004 Patients with Surveillance Angiography.

Objective

Methods

Results

Conclusions

Publications

Machine Learning Identifies New Predictors on Restenosis Risk after Coronary Artery Stenting in 10,004 Patients with Surveillance Angiography.

Similar Datasets

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