Early-, Late-, and Very Late-Term Prediction of Target Lesion Failure in Coronary Artery Stent Patients: An International Multi-Site Study

Autor: Narges Ahmidi, Alireza Zamanian, Calvin Bahr, Myriam Stieler, Elisabeth Pachl
Přispěvatelé: Publica
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Target lesion
medicine.medical_specialty
Technology
QH301-705.5
QC1-999
Coronary artery stent
stent intervention
030204 cardiovascular system & hematology
Coronary artery disease
03 medical and health sciences
0302 clinical medicine
medicine
General Materials Science
030212 general & internal medicine
Biology (General)
Prospective cohort study
Instrumentation
QD1-999
Fluid Flow and Transfer Processes
Framingham Risk Score
business.industry
Process Chemistry and Technology
Physics
General Engineering
Multi site
medicine.disease
Engineering (General). Civil engineering (General)
Computer Science Applications
Term (time)
Machine Learning
Multi-site Clinical Cohort
Stent Intervention
Tlf Prediction
Chemistry
machine learning
TLF prediction
Cohort
multi-site clinical cohort
Radiology
TA1-2040
business
Zdroj: Applied Sciences, Vol 11, Iss 6986, p 6986 (2021)
Appl. Sci. 11:6986 (2021)
Applied Sciences
Volume 11
Issue 15
ISSN: 2076-3417
Popis: The main intervention for coronary artery disease is stent implantation. We aim to predict post-intervention target lesion failure (TLF) months before its onset, an extremely challenging task in clinics. This post-intervention decision support tool helps physicians to identify at-risk patients much earlier and to inform their follow-up care. We developed a novel machine-learning model with three components: a TLF predictor at discharge via a combination of nine conventional models and a super-learner, a risk score predictor for time-to-TLF, and an update function to manage the size of the at-risk cohort. We collected data in a prospective study from 120 medical centers in over 25 countries. All 1975 patients were enrolled during Phase I (2016–2020) and were followed up for five years post-intervention. During Phase I, 151 patients (7.6%) developed TLF, which we used for training. Additionally, 12 patients developed TLF after Phase I (right-censored). Our algorithm successfully classifies 1635 patients as not at risk (TNR = 90.23%) and predicts TLF for 86 patients (TPR = 52.76%), outperforming its training by identifying 33% of the right-censored patients. We also compare our model against five state of the art models, outperforming them all. Our prediction tool is able to optimize for both achieving higher sensitivity and maintaining a reasonable size for the at-risk cohort over time.
Databáze: OpenAIRE
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