Risk prediction of in-stent restenosis among patients with coronary drug-eluting stents: current clinical approaches and challenges

Sakamoto, Atsushi; Sato, Yu; Kawakami, Rika; Cornelissen, Anne; Mori, Masayuki; Kawai, Kenji; Fernandez, Raquel; Fuller, Daniela; Gadhoke, Neel; Guo, Liang; Romero, Maria; Kolodgie, Frank D.; Virmani, Renu; Finn, Aloke V.

doi:10.1080/14779072.2021.1856657

Cited by 13 publications

(10 citation statements)

References 164 publications

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“…One of LR’s limitations is when multiple variables with correlations are included, serious deviation would be generated and lead to overfitting [ 5 ]. The predictors of ISR are of multiple correlation and interaction [ 11 ]. Patients with diabetes are associated with renal dysfunction, dyslipidemia, long lesion, and smaller vascular diameter [ 16 – 18 ].…”

Section: Discussionmentioning

confidence: 99%

“…15 ISR predictors were selected according to documentation, including patient age, male gender, smoking history, clinical presentation of acute coronary syndrome (ACS), diabetes, hypertension, dyslipidemia, chronic kidney disease (CKD) stage, number of stenotic vessels (>50% of luminal diameter by QCA), number of stenting vessels, minimum stent diameter, total stent length, left main artery stenting, bifurcation stenting (left main), and stenting in complex lesions (type B2 and C) [ 11 ]. The CKD stage is classified by calculating the estimated glomerular filtration rate using the Modification of Diet in Renal Disease Study equation [ 12 ].…”

Section: Methodsmentioning

confidence: 99%

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Random forest vs. logistic regression: Predicting angiographic in-stent restenosis after second-generation drug-eluting stent implantation

et al. 2022

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As the rate of percutaneous coronary intervention increases, in-stent restenosis (ISR) has become a burden. Random forest (RF) could be superior to logistic regression (LR) for predicting ISR due to its robustness. We developed an RF model and compared its performance with the LR one for predicting ISR. We retrospectively included 1501 patients (age: 64.0 ± 10.3; male: 76.7%; ISR events: 279) who underwent coronary angiography at 9 to 18 months after implantation of 2nd generation drug-eluting stents. The data were randomly split into a pair of train and test datasets for model development and validation with 50 repeats. The predictive performance was assessed by the area under the curve (AUC) of the receiver operating characteristic (ROC). The RF models predicted ISR with larger AUC-ROCs of 0.829 ± 0.025 compared to 0.784 ± 0.027 of the LR models. The difference was statistically significant in 29 of the 50 repeats. The RF and LR models had similar sensitivity using the same cutoff threshold, but the specificity was significantly higher in the RF models, reducing 25% of the false positives. By removing the high leverage outliers, the LR models had comparable AUC-ROC to the RF models. Compared to the LR, the RF was more robust and significantly improved the performance for predicting ISR. It could cost-effectively identify patients with high ISR risk and help the clinical decision of coronary stenting.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Random forest vs. logistic regression: Predicting angiographic in-stent restenosis after second-generation drug-eluting stent implantation

et al. 2022

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“…In clinic therapy, the long-term dosing in early stage has low absolute bioavailability and undesirable side effects [3]. For serious pathology, percutaneous stent implantation is widely used to restore the blood flow [4], but subsequent in-stent thrombosis and in-stent restenosis become an unforeseen fatal risk [5,6]. Recently, nanodrug delivery system has receive considerable attentions as a noninvasive therapeutic platform for the most diseases because of the attractive performances in medicament improvement, such as solubilizing and stabilizing drug delivery, enhancing therapeutic bioavailability by the target drug delivery and the controllable drug release, and significantly reducing drug toxicity and side effects [7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Macrophage membrane camouflaged reactive oxygen species responsive nanomedicine for efficiently inhibiting the vascular intimal hyperplasia

et al. 2021

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Background Intimal hyperplasia caused by vascular injury is an important pathological process of many vascular diseases, especially occlusive vascular disease. In recent years, Nano-drug delivery system has attracted a wide attention as a novel treatment strategy, but there are still some challenges such as high clearance rate and insufficient targeting. Results In this study, we report a biomimetic ROS-responsive MM@PCM/RAP nanoparticle coated with macrophage membrane. The macrophage membrane with the innate “homing” capacity can superiorly regulate the recruitment of MM@PCM/RAP to inflammatory lesion to enhance target efficacy, and can also disguise MM@PCM/RAP nanoparticle as the autologous cell to avoid clearance by the immune system. In addition, MM@PCM/RAP can effectively improve the solubility of rapamycin and respond to the high concentration level of ROS accumulated in pathological lesion for controlling local cargo release, thereby increasing drug availability and reducing toxic side effects. Conclusions Our findings validate that the rational design, biomimetic nanoparticles MM@PCM/RAP, can effectively inhibit the pathological process of intimal injury with excellent biocompatibility. Graphical Abstract

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“…Coronary stents are deployed to treat this disease by reopening stenotic regions in arteries to restore blood flow. But after stent implantation, the rate of the in-stent restenosis (ISR) is high (20-30%) [1]. One of the reasons for this phenomenon is that stent implantation induces changes in local hemodynamic environment, and has an adverse effect on the physiological behaviors of endothelial cells and other biomacromolecules [1].…”

mentioning

confidence: 99%

“…But after stent implantation, the rate of the in-stent restenosis (ISR) is high (20-30%) [1]. One of the reasons for this phenomenon is that stent implantation induces changes in local hemodynamic environment, and has an adverse effect on the physiological behaviors of endothelial cells and other biomacromolecules [1]. Therefore, carrying out studies about blood flow environment after stent implantation is of vital importance.…”

mentioning

confidence: 99%

Local hemodynamic analysis after coronary stent implantation based on Euler-Lagrange method

Wang

Zhan²,

Bian

et al. 2021

J Biol Phys

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Coronary stents are deployed to treat the coronary artery disease (CAD) by reopening stenotic regions in arteries to restore blood flow, but the risk of the in-stent restenosis (ISR) is high after stent implantation. One of the reasons is that stent implantation induces changes in local hemodynamic environment, so it is of vital importance to study the blood flow in stented arteries. Based on regarding the red blood cell (RBC) as a rigid solid particle and regarding the blood (including RBCs and plasma) as particle suspensions, a non-Newtonian particle suspensions model is proposed to simulate the realistic blood flow in this work. It considers the blood's flow pattern and non-Newtonian characteristic, the blood cell-cell interactions, and the additional effects owing to the bi-concave shape and rotation of the RBC. Then, it is compared with other four common hemodynamic models (Newtonian single-phase flow model, Newtonian Eulerian two-phase flow model, non-Newtonian single-phase flow model, non-Newtonian Eulerian two-phase flow model), and the comparison results indicate that the models with the non-Newtonian characteristic are more suitable to describe the realistic blood flow. Afterwards, based on the non-Newtonian particle suspensions model, the local hemodynamic environment in stented arteries is investigated. The result shows that the stent strut protrusion into the flow stream would be likely to produce the flow stagnation zone. And the stent implantation can make the pressure gradient distribution uneven. Besides, the wall shear stress (WSS) of the region adjacent to every stent strut is lower than 0.5 Pa, and along the flow direction, the low-WSS zone near the strut behind is larger than that near the front strut. What's more, in the regions near the struts in the proximal of the stent, the RBC particle stagnation zone is easy to be formed, and the erosion and deposition of RBCs are prone to occur. These hemodynamic analyses illustrate that the risk of ISR is high in the regions adjacent to the struts in the proximal and the distal ends of the stent when compared with struts in other positions of the stent. So the research can provide a suggestion on the stent design, which indicates that the strut structure in these positions of a stent should be optimized further.

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Risk prediction of in-stent restenosis among patients with coronary drug-eluting stents: current clinical approaches and challenges

Cited by 13 publications

References 164 publications

Random forest vs. logistic regression: Predicting angiographic in-stent restenosis after second-generation drug-eluting stent implantation

Random forest vs. logistic regression: Predicting angiographic in-stent restenosis after second-generation drug-eluting stent implantation

Macrophage membrane camouflaged reactive oxygen species responsive nanomedicine for efficiently inhibiting the vascular intimal hyperplasia

Local hemodynamic analysis after coronary stent implantation based on Euler-Lagrange method

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