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. 2021 Sep 29;20(1):118.
doi: 10.1186/s12944-021-01553-2.

A prediction model based on platelet parameters, lipid levels, and angiographic characteristics to predict in-stent restenosis in coronary artery disease patients implanted with drug-eluting stents

Min-Tao Gai #  1 , Bing Zhu #  2 , Xiao-Cui Chen  1 , Fen Liu  1 , Xiang Xie  2 , Xiao-Ming Gao  2 , Xiang Ma  2 , Zhen-Yan Fu  2 , Yi-Tong Ma  3   4 , Bang-Dang Chen  5   6
Affiliations

A prediction model based on platelet parameters, lipid levels, and angiographic characteristics to predict in-stent restenosis in coronary artery disease patients implanted with drug-eluting stents

Min-Tao Gai et al. Lipids Health Dis. .

Abstract

Background: The present study was aimed to establish a prediction model for in-stent restenosis (ISR) in subjects who had undergone percutaneous coronary intervention (PCI) with drug-eluting stents (DESs).

Materials and methods: A retrospective cohort study was conducted. From September 2010 to September 2013, we included 968 subjects who had received coronary follow-up angiography after primary PCI. The logistic regression analysis, receiver operator characteristic (ROC) analysis, nomogram analysis, Hosmer-Lemeshow χ2 statistic, and calibration curve were applied to build and evaluate the prediction model.

Results: Fifty-six patients (5.79%) occurred ISR. The platelet distribution width (PDW), total cholesterol (TC), systolic blood pressure (SBP), low-density lipoprotein cholesterol (LDL-C), and lesion vessels had significant differences between ISR and non-ISR groups (all P < 0.05). And these variables were independently associated with ISR (all P < 0.05). Furthermore, they were identified as predictors (all AUC > 0.5 and P < 0.05) to establish a prediction model. The prediction model showed a good value of area under curve (AUC) (95%CI): 0.72 (0.64-0.80), and its optimized cut-off was 6.39 with 71% sensitivity and 65% specificity to predict ISR.

Conclusion: The incidence of ISR is 5.79% in CAD patients with DES implantation in the Xinjiang population, China. The prediction model based on PDW, SBP, TC, LDL-C, and lesion vessels was an effective model to predict ISR in CAD patients with DESs implantation.

Keywords: Coronary heart disease; In-stent restenosis; Percutaneous coronary intervention; Risk factors.

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Conflict of interest statement

None competing interests.

Figures

Fig. 1
Fig. 1
Scoring and evaluating the prediction model. A nomogram was performed for the prediction model. The using instruction for the nomogram is that a line on the top labeled “Points” displays the corresponding score for the values of each variable; a line labeled “Total points”, representing an individual’s total points of all variables in the model, is corresponded to the line labeled “Risk” which indicates the risk of ISR for the individual. Abbreviations: LDL-C, low-density lipoprotein cholesterol; PDW, platelet distribution width; SBP, systolic blood pressure; TC, total cholesterol
Fig. 2
Fig. 2
Overview of the prediction model of ISR. The figure summarizes the content of the prediction model, including variables in the model, the lowest and highest risk score for each variable, and suggestions for risk factor control. Abbreviations: LDL, low-density lipoprotein cholesterol; PDW, platelet distribution width; SBP, systolic blood pressure; TC, total cholesterol
See this image and copyright information in PMC

References

    1. Lekshmi KM, Che HL, Cho CS, Park IK. Drug- and Gene-eluting Stents for Preventing Coronary Restenosis. Chonnam Med J. 2017;53:14–27. doi: 10.4068/cmj.2017.53.1.14. - DOI - PMC - PubMed
    1. Timmis A, Townsend N, Gale CP, Torbica A, Lettino M, Petersen SE, et al. European Society of Cardiology: Cardiovascular Disease Statistics 2019. Eur Heart J. 2020:41, 12–85. 10.1093/eurheartj/ehz859. - PubMed
    1. Levine GN, Bates ER, Blankenship JC, Bailey SR, Bittl JA, Cercek B, et al. 2015 ACC/AHA/SCAI focused update on primary percutaneous coronary intervention for patients with ST-elevation myocardial infarction: an update of the 2011 ACCF/AHA/SCAI guideline for percutaneous coronary intervention and the 2013 ACCF/AHA guideline for the management of ST-elevation myocardial infarction. J Am Coll Cardiol. 2016;67:1235–1250. doi: 10.1016/j.jacc.2015.10.005. - DOI - PubMed
    1. Byrne RA, Joner M, Kastrati A. Stent thrombosis and restenosis: what have we learned and where are we going? The Andreas Gruntzig Lecture ESC 2014. Eur Heart J. 2015;36:3320–3331. doi: 10.1093/eurheartj/ehv511. - DOI - PMC - PubMed
    1. Stefanini GG, Holmes DR., Jr Drug-eluting coronary-artery stents. N Engl J Med. 2013;368:254–265. doi: 10.1056/NEJMra1210816. - DOI - PubMed