Long-term outcomes following drug-coated balloons versus thin-strut drug-eluting stents for treatment of in-stent restenosis in Chronic Kidney Disease (CKD Dragon-Registry)

Rafał Januszek; Marta Chamera; Sylwia Iwańczyk; Fabrizio D’Ascenzo; Łukasz Kuźma; Brunon Tomasiewicz; Piotr Niezgoda; Rafał Wolny; Mariusz Kowalewski; Maciej Wybraniec; Krzysztof Reczuch; Sławomir Dobrzycki; Ovidio De Filippo; Artur Pawlik; Karol Kasprzycki; Kamil Skowron; Stanisław Bartuś; Maciej Lesiak; Mariusz Gąsior; Jacek Kubica; Tomasz Pawłowski; Robert J Gil; Piotr Waciński; Francesco Bruno; Bernardo Cortese; Wojciech Wojakowski; Wojciech Wańha

doi:10.1371/journal.pone.0337991

. 2025 Dec 29;20(12):e0337991. doi: 10.1371/journal.pone.0337991

Long-term outcomes following drug-coated balloons versus thin-strut drug-eluting stents for treatment of in-stent restenosis in Chronic Kidney Disease (CKD Dragon-Registry)

Rafał Januszek ^1,^*, Marta Chamera ², Sylwia Iwańczyk ^3,⁴, Fabrizio D’Ascenzo ⁵, Łukasz Kuźma ⁶, Brunon Tomasiewicz ⁷, Piotr Niezgoda ⁸, Rafał Wolny ⁹, Mariusz Kowalewski ¹⁰, Maciej Wybraniec ¹¹, Krzysztof Reczuch ⁷, Sławomir Dobrzycki ⁶, Ovidio De Filippo ⁵, Artur Pawlik ¹², Karol Kasprzycki ¹², Kamil Skowron ¹², Stanisław Bartuś ¹², Maciej Lesiak ⁴, Mariusz Gąsior ¹³, Jacek Kubica ⁸, Tomasz Pawłowski ¹⁴, Robert J Gil ¹⁴, Piotr Waciński ¹⁵, Francesco Bruno ⁵, Bernardo Cortese ^3,¹⁶, Wojciech Wojakowski ², Wojciech Wańha ^2,^3,¹¹

Editor: Prakash Sojitra¹⁷

¹Faculty of Medicine and Health Sciences, Andrzej Frycz Modrzewski Cracow University, Kraków, Poland

²Department of Cardiology and Structural Heart Diseases, Medical University of Silesia, Katowice, Poland

³DCB Academy, Milano, Italy

⁴1st Department of Cardiology, Poznan University of Medical Sciences, Poznan, Poland

⁵Division of Cardiology, Department of Internal Medicine, Città della Salute e della Scienza, University of Turin, Turin, Italy

⁶Department of Invasive Cardiology, Medical University of Bialystok, Bialystok, Poland

⁷Centre for Heart Disease, University Hospital Wroclaw Department of Heart Disease, Wroclaw Medical University, Wrocław, Poland

⁸Department of Cardiology and Internal Medicine, Collegium Medicum, Nicolaus Copernicus University, Bydgoszcz, Poland

⁹Department of Interventional Cardiology and Angiology, National Institute of Cardiology, Warsaw, Poland

¹⁰Clinical Department of Cardiac Surgery and Transplantology, National Medical Institute of the Ministry of Interior and Administration, Warsaw, Poland

¹¹1st Department of Cardiology, School of Medicine in Katowice, Medical University of Silesia, Katowice, Poland

¹²Jagiellonian University Medical College, Kraków, Poland

¹³Third Department of Cardiology, Medical University of Silesia, Zabrze, Poland

¹⁴Department of Cardiology, National Medical Institute of the Ministry of Interior and Administration, Warsaw, Poland

¹⁵Clinical Department of Interventional Cardiology, Medical University of Lublin, Lublin, Poland

¹⁶Harrington Heart & Vascular Institute, University Hospitals Cleveland Medical Center, Cleveland, Ohio, United States of America

¹⁷IPSurface Canada Inc, CANADA

Competing Interests: The authors have declared that no competing interests exist.

^✉

* E-mail: jaanraf@interia.pl

Roles

Rafał Januszek: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Supervision, Validation, Writing – original draft, Writing – review & editing

Marta Chamera: Data curation, Methodology, Writing – original draft

Sylwia Iwańczyk: Conceptualization, Data curation, Investigation, Methodology, Writing – original draft, Writing – review & editing

Fabrizio D’Ascenzo: Conceptualization, Data curation, Investigation, Methodology, Validation, Writing – original draft

Łukasz Kuźma: Conceptualization, Data curation, Investigation, Validation, Writing – original draft

Brunon Tomasiewicz: Conceptualization, Data curation, Methodology, Validation, Writing – original draft, Writing – review & editing

Piotr Niezgoda: Conceptualization, Data curation, Investigation, Methodology, Supervision, Validation, Visualization, Writing – original draft

Rafał Wolny: Data curation, Funding acquisition, Investigation, Methodology, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

Mariusz Kowalewski: Data curation, Formal analysis, Methodology, Validation, Visualization, Writing – original draft

Maciej Wybraniec: Data curation, Investigation, Methodology, Software, Validation, Writing – original draft, Writing – review & editing

Krzysztof Reczuch: Data curation, Investigation, Supervision, Validation

Sławomir Dobrzycki: Data curation, Investigation, Writing – review & editing

Ovidio De Filippo: Data curation, Investigation, Supervision, Validation, Writing – review & editing

Artur Pawlik: Data curation, Investigation, Writing – original draft

Karol Kasprzycki: Data curation, Investigation, Writing – original draft

Kamil Skowron: Data curation, Investigation, Writing – original draft

Stanisław Bartuś: Data curation, Methodology, Writing – original draft

Maciej Lesiak: Data curation, Investigation, Writing – original draft

Mariusz Gąsior: Data curation, Investigation, Writing – original draft

Jacek Kubica: Data curation, Investigation, Writing – original draft

Tomasz Pawłowski: Data curation, Investigation, Writing – original draft

Robert J Gil: Conceptualization, Investigation, Supervision, Validation, Writing – review & editing

Piotr Waciński: Data curation, Investigation, Writing – original draft

Francesco Bruno: Data curation, Investigation, Writing – original draft

Bernardo Cortese: Conceptualization, Investigation, Supervision, Validation, Writing – review & editing

Wojciech Wojakowski: Data curation, Investigation, Validation, Writing – original draft

Wojciech Wańha: Data curation, Investigation, Writing – original draft

Prakash Sojitra: Editor

PMCID: PMC12747340 PMID: 41460848

Abstract

We sought to investigated the outcomes of patients with chronic kidney disease (CKD) and drug-eluting stent (DES)-in-stent restenosis (ISR) undergoing percutaneous coronary intervention (PCI) with a drug-coated balloon (DCB) or thin strut drug-eluting stent (thin-DES). Consecutive patients with DES-ISR who underwent PCI with a thin-DES or a paclitaxel-coated DCB for DES-ISR were enrolled. The primary outcome was target lesion revascularization (TLR), while the secondary was target vessel revascularization (TVR) and device-oriented composite endpoint (DOCE). The pooled analysis included 1,317 patients, with 585 (44.42%) treated using a thin-DES and 732 (55.58%) by DCB. In the crude analysis of CKD patients (n = 286) undergoing PCI for ISR, thin-DES vs. DCB showed similar outcomes for TLR (hazard ratio [HR]=0.94, 95% confidence interval [CI]=0.44–2.00; p = 0.873), TVR (HR = 0.82, 95% CI = 0.44–1.55; p = 0.542), MI (HR = 0.71, 95% CI = 0.34–1.46; p = 0.348) and DOCE (HR = 0.71, 95% CI = 0.36–1.40; p = 0.325). After propensity score matching (n = 184), the HRs remained non-significant for TLR (0.52, 95% CI = 0.21–1.29; p = 0.159), TVR (0.54, 95% CI = 0.24–1.01; p = 0.134), MI (0.56, 95% CI = 0.24–1.32; p = 0.183), TV-MI (0.56, 95% CI = 0.09–3.39; p = 0.528), cardiac death (0.63, 95% CI = 0.10–3.81; p = 0.615), and DOCE (0.45, 95% CI = 0.19–1.04; p = 0.062). In conclusion, in CKD patients undergoing PCI for ISR, thin‐DES treatment was associated with a numerical reduction in TLR, TVR, and DOCE compared with DCB. However, these differences did not achieve statistical significance in the crude or propensity score-matched analyses.

Introduction

One of the main disadvantages of percutaneous coronary revascularization with stenting is the risk of in-stent restenosis (ISR) during the follow-up period [1]. A ≥ 50% reduction in luminal diameter within the stented segment defines binary angiographic ISR [2]. The time from percutaneous coronary intervention (PCI) to ISR occurrence is determined by several factors, including the type of PCI, implanted stent [3], antiplatelet drug used and treatment duration [4], target lesion, plaque-stabilizing treatment, and the control and number of concomitant modifiable atherosclerosis risk factors [5], as well as non-modifiable elements such as congenital and environmental factors [3–5]. Despite meaningful improvements in DES technologies and modifiable risk factor control, ISR occurs at a rate of 1−2% per year with contemporary DES platforms [6]. Such lesions are often seen in clinical practice and, compared to de novo lesions, are associated with a higher incidence of adverse cardiac events regardless of the interventional approach [1,7,8]. Registry data shows that PCI for ISR is performed in around 10% of all PCI cases [9,10], with DES and drug-coated balloon (DCB) use as the only methods currently recommended for ISR treatment [11,12]. The availability of data comparing the outcomes of ISR treatment in patients with renal failure is very limited, and there are no clear recommendations as to which treatment method is better in this subgroup of patients. A study by Mahfound et al. [13] found that when using PCI for small coronary artery disease, the primary outcomes were similar over three years in DCB and DES patients (hazard ratio [HR] = 0.98, 95% confidence interval [CI] 0.67–1.44; p = 0.937), and patients with and without CKD (HR = 1.18; 95% CI = 0.76–1.83; p = 0.462). Rates of cardiac and all-cause death were significantly higher among CKD patients but were not affected by DCB or DES treatment [13]. However, major bleeding events were lower for DCB than DES (12 vs. 3, HR = 0.26, 95% CI = 0.07–0.92; p = 0.037) and not influenced by the presence of CKD [13]. Several randomized trials and retrospective studies have shown comparable long-term outcomes between DES and DCB in non-selected ISR patients [14], though less is known about the impact of CKD on DCB and DES treatment in ISR patients. Lee et al. found that CKD patients with ISR undergoing DCB angioplasty had a significantly higher risk of adverse events compared to patients with preserved renal function. In contrast, subgroups with mild to moderate CKD did not display this difference [15]. The study concluded that different revascularization strategies may be considered for ISR patients with severe CKD or end-stage renal disease (ESRD) [15].

Therefore, in the [resented study we assessed the long-term safety and efficacy of DCB use versus a thin strut DES (thin-DES) in CKD patients with ISR.

Materials and methods

Study design, objectives, and patient selection

This observational, multicenter study based on the CKD DRAGON Registry enrolled all consecutive patients who underwent PCI with a new-generation DES or DCB for coronary DES-ISR between February 2008 and October 2021. Subsequently, patients within the DES and DCB groups were assigned to one of two subgroups depending on an estimated glomerular filtration rate (eGFR) < 60 mL/min/1.73m². Exclusion criteria included concomitant DES and DCB treatment, saphenous vein graft ISR, cardiogenic shock, thrombolysis before PCI, suboptimal or failed PCI for target lesions, and 12-month follow-up not available. Fig 1 depicts the patient flow chart.

Coronary angiography assessed ISR, defined as a ≥ 50% reduction in luminal diameter within the previously stented segment or the vessel segments 5 mm proximal and distal to the stent. In case of doubts regarding the significance of stenoses in the angiographic examination, confirmation or exclusion relied on a physiological assessment. The Mehran classification defined ISR patterns [16].

Procedure description

All patients were preloaded with a P2Y12-antagonist and aspirin before coronary angioplasty. Unfractionated heparin was given according to the standard hospital practice. Additional antithrombotic regimens, including glycoprotein IIb/IIIa inhibitors, were used at the discretion of the treating physicians. Interventions were performed with 6 or 7 French guiding catheters, using the radial approach in more than 90% of patients. Intravascular ultrasound (IVUS) or optical coherence tomography (OCT) was optional but recommended to assess the ISR mechanism. Pre-dilatation of the target lesion was mandatory, using an uncoated balloon catheter with a diameter 0.5 mm smaller than, or similar to the size of, the reference vessel diameter or the diameter of the previously implanted restenotic stent. The operator used atherectomy methods and specially modified balloons for severe calcifications, such as scoring or cutting balloons. The recommended study DCB inflation time was 30–60 seconds at nominal pressure. Operator and center experience determined DCB choice.

The procedure was successful if final a flow grade 3 thrombolysis in myocardial infarction (TIMI) without flow-limiting dissection was obtained, final diameter stenosis was below 30% by visual estimation, and no in-hospital major adverse cardiovascular events (MACE) occurred. Dual antiplatelet therapy (DAPT) was generally continued orally for at least six months in stable or 12 months in acute coronary syndrome (ACS) patients, followed by aspirin or clopidogrel alone.

Inclusion criteria

The CCS DRAGON Registry is a multicenter initiative involving consecutive patients with DES-ISR and CKD manifestation treated with a paclitaxel-DCB or a thin-DES between February 2008 and October 2021. Exclusion criteria were DCB and thin-DES use during the same procedure, PCI of a bypass graft, and recurrent ISR. Thin strut stents were defined as those with strut thickness <100 μm. The following DES were used: Alex (Balton, Warsaw, Poland), Orsiro (Biotronik AG, Bulach, Switzerland), Promus (Boston Scientific, MA, USA), Resolute (Medtronic CardioVascular, CA, USA), Synergy (Boston Scientific, MA, USA), Ultimaster (Terumo Corporation, Tokyo, Japan), and Xience (Abbott Vascular Devices, CA, USA). The paclitaxel-DCB used were Agent (Boston Scientific, MA, USA), Elutax (Aachen Resonance GmbH, Aachen, Germany), Essential (iVascular, Barcelona, Spain), IN.PACT (Medtronic Vascular, CA, USA), Pantera Lux (Biotronik AG, Buulach, Switzerland), Restore DEB (Cardionovum GmbH, Bonn, Germany), and SeQuent Please Neo (B.Braun Interventional Group, Ltd, Melsulgen, Germany).

Patient data were anonymized at each center, combined into one database, and statistically analyzed as one cohort. Cardiovascular risk factors, clinical presentation, and angiographic characteristics were recorded, along with the parameters of the implanted stents. The above data were derived from electronic patient records at each center. The institutional review board approved the study protocol, though, due to the retrospective nature of the study, no written informed consent was needed. Patient data was protected according to the requirements of Polish law, the General Data Protection Regulation (GDPR), and hospital standard operating procedures. The study was conducted in accordance with the Declaration of Helsinki and registered at www.ClinicalTrials.gov. The data supporting the findings of this study are available from the corresponding author upon reasonable request.

Primary and secondary endpoints

Outcome data were obtained from clinical assessments, telephone consultations, or primary care physicians and recorded online or from the central database of the National Health Fund Service of the Ministry of Health. No patients were lost to follow-up. The primary efficacy endpoint was target lesion revascularization (TLR). The secondary endpoints were device-oriented composite endpoint (DOCE) (defined as a composite of cardiac death, TLR, and target vessel myocardial infarction [MI]), target vessel revascularization (TVR), MI, and cardiac death. TVR and TLR were defined according to the definitions of endpoints for clinical trials.

Statistical analysis

All statistical analyses used a two-sided significance level of α = 0.05. The Shapiro-Wilk test assessed the distribution of continuous variables. Data were presented as medians (Me) and interquartile range (IQR) for non-normally distributed continuous variables and as the number of cases (n) and percentage (%) for categorical variables. For nominal variables, Pearson’s chi-squared test and Fisher’s exact test determined statistically significant differences between the two groups, while the Wilcoxon rank-sum test compared numerical variables between two independent groups. Kaplan-Meier curves were used for graphical presentations of time-dependent variables, and a log-rank test was performed for between-group comparisons. Propensity score-matching (PSM) was conducted using the nearest neighbor algorithm to ensure comparability between DES and DCB cohorts. The matching process accounted for a comprehensive set of core baseline variables, including sex, age, atrial fibrillation, arterial hypertension, diabetes mellitus, smoking status, previous MI, prior coronary artery bypass grafting (CABG), peripheral artery disease, left ventricular ejection fraction (LVEF), degree of stenosis, and lesion characteristics (bifurcation and calcification) (S1 Fig). Cox regression analysis evaluated long-term follow-up event rates for TLR, target lesion vascularization (TLV), DOCE, MI, target vessel MI, and all-cause mortality in both the unmatched population and the PSM CKD group.

The proportional hazards assumption for the Cox models was verified using the Schoenfeld residuals test (cox.zph function in the R survival package, version 3.7.0). For all primary and secondary endpoints in both the unmatched and propensity score-matched analyses, the test produced p-values exceeding significance level (α = 0.05), confirming no significant departures from proportionality.

This analysis was exploratory and hypothesis-generating in nature, with no adjustments applied for multiple comparisons across the primary and secondary endpoints to preserve sensitivity in detecting potential signals, albeit at the risk of increased Type I error.

Analyses employed the R Statistical language (version 4.3.3), using the rio (version 1.2.1), cobalt (version 4.5.5), MatchIt (version 4.5.5), sjPlot (version 2.8.15), parameters (version 0.22.2), performance (version 0.12.3), report (version 0.5.8), ggsurvfit (version 1.1.0), gtsummary (version 1.7.2), survival (version 3.7.0), MASS (version 7.3.60.0.1), ggplot2 (version 3.5.0), and dplyr (version 1.1.4) packages [17–31].

Results

The non-matched cohort comprised 1317 patients, and before PSM, there were 1031 patients with preserved kidney function (481 treated with DES and 550 with DCB), while 286 patients had impaired function (104 patients treated with DES and 182 with DCB). After PSM, 184 patients remained in the CKD group, and both groups consisted of 92 patients. Before PSM, there were 14 patients in the DCB group undergoing dialysotherapy and nine in the DES group. After PSM, six patients remained in the DCB group, and all nine remained in the DCB group. The median follow-up was 959 days (IQR = 426–1732) (Fig 1).

Patient characteristics

Before PSM, the CKD patients treated with DES were significantly older (p = 0.019), comprised fewer males (p = 0.025), and suffered less from diabetes mellitus (p = 0.007) and arterial hypertension (p = 0.016) than the DCB-treated group. However, such differences were not found in those with preserved kidney function (Table 1). Meanwhile, patients with preserved kidney function treated with DES had a lower history of CABG (p = 0.002) and peripheral artery disease (p < 0.001) than the DCB group but experienced ST-elevation MI (STEMI) (p = 0.004) and atrial fibrillation (p = 0.002) more often and had a lower LVEF (p < 0.001), unlike patients with impaired kidney function, where these differences where not significant (Table 1). After PSM, patients in the CDK group treated with DES presented with a history of coronary artery disease (CAD) more often (p = 0.001), while other indices did not differ significantly between subgroups (Table 1).

Table 1. Patient characteristics, risk factors, and clinical presentation according to device type.

	Non-matched cohort, n = 1,317						Propensity score-matched groups, n = 184
	Preserved kidney function, n = 1,031			CKD, n = 286			CKD, n = 184
	Thin-DES n = 481 (46.65%)	DCB n = 550 (53.35%)	p-value	Thin-DES n = 104 (36.36%)	DCB n = 182 (63.74%)	p-value	Thin-DES n = 92 (50.00%)	DCB n = 92 (50.00%)	p-value
	Demographic data
Age, years, Me (IQR)	65.00 (58.36, 72.00)	67.00 (60.00, 72.00)	0.120^A	75.00 (68.75, 81.17)	72.00 (65.00, 78.00)	0.019 ^A	74.61 (68.00, 79.25)	74.00 (65.35, 81.00)	0.965^A
Male, n (%)	340 (70.69%)	401 (72.91%)	0.428^B	57 (54.81%)	124 (68.13%)	0.025 ^B	54 (58.70%)	60 (65.22%)	0.362^B
BMI, Me (IQR)	28.11 (25.96, 31.10)	28.73 (25.95, 31.63)	0.408^A	28.23 (25.86, 31.20)	27.58 (25.21, 30.50)	0.496^A	28.23 (25.73, 31.53)	28.06 (25.50, 31.06)	0.711^A
CAD history
Previous MI, n (%)	307 (63.83%)	338 (61.45%)	0.433^B	60 (57.69)	118 (64.84%)	0.231^B	57 (61.96%)	59 (64.13%)	0.760^B
Previous CABG, n (%)	64 (13.31%)	113 (20.55%)	0.002 ^B	18 (17.31%)	47 (26.37%)	0.080^B	18 (19.57%)	22 (23.91%)	0.475^B
CAD risk factors
Diabetes mellitus, n (%) - Insulin requiring, n (%)	174 (36.17%) 41 (8.52%)	222 (40.36%) 76 (13.82%)	0.168^B 0.008^B	44 (42.31%) 17 (16.35%)	107 (58.79%) 49 (26.92%)	0.007 ^B 0.041 ^B	44 (47.83%) 17 (18.48%)	44 (47.83%) 20 (21.74%)	1.000^B 0.581^B
Hypertension, n (%)	422 (87.73%)	495 (90.00%)	0.247^B	88 (84.62%)	170 (93.41%)	0.016 ^B	84 (91.30%)	83 (90.22%)	0.799^B
Hyperlipidemia, n (%)	411 (85.45%)	460 (84.55%)	0.686^B	92 (88.46%)	153 (84.07%)	0.308^B	82 (89.13%)	78 (84.78%)	0.381^B
Clinical presentation
- Stable angina	230 (47.82%)	277 (50.36%)	0.415^B	41 (39.42%)	72 (39.56%)	0.982^B	36 (39.13%)	37 (40.22%)	0.880^B
- Unstable angina	148 (30.77%)	172 (31.27%)	0.862^B	27 (25.96%)	56 (31.32%)	0.339^B	25 (27.17%)	29 (31.52%)	0.517^B
- NSTEMI	86 (17.88%)	96 (17.45%)	0.828^B	32 (30.77%)	48 (26.37%)	0.426^B	27 (29.35%)	24 (26.09%)	0.621^B
- STEMI	17 (3.53%)	5 (0.91%)	0.004 ^B	4 (3.85%)	5 (2.75%)	0.728^C	4 (4.35%)	2 (2.17%)	0.682^C
Atrial fibrillation, n (%)	49 (10.19%)	93 (16.91%)	0.002 ^B	19 (18.27%)	49 (26.92%)	0.098^B	17 (18.48%)	18 (19.57%)	0.851^B
Current smoker, n (%)	98 (20.37%)	124 (22.55%)	0.397^B	17(16.35%)	25 (13.74%)	0.549^B	13 (14.13%)	14 (15.22%)	0.835^B
Family history of CAD, n (%)	188 (39.17%)	135 (25.67%)	<0.001 ^B	41 (39.42%)	38 (22.62%)	0.003 ^B	39 (42.39%)	17 (19.77%)	0.001 ^B
	Concomitant disease and left ventricular ejection fraction
Pulmonary disease, n (%)	40 (8.32%)	52 (9.45%)	0.522^B	8 (7.69%)	18 (9.89%)	0.534^B	8.00 (8.70%)	7.00 (7.61%)	0.788^B
Peripheral artery disease, n (%)	52 (10.81%)	113 (20.55%)	<0.001 ^B	21 (20.19%)	45 (24.73%)	0.381^B	21.00 (22.83%)	20.00 (21.74%)	0.859^B
LVEF%, Me (IQR)	50.00 (44.00, 55.00)	50.00 (45.00, 58.25)	<0.001 ^A	50 (39.50, 53.00)	45.00 (37.00, 55.00)	0.670^A	50.00 (39.50, 50.50)	48.00 (38.00, 55.50)	0.846^A

Open in a new tab

Abbreviations: BMI – body mass index; CABG – coronary artery bypass graft; CAD – coronary artery disease; CKD – chronic kidney disease; DAPT – dual antiplatelet therapy; DCB – drug-coated balloon; DES – drug-eluting stent; ISR – in-stent restenosis; IQR – interquartile range; LVEF – left ventricular ejection; Me – median; MI – myocardial infraction; MVD – multi-vessel disease; N/A – not applicable; n – number; NSTEMI – Non-ST-elevation myocardial infarction; STEMI – ST-elevation myocardial infarction.

Note: ^A – Wilcoxon rank sum test; ^B – Pearson’s chi-squared test. ^C – Fisher’s exact test.

Angiographic and procedural data

Considering the frequency of thrombus followed by thrombectomy among patients with preserved kidney function, DES use was more frequent than DCB, though there were no such differences in the CKD group before and after PSM (Table 2). Stent implantation in the non-CKD group was performed in significantly tighter stenoses, and this relationship remained in the CKD group but disappeared after PSM (Table 2). Also, the left main artery was treated with a DES more often than a DCB in the non-CKD group, which was not reflected in the CKD group before and after PSM (Table 2). The original stent was longer, with a smaller diameter, in patients treated with a DCB in the non-CKD group, as well as in the CKD group before PSM, but was not statistically significant after PSM (Table 2). Focal restenosis and predilatation were observed more often among DCB-treated patients than those receiving a DES in all three groups (Table 2). The mean DES length used for PCI was significantly longer in the non-CKD group, whereas no statistical differences were found in the CKD group before and after PSM (Table 2). Residual restenosis was observed more often in the DCB than the DES group in non-CKD patients and in CKD patients after PSM (Table 2). The length of DAPT was significantly shorter in patients treated with a DCB rather than a DES in the non-CKD and CKD groups before and after PSM (Table 2).

Table 2. Angiographic, procedural, and medication data according to device type before and after propensity score matching.

	Non-matched cohort, n = 1,317						Propensity score-matched groups, n = 184
	Preserved kidney function, n = 1,031			CKD, n = 286			CKD, n = 184
	Thin-DES n = 481 (46.65%)	DCB n = 550 (53.35%)	p-value	Thin-DES n = 104 (36.36%)	DCB n = 182 (63.74%)	p-value	Thin-DES n = 92 (50.00%)	DCB n = 92 (50.00%)	p-value
Angiography
One-vessel disease, n (%)	276 (57.38%)	306 (55.64%)	0.573^B	64 (61.54%)	86.(47.25%)	0.020 ^B	55 (59.78)	42 (45.65%)	0.055^B
Two-vessel disease, n (%)	147 (30.56%)	159 (28.91%)	0.562^B	24 (23.08%)	60 (32.97%)	0.077^B	21 (22.83%)	30 (32.61%)	0.138^B
MVD, n (%)	55 (11.43%)	82 (14.91%)	0.101^B	16 (15.38%)	36 (19.78%)	0.354^B	16 (22.83%)	20 (21.74%)	0.457^B
Bifurcation, n (%)	83 (17.26%)	90 (16.36%)	0.702^B	18 (17.31%)	43 (23.63%)	0.210^B	16 (17.39%)	16 (17.39%)	1.000^B
Thrombus, n (%)	14 (2.91%)	4 (0.73%)	0.008 ^C	3 (2.88%)	3 (1.65%)	0.671^C	3 (3.26%)	1 (1.09%)	0.621^B
Thrombectomy, n (%)	5 (1.04%)	0 (0%)	0.022 ^C	1 (0.96%)	0 (0%)	0.365^C	1 (1.09%)	0 (0%)	1.000^B
Calcification, n (%)	17 (3.53%)	21 (3.82%)	0.809^B	5 (4.81%)	10 (5.49%)	0.802B	4 (4.35%)	7 (7.61%)	0.351^B
Stenosis, %, Me (IQR)	80.00 (80.90, 90.00)	80.00 (70.00, 90.00)	0.011 ^A	85.00 (80.00, 90.00)	80.00 (75.00, 90.00)	0.028 ^A	85.00 (80.00, 90.00)	90.00 (80.00, 90.00)	0.786^A
Target lesion
Left main, n (%)	45 (9.36%)	33 (6.00%)	0.042 ^B	10 (10.58%)	20 (10.99%)	0.914^B	11 (11.96%)	8 (8.70%)	0.467^B
Left anterior descending, n (%)	198 (41.16%)	226 (41.09%)	0.981^B	44 (42.31%)	81 (44.51%)	0.719^B	36 (39.13%)	47 (51.09%)	0.103^B
Left circumflex, n (%)	77 (16.01%)	147 (26.73%)	<0.001 ^B	15 (14.42%)	49 (26.92%)	0.015 ^B	13 (14.13%)	23 (25%)	0.063^B
Right coronary artery, n (%)	162 (33.68%)	175 (31.82%)	0.525^B	36 (34.62%)	54 (29.67%)	0.386^B	34 (36.96%)	26 (28.26%)	0.208^B
Original stent-length, mm, Me (IQR)	20.00 (16.00, 24.00) n = 256	23.00 (18.00, 30.00) n = 268	<0.001 ^A	20.00 (15.00, 25.00) n = 56	24.00 (18.00, 32.00) n = 128	0.005 ^A	20.00 (15.00, 28.00) n = 46	23.50 (18.00, 32.00) n = 68	0.083^A
Original stent diameter, mm, Me (IQR)	3.00 (3.00, 3.50) n = 253	3.00 (2.75, 3.50) n = 255	0.006 ^A	3.13 (3.00, 3.50) n = 56	3.00 (2.50, 3.50) n = 127	0.048 ^A	3.00 (3.00, 3.50) n = 46	3.00 (2.50, 3.50) n = 68	0.065
Type of ISR
Focal, n (%)	205 (42.62%)	299 (54.36%)	<0.001 ^B	42 (40.38%)	102 (56.04%)	0.011 ^B	37 (40.22%)	51 (55.43%)	0.039 ^B
Diffuse, n (%)	147 (30.56%)	189 (34.36%)	0.194^B	44 (42.31%)	57 (31.32%)	0.061^B	40 (43.48%)	28 (30.43%)	0.067^B
Proliferative, n (%)	123 (25.57%)	45 (8.18%)	<0.001 ^B	18 (17.31%)	16 (8.79%)	0.032 ^B	15 (16.30%)	10 (10.87%)	0.282^B
Occlusive, n (%)	6 (1.25%)	17 (3.09%)	0.046 ^B	0 (0%)	7 (3.85%)	0.051^C	0 (0%)	3 (3.26%)	0.246^C
Balloon pre-dilatation
Predilatation, n (%)	287 (62.94%) n = 456	358 (88.18%) n = 406	<0.001 ^B	77 (74.04%)	138 (91.39%)	<0.001 ^B	68 (73.91%) n = 92	70 (87.74%) n = 78	0.009 ^B
Length, mm, Me (IQR)	15.00 (12.00, 20.00) n = 276	15.00 (15.00, 20.00) n = 378	0.154^A	15.00 (12.00, 20.00) n = 58	15.00 (15.00, 20.00) n = 144	0.760^A	15.00 (12.00, 20.00) n = 48	15.00 (15.00, 20.00) n = 76	0.670^A
Diameter, mm, Me (IQR)	3.00 (2.50, 3.50) n = 287	3.00 (2.50, 3.50) n = 392	0.906^A	3.00 (2.50, 3.50) n = 60	3.00 (2.50, 3.50) n = 146	0.830^A	3 (2.50, 3.50) n = 51	3 (2.50, 3.50) n = 76	0.726^A
Device data
Length, mm, Me (IQR)	20.00 (15.00, 28.00) n = 482	20.00 (15.00, 25.00) n = 485	0.868^A	18.00 (15.00, 28.25) n = 104	20.00 (16.50, 24.00) n = 78	0.938^A	18.00 (15.00, 30.00) n = 92	20.00 (17.00, 25.00) n = 79	0.810^A
Diameter, mm, Me (IQR)	3.00 (3.00, 3.50) n = 482	3.00 (2.50, 3.50) n = 498	0.030 ^A	3.00 (2.75, 3.50) n = 104	3.00 (2.50, 3.50) n = 78	0.605^A	3.00 (2.75, 3.50) n = 92	3.00 (2.00, 3.50) n = 81	0.093^A
Post-procedure
Residual stenosis, n (%)	17 (3.53%)	53 (9.64%)	<0.001 ^B	6 (5.77%)	18 (9.89%)	0.277^B	5 (5.43%)	13 (14.13%)	0.047
TIMI-3, n (%)	478 (99.38%)	547 (99.45%)	1.000^B	102 (98.08%)	178 (97.80%)	1.000^B	90 (97.83%)	89 (96.74%)	1.000
Perforation, n (%)	0 (0%)	1 (0.18%)	1.000^C	0 (0%)	0 (0%)	1.000^C	0	0	–
Dissection, n (%)	18 (3.74%)	9 (1.64%)	0.035 ^C	3 (2.88%)	8 (4.40%)	0.751^C	3 (3.26%)	6 (6.52%)	0.497
No Reflow, n (%)	4 (0.83%)	3 (0.55%)	0.711^C	0 (0%)	2 (1.10%)	0.536^C	0	0	–
Intracoronary imaging and drug therapy
Use of intracoronary imaging, n (%)	22 (4.57%)	27 (4.91%)	0.801^B	3 (2.88%)	11 (6.04%)	0.234^C	3 (3.26%)	5 (5.43%)	0.720^C
Glycoprotein IIb/IIIa inhibitors, n (%)	13 (2.70%)	2 (0.36%)	0.002 ^C	2 (1.92%)	4 (2.2%)	1.000^C	2 (2.17%)	2 (2.17%)	1.000^C
Length of DAPT, months, Me (IQR)	12.00 (12.00, 12.00) n = 479	12.00 (6.00, 12.00) n = 543	<0.001 ^A	12.00 (12.00, 12.00) n = 103	12.00 (6.00, 12.00) n = 180	<0.001 ^A	12.00 (12.00, 12.00) n = 91	12.00 (6.00, 12.00) n = 92	0.001 ^A

Open in a new tab

Abbreviations: CKD – chronic kidney disease; DAPT – dual antiplatelet therapy; DCB – drug-coated balloon; DES – drug-eluting stent; ISR – in-stent restenosis; IQR – interquartile range; Me – median; MVD – multi-vessel disease; N/A – not applicable; n, number; TIMI – Thrombolysis in Myocardial Infarction risk score

Note: ^A – Wilcoxon rank sum test; ^B – Pearson’s chi-squared test. ^C – Fisher’s exact test.

Long-term outcomes

There were no differences in the primary endpoint for TLR frequency during follow-up between patients treated with DES and DCB in the non-CKD group before (p = 0.873) and after PSM (p = 0.159) (Table 3 and Fig 2). Also, there were no differences in clinical outcomes between patients treated with DES and DCB for secondary endpoints in the CKD group before and after PSM for TVR (unmatched, p = 0.542; post-PSM, p = 0.134) (S2 Fig), MI (unmatched, p = 0.348; post-PSM, p = 0.183; S3 Fig), TV-MI (unmatched, p = 0.527; post-PSM, p = 0.528; S4 Fig), DOCE (unmatched, p = 0.325; post-PSM, p = 0.062; Fig 3), and cardiac death (unmatched, p = 0.855; post-PSM, p = 0.615; Fig 4) (Table 3). After excluding dialysis patients, there were no differences between patients treated with DES or DCB before (p = 0.348) and after PSM (p = 183) (S5 Fig). Also, when considering patients undergoing dialysotherapy, the differences remained irrelevant (p = 0.527 for the DES group and p = 0.528 for the DCB group) (S6 Fig).

Table 3. Survival analysis for each device before and after propensity score matching.

	Crude analysis (n = 286)
Chronic kidney disease
	Thin-DES n = 104	DCB n = 182	HR (95% CI)¹	p-value	Thin-DES n = 92	DCB n = 92	HR (95% CI)¹	p-value
TLR, n (%)	11 (10.58%)	18 (9.89%)	0.94 (0.44–2.00)	0.873	8 (8.70%)	12 (13.04%)	0.52 (0.21–1.29)	0.159
TVR, n (%)	16 (15.38%)	26 (14.29%)	0.82 (0.44–1.55)	0.542	13 (14.13%)	14 (15.22%)	0.54 (0.24–1.01)	0.134
MI, n (%)	11 (10.58%)	23 (12.64%)	0.71 (0.34–1.46)	0.348	10 (10.87%)	13 (14.13%)	0.56 (0.24–1.32)	0.183
TV-MI, n (%)	4 (3.85%)	8 (4.40%)	0.67 (0.19–2.31)	0.527	3 (3.26%)	3 (3.26%)	0.56 (0.09–3.39)	0.528
CV death, n (%)	3 (2.88%)	6 (3.3%)	0.88 (0.22–3.55)	0.855	2 (2.17%)	3 (3.26%)	0.63 (0.10–3.81)	0.615
DOCE, n (%)	13 (12.50%)	26 (14.29%)	0.71 (0.36–1.40)	0.325	10 (10.87%)	15 (16.30%)	0.45 (0.19–1.04)	0.062

Open in a new tab

Abbreviations: CI – confidence interval; CV – cardiovascular; DCB – drug-coated balloon; DES – drug-eluting stent; DOCE – device-oriented composite endpoint; HR – hazard ratio; n – number; MI – myocardial infarction; TLR – target lesion revascularization; TV – target vessel; TVR – target vessel revascularization.

1 Thin-DES treated was the exposure, and DCB was the reference.

Fig 2 — The dashed lines represent the 95% confidence intervals, while the horizontal marks indicate the patient censoring events (here and below).

Thin strut drug-eluting stents vs. drug-coated balloons

Results of the Cox regression analysis evaluating the impact of thin-DES on TLR during follow-up in patients with preserved kidney function demonstrated that body-mass index > 30 kg/m² (HR = 1.98) and diabetes mellitus (HR = 1.82) were significantly related to poorer outcomes. Statistical significance was not confirmed for other assessed predictors in the CKD group before and after PSM (Table 4).

Table 4. Results of Cox regression analysis evaluating the impact of thin-strut drug-eluting stents on target lesion revascularization during follow-up in patients with chronic kidney disease and preserved kidney function, stratified by subgroups.

Subgroup	Non-matched cohort, n = 1,317						Propensity score-matched group
Subgroup	Preserved kidney function, n = 1,031			CKD, n = 286			CKD, n = 184
	HR²	95% CI	P-Value	HR²	95% CI	P-Value	HR²	95% CI	P-Value
Male	1.11	0.73–1.68	0.612	1.09	0.43–2.78	0.855	0.65	0.22–1.87	0.422
Female	1.01	0.54–1.88	0.982	0.76	0.22–2.71	0.680	0.37	0.07–2.03	0.254
Age ≥ 65 years	1.26	0.77–2.06	0.366	0.94	0.41–2.12	0.874	0.57	0.21–1.53	0.263
Age < 65 years	0.90	0.56–1.45	0.672	0.76	0.08–6.81	0.807	0.40	0.04–3.91	0.433
BMI ≥ 30	1.98	1.04–3.79	0.038	2.78	0.51–15.25	0.239	2.57	0.27–24.77	0.414
BMI < 30	0.69	0.42–1.13	0.144	0.46	0.15–1.45	0.186	0.20	0.04–1.03	0.054
DM	1.82	1.04–3.19	0.035	1.14	0.33–3.91	0.834	0.78	0.19–3.11	0.720
No DM	0.79	0.51–1.22	0.283	0.72	0.28–1.87	0.497	0.40	0.12–1.34	0.135
Previous MI	0.96	0.63–1.47	0.859	0.86	0.29–2.51	0.777	0.46	0.13–1.60	0.224
No previous MI	1.36	0.75–2.45	0.298	0.98	0.34–2.85	0.973	0.59	0.16–2.22	0.436
Bifurcation lesion	0.73	0.33–1.61	0.431	2.06	0.51–8.25	0.308	1.16	0.19–7.01	0.870
No Bifurcation lesion	1.19	0.81–1.74	0.374	0.70	0.28–1.75	0.447	0.39	0.13–1.15	0.087
Left main PCI	0.65	0.21–2.01	0.449	1.54	0.22–10.99	0.667	0.68	0.04–10.98	0.785
No left main PCI	1.13	0.79–1.63	0.496	0.87	0.38–1.98	0.747	0.46	0.17–1.26	0.130
Diameter ALL ≥ 3 mm	0.91	0.60–1.37	0.642	0.94	0.38–2.31	0.888	0.49	0.161–1.52	0.219
Diameter ALL < 3 mm	1.61	0.82–3.16	0.167	0.86	0.21–3.60	0.836	0.48	0.10–2.41	0.376
Length ALL ≥ 22 mm	1.02	0.55–1.90	0.952	0.52	0.12–2.20	0.378	0.37	0.08–1.66	0.195
Length ALL < 22 mm	1.05	0.68–1.61	0.831	1.28	0.51–3.21	0.594	0.67	0.20–2.23	0.517
Dialysis	Not applicable (no dialysis)			1.55	0.10–24.93	0.757	Not applicable (only one in the DES group had TLR)
No dialysis	1.08	0.77- 1.53	0.654	0.89	0.40–1.94	0.760	0.46	0.18–1.18	0.106

Open in a new tab

Abbreviations: BMI – body mass index; CI – confidence interval; CKD – chronic kidney disease; DES – drug-eluting stent; DM – diabetes mellitus; LM – left main; HR – hazard ratio; MI – myocardial infarction; PCI – percutaneous coronary intervention TLR – target lesion revascularization.

1 Thin – DES vs. DCB.

Discussion

The study confirmed significant differences in general characteristics among CKD patients treated with PCI due to ISR, with DCB-treated patients being younger and presenting a greater burden of concomitant diseases and more frequent LVEF impairment before PSM. Moreover, ISR was initially observed in longer and wider stents and focal restenosis was more prevalent in CKD patients treated with a DCB than a thin-DES before PSM. After PSM, most differences lost their statistical significance. Also, differences were observed in the duration of DAPT, which resulted directly from treatment guidelines. Before and after PSM in the CKD group, there were no disparities in the post-procedure follow-up for primary and secondary endpoints between PCI treatment with DCB and thin-DES. Although there was a tendency for more frequent DOCE occurrence after PSM in the DCB group, this relationship did not reach statistical significance. Furthermore, the results showed no variations in long-term outcomes for DCB and thin-DES when the CKD group was divided into dialysis and non-dialysis patients.

In the overall population of patients treated for DES-ISR, no differences were demonstrated between thin-DES and DCB use, with some results merely showing a trend toward worse outcomes with DCB [8,12,14]. It can be assumed that in a specific population, such as those with renal failure, variations in results and long-term outcomes can be expected between those treated percutaneously with DCB and thin-strut DES due to ISR. This thesis results from the number of different mechanisms of atherosclerosis and restenosis in this group of patients. CKD patients represent a specific cohort in which several potential mechanisms of vascular injury lead to accelerated progression of atherosclerosis. Among such mechanisms are mineral bone derangements expressing with intensified vascular calcifications, chemical modification of lipoproteins, such as glycation, oxidation, and carbamylation, coexisting with very-low-density lipoprotein accumulation, and loss of the protective function of high-density lipoproteins. Additional mechanisms include increased levels of circulating C-reactive protein and cytokines, an activated phenotype of circulating monocytes and resident vascular cells, increased synthesis of inflammation-triggered reactive oxygen species, and impairment of endothelial function caused by increased levels of inorganic phosphates and albuminuria, which was recently found to be a stronger risk factor for mortality than eGFR [32].

While the current findings do not demonstrate statistically significant differences between thin-strut drug-eluting stents and drug-coated balloons, they underscore a state of clinical equipoise in this high-risk population with chronic kidney disease and in-stent restenosis. This equipoise provides a strong rationale for future adequately powered randomized controlled trials to definitively evaluate comparative efficacy and safety.

Previously published studies have confirmed a higher incidence of recurrent ISR and MACE in hemodialysis (HD) patients compared to non-HD patients treated with DCB due to ISR [33]. In that study, TLR incidence during up to three years of follow-up reached 41% in the HD group vs. 9.6% in the non-HD (p < 0.0001) [33]. Similar long-term observations with a follow-up period of up to 1800 days were presented in a study published by Lee et al., which showed a significantly higher incidence of target vessel failure (TVF), repeated revascularization, and all-cause mortality in patients treated with DCB due to ISR and impaired renal function compared to those with preserved renal function [15].

At the beginning of this century, attempts were made to reduce the frequency of restenosis in patients with impaired renal function using various devices, one of them being intracoronary radiation, although they did not show any improvement and were even characterized by a number of periprocedural complications and ultimately performed worse than conventional treatment [34]. Studies published so far have confirmed that DES use in dialysis patients yields a significant decrease in the risk of mortality, MACE, and TL/VR [35]. However, several studies did not confirm these findings and showed no additive effect of DES treatment over bare-metal stents [36]. The introduction of new stents with thin layers did not significantly improve the treatment outcomes of CKD patients in the general population [37]. Similarly, no effect of the type of antimitotic drug released was demonstrated on the clinical outcomes of PCI treatment in patients with CKD in the general population [38]. The data available in the literature regarding the influence of the presence and severity of chronic renal failure on ISR treatment using thin-strut DES and DCB are very limited. Therefore, the present analysis is pioneering and indicates that renal failure has an equal impact on the long-term results of treatment using both techniques. Confirmation of this thesis requires observations on larger groups of patients from randomized studies.

Study limitations

This investigation is subject to several inherent limitations that must be acknowledged when interpreting the results, particularly with respect to the evaluation of long-term clinical outcomes following percutaneous coronary intervention for in-stent restenosis in patients with chronic kidney disease. Chief among these is the constrained sample size of the propensity score-matched cohort, comprising 184 patients (92 per treatment arm), coupled with low cumulative event rates for the assessed endpoints (e.g., 13.6% for the device-oriented composite endpoint [DOCE] and 3.3% for target vessel myocardial infarction). This configuration yields substantial underpowering, as demonstrated by post-hoc power analyses utilizing the Schoenfeld approximation for the Cox proportional hazards model. For instance, with 25 observed DOCE events and a hazard ratio (HR) of 0.45, the study achieved only 51% power to detect this effect at α = 0.05 (two-sided), conferring a high risk of Type II error – specifically, a greater than 49% probability of failing to identify a true difference despite the observed trend (p = 0.062). Comparable post-hoc powers for secondary endpoints were even lower, ranging from 7% for cardiovascular death to 36% for target vessel revascularization, further amplifying the potential for false-negative conclusions.

An a priori power calculation, absent from the original study design, would have been indispensable for ensuring adequate sensitivity. Hypothetically, for planning future studies in similar populations, detecting an HR of 0.45 (aligned with the observed point estimate) with 80% power and α = 0.05, under an assumed event rate of 13.6%, would necessitate approximately 49 events and a total sample of 362 patients (181 per group). For a more conservative anticipated effect size (HR = 0.50), the requirements escalate to 65 events and 481 patients (241 per group). These estimates highlight the current study’s insufficiency in event accrual and cohort size, which may obscure clinically relevant benefits of thin-strut drug-eluting stents over drug-coated balloons, even amid numerical trends favoring the former.

Beyond statistical power, the retrospective nature of the registry introduces potential selection and confounding biases, notwithstanding propensity score matching for key baseline covariates such as age, diabetes mellitus, and lesion morphology. Residual confounding from unmeasured factors, including the underlying mechanisms of restenosis (due to the lack of routine intravascular imaging) and operator-dependent treatment selections, cannot be fully excluded. Furthermore, the absence of centralized core laboratory adjudication for angiographic parameters may compromise the precision of lesion characterizations. The findings’ generalizability is confined to patients with chronic kidney disease and in-stent restenosis, potentially limiting applicability to broader populations. Although the median follow-up duration of 959 days is clinically pertinent, it may not capture late-emerging disparities in outcomes.

Furthermore, as an exploratory, hypothesis-generating study evaluating multiple endpoints without corrections for multiplicity, the results are susceptible to inflated Type I error rates, potentially leading to spurious associations. Larger, prospective randomized trials with prespecified adjustments for multiple testing are warranted to validate these preliminary observations.

Conclusions

In CKD patients undergoing PCI for ISR, thin‐DES treatment was associated with a numerical reduction in TLR, TVR, and DOCE compared with DCB. However, these differences did not achieve statistical significance in the crude or PSM analyses.

Supporting information

S1 Fig. Covariate balance before and after propensity score matching.

(JPG)

pone.0337991.s001.jpg^{(183.4KB, jpg)}

S2 Fig. Kaplan -Meyer curves for estimated survival function of TVR in unmatched CKD cohort (N = 286, left) and post-PSM CKD cohort (N = 184, right).

(JPG)

pone.0337991.s002.jpg^{(357.6KB, jpg)}

S3 Fig. Kaplan -Meyer curves for estimated survival function of MI in unmatched CKD cohort (N = 286, left) and post-PSM CKD cohort (N = 184, right).

(JPG)

pone.0337991.s003.jpg^{(356.3KB, jpg)}

S4 Fig. Kaplan -Meyer curves for estimated survival function of MI-TVR in unmatched CKD cohort (N = 286, left) and post-PSM CKD cohort (N = 184, right).

(JPG)

pone.0337991.s004.jpg^{(312.5KB, jpg)}

S5 Fig. Kaplan -Meyer curves for estimated survival function of TLR in unmatched CKD cohort (N = 263, left) and post-PSM CKD cohort (N = 169, right) for non-dialyzed patients.

The dashed lines represent the 95% confidence intervals, while the horizontal marks indicate the patient censoring events.

(JPG)

pone.0337991.s005.jpg^{(364.9KB, jpg)}

S6 Fig. Kaplan -Meyer curves for estimated survival function of TLR in unmatched CKD cohort (N = 23, left) and post-PSM CKD cohort (N = 15, right) for dialyzed patients.

The dashed lines represent the 95% confidence intervals, while the horizontal marks indicate the patient censoring events.

(JPG)

pone.0337991.s006.jpg^{(260KB, jpg)}

Acknowledgments

The Dragon Registry was initiated on the Club 30 Scientific Platform of the Polish Cardiac Society.

Data Availability

All relevant data are within the manuscript and its Supporting Information files.

Funding Statement

The author(s) received no specific funding for this work.

References

1.Alfonso F, Coughlan JJ, Giacoppo D, Kastrati A, Byrne RA. Management of in-stent restenosis. EuroIntervention. 2022;18(2):e103–23. doi: 10.4244/EIJ-D-21-01034 [DOI] [PMC free article] [PubMed] [Google Scholar]
2.Kuntz RE, Baim DS. Defining coronary restenosis. Newer clinical and angiographic paradigms. Circulation. 1993;88(3):1310–23. doi: 10.1161/01.cir.88.3.1310 [DOI] [PubMed] [Google Scholar]
3.Stefanini GG, Holmes DR Jr. Drug-eluting coronary-artery stents. N Engl J Med. 2013;368(3):254–65. doi: 10.1056/NEJMra1210816 [DOI] [PubMed] [Google Scholar]
4.Sharma A, Agrawal S, Garg A, Vallakati A, Lavie CJ, Helft G. Duration of dual antiplatelet therapy following drug-eluting stent implantation: A systemic review and meta-analysis of randomized controlled trials with longer follow up. Catheter Cardiovasc Interv. 2017;90(1):31–7. doi: 10.1002/ccd.27123 [DOI] [PubMed] [Google Scholar]
5.Kim C, Kim B-K, Lee S-Y, Shin D-H, Kim J-S, Ko Y-G, et al. Incidence, clinical presentation, and predictors of early neoatherosclerosis after drug-eluting stent implantation. Am Heart J. 2015;170(3):591–7. doi: 10.1016/j.ahj.2015.06.005 [DOI] [PubMed] [Google Scholar]
6.Madhavan MV, Kirtane AJ, Redfors B, Généreux P, Ben-Yehuda O, Palmerini T, et al. Stent-Related Adverse Events ≥1 Year After Percutaneous Coronary Intervention. J Am Coll Cardiol. 2020;75:590–604. [DOI] [PubMed] [Google Scholar]
7.Elbadawi A, Dang AT, Mahana I, Elzeneini M, Alonso F, Banerjee S, et al. Outcomes of Percutaneous Coronary Intervention for In-Stent Restenosis Versus De Novo Lesions: A Meta-Analysis. J Am Heart Assoc. 2023;12(13):e029300. doi: 10.1161/JAHA.122.029300 [DOI] [PMC free article] [PubMed] [Google Scholar]
8.Wańha W, Bil J, Januszek R, Gilis-Malinowska N, Figatowski T, Milewski M, et al. Long-Term Outcomes Following Drug-Eluting Balloons Versus Thin-Strut Drug-Eluting Stents for Treatment of In-Stent Restenosis (DEB-Dragon-Registry). Circ Cardiovasc Interv. 2021;14(9):e010868. doi: 10.1161/CIRCINTERVENTIONS.121.010868 [DOI] [PubMed] [Google Scholar]
9.Moussa ID, Mohananey D, Saucedo J, Stone GW, Yeh RW, Kennedy KF, et al. Trends and Outcomes of Restenosis After Coronary Stent Implantation in the United States. J Am Coll Cardiol. 2020;76:1521–31. [DOI] [PubMed] [Google Scholar]
10.Tamez H, Secemsky EA, Valsdottir LR, Moussa ID, Song Y, Simonton CA, et al. Long-term outcomes of percutaneous coronary intervention for in-stent restenosis among Medicare beneficiaries. EuroIntervention. 2021;17(5):e380–7. doi: 10.4244/EIJ-D-19-01031 [DOI] [PMC free article] [PubMed] [Google Scholar]
11.Neumann F-J, Sousa-Uva M, Ahlsson A, Alfonso F, Banning AP, Benedetto U, et al. , ESC Scientific Document Group. 2018 ESC/EACTS Guidelines on myocardial revascularization. Eur Heart J. 2019;40(2):87–165. doi: 10.1093/eurheartj/ehy394 [DOI] [PubMed] [Google Scholar]
12.Wolny R, Kowalik I, Januszek R, Bil J, Figatowski T, Milewski M, et al. Long-term outcomes following drug-eluting balloons vs. thin-strut drug-eluting stents for treatment of recurrent restenosis in drug-eluting stents. Kardiol Pol. 2022;80(7–8):765–73. doi: 10.33963/KP.a2022.0106 [DOI] [PubMed] [Google Scholar]
13.Mahfoud F, Farah A, Ohlow M-A, Mangner N, Wöhrle J, Möbius-Winkler S, et al. Drug-coated balloons for small coronary artery disease in patients with chronic kidney disease: a pre-specified analysis of the BASKET-SMALL 2 trial. Clin Res Cardiol. 2022;111(7):806–15. doi: 10.1007/s00392-022-01995-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
14.Baan J Jr, Claessen BE, Dijk KB, Vendrik J, van der Schaaf RJ, Meuwissen M, et al. A Randomized Comparison of Paclitaxel-Eluting Balloon Versus Everolimus-Eluting Stent for the Treatment of Any In-Stent Restenosis: The DARE Trial. JACC Cardiovasc Interv. 2018;11:275–83. [DOI] [PubMed] [Google Scholar]
15.Lee H-F, Cheng Y-W, Peng J-R, Hsu C-Y, Yang C-H, Chan Y-H, et al. Impact of chronic kidney disease on long-term outcomes for coronary in-stent restenosis after drug-coated balloon angioplasty. J Cardiol. 2021;78(6):564–70. doi: 10.1016/j.jjcc.2021.08.010 [DOI] [PubMed] [Google Scholar]
16.Mehran R, Dangas G, Abizaid AS, Mintz GS, Lansky AJ, Satler LF, et al. Angiographic patterns of in-stent restenosis: classification and implications for long-term outcome. Circulation. 1999;100(18):1872–8. doi: 10.1161/01.cir.100.18.1872 [DOI] [PubMed] [Google Scholar]
17.R Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2024. Available from: https://www.R-project.org/ [Google Scholar]
18.Chan C, Leeper T, Becker J, Schoch D. Rio: A Swiss-army knife for data file I/O. 2023. Available from: https://cran.r-project.org/package=rio
19.Greifer N. cobalt: Covariate Balance Tables and Plots. R package version 4.5.5. 2024. Available from: https://CRAN.R-project.org/package=cobalt
20.Ho D, Imai K, King G, Stuart E. MatchIt: Nonparametric Preprocessing for Parametric Causal Inference. J Stat Softw. 2011;42:1–28. [Google Scholar]
21.Lüdecke D. sjPlot: Data Visualization for Statistics in Social Science. R package version 2.8.15. 2023. Available from: https://CRAN.R-project.org/package=sjPlot
22.Lüdecke D, Ben-Shachar M, Patil I, Makowski D. Extracting, Computing and Exploring the Parameters of Statistical Models using R. J Open Source Softw. 2023;5:2445. doi: 10.21105/joss.02445 [DOI] [Google Scholar]
23.Lüdecke D, Ben-Shachar M, Patil I, Waggoner P, Makowski D. performance: An R Package for Assessment, Comparison and Testing of Statistical Models. J Open Source Softw. 2021;6(60):3139. doi: 10.21105/joss.03139 [DOI] [Google Scholar]
24.Makowski D, Lüdecke D, Patil I, Thériault R, Ben-Shachar M, Wiernik B. Automated Results Reporting as a Practical Tool to Improve Reproducibility and Methodological Best Practices Adoption. CRAN. 2023. Available from: https://easystats.github.io/report/
25.Sjoberg D, Baillie M, Fruechtenicht C, Haesendonckx S, Treis T. Ggsurvfit: Flexible Time-to-Event Figures. R package version 1.1.0. 2024. Available from: https://CRAN.R-project.org/package=ggsurvfit
26.Sjoberg D D, Whiting K, Curry M, Lavery J A, Larmarange J. Reproducible Summary Tables with the gtsummary Package. R J. 2021;13(1):570. doi: 10.32614/rj-2021-053 [DOI] [Google Scholar]
27.Therneau T. A Package for Survival Analysis in R. R package version 3.7-0. 2024. Available from: https://CRAN.R-project.org/package=survival
28.Therneau TM, Grambsch PM. Modeling survival data: extending the Cox model. New York: Springer; 2000. [Google Scholar]
29.Venables WN, Ripley BD. Modern Applied Statistics with S. 4 edition. New York: Springer; 2002. [Google Scholar]
30.Wickham H. ggplot2: Elegant Graphics for Data Analysis. New York: Springer; 2016. [Google Scholar]
31.Wickham H, François R, Henry L, Müller K, Vaughan D. dplyr: A Grammar of Data Manipulation. R package version 1.1.4. 2023. Available from: https://CRAN.R-project.org/package=dplyr
32.Valdivielso JM, Rodríguez-Puyol D, Pascual J, Barrios C, Bermúdez-López M, Sánchez-Niño MD. Atherosclerosis in chronic kidney disease: more, less, or just different? Arterioscler Thromb Vasc Biol. 2019;39:1938–66. [DOI] [PubMed] [Google Scholar]
33.Kiriyama H, Kodera S, Minatsuki S, Kaneko H, Kikuchi H, Kiyosue A, et al. Short-Term and Long-Term Efficacy of Drug-Coated Balloon for In-Stent Restenosis in Hemodialysis Patients with Coronary Artery Disease. Int Heart J. 2019;60(5):1070–6. doi: 10.1536/ihj.18-533 [DOI] [PubMed] [Google Scholar]
34.Gruberg L, Waksman R, Ajani AE, Kim HS, White RL, Pinnow E, et al. The effect of intracoronary radiation for the treatment of recurrent in-stent restenosis in patients with chronic renal failure. J Am Coll Cardiol. 2001;38(4):1049–53. doi: 10.1016/s0735-1097(01)01500-5 [DOI] [PubMed] [Google Scholar]
35.Li S, Ye D, Chen G, Xu W. Meta-Analysis of Comparison of Drug-Eluting Stents and Bare-Metal Stents in Patients on Dialysis. Am J Cardiol. 2017;119(8):1186–92. doi: 10.1016/j.amjcard.2016.12.036 [DOI] [PubMed] [Google Scholar]
36.Edfors R, James S, Szummer K, Evans M, Carrero J-J, Faxén J, et al. SWEDEHEART-1-year data show no benefit of newer generation drug-eluting stents over bare-metal stents in patients with severe kidney dysfunction following percutaneous coronary intervention. Coron Artery Dis. 2020;31(1):49–58. doi: 10.1097/MCA.0000000000000814 [DOI] [PubMed] [Google Scholar]
37.Wańha W, Kawecki D, Roleder T, Pluta A, Marcinkiewicz K, Morawiec B. Cardiorenal Med. 2017;7:85–95. [DOI] [PMC free article] [PubMed] [Google Scholar]
38.Sakakibara T, Ishii H, Toriyama T, Aoyama T, Takahashi H, Kamoi D, et al. Sirolimus-eluting stent vs. everolimus-eluting stent for coronary intervention in patients on chronic hemodialysis. Circ J. 2012;76(2):351–5. doi: 10.1253/circj.cj-11-0814 [DOI] [PubMed] [Google Scholar]

PLoS One. doi: 10.1371/journal.pone.0337991.r001

Decision Letter 0

Prakash Sojitra

7 Jul 2025

Dear Dr. Januszek,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by Aug 21 2025 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org . When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols . Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols .

We look forward to receiving your revised manuscript.

Kind regards,

Prakash Sojitra, PhD

Academic Editor

PLOS ONE

Journal Requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

2. In this instance it seems there may be acceptable restrictions in place that prevent the public sharing of your minimal data. However, in line with our goal of ensuring long-term data availability to all interested researchers, PLOS’ Data Policy states that authors cannot be the sole named individuals responsible for ensuring data access (http://journals.plos.org/plosone/s/data-availability#loc-acceptable-data-sharing-methods).

Data requests to a non-author institutional point of contact, such as a data access or ethics committee, helps guarantee long term stability and availability of data. Providing interested researchers with a durable point of contact ensures data will be accessible even if an author changes email addresses, institutions, or becomes unavailable to answer requests.

Before we proceed with your manuscript, please also provide non-author contact information (phone/email/hyperlink) for a data access committee, ethics committee, or other institutional body to which data requests may be sent. If no institutional body is available to respond to requests for your minimal data, please consider if there any institutional representatives who did not collaborate in the study, and are not listed as authors on the manuscript, who would be able to hold the data and respond to external requests for data access? If so, please provide their contact information (i.e., email address). Please also provide details on how you will ensure persistent or long-term data storage and availability.

3. Please include captions for your Supporting Information files at the end of your manuscript, and update any in-text citations to match accordingly. Please see our Supporting Information guidelines for more information: http://journals.plos.org/plosone/s/supporting-information .

4. Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Additional Editor Comments:

Introduction cites Mahfound et al reference but not listed in references section.

Introduction is more focused on coronary treatment and it need more clear references and clinical emphasis on renal indications.

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously? -->?>

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available??>

The PLOS Data policy

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #1: Yes

Reviewer #2: Yes

**********

Reviewer #1: Hello,

Thanks for submitting this manuscript titled "Long-Term Outcomes Following Drug-Coated Balloons Versus Thin-Strut Drug-Eluting Stents for Treatment of In-Stent Restenosis in Chronic Kidney Disease" to this journal. It is a well written article however, following points will make it clearer.

1. The title mentions long term however, the mean/median (IQR) duration of the follow up of angiogram is not mentioned

2. Were all the implanted patients followed up clinically only and repeat angiogram was done only once the procedure was left to the discretion of cardiologist ?

3. What were the confounding factors and what were the propensity matching variables in the study.

Thanks

Reviewer #2: The study provides valuable information on the treatment of in-stent restenosis in patients with chronic kidney disease, but the aforementioned limitations should be considered when interpreting the results. Lack of Imaging Data: The absence of intravascular imaging data limits the ability to assess the morphology of lesions and the effectiveness of the treatment.

Variability in Treatment: The choice of treatment was based on the operator's preference, which may introduce bias and limit the generalizability of the results.

**********

what does this mean? ). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy

Reviewer #1: No

Reviewer #2: Yes: Maria Antonieta Albanez A de Medeiros Lopes

**********

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/ . PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org

PLoS One. 2025 Dec 29;20(12):e0337991. doi: 10.1371/journal.pone.0337991.r002

Author response to Decision Letter 1

26 Aug 2025

I have attached data in excel file.

So then your request for other contact (non-author) is no longer valid.

PONE-D-25-26896

Long-Term Outcomes Following Drug-Coated Balloons Versus Thin-Strut Drug-Eluting Stents for Treatment of In-Stent Restenosis in Chronic Kidney Disease

PLOS ONE

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

Journal Requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

This was corrected according to the journals guidelines.

We collected the data ourselves, it is in my possession and that of Dr. Wojciech Wańha, but he is also a co-author. I cannot guarantee access to the data indefinitely, but as long as Dr. Wańha and I are able to respond to emails, it is possible to share the data.

This was corrected, the links to the appendix in the text have been changed and an appropriate description of the links has been added at the end of the text.

The references were checked again, the spelling was systematized, and grammatical and punctuation errors were corrected.

Additional Editor Comments:

Introduction cites Mahfound et al reference but not listed in references section.

It is listed, position 13.

Introduction is more focused on coronary treatment and it need more clear references and clinical emphasis on renal indications.

References regarding the treatment of ISR in patients with CKD are limited, so some are included in the introduction, while most are included in the discussion. To highlight the issue relevant to this publication, the general section of the introduction regarding coronary arteries has been shortened.

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #1: Hello,

1. The title mentions long term however, the mean/median (IQR) duration of the follow up of angiogram is not mentioned

Follow-up angiograms were not routinely performed, only when clinically indicated.

2. Were all the implanted patients followed up clinically only and repeat angiogram was done only once the procedure was left to the discretion of cardiologist ?

Yes, that's exactly what it was, it's a retrospective study, not a prospective study with specific visits and examinations scheduled at specific time points.

3. What were the confounding factors and what were the propensity matching variables in the study.

They are include into the methods section, subsection statistical analysis.

Lack of Imaging Data: The absence of intravascular imaging data limits the ability to assess the morphology of lesions and the effectiveness of the treatment.

Variability in Treatment: The choice of treatment was based on the operator's preference, which may introduce bias and limit the generalizability of the results.

It is true that these factors may modify random results to some extent, which is why we have placed these issues in the limitations section.

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

This was done, and no significant issues were suggested.

PLoS One. doi: 10.1371/journal.pone.0337991.r003

Decision Letter 1

Prakash Sojitra

14 Oct 2025

Dear Dr. Januszek,

Please submit your revised manuscript by Nov 28 2025 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org . When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

We look forward to receiving your revised manuscript.

Kind regards,

Prakash Sojitra, PhD

Academic Editor

PLOS ONE

Journal Requirements:

If the reviewer comments include a recommendation to cite specific previously published works, please review and evaluate these publications to determine whether they are relevant and should be cited. There is no requirement to cite these works unless the editor has indicated otherwise.

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #1: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions??>

Reviewer #1: Partly

**********

3. Has the statistical analysis been performed appropriately and rigorously? -->?>

Reviewer #1: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available??>

The PLOS Data policy

Reviewer #1: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #1: No

**********

Reviewer #1: Hello,

Though the authors have answered some of the points but the clarity for the questions sought in the first revision is not yet met.

Let me expand my previous questions in more detailed and elaborate to the point analysis for further clarifications-

1. The CKD cohort after propensity score matching (PSM) contains only 92 patients per group (184 total). Given the relatively low event rates (TLR: 8.70% vs 13.04%; DOCE: 10.87% vs 16.30%), the study is substantially underpowered to detect clinically meaningful differences. The authors report trends toward better outcomes with thin-DES (HR 0.45 for DOCE, p=0.062) but dismiss these as non-significant without acknowledging the Type II error risk. A proper power calculation should have been performed a priori and reported. Include power calculations and explicitly discuss the study's limitations in detecting differences given the sample size and event rates.

2.The median follow-up of 959 days (IQR 426-1732) shows substantial variability. The wide interquartile range suggests that many patients had relatively short follow-up periods. For a study examining "long-term outcomes," this is problematic. The manuscript does not:

Report mean follow-up duration, and provide follow-up completeness at specific time points (1-year, 2-year)

3.With 104 DES and 182 DCB patients pre-matching, achieving 92:92 matching suggests substantial patient exclusion—the characteristics of excluded patients are not described

4. Multiple inconsistencies undermine confidence in the data:

Table 2: Reports "10.58%" for one group but this appears to be an error (mixing percentage with decimal notation)

Dialysis subgroup: Before PSM, 14 DCB and 9 DES patients were on dialysis. After PSM, the text states "six patients remained in the DCB group, and all nine remained in the DCB group"—this appears to be a typographical error (should likely be "DES group" for the second mention)

Original stent data: Only available for approximately 50% of patients (n=256/481 for DES group)—this massive missing data issue is not addressed

5.CKD staging: Patients are dichotomized at eGFR <60 mL/min/1.73m², but no breakdown by CKD stage (3a, 3b, 4, 5, 5D) is provided. This is critical as treatment effects likely vary by severity.

Bleeding complications: Given the different DAPT duration between groups (shorter with DCB, as shown in Table 2), bleeding outcomes are highly relevant but completely omitted.

Stent thrombosis: Not reported despite being a key safety endpoint.

Renal function trajectories: No data on whether renal function changed during follow-up or how this related to outcomes.

6.Cox regression assumptions: No verification of proportional hazards assumption reported

Variable selection: The rationale for predictor selection in Table 4 is not explained

Missing data handling: Not addressed despite substantial missingness (e.g., original stent parameters)

Multiple testing: No adjustment despite multiple endpoints and numerous subgroup analyses

7. Ethical and Regulatory Statement

The statement "due to the retrospective nature of the study, no written informed consent was needed" may not satisfy all journal requirements. Many journals now require documentation that the institutional review board specifically approved the waiver of consent.

8.Add power calculations and explicitly state the study is exploratory and hypothesis-generating

Correct all data errors (dialysis patient numbers, Table 2 inconsistencies, missing data explanations)

9.The study's greatest value may be in demonstrating equipoise for a future randomized trial, but this perspective is not emphasized. With substantial revisions addressing the issues outlined above, this could become a valuable contribution to the literature on ISR management in CKD patients.

Thanks

**********

what does this mean? ). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy

Reviewer #1: No

**********

PLoS One. 2025 Dec 29;20(12):e0337991. doi: 10.1371/journal.pone.0337991.r004

Author response to Decision Letter 2

5 Nov 2025

Reviewer #1: Hello,

Though the authors have answered some of the points but the clarity for the questions sought in the first revision is not yet met.

Let me expand my previous questions in more detailed and elaborate to the point analysis for further clarifications-

Study limitations

This investigation is subject to several inherent limitations that must be acknowledged when interpreting the results, particularly with respect to the evaluation of long-term clinical outcomes following percutaneous coronary intervention for in-stent restenosis in patients with chronic kidney disease. Chief among these is the constrained sample size of the propensity score-matched cohort, comprising 184 patients (92 per treatment arm), coupled with low cumulative event rates for the assessed endpoints (e.g., 13.6% for the device-oriented composite endpoint [DOCE] and 3.3% for target vessel myocardial infarction). This configuration yields substantial underpowering, as demonstrated by post-hoc power analyses utilizing the Schoenfeld approximation for the Cox proportional hazards model. For instance, with 25 observed DOCE events and a hazard ratio (HR) of 0.45, the study achieved only 51% power to detect this effect at α=0.05 (two-sided), conferring a high risk of Type II error – specifically, a greater than 49% probability of failing to identify a true difference despite the observed trend (p=0.062). Comparable post-hoc powers for secondary endpoints were even lower, ranging from 7% for cardiovascular death to 36% for target vessel revascularization, further amplifying the potential for false-negative conclusions.

An a priori power calculation, absent from the original study design, would have been indispensable for ensuring adequate sensitivity. Hypothetically, for planning future studies in similar populations, detecting an HR of 0.45 (aligned with the observed point estimate) with 80% power and α=0.05, under an assumed event rate of 13.6%, would necessitate approximately 49 events and a total sample of 362 patients (181 per group). For a more conservative anticipated effect size (HR=0.50), the requirements escalate to 65 events and 481 patients (241 per group). These estimates highlight the current study's insufficiency in event accrual and cohort size, which may obscure clinically relevant benefits of thin-strut drug-eluting stents over drug-coated balloons, even amid numerical trends favoring the former.

Beyond statistical power, the retrospective nature of the registry introduces potential selection and confounding biases, notwithstanding propensity score matching for key baseline covariates such as age, diabetes mellitus, and lesion morphology. Residual confounding from unmeasured factors, including the underlying mechanisms of restenosis (due to the lack of routine intravascular imaging) and operator-dependent treatment selections, cannot be fully excluded. Furthermore, the absence of centralized core laboratory adjudication for angiographic parameters may compromise the precision of lesion characterizations. The findings' generalizability is confined to patients with chronic kidney disease and in-stent restenosis, potentially limiting applicability to broader populations. Although the median follow-up duration of 959 days is clinically pertinent, it may not capture late-emerging disparities in outcomes.

Report mean follow-up duration, and provide follow-up completeness at specific time points (1-year, 2-year)

The mean follow-up duration was 1254.9 days (standard deviation = 893.7 days) which corresponds to approximately 3.4 years and accounts for the right-skewed distribution of follow-up times.

To quantify follow-up completeness at clinically relevant milestones and mitigate concerns about truncated observation in a subset of participants, in table 1 we report the proportion of patients remaining under follow-up (i.e., not censored prior to the landmark) at predefined time points, derived from the Kaplan-Meier estimator for the composite endpoint of all-cause mortality (as a conservative proxy for overall retention, given the absence of loss to follow-up).

Table 1. Completeness of Patient follow-up at specified landmark time points in the overall cohort (N = 1,317).

Time Point Duration

(Days) Proportion Remaining

Under Follow-Up (%) Number of Patients

(n)

1 Year 365 82.7 1,089

2 Years 730 64.8 853

3 Years 1,095 50.0 659

4 Years 1,460 36.4 480

5 Years 1,825 24.9 328

Notes: The total cohort size (N=1,317) represents all enrolled patients; n reflects those not censored prior to each landmark.

3.With 104 DES and 182 DCB patients pre-matching, achieving 92:92 matching suggests substantial patient exclusion—the characteristics of excluded patients are not described

In our study, the absence of a dedicated description of excluded patients is justifiable given that we have already reported comprehensive baseline characteristics for the full unmatched CKD sample (N=286) and the matched subsample (N=184), alongside the crude analysis results and covariate balance visualization. This fulfills STROBE's core recommendations by enabling readers to compare pre- and post-matching populations and assess the matching's impact on confounding. The description of patients excluded from the matched cohort is not a mandatory requirement under established reporting guidelines. Additional guidance from systematic reviews and best practice recommendations for PSM in clinical research, reinforces this approach.

4. Multiple inconsistencies undermine confidence in the data:

Table 2: Reports "10.58%" for one group but this appears to be an error (mixing percentage with decimal notation

We’ve provided correction for Left main parameter in CKD for Thin-DES N = 104 (see fragment of table 2). This typo not affect any results.

This is not the narrative of my analysis. The correct version for revising in manuscript: six patients remained in the DCB group, and all nine remained in the DCB DES group

Original stent data: Only available for approximately 50% of patients (n=256/481 for DES group)—this massive missing data issue is not addressed

This is a retrospective study and in itself carries some data gaps.

Table 2. Angiographic, procedural, medications data according to the type of device before and after propensity score matching

Non-matched cohort, N = 1,317 Propensity score-matched groups, N = 184

Preserved kidney function, N = 1,031 CKD, N = 286 CKD, N = 184

Thin-DES

N = 481

(46.65%) DCB

N = 550 (53.35%) P-Value Thin-DES

N = 104 (36.36%) DCB

N = 182

(63.74%) P-Value Thin-DES

N = 92

(50.00%) DCB

N = 92

(50.00%) P-Value

Target lesion

Left main, n (%) 45 (9.36%) 33 (6.00%) 0.042B 11 (10.58%) 20 (10.99%) 0.914B 11 (11.96%) 8 (8.70%) 0.467B

5.CKD staging: Patients are dichotomized at eGFR <60 mL/min/1.73m², but no breakdown by CKD stage (3a, 3b, 4, 5, 5D) is provided. This is critical as treatment effects likely vary by severity.

This binary classification was employed in alignment with established cardiovascular risk stratification guidelines, such as those from the Kidney Disease: Improving Global Outcomes consortium, which recognize eGFR <60 mL/min/1.73m² as a marker of moderate-to-severe CKD associated with heightened risks of adverse percutaneous coronary intervention outcomes, including in-stent restenosis and revascularization. While detailed staging could elucidate severity-dependent treatment effects – given evidence that advanced CKD (e.g., stages 4–5) exacerbates neointimal hyperplasia and ISR progression through mechanisms like vascular calcification and inflammation – the registry protocol captured CKD status dichotomously, supplemented by dialysis as a proxy for end-stage disease.

Bleeding complications: Given the different DAPT duration between groups (shorter with DCB, as shown in Table 2), bleeding outcomes are highly relevant but completely omitted.

Clinically, CKD confers a heightened bleeding propensity due to uremic platelet dysfunction and altered pharmacokinetics, and prolonged DAPT has been associated with increased bleeding events in this population, as evidenced by meta-analyses demonstrating a delicate ischemic-bleeding balance. However, the Dragon Registry prioritized device-oriented efficacy endpoints (e.g., target lesion revascularization, device-oriented composite endpoint) aligned with ISR-focused trials, and did not systematically capture long-term bleeding complications such as those classified by Bleeding Academic Research Consortium criteria. This omission reflects the registry's design emphasis on restenosis-related outcomes rather than comprehensive safety surveillance, though we recognize its relevance given trials showing reduced bleeding with abbreviated DAPT in high-risk cohorts.

Stent thrombosis: Not reported despite being a key safety endpoint.

In our registry, stent thrombosis was recorded only during the index hospitalization ("Stent thrombosis during hospitalization"), revealing a low incidence (0.7% overall in the CKD cohort, 1.0% in thin-DES and 0.5% in DCB). Long-term thrombosis was not prospectively reported, reflecting the registry's focus on restenosis-centric endpoints rather than comprehensive thrombotic surveillance, though this aligns with some ISR trials prioritizing efficacy over rare safety events.

Renal function trajectories: No data on whether renal function changed during follow-up or how this related to outcomes.

The Dragon Registry did not collect serial eGFR measurements or AKI incidences during follow-up, limiting our ability to evaluate dynamic renal changes or their interplay with ISR outcomes. This design choice prioritized endpoint ascertainment via national databases over longitudinal renal surveillance, though we recognize that favorable renal trajectories (e.g., improved function from enhanced cardiac output) or deteriorations could modulate observed trends.

6.Cox regression assumptions: No verification of proportional hazards assumption reported

Materials and methods (Statistical analysis subsection):

Variable selection: The rationale for predictor selection in Table 4 is not explained

The subgroup analyses evaluating the impact of thin-strut drug-eluting stents on target lesion revascularization (Table 4) were stratified by clinically pertinent predictors selected a priori based on their documented associations with adverse PCI outcomes, including restenosis and revascularization rates, in high-risk populations such as those with CKD. Sex and age were included due to evidence indicating differential risks, with older age and female sex linked to higher ISR incidence owing to vascular biology differences and comorbidity burden. Body mass index was chosen as obesity is a recognized modifier of neointimal hyperplasia and inflammatory responses post-stenting. Diabetes mellitus was prioritized given its strong independent association with accelerated restenosis through mechanisms involving endothelial dysfunction and hyperglycemia. Previous myocardial infarction reflects underlying atherosclerotic burden and impaired myocardial reserve, which may influence procedural success and long-term patency. Procedural factors, including bifurcation lesions, left main PCI, vessel diameter, and lesion length, were selected as they represent anatomical complexities known to elevate TLR risk via incomplete stent apposition, flow disturbances, or increased neointimal proliferation. Finally, dialysis status was incorporated specifically for the CKD cohort, acknowledging the profound impact of end-stage renal disease on vascular calcification and restenosis propensity. These predictors align with established risk factors identified in meta-analyses and registries, such as diabetes, CKD severity, advanced age, and lesion morphology, which modulate outcomes in ISR interventions.

Missing data handling: Not addressed despite substantial missingness (e.g., original stent parameters)

It was already mentioned in #4.

Multiple testing: No adjustment despite multiple endpoints and numerous subgroup analyses

As outlined in our response to Comment 8, we have explicitly characterized this registry-based investigation as exploratory and hypothesis-generating in nature. This designation reflects the study's retrospective design and its primary aim to identify potential signals warranting further investigation, rather than to provide definitive confirmatory evidence. Consistent with this approach, no formal corrections for multiplicity were applied to preserve sensitivity in detecting trends, though we recognize this increases the risk of Type I errors. To address this limitation transparently, we have incorporated clarifying statements in the Methods (Statistical Analysis subsection) and Discussion (Limitations subsection)

7. Ethical and Regulatory Statement

I have not encountered any Journal where this would be mandatory, including my previous publications in PlosOne and the series of publications from the DEB Dragon registry so far. I have considerable experience in publishing registry data.

8.Add power calculations

We have mentioned it in #1

and explicitly state the study is exploratory and hypothesis-generating

Materials and methods (Statistical analysis subsection):

Discussion (Study limitations subsection):

Correct all data errors (dialysis patient numbers, Table 2 inconsistencies, missing data explanations)

It was already mentioned in #4

9.The study's greatest value may be in demonstrating equipoise for a future randomized trial, but this perspective is not emphasized.

While the current findings do not demonstrate statistically significant differences between thin-strut drug-eluting stents and drug-coated balloons, they underscore a state of clinic

Attachment

Submitted filename: Responses to the reviewers comments R2.docx

pone.0337991.s009.docx^{(33.1KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0337991.r005

Decision Letter 2

Prakash Sojitra

17 Nov 2025

Long-Term Outcomes Following Drug-Coated Balloons Versus Thin-Strut Drug-Eluting Stents for Treatment of In-Stent Restenosis in Chronic Kidney Disease

PONE-D-25-26896R2

Dear Dr. Rafał Januszek,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice will be generated when your article is formally accepted. Please note, if your institution has a publishing partnership with PLOS and your article meets the relevant criteria, all or part of your publication costs will be covered. Please make sure your user information is up-to-date by logging into Editorial Manager at Editorial Manager® and clicking the ‘Update My Information' link at the top of the page. For questions related to billing, please contact billing support .

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Prakash Sojitra, PhD

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

All answers are satisfactory now.

Reviewers' comments:

PLoS One. doi: 10.1371/journal.pone.0337991.r006

Acceptance letter

Prakash Sojitra

PONE-D-25-26896R2

PLOS One

Dear Dr. Januszek,

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS One. Congratulations! Your manuscript is now being handed over to our production team.

At this stage, our production department will prepare your paper for publication. This includes ensuring the following:

* All references, tables, and figures are properly cited

* All relevant supporting information is included in the manuscript submission,

* There are no issues that prevent the paper from being properly typeset

You will receive further instructions from the production team, including instructions on how to review your proof when it is ready. Please keep in mind that we are working through a large volume of accepted articles, so please give us a few days to review your paper and let you know the next and final steps.

Lastly, if your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

You will receive an invoice from PLOS for your publication fee after your manuscript has reached the completed accept phase. If you receive an email requesting payment before acceptance or for any other service, this may be a phishing scheme. Learn how to identify phishing emails and protect your accounts at https://explore.plos.org/phishing.

If we can help with anything else, please email us at customercare@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Prakash Sojitra

Academic Editor

PLOS One

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Fig. Covariate balance before and after propensity score matching.

(JPG)

pone.0337991.s001.jpg^{(183.4KB, jpg)}

S2 Fig. Kaplan -Meyer curves for estimated survival function of TVR in unmatched CKD cohort (N = 286, left) and post-PSM CKD cohort (N = 184, right).

(JPG)

pone.0337991.s002.jpg^{(357.6KB, jpg)}

S3 Fig. Kaplan -Meyer curves for estimated survival function of MI in unmatched CKD cohort (N = 286, left) and post-PSM CKD cohort (N = 184, right).

(JPG)

pone.0337991.s003.jpg^{(356.3KB, jpg)}

S4 Fig. Kaplan -Meyer curves for estimated survival function of MI-TVR in unmatched CKD cohort (N = 286, left) and post-PSM CKD cohort (N = 184, right).

(JPG)

pone.0337991.s004.jpg^{(312.5KB, jpg)}

S5 Fig. Kaplan -Meyer curves for estimated survival function of TLR in unmatched CKD cohort (N = 263, left) and post-PSM CKD cohort (N = 169, right) for non-dialyzed patients.

The dashed lines represent the 95% confidence intervals, while the horizontal marks indicate the patient censoring events.

(JPG)

pone.0337991.s005.jpg^{(364.9KB, jpg)}

S6 Fig. Kaplan -Meyer curves for estimated survival function of TLR in unmatched CKD cohort (N = 23, left) and post-PSM CKD cohort (N = 15, right) for dialyzed patients.

The dashed lines represent the 95% confidence intervals, while the horizontal marks indicate the patient censoring events.

(JPG)

pone.0337991.s006.jpg^{(260KB, jpg)}

Attachment

Submitted filename: Responses to the reviewers comments R2.docx

pone.0337991.s009.docx^{(33.1KB, docx)}

Data Availability Statement

All relevant data are within the manuscript and its Supporting Information files.

[pone.0337991.ref001] 1.Alfonso F, Coughlan JJ, Giacoppo D, Kastrati A, Byrne RA. Management of in-stent restenosis. EuroIntervention. 2022;18(2):e103–23. doi: 10.4244/EIJ-D-21-01034 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0337991.ref002] 2.Kuntz RE, Baim DS. Defining coronary restenosis. Newer clinical and angiographic paradigms. Circulation. 1993;88(3):1310–23. doi: 10.1161/01.cir.88.3.1310 [DOI] [PubMed] [Google Scholar]

[pone.0337991.ref003] 3.Stefanini GG, Holmes DR Jr. Drug-eluting coronary-artery stents. N Engl J Med. 2013;368(3):254–65. doi: 10.1056/NEJMra1210816 [DOI] [PubMed] [Google Scholar]

[pone.0337991.ref004] 4.Sharma A, Agrawal S, Garg A, Vallakati A, Lavie CJ, Helft G. Duration of dual antiplatelet therapy following drug-eluting stent implantation: A systemic review and meta-analysis of randomized controlled trials with longer follow up. Catheter Cardiovasc Interv. 2017;90(1):31–7. doi: 10.1002/ccd.27123 [DOI] [PubMed] [Google Scholar]

[pone.0337991.ref005] 5.Kim C, Kim B-K, Lee S-Y, Shin D-H, Kim J-S, Ko Y-G, et al. Incidence, clinical presentation, and predictors of early neoatherosclerosis after drug-eluting stent implantation. Am Heart J. 2015;170(3):591–7. doi: 10.1016/j.ahj.2015.06.005 [DOI] [PubMed] [Google Scholar]

[pone.0337991.ref006] 6.Madhavan MV, Kirtane AJ, Redfors B, Généreux P, Ben-Yehuda O, Palmerini T, et al. Stent-Related Adverse Events ≥1 Year After Percutaneous Coronary Intervention. J Am Coll Cardiol. 2020;75:590–604. [DOI] [PubMed] [Google Scholar]

[pone.0337991.ref007] 7.Elbadawi A, Dang AT, Mahana I, Elzeneini M, Alonso F, Banerjee S, et al. Outcomes of Percutaneous Coronary Intervention for In-Stent Restenosis Versus De Novo Lesions: A Meta-Analysis. J Am Heart Assoc. 2023;12(13):e029300. doi: 10.1161/JAHA.122.029300 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0337991.ref008] 8.Wańha W, Bil J, Januszek R, Gilis-Malinowska N, Figatowski T, Milewski M, et al. Long-Term Outcomes Following Drug-Eluting Balloons Versus Thin-Strut Drug-Eluting Stents for Treatment of In-Stent Restenosis (DEB-Dragon-Registry). Circ Cardiovasc Interv. 2021;14(9):e010868. doi: 10.1161/CIRCINTERVENTIONS.121.010868 [DOI] [PubMed] [Google Scholar]

[pone.0337991.ref009] 9.Moussa ID, Mohananey D, Saucedo J, Stone GW, Yeh RW, Kennedy KF, et al. Trends and Outcomes of Restenosis After Coronary Stent Implantation in the United States. J Am Coll Cardiol. 2020;76:1521–31. [DOI] [PubMed] [Google Scholar]

[pone.0337991.ref010] 10.Tamez H, Secemsky EA, Valsdottir LR, Moussa ID, Song Y, Simonton CA, et al. Long-term outcomes of percutaneous coronary intervention for in-stent restenosis among Medicare beneficiaries. EuroIntervention. 2021;17(5):e380–7. doi: 10.4244/EIJ-D-19-01031 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0337991.ref011] 11.Neumann F-J, Sousa-Uva M, Ahlsson A, Alfonso F, Banning AP, Benedetto U, et al. , ESC Scientific Document Group. 2018 ESC/EACTS Guidelines on myocardial revascularization. Eur Heart J. 2019;40(2):87–165. doi: 10.1093/eurheartj/ehy394 [DOI] [PubMed] [Google Scholar]

[pone.0337991.ref012] 12.Wolny R, Kowalik I, Januszek R, Bil J, Figatowski T, Milewski M, et al. Long-term outcomes following drug-eluting balloons vs. thin-strut drug-eluting stents for treatment of recurrent restenosis in drug-eluting stents. Kardiol Pol. 2022;80(7–8):765–73. doi: 10.33963/KP.a2022.0106 [DOI] [PubMed] [Google Scholar]

[pone.0337991.ref013] 13.Mahfoud F, Farah A, Ohlow M-A, Mangner N, Wöhrle J, Möbius-Winkler S, et al. Drug-coated balloons for small coronary artery disease in patients with chronic kidney disease: a pre-specified analysis of the BASKET-SMALL 2 trial. Clin Res Cardiol. 2022;111(7):806–15. doi: 10.1007/s00392-022-01995-3 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0337991.ref014] 14.Baan J Jr, Claessen BE, Dijk KB, Vendrik J, van der Schaaf RJ, Meuwissen M, et al. A Randomized Comparison of Paclitaxel-Eluting Balloon Versus Everolimus-Eluting Stent for the Treatment of Any In-Stent Restenosis: The DARE Trial. JACC Cardiovasc Interv. 2018;11:275–83. [DOI] [PubMed] [Google Scholar]

[pone.0337991.ref015] 15.Lee H-F, Cheng Y-W, Peng J-R, Hsu C-Y, Yang C-H, Chan Y-H, et al. Impact of chronic kidney disease on long-term outcomes for coronary in-stent restenosis after drug-coated balloon angioplasty. J Cardiol. 2021;78(6):564–70. doi: 10.1016/j.jjcc.2021.08.010 [DOI] [PubMed] [Google Scholar]

[pone.0337991.ref016] 16.Mehran R, Dangas G, Abizaid AS, Mintz GS, Lansky AJ, Satler LF, et al. Angiographic patterns of in-stent restenosis: classification and implications for long-term outcome. Circulation. 1999;100(18):1872–8. doi: 10.1161/01.cir.100.18.1872 [DOI] [PubMed] [Google Scholar]

[pone.0337991.ref017] 17.R Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2024. Available from: https://www.R-project.org/ [Google Scholar]

[pone.0337991.ref018] 18.Chan C, Leeper T, Becker J, Schoch D. Rio: A Swiss-army knife for data file I/O. 2023. Available from: https://cran.r-project.org/package=rio

[pone.0337991.ref019] 19.Greifer N. cobalt: Covariate Balance Tables and Plots. R package version 4.5.5. 2024. Available from: https://CRAN.R-project.org/package=cobalt

[pone.0337991.ref020] 20.Ho D, Imai K, King G, Stuart E. MatchIt: Nonparametric Preprocessing for Parametric Causal Inference. J Stat Softw. 2011;42:1–28. [Google Scholar]

[pone.0337991.ref021] 21.Lüdecke D. sjPlot: Data Visualization for Statistics in Social Science. R package version 2.8.15. 2023. Available from: https://CRAN.R-project.org/package=sjPlot

[pone.0337991.ref022] 22.Lüdecke D, Ben-Shachar M, Patil I, Makowski D. Extracting, Computing and Exploring the Parameters of Statistical Models using R. J Open Source Softw. 2023;5:2445. doi: 10.21105/joss.02445 [DOI] [Google Scholar]

[pone.0337991.ref023] 23.Lüdecke D, Ben-Shachar M, Patil I, Waggoner P, Makowski D. performance: An R Package for Assessment, Comparison and Testing of Statistical Models. J Open Source Softw. 2021;6(60):3139. doi: 10.21105/joss.03139 [DOI] [Google Scholar]

[pone.0337991.ref024] 24.Makowski D, Lüdecke D, Patil I, Thériault R, Ben-Shachar M, Wiernik B. Automated Results Reporting as a Practical Tool to Improve Reproducibility and Methodological Best Practices Adoption. CRAN. 2023. Available from: https://easystats.github.io/report/

[pone.0337991.ref025] 25.Sjoberg D, Baillie M, Fruechtenicht C, Haesendonckx S, Treis T. Ggsurvfit: Flexible Time-to-Event Figures. R package version 1.1.0. 2024. Available from: https://CRAN.R-project.org/package=ggsurvfit

[pone.0337991.ref026] 26.Sjoberg D D, Whiting K, Curry M, Lavery J A, Larmarange J. Reproducible Summary Tables with the gtsummary Package. R J. 2021;13(1):570. doi: 10.32614/rj-2021-053 [DOI] [Google Scholar]

[pone.0337991.ref027] 27.Therneau T. A Package for Survival Analysis in R. R package version 3.7-0. 2024. Available from: https://CRAN.R-project.org/package=survival

[pone.0337991.ref028] 28.Therneau TM, Grambsch PM. Modeling survival data: extending the Cox model. New York: Springer; 2000. [Google Scholar]

[pone.0337991.ref029] 29.Venables WN, Ripley BD. Modern Applied Statistics with S. 4 edition. New York: Springer; 2002. [Google Scholar]

[pone.0337991.ref030] 30.Wickham H. ggplot2: Elegant Graphics for Data Analysis. New York: Springer; 2016. [Google Scholar]

[pone.0337991.ref031] 31.Wickham H, François R, Henry L, Müller K, Vaughan D. dplyr: A Grammar of Data Manipulation. R package version 1.1.4. 2023. Available from: https://CRAN.R-project.org/package=dplyr

[pone.0337991.ref032] 32.Valdivielso JM, Rodríguez-Puyol D, Pascual J, Barrios C, Bermúdez-López M, Sánchez-Niño MD. Atherosclerosis in chronic kidney disease: more, less, or just different? Arterioscler Thromb Vasc Biol. 2019;39:1938–66. [DOI] [PubMed] [Google Scholar]

[pone.0337991.ref033] 33.Kiriyama H, Kodera S, Minatsuki S, Kaneko H, Kikuchi H, Kiyosue A, et al. Short-Term and Long-Term Efficacy of Drug-Coated Balloon for In-Stent Restenosis in Hemodialysis Patients with Coronary Artery Disease. Int Heart J. 2019;60(5):1070–6. doi: 10.1536/ihj.18-533 [DOI] [PubMed] [Google Scholar]

[pone.0337991.ref034] 34.Gruberg L, Waksman R, Ajani AE, Kim HS, White RL, Pinnow E, et al. The effect of intracoronary radiation for the treatment of recurrent in-stent restenosis in patients with chronic renal failure. J Am Coll Cardiol. 2001;38(4):1049–53. doi: 10.1016/s0735-1097(01)01500-5 [DOI] [PubMed] [Google Scholar]

[pone.0337991.ref035] 35.Li S, Ye D, Chen G, Xu W. Meta-Analysis of Comparison of Drug-Eluting Stents and Bare-Metal Stents in Patients on Dialysis. Am J Cardiol. 2017;119(8):1186–92. doi: 10.1016/j.amjcard.2016.12.036 [DOI] [PubMed] [Google Scholar]

[pone.0337991.ref036] 36.Edfors R, James S, Szummer K, Evans M, Carrero J-J, Faxén J, et al. SWEDEHEART-1-year data show no benefit of newer generation drug-eluting stents over bare-metal stents in patients with severe kidney dysfunction following percutaneous coronary intervention. Coron Artery Dis. 2020;31(1):49–58. doi: 10.1097/MCA.0000000000000814 [DOI] [PubMed] [Google Scholar]

[pone.0337991.ref037] 37.Wańha W, Kawecki D, Roleder T, Pluta A, Marcinkiewicz K, Morawiec B. Cardiorenal Med. 2017;7:85–95. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0337991.ref038] 38.Sakakibara T, Ishii H, Toriyama T, Aoyama T, Takahashi H, Kamoi D, et al. Sirolimus-eluting stent vs. everolimus-eluting stent for coronary intervention in patients on chronic hemodialysis. Circ J. 2012;76(2):351–5. doi: 10.1253/circj.cj-11-0814 [DOI] [PubMed] [Google Scholar]

PERMALINK

Long-term outcomes following drug-coated balloons versus thin-strut drug-eluting stents for treatment of in-stent restenosis in Chronic Kidney Disease (CKD Dragon-Registry)

Rafał Januszek

Marta Chamera

Sylwia Iwańczyk

Fabrizio D’Ascenzo

Łukasz Kuźma

Brunon Tomasiewicz

Piotr Niezgoda

Rafał Wolny

Mariusz Kowalewski

Maciej Wybraniec

Krzysztof Reczuch

Sławomir Dobrzycki

Ovidio De Filippo

Artur Pawlik

Karol Kasprzycki

Kamil Skowron

Stanisław Bartuś

Maciej Lesiak

Mariusz Gąsior

Jacek Kubica

Tomasz Pawłowski

Robert J Gil

Piotr Waciński

Francesco Bruno

Bernardo Cortese

Wojciech Wojakowski

Wojciech Wańha

Roles

Abstract

Introduction

Materials and methods

Study design, objectives, and patient selection

Fig 1. Patient flow chart.

Procedure description

Inclusion criteria

Primary and secondary endpoints

Statistical analysis

Results

Patient characteristics

Table 1. Patient characteristics, risk factors, and clinical presentation according to device type.

Angiographic and procedural data

Table 2. Angiographic, procedural, and medication data according to device type before and after propensity score matching.

Long-term outcomes

Table 3. Survival analysis for each device before and after propensity score matching.

Fig 2. Kaplan-Meyer curves for the estimated survival function of target vessel revascularization (TLR) in the unmatched chronic kidney disease (CKD) (n = 286, left) and post-PSM CKD cohorts (n = 184, right).

Fig 3. Kaplan-Meyer curves for the estimated survival function of the device-oriented composite endpoint (DOCE) in the unmatched chronic kidney disease (CKD) (n = 286, left) and post-PSM CKD cohorts (n = 184, right).

Fig 4. Kaplan-Meyer curves for the estimated survival function of cardiovascular death in the unmatched chronic kidney disease (CKD) (n = 286, left) and post-PSM CKD cohorts (n = 184, right).

Thin strut drug-eluting stents vs. drug-coated balloons

Table 4. Results of Cox regression analysis evaluating the impact of thin-strut drug-eluting stents on target lesion revascularization during follow-up in patients with chronic kidney disease and preserved kidney function, stratified by subgroups.

Discussion

Study limitations

Conclusions

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Prakash Sojitra

Roles

Author response to Decision Letter 1

Decision Letter 1

Prakash Sojitra

Roles

Author response to Decision Letter 2

Decision Letter 2

Prakash Sojitra

Roles

Acceptance letter

Prakash Sojitra

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

Similar articles