Prognostic Value of the Meta‐Analysis Global Group in Chronic Heart Failure Risk Score in Patients Undergoing Mitral Valve Transcatheter Edge‐to‐Edge Repair

Yohei Nishimura; Masahiko Asami; Yu Horiuchi; Jun Tanaka; Masanori Taniwaki; Kota Komiyama; Hitomi Yuzawa; Kengo Tanabe; Masanori Yamamoto; Shunsuke Kubo; Mike Saji; Yuki Izumi; Atsushi Sugiura; Tetsuro Shimura; Yusuke Enta; Shinichi Shirai; Masaki Izumo; Shingo Mizuno; Yusuke Watanabe; Makoto Amaki; Kazuhisa Kodama; Hisao Otsuki; Toru Naganuma; Hiroki Bota; Yohei Ohno; Masahiro Yamawaki; Hiroshi Ueno; Gaku Nakazawa; Daisuke Hachinohe; Toshiaki Otsuka; Kentaro Hayashida; OCEAN‐Mitral Investigators

doi:10.1161/JAHA.125.048383

. 2026 Jul 3;15(13):e048383. doi: 10.1161/JAHA.125.048383

Prognostic Value of the Meta‐Analysis Global Group in Chronic Heart Failure Risk Score in Patients Undergoing Mitral Valve Transcatheter Edge‐to‐Edge Repair

Yohei Nishimura ¹, Masahiko Asami ^1,^✉, Yu Horiuchi ¹, Jun Tanaka ¹, Masanori Taniwaki ¹, Kota Komiyama ¹, Hitomi Yuzawa ¹, Kengo Tanabe ¹, Masanori Yamamoto ², Shunsuke Kubo ³, Mike Saji ^4,⁵, Yuki Izumi ⁴, Atsushi Sugiura ⁶, Tetsuro Shimura ⁷, Yusuke Enta ⁸, Shinichi Shirai ⁹, Masaki Izumo ¹⁰, Shingo Mizuno ¹¹, Yusuke Watanabe ¹², Makoto Amaki ¹³, Kazuhisa Kodama ¹⁴, Hisao Otsuki ¹⁵, Toru Naganuma ¹⁶, Hiroki Bota ¹⁷, Yohei Ohno ¹⁸, Masahiro Yamawaki ¹⁹, Hiroshi Ueno ²⁰, Gaku Nakazawa ²¹, Daisuke Hachinohe ²², Toshiaki Otsuka ²³, Kentaro Hayashida ²⁴; OCEAN‐Mitral Investigators ^*

^¹Division of Cardiology, Mitsui Memorial Hospital, Tokyo, Japan

^²Department of Cardiology, Toyohashi Heart Center, Toyohashi, Japan

^³Department of Cardiology, Kurashiki Central Hospital, Kurashiki, Japan

^⁴Department of Cardiology, Sakakibara Heart Institute, Tokyo, Japan

^⁵Division of Cardiovascular Medicine, Department of Internal Medicine, Toho University Faculty of Medicine, Tokyo, Japan

^⁶Department of Cardiology, Nagoya Heart Center, Nagoya, Japan

^⁷Department of Cardiology, Gifu Heart Center, Gifu, Japan

^⁸Department of Cardiology, Sendai Kosei Hospital, Sendai, Japan

^⁹Division of Cardiology, Kokura Memorial Hospital, Kitakyushu, Japan

^¹⁰Division of Cardiology, St. Marianna University School of Medicine Hospital, Kawasaki, Japan

^¹¹Department of Cardiology, Shonan Kamakura General Hospital, Kamakura, Kanagawa, Japan

^¹²Department of Cardiology, Teikyo University School of Medicine, Tokyo, Japan

^¹³Department of Cardiology, National Cerebral and Cardiovascular Center, Suita, Japan

^¹⁴Division of Cardiology, Saiseikai Kumamoto Hospital Cardiovascular Center, Kumamoto, Japan

^¹⁵Department of Cardiology, Tokyo Women's Medical University, Tokyo, Japan

^¹⁶Department of Cardiology, New Tokyo Hospital, Chiba, Japan

^¹⁷Department of Cardiology, Sapporo Higashi Tokushukai Hospital, Sapporo, Japan

^¹⁸Department of Cardiology, Tokai University School of Medicine, Isehara, Japan

^¹⁹Department of Cardiology, Saiseikai Yokohama City Eastern Hospital, Yokohama, Kanagawa, Japan

^²⁰Second Department of Internal Medicine, Toyama University Hospital, Toyama, Japan

^²¹Department of Cardiology, Kindai University Faculty of Medicine, Osaka, Japan

^²²Cardiovascular Medicine, Sapporo Heart Center, Sapporo Cardiovascular Clinic, Sapporo, Japan

^²³Department of Hygiene and Public Health, Nippon Medical School, Tokyo, Japan

^²⁴Department of Cardiology, Keio University School of Medicine, Tokyo, Japan

Correspondence to: Masahiko Asami, MD, Division of Cardiology, Mitsui Memorial Hospital, Kanda‐Izumicho 1, Chiyoda‐ku, Tokyo 101‐8643, Japan. Email: asami560725@yahoo.co.jp

A complete list of the OCEAN‐Mitral Investigators can be found in the appendix at the end of the manuscript.

^✉

Corresponding author.

PMCID: PMC13477313 PMID: 42396786

Abstract

Background

The prognostic value of the Meta‐Analysis Global Group in Chronic Heart Failure (MAGGIC) score in patients undergoing mitral valve transcatheter edge‐to‐edge repair (M‐TEER) remains unclear. This study evaluated the MAGGIC score to predict outcomes after M‐TEER in patients with functional (FMR) or degenerative mitral regurgitation (DMR) and compared its performance with conventional surgical and M‐TEER–specific risk models.

Methods

We analyzed 3609 patients (FMR, 71%; DMR, 29%) who underwent M‐TEER in the OCEAN (Optimized Catheter Valvular Intervention)‐Mitral registry. Patients were stratified into MAGGIC score tertiles. The primary outcome was all‐cause death at 1 and 3 years. Secondary outcomes included cardiovascular death. Predictive performance was compared with Society of Thoracic Surgeons score, European System for Cardiac Operative Risk Evaluation II, MitraScore, and COAPT (Cardiovascular Outcomes Assessment of the MitraClip Percutaneous Therapy for Heart Failure Patients With Functional Mitral Regurgitation) score.

Results

The median age was 81 years, 55.3% were men, and median MAGGIC score was 30. In the FMR cohort, patients in tertile 3 had higher risks of all‐cause death (1 year: adjusted hazard ratio [HR_adj], 4.15 [95% CI, 2.34–7.36]; 3 years: HR_adj, 1.83 [95% CI, 1.22–2.77]) and cardiovascular death (1 year: HR_adj, 2.31 [95% CI, 1.56–3.43]; 3 years: HR_adj, 1.61 [95% CI, 1.13–2.31]). In the DMR cohort, tertile 3 was associated with higher all‐cause death (1 year: HR_adj, 2.84 [95% CI, 1.18–6.86]; 3 years: HR_adj, 2.25 [95% CI, 1.07–4.73]) and cardiovascular death at 3 years (HR_adj, 3.49 [95% CI, 1.23–9.84]). The MAGGIC score demonstrated comparable or superior discrimination on the basis of the concordance index.

Conclusions

The MAGGIC score independently predicted 1‐ and 3‐year all‐cause death after M‐TEER in both FMR and DMR cohorts and cardiovascular death in the FMR cohort and at 3 years in the DMR cohort. It demonstrated comparable or superior predictive performance to conventional surgical and M‐TEER–specific risk models.

Keywords: EuroSCORE II, heart failure, MitraClip, mitral regurgitation, STS score

Subject Categories: Valvular Heart Disease, Heart Failure, Clinical Studies, Prognosis, Mortality/Survival

Nonstandard Abbreviations and Acronyms

COAPT: Cardiovascular Outcomes Assessment of the MitraClip Percutaneous Therapy for Heart Failure Patients With Functional Mitral Regurgitation
DMR: degenerative mitral regurgitation
EuroSCORE: European System for Cardiac Operative Risk Evaluation
EuroSMR: European Registry of Transcatheter Repair for Secondary Mitral Regurgitation
FMR: functional mitral regurgitation
MAGGIC: Meta‐Analysis Global Group in Chronic Heart Failure
MR: mitral regurgitation
M‐TEER: mitral valve transcatheter edge‐to‐edge repair
OCEAN: Optimized Catheter Valvular Intervention
STS: Society of Thoracic Surgeons
TVT: Transcatheter Valve Therapy

Clinical Perspective.

What Is New?

The Meta‐Analysis Global Group in Chronic Heart Failure score is an independent predictor of all‐cause death at 1 and 3 years in patients undergoing mitral valve transcatheter edge‐to‐edge repair in both functional and degenerative mitral regurgitation cohorts, and higher Meta‐Analysis Global Group in Chronic Heart Failure tertiles are associated with progressively worse clinical outcomes, including all‐cause death, cardiovascular death, and in‐hospital death in the functional mitral regurgitation cohort and all‐cause death in the degenerative mitral regurgitation cohort.
The Meta‐Analysis Global Group in Chronic Heart Failure score demonstrated comparable or superior predictive performance to conventional surgical and mitral valve transcatheter edge‐to‐edge repair–specific risk models at 1 and 3 years in both functional and degenerative mitral regurgitation cohorts.

What Are the Clinical Implications?

The Meta‐Analysis Global Group in Chronic Heart Failure score offers a simple and effective tool for midterm risk stratification in patients undergoing mitral valve transcatheter edge‐to‐edge repair, supporting its use in routine clinical practice to enhance risk assessment and guide clinical decision making.

Mitral valve transcatheter edge‐to‐edge repair (M‐TEER) with the MitraClip system (Abbott Vascular, Santa Clara, CA) has become an effective treatment for symptomatic functional (FMR) and degenerative mitral regurgitation (DMR). ¹ , ² , ³ The Meta‐Analysis Global Group in Chronic Heart Failure (MAGGIC) risk score, derived from data on 39 372 patients with heart failure (HF), predicts 1‐ and 3‐year death. ⁴ Subsequent validations have confirmed its predictive value not only in patients with HF ⁵ , ⁶ but also in those with valvular heart disease, ⁷ valve surgery, ⁸ and transcatheter aortic valve replacement. ⁹ However, its role in patients undergoing M‐TEER remains underexplored. Previous retrospective studies evaluated the prognostic value of the MAGGIC score in small cohorts but did not compare it directly with established surgical risk models, such as the Society of Thoracic Surgeons (STS) score and the European System for Cardiac Operative Risk Evaluation (EuroSCORE) II, in patients undergoing M‐TEER. ¹⁰ , ¹¹

While the STS score and EuroSCORE II are widely used prognostic models in cardiac surgery, their utility in nonsurgical treatments such as M‐TEER may be limited. ¹² These models primarily emphasize surgical risk factors and may not capture the complex interplay of HF and valvular disease in patients undergoing M‐TEER. In addition, unlike surgical repair, which typically eliminates mitral regurgitation (MR), M‐TEER often results in residual MR, and postprocedural prognosis may therefore be driven more by the longitudinal burden of HF and residual valvular dysfunction than by procedural risk alone. In this context, surgical risk scores may be better suited to patients undergoing valvular surgery, whereas the MAGGIC score, developed for patients with HF, may better reflect the determinants of outcomes after M‐TEER. As a result, the MAGGIC score may provide more accurate and clinically relevant risk stratification, potentially resulting in superior prognostic performance in this population.

In recent years, several M‐TEER–specific prognostic scores have been proposed to improve risk stratification in this setting. ¹³ , ¹⁴ , ¹⁵ However, most of these models have been derived from selected cohorts, and data on external validation remain limited, leaving their generalizability and clinical utility insufficiently established.

Given the growing role of M‐TEER in HF management, we hypothesized that the MAGGIC score would predict morbidity and death in patients undergoing M‐TEER and outperform traditional surgical risk models. In this study, we aimed to evaluate the prognostic value of the MAGGIC score and compare its performance with that of other established surgical risk models, as well as with recently proposed M‐TEER–specific prognostic scores, with a focus on accuracy and clinical applicability.

Methods

The data, analytic methods, and study materials will not be made available to other researchers for purposes of reproducing the results or replicating the procedure.

Study Design and Study Population

We conducted a retrospective analysis using data from the OCEAN (Optimized Catheter Valvular Intervention)‐Mitral registry, a prospective, multicenter, investigator‐initiated, observational study designed to evaluate the safety and efficacy of M‐TEER in patients with FMR or DMR. This registry includes patients treated between June 2018 and June 2023 at 21 participating centers in Japan. Patients with missing data required for the MAGGIC score calculation were excluded. The remaining patients were categorized into tertiles on the basis of their MAGGIC score.

The OCEAN‐Mitral registry is registered with the University Hospital Medical Information Network (UMIN000023653) and was approved by the institutional review board of each participating center. All participants provided informed consent. Data collection and analysis followed the Declaration of Helsinki and the Ministry of Health, Labor, and Welfare of Japan's guidelines for epidemiological research.

Calculation for Risk Scores

The MAGGIC score was calculated at baseline for each patient using variables including age, left ventricular ejection fraction (LVEF), New York Heart Association class, systolic blood pressure, body mass index, serum creatinine, smoking status, diabetes, chronic obstructive pulmonary disease, time since initial HF diagnosis, and use of β blockers and renin–angiotensin system inhibitors (Table S1). The MAGGIC score originally incorporates a variable assessing whether HF was first diagnosed within the past 18 months; however, we applied a 12‐month threshold due to the data structure of the OCEAN‐Mitral registry. In addition to the MAGGIC score, the STS score for surgical mitral valve (MV) replacement and repair (version 2.0.6), EuroSCORE II, the MitraScore, and the COAPT (Cardiovascular Outcomes Assessment of the MitraClip Percutaneous Therapy for Heart Failure Patients With Functional Mitral Regurgitation) risk score were also evaluated at baseline.

Clinical Outcomes

The primary outcome was all‐cause death at 1 and 3 years. Secondary outcomes included cardiovascular death, HF rehospitalization, and the composite of all‐cause death or HF rehospitalization at 1 and 3 years. Additional outcomes included in‐hospital death and procedural measures, including acute procedural success, total device time, procedural time, fluoroscopy duration, number of clips implanted, residual MR severity, and mean transmitral pressure gradient at discharge. We also assessed single‐leaflet device attachment, device embolization, and leaflet tear at 30 days and 1 year. Mitral valve surgery was assessed at 30 days, 1 year, and 3 years. The length of hospital stay was recorded at discharge. Acute procedural success was defined as effective device deployment resulting in MR severity of grade ≤2+ on the discharge echocardiogram. MR severity was categorized from 0+ to 4+.

Data Collection

Clinical follow‐up data were obtained at baseline, discharge, 30 days, and 1 and 3 years through standardized interviews, records from referring physicians, and hospital discharge summaries. Telephone follow‐ups were conducted when in‐person visits were not feasible. Echocardiographic evaluations were performed at baseline, discharge, 30 days, and 1 year in accordance with the American Society of Echocardiography guidelines. ¹⁶ MR severity and mean transmitral pressure gradient were evaluated at baseline and discharge, while single‐leaflet device attachment and leaflet tear were assessed for presence or absence at 30 days and 1 year. All data collection followed the registry protocol to ensure accuracy and consistency.

Statistical Analysis

Because FMR and DMR represent distinct disease entities with different pathophysiology, all analyses were performed separately in the FMR and DMR cohorts.

Baseline, echocardiographic, and procedural characteristics, as well as procedural outcomes, were summarized using standard descriptive statistics. Continuous variables were reported as means±SDs or as medians with interquartile ranges (IQRs), depending on distribution. Categorical variables were expressed as counts and percentages. The Mann–Whitney U test was used to compare continuous variables, and the χ² test or Fisher's exact test was applied to categorical variables.

Kaplan–Meier survival curves were generated to visualize all‐cause death, cardiovascular death, HF rehospitalization, and the composite of all‐cause death or HF rehospitalization across MAGGIC score tertiles. Events were reported as counts of first occurrence for each type of event within the 1‐ and 3‐year follow‐up periods. Cox proportional hazards regression was used to compare tertile 2 versus tertile 1 and tertile 3 versus tertile 1. For HF rehospitalization, additional competing risk analyses were performed treating all‐cause death as a competing event. Subdistribution hazard ratios (sHRs) were estimated using Fine–Gray competing risk regression models. Univariable and multivariable analyses were performed to identify predictive factors for outcomes. Because NT‐proBNP (N‐terminal pro‐B‐type natriuretic peptide) showed a markedly right‐skewed distribution (Figure S1), logarithmic transformation was applied to improve model stability and facilitate appropriate modeling in Cox proportional hazards analyses. Variables related to components of the MAGGIC score were excluded from the multivariable analysis to avoid collinearity. All other variables with a P value <0.10 in univariable analysis were entered into the multivariable prediction model.

Model performance was primarily assessed in terms of discrimination for predicting 1‐ and 3‐year all‐cause death. Discrimination was evaluated using the concordance index, and the area under the receiver operating characteristic curve. Calibration and reclassification were evaluated as supportive assessments and were not used as primary measures of model performance. Calibration was assessed using calibration curves generated with 1000 bootstrap repetitions. Continuous net reclassification improvement and integrated discrimination improvement were applied to compare the reclassification ability of the MAGGIC score with other prognostic models. Comparisons were performed with conventional surgical and M‐TEER–specific risk scores, including the STS score for MV replacement in the FMR cohort and for MV repair in the DMR cohort, as well as EuroSCORE II, the MitraScore, and the COAPT score. For comparisons of risk scores, complete‐case analyses were performed.

Subgroup analyses and interaction tests were conducted to evaluate the predictive value of the MAGGIC score for 1‐ and 3‐year death across patient subgroups. Prespecified subgroup analyses of the primary outcome were performed separately in the FMR and DMR cohorts. In the FMR cohort, patients were stratified by mean transmitral pressure gradient at discharge, residual MR at discharge, STS score for MV replacement categories, EuroSCORE II categories, FMR pathogenesis at baseline (atrial or ventricular FMR), and baseline LVEF. In the DMR cohort, stratification was performed by mean transmitral pressure gradient at discharge, residual MR at discharge, STS score for MV repair categories, EuroSCORE II categories, and baseline LVEF. For both cohorts, STS score categories for MV replacement and repair were defined according to the cutoffs proposed in the Mitral Valve Academic Research Consortium consensus document. ¹⁷ Results of subgroup analyses were displayed as forest plots.

All statistical analyses were conducted using R software (version 4.4.1, R Foundation for Statistical Computing, Vienna, Austria), with statistical significance defined as P<0.05. The analysis of the primary outcome accounted for type I error; however, subgroup analyses were exploratory and were not adjusted for multiple comparisons.

Results

Study Population and Baseline Characteristics

Of the 3764 patients in the OCEAN‐Mitral registry, 3609 (96%) had complete data for the MAGGIC score and were included in this analysis. Details on missing or invalid MAGGIC score components in excluded patients are presented in Table S2. The distribution of the MAGGIC score (median, 30 [IQR, 26–33]) is shown in Figure S2. Among patients with FMR (n=2552), the median MAGGIC score was 30 [IQR, 26–34], whereas among those with DMR (n=1057), it was 29 [IQR, 26–32]. Patients were stratified into tertiles based on the MAGGIC score: tertile 1 (≤27; n=1243), tertile 2 (28–32; n=1256), and tertile 3 (≥33; n=1109) (Figure S3). The median follow‐up duration was 428 (IQR, 305–821) days.

Baseline characteristics of the overall study population are summarized in Table 1. The median age was 81.0 [IQR, 73.0–85.0] years, and 1996 patients (55.3%) were men. Patients in the MAGGIC tertile 3 were older (tertile 3 versus tertile 2 versus tertile 1: 83.0 versus 82.0 versus 75.0 years; P<0.001), more often men (63.6% versus 50.0% versus 53.3%; P<0.001), had a lower body mass index (20.0 versus 20.6 versus 22.0 kg/m²; P<0.001), and had higher prevalences of diabetes, chronic obstructive pulmonary disease, coronary artery disease, and prior myocardial infarction. Furthermore, patients in tertile 3 had higher values of the STS score for MV replacement (13.0 versus 9.1 versus 6.2; P<0.001), the STS score for MV repair (10.0 versus 6.7 versus 3.9; P<0.001), EuroSCORE II (7.4 versus 5.0 versus 3.6; P<0.001), the COAPT score (5.0 versus 3.0 versus 2.0; P<0.001), and the MitraScore (5.0 versus 4.0 versus 3.0; P<0.001), as well as higher median NT‐proBNP levels (4900.0 versus 2569.0 versus 1688.3 ng/L; P<0.001), whereas showing lower median estimated glomerular filtration rates (25.7 versus 38.8 versus 45.6 mL/min per 1.73 m²; P<0.001). The use of guideline‐directed medical therapy, including renin–angiotensin system inhibitors, β blockers, and mineralocorticoid receptor antagonists, was less frequent in tertile 3. Among the overall cohort, 2552 (70.7%) had FMR and 1057 patients (29.3%) had DMR. Tables S3 and S4 provide additional baseline comparisons within the FMR and DMR cohorts.

Table 1.

Baseline Characteristics

	Overall	MAGGIC tertile 1	MAGGIC tertile 2	MAGGIC tertile 3	P value
	(n=3609)	(n=1243)	(n=1256)	(n=1110)	P value
Age, y	81.0 (73.0–85.0)	75.0 (68.0–82.0)	82.0 (76.8–86.0)	83.0 (78.0–87.0)	<0.001
Male sex	1996 (55.3)	662 (53.3)	628 (50.0)	706 (63.6)	<0.001
Body mass index, kg/m²	21.0 (18.8–23.3)	22.0 (20.1–24.6)	20.6 (18.5–22.9)	20.0 (18.3–22.2)	<0.001
Hypertension	2346 (65.0)	812 (65.3)	826 (65.8)	708 (63.8)	0.576
Dyslipidemia	1756 (48.7)	599 (48.2)	596 (47.5)	561 (50.5)	0.299
Diabetes	951 (26.4)	207 (16.7)	306 (24.4)	438 (39.5)	<0.001
Chronic obstructive pulmonary disease	314 (8.7)	68 (5.5)	111 (8.8)	135 (12.2)	<0.001
Atrial fibrillation/atrial flutter	2227 (61.7)	735 (59.1)	806 (64.2)	686 (61.8)	0.035
Prior stroke	416 (11.5)	127 (10.2)	149 (11.9)	140 (12.6)	0.173
Coronary artery disease	1270 (35.2)	342 (27.5)	415 (33.0)	513 (46.2)	<0.001
Prior myocardial infarction	813 (22.5)	225 (18.1)	253 (20.1)	335 (30.2)	<0.001
Prior cardiac surgery	363 (10.1)	130 (10.5)	120 (9.6)	113 (10.2)	0.744
Cardiac resynchronization therapy	363 (10.1)	113 (9.1)	110 (8.8)	140 (12.6)	0.003
Chronic kidney disease	3125 (86.6)	979 (78.8)	1103 (87.8)	1043 (94.0)	<0.001
Hemodialysis	220 (6.1)	36 (2.9)	54 (4.3)	130 (11.7)	<0.001
Heart failure hospitalization within 1 y	1700 (47.1)	484 (38.9)	634 (50.5)	582 (52.4)	<0.001
New York Heart Association class III/IV	2285 (63.3)	445 (35.8)	850 (67.6)	990 (89.2)	<0.001
Laboratory values
Albumin, g/dL	3.7 (2.4–4.6)	3.9 (2.7–4.7)	3.6 (2.5–4.5)	3.4 (2.2–4.4)	<0.001
Hemoglobin, g/dL	11.6 (10.3–12.9)	12.2 (11.0–13.4)	11.5 (10.4–12.8)	11.0 (9.8–12.2)	<0.001
Estimated glomerular filtration rate, mL/min per 1.73 m²	37.3 (25.6–50.5)	45.6 (34.8–57.4)	38.8 (28.2–49.7)	25.7 (17.0–36.8)	<0.001
B‐type natriuretic peptide, pg/mL (n=2604, 72.2%)	354.2 (171.5–710.0)	263.9 (135.6–538.9)	324.8 (159.6–602.0)	550.5 (252.7–1144.3)	<0.001
NT‐proBNP, ng/L (n=1913, 53.0%)	2639.0 (1186.0–5489.0)	1688.3 (760.0–3440.0)	2569.0 (1215.0–4866.5)	4900.0 (2338.0–108 06.0)	<0.001
Risk assessment
MAGGIC risk score	30 (26–33)	24 (22–26)	30 (29–31)	35 (34–38)	<0.001
STS score for mitral valve replacement (n=3400, 94.2%)	9.0 (5.7–14.0)	6.2 (3.9–9.6)	9.1 (6.1–13.1)	13.0 (8.8–20.0)	<0.001
STS score for mitral valve repair (n=2810, 77.9%)	6.5 (3.7–10.6)	3.9 (2.2–6.7)	6.7 (4.2–10.0)	10.0 (6.5–16.2)	<0.001
EuroSCORE II (n=3087, 85.5%)	5.0 (3.3–8.7)	3.6 (2.3–5.9)	5.0 (3.5–8.1)	7.4 (4.7–13.0)	<0.001
COAPT risk score (n=3029, 83.9%)	3.0 (1.0–5.0)	2.0 (1.0–4.0)	3.0 (2.0–5.0)	5.0 (3.0–7.0)	<0.001
MitraScore (n=3588, 99.4%)	4.0 (3.0–5.0)	3.0 (2.0–4.0)	4.0 (3.0–5.0)	5.0 (4.0–5.0)	<0.001
Medication at admission
Renin–angiotensin system inhibitor	1886 (52.3)	791 (63.6)	662 (52.7)	433 (39.0)	<0.001
β blocker	2670 (74.0)	1050 (84.5)	933 (74.3)	687 (61.9)	<0.001
Mineralocorticoid receptor antagonist	2002 (55.6)	727 (58.6)	704 (56.2)	571 (51.5)	0.002
Sodium–glucose cotransporter‐2 inhibitor	763 (21.1)	261 (21.0)	259 (20.6)	243 (21.9)	0.743
Angiotensin receptor–neprilysin inhibitor	413 (11.5)	135 (10.9)	145 (11.5)	133 (12.0)	0.689

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Values are n (%) or median (interquartile range).

COAPT indicates Cardiovascular Outcomes Assessment of the MitraClip Percutaneous Therapy for Heart Failure Patients With Functional Mitral Regurgitation; EuroSCORE, European System for Cardiac Operative Risk Evaluation; MAGGIC, Meta‐Analysis Global Group in Chronic Heart Failure; NT‐proBNP, N‐terminal pro‐B‐type natriuretic peptide; and STS, Society of Thoracic Surgeons.

Echocardiographic measurements at baseline are summarized in Table 2. In the FMR cohort, atrial FMR was present in 20.1% of patients, and ventricular FMR in 79.9%. In the FMR cohort, patients in higher MAGGIC tertiles had a lower LVEF (38.6% versus 36.9% versus 31.8%; P<0.001) and a higher tricuspid regurgitation pressure gradient (29 versus 31 versus 34 mm Hg; P<0.001). In the DMR cohort, LVEF did not differ significantly across tertiles (62.9% versus 62.5% versus 61.0%; P=0.172), whereas patients in higher MAGGIC tertiles had a higher tricuspid regurgitation pressure gradient (31 versus 34 versus 34 mm Hg; P=0.002).

Table 2.

Echocardiographic Characteristics

	Overall	MAGGIC tertile 1	MAGGIC tertile 2	MAGGIC tertile 3	P value
FMR cohort	2552	858	821	873
Mitral regurgitation pathogenesis
Atrial functional	512 (20.1)	194 (22.6)	202 (24.6)	116 (13.3)	<0.001
Ventricular functional	2040 (79.9)	664 (77.4)	619 (75.4)	757 (86.7)	<0.001
Effective regurgitant orifice area, cm²	0.31 (0.23–0.41)	0.30 (0.22–0.40)	0.30 (0.23–0.40)	0.32 (0.23–0.43)	0.042
Left ventricular ejection fraction, %	35.4 (27.8–48.6)	38.6 (30.7–51.1)	36.9 (28.4–51.0)	31.8 (25.0–41.9)	<0.001
≤30%	819 (32.1)	189 (22.0)	189 (22.0)	382 (43.8)	<0.001
Left ventricular systolic diameter, mm	48.0 (38.0–57.0)	48.0 (38.0–57.8)	47.0 (36.0–55.0)	50.3 (41.0–59.0)	<0.001
Left ventricular diastolic diameter, mm	59.0 (52.0–66.0)	59.0 (52.0–67.0)	58.0 (50.0–65.0)	60.0 (53.0–67.0)	<0.001
Left ventricular end‐systolic volume, mL	97.0 (56.0–143.5)	90.0 (52.0–138.2)	87.5 (51.0–134.0)	111.0 (68.0–154.0)	<0.001
Left ventricular end‐diastolic volume, mL	149.0 (104.0–204.0)	146.0 (103.2–201.5)	137.0 (96.0–189.0)	163.0 (114.8–213.0)	<0.001
Left atrial diameter, mm	49.0 (44.0–55.0)	50.0 (44.0–57.0)	49.0 (43.0–54.0)	49.0 (44.0–54.0)	0.015
Tricuspid regurgitation pressure gradient, mm Hg	31.0 (24.0–41.2)	29.0 (23.0–39.0)	31.0 (24.0–41.0)	34.0 (25.0–44.0)	<0.001
Mitral valve mean pressure gradient, mm Hg	1.5 (1.0–2.0)	1.4 (1.0–2.0)	1.5 (1.0–2.1)	1.6 (1.0–2.2)	0.024
Mitral valve area, cm²	5.0 (4.1–6.1)	5.3 (4.3–6.6)	4.9 (4.0–6.0)	4.9 (3.9–5.9)	<0.001
Moderate or greater tricuspid regurgitation	914 (35.9)	291 (34.0)	313 (38.2)	310 (35.6)	0.200
DMR cohort	1057	385	435	237
Effective regurgitant orifice area, cm²	0.44 (0.35–0.58)	0.43 (0.35–0.54)	0.45 (0.36–0.58)	0.43 (0.32–0.59)	0.604
Left ventricular ejection fraction, %	62.3 (55.1–67.2)	62.9 (55.6–67.0)	62.5 (56.0–67.4)	61.0 (51.3–68.1)	0.172
≤30%	20 (1.9)	3 (0.8)	5 (1.1)	12 (5.1)	<0.001
Left ventricular systolic diameter, mm	32.0 (28.0–37.0)	32.0 (29.0–38.0)	32.0 (28.0–36.0)	32.0 (27.9–38.0)	0.191
Left ventricular diastolic diameter, mm	50.0 (45.0–56.0)	51.0 (47.0–57.0)	50.0 (45.0–55.0)	50.0 (44.0–56.0)	0.002
Left ventricular end‐systolic volume, mL	40.0 (30.0–58.0)	42.0 (31.0–61.0)	39.0 (29.0–54.9)	40.0 (29.0–60.7)	0.165
Left ventricular end‐diastolic volume, mL	106.0 (81.0–135.2)	109.0 (87.5–140.2)	104.0 (81.0–131.0)	103.0 (78.0–135.0)	0.056
Left atrial diameter, mm	47.0 (43.0–53.0)	48.0 (43.0–53.0)	47.0 (42.0–53.0)	47.0 (41.0–53.0)	0.034
Tricuspid regurgitation pressure gradient, mm Hg	33.0 (25.0–43.0)	31.0 (25.0–41.0)	34.0 (26.6–45.0)	34.0 (25.0–45.0)	0.002
Mitral valve mean pressure gradient, mm Hg	2.0 (1.1–3.0)	2.0 (1.0–2.6)	2.0 (1.2–3.0)	2.0 (1.1–3.0)	0.129
Mitral valve area, cm²	5.0 (4.2–6.0)	5.1 (4.3–6.2)	4.8 (4.1–6.0)	4.9 (4.1–5.9)	0.032
Moderate or greater tricuspid regurgitation	429 (40.8)	128 (33.5)	193 (44.4)	108 (46.2)	0.001

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Values are n (%) or median (interquartile range).

DMR indicates degenerative mitral regurgitation; FMR, functional mitral regurgitation; and MAGGIC, Meta‐Analysis Global Group in Chronic Heart Failure.

Procedural Characteristics and Outcomes

Procedural characteristics and outcomes are summarized in Table 3. Acute procedural success was achieved in 96.4% of patients in the FMR cohort and 91.9% in the DMR cohort, with no significant differences among tertiles (tertile 3 versus tertile 2 versus tertile 1: FMR, 96.2% versus 97.6% versus 95.6%; P=0.090; DMR, 93.8% versus 91.5% versus 89.5%; P=0.150). Patients in tertile 3 had a significantly longer hospital stay than those in tertiles 1 and 2 in both cohorts (FMR, 20 [IQR, 10–36] days versus 15 [IQR, 8–28] days versus 12 [IQR, 7–24] days; P<0.001; DMR, 18 [IQR, 11–32] days versus 13 [IQR, 8–24] days versus 10 [IQR, 7–16] days; P<0.001). In the FMR cohort, no significant differences across tertiles were observed in the incidence of single‐leaflet device attachment, device embolization, or leaflet tear at 30 days and 1 year, or in mitral valve surgery at 30 days, 1 year, and 3 years. In the DMR cohort, leaflet tear at 30 days (0.8% versus 0.0% versus 1.7%; P=0.034) and mitral valve surgery at 30 days (0.8% versus 0.5% versus 3.0%; P=0.010) occurred more frequently in tertile 3; however, no significant differences were observed otherwise.

Table 3.

Procedural Characteristics and Outcomes

	Overall	MAGGIC tertile 1	MAGGIC tertile 2	MAGGIC tertile 3	P value
FMR cohort	2552	858	821	873
Total device time, min	52.0 (36.0–75.0)	56.0 (39.0–81.0)	50.0 (36.0–70.0)	50.0 (35.0–75.0)	0.013
Procedural time, min	79.0 (57.0–110.0)	84.0 (60.0–113.0)	76.0 (56.0–105.0)	79.0 (57.0–109.0)	0.007
Fluoroscopy duration, min	23.9 (16.0–35.1)	24.0 (15.9–39.0)	23.0 (16.0–33.4)	24.0 (16.0–35.0)	0.183
Number of clips implanted
0	14 (0.5)	7 (0.8)	3 (0.4)	4 (0.5)	0.415
1	1916 (75.1)	621 (72.4)	636 (77.5)	659 (75.5)	0.052
2 or 3	622 (24.4)	230 (26.8)	182 (22.2)	210 (24.1)	0.083
Acute procedural success	2461 (96.4)	825 (96.2)	801 (97.6)	835 (95.6)	0.090
MR severity at discharge
0	498 (19.7)	178 (20.8)	171 (21.1)	149 (17.4)	0.102
1+	1499 (59.4)	485 (56.7)	487 (60.0)	527 (61.4)	0.129
2+	462 (18.3)	169 (19.8)	142 (17.5)	151 (17.6)	0.393
3+ or 4+	66 (2.6)	23 (2.7)	12 (1.5)	31 (3.6)	0.024
Mean transmitral pressure gradient at discharge, mm Hg	2.6 (2.0–3.6)	2.6 (2.0–3.5)	2.6 (2.0–3.6)	2.7 (2.0–3.7)	0.376
≥5 mm Hg	236 (10.2)	69 (9.0)	74 (9.8)	93 (11.7)	0.183
Length of hospital stay, d	15.0 (8.0–30.0)	12.0 (7.3–24.0)	15.0 (8.0–28.0)	20.0 (10.0–36.0)	<0.001
Single‐leaflet device attachment at 30 d	20 (0.8)	5 (0.6)	7 (0.9)	8 (0.9)	0.707
Single‐leaflet device attachment at 1 y	25 (1.0)	9 (1.1)	8 (1.0)	8 (0.9)	0.958
Device embolization at 30 d	0 (0.0)	0 (0.0)	0 (0.0)	0 (0.0)	NA
Device embolization at 1 y	0 (0.0)	0 (0.0)	0 (0.0)	0 (0.0)	NA
Leaflet tear at 30 d	27 (1.1)	7 (0.8)	8 (1.0)	12 (1.4)	0.518
Leaflet tear at 1 y	34 (1.4)	9 (1.1)	10 (1.3)	15 (1.8)	0.466
Mitral valve surgery at 30 d	20 (0.8)	4 (0.5)	8 (1.0)	8 (0.9)	0.424
Mitral valve surgery at 1 y	46 (1.9)	16 (1.9)	15 (1.9)	15 (1.8)	0.964
Mitral valve surgery at 3 y	63 (2.5)	24 (2.9)	21 (2.7)	18 (2.1)	0.584
DMR cohort	1057	385	435	237
Total device time, min	58.0 (40.0–86.0)	57.0 (39.8–86.0)	56.0 (40.0–84.0)	61.0 (43.0–88.0)	0.326
Procedural time, min	85.0 (62.3–117.0)	83.5 (61.8–115.3)	84.0 (61.0–114.8)	90.0 (66.0–125.0)	0.158
Fluoroscopy duration, min	25.2 (17.1–38.5)	23.0 (15.0–38.9)	26.0 (18.1–37.8)	25.6 (19.1–39.5)	0.266
Number of clips implanted
0	10 (0.9)	3 (0.8)	5 (1.1)	2 (0.8)	0.847
1	683 (64.6)	259 (67.3)	278 (63.9)	146 (61.6)	0.329
2 or 3	364 (34.4)	123 (31.9)	152 (34.9)	89 (37.6)	0.346
Acute procedural success	971 (91.9)	361 (93.8)	398 (91.5)	212 (89.5)	0.150
MR severity at discharge
0	207 (19.7)	88 (22.9)	73 (16.9)	46 (19.7)	0.101
1+	548 (52.2)	202 (52.6)	231 (53.6)	115 (49.1)	0.539
2+	228 (21.7)	76 (19.8)	97 (22.5)	55 (23.5)	0.488
3+ or 4+	66 (6.3)	18 (4.7)	30 (7.0)	18 (7.7)	0.249
Mean transmitral pressure gradient at discharge, mm Hg	2.8 (2.0–4.0)	2.7 (2.0–3.5)	3.0 (2.0–4.0)	2.9 (2.0–4.0)	0.159
≥5 mm Hg	113 (11.7)	37 (10.4)	44 (11.1)	32 (15.0)	0.235
Length of hospital stay, d	12.0 (8.0–24.0)	10.0 (7.0–16.0)	13.0 (8.0–24.0)	18.0 (11.0–32.0)	<0.001
Single‐leaflet device attachment at 30 d	16 (1.5)	6 (1.6)	7 (1.6)	3 (1.3)	0.936
Single‐leaflet device attachment at 1 y	22 (2.1)	9 (2.4)	9 (2.1)	4 (1.7)	0.853
Device embolization at 30 d	0 (0.0)	0 (0.0)	0 (0.0)	0 (0.0)	NA
Device embolization at 1 y	4 (0.1)	1 (0.1)	2 (0.2)	1 (0.1)	0.892
Leaflet tear at 30 d	7 (0.7)	3 (0.8)	0 (0.0)	4 (1.7)	0.034
Leaflet tear at 1 y	10 (1.0)	4 (1.1)	2 (0.5)	4 (1.7)	0.284
Mitral valve surgery at 30 d	12 (1.2)	3 (0.8)	2 (0.5)	7 (3.0)	0.010
Mitral valve surgery at 1 y	35 (3.4)	15 (4.0)	11 (2.6)	9 (3.9)	0.487
Mitral valve surgery at 3 y	51 (4.9)	21 (5.6)	18 (4.2)	12 (5.2)	0.659

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Values are n (%) or median (interquartile range).

DMR indicates degenerative mitral regurgitation; FMR, functional mitral regurgitation; MAGGIC, Meta‐Analysis Global Group in Chronic Heart Failure; MR, mitral regurgitation; and NA, not applicable.

Clinical Outcomes

The associations between the MAGGIC score and primary and secondary outcomes are illustrated by Kaplan–Meier curves of event‐free survival stratified by MAGGIC score tertiles in the FMR cohort (Figure 1) and the DMR cohort (Figure 2). Event rates with crude hazard ratios (HRs) and adjusted HR (HR_adj) for clinical outcomes at 1 and 3 years are provided for the FMR cohort in Table 4 and Table S5 and for the DMR cohort in Table 5 and Table S6.

A, Event‐free survival of all‐cause death, (B) cardiovascular death, (C) HF rehospitalization, and (D) the composite of all‐cause death or HF rehospitalization within 3 years, according to MAGGIC score tertiles. Adj.HR indicates adjusted hazard ratio; CV, cardiovascular; FMR, functional mitral regurgitation; HF, heart failure; MAGGIC, Meta‐Analysis Global Group in Chronic Heart Failure; and rehosp., rehospitalization.

Table 4.

Midterm Clinical Outcomes According to MAGGIC Tertiles in Patients With FMR, With Adjusted HRs

	FMR cohort	MAGGIC tertile 1	MAGGIC tertile 2	MAGGIC tertile 3	Tertile 2 vs 1		Tertile 3 vs 1
	(n=2552)	(n=858)	(n=821)	(n=873)	Adjusted HR^* (95% CI)	P value	Adjusted HR^* (95% CI)	P value
1‐y follow‐up	n=2076	n=699	n=671	n=706
All‐cause death	341 (16.4)	46 (6.6)	102 (15.2)	193 (27.3)	2.24 (1.23–4.06)	0.008	4.15 (2.34–7.36)	<0.001
Cardiovascular death	200 (9.6)	27 (3.9)	60 (8.9)	113 (16.0)	1.78 (0.80–3.95)	0.158	4.07 (1.94–8.55)	<0.001
HF rehospitalization	369 (17.8)	94 (13.4)	113 (16.8)	162 (22.9)	1.22 (0.81–1.84)	0.344	1.83 (1.22–2.77)	0.004
All‐cause death or HF rehospitalization	611 (29.4)	127 (18.2)	180 (26.8)	304 (43.1)	1.43 (1.00–2.03)	0.049	2.34 (1.66–3.30)	<0.001
3‐y follow‐up	n=1210	n=358	n=372	n=480
All‐cause death	600 (49.6)	112 (31.3)	187 (50.3)	301 (62.7)	1.75 (1.18–2.58)	0.005	2.31 (1.56–3.43)	<0.001
Cardiovascular death	361 (29.8)	73 (20.4)	109 (29.3)	179 (37.3)	1.32 (0.80–2.16)	0.276	2.01 (1.24–3.26)	0.005
HF rehospitalization	536 (44.3)	149 (41.6)	165 (44.4)	222 (46.2)	1.08 (0.76–1.53)	0.671	1.61 (1.13–2.31)	0.009
All‐cause death or HF rehospitalization	900 (74.4)	211 (58.9)	275 (73.9)	414 (86.2)	1.26 (0.94–1.67)	0.121	1.87 (1.40–2.51)	<0.001
In‐hospital death	82 (3.2)	7 (0.8)	17 (2.1)	58 (6.6)	3.70 (0.73–18.88)	0.115	8.85 (1.89–41.34)	0.006

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Values are n (%) unless otherwise indicated. HRs and P values were calculated with the use of Cox proportional hazards survival models.

FMR indicates functional mitral regurgitation; HF, heart failure; HR, hazard ratio; and MAGGIC, Meta‐Analysis Global Group in Chronic Heart Failure.

Multivariate adjustment excluded variables related to components of the MAGGIC score and was performed using dyslipidemia, coronary artery disease, atrial fibrillation or atrial flutter, history of stroke, hemoglobin levels, N‐terminal pro‐B‐type natriuretic peptide levels, use of angiotensin receptor–neprilysin inhibitors, use of sodium–glucose cotransporter‐2 inhibitors, moderate or greater tricuspid regurgitation, and mean transmitral pressure gradient, all assessed before the procedure.

Table 5.

Midterm Clinical Outcomes According to MAGGIC Tertiles in Patients With DMR, With Adjusted HRs

	DMR cohort	MAGGIC tertile 1	MAGGIC tertile 2	MAGGIC tertile 3	Tertile 2 vs 1		Tertile 3 vs 1
	(n=1057)	(n=385)	(n=435)	(n=237)	Adjusted HR^* (95% CI)	P value	Adjusted HR^* (95% CI)	P value
1‐y follow‐up	n=833	n=298	n=349	n=186
All‐cause death	101 (12.1)	14 (4.7)	37 (10.6)	50 (26.9)	2.01 (0.86–4.69)	0.107	2.84 (1.18–6.86)	0.020
Cardiovascular death	53 (6.4)	6 (2.0)	21 (6.0)	26 (14.0)	2.70 (0.85–8.58)	0.091	3.24 (0.97–10.91)	0.057
HF rehospitalization	80 (9.6)	20 (6.7)	36 (10.3)	24 (12.9)	3.62 (1.18–11.07)	0.024	3.40 (1.00–11.59)	0.051
All‐cause death or HF rehospitalization	165 (19.8)	33 (11.1)	68 (19.5)	64 (34.4)	2.34 (1.18–4.62)	0.015	2.47 (1.17–5.21)	0.018
3‐y follow‐up	n=376	n=103	n=166	n=107
All‐cause death	168 (44.7)	29 (28.2)	70 (42.2)	69 (64.5)	1.48 (0.74–2.95)	0.264	2.25 (1.07–4.73)	0.033
Cardiovascular death	89 (23.7)	11 (10.7)	42 (25.3)	36 (33.6)	2.58 (1.01–6.64)	0.048	3.49 (1.23–9.84)	0.018
HF rehospitalization	115 (30.6)	30 (29.1)	55 (33.1)	30 (28.0)	1.54 (0.67–3.53)	0.313	1.13 (0.43–2.96)	0.807
All‐cause death or HF rehospitalization	245 (65.2)	52 (50.5)	108 (65.1)	85 (79.4)	1.40 (0.80–2.44)	0.236	1.51 (0.82–2.78)	0.187
In‐hospital death	19 (1.8)	1 (0.3)	6 (1.4)	12 (5.1)	…	…	…	…^†

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Values are n (%) unless otherwise indicated. HRs and P values were calculated with the use of Cox proportional hazards survival models.

DMR indicates degenerative mitral regurgitation; HF, heart failure; HR, hazard ratio; and MAGGIC, Meta‐Analysis Global Group in Chronic Heart Failure.

^{^†}

Given the limited number of in‐hospital death events in the DMR cohort, multivariable adjustment was not feasible. Accordingly, only crude analyses were performed.

After multivariable adjustment for clinically relevant variables not included in the MAGGIC score, in the FMR cohort, patients in tertile 3 had significantly higher risks of all‐cause death, cardiovascular death, HF rehospitalization, the composite of all‐cause death or HF rehospitalization at both 1 and 3 years, and in‐hospital death. In the DMR cohort, patients in tertile 3 had significantly higher risks of all‐cause death and the composite of all‐cause death or HF rehospitalization at 1 year, and in all‐cause death and cardiovascular death at 3 years. For HF rehospitalization, cumulative incidence differed across tertiles by Gray's test (FMR, P=0.0076; DMR, P=0.0312) (Figure S4). However, after multivariable Fine–Gray adjustment, MAGGIC score tertiles were not independently associated with HF rehospitalization (FMR: tertile 2 versus 1, sHR, 0.85; P=0.350; tertile 3 versus 1, sHR, 0.92; P=0.630; DMR: tertile 2 versus 1, sHR, 1.27; P=0.540; tertile 3 versus 1, sHR, 0.68; P=0.420).

In the multivariable analysis, in the FMR cohort, independent predictors of 1‐year mortality after M‐TEER included MAGGIC score tertiles (HR_adj, 1.10 [95% CI, 1.05–1.15; P<0.001]), hemoglobin (HR_adj, 0.74 [95% CI, 0.64–0.84]; P<0.001), and log‐transformed NT‐proBNP (HR_adj, 1.36 [95% CI, 1.08–1.72]; P=0.009). For 3‐year death, independent predictors were MAGGIC score tertiles (HR_adj, 1.06 [95% CI, 1.03–1.10]; P<0.001), hemoglobin (HR_adj, 0.83 [95% CI, 0.76–0.92]; P<0.001), log‐transformed NT‐proBNP (HR_adj, 1.50 [95% CI, 1.23–1.82]; P<0.001), and use of angiotensin receptor–neprilysin inhibitors (HR_adj, 2.27 [95% CI, 1.39–3.68]; P=0.001; Tables S7 and S8). In the DMR cohort, independent predictors of 1‐year death after M‐TEER included MAGGIC score tertiles (HR_adj, 1.08 [95% CI, 1.02–1.15]; P<0.001), log‐transformed NT‐proBNP (HR_adj, 2.24 [95% CI, 1.53–3.28]; P<0.001), and residual MR severity at discharge of grade ≥3+ (HR_adj, 2.47 [95% CI, 1.12–5.48]; P=0.026). For 3‐year death, independent predictors were MAGGIC score tertiles (HR_adj, 1.05 [95% CI, 1.00–1.10]; P=0.039), log‐transformed NT‐proBNP (HR_adj, 1.81 [95% CI, 1.31–2.50]; P<0.001), and residual MR severity at discharge of grade ≥3+ (HR_adj, 2.24 [95% CI, 1.19–4.21]; P=0.012; Tables S9 and S10).

Comparison of the MAGGIC Score, the STS Score for MV Replacement and Repair, EuroSCORE II, the MitraScore, and the COAPT Risk Score

Receiver operating characteristic curves for predicting 1‐ and 3‐year all‐cause death after M‐TEER in the FMR and DMR cohorts are shown in Figures S5 and S6. In the FMR cohort, the discriminative ability of the MAGGIC score was comparable with that of the STS score for MV replacement at both 1 year (0.70 [95% CI, 0.67–0.73] versus 0.70 [95% CI, 0.67–0.73]; P=0.874) and 3 years (0.65 [95% CI, 0.62–0.69] versus 0.64 [95% CI, 0.61–0.68]; P=0.493; Table S11). Compared with EuroSCORE II, the MAGGIC score demonstrated a higher concordance index at 1 year (0.69 [95% CI, 0.66–0.72] versus 0.65 [95% CI, 0.62–0.69]; P=0.048), and comparable at 3 years (0.65 [95% CI, 0.62–0.68] versus 0.62 [95% CI, 0.59–0.65]; P=0.098) (Table S12). When compared with M‐TEER–specific prognostic scores, the MAGGIC score showed significantly higher concordance index values than the COAPT score at both 1 year (0.70 [95% CI, 0.66–0.73] versus 0.59 [95% CI, 0.56–0.63]; P<0.001) and 3 years (0.66 [95% CI, 0.63–0.69] versus 0.57 [95% CI, 0.54–0.61]; P<0.001), and than the MitraScore at both 1 year (0.69 [95% CI, 0.66–0.72] versus 0.65 [95% CI, 0.62–0.68]; P=0.003) and 3 years (0.66 [95% CI, 0.63–0.69] versus 0.61 [95% CI, 0.58–0.64]; P=0.003; Tables S13 and S14).

In the DMR cohort, the MAGGIC score demonstrated higher concordance index values at 1 year than the STS score for MV repair (0.74 [95% CI, 0.68–0.80] versus 0.67 [95% CI, 0.60–0.73]; P=0.017), EuroSCORE II (0.74 [95% CI, 0.68–0.79] versus 0.67 [95% CI, 0.61–0.73]; P=0.049), and the MitraScore (0.73 [95% CI, 0.68–0.79] versus 0.66 [95% CI, 0.60–0.71]; P=0.011), and comparable at 3 years (STS, 0.68 [95% CI, 0.62–0.74] versus 0.65 [95% CI, 0.59–0.72]; P=0.462; EuroSCORE II: 0.66 [95% CI, 0.61–0.72] versus 0.66 [95% CI, 0.60–0.72]; P=0.911; MitraScore: 0.67 [95% CI, 0.62–0.73] versus 0.65 [95% CI, 0.60–0.70]; P=0.457; Tables S15, S16, and S18). The MAGGIC score also showed higher concordance index values than the COAPT score at both 1 year (0.73 [95% CI, 0.67–0.79] versus 0.64 [95% CI, 0.58–0.70]; P=0.002) and 3 years (0.67 [95% CI, 0.61–0.73] versus 0.59 [95% CI, 0.53–0.65]; P=0.013; Table S17).

Calibration plots for 1‐ and 3‐year survival after M‐TEER in both cohorts revealed excellent calibration of the MAGGIC score, comparable calibration of the STS score for MV replacement and repair as well as the MitraScore, and less favorable calibration of EuroSCORE II and the COAPT score (Figures S7 and S8).

Reclassification metrics were also evaluated. In the FMR cohort, when compared with the MAGGIC score, both continuous net reclassification improvement and integrated discrimination improvement were consistently significant for EuroSCORE II, and the M‐TEER–specific scores, whereas they were not consistently significant for the STS score for MV replacement. In the DMR cohort, when compared with the MAGGIC score, both continuous net reclassification improvement and integrated discrimination improvement were significant at 1 year for the STS score for MV repair, EuroSCORE II, and the M‐TEER–specific scores. At 3 years, they were significant for the COAPT score but not for the other risk scores (Tables S11 through S18).

Subgroup Analyses

Subgroup analyses showed that the MAGGIC score consistently predicted 1‐ and 3‐year death across all prespecified subgroups (Figures 3A, 3B, 4A, and 4B). However, significant interactions were observed in subgroups defined by the STS score for MV repair categories for 3‐year death (P for interaction=0.015). Despite these interactions, the positive association between higher MAGGIC scores and increased mortality risk remained consistent across subgroups.

The black dotted vertical line represents the hazard ratio of 1.0. The MAGGIC score was analyzed as a continuous variable, with hazard ratios calculated per 1‐point increase. The MAGGIC score consistently predicted 1‐ and 3‐year death across prespecified subgroups, including mean transmitral pressure gradient at discharge, residual MR at discharge, STS score (for MV replacement) categories, EuroSCORE II categories, FMR pathogenesis, and LVEF. EuroSCORE indicates European System for Cardiac Operative Risk Evaluation; FMR, functional mitral regurgitation; LVEF, left ventricular ejection fraction; MAGGIC, Meta‐Analysis Global Group in Chronic Heart Failure; MR, mitral regurgitation; MV, mitral valve; STS, Society of Thoracic Surgeons; and TMPG, transmitral pressure gradient.

Discussion

In this large, contemporary, real‐world, multicenter cohort of 3609 patients undergoing M‐TEER, the key findings of our analysis are as follows:

Among the overall cohort, the median MAGGIC score was 30 (IQR, 26–33). Stratified by MR pathogenesis, it was 30 (IQR, 26–34) in patients with FMR and 29 (IQR, 26–32) in those with DMR.
The MAGGIC score was a significant independent predictor of all‐cause death at both 1 and 3 years, with consistent associations across prespecified subgroups in both the FMR and DMR cohorts.
In the FMR cohort, higher MAGGIC score tertiles were associated with stepwise increases in all‐cause death, cardiovascular death, and the composite of all‐cause death or HF rehospitalization at both 1 and 3 years, and in‐hospital death, whereas for HF rehospitalization alone the association was attenuated after accounting for death as a competing risk.
In the DMR cohort, higher MAGGIC score tertiles were associated with stepwise increases in all‐cause death and the composite of all‐cause death or HF rehospitalization at 1 year, and in all‐cause death and cardiovascular death at 3 years.
The predictive performance of the MAGGIC score was comparable with or superior to that of conventional surgical and M‐TEER–specific risk scores for 1‐ and 3‐year death.

To the best of our knowledge, this is the first study to establish the MAGGIC score as an independent prognostic factor after M‐TEER and to directly compare its performance with conventional surgical and M‐TEER–specific risk scores in this patient population, demonstrating superior predictive performance. Moreover, the baseline STS score in our cohort (median, 9.0 [IQR, 5.7–14.0]) was comparable with that reported in the TVT (Transcatheter Valve Therapy) registry (median, 9.2 [IQR, 6.0–14.1]) and the COAPT postapproval registry (mean, 8.9±7.5), ¹⁸ , ¹⁹ supporting the generalizability of our findings to contemporary M‐TEER populations. Notably, by confirming the prognostic value of the MAGGIC score in both FMR and DMR, our study underscores that underlying HF status plays a central role in determining outcomes after M‐TEER, irrespective of MR pathogenesis. These findings highlight that a risk model originally derived from HF cohorts may be more relevant for patients undergoing M‐TEER than conventional surgical risk scores, which primarily capture perioperative risk.

The performance of the MAGGIC score should be interpreted according to the therapeutic context of MR. In surgical settings, MR is usually eliminated, and early outcomes are primarily determined by perioperative risk and procedural factors. Nevertheless, the MAGGIC score has shown prognostic performance comparable to STS and EuroSCORE II after valve surgery. ⁸ In observational cohorts of patients with MR managed conservatively, death is mainly driven by ventricular dysfunction and systemic comorbidities, and the MAGGIC score has demonstrated superior predictive performance compared with EuroSCORE II. ⁷ In contrast, after M‐TEER, MR is reduced but not eliminated, and long‐term outcomes remain strongly influenced by underlying HF burden. Therefore, an HF‐derived risk model such as the MAGGIC score may be particularly appropriate in this setting.

Beyond conventional surgical risk models, several M‐TEER–specific prognostic models have been developed in recent years, including the MitraScore, ¹³ the COAPT score, ¹⁴ and the EuroSMR (European Registry of Transcatheter Repair for Secondary Mitral Regurgitation) risk score. ¹⁵ However, their external validation studies have demonstrated modest prognostic performance. The MitraScore showed acceptable discriminative performance in its initial external validation (concordance index, 0.70), ¹³ whereas subsequent validations in 2 other independent registries demonstrated only moderate discriminative ability, with concordance indices of 0.61 and 0.63. ²⁰ , ²¹ The COAPT score demonstrated similarly limited discrimination (area under the receiver operating characteristic curve, 0.62). ²² External validation of the EuroSMR risk score has not yet been reported, and some of its variables, such as right midventricular diameter and right atrial area, are not routinely collected in daily practice, which may limit its practicality and widespread adoption. In contrast, the MAGGIC score is composed of readily available clinical variables that are routinely collected, offering both practicality and strong predictive accuracy. Consistent with these advantages, the present study demonstrated that the MAGGIC score significantly outperformed the MitraScore and the COAPT score in predicting 1‐ and 3‐year death in both the FMR and DMR cohorts. Therefore, the MAGGIC score is particularly appealing as a simple and broadly applicable tool for real‐world risk stratification after M‐TEER.

Several factors may explain the MAGGIC score's superior predictive ability. First, unlike the STS score and EuroSCORE II, which are designed primarily to assess perioperative risk within 30 days, the MAGGIC score focuses on midterm death at 1 and 3 years. This emphasis on midterm outcomes is particularly relevant in patients undergoing M‐TEER, as the benefits of the procedure are most evident within the first 2 to 3 years after M‐TEER. ²³ During this early to midterm period, death remains largely driven by HF‐related pathophysiology, which is directly captured by the variables incorporated in the MAGGIC score. Furthermore, another study reported that at 2 years after M‐TEER, the cumulative incidence of all‐cause death was 19%, whereas the cumulative incidence of noncardiovascular death reached 12%. ²⁴ These findings indicate that noncardiovascular causes constitute a substantial component of midterm death. As death reflects both HF‐related and non‐HF mechanisms over time, the relative contribution of HF‐specific predictors may be attenuated, thereby potentially explaining the modest decline in discriminative performance observed at 3 years in the present study.

Second, the MAGGIC score incorporates a broader range of comorbidities, thereby capturing multifaceted risk factors relevant to midterm prognosis in HF. Several variables included in the MAGGIC score, such as age, body mass index, renal dysfunction, diabetes, and the use of β blockers and renin–angiotensin system inhibitors, have been reported as independent predictors in patients undergoing M‐TEER, ²⁵ , ²⁶ , ²⁷ , ²⁸ , ²⁹ while the duration since the initial HF diagnosis has been identified as an independent predictor in patients with HF. ³⁰ In contrast, EuroSCORE II includes only 2 of these variables, age and renal dysfunction, omitting several important HF‐specific predictors, which may explain the superior performance of the MAGGIC score. The STS score includes most of these predictors except β blocker use and HF duration, which likely accounts for the similar performance of the MAGGIC and the STS scores, particularly in the FMR cohort and for 3‐year death in the DMR cohort. The superior performance of the MAGGIC score over the STS score for 1‐year death in the DMR cohort may be related to the pathophysiology of DMR, which is typically associated with fewer adverse comorbidities, allowing HF‐specific factors incorporated in the MAGGIC score to better capture early risk after M‐TEER. In contrast, the more complex and multifactorial determinants of outcomes in FMR may attenuate differences between the 2 scores. Similarly, the superior performance of the MAGGIC score compared with M‐TEER–specific prognostic scores observed in both the FMR and DMR cohorts may reflect fundamental differences in model design. While the MitraScore and the COAPT score emphasize procedural characteristics and valve‐related factors, the MAGGIC score focuses on patient‐level comorbidities and the chronic HF disease trajectory, which are major determinants of death after M‐TEER regardless of MR pathogenesis.

In addition, subgroup analyses showed that the prognostic ability of the MAGGIC score for 3‐year death was greater among patients with a high STS score for MV repair in the DMR cohort. This finding suggests that patients with DMR in the higher STS score subgroup may have a broader distribution of HF‐related comorbidities, allowing the MAGGIC score to more effectively discriminate 3‐year risk. In contrast, patients with lower surgical risk tend to have fewer comorbidities and lower event rates, which may limit risk heterogeneity and attenuate discrimination for 3‐year death.

From a clinical perspective, our findings support a complementary approach to risk stratification in patients undergoing M‐TEER. Surgical risk models such as the STS score may be particularly informative for estimating short‐term procedural risk, whereas HF‐based models such as the MAGGIC score may provide additional insight into the midterm prognosis. All risk models are accessible via online calculators; however, the simplicity and superior predictive performance of the MAGGIC score make it a practical and user‐friendly tool for widespread adoption in clinical practice.

Limitations

This study has some limitations. It was based on prospectively collected data; however, its retrospective design may have introduced potential biases. A key limitation is that medication adjustments during follow‐up were made at the discretion of attending physicians, creating variability that could influence outcomes and their interpretation. Furthermore, the data were derived from the OCEAN‐Mitral registry, which focuses on a Japanese cohort, potentially limiting the generalizability of the findings to non‐Asian populations. All M‐TEER procedures were performed using the MitraClip device, so outcomes with other devices were not assessed. Furthermore, the duration since the initial diagnosis of HF was recorded using a 12‐month threshold rather than the original 18‐month threshold defined in the MAGGIC score, which may have led to underestimation in some patients. Finally, the absence of core laboratory adjudication for imaging and biomarker data may have introduced inconsistencies in interpretation, although the registry protocol was consistently applied across all participating centers. Further research is needed to address these limitations and strengthen the robustness of future findings.

Conclusions

In this study, the MAGGIC score was a significant independent predictor of 1‐ and 3‐year all‐cause death in patients undergoing M‐TEER, with consistent associations across prespecified subgroups in both the FMR and DMR cohorts. Higher MAGGIC score tertiles were associated with stepwise increases in adverse clinical outcomes. Across each cohort, the predictive performance of the MAGGIC score was comparable with or superior to that of conventional surgical and M‐TEER–specific risk scores for 1‐ and 3‐year death. These findings suggest that the MAGGIC score may serve as a valuable and straightforward tool for midterm risk stratification in routine clinical practice for patients undergoing M‐TEER.

Appendix

OCEAN‐Mitral Investigators

Keio University

Toyohashi Heart Center

Masanori Yamamoto, Mitsuru Sago, Tatsuya Tsunaki, Ryo Yamaguchi, Junji Yanagisawa, Yuki Okubo.

Nagoya Heart Center

Ai Kagase, Takahiro Tokuda, Yuki Nakashima, Toshihiro Kobayashi, Kenichi Shibata, Ryoutaku Kawahata, Hiroto Nishio, Yuki Kondo, Tomofumi Nakamura, Hiroshi Tsunamoto.

Gifu Heart Center

Tetsuro Shimura, Yutaka Koyama, Munenori Okubo, Shunsuke Imai, Tokuya Sakakura, Yoshihiro Harano, Masahiro Inagaki, Hirooki Higami, Yoji Kuze, Takashi Nagai, Azusa Kurita.

Kokura Memorial Hospital

Shinichi Shirai, Akihiro Isotani, Kenichi Ishizu, Masato Fukunaga, Hiroyuki Tabata, Toru Morofuji, Hiroyuki Kono, Maiko Kuroda, Euihong Ko, Norihisa Miyawaki, Kenji Nakano, Katsunori Miyahara, Ko Yamamoto, Koumei Onuki, Yasuo Tsuru, Tomohiro Suenaga, Akira Otani.

Teikyo University

Yusuke Watanabe, Taiga Katayama, Hirofumi Hioki, Junichi Nishikawa, Yosei Iseki, Yasuyuki Tsuchida, Kento Kito, Joe Omiya, Masataka Arakawa, Maki Okamoto, Masafumi Saito, Mizuki Miura.

New Tokyo Hospital

Toru Naganuma, Toru Ouchi, Haruhito Yuki.

Saiseikai Yokohama City Eastern Hospital

Masahiro Yamawaki.

Sendai Kousei Hospital

Yusuke Enta, Norio Tada, Masaki Nakashima, Makoto Saigan, Yoshiko Munehisa, Masaki Miyasaka, Natsuko Satomi, Yuta Kobayashi, Daishi Tazawa, Yun Teng.

Shonan Kamakura General Hospital

Shingo Mizuno, Futoshi Yamanaka, Koki Shishido, Tomoki Ochiai, Tsuyoshi Yamabe, Noriaki Moriyama, Takashi Matsumoto, Hirokazu Miyashita, Yoichi Sugiyama, Kunihiko Shimizu, Daisuke Sato, Yuka Murakami.

Toyama University Hospital

Hiroshi Ueno, Nobuyuki Fukuda, Shuhei Tanaka, Hiroshi Onoda, Toshio Doi, Mitsuo Sobajima, Yohei Ueno, Teruhiko Imamura, Ryuichi Ushijima, Yuki Hida, Kazuya Nakashima.

Tokai University

Yohei Ohno, Junichi Miyamoto, Tsutomu Murakami, Sho Torii, Makoto Natsumeda, Shigemitsu Tanaka, Takeshi Ijichi, Hitomi Horinouchi, Kazuki Aihara, Katsuaki Sakai, Shingo Matsumoto, Norihiko Kamioka, Norihito Nakamura, Satoshi Noda, Yu Sato.

St. Marianna University School of Medicine

Masaki Izumo, Daisuke Miyahara, Shingo Kuwata, Masashi Koga, Toshiki Kaihara, Takumi Higuma, Kei Honda, Yoshikuni Kobayashi, Risako Murata, Kenichi Sasaki, Daisuke Togashi, Takahiko Kai, Taishi Okuno, Toshiya Yoshida, Keita Sone, Karin Terauchi.

Sapporo Higashi Tokushukai Hospital

Hiroki Bota, Kazumasa Yamasaki, Tomoyuki Tani, Mamoru Miyazaki, Yuki Katagiri.

Saiseikai Kumamoto Hospital

Kazuhisa Kodama, Hiroto Suzuyama, Hideharu Okamatsu, Tomohiro Sakamoto, Yoko Horibata, Yutaka Konami, Masahiro Yamada, Eiji Horio, Mika Maeda, Satoko Hasegawa, Megumi Koga, Mikiko Ohara, Shota Fukushige.

Tokyo Women's Medical University

Junichi Yamaguchi, Hisao Otsuki, Yusuke Inagaki, Chihiro Koyanagi, Tomohito Kogure, Takanori Kawamoto, Masafumi Yoshikawa, Eiji Shibahashi, Kazuki Tanaka.

Kurashiki Central Hospital

Shunsuke Kubo, Mikitaka Fujita, Sachiyo Ono, Kohei Osakada, Kazunori Mushiake, Takeshi Maruo, Naoki Nishiura.

National Cerebral and Cardiovascular Center

Makoto Amaki, Kensuke Takagi, Takeshi Kitai, Atsushi Okada, Tasuku Hada, Shoko Nakagawa, Marina Arai, Yuki Irie, Takahiro Sakamoto, Shinichi Kurashima.

Mitsui Memorial Hospital

Masahiko Asami, Jun Tanaka, Yu Horiuchi, Naoki Hosoda, Hideaki Nonaka, Yuki Gonda, Yohei Nishimura.

Sakakibara Heart Institute

Mike Saji, Yuki Izumi, Ryosuke Higuchi, Ryo Abe, Takashi Hiruma, Satonori Maekawara, Koya Okabe, Yoshiko Nemoto.

Kindai University

Gaku Nakazawa, Masakazu Yasuda, Kosuke Fujita, Tatsuya Miyoshi, Naoko Soejima, Masahiro Maruyama, Motohide Tanaka, Takayuki Kawamura, Honoka Kanno, Ayano Yoshida, Kyohei Onishi, Nobuhiro Yamada.

Juntendo University Hospital

Shinya Okazaki, Minoru Tabata, Shinichiro Doi, Ryota Nishio, Nobuyuki Kagiyama, Tomohiro Kaneko, Sakiko Miyazaki, Norihito Takahashi.

Sapporo Heart Center Sapporo Cardiovascular Clinic

Daisuke Hachinohe, Ken Kobayashi, Kazuki Mizutani, Hidemasa Shitan, Ryo Horita, Ryo Otake.

Toho University Omori Medical Center

Mike Saji, Hiroshi Ohara, Hideki Koike.

Sources of Funding

The OCEAN‐Mitral registry, which is a part of the OCEAN‐SHD (Optimized Catheter Valvular Intervention–Structural Heart Disease) registry, was supported by Edwards Lifesciences, Medtronic Japan, Boston Scientific, Abbott Medical Japan, and the Daiichi‐Sankyo Company. The sponsors were not involved in the study, data collection, statistical analyses, or manuscript writing.

Disclosures

Clinical proctor of M‐TEER for Abbott Medical: Drs Yamamoto, Asami, Izumi, Saji, Enta, Shirai, Izumo, Kuwata, Mizuno, Watanabe, Amaki, Bota, Ohno, Ueno, Kubo, and Hayashida. Lecture fees from Abbott Medical: Drs Otsuki, Yamamoto, Asami, Tanabe, Enta, Saji, Shirai, Izumo, Kuwata, Mizuno, Watanabe, Amaki, Kodama, Bota, Ohno, and Kubo. Consultant fees from Abbott Medical: Drs Yamamoto, Saji, Enta, Izumo, Kuwata, Shirai, Mizuno, Watanabe, Amaki, Bota, Ohno, and Kubo. Scholarship donation from Abbott Medical: Dr Otsuki. Advisor of Abbott Medical: Dr Ohno. The remaining authors have no disclosures to report.

Supporting information

Tables S1–S18

Figures S1–S8

JAH3-15-e048383-s002.pdf^{(1.2MB, pdf)}

STROBE Checklist

JAH3-15-e048383-s001.pdf^{(234.6KB, pdf)}

Acknowledgments

The authors thank all OCEAN‐Mitral investigators who participated in this registry. No artificial intelligence programs have contributed to the compilation of the submitted manuscript.

This manuscript was sent to Michel Pompeu Sá, MD, MSc, MHBA, PhD, FACC, FAHA, Associate Editor, for review by expert referees, editorial decision, and final disposition.

Supplemental Material is available at https://www.ahajournals.org/doi/suppl/10.1161/JAHA.125.048383

For Sources of Funding and Disclosures, see page 18.

Contributor Information

Masahiko Asami, Email: asami560725@yahoo.co.jp.

OCEAN‐Mitral Investigators:

Kentaro Hayashida, Makoto Tanaka, Hikaru Tsuruta, Tetsuya Saito, Yusuke Kobari, Toshinobu Ryuzaki, Tatsuo Takahashi, Shinichi Goto, Shohei Imaeda, Shingo Sakata, Yoshinori Katsumata, Kohsuke Shirakawa, Juri Iwata, Keitaro Shinada, Akiyoshi Kajino, Jungo Kato, Ryo Arita, Tomonari Moriizumi, Masanori Yamamoto, Mitsuru Sago, Tatsuya Tsunaki, Ryo Yamaguchi, Junji Yanagisawa, Yuki Okubo, Ai Kagase, Takahiro Tokuda, Yuki Nakashima, Toshihiro Kobayashi, Kenichi Shibata, Ryoutaku Kawahata, Hiroto Nishio, Yuki Kondo, Tomofumi Nakamura, Hiroshi Tsunamoto, Tetsuro Shimura, Yutaka Koyama, Munenori Okubo, Shunsuke Imai, Tokuya Sakakura, Yoshihiro Harano, Masahiro Inagaki, Hirooki Higami, Yoji Kuze, Takashi Nagai, Azusa Kurita, Shinichi Shirai, Akihiro Isotani, Kenichi Ishizu, Masato Fukunaga, Hiroyuki Tabata, Toru Morofuji, Hiroyuki Kono, Maiko Kuroda, Euihong Ko, Norihisa Miyawaki, Kenji Nakano, Katsunori Miyahara, Ko Yamamoto, Koumei Onuki, Yasuo Tsuru, Tomohiro Suenaga, Akira Otani, Yusuke Watanabe, Taiga Katayama, Hirofumi Hioki, Junichi Nishikawa, Yosei Iseki, Yasuyuki Tsuchida, Kento Kito, Joe Omiya, Masataka Arakawa, Maki Okamoto, Masafumi Saito, Mizuki Miura, Toru Naganuma, Toru Ouchi, Haruhito Yuki, Masahiro Yamawaki, Yusuke Enta, Norio Tada, Masaki Nakashima, Makoto Saigan, Yoshiko Munehisa, Masaki Miyasaka, Natsuko Satomi, Yuta Kobayashi, Daishi Tazawa, Yun Teng, Shingo Mizuno, Futoshi Yamanaka, Koki Shishido, Tomoki Ochiai, Tsuyoshi Yamabe, Noriaki Moriyama, Takashi Matsumoto, Hirokazu Miyashita, Yoichi Sugiyama, Kunihiko Shimizu, Daisuke Sato, Yuka Murakami, Hiroshi Ueno, Nobuyuki Fukuda, Shuhei Tanaka, Hiroshi Onoda, Toshio Doi, Mitsuo Sobajima, Yohei Ueno, Teruhiko Imamura, Ryuichi Ushijima, Yuki Hida, Kazuya Nakashima, Yohei Ohno, Junichi Miyamoto, Tsutomu Murakami, Sho Torii, Makoto Natsumeda, Shigemitsu Tanaka, Takeshi Ijichi, Hitomi Horinouchi, Kazuki Aihara, Katsuaki Sakai, Shingo Matsumoto, Norihiko Kamioka, Norihito Nakamura, Satoshi Noda, Yu Sato, Masaki Izumo, Daisuke Miyahara, Shingo Kuwata, Masashi Koga, Toshiki Kaihara, Takumi Higuma, Kei Honda, Yoshikuni Kobayashi, Risako Murata, Kenichi Sasaki, Daisuke Togashi, Takahiko Kai, Taishi Okuno, Toshiya Yoshida, Keita Sone, Karin Terauchi, Hiroki Bota, Kazumasa Yamasaki, Tomoyuki Tani, Mamoru Miyazaki, Yuki Katagiri, Kazuhisa Kodama, Hiroto Suzuyama, Hideharu Okamatsu, Tomohiro Sakamoto, Yoko Horibata, Yutaka Konami, Masahiro Yamada, Eiji Horio, Mika Maeda, Satoko Hasegawa, Megumi Koga, Mikiko Ohara, Shota Fukushige, Junichi Yamaguchi, Hisao Otsuki, Yusuke Inagaki, Chihiro Koyanagi, Tomohito Kogure, Takanori Kawamoto, Masafumi Yoshikawa, Eiji Shibahashi, Kazuki Tanaka, Shunsuke Kubo, Mikitaka Fujita, Sachiyo Ono, Kohei Osakada, Kazunori Mushiake, Takeshi Maruo, Naoki Nishiura, Makoto Amaki, Kensuke Takagi, Takeshi Kitai, Atsushi Okada, Tasuku Hada, Shoko Nakagawa, Marina Arai, Yuki Irie, Takahiro Sakamoto, Shinichi Kurashima, Masahiko Asami, Jun Tanaka, Yu Horiuchi, Naoki Hosoda, Hideaki Nonaka, Yuki Gonda, Yohei Nishimura, Mike Saji, Yuki Izumi, Ryosuke Higuchi, Ryo Abe, Takashi Hiruma, Satonori Maekawara, Koya Okabe, Yoshiko Nemoto, Gaku Nakazawa, Masakazu Yasuda, Kosuke Fujita, Tatsuya Miyoshi, Naoko Soejima, Masahiro Maruyama, Motohide Tanaka, Takayuki Kawamura, Honoka Kanno, Ayano Yoshida, Kyohei Onishi, Nobuhiro Yamada, Shinya Okazaki, Minoru Tabata, Shinichiro Doi, Ryota Nishio, Nobuyuki Kagiyama, Tomohiro Kaneko, Sakiko Miyazaki, Norihito Takahashi, Daisuke Hachinohe, Ken Kobayashi, Kazuki Mizutani, Hidemasa Shitan, Ryo Horita, Ryo Otake, Mike Saji, Hiroshi Ohara, and Hideki Koike

References

1. Anker SD, Friede T, von Bardeleben R‐S, Butler J, Khan M‐S, Diek M, Heinrich J, Geyer M, Placzek M, Ferrari R, et al. Transcatheter valve repair in heart failure with moderate to severe mitral regurgitation. N Engl J Med. 2024;391:1799–1809. doi: 10.1056/NEJMoa2314328 [DOI] [PubMed] [Google Scholar]
2. Baldus S, Doenst T, Pfister R, Gummert J, Kessler M, Boekstegers P, Lubos E, Schröder J, Thiele H, Walther T, et al. Transcatheter repair versus mitral‐valve surgery for secondary mitral regurgitation. N Engl J Med. 2024;391:1787–1798. doi: 10.1056/NEJMoa2408739 [DOI] [PubMed] [Google Scholar]
3. Makkar RR, Chikwe J, Chakravarty T, Chen Q, O'Gara PT, Gillinov M, Mack MJ, Vekstein A, Patel D, Stebbins AL, et al. Transcatheter mitral valve repair for degenerative mitral regurgitation. JAMA. 2023;329:1778–1788. doi: 10.1001/jama.2023.7089 [DOI] [PMC free article] [PubMed] [Google Scholar]
4. Pocock SJ, Ariti CA, McMurray JJV, Maggioni A, Køber L, Squire IB, Swedberg K, Dobson J, Poppe KK, Whalley GA, et al. Predicting survival in heart failure: a risk score based on 39 372 patients from 30 studies. Eur Heart J. 2013;34:1404–1413. doi: 10.1093/eurheartj/ehs337 [DOI] [PubMed] [Google Scholar]
5. Sartipy U, Dahlström U, Edner M, Lund LH. Predicting survival in heart failure: validation of the MAGGIC heart failure risk score in 51,043 patients from the Swedish Heart Failure Registry. Eur J Heart Fail. 2014;16:173–179. doi: 10.1111/ejhf.32 [DOI] [PubMed] [Google Scholar]
6. Rich JD, Burns J, Freed BH, Maurer MS, Burkhoff D, Shah SJ. Meta‐analysis global group in chronic (MAGGIC) heart failure risk score: validation of a simple tool for the prediction of morbidity and mortality in heart failure with preserved ejection fraction. J Am Heart Assoc. 2018;7:e009594. doi: 10.1161/JAHA.118.009594 [DOI] [PMC free article] [PubMed] [Google Scholar]
7. Lv J, Xu H, Ye Y, Li Z, Wang W, Zhang B, Zhao Q, Zhang H, Zhao Z, Liu Q, et al. Meta‐analysis global group in chronic heart failure score for the prediction of mortality in valvular heart disease. ESC Heart Fail. 2024;11:349–365. doi: 10.1002/ehf2.14586 [DOI] [PMC free article] [PubMed] [Google Scholar]
8. Zhuo DX, Bilchick KC, Shah KP, Mehta NK, Mwansa H, Nkanza‐Kabaso K, Kwon Y, Breathett KK, Hilton‐Buchholz EJ, Mazimba S. MAGGIC, STS, and EuroSCORE II risk score comparison after aortic and mitral valve surgery. J Cardiothorac Vasc Anesth. 2021;35:1806–1812. doi: 10.1053/j.jvca.2020.11.053 [DOI] [PMC free article] [PubMed] [Google Scholar]
9. Hioki H, Watanabe Y, Kozuma K, Kawashima H, Nagura F, Nakashima M, Kataoka A, Yamamoto M, Naganuma T, Araki M, et al. The MAGGIC risk score predicts mortality in patients undergoing transcatheter aortic valve replacement: sub‐analysis of the OCEAN‐TAVI registry. Heart Vessels. 2019;34:1976–1983. doi: 10.1007/s00380-019-01443-9 [DOI] [PubMed] [Google Scholar]
10. Schau T, Isotani A, Neuss M, Schöpp M, Seifert M, Höpfner C, Burkhoff D, Butter C. Long‐term survival after MitraClip(®) therapy in patients with severe mitral regurgitation and severe congestive heart failure: a comparison among survivals predicted by heart failure models. J Cardiol. 2016;67:287–294. doi: 10.1016/j.jjcc.2015.05.015 [DOI] [PubMed] [Google Scholar]
11. Heyl S, Luu B, Wieszner M, Nikkhoo A, Seeger F, Hemmann K, Assmus B, Kaess B, Zeiher AM, Walther C, et al. Differential prognostic accuracy of right ventricular dysfunction, the Seattle heart failure model and the MAGGIC score in patients with severe mitral regurgitation undergoing the MitraClip® procedure. Int J Cardiol Heart Vasc. 2020;31:100641. doi: 10.1016/j.ijcha.2020.100641 [DOI] [PMC free article] [PubMed] [Google Scholar]
12. Kortlandt FA, van't Klooster CC, Bakker ALM, Swaans MJ, Kelder JC, de Kroon TL, Rensing BJWM, Eefting FD, van der Heyden JAS, Post MC. The predictive value of conventional surgical risk scores for periprocedural mortality in percutaneous mitral valve repair. Neth Heart J. 2016;24:475–480. doi: 10.1007/s12471-016-0841-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
13. Raposeiras‐Roubin S, Adamo M, Freixa X, Arzamendi D, Benito‐González T, Montefusco A, Pascual I, Nombela‐Franco L, Rodes‐Cabau J, Shuvy M, et al. A score to assess mortality after percutaneous mitral valve repair. J Am Coll Cardiol. 2022;79:562–573. doi: 10.1016/j.jacc.2021.11.041 [DOI] [PubMed] [Google Scholar]
14. Shah N, Madhavan MV, Gray WA, Brener SJ, Ahmad Y, Lindenfeld J, Abraham WT, Grayburn PA, Kar S, Lim DS, et al. Prediction of death or HF hospitalization in patients with severe FMR: the COAPT risk score. JACC Cardiovasc Interv. 2022;15:1893–1905. doi: 10.1016/j.jcin.2022.08.005 [DOI] [PubMed] [Google Scholar]
15. Hausleiter J, Lachmann M, Stolz L, Bedogni F, Rubbio AP, Estévez‐Loureiro R, Raposeiras‐Roubin S, Boekstegers P, Karam N, Rudolph V, et al. Artificial intelligence‐derived risk score for mortality in secondary mitral regurgitation treated by transcatheter edge‐to‐edge repair: the EuroSMR risk score. Eur Heart J. 2024;45:922–936. doi: 10.1093/eurheartj/ehad871 [DOI] [PubMed] [Google Scholar]
16. Mitchell C, Rahko PS, Blauwet LA, Canaday B, Finstuen JA, Foster MC, Horton K, Ogunyankin KO, Palma RA, Velazquez EJ. Guidelines for performing a comprehensive transthoracic echocardiographic examination in adults: recommendations from the American Society of Echocardiography. J Am Soc Echocardiogr. 2019;32:1–64. doi: 10.1016/j.echo.2018.06.004 [DOI] [PubMed] [Google Scholar]
17. Stone GW, Vahanian AS, Adams DH, Abraham WT, Borer JS, Bax JJ, Schofer J, Cutlip DE, Krucoff MW, Blackstone EH, et al. Clinical trial design principles and endpoint definitions for transcatheter mitral valve repair and replacement: part 1: clinical trial design principles: a consensus document from the mitral valve academic research consortium. J Am Coll Cardiol. 2015;66:278–307. doi: 10.1016/j.jacc.2015.05.046 [DOI] [PubMed] [Google Scholar]
18. Sorajja P, Vemulapalli S, Feldman T, Mack M, Holmes DR Jr, Stebbins A, Kar S, Thourani V, Ailawadi G. Outcomes with transcatheter mitral valve repair in the United States: an STS/ACC TVT registry report. J Am Coll Cardiol. 2017;70:2315–2327. doi: 10.1016/j.jacc.2017.09.015 [DOI] [PubMed] [Google Scholar]
19. Goel K, Lindenfeld J, Makkar R, Naik H, Atmakuri S, Mahoney P, Morse MA, Thourani VH, Yadav P, Batchelor W, et al. Transcatheter edge‐to‐edge repair in 5,000 patients with secondary mitral regurgitation: COAPT post‐approval study. J Am Coll Cardiol. 2023;82:1281–1297. doi: 10.1016/j.jacc.2023.07.015 [DOI] [PubMed] [Google Scholar]
20. Spieker M, Zweck E, Pfister R, Ulrich Becher M, Westenfeld R. Risk scores for mortality prediction after transcatheter mitral valve repair. J Am Coll Cardiol. 2022;79:e477–e478. doi: 10.1016/j.jacc.2022.03.382 [DOI] [PubMed] [Google Scholar]
21. Nonaka H, Asami M, Horiuchi Y, Tanaka J, Gonda Y, Taniwaki M, Komiyama K, Tanabe K, Yamamoto M, Kubo S, et al. Validation of MitraScore in diverse mitral regurgitation subtypes: insights from the OCEAN‐mitral registry. Int J Cardiol. 2025;436:133427. doi: 10.1016/j.ijcard.2025.133427 [DOI] [PubMed] [Google Scholar]
22. Adamo M, Rubbio AP, Zaccone G, Pighi M, Massussi M, Tomasoni D, Pancaldi E, Testa L, Tusa MB, De Marco F, et al. Prediction of mortality and heart failure hospitalisations in patients undergoing M‐TEER: external validation of the COAPT risk score. EuroIntervention. 2023;18:1408–1417. doi: 10.4244/EIJ-D-22-00992 [DOI] [PMC free article] [PubMed] [Google Scholar]
23. Stone GW, Abraham WT, Lindenfeld J, Kar S, Grayburn PA, Lim DS, Mishell JM, Whisenant B, Rinaldi M, Kapadia SR, et al. Five‐year follow‐up after transcatheter repair of secondary mitral regurgitation. N Engl J Med. 2023;388:2037–2048. doi: 10.1056/NEJMoa2300213 [DOI] [PubMed] [Google Scholar]
24. Esposito L, Di Maio M, Baldi C, Di Lorenzo E, Bellino M, Silverio A, Adamo M, Giordano A, De Felice F, Grasso C, et al. Competing risks of cardiac and noncardiac mortality in patients with secondary mitral regurgitation undergoing transcatheter edge‐to‐edge repair. J Am Heart Assoc. 2025;14:e040496. doi: 10.1161/JAHA.124.040496 [DOI] [PMC free article] [PubMed] [Google Scholar]
25. Song C, Madhavan MV, Lindenfeld J, Abraham WT, Kar S, Lim DS, Grayburn PA, Kapadia SR, Kotinkaduwa LN, Mack MJ, et al. Age‐related outcomes after transcatheter mitral valve repair in patients with heart failure: analysis from COAPT. JACC Cardiovasc Interv. 2022;15:397–407. doi: 10.1016/j.jcin.2021.11.037 [DOI] [PubMed] [Google Scholar]
26. Kalbacher D, Tigges E, Boekstegers P, Puls M, Plicht B, Eggebrecht H, Nickenig G, von Bardeleben RS, Zuern CS, Franke J, et al. Underweight is associated with inferior short and long‐term outcomes after MitraClip implantation: results from the German TRAnscatheter mitral valve interventions (TRAMI) registry. Am Heart J. 2020;222:73–82. doi: 10.1016/j.ahj.2019.12.022 [DOI] [PubMed] [Google Scholar]
27. Beohar N, Ailawadi G, Kotinkaduwa LN, Redfors B, Simonato M, Zhang Z, Morgan LG, Escolar E, Kar S, Lim DS, et al. Impact of baseline renal dysfunction on cardiac outcomes and end‐stage renal disease in heart failure patients with mitral regurgitation: the COAPT trial. Eur Heart J. 2022;43:1639–1648. doi: 10.1093/eurheartj/ehac026 [DOI] [PubMed] [Google Scholar]
28. Shahim B, Ben‐Yehuda O, Chen S, Redfors B, Madhavan MV, Kar S, Lim DS, Asch FM, Weissman NJ, Cohen DJ, et al. Impact of diabetes on outcomes after transcatheter mitral valve repair in heart failure: COAPT trial. JACC Heart Fail. 2021;9:559–567. doi: 10.1016/j.jchf.2021.03.011 [DOI] [PubMed] [Google Scholar]
29. Higuchi S, Orban M, Adamo M, Giannini C, Melica B, Karam N, Praz F, Kalbacher D, Koell B, Stolz L, et al. Guideline‐directed medical therapy in patients undergoing transcatheter edge‐to‐edge repair for secondary mitral regurgitation. Eur J Heart Fail. 2022;24:2152–2161. doi: 10.1002/ejhf.2613 [DOI] [PubMed] [Google Scholar]
30. Böhm M, Komajda M, Borer JS, Ford I, Maack C, Tavazzi L, Moyne A, Swedberg K; SHIFT Investigators . Duration of chronic heart failure affects outcomes with preserved effects of heart rate reduction with ivabradine: findings from SHIFT. Eur J Heart Fail. 2018;20:373–381. doi: 10.1002/ejhf.1021 [DOI] [PubMed] [Google Scholar]

Associated Data

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

Supplementary Materials

Tables S1–S18

Figures S1–S8

JAH3-15-e048383-s002.pdf^{(1.2MB, pdf)}

STROBE Checklist

JAH3-15-e048383-s001.pdf^{(234.6KB, pdf)}

[jah370704-bib-0001] 1. Anker SD, Friede T, von Bardeleben R‐S, Butler J, Khan M‐S, Diek M, Heinrich J, Geyer M, Placzek M, Ferrari R, et al. Transcatheter valve repair in heart failure with moderate to severe mitral regurgitation. N Engl J Med. 2024;391:1799–1809. doi: 10.1056/NEJMoa2314328 [DOI] [PubMed] [Google Scholar]

[jah370704-bib-0002] 2. Baldus S, Doenst T, Pfister R, Gummert J, Kessler M, Boekstegers P, Lubos E, Schröder J, Thiele H, Walther T, et al. Transcatheter repair versus mitral‐valve surgery for secondary mitral regurgitation. N Engl J Med. 2024;391:1787–1798. doi: 10.1056/NEJMoa2408739 [DOI] [PubMed] [Google Scholar]

[jah370704-bib-0003] 3. Makkar RR, Chikwe J, Chakravarty T, Chen Q, O'Gara PT, Gillinov M, Mack MJ, Vekstein A, Patel D, Stebbins AL, et al. Transcatheter mitral valve repair for degenerative mitral regurgitation. JAMA. 2023;329:1778–1788. doi: 10.1001/jama.2023.7089 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah370704-bib-0004] 4. Pocock SJ, Ariti CA, McMurray JJV, Maggioni A, Køber L, Squire IB, Swedberg K, Dobson J, Poppe KK, Whalley GA, et al. Predicting survival in heart failure: a risk score based on 39 372 patients from 30 studies. Eur Heart J. 2013;34:1404–1413. doi: 10.1093/eurheartj/ehs337 [DOI] [PubMed] [Google Scholar]

[jah370704-bib-0005] 5. Sartipy U, Dahlström U, Edner M, Lund LH. Predicting survival in heart failure: validation of the MAGGIC heart failure risk score in 51,043 patients from the Swedish Heart Failure Registry. Eur J Heart Fail. 2014;16:173–179. doi: 10.1111/ejhf.32 [DOI] [PubMed] [Google Scholar]

[jah370704-bib-0006] 6. Rich JD, Burns J, Freed BH, Maurer MS, Burkhoff D, Shah SJ. Meta‐analysis global group in chronic (MAGGIC) heart failure risk score: validation of a simple tool for the prediction of morbidity and mortality in heart failure with preserved ejection fraction. J Am Heart Assoc. 2018;7:e009594. doi: 10.1161/JAHA.118.009594 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah370704-bib-0007] 7. Lv J, Xu H, Ye Y, Li Z, Wang W, Zhang B, Zhao Q, Zhang H, Zhao Z, Liu Q, et al. Meta‐analysis global group in chronic heart failure score for the prediction of mortality in valvular heart disease. ESC Heart Fail. 2024;11:349–365. doi: 10.1002/ehf2.14586 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah370704-bib-0008] 8. Zhuo DX, Bilchick KC, Shah KP, Mehta NK, Mwansa H, Nkanza‐Kabaso K, Kwon Y, Breathett KK, Hilton‐Buchholz EJ, Mazimba S. MAGGIC, STS, and EuroSCORE II risk score comparison after aortic and mitral valve surgery. J Cardiothorac Vasc Anesth. 2021;35:1806–1812. doi: 10.1053/j.jvca.2020.11.053 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah370704-bib-0009] 9. Hioki H, Watanabe Y, Kozuma K, Kawashima H, Nagura F, Nakashima M, Kataoka A, Yamamoto M, Naganuma T, Araki M, et al. The MAGGIC risk score predicts mortality in patients undergoing transcatheter aortic valve replacement: sub‐analysis of the OCEAN‐TAVI registry. Heart Vessels. 2019;34:1976–1983. doi: 10.1007/s00380-019-01443-9 [DOI] [PubMed] [Google Scholar]

[jah370704-bib-0010] 10. Schau T, Isotani A, Neuss M, Schöpp M, Seifert M, Höpfner C, Burkhoff D, Butter C. Long‐term survival after MitraClip(®) therapy in patients with severe mitral regurgitation and severe congestive heart failure: a comparison among survivals predicted by heart failure models. J Cardiol. 2016;67:287–294. doi: 10.1016/j.jjcc.2015.05.015 [DOI] [PubMed] [Google Scholar]

[jah370704-bib-0011] 11. Heyl S, Luu B, Wieszner M, Nikkhoo A, Seeger F, Hemmann K, Assmus B, Kaess B, Zeiher AM, Walther C, et al. Differential prognostic accuracy of right ventricular dysfunction, the Seattle heart failure model and the MAGGIC score in patients with severe mitral regurgitation undergoing the MitraClip® procedure. Int J Cardiol Heart Vasc. 2020;31:100641. doi: 10.1016/j.ijcha.2020.100641 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah370704-bib-0012] 12. Kortlandt FA, van't Klooster CC, Bakker ALM, Swaans MJ, Kelder JC, de Kroon TL, Rensing BJWM, Eefting FD, van der Heyden JAS, Post MC. The predictive value of conventional surgical risk scores for periprocedural mortality in percutaneous mitral valve repair. Neth Heart J. 2016;24:475–480. doi: 10.1007/s12471-016-0841-7 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah370704-bib-0013] 13. Raposeiras‐Roubin S, Adamo M, Freixa X, Arzamendi D, Benito‐González T, Montefusco A, Pascual I, Nombela‐Franco L, Rodes‐Cabau J, Shuvy M, et al. A score to assess mortality after percutaneous mitral valve repair. J Am Coll Cardiol. 2022;79:562–573. doi: 10.1016/j.jacc.2021.11.041 [DOI] [PubMed] [Google Scholar]

[jah370704-bib-0014] 14. Shah N, Madhavan MV, Gray WA, Brener SJ, Ahmad Y, Lindenfeld J, Abraham WT, Grayburn PA, Kar S, Lim DS, et al. Prediction of death or HF hospitalization in patients with severe FMR: the COAPT risk score. JACC Cardiovasc Interv. 2022;15:1893–1905. doi: 10.1016/j.jcin.2022.08.005 [DOI] [PubMed] [Google Scholar]

[jah370704-bib-0015] 15. Hausleiter J, Lachmann M, Stolz L, Bedogni F, Rubbio AP, Estévez‐Loureiro R, Raposeiras‐Roubin S, Boekstegers P, Karam N, Rudolph V, et al. Artificial intelligence‐derived risk score for mortality in secondary mitral regurgitation treated by transcatheter edge‐to‐edge repair: the EuroSMR risk score. Eur Heart J. 2024;45:922–936. doi: 10.1093/eurheartj/ehad871 [DOI] [PubMed] [Google Scholar]

[jah370704-bib-0016] 16. Mitchell C, Rahko PS, Blauwet LA, Canaday B, Finstuen JA, Foster MC, Horton K, Ogunyankin KO, Palma RA, Velazquez EJ. Guidelines for performing a comprehensive transthoracic echocardiographic examination in adults: recommendations from the American Society of Echocardiography. J Am Soc Echocardiogr. 2019;32:1–64. doi: 10.1016/j.echo.2018.06.004 [DOI] [PubMed] [Google Scholar]

[jah370704-bib-0017] 17. Stone GW, Vahanian AS, Adams DH, Abraham WT, Borer JS, Bax JJ, Schofer J, Cutlip DE, Krucoff MW, Blackstone EH, et al. Clinical trial design principles and endpoint definitions for transcatheter mitral valve repair and replacement: part 1: clinical trial design principles: a consensus document from the mitral valve academic research consortium. J Am Coll Cardiol. 2015;66:278–307. doi: 10.1016/j.jacc.2015.05.046 [DOI] [PubMed] [Google Scholar]

[jah370704-bib-0018] 18. Sorajja P, Vemulapalli S, Feldman T, Mack M, Holmes DR Jr, Stebbins A, Kar S, Thourani V, Ailawadi G. Outcomes with transcatheter mitral valve repair in the United States: an STS/ACC TVT registry report. J Am Coll Cardiol. 2017;70:2315–2327. doi: 10.1016/j.jacc.2017.09.015 [DOI] [PubMed] [Google Scholar]

[jah370704-bib-0019] 19. Goel K, Lindenfeld J, Makkar R, Naik H, Atmakuri S, Mahoney P, Morse MA, Thourani VH, Yadav P, Batchelor W, et al. Transcatheter edge‐to‐edge repair in 5,000 patients with secondary mitral regurgitation: COAPT post‐approval study. J Am Coll Cardiol. 2023;82:1281–1297. doi: 10.1016/j.jacc.2023.07.015 [DOI] [PubMed] [Google Scholar]

[jah370704-bib-0020] 20. Spieker M, Zweck E, Pfister R, Ulrich Becher M, Westenfeld R. Risk scores for mortality prediction after transcatheter mitral valve repair. J Am Coll Cardiol. 2022;79:e477–e478. doi: 10.1016/j.jacc.2022.03.382 [DOI] [PubMed] [Google Scholar]

[jah370704-bib-0021] 21. Nonaka H, Asami M, Horiuchi Y, Tanaka J, Gonda Y, Taniwaki M, Komiyama K, Tanabe K, Yamamoto M, Kubo S, et al. Validation of MitraScore in diverse mitral regurgitation subtypes: insights from the OCEAN‐mitral registry. Int J Cardiol. 2025;436:133427. doi: 10.1016/j.ijcard.2025.133427 [DOI] [PubMed] [Google Scholar]

[jah370704-bib-0022] 22. Adamo M, Rubbio AP, Zaccone G, Pighi M, Massussi M, Tomasoni D, Pancaldi E, Testa L, Tusa MB, De Marco F, et al. Prediction of mortality and heart failure hospitalisations in patients undergoing M‐TEER: external validation of the COAPT risk score. EuroIntervention. 2023;18:1408–1417. doi: 10.4244/EIJ-D-22-00992 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah370704-bib-0023] 23. Stone GW, Abraham WT, Lindenfeld J, Kar S, Grayburn PA, Lim DS, Mishell JM, Whisenant B, Rinaldi M, Kapadia SR, et al. Five‐year follow‐up after transcatheter repair of secondary mitral regurgitation. N Engl J Med. 2023;388:2037–2048. doi: 10.1056/NEJMoa2300213 [DOI] [PubMed] [Google Scholar]

[jah370704-bib-0024] 24. Esposito L, Di Maio M, Baldi C, Di Lorenzo E, Bellino M, Silverio A, Adamo M, Giordano A, De Felice F, Grasso C, et al. Competing risks of cardiac and noncardiac mortality in patients with secondary mitral regurgitation undergoing transcatheter edge‐to‐edge repair. J Am Heart Assoc. 2025;14:e040496. doi: 10.1161/JAHA.124.040496 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah370704-bib-0025] 25. Song C, Madhavan MV, Lindenfeld J, Abraham WT, Kar S, Lim DS, Grayburn PA, Kapadia SR, Kotinkaduwa LN, Mack MJ, et al. Age‐related outcomes after transcatheter mitral valve repair in patients with heart failure: analysis from COAPT. JACC Cardiovasc Interv. 2022;15:397–407. doi: 10.1016/j.jcin.2021.11.037 [DOI] [PubMed] [Google Scholar]

[jah370704-bib-0026] 26. Kalbacher D, Tigges E, Boekstegers P, Puls M, Plicht B, Eggebrecht H, Nickenig G, von Bardeleben RS, Zuern CS, Franke J, et al. Underweight is associated with inferior short and long‐term outcomes after MitraClip implantation: results from the German TRAnscatheter mitral valve interventions (TRAMI) registry. Am Heart J. 2020;222:73–82. doi: 10.1016/j.ahj.2019.12.022 [DOI] [PubMed] [Google Scholar]

[jah370704-bib-0027] 27. Beohar N, Ailawadi G, Kotinkaduwa LN, Redfors B, Simonato M, Zhang Z, Morgan LG, Escolar E, Kar S, Lim DS, et al. Impact of baseline renal dysfunction on cardiac outcomes and end‐stage renal disease in heart failure patients with mitral regurgitation: the COAPT trial. Eur Heart J. 2022;43:1639–1648. doi: 10.1093/eurheartj/ehac026 [DOI] [PubMed] [Google Scholar]

[jah370704-bib-0028] 28. Shahim B, Ben‐Yehuda O, Chen S, Redfors B, Madhavan MV, Kar S, Lim DS, Asch FM, Weissman NJ, Cohen DJ, et al. Impact of diabetes on outcomes after transcatheter mitral valve repair in heart failure: COAPT trial. JACC Heart Fail. 2021;9:559–567. doi: 10.1016/j.jchf.2021.03.011 [DOI] [PubMed] [Google Scholar]

[jah370704-bib-0029] 29. Higuchi S, Orban M, Adamo M, Giannini C, Melica B, Karam N, Praz F, Kalbacher D, Koell B, Stolz L, et al. Guideline‐directed medical therapy in patients undergoing transcatheter edge‐to‐edge repair for secondary mitral regurgitation. Eur J Heart Fail. 2022;24:2152–2161. doi: 10.1002/ejhf.2613 [DOI] [PubMed] [Google Scholar]

[jah370704-bib-0030] 30. Böhm M, Komajda M, Borer JS, Ford I, Maack C, Tavazzi L, Moyne A, Swedberg K; SHIFT Investigators . Duration of chronic heart failure affects outcomes with preserved effects of heart rate reduction with ivabradine: findings from SHIFT. Eur J Heart Fail. 2018;20:373–381. doi: 10.1002/ejhf.1021 [DOI] [PubMed] [Google Scholar]

PERMALINK

Prognostic Value of the Meta‐Analysis Global Group in Chronic Heart Failure Risk Score in Patients Undergoing Mitral Valve Transcatheter Edge‐to‐Edge Repair

Yohei Nishimura, MD

Masahiko Asami, MD

Yu Horiuchi, MD

Jun Tanaka, MD

Masanori Taniwaki, MD

Kota Komiyama, MD

Hitomi Yuzawa, MD

Kengo Tanabe, MD

Masanori Yamamoto, MD

Shunsuke Kubo, MD

Mike Saji, MD

Yuki Izumi, MD

Atsushi Sugiura, MD

Tetsuro Shimura, MD

Yusuke Enta, MD

Shinichi Shirai, MD

Masaki Izumo, MD

Shingo Mizuno, MD

Yusuke Watanabe, MD

Makoto Amaki, MD

Kazuhisa Kodama, MD

Hisao Otsuki, MD

Toru Naganuma, MD

Hiroki Bota, MD

Yohei Ohno, MD

Masahiro Yamawaki, MD

Hiroshi Ueno, MD

Gaku Nakazawa, MD

Daisuke Hachinohe, MD

Toshiaki Otsuka, MD

Kentaro Hayashida, MD

Abstract

Background

Methods

Results

Conclusions

Nonstandard Abbreviations and Acronyms

Clinical Perspective.

What Is New?

What Are the Clinical Implications?

Methods

Study Design and Study Population

Calculation for Risk Scores

Clinical Outcomes

Data Collection

Statistical Analysis

Results

Study Population and Baseline Characteristics

Table 1.

Table 2.

Procedural Characteristics and Outcomes

Table 3.

Clinical Outcomes

Figure 1. Kaplan–Meier curves of event‐free survival stratified by tertiles of the MAGGIC score in the FMR cohort.

Figure 2. Kaplan–Meier curves of event‐free survival stratified by tertiles of the MAGGIC score in the DMR cohort.

Table 4.

Table 5.

Comparison of the MAGGIC Score, the STS Score for MV Replacement and Repair, EuroSCORE II, the MitraScore, and the COAPT Risk Score

Subgroup Analyses

Figure 3. Forest plots of hazard ratios for (A) 1‐ and (B) 3‐year all‐cause death according to subgroups in the FMR cohort.

Figure 4. Forest plots of hazard ratios for (A) 1‐ and (B) 3‐year all‐cause death according to subgroups in the DMR cohort.

Discussion

Limitations

Conclusions

Appendix

Sources of Funding

Disclosures

Supporting information

Acknowledgments

Contributor Information

References

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

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