Abstract
BACKGROUND:
High-volume centers report acceptable outcomes for robotic mitral repair, yet population level data are limited. We compared late mortality and re-intervention rates for robotic versus non-robotic mitral repair in the United States.
METHODS:
The Centers for Medicare and Medicaid database validated against clinical records was used to identify 26,524 isolated first-time non-emergent mitral ± tricuspid repairs or ablations without other concomitant procedures. Of these, 2,227 (8.3%) underwent robotic repair and 24,297 (91.7%) non-robotic repair. Propensity score matching was performed on 30 baseline characteristics. The primary endpoint was a composite of death or mitral reintervention, and the secondary endpoint was all-cause mortality. Both were compared in a Cox proportional hazards model. Falsification endpoint analysis assessed for potential unmeasured confounders with death as a competing risk.
RESULTS:
Matching yielded 2,226 patient pairs (mean age 72 years; 44% female; 9% with concomitant tricuspid repair; 7% ablation). Thirty-day mortality did not differ between groups (1.3% robotic vs. 1.3% non-robotic, p=0.90). Robotic repair was associated with lower postoperative atrial fibrillation (19.1% vs. 23.2%, p=0.001) and a shorter hospital stay (median 5 days [IQR 4–7] vs. 7 days [IQR 5–9], p<0.001). At 5 years, the composite of death or mitral reintervention (17.8% vs.18.6%, HR: 0.93, 95%CI: 0.79-1.09, p=0.37) and all-cause mortality (14.9% vs.15.6%, HR: 0.93, 95%CI: 0.77-1.11, p=0.40) were similar, with falsification testing confirming minimal confounding (p=0.20).
CONCLUSIONS:
Robotic mitral repair in the United States is safe, yielding outcomes comparable to those of non-robotic repair.
Keywords: robotic mitral repair, population-level analysis, mortality, reintervention
Graphical Abstract
Degenerative mitral regurgitation (DMR) is the most common valvular heart lesion in developed countries, affecting 1-2% of the population, half of whom require intervention within 5-years.1,2 The standard of care treatment remains open surgical correction via sternotomy or minimally invasive approaches, although uptake of transcatheter procedures is increasing.1 Mitral repair is performed safely in the United States with mortality under 1% in most patients. Sternotomy remains the preferred approach in the United States though a recent analysis of the Society of Thoracic Surgeons Adult Cardiac Dataset (STS/ACSD) showed a 40% increase in robotic mitral repair over the past decade.3 Uptake of robotic mitral surgery may be limited by concerns for a perceived learning curve, prolonged operative times, and uncertain long-term durability. Expert centers have shown that robotic mitral repair can be performed safely, and STS/ACSD analyses showed comparable early outcomes to conventional surgery.3–5 However, population-level data on late outcomes remain lacking. The recently published UK Mini Mitral trial from 10 tertiary care centers in the United Kingdom showed no difference in the primary outcome of physical function recovery at 12 weeks.6 Secondary outcomes of death, stroke or reintervention were similar at 1-year. However, there were some concerning signals from this trial; 5% of patients in the minithoracotomy group required conversion to open repair, and 5% of patients in both groups left the operating room with moderate or worse mitral regurgitation (MR). As such, given patient preferences for a less invasive but durable option in the era of transcatheter mitral repair, understanding late outcomes of minimally invasive robotic mitral repair compared to conventional surgical repair is crucial at the population level.
MATERIAL AND METHODS
Study overview
We conducted a retrospective cohort study comparing 5-year outcomes in patients undergoing first-time isolated mitral valve repair with and without robotic assistance using the Centers for Medicaid and Medicare Services administrative data between 2010-2019. International Classification of Diseases, 9th Revision (ICD-9) or International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10) diagnosis and procedure codes (Supplemental Table 1) were used to identify patients undergoing isolated mitral valve repair. The ICD-9 or ICD-10 diagnosis codes documented in the index hospitalization record and all inpatient admissions within the prior two years were used to identify baseline comorbidities (Supplemental Table 2). After further exclusion of patients with prior cardiac surgery, endocarditis, other concomitant cardiac surgery (except tricuspid surgery or surgical ablation), emergency patients, and patients without Medicare continuous enrollment or at least 1-year of Medicare coverage, we identified 26,524 isolated mitral ± tricuspid repairs or ablations, of which 2,227 (8.3%) received robotic and 24,297 (91.7%) received non-robotic mitral repair (Figure 1). Patients were classified as having degenerative disease if diagnosed with mitral valve prolapse or if no alternative etiology was present, including congenital disease, cardiomyopathy, prior myocardial infarction, left ventricular assist device or transplantation, or concomitant/prior coronary bypass grafting, consistent with prior definitions.7 The Institutional Review Board at Cedars-Sinai Medical Center approved this study with a waiver of informed consent (STUDY00001188, approved on 2/19/2021).
Figure 1.
Consort diagram
Study endpoints
The primary endpoint was the composite of death or mitral valve reintervention (surgery or transcatheter) during a median follow-up time of 4.18 years (maximum: 10 years, interquartile range [IQR]: 4.06-4.32 years) by the reverse Kaplan-Meier method. The secondary endpoint was all-cause mortality. Other endpoints included 30-day mortality, 30-day stroke, new post-operative atrial fibrillation, major bleeding, index hospitalization length of stay, and heart-failure readmission. Deaths were identified from the Master Beneficiary Summary File, and non-fatal secondary endpoints were defined using ICD-9 or ICD-10 diagnosis and procedure codes (Supplemental Table 3). Stroke was defined as any cerebrovascular accidents documented during the index hospitalization (that was not present on admission) and any subsequent hospital admission where the principal diagnosis was hemorrhagic or ischemic stroke. Heart failure readmissions included any subsequent hospital admission where the principal diagnosis was heart failure or cardiomyopathy. To identify the incidence of new postoperative atrial fibrillation, patients with a history of atrial fibrillation were excluded from this specific secondary endpoint analysis. We censored patients without one of the above events of interest on December 31, 2019.
Statistical analysis
Trends in utilization of robotic mitral repair out of all mitral repair patients were assessed using linear regression with time. Baseline characteristics were first compared in the overall sample between those receiving robotic vs. non-robotic mitral repair. Student’s t-test was used for normally distributed continuous variables, Wilcoxon rank-sum test for non-normally distributed continuous variables, while the Chi-square test was used for categorical variables. Propensity score matching was performed to account for baseline differences in patient characteristics between robotic and non-robotic mitral repair to reduce the effects of confounding. The propensity score for each patient was estimated using a multivariable logistic regression model, with procedure type (robotic vs non-robotic) regressed on 30 covariates that may influence the choice of intervention or that were prognostically important for the outcome (Table 1). Subjects were matched on the logit of the propensity score using 1:1 greedy nearestneighbour matching with a caliper distance of 0.1 times the standard deviation of the logit of the propensity score. Success of matching was assessed by computing the standardized difference of each covariate with a cut-off of 0.1 to denote acceptable balance.8,9 Early events were compared between the two cohorts using the McNemar test for binary outcomes and paired t-test and the Wilcoxon signed rank test for normally and non-normally distributed continuous variables, respectively.
Table 1.
Baseline characteristics before and after propensity matching stratified by procedure type
| Before propensity matching | After propensity matching | |||||
|---|---|---|---|---|---|---|
| Non-robotic mitral repair (n=24,297) | Robotic mitral repair (n=2,227) | P-value | Non-robotic mitral repair (n=2,226) | Robotic mitral repair (n=2,226) | SMD % | |
| Age, years | 72.47 (8.02) | 72.26 (6.66) | 0.22 | 72.22 (7.18) | 72.25 (6.65) | 1 |
| Race | 0.002 | 4 | ||||
| Asian | 239 (1.0) | 26 (1.2) | 25 (1.1) | 26 (1.2) | ||
| Black | 1214 (5.0) | 92 (4.1) | 101 (4.5) | 92 (4.1) | ||
| Hispanic | 176 (0.7) | 6 (0.3) | 4 (0.2) | 6 (0.3) | ||
| North American Native | 62 (0.3) | 6 (0.3) | 7 (0.3) | 6 (0.3) | ||
| Other | 359 (1.5) | 38 (1.7) | 31 (1.4) | 38 (1.7) | ||
| Unknown | 341 (1.4) | 50 (2.2) | 50 (2.2) | 50 (2.2) | ||
| White | 21906 (90.2) | 2009 (90.2) | 2008 (90.2) | 2008 (90.2) | ||
| Female sex | 12625 (52.0) | 966 (43.4) | <0.001 | 986 (44.3) | 966 (43.4) | 2 |
| Prior percutaneous coronary intervention | 2322 (9.6) | 203 (9.1) | 0.52 | 210 (9.4) | 202 (9.1) | 1 |
| Degenerative mitral regurgitation | 15512 (63.8) | 1666 (74.8) | <0.001 | 1659 (74.5) | 1665 (74.8) | 1 |
| Hypertension | 20456 (84.2) | 1745 (78.4) | <0.001 | 1736 (78.0) | 1744 (78.3) | 1 |
| Atrial fibrillation | 13836 (56.9) | 950 (42.7) | <0.001 | 938 (42.1) | 950 (42.7) | 1 |
| Prior myocardial infarction | 2764 (11.4) | 171 (7.7) | <0.001 | 165 (7.4) | 171 (7.7) | 1 |
| Coronary artery disease | 14636 (60.2) | 1273 (57.2) | 0.01 | 1275 (57.3) | 1272 (57.1) | 1 |
| Congestive heart failure | 16902 (69.6) | 1433 (64.3) | <0.001 | 1400 (62.9) | 1432 (64.3) | 1 |
| Cardiomyopathy | 5413 (22.3) | 305 (13.7) | <0.001 | 302 (13.6) | 305 (13.7) | 4 |
| Congenital heart disease | 653 (2.7) | 52 (2.3) | 0.36 | 45 (2.0) | 52 (2.3) | 1 |
| Hyperlipidemia | 16607 (68.4) | 1478 (66.4) | 0.06 | 1468 (65.9) | 1477 (66.4) | 1 |
| Diabetes | 5378 (22.1) | 383 (17.2) | <0.001 | 397 (17.8) | 383 (17.2) | 2 |
| Peripheral vascular disease | 4658 (19.2) | 528 (23.7) | <0.001 | 536 (24.1) | 527 (23.7) | 1 |
| Stroke | 1006 (4.1) | 77 (3.5) | 0.13 | 91 (4.1) | 77 (3.5) | 3 |
| Thromboembolism | 996 (4.1) | 78 (3.5) | 0.19 | 78 (3.5) | 78 (3.5) | 1 |
| Cerebrovascular disease | 4995 (20.6) | 363 (16.3) | <0.001 | 362 (16.3) | 363 (16.3) | 1 |
| Dialysis | 596 (2.5) | 21 (0.9) | <0.001 | 20 (0.9) | 21 (0.9) | 1 |
| Chronic kidney disease | 5288 (21.8) | 326 (14.6) | <0.001 | 316 (14.2) | 326 (14.6) | 1 |
| Chronic obstructive pulmonary disease | 6566 (27.0) | 454 (20.4) | <0.001 | 446 (20.0) | 454 (20.4) | 1 |
| Liver disease | 3636 (15.0) | 305 (13.7) | 0.11 | 306 (13.7) | 305 (13.7) | 1 |
| Coagulopathy | 3651 (15.0) | 297 (13.3) | 0.04 | 292 (13.1) | 297 (13.3) | 1 |
| Cancer | 6241 (25.7) | 560 (25.1) | 0.59 | 542 (24.3) | 559 (25.1) | 2 |
| Dementia | 1220 (5.0) | 108 (4.8) | 0.76 | 117 (5.3) | 108 (4.9) | 2 |
| Concomitant tricuspid surgery | 3688 (15.2) | 185 (8.3) | <0.001 | 199 (8.9) | 185 (8.3) | 2 |
| Concomitant surgical ablation | 2300 (9.5) | 146 (6.6) | <0.001 | 158 (7.1) | 146 (6.6) | 2 |
| Urgent status | 2601 (10.7) | 75 (3.4) | <0.001 | 80 (3.6) | 75 (3.4) | 1 |
| CHA2DS2-VASc score | 3.00 [3.00, 4.00] | 3.00 [2.00, 4.00] | <0.001 | 3.00 [2.00, 4.00] | 3.00 [2.00, 4.00] | 1 |
| Annual institution mitral volume | 39.00 [19.00, 80.00] | 72.00 [39.00, 114.00] | <0.001 | 40.00 [19.00, 82.00] | 72.00 [39.00, 114.00] | 33 |
| Year of procedure | <0.001 | 5 | ||||
| 2010 | 2486 (10.2) | 134 (6.0) | 127 (5.7) | 134 (6.0) | ||
| 2011 | 2491 (10.3) | 173 (7.8) | 169 (7.6) | 173 (7.8) | ||
| 2012 | 2302 (9.5) | 194 (8.7) | 208 (9.3) | 194 (8.7) | ||
| 2013 | 2474 (10.2) | 238 (10.7) | 244 (11.0) | 238 (10.7) | ||
| 2014 | 2431 (10.0) | 221 (9.9) | 219 (9.8) | 221 (9.9) | ||
| 2015 | 2500 (10.3) | 219 (9.8) | 215 (9.7) | 219 (9.8) | ||
| 2016 | 2479 (10.2) | 233 (10.5) | 227 (10.2) | 233 (10.5) | ||
| 2017 | 2446 (10.1) | 278 (12.5) | 279 (12.5) | 277 (12.4) | ||
| 2018 | 2286 (9.4) | 272 (12.2) | 296 (13.3) | 272 (12.2) | ||
| 2019 | 2402 (9.9) | 265 (11.9) | 242 (10.9) | 265 (11.9) | ||
Values are expressed in mean (standard deviation), median [interquartile range], or n (%)
Abbreviations: standardized mean difference (SMD), congestive heart failure, hypertension, age ≥75, diabetes, stroke, vascular disease, age 65 to 74 and sex category (CHA2DS2-VASc)
The primary composite endpoint of death or mitral reintervention was analyzed using Kaplan- Meier event curves. Differences between groups in the matched cohort were assessed using a stratified log-rank test, and hazard ratios were estimated using a Cox-proportional hazards model with a robust variance estimator to account for the matched design.10,11 For other late non-fatal outcomes (reintervention, stroke, heart failure readmission), cumulative incidence functions were used to estimate the incidence of these events after accounting for death as a competing risk. In the matched sample, equality of cumulative incidence function was assessed using a univariate Fine-Gray sub distribution hazard model in which the sub distribution hazard of the outcome was regressed on a single variable denoting treatment status, with a robust variance estimator to account for the matched nature of the sample.11
Falsification endpoint analysis
To assess for the risk of residual potential unmeasured confounders after propensity score matching, the association between treatment allocation (robotic versus non-robotic mitral repair) and the incidence of a composite falsification endpoint of hospitalization for urinary tract infection or pneumonia was compared.12 This endpoint was chosen as it is unlikely to be related to treatment assignment. The cumulative incidence of this endpoint was evaluated with death as a competing risk.
Statistical significance was assumed for p<0.05. The primary endpoint was tested initially, while there was no adjustment for multiplicity for secondary or early outcomes. All analyses were conducted with RStudio (version 1.3.959, RStudio: Integrated Development for R. RStudio, PBC, Boston, MA).
RESULTS
Primary matched analysis
In total, there were 2,227 patients in the robotic mitral repair group and 24,297 patients in the non-robotic mitral repair group (Figure 1). Over the entire study, there was an increase in the proportion of patients undergoing robotic mitral repair from 5% in 2010 to 10% in 2019 (Figure 2, p<0.05). Overall, there were few significant differences in important baseline characteristics between the robotic and non-robotic mitral repair patients before propensity matching (Table 1). Those who received robotic repair were more often male, more likely to have degenerative mitral regurgitation, lower incidence of a history of cerebrovascular disease, and less likely to require urgent surgery. Propensity matching on 30 baseline covariates (including year of procedure) yielded 2,226 pairs of patients (i.e., 99% of robotic mitral repair patients were matched to a non-robotic mitral repair patient), who were well-matched with standardized mean differences <0.10 for all covariates.
Figure 2.
Trends in mitral surgery stratified by surgical approach
In the propensity matched patients, there was no difference in early 30-day mortality between the robotic and non-robotic mitral repair patients (1.3% vs 1.3%, p=0.90). There was a lower rate of new post-operative atrial fibrillation in the robotic mitral repair group (19.1% vs 23.2%, p=0.001). The rate of new in-hospital stroke was similar between robotic and non-robotic mitral repair patients (2.2% vs 2.0%, p=0.75). There was no difference in the rate of in-hospital bleeding events between robotic and non-robotic mitral repair patients (5.9% vs. 6.3%, p=0.66). The index length of hospitalization was lower in robotic mitral repair patients (median 5.0 days [IQR: 4.0-7.0] vs 7.0 [5.0-9.0], p<0.001). Early outcomes before and after propensity matching are provided in Table 2.
Table 2.
Early outcomes before and after propensity matching stratified by procedure type
| Before propensity matching | After propensity matching | |||||
|---|---|---|---|---|---|---|
| Non-robotic mitral repair (n=24,297) | Robotic mitral repair (n=2,227) | P-value | Non-robotic mitral repair (n=2,226) | Robotic mitral repair (n=2,226) | P-value | |
| In-hospital mitral reintervention | 90 (0.4) | 4 (0.2) | 0.21 | 9 (0.4) | 4 (0.2) | 0.27 |
| In-hospital stroke | 446 (1.8) | 48 (2.2) | 0.32 | 44 (2.0) | 48 (2.2) | 0.75 |
| In-hospital bleeding | 1787 (7.4) | 132 (5.9) | 0.01 | 140 (6.3) | 132 (5.9) | 0.66 |
| In-hospital post-op atrial fibrillation | 4087 (16.8) | 426 (19.1) | 0.006 | 513 (23.2) | 426 (19.1) | 0.001 |
| 30-day death | 539 (2.2) | 30 (1.3) | 0.008 | 28 (1.3) | 30 (1.3) | 0.90 |
| Index length of stay, days | 7.00 [6.00, 10.00] | 5.00 [4.00, 7.00] | <0.001 | 7.00 [5.00, 9.00] | 5.00 [4.00, 7.00] | <0.001 |
Values are expressed in n (%) or median [interquartile range]
At 5-year follow-up, the primary endpoint of death or mitral valve reintervention was similar between the robotic and non-robotic mitral repair patients (17.8% vs.18.6%, HR: 0.93, 95%CI: 0.79-1.09, p=0.37, Figure 3). There was no difference in 5-year all-cause mortality between the two groups (14.9% vs.15.6%, HR: 0.93, 95%CI: 0.77-1.11, p=0.40, Figure 4). In competing risk models, the incidence of mitral valve reintervention (3.4% vs 3.8%, HR: 0.88, 95%CI: 0.62-1.24, p=0.46, Figure 5), stroke (6.1% vs. 5.6%, HR: 1.09, 95%CI: 0.84-1.43, p=0.50, Supplemental Figure 1), and heart failure readmissions (15.4% vs. 17.1%, HR: 0.89, 95%CI: 0.77-1.04, p=0.14, Supplemental Figure 2) were similar between the robotic and non-robotic mitral repair patients at 5-years.
Figure 3.
5-year cumulative incidence of death or mitral valve reintervention stratified by surgical approach
Figure 4.
5-year cumulative incidence of death stratified by surgical approach
Figure 5.
5-year cumulative incidence of mitral valve reintervention, stratified by surgical approach with death as a competing risk
In falsification endpoint analysis, there was no difference in the 5-year cumulative incidence of the urinary tract infection or pneumonia in the matched cohort (8.4% vs 9.7%, HR: 0.87, 95%CI: 0.71-1.08, p=0.20, Supplemental Figure 3) between robotic and non-robotic mitral repair.
COMMENT
Most longitudinal studies in the literature comparing robotic and non-robotic mitral repair are from single centers of excellence and may not be generalizable to the entire population. Previous work by Wang and colleagues linked data from the STS adult cardiac surgery dataset to Medicare and found no difference in all-cause mortality, heart failure readmissions, or mitral valve reintervention in 503 matched pair of patients from 2011-2014.13 Our population-based analysis of robotic vs non-robotic mitral repair builds on this previous study with more contemporaneous data (2010-2019), longer overall follow-up, and a larger sample size and showed several important findings. First, robotic assistance is used in the minority of mitral repairs, but its use doubled during the study period, highlighting a trend towards less invasive means to treat mitral valve disease. Importantly, there were no differences in early death or stroke with robotic mitral repair, a particular concern given the use of peripheral arterial cannulation in these minimally invasive cases, which has been suggested to increase the risk of stroke due to retrograde arterial perfusion. Finally, there was no difference in the primary endpoint of death or mitral valve reintervention at the population level, highlighting that robotic mitral repair is safe, generalizable, and durable.
In the era of transcatheter valve interventions, patients are seeking less invasive but durable means to treat their mitral regurgitation.14 Randomized evidence to support transcatheter edge-to-edge repair in degenerative mitral regurgitation has been limited to high surgical risk patients that demonstrated inferiority compared to conventional surgery.15 In a clinical registry of over 19,000 patients undergoing transcatheter edge-to-edge with degenerative mitral regurgitation, >10% of patients were left with moderate or greater residual MR and this was associated with worse 1-year mortality.16 As such, surgical mitral repair, which carries 0.5% operative mortality for patients in North America remains the gold standard for treating degenerative MR: restoring life expectancy and improving symptoms.17 Given patient preferences for less invasive treatment of DMR without compromising durability and quality of repair, understanding both early and late outcomes between conventional mitral repair and robotic repair is paramount. The incidence of reintervention in this study was 3% at 5-years follow-up. Our findings at the population level are consistent with results from large single center series showing mitral valve reintervention rates of 3-4% at 5-years follow-up and are comparable to conventional sternotomy mitral repairs performed by expert surgeons.18,19
In this analysis, we showed that utilization of robotic assistance increased over the study period yet was used in <10% of all cases. There are some barriers to widespread adoption of a robotic mitral repair platform. First, there is the concern for a perceived learning curve that increases operative time, morbidity, and mortality.20 Early national outcomes showed increased risk of early stroke with robotic mitral repair, while more recent analysis of the same dataset showed improvement with no differences compared to conventional surgery.3,21Given that conventional mitral repair has been shown to be safe and can be performed with very low morbidity and mortality; any new advances in the field must at least match if not exceed that of conventional gold standard. Several studies have been performed to assess the learning curve for robotic mitral repair.22 Using cumulative sum analyses, these studies suggest that a learning curve of 50-100 cases are required before outcomes stabilize at the institution level.23 However, a more recent study from a high-volume center showed that the learning curve for this procedure may be mitigated with expert proctorship and supervision.24 Indeed, later surgeons that joined the robotic mitral repair program had improved operative times compared to the first surgeon in the program at their initial start. Furthermore, the robotic platform offers unparalleled exposure and visualization to all aspects of the mitral valve and thus is an excellent teaching platform given that both teacher and student have the same view.
Limitations
This study must be interpreted in the context of several limitations. First, although propensity matching was used to account for known confounders, unmeasured confounding may remain. Patients were already well balanced before matching, and nearly all (99%) in the treatment group were successfully matched, with falsification endpoint analysis showing minimal residual bias. The Medicare dataset lacks granular details on mitral valve repair techniques (e.g., endoballoon vs. Chitwood clamp, femoral vs. axillary cannulation), surgical approaches (sternotomy, thoracotomy, or non-robotic minimally invasive), and cannot reliably differentiate mitral valve dysfunction etiologies, creating a heterogeneous population that may limit disease-specific generalizability. Data on intraoperative conversions, as well as cross-clamp and cardiopulmonary bypass times, were also unavailable. While mitral valve reintervention was used as a surrogate for durability, follow-up echocardiography was not available to define etiology (residual/progressive MR or transmitral gradients). Given the mean age of 72 years, results may not be generalizable to younger patients. Finally, cost data were not available, precluding evaluation of the economic implications relative to the procedural costs of robotic surgery.
Conclusions
Our population-based analysis demonstrates that robotic mitral repair achieves 5-year mortality and reintervention outcomes equivalent to conventional non-robotic repair, while offering the additional benefits of reduced postoperative atrial fibrillation and shorter hospital stays. These findings support the safety and durability of robotic repair for mitral regurgitation, reinforcing its role as a viable minimally invasive alternative to the traditional sternotomy approach. Although currently utilized in fewer than 10% of cases, the national experience indicates that broader adoption of robotic techniques may enhance patient recovery without compromising long-term repair quality.
Supplementary Material
FUNDING:
Allen A. Razavi and Aminah Sallam are supported by grants from the National Institutes of Health for advanced heart disease research (T32HL116273).
GLOSSARY OF ABBREVIATIONS
- (DMR)
Degenerative mitral regurgitation
- (STS/ACSD)
Society of Thoracic Surgeons Adult Cardiac Dataset
- (MR)
Mitral regurgitation
- (ICD-9)
International Classification of Diseases, 9th Revision
- (ICD-10)
International Classification of Diseases, 10th Revision
- (SMD)
Standardized mean difference
- (CHA2DS2-VASc)
Congestive heart failure, hypertension, age ≥75, diabetes, stroke, vascular disease, age 65 to 74 and sex category
Footnotes
Meeting Presentation: The abstract associated with this work was presented at the American Association for Thoracic Surgery 2025 annual meeting in Seattle, WA (May 2nd-5th)
Declaration of interests
The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:
Joanna Chikwe serves as primary investigator on the Percutaneous or Surgical Mitral Valve Repair (PRIMARY) Trial (NCT05051033) If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Data Availability
The data supporting our findings are available upon request.
REFERENCES
- 1.Otto CM, Nishimura RA, Bonow RO, et al. 2020 ACC/AHA Guideline for the Management of Patients With Valvular Heart Disease: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. Circulation. 2021;143(5):e72–e227. doi: 10.1161/cir.0000000000000923 [DOI] [PubMed] [Google Scholar]
- 2.Delling FN, Vasan RS. Epidemiology and Pathophysiology of Mitral Valve Prolapse. Circulation. 2014;129(21):2158–2170. doi: 10.1161/circulationaha.113.006702 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Mori M, Parsons N, Krane M, et al. Robotic Mitral Valve Repair for Degenerative Mitral Regurgitation. The Annals of Thoracic Surgery. 2024;117(1):96–104. doi: 10.1016/j.athoracsur.2023.07.047 [DOI] [PubMed] [Google Scholar]
- 4.Gillinov AM, Mihaljevic T, Javadikasgari H, et al. Early results of robotically assisted mitral valve surgery: Analysis of the first 1000 cases. The Journal of Thoracic and Cardiovascular Surgery. 2018;155(1):82–91.e2. doi: 10.1016/j.jtcvs.2017.07.037 [DOI] [PubMed] [Google Scholar]
- 5.Chitwood WR. Robotic Mitral Valve Repair: How I Teach It. The Annals of Thoracic Surgery. 2019;107(5):1297–1301. doi: 10.1016/j.athoracsur.2019.03.002 [DOI] [PubMed] [Google Scholar]
- 6.Akowuah EF, Maier RH, Hancock HC, et al. Minithoracotomy vs Conventional Sternotomy for Mitral Valve Repair. JAMA. 2023;329(22):1957–1966. doi: 10.1001/jama.2023.7800 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Chikwe J, Chen Q, Bowdish ME, et al. Surgery and transcatheter intervention for degenerative mitral regurgitation in the United States. The Journal of Thoracic and Cardiovascular Surgery. 2025;169(1):80–88.e19. doi: 10.1016/j.jtcvs.2024.01.014 [DOI] [PubMed] [Google Scholar]
- 8.Austin PC. Optimal caliper widths for propensity-score matching when estimating differences in means and differences in proportions in observational studies. Pharmaceutical statistics. 2011;10(2):150–161. doi: 10.1002/pst.433 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Austin PC. Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples. Statistics in medicine. 2009;28(25):3083–3107. doi: 10.1002/sim.3697 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Austin PC, Schuster T. The performance of different propensity score methods for estimating absolute effects of treatments on survival outcomes: A simulation study. Statistical methods in medical research. 2016;25(5):2214–2237. doi: 10.1177/0962280213519716 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Austin PC, Fine JP. Propensity-score matching with competing risks in survival analysis. Statistics in medicine. 2019;38(5):751–777. doi: 10.1002/sim.8008 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Groenwold RHH. Falsification end points for observational studies. JAMA: The Journal of the American Medical Association. 2013;309(17):1769–1770. doi: 10.1001/jama.2013.3089 [DOI] [PubMed] [Google Scholar]
- 13.Wang A, Brennan JM, Zhang S, et al. Robotic Mitral Valve Repair in Older Individuals: An Analysis of The Society of Thoracic Surgeons Database. The Annals of Thoracic Surgery. 2018;106(5):1388–1393. doi: 10.1016/j.athoracsur.2018.05.074 [DOI] [PubMed] [Google Scholar]
- 14.Alabbadi S, Malas J, Chen Q, et al. Guidelines vs Practice: Surgical Versus Transcatheter Aortic Valve Replacement in Adults ≤60 Years. The Annals of Thoracic Surgery. 2025;119(4):861–869. doi: 10.1016/j.athoracsur.2024.07.036 [DOI] [PubMed] [Google Scholar]
- 15.Feldman T, Foster E, Glower DD, et al. Percutaneous Repair or Surgery for Mitral Regurgitation. N Engl J Med. 2011;364(15):1395–1406. doi: 10.1056/NEJMoa1009355 [DOI] [PubMed] [Google Scholar]
- 16.Makkar RR, Chikwe J, Chakravarty T, et al. Transcatheter Mitral Valve Repair for Degenerative Mitral Regurgitation. JAMA. 2023;329(20):1778. doi: 10.1001/jama.2023.7089 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Suri RM, Antiel RM, Burkhart HM, et al. Quality of Life After Early Mitral Valve Repair Using Conventional and Robotic Approaches. The Annals of Thoracic Surgery. 2012;93(3):761–769. doi: 10.1016/j.athoracsur.2011.11.062 [DOI] [PubMed] [Google Scholar]
- 18.Murphy DA, Moss E, Binongo J, et al. The Expanding Role of Endoscopic Robotics in Mitral Valve Surgery: 1,257 Consecutive Procedures. The Annals of Thoracic Surgery. 2015;100(5):1675–1682. doi: 10.1016/j.athoracsur.2015.05.068 [DOI] [PubMed] [Google Scholar]
- 19.David TE, Armstrong S, McCrindle BW, Manlhiot C. Late Outcomes of Mitral Valve Repair for Mitral Regurgitation Due to Degenerative Disease. Circulation. 2013;127(14):1485–1492. doi: 10.1161/CIRCULATIONAHA.112.000699 [DOI] [PubMed] [Google Scholar]
- 20.Hasan I, Amabile A, Tam DY. Robotic mitral valve surgery: evolving history, techniques, and training paths. Current Opinion in Cardiology. 2025;40(2):72–74. doi: 10.1097/HCO.0000000000001194 [DOI] [PubMed] [Google Scholar]
- 21.Gammie JS, Zhao Y, Peterson ED, O’Brien SM, Rankin JS, Griffith BP. Less-Invasive Mitral Valve Operations: Trends and Outcomes From The Society of Thoracic Surgeons Adult Cardiac Surgery Database. The Annals of Thoracic Surgery. 2010;90(5):1401–1410.e1. doi: 10.1016/j.athoracsur.2010.05.055 [DOI] [PubMed] [Google Scholar]
- 22.Ramzy D, Trento A, Cheng W, et al. Three hundred robotic-assisted mitral valve repairs: The Cedars-Sinai experience. The Journal of Thoracic and Cardiovascular Surgery. 2014;147(1):228–235. doi: 10.1016/j.jtcvs.2013.09.035 [DOI] [PubMed] [Google Scholar]
- 23.Gillinov AM, Mihaljevic T, Javadikasgari H, et al. Early results of robotically assisted mitral valve surgery: Analysis of the first 1000 cases. The Journal of Thoracic and Cardiovascular Surgery. 2018;155(1):82–91.e2. doi: 10.1016/j.jtcvs.2017.07.037 [DOI] [PubMed] [Google Scholar]
- 24.Sandoval E, Bhoera RA, Tomšič A, et al. Learning curve of robotic mitral repair: prospective two-centre study of proficiency and clinical outcomes. European Journal of Cardio-Thoracic Surgery. 2024;66(6):ezae426. doi: 10.1093/ejcts/ezae426 [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The data supporting our findings are available upon request.