Mid-term Outcomes of Robotic Assisted versus Conventional Sternotomy for Mitral Valve Replacement: Inverse Probability of Treatment Weighting Survival Analysis  

Mid-term Outcomes of Robotic Assisted versus Conventional Sternotomy for Mitral Valve Replacement: Inverse Probability of Treatment Weighting Survival Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Mid-term Outcomes of Robotic Assisted versus Conventional Sternotomy for Mitral Valve Replacement: Inverse Probability of Treatment Weighting Survival Analysis Yu-san Chien, Ching-hu Chung, Jiun-yi Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6852734/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 16 Jul, 2025 Read the published version in Journal of Robotic Surgery → Version 1 posted 17 You are reading this latest preprint version Abstract Background This study aimed to compare survival, complications, and healthcare costs between robotic-assisted and conventional sternotomy mitral valve replacement, using a nationwide population-based dataset from Taiwan. Methods Patients who underwent isolated surgical mitral valve replacement between 2016 and 2021 were identified from Taiwan’s National Health Insurance Research Database. Inverse probability of treatment weighting was used to adjust for baseline differences. Survival, postoperative complications, and medical costs were compared between the two surgical approaches. Results After adjustment, a total of 5,660 patients who underwent conventional sternotomy and 5640 who received robotic-assisted mitral valve replacement were used for analysis. The robotic-assisted group had significantly better survival (hazard ratio: 0.37, 95% confidence interval: 0.33–0.41, p < 0.01), shorter hospital stays (16.5 vs. 22.7 days, p < 0.01), shorter intensive care stays (4.4 vs. 9.1 days, p < 0.01), and lower rates of dialysis and stroke. The additional medical expenses incurred within one year after surgery, excluding the cost of the initial hospitalization, were also significantly lower in the robotic-assisted group (1,640 vs. 4,003 United States dollars, p < 0.01). Conclusion In this national population-based analysis, robotic-assisted mitral valve replacement was associated with better mid-term survival, shorter hospital stays, and reduced medical costs compared with conventional sternotomy. These findings support the use of robotic-assisted surgery as a safe and effective alternative in selected patients undergoing mitral valve replacement. Mitral valve replacement robot sternotomy cost Figures Figure 1 Figure 2 Introduction Adult mitral valve disease can result from valve degeneration, rheumatic heart disease, ischemic heart disease, or infective endocarditis. Current practice guidelines recommend treatment options based on the underlying etiology, patients’ eligibility for transcatheter procedures, and their surgical risks [ 1 , 2 ]. For patients requiring mitral valve replacement and deemed unsuitable for repair, both conventional sternotomy and minimally invasive approaches have been developed. Since the late 1990s, robotic-assisted mitral valve surgery has emerged as an alternative to conventional thoracotomy, offering a less invasive approach with the assistance of robotic arms [ 3 ]. In 2002, the Food and Drug Administration of the United States approved the use of robotic da Vinci system in mitral valve surgery [ 4 ]. Since then, its use has expanded rapidly, with a 75% increase in robotic system sales within two years [ 5 ] and a six-fold increase in the number of surgeries performed within four years [ 6 ]. Despite this growing adoption, robotic-assisted cardiac procedures remain predominantly focused on coronary artery bypass surgery rather than valve operations. A study analyzing 5199 robotic cardiac surgeries performed between 2008 and 2011 found that only 11% were valve-related, whereas 43% involved coronary artery procedures [ 6 ]. Similarly, a 2020 review reported that nearly half of robotic-assisted cardiac surgeries were coronary artery bypass operations [ 3 ]. Whether the clinical outcomes of robotic-assisted mitral valve surgery (RAMVS) is equal or superior to conventional sternotomy mitral valve surgery (CSMVS) remains uncertain. A 2022 meta-analysis combining data from 14 studies and comparing the results of 2804 RAMVS with 3537 CSMVS showed that RAMVS had significantly lower overall mortality, shorter ICU and hospital stays, and less transfusion, but with longer aortic cross-clamp time in both unmatched and matched cohorts [ 7 ]. Despite these promising results, the meta-analysis had significant limitations. Only two of the 14 studies specifically focused on mitral valve replacement, while the majority included either mitral valve repair alone or a combination of repair and replacement. This heterogeneity introduces potential biases, given the distinct surgical indications, patient populations, and long-term prognoses associated with repair versus replacement. Additionally, only two studies reported follow-up durations exceeding two years, and all but one small cohort study (involving 47 RAMVS patients) were conducted in Western countries, leaving a gap in knowledge regarding outcomes in other populations [ 8 ]. Real-world data on the mid-term outcomes of robotic-assisted mitral valve replacement (RAMVR), especially in the Asian population, is limited. To address this gap, our study utilized Taiwan's National Health Insurance database to assess survival and complication rates following RAMVR and compared these outcomes with those of conventional sternotomy mitral valve replacement (CSMVR). Methods Data Source and Patient Selection This is a retrospective population-based cohort study, analysing data from Taiwan National Health Insurance (NHI) Research Database between January 2016 and December 2022. National Health Insurance is a mandatory health care system in Taiwan with a coverage rate of over 99% for all eligible residents. Its database houses comprehensive information on medical services, used devices and related charges for all registered beneficiaries, totalling 23,603,121 individuals in 2019. Access to this database can be obtained by applying to the Health and Welfare Data Science Centre of the Ministry of Health and Welfare, Taiwan, and our application was approved with the certification number of H112333. Our study population were patients diagnosed with mitral valve diseases between 2016 and 2021, identified by the presence of the International Classification of Diseases (ICD)-10 code I34 in the diagnosis of at least two outpatient clinic visits or one or more hospitalizations within a year. From this population, those who underwent surgical mitral valve replacement were selected using ICD-10 code 02RG. Patients with ICD-10 code 02RG0 were categorized in the group of conventional sternotomy mitral valve replacement (CSMVR), and those coded with ICD-10 code 02RG4 (Percutaneous endoscopic approach) and Bureau of National Health Insurance procedure code 26001, 26006, or 26003 were categorized in the robotic-assisted mitral valve replacement group (RAMVR). Patients with the ICD-10 code of 02RG4 but without the compatible procedure codes indicating use of robotic assistance were excluded. Patients who underwent concurrent aortic valve replacement or coronary artery bypass were also excluded. The flow chart of patient selection was summarized in Fig. 1 . Assessment and Statistics Analysis We compared baseline characteristics between CSMVR and RSMVR in the unmatched cohort. Next, we applied inverse probability of treatment weighting (IPTW), adjusting for age, gender, and Charlson Comorbidity Index (CCI) score [ 9 ], to assess the impact of RAMVR on hospital stay, intensive care unit (ICU) stay, postoperative complications, and healthcare expenditures. A time-to-event analysis using Kaplan-Meier survival curves was conducted, with a stratified log-rank test used to evaluate the equality of the estimated survival curves for all-cause mortality. The time-to-event was defined as the period from the day of the procedure to the date of death. We also performed a Cox proportional hazards model, adjusting for age, gender, and underlying comorbidities. Differences in comorbidities across the two treatment groups were tested using the chi-square test. Length of hospital stay was measured as the total number of hospitalization days related to mitral valve replacement treatment. For cost analysis, only reimbursement by NHI recorded in the research database was used for analysis and the exchange rate of Taiwan dollars to USD was 1:32.5. The costs of the indexed hospitalization when mitral valve replacement happened, and the total medical costs in the following year were compared between groups. The NHI provided comprehensive coverage for MVR, including a fixed rate of USD 1362.90 per device and USD 2150.10 per standard sternotomy procedure. If the patient opted for robotic assisted surgery or newer generation bioprosthetic valves, the reimbursement from NHI would be equivalent to standard procedure and the patients are required to pay the difference out of their own pockets [ 10 ]. ​ Data analyses were performed using SAS 9.4 (SAS Institute Inc., Cary, NC). Variable measures were identified based on the criteria described above. Categorical variables were reported using frequencies or percentages, while continuous variables were described using the mean ± standard deviation (SD). The Cox proportional hazards model included the following variables: Robotic-assisted mitral valve replacements, age, gender, diabetes, hypertension, congestive heart failure, active endocarditis, peripheral vascular disease, cerebrovascular disease, chronic pulmonary disease, renal disease, end-stage renal disease under haemodialysis, liver cirrhosis, and hospital level. Pre-existing comorbidities diagnosed within a year prior to the index operation using the corresponding ICD diagnostic codes were used to identify underlying diseases for each patient and calculate the Charlson Comorbidity Index. Research Ethics Approval The study protocol was approved by the MacKay Memorial Hospital Institutional Review Board Taiwan R.O.C. (Approval Number: 23MMHIS386e). Giving the retrospective nature, the requirement for informed consent was waived. Data from NHIRD was provided in encrypted form, with all personal identification removed. Results Sample Description Between 2016 and 2021, a total of 5,736 patients underwent surgical mitral valve replacement, including 5,547 CSMVR (96.7%), 113 RAMVR (2.0%), and 76 (1.3%) through mini thoracotomy. Only patients receiving CSMVR and RAMVR were included in this study. Table 1 showed the demographic features of patients receiving RAMVR and CSMVR. Half of the enrolled patients were male, with a mean age of 62.98 ± 12.97 and 60.13 ± 12.72 years in the CSMVR and RAMVR groups, respectively (p = 0.37). The mean CCI score did not differ significantly between groups (1.58 ± 1.67 vs. 1.71 ± 1.62, p = 0.37), and the proportion of patients with CCI > 3 was also similar (14.16% vs. 14.75%, p = 0.85). The prevalence of most comorbidities was comparable across groups, including diabetes, hypertension, renal disease, and cerebrovascular disease. Congestive heart failure was more common in the RAMVR group (57.52% vs. 47.58%, p = 0.04) while liver cirrhosis was observed in 2.07% of CSMVR patients and none in the RAMVR group ( p = 0.12). A significant difference was observed in the distribution of hospital level between groups. RAMVR were predominantly performed in medical centers (90.27% vs. 64.25%, p < 0.01), while CSMVR procedures were more likely to be conducted in regional or district hospitals (p < 0.01). Table 1 Comparison of patient characteristics between patients receiving CSMVR and RAMVR before and after adjustment with inverse probability of treatment weighting. Unmatched IPTW CSMVR RAMVR p -value CSMVR RAMVR p -value 5547 113 5660 5640 Male 2585 (46.60%) 57 (50.44%) 0.4179 2636 (46.57%) 2845 (50.44%) < .0001 Mean age ± SD (years) 62.98 ± 12.97 60.13 ± 12.72 0.3678 62.84 ± 13.13 62.40 ± 85.40 0.7080 Charlson Comorbidity Score Mean ± SD 1.71 ± 1.62 1.58 ± 1.67 0.3678 1.71 ± 1.64 1.73 ± 12.32 0.9229 CCI score > 3 820 (14.75%) 16 (14.16%) 0.8533 834 (14.73%) 943 (16.72%) 0.0038 Underlying disease Diabetes 997 (17.97%) 19 (16.81%) 0.7505 1015 (17.93%) 1090 (19.33%) 0.0578 Hypertension 1733 (31.24%) 30 (25.43%) 0.2862 1766 (31.20%) 1512 (26.81%) < .0001 Congestive heart failure 2639 (47.58%) 65 (57.52%) 0.0361 2691 (47.54%) 3412 (60.50%) < .0001 Active endocarditis 851 (15.34%) 19 (16.81%) 0.6675 870 (15.37%) 825 (14.63%) 0.2641 Peripheral vascular disease 186 (3.35%) 4 (3.53%) 0.9132 190 (3.35%) 200 (3.55%) 0.5559 Chronic lung disease 802 (14.46%) 12 (10.62%) 0.2496 817 (14.43%) 683 (12.11%) 0.0003 Renal disease 804 (14.46%) 11 (9.73%) 0.1537 818 (15.04%) 624 (11.06%) < .0001 End stage renal disease under dialysis 272 (4.90%) 3 (2.65%) 0.2711 277 (4.89%) 147 (2.61%) < .0001 Cerebral vascular disease 819 (14.76%) 13 (11.50%) 0.3326 835 (14.75%) 625 (11.08%) < .0001 Liver cirrhosis 115 (2.07%) 0 0.122 117 (20.67%) 0 < .0001 Hospital Level Medical Center 3564 (64.25%) 102 (90.27%) < .0001 3637 (64.26%) 5059 (89.70%) < .0001 Regional Hospital 1893 (34.13%) 11 (9.73%) < .0001 1931 (34.12%) 581 (10.30%) < .0001 District Hospital 90 (1.62%) 0 < .0001 92 (1.63%) 0 < .0001 IPTW: inverse probability of treatment weighting CCI: Charlson Comorbidity Index SD: standard deviation ESRD: end-stage renal disease IPTW was applied to match patients’ age, gender, and Charlson Comorbidity Index (CCI) score, and this adjustment process yielded 5,660 patients undergoing CSMVR and 5,640 patients receiving RAMVR for further analysis. Survival Analysis Figure 2 showed the survival trends of patients receiving CSMVR and RAMVR during the study period. Before IPTW adjustment (Fig. 2 A), the RAMVR group exhibited a trend toward improved survival compared to CSMVR throughout the follow-up period, despite the significantly smaller sample size of RAMVR patients. After IPTW adjustment (Fig. 2 B), which accounted for potential confounding factors, the RAMVR group demonstrated a clear survival advantage over CSMVR. The survival curves diverged early postoperatively and continued to separate over time, with RAMVR maintaining superior long-term survival rates and the CSMVR group exhibited a higher cumulative mortality risk. Multivariate regression analysis of our study population Table 2 showed the multivariate logistic regression analysis and we identified several independent predictors of mortality following mitral valve replacement. Patients undergoing RAMVR had significantly lower mortality risk compared to those undergoing CSMVR, both in the unmatched model (HR: 0.34, 95% CI: 0.19–0.67, p = 0.00) and after IPTW adjustment (HR: 0.37, 95% CI: 0.33–0.41, p < 0.01). Table 2 Multivariate logistic regression analysis of risk factors for mortality after mitral valve replacement. Unmatched IPTW HR 95%CI p -value HR 95%CI p -value Gender Male vs female 1.079 0.967 1.205 0.1742 1.032 0.941 1.132 0.5019 Age ≥ 50 vs < 50 (years) 1.988 1.623 2.435 < .0001 2.733 2.244 3.33 < .0001 Surgery RAMVR vs CSMVR 0.358 0.192 0.669 0.0013 0.365 0.327 0.406 < .0001 Hospital stay ≥ 20 vs 3 1.17 0.994 1.377 0.0594 1.062 0.892 1.263 0.4994 Peripheral vascular disease 1.277 0.911 1.791 0.1556 1.372 0.982 1.917 0.0635 Chronic pulmonary disease 0.748 0.615 0.909 0.0034 0.856 0.728 1.007 0.0569 Renal disease 1.464 1.218 1.76 < .0001 1.803 1.539 2.112 < .0001 Dialysis 1.612 1.295 2.007 < .0001 1.24 1.005 1.53 0.0445 Cerebral vascular disease 0.953 0.787 1.155 0.6251 0.845 0.702 1.018 0.0765 Liver cirrhosis 1.245 0.666 2.328 0.4915 1.388 0.748 2.576 0.2984 Hospital level Medical center 0.908 0.811 1.018 0.0984 1.017 0.912 1.134 0.7574 IPTW: inverse probability of treatment weighting HR: hazard ratio 95 CI: 95% confidence interval CCI: Charlson Comorbidity Index Age ≥ 50 years was associated with a significantly increased risk of mortality in both models (Unmatched HR: 1.99, p < 0.01; IPTW HR: 2.73, p < 0.01). Similarly, hospital stay ≥ 20 days was linked to higher mortality (Unmatched HR: 1.16, p = 0.01; IPTW HR: 1.117, p = 0.02). Renal disease and dialysis dependence were also strong predictors of mortality. Renal disease was associated with elevated risk in both unmatched (HR: 1.46, p < 0.01) and IPTW models (HR: 1.80, p < 0.01), while dialysis showed a weaker but still significant association (IPTW HR: 1.24, p = 0.04). Chronic pulmonary disease was associated with lower mortality in the unmatched analysis (HR: 0.75, p = 0.00), but this association lost significance after adjustment (IPTW HR: 0.86, p = 0.06). Hospitalization-related outcome and cost analysis Hospitalization-related outcome and cost analysis Table 3 showed the hospitalization-related outcomes and the results of cost analysis. Patients who underwent RAMVR experienced significantly more favorable perioperative metrics compared to those who received CSMVR. In both unmatched and IPTW-adjusted analyses, the RAMVR group had significantly shorter hospital stays (16.60 vs. 22.73 days, p < 0.01) and ICU stays (4.39 vs. 9.11 days, p < 0.01). In terms of post-operative complications, new-onset dialysis occurred only in the CSMVR group (9 cases, 0.16%), reaching significance after IPTW adjustment ( p = 0.00). Stroke rates were slightly lower in RAMVR (8.40% vs. 10.32%, p = 0.00 after IPTW), and the rates of sternum wound infection and re-operations were low and statistically comparable between groups. Table 3 Hospitalization outcome and cost analysis for mitral valve replacement. Unmatched IPTW CSMVR RAMVR p -value CSMVR RAMVR p -value Hospital stay (days) 22.73 ± 13.37 16.60 ± 10.44 < .0001 22.73 ± 13.51 16.50 ± 70.34 < .0001 ICU stay (days) 9.11 ± 12.53 4.39 ± 7.78 < .0001 9.11 ± 12.66 4.36 ± 51.73 < .0001 Post-operative complications New dialysis (including CVVH) 9 (0.16%) 0 0.6683 9 (0.16%) 0 0.0025 Stroke 573 (10.33%) 10 (8.85%) 0.6083 584 (10.32%) 474 (8.40%) 0.0004 Sternum wound infection 127 (2.29%) ≤ 3 (≤ 2.65%) 0.7141 130 (2.30%) 119 (2.11%) 0.504 Re-operation 17 (0.31%) 0 0.5556 17 (0.30%) 0 < .0001 Medical cost (USD) Index hospitalization cost 16720.65 ± 10182.00 12875.32 ± 5269.38 < .0001 16719.08 ± 10283.51 12881.35 ± 3602.84 < .0001 Additional medical cost 1–3 months after MVR 964.77 ± 412.92 774.03 ± 470.80 0.6699 963.48 ± 416.81 760.77 ± 313.35 0.631 Additional medical cost 4–6 months after MVR 775.75 ± 365.58 212.34 ± 95.38 < .0001 774.80 ± 369.15 243.66 ± 74.39 < .0001 Additional medical cost 7–12 months after MVR 1064.58 ± 526.50 364.89.60 ± 169.07 < .0001 1063.32 ± 531.48 388.28 ± 122.68 < .0001 ICU: intensive care uint CVVH: continuous veno-venous hemodilaysis MVR: mitral valve replacement For the cost analysis, because the National Health Insurance in Taiwan does not reimburse the procedure fee for robotic-assisted surgery during the index hospitalization, and patient co-payments are not captured in the claims data. As a result, the recorded hospitalization cost appeared significantly lower in the RAMVR group (adjusted mean: USD $ 12,881.35 ± 3602.84 vs. $ 16,719.08 ± 10283.51, p < 0.01). To estimate the broader economic impact, we analyzed the post-discharge medical expenses. No significant difference was observed during the first three months after surgery. However, the robotic group incurred substantially lower additional medical costs during months 4–6 (243.66 ± 74.39 vs. 774.80 ± 369.15 USD, p < 0.01) and months 7–12 (1063.32 ± 531.48 vs. 388.28 ± 122.68 USD, p < 0.01). When calculating the total additional medical expenses within one- year post-surgery, it was also significantly lower in the RAMVR group (2801.60 ± 470.54 vs. 1392.71 ± 1506.08 USD, p < 0.01), suggesting reduced postoperative resource utilization. Discussion This is a nationwide population-based research to systematically evaluate the mid-term outcomes of robotic-assisted mitral valve replacement in real-world setting. In this study, we demonstrated that RAMVR was associated with a significant survival benefit compared to CSMVR. Additionally, patients undergoing RAMVR experienced a significantly shorter hospital stay and ICU stay, which may contribute to improved postoperative recovery and reduced healthcare resource utilization. Compared to mitral valve repair, RAMVR is considered more technically challenging due to the small surgical ports, which can complicate prosthesis implantation and suturing, leading to significantly longer aortic clamp and cardiopulmonary bypass durations [ 11 , 12 ]. As a result, most studies have focused on robotic-assisted mitral valve repair rather than replacement, and the limited research available on RAMVR has primarily been conducted in highly specialized centers, where surgeries were performed by dedicated teams of experienced surgeons, anesthesiologists, and nurses, and comprehensive surgical details were kept [ 8 , 11 – 14 ]. The three studies that compared the clinical outcomes of RAMVR and CSMVR reported similar rates of all-cause mortality and perioperative complications [ 8 , 12 , 14 ]. In contrast, our study, which included a broader patient population across multiple hospitals over six years, demonstrated superior survival rates for the RAMVR group in both unadjusted analyses and after adjustment using the inverse probability of treatment weighting method. Several factors may explain this discrepancy. First, most previous studies were conducted in high-volume institutions with strong surgical teams for both RAMVR and CSMVR, minimizing performance variability. In our nationwide cohort, RAMVR was almost exclusively performed in medical centers, while CSMVR was more frequently carried out in regional or district hospitals. This may reflect differences in institutional expertise and perioperative care, which could have influenced patient outcomes despite statistical adjustment. Second, some confounding factors might not be fully captured by the Charlson comorbidity score, such as critical preoperative status, active endocarditis, significantly reduced left ventricular ejection fraction, and pulmonary hypertension. To further minimize selection bias, incorporating a well-validated risk assessment tool specifically designed for cardiac surgeries, such as the European System for Cardiac Operative Risk Evaluation II (EuroSCORE II) or Society of Thoracic Surgeons Score, would be more ideal [ 15 ]. Third, patient selection for RAMVR in Taiwan may inherently favor higher-performing institutions and better-coordinated multidisciplinary care, even though baseline clinical characteristics were similar after IPTW adjustment. Forth, the survival benefit may be more apparent in our cohort because we focused specifically on mitral valve replacement rather than repair. MVR is typically reserved for patients with more advanced disease or unsuitable anatomy for repair, and the benefits of a less invasive approach—such as reduced surgical trauma, shorter ICU stays, and fewer complications—may translate more clearly into survival advantages in this higher-risk population. Fourth, prior studies often had limited follow-up periods and small RAMVR sample sizes, making them less likely to detect survival differences over time. Our study included follow-up up to 5–6 years, allowing the survival curves to separate more distinctly in the mid-term. Finally, advances in robotic technology, increased team experience, and improved perioperative management in recent years may have contributed to better outcomes in RAMVR patients compared to those reported in earlier studies. Despite its potential benefits, RAMVR remained infrequent in our national database, accounting for only 2% of all mitral valve replacements during the study period. Several factors may have accounted for this low adoption rate. First, the high material and infrastructure costs for both hospitals and patients result in highly selective patient eligibility for robotic-assisted surgery. Second, the significantly longer operative time may reduce cost-effectiveness in operating room utilization. Third, RAMVR requires extensive training for surgeons to achieve consistent outcomes. A recent report from Germany found that material and infrastructure costs of RAMVR were significantly higher than the national benchmark for non-robotic surgeries by €1,384 vs. €985, respectively ( p < 0.01), but the overall cost was not significantly higher, indicating the high cost of RAMVR could be partially offset by a shorter hospital stay [ 13 ]. Regarding operative time, studies from high-volume centers have shown that RAMVR typically took around 240 to 320 minutes to perform, which was about one to two hours longer than CSMVR [ 11 – 13 ]. However, with six months of structured team training or experience exceeding 50 cases, the operative time could be reduced to approximately 200 minutes [ 12 , 13 ]. It is reasonable to deduce that cost-effectiveness of RAMVR could also be improved over time in high-volume centers [ 16 ]. Our study has several limitations. First, although the analysis of NHIRD data offers multiple advantages, it lacks important clinical details such as surgical time, cardiopulmonary bypass duration, aortic clamp time, and the amount of transfusion. Second, the study design relied heavily on the ICD system and procedure codes, which may introduce coding errors, misclassifications, or variations between hospitals, potentially affecting the results. Third, the longest follow-up period for RAMVR in this study was five to six years, which is insufficient to fully assess long-term outcomes. Finally, advancements in medical equipment, medication, and overall care quality during the observation period could have influenced the clinical outcomes. Future research should focus on long-term clinical outcomes, cost-effectiveness analyses, and strategies to optimize patient selection for RAMVR, especially in more diverse populations and across different healthcare systems. Additionally, incorporating standardized risk assessment tools such as EuroSCORE II could enhance the accuracy of outcome comparisons and refine best practices for mitral valve replacement. Conclusions In this population-based national study, we demonstrated the survival benefit of RAMVR compared with CSMVR. Patients receiving a RAMVR also demonstrated a significantly shorter length of hospital and ICU stay. Declarations Funding Statement This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors Conflict of Interest Statement None declared. 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Xuan, Evidence-Based Determination of Cut-Off Points for Increased Cardiac-Surgery Mortality Risk With EuroSCORE II and STS: The Best-Performing Risk Scoring Models in a Single-Centre Australian Population, Heart Lung Circ 31(4) (2022) 590-601. G. Coyan, L.M. Wei, A. Althouse, H.G. Roberts, D. Schauble, T. Murashita, C.C. Cook, J.S. Rankin, V. Badhwar, Robotic mitral valve operations by experienced surgeons are cost-neutral and durable at 1 year, J. Thorac. Cardiovasc. Surg. 156(3) (2018) 1040-1047. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 16 Jul, 2025 Read the published version in Journal of Robotic Surgery → Version 1 posted Editorial decision: Revision requested 18 Jun, 2025 Reviewers agreed at journal 18 Jun, 2025 Reviews received at journal 18 Jun, 2025 Reviews received at journal 17 Jun, 2025 Reviews received at journal 16 Jun, 2025 Reviewers agreed at journal 16 Jun, 2025 Reviews received at journal 15 Jun, 2025 Reviewers agreed at journal 15 Jun, 2025 Reviewers agreed at journal 14 Jun, 2025 Reviewers agreed at journal 13 Jun, 2025 Reviewers agreed at journal 13 Jun, 2025 Reviewers agreed at journal 12 Jun, 2025 Reviewers agreed at journal 12 Jun, 2025 Reviewers invited by journal 12 Jun, 2025 Editor assigned by journal 11 Jun, 2025 Submission checks completed at journal 11 Jun, 2025 First submitted to journal 09 Jun, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6852734","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":471472499,"identity":"15fd5b35-8cbe-4f90-ac9b-61b3de6b2040","order_by":0,"name":"Yu-san Chien","email":"","orcid":"","institution":"Mackay Memorial Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yu-san","middleName":"","lastName":"Chien","suffix":""},{"id":471472500,"identity":"6ddd950c-9fae-487d-82e2-edc5daecaadc","order_by":1,"name":"Ching-hu Chung","email":"","orcid":"","institution":"Mackay Medical College","correspondingAuthor":false,"prefix":"","firstName":"Ching-hu","middleName":"","lastName":"Chung","suffix":""},{"id":471472501,"identity":"94e1be2e-1183-418c-b5a6-a7b6fc27c6a0","order_by":2,"name":"Jiun-yi Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVRIiWNgGAWjYBACxgbm9g8fKiTkQJwDD4jTwtjGOOOMhTFYSwKR9rQx87ZVJDaA2ERpYZ6R2PZwZptE+vywww+BttjJ6TYQsmNGYrvBh3MSuRtvpxkAtSQbmx0grKVBckYZUMvsBJCWA4nbiNEizcMmkW44O/0D0VrapHnaJBLkpXOItaXnYbPhjDMShhukcwoOJBgQ4RfD9uSDDz5U1MnLz07fDIxTOznCWiYkQBgGYJUGBJSDgDw/1FD5BiJUj4JRMApGwcgEAN2IS9e8/16aAAAAAElFTkSuQmCC","orcid":"","institution":"Mackay Memorial Hospital","correspondingAuthor":true,"prefix":"","firstName":"Jiun-yi","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2025-06-09 09:08:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6852734/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6852734/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11701-025-02564-2","type":"published","date":"2025-07-16T16:05:40+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":84780831,"identity":"110922bb-ce1d-412f-998c-7374d3b229a3","added_by":"auto","created_at":"2025-06-17 09:28:19","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":108577,"visible":true,"origin":"","legend":"\u003cp\u003eThe enrolment flow chart of this study.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6852734/v1/342d9feb1c24444507eef200.png"},{"id":84780830,"identity":"7f20bdf2-7cef-4c54-ade2-fad88c438acf","added_by":"auto","created_at":"2025-06-17 09:28:19","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":107032,"visible":true,"origin":"","legend":"\u003cp\u003eSurvival trends of patients receiving conventional sternotomy and robotic-assisted mitral valve replacement.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6852734/v1/828651c544daad8d7f1912b8.png"},{"id":87220536,"identity":"6a45280f-50c8-47b9-b776-e8855f6334b7","added_by":"auto","created_at":"2025-07-21 16:12:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1168816,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6852734/v1/b30ae480-bb9b-4281-a173-b2879762b13e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Mid-term Outcomes of Robotic Assisted versus Conventional Sternotomy for Mitral Valve Replacement: Inverse Probability of Treatment Weighting Survival Analysis ","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAdult mitral valve disease can result from valve degeneration, rheumatic heart disease, ischemic heart disease, or infective endocarditis. Current practice guidelines recommend treatment options based on the underlying etiology, patients\u0026rsquo; eligibility for transcatheter procedures, and their surgical risks [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. For patients requiring mitral valve replacement and deemed unsuitable for repair, both conventional sternotomy and minimally invasive approaches have been developed. Since the late 1990s, robotic-assisted mitral valve surgery has emerged as an alternative to conventional thoracotomy, offering a less invasive approach with the assistance of robotic arms [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn 2002, the Food and Drug Administration of the United States approved the use of robotic da Vinci system in mitral valve surgery [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Since then, its use has expanded rapidly, with a 75% increase in robotic system sales within two years [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] and a six-fold increase in the number of surgeries performed within four years [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Despite this growing adoption, robotic-assisted cardiac procedures remain predominantly focused on coronary artery bypass surgery rather than valve operations. A study analyzing 5199 robotic cardiac surgeries performed between 2008 and 2011 found that only 11% were valve-related, whereas 43% involved coronary artery procedures [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Similarly, a 2020 review reported that nearly half of robotic-assisted cardiac surgeries were coronary artery bypass operations [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhether the clinical outcomes of robotic-assisted mitral valve surgery (RAMVS) is equal or superior to conventional sternotomy mitral valve surgery (CSMVS) remains uncertain. A 2022 meta-analysis combining data from 14 studies and comparing the results of 2804 RAMVS with 3537 CSMVS showed that RAMVS had significantly lower overall mortality, shorter ICU and hospital stays, and less transfusion, but with longer aortic cross-clamp time in both unmatched and matched cohorts [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Despite these promising results, the meta-analysis had significant limitations. Only two of the 14 studies specifically focused on mitral valve replacement, while the majority included either mitral valve repair alone or a combination of repair and replacement. This heterogeneity introduces potential biases, given the distinct surgical indications, patient populations, and long-term prognoses associated with repair versus replacement. Additionally, only two studies reported follow-up durations exceeding two years, and all but one small cohort study (involving 47 RAMVS patients) were conducted in Western countries, leaving a gap in knowledge regarding outcomes in other populations [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eReal-world data on the mid-term outcomes of robotic-assisted mitral valve replacement (RAMVR), especially in the Asian population, is limited. To address this gap, our study utilized Taiwan's National Health Insurance database to assess survival and complication rates following RAMVR and compared these outcomes with those of conventional sternotomy mitral valve replacement (CSMVR).\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Source and Patient Selection\u003c/h2\u003e \u003cp\u003eThis is a retrospective population-based cohort study, analysing data from Taiwan National Health Insurance (NHI) Research Database between January 2016 and December 2022. National Health Insurance is a mandatory health care system in Taiwan with a coverage rate of over 99% for all eligible residents. Its database houses comprehensive information on medical services, used devices and related charges for all registered beneficiaries, totalling 23,603,121 individuals in 2019. Access to this database can be obtained by applying to the Health and Welfare Data Science Centre of the Ministry of Health and Welfare, Taiwan, and our application was approved with the certification number of H112333.\u003c/p\u003e \u003cp\u003eOur study population were patients diagnosed with mitral valve diseases between 2016 and 2021, identified by the presence of the International Classification of Diseases (ICD)-10 code I34 in the diagnosis of at least two outpatient clinic visits or one or more hospitalizations within a year. From this population, those who underwent surgical mitral valve replacement were selected using ICD-10 code 02RG. Patients with ICD-10 code 02RG0 were categorized in the group of conventional sternotomy mitral valve replacement (CSMVR), and those coded with ICD-10 code 02RG4 (Percutaneous endoscopic approach) and Bureau of National Health Insurance procedure code 26001, 26006, or 26003 were categorized in the robotic-assisted mitral valve replacement group (RAMVR). Patients with the ICD-10 code of 02RG4 but without the compatible procedure codes indicating use of robotic assistance were excluded. Patients who underwent concurrent aortic valve replacement or coronary artery bypass were also excluded. The flow chart of patient selection was summarized in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAssessment and Statistics Analysis\u003c/h3\u003e\n\u003cp\u003eWe compared baseline characteristics between CSMVR and RSMVR in the unmatched cohort. Next, we applied inverse probability of treatment weighting (IPTW), adjusting for age, gender, and Charlson Comorbidity Index (CCI) score [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], to assess the impact of RAMVR on hospital stay, intensive care unit (ICU) stay, postoperative complications, and healthcare expenditures. A time-to-event analysis using Kaplan-Meier survival curves was conducted, with a stratified log-rank test used to evaluate the equality of the estimated survival curves for all-cause mortality. The time-to-event was defined as the period from the day of the procedure to the date of death. We also performed a Cox proportional hazards model, adjusting for age, gender, and underlying comorbidities. Differences in comorbidities across the two treatment groups were tested using the chi-square test. Length of hospital stay was measured as the total number of hospitalization days related to mitral valve replacement treatment.\u003c/p\u003e \u003cp\u003eFor cost analysis, only reimbursement by NHI recorded in the research database was used for analysis and the exchange rate of Taiwan dollars to USD was 1:32.5. The costs of the indexed hospitalization when mitral valve replacement happened, and the total medical costs in the following year were compared between groups. The NHI provided comprehensive coverage for MVR, including a fixed rate of USD 1362.90 per device and USD 2150.10 per standard sternotomy procedure. If the patient opted for robotic assisted surgery or newer generation bioprosthetic valves, the reimbursement from NHI would be equivalent to standard procedure and the patients are required to pay the difference out of their own pockets [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. ​\u003c/p\u003e \u003cp\u003eData analyses were performed using SAS 9.4 (SAS Institute Inc., Cary, NC). Variable measures were identified based on the criteria described above. Categorical variables were reported using frequencies or percentages, while continuous variables were described using the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD). The Cox proportional hazards model included the following variables: Robotic-assisted mitral valve replacements, age, gender, diabetes, hypertension, congestive heart failure, active endocarditis, peripheral vascular disease, cerebrovascular disease, chronic pulmonary disease, renal disease, end-stage renal disease under haemodialysis, liver cirrhosis, and hospital level. Pre-existing comorbidities diagnosed within a year prior to the index operation using the corresponding ICD diagnostic codes were used to identify underlying diseases for each patient and calculate the Charlson Comorbidity Index.\u003c/p\u003e\n\u003ch3\u003eResearch Ethics Approval\u003c/h3\u003e\n\u003cp\u003e The study protocol was approved by the MacKay Memorial Hospital Institutional Review Board Taiwan R.O.C. (Approval Number: 23MMHIS386e). Giving the retrospective nature, the requirement for informed consent was waived. Data from NHIRD was provided in encrypted form, with all personal identification removed.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eSample Description\u003c/h2\u003e \u003cp\u003eBetween 2016 and 2021, a total of 5,736 patients underwent surgical mitral valve replacement, including 5,547 CSMVR (96.7%), 113 RAMVR (2.0%), and 76 (1.3%) through mini thoracotomy. Only patients receiving CSMVR and RAMVR were included in this study. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e showed the demographic features of patients receiving RAMVR and CSMVR. Half of the enrolled patients were male, with a mean age of 62.98\u0026thinsp;\u0026plusmn;\u0026thinsp;12.97 and 60.13\u0026thinsp;\u0026plusmn;\u0026thinsp;12.72 years in the CSMVR and RAMVR groups, respectively (p\u0026thinsp;=\u0026thinsp;0.37). The mean CCI score did not differ significantly between groups (1.58\u0026thinsp;\u0026plusmn;\u0026thinsp;1.67 vs. 1.71\u0026thinsp;\u0026plusmn;\u0026thinsp;1.62, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.37), and the proportion of patients with CCI\u0026thinsp;\u0026gt;\u0026thinsp;3 was also similar (14.16% vs. 14.75%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.85). The prevalence of most comorbidities was comparable across groups, including diabetes, hypertension, renal disease, and cerebrovascular disease. Congestive heart failure was more common in the RAMVR group (57.52% vs. 47.58%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.04) while liver cirrhosis was observed in 2.07% of CSMVR patients and none in the RAMVR group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.12). A significant difference was observed in the distribution of hospital level between groups. RAMVR were predominantly performed in medical centers (90.27% vs. 64.25%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), while CSMVR procedures were more likely to be conducted in regional or district hospitals (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of patient characteristics between patients receiving CSMVR and RAMVR before and after adjustment with inverse probability of treatment weighting.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eUnmatched\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eIPTW\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCSMVR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRAMVR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCSMVR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRAMVR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5547\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5660\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5640\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMale\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2585 (46.60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57 (50.44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.4179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2636 (46.57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2845 (50.44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMean age\u0026thinsp;\u0026plusmn;\u0026thinsp;SD (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e62.98\u0026thinsp;\u0026plusmn;\u0026thinsp;12.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60.13\u0026thinsp;\u0026plusmn;\u0026thinsp;12.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.3678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e62.84\u0026thinsp;\u0026plusmn;\u0026thinsp;13.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e62.40\u0026thinsp;\u0026plusmn;\u0026thinsp;85.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.7080\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCharlson Comorbidity Score\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.71\u0026thinsp;\u0026plusmn;\u0026thinsp;1.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.58\u0026thinsp;\u0026plusmn;\u0026thinsp;1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.3678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.71\u0026thinsp;\u0026plusmn;\u0026thinsp;1.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.73\u0026thinsp;\u0026plusmn;\u0026thinsp;12.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.9229\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCI score\u0026thinsp;\u0026gt;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e820 (14.75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (14.16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.8533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e834 (14.73%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e943 (16.72%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0038\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUnderlying disease\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e997 (17.97%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (16.81%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.7505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1015 (17.93%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1090 (19.33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0578\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1733 (31.24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 (25.43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2862\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1766 (31.20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1512 (26.81%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCongestive heart failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2639 (47.58%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65 (57.52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2691 (47.54%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3412 (60.50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActive endocarditis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e851 (15.34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (16.81%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.6675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e870 (15.37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e825 (14.63%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.2641\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeripheral vascular disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e186 (3.35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (3.53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e190 (3.35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e200 (3.55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.5559\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic lung disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e802 (14.46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (10.62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e817 (14.43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e683 (12.11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRenal disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e804 (14.46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (9.73%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1537\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e818 (15.04%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e624 (11.06%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnd stage renal disease under dialysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e272 (4.90%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (2.65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2711\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e277 (4.89%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e147 (2.61%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCerebral vascular disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e819 (14.76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (11.50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.3326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e835 (14.75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e625 (11.08%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiver cirrhosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e115 (2.07%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e117 (20.67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHospital Level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedical Center\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3564 (64.25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e102 (90.27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3637 (64.26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5059 (89.70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegional Hospital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1893 (34.13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (9.73%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1931 (34.12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e581 (10.30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistrict Hospital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e90 (1.62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e92 (1.63%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eIPTW: inverse probability of treatment weighting\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eCCI: Charlson Comorbidity Index\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eSD: standard deviation\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eESRD: end-stage renal disease\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIPTW was applied to match patients\u0026rsquo; age, gender, and Charlson Comorbidity Index (CCI) score, and this adjustment process yielded 5,660 patients undergoing CSMVR and 5,640 patients receiving RAMVR for further analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSurvival Analysis\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e showed the survival trends of patients receiving CSMVR and RAMVR during the study period. Before IPTW adjustment (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA), the RAMVR group exhibited a trend toward improved survival compared to CSMVR throughout the follow-up period, despite the significantly smaller sample size of RAMVR patients.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAfter IPTW adjustment (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB), which accounted for potential confounding factors, the RAMVR group demonstrated a clear survival advantage over CSMVR. The survival curves diverged early postoperatively and continued to separate over time, with RAMVR maintaining superior long-term survival rates and the CSMVR group exhibited a higher cumulative mortality risk.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMultivariate regression analysis of our study population\u003c/h3\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e showed the multivariate logistic regression analysis and we identified several independent predictors of mortality following mitral valve replacement. Patients undergoing RAMVR had significantly lower mortality risk compared to those undergoing CSMVR, both in the unmatched model (HR: 0.34, 95% CI: 0.19\u0026ndash;0.67, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00) and after IPTW adjustment (HR: 0.37, 95% CI: 0.33\u0026ndash;0.41, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate logistic regression analysis of risk factors for mortality after mitral valve replacement.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eUnmatched\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e \u003cp\u003eIPTW\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eHR\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u003cb\u003e95%CI\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003cb\u003e-value\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eHR\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e\u003cb\u003e95%CI\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003cb\u003e-value\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMale vs female\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.967\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.1742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.5019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e\u0026ge; 50 vs\u0026thinsp;\u0026lt;\u0026thinsp;50 (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSurgery\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eRAMVR vs CSMVR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.669\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.406\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHospital stay\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e\u0026ge; 20 vs\u0026thinsp;\u0026lt;\u0026thinsp;20 (days)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.0206\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003e\u003cb\u003eUnderlying disease\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCCI score\u0026thinsp;\u0026gt;\u0026thinsp;3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0594\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.4994\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePeripheral vascular disease\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.911\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.791\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.1556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.982\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.917\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.0635\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eChronic pulmonary disease\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.748\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.728\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.0569\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eRenal disease\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.464\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.539\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eDialysis\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.0445\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCerebral vascular disease\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.953\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.6251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.702\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.0765\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eLiver cirrhosis\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.666\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.4915\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.748\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.576\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.2984\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHospital level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMedical center\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.908\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.7574\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eIPTW: inverse probability of treatment weighting\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eHR: hazard ratio\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e95 CI: 95% confidence interval\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eCCI: Charlson Comorbidity Index\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAge\u0026thinsp;\u0026ge;\u0026thinsp;50 years was associated with a significantly increased risk of mortality in both models (Unmatched HR: 1.99, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01; IPTW HR: 2.73, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Similarly, hospital stay\u0026thinsp;\u0026ge;\u0026thinsp;20 days was linked to higher mortality (Unmatched HR: 1.16, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01; IPTW HR: 1.117, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02). Renal disease and dialysis dependence were also strong predictors of mortality. Renal disease was associated with elevated risk in both unmatched (HR: 1.46, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and IPTW models (HR: 1.80, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), while dialysis showed a weaker but still significant association (IPTW HR: 1.24, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.04). Chronic pulmonary disease was associated with lower mortality in the unmatched analysis (HR: 0.75, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00), but this association lost significance after adjustment (IPTW HR: 0.86, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.06).\u003c/p\u003e\n\u003ch3\u003eHospitalization-related outcome and cost analysis\u003c/h3\u003e\n\u003cdiv class=\"Heading\"\u003eHospitalization-related outcome and cost analysis\u003c/div\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e showed the hospitalization-related outcomes and the results of cost analysis. Patients who underwent RAMVR experienced significantly more favorable perioperative metrics compared to those who received CSMVR. In both unmatched and IPTW-adjusted analyses, the RAMVR group had significantly shorter hospital stays (16.60 vs. 22.73 days, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and ICU stays (4.39 vs. 9.11 days, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). In terms of post-operative complications, new-onset dialysis occurred only in the CSMVR group (9 cases, 0.16%), reaching significance after IPTW adjustment (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00). Stroke rates were slightly lower in RAMVR (8.40% vs. 10.32%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00 after IPTW), and the rates of sternum wound infection and re-operations were low and statistically comparable between groups.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHospitalization outcome and cost analysis for mitral valve replacement.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eUnmatched\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eIPTW\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCSMVR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRAMVR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCSMVR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRAMVR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHospital stay (days)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.73\u0026thinsp;\u0026plusmn;\u0026thinsp;13.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.60\u0026thinsp;\u0026plusmn;\u0026thinsp;10.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.73\u0026thinsp;\u0026plusmn;\u0026thinsp;13.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.50\u0026thinsp;\u0026plusmn;\u0026thinsp;70.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eICU stay (days)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.11\u0026thinsp;\u0026plusmn;\u0026thinsp;12.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.39\u0026thinsp;\u0026plusmn;\u0026thinsp;7.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.11\u0026thinsp;\u0026plusmn;\u0026thinsp;12.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.36\u0026thinsp;\u0026plusmn;\u0026thinsp;51.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePost-operative complications\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNew dialysis (including CVVH)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (0.16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6683\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9 (0.16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStroke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e573 (10.33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (8.85%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e584 (10.32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e474 (8.40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSternum wound infection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e127 (2.29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;3 (\u0026le;\u0026thinsp;2.65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.7141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e130 (2.30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e119 (2.11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.504\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRe-operation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (0.31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17 (0.30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedical cost (USD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndex hospitalization cost\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16720.65\u0026thinsp;\u0026plusmn;\u0026thinsp;10182.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12875.32\u0026thinsp;\u0026plusmn;\u0026thinsp;5269.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16719.08\u0026thinsp;\u0026plusmn;\u0026thinsp;10283.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12881.35\u0026thinsp;\u0026plusmn;\u0026thinsp;3602.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdditional medical cost\u003c/p\u003e \u003cp\u003e1\u0026ndash;3 months after MVR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e964.77\u0026thinsp;\u0026plusmn;\u0026thinsp;412.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e774.03\u0026thinsp;\u0026plusmn;\u0026thinsp;470.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e963.48\u0026thinsp;\u0026plusmn;\u0026thinsp;416.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e760.77\u0026thinsp;\u0026plusmn;\u0026thinsp;313.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.631\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdditional medical cost\u003c/p\u003e \u003cp\u003e4\u0026ndash;6 months after MVR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e775.75\u0026thinsp;\u0026plusmn;\u0026thinsp;365.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e212.34\u0026thinsp;\u0026plusmn;\u0026thinsp;95.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e774.80\u0026thinsp;\u0026plusmn;\u0026thinsp;369.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e243.66\u0026thinsp;\u0026plusmn;\u0026thinsp;74.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdditional medical cost\u003c/p\u003e \u003cp\u003e7\u0026ndash;12 months after MVR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1064.58\u0026thinsp;\u0026plusmn;\u0026thinsp;526.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e364.89.60\u0026thinsp;\u0026plusmn;\u0026thinsp;169.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1063.32\u0026thinsp;\u0026plusmn;\u0026thinsp;531.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e388.28\u0026thinsp;\u0026plusmn;\u0026thinsp;122.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eICU: intensive care uint\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eCVVH: continuous veno-venous hemodilaysis\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eMVR: mitral valve replacement\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e For the cost analysis, because the National Health Insurance in Taiwan does not reimburse the procedure fee for robotic-assisted surgery during the index hospitalization, and patient co-payments are not captured in the claims data. As a result, the recorded hospitalization cost appeared significantly lower in the RAMVR group (adjusted mean: USD \u003cspan\u003e$\u003c/span\u003e12,881.35\u0026thinsp;\u0026plusmn;\u0026thinsp;3602.84 vs. \u003cspan\u003e$\u003c/span\u003e16,719.08\u0026thinsp;\u0026plusmn;\u0026thinsp;10283.51, \u003cem\u003ep\u003c/em\u003e \u0026thinsp;\u0026lt;\u0026thinsp;0.01). To estimate the broader economic impact, we analyzed the post-discharge medical expenses. No significant difference was observed during the first three months after surgery. However, the robotic group incurred substantially lower additional medical costs during months 4\u0026ndash;6 (243.66\u0026thinsp;\u0026plusmn;\u0026thinsp;74.39 vs. 774.80\u0026thinsp;\u0026plusmn;\u0026thinsp;369.15 USD, \u003cem\u003ep\u003c/em\u003e \u0026thinsp;\u0026lt;\u0026thinsp;0.01) and months 7\u0026ndash;12 (1063.32\u0026thinsp;\u0026plusmn;\u0026thinsp;531.48 vs. 388.28\u0026thinsp;\u0026plusmn;\u0026thinsp;122.68 USD, \u003cem\u003ep\u003c/em\u003e \u0026thinsp;\u0026lt;\u0026thinsp;0.01). When calculating the total additional medical expenses within one- year post-surgery, it was also significantly lower in the RAMVR group (2801.60\u0026thinsp;\u0026plusmn;\u0026thinsp;470.54 vs. 1392.71\u0026thinsp;\u0026plusmn;\u0026thinsp;1506.08 USD, \u003cem\u003ep\u003c/em\u003e \u0026thinsp;\u0026lt;\u0026thinsp;0.01), suggesting reduced postoperative resource utilization. \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis is a nationwide population-based research to systematically evaluate the mid-term outcomes of robotic-assisted mitral valve replacement in real-world setting. In this study, we demonstrated that RAMVR was associated with a significant survival benefit compared to CSMVR. Additionally, patients undergoing RAMVR experienced a significantly shorter hospital stay and ICU stay, which may contribute to improved postoperative recovery and reduced healthcare resource utilization.\u003c/p\u003e \u003cp\u003eCompared to mitral valve repair, RAMVR is considered more technically challenging due to the small surgical ports, which can complicate prosthesis implantation and suturing, leading to significantly longer aortic clamp and cardiopulmonary bypass durations [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. As a result, most studies have focused on robotic-assisted mitral valve repair rather than replacement, and the limited research available on RAMVR has primarily been conducted in highly specialized centers, where surgeries were performed by dedicated teams of experienced surgeons, anesthesiologists, and nurses, and comprehensive surgical details were kept [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan additionalcitationids=\"CR12 CR13\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The three studies that compared the clinical outcomes of RAMVR and CSMVR reported similar rates of all-cause mortality and perioperative complications [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn contrast, our study, which included a broader patient population across multiple hospitals over six years, demonstrated superior survival rates for the RAMVR group in both unadjusted analyses and after adjustment using the inverse probability of treatment weighting method. Several factors may explain this discrepancy. First, most previous studies were conducted in high-volume institutions with strong surgical teams for both RAMVR and CSMVR, minimizing performance variability. In our nationwide cohort, RAMVR was almost exclusively performed in medical centers, while CSMVR was more frequently carried out in regional or district hospitals. This may reflect differences in institutional expertise and perioperative care, which could have influenced patient outcomes despite statistical adjustment.\u003c/p\u003e \u003cp\u003eSecond, some confounding factors might not be fully captured by the Charlson comorbidity score, such as critical preoperative status, active endocarditis, significantly reduced left ventricular ejection fraction, and pulmonary hypertension. To further minimize selection bias, incorporating a well-validated risk assessment tool specifically designed for cardiac surgeries, such as the European System for Cardiac Operative Risk Evaluation II (EuroSCORE II) or Society of Thoracic Surgeons Score, would be more ideal [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Third, patient selection for RAMVR in Taiwan may inherently favor higher-performing institutions and better-coordinated multidisciplinary care, even though baseline clinical characteristics were similar after IPTW adjustment. Forth, the survival benefit may be more apparent in our cohort because we focused specifically on mitral valve replacement rather than repair. MVR is typically reserved for patients with more advanced disease or unsuitable anatomy for repair, and the benefits of a less invasive approach\u0026mdash;such as reduced surgical trauma, shorter ICU stays, and fewer complications\u0026mdash;may translate more clearly into survival advantages in this higher-risk population. Fourth, prior studies often had limited follow-up periods and small RAMVR sample sizes, making them less likely to detect survival differences over time. Our study included follow-up up to 5\u0026ndash;6 years, allowing the survival curves to separate more distinctly in the mid-term. Finally, advances in robotic technology, increased team experience, and improved perioperative management in recent years may have contributed to better outcomes in RAMVR patients compared to those reported in earlier studies.\u003c/p\u003e \u003cp\u003eDespite its potential benefits, RAMVR remained infrequent in our national database, accounting for only 2% of all mitral valve replacements during the study period. Several factors may have accounted for this low adoption rate. First, the high material and infrastructure costs for both hospitals and patients result in highly selective patient eligibility for robotic-assisted surgery. Second, the significantly longer operative time may reduce cost-effectiveness in operating room utilization. Third, RAMVR requires extensive training for surgeons to achieve consistent outcomes. A recent report from Germany found that material and infrastructure costs of RAMVR were significantly higher than the national benchmark for non-robotic surgeries by \u0026euro;1,384 vs. \u0026euro;985, respectively (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), but the overall cost was not significantly higher, indicating the high cost of RAMVR could be partially offset by a shorter hospital stay [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Regarding operative time, studies from high-volume centers have shown that RAMVR typically took around 240 to 320 minutes to perform, which was about one to two hours longer than CSMVR [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, with six months of structured team training or experience exceeding 50 cases, the operative time could be reduced to approximately 200 minutes [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. It is reasonable to deduce that cost-effectiveness of RAMVR could also be improved over time in high-volume centers [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur study has several limitations. First, although the analysis of NHIRD data offers multiple advantages, it lacks important clinical details such as surgical time, cardiopulmonary bypass duration, aortic clamp time, and the amount of transfusion. Second, the study design relied heavily on the ICD system and procedure codes, which may introduce coding errors, misclassifications, or variations between hospitals, potentially affecting the results. Third, the longest follow-up period for RAMVR in this study was five to six years, which is insufficient to fully assess long-term outcomes. Finally, advancements in medical equipment, medication, and overall care quality during the observation period could have influenced the clinical outcomes. Future research should focus on long-term clinical outcomes, cost-effectiveness analyses, and strategies to optimize patient selection for RAMVR, especially in more diverse populations and across different healthcare systems. Additionally, incorporating standardized risk assessment tools such as EuroSCORE II could enhance the accuracy of outcome comparisons and refine best practices for mitral valve replacement.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn this population-based national study, we demonstrated the survival benefit of RAMVR compared with CSMVR. Patients receiving a RAMVR also demonstrated a significantly shorter length of hospital and ICU stay.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone declared.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eC.M. Otto, R.A. Nishimura, R.O. Bonow, B.A. Carabello, J.P. Erwin, F. Gentile, H. Jneid, E.V. Krieger, M. Mack, C. McLeod, P.T. O\u0026rsquo;Gara, V.H. Rigolin, T.M. Sundt, A. Thompson, C. Toly, 2020 ACC/AHA Guideline for the Management of Patients With Valvular Heart Disease: Executive Summary: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines, Circulation 143(5) (2021) e35-e71.\u003c/li\u003e\n \u003cli\u003eA. Vahanian, F. Beyersdorf, F. Praz, M. Milojevic, S. Baldus, J. Bauersachs, D. Capodanno, L. Conradi, M. De Bonis, R. De Paulis, V. Delgado, N. Freemantle, M. Gilard, K.H. Haugaa, A. Jeppsson, P. J\u0026uuml;ni, L. Pierard, B.D. Prendergast, J.R. S\u0026aacute;daba, C. Tribouilloy, W. Wojakowski, E.E.S.D. Group, E.S.C.N.C. Societies, 2021 ESC/EACTS Guidelines for the management of valvular heart disease: Developed by the Task Force for the management of valvular heart disease of the European Society of Cardiology (ESC) and the European Association for Cardio-Thoracic Surgery (EACTS), Eur. Heart J. 43(7) (2022) 561-632.\u003c/li\u003e\n \u003cli\u003eJ.M. Hemli, N.C. Patel, Robotic Cardiac Surgery, Surg. Clin. North Am. 100(2) (2020) 219-236.\u003c/li\u003e\n \u003cli\u003eL.W. Nifong, W.R. Chitwood, P.S. Pappas, C.R. Smith, M. Argenziano, V.A. Starnes, P.M. Shah, Robotic mitral valve surgery: a United States multicenter trial, J. Thorac. Cardiovasc. Surg. 129(6) (2005) 1395-404.\u003c/li\u003e\n \u003cli\u003eG.I. Barbash, S.A. Glied, New technology and health care costs--the case of robot-assisted surgery, N. Engl. J. Med. 363(8) (2010) 701-4.\u003c/li\u003e\n \u003cli\u003eF. Yanagawa, M. Perez, T. Bell, R. Grim, J. Martin, V. Ahuja, Critical Outcomes in Nonrobotic vs Robotic-Assisted Cardiac Surgery, JAMA Surg 150(8) (2015) 771-7.\u003c/li\u003e\n \u003cli\u003eM.L. Williams, B. Hwang, L. Huang, A. Wilson-Smith, J. Brookes, A. Eranki, T.D. Yan, T.S. Guy, J. Bonatti, Robotic versus conventional sternotomy mitral valve surgery: a systematic review and meta-analysis, Annals of cardiothoracic surgery 11(5) (2022) 490-503.\u003c/li\u003e\n \u003cli\u003eH. Zhao, H. Zhang, M. Yang, C. Xiao, Y. Wang, C. Gao, R. Wang, [Comparison of quality of life and long-term outcomes following mitral valve replacement through robotically assisted versus median sternotomy approach], Nan Fang Yi Ke Da Xue Xue Bao 40(11) (2020) 1557-1563.\u003c/li\u003e\n \u003cli\u003eH. Quan, V. Sundararajan, P. Halfon, A. Fong, B. Burnand, J.C. Luthi, L.D. Saunders, C.A. Beck, T.E. Feasby, W.A. Ghali, Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data, Med Care 43(11) (2005) 1130-9.\u003c/li\u003e\n \u003cli\u003eH.Y. Tsai, Y.W. Huang, S.Y. Chang, L.Y. Huang, C.J. Lin, P.C. Lee, The reimbursement coverage decisions and pricing rules for medical devices in Taiwan, GMS Health Innov Technol 16 (2022) Doc02.\u003c/li\u003e\n \u003cli\u003eC.-C. Kuo, H.-H. Chang, C.-H. Hsing, H.-P. Hii, N.-C. Wu, C.-M. Hsu, C.-I. Chen, B.-C. Cheng, Robotic mitral valve replacements with bioprosthetic valves in 52 patients: experience from a tertiary referral hospital, Eur. J. Cardiothorac. Surg. 54(5) (2018) 853-859.\u003c/li\u003e\n \u003cli\u003eW. Yan, Y. Wang, W. Wang, Q. Wang, X. Zheng, S. Yang, Propensity-matched analysis of robotic versus sternotomy approaches for mitral valve replacement, J. Robot. Surg. 17(5) (2023) 2375-2386.\u003c/li\u003e\n \u003cli\u003eU.F.W. Franke, F. Huether, M. Ghinescu, M. Ortega Gaviria, M.I. Rufa, M. Albert, A. Ursulescu, N. Goebel, Robotically assisted mitral valve surgery-experience during the restart of a robotic program in Germany, Annals of cardiothoracic surgery 11(6) (2022) 596-604.\u003c/li\u003e\n \u003cli\u003eH. Sicim, M. Kadan, G. Erol, V. Yildirim, C. Bolcal, U. Demirkilic, Comparison of postoperative outcomes between robotic mitral valve replacement and conventional mitral valve replacement, J. Card. Surg. 36(4) (2021) 1411-1418.\u003c/li\u003e\n \u003cli\u003eS.K. Koo, R. Dignan, E.Y.W. Lo, C. Williams, W. Xuan, Evidence-Based Determination of Cut-Off Points for Increased Cardiac-Surgery Mortality Risk With EuroSCORE II and STS: The Best-Performing Risk Scoring Models in a Single-Centre Australian Population, Heart Lung Circ 31(4) (2022) 590-601.\u003c/li\u003e\n \u003cli\u003eG. Coyan, L.M. Wei, A. Althouse, H.G. Roberts, D. Schauble, T. Murashita, C.C. Cook, J.S. Rankin, V. Badhwar, Robotic mitral valve operations by experienced surgeons are cost-neutral and durable at 1 year, J. Thorac. Cardiovasc. Surg. 156(3) (2018) 1040-1047.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-robotic-surgery","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jors","sideBox":"Learn more about [Journal of Robotic Surgery](http://link.springer.com/journal/11701)","snPcode":"11701","submissionUrl":"https://submission.nature.com/new-submission/11701/3","title":"Journal of Robotic Surgery","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Mitral valve replacement, robot, sternotomy, cost","lastPublishedDoi":"10.21203/rs.3.rs-6852734/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6852734/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study aimed to compare survival, complications, and healthcare costs between robotic-assisted and conventional sternotomy mitral valve replacement, using a nationwide population-based dataset from Taiwan.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatients who underwent isolated surgical mitral valve replacement between 2016 and 2021 were identified from Taiwan’s National Health Insurance Research Database. Inverse probability of treatment weighting was used to adjust for baseline differences. Survival, postoperative complications, and medical costs were compared between the two surgical approaches.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter adjustment, a total of 5,660 patients who underwent conventional sternotomy and 5640 who received robotic-assisted mitral valve replacement were used for analysis. The robotic-assisted group had significantly better survival (hazard ratio: 0.37, 95% confidence interval: 0.33–0.41, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01), shorter hospital stays (16.5 vs. 22.7 days, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01), shorter intensive care stays (4.4 vs. 9.1 days, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01), and lower rates of dialysis and stroke. The additional medical expenses incurred within one year after surgery, excluding the cost of the initial hospitalization, were also significantly lower in the robotic-assisted group (1,640 vs. 4,003 United States dollars, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this national population-based analysis, robotic-assisted mitral valve replacement was associated with better mid-term survival, shorter hospital stays, and reduced medical costs compared with conventional sternotomy. These findings support the use of robotic-assisted surgery as a safe and effective alternative in selected patients undergoing mitral valve replacement.\u003c/p\u003e","manuscriptTitle":"Mid-term Outcomes of Robotic Assisted versus Conventional Sternotomy for Mitral Valve Replacement: Inverse Probability of Treatment Weighting Survival Analysis ","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-17 09:28:15","doi":"10.21203/rs.3.rs-6852734/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-18T22:40:21+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"338072576791378218496915928810070512192","date":"2025-06-18T16:51:54+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-18T11:09:29+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-17T15:57:13+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-17T01:58:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"295975938262799882960413891396839264190","date":"2025-06-16T18:28:16+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-15T11:53:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"37952794628020540201390213738052491955","date":"2025-06-15T11:44:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"160239670057156704436970840597493514468","date":"2025-06-14T17:48:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"205805099518843622740390764948565729993","date":"2025-06-13T15:56:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"147412977871619433562576029705870424844","date":"2025-06-13T08:28:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"62897875564429702494898230301976986075","date":"2025-06-13T03:44:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"235484829607754671623807868685590515806","date":"2025-06-13T02:52:00+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-13T02:49:11+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-11T15:27:27+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-11T08:52:47+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Robotic Surgery","date":"2025-06-09T09:04:07+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-robotic-surgery","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jors","sideBox":"Learn more about [Journal of Robotic Surgery](http://link.springer.com/journal/11701)","snPcode":"11701","submissionUrl":"https://submission.nature.com/new-submission/11701/3","title":"Journal of Robotic Surgery","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"bf7fadfc-725b-4ac8-83a4-d95370f3fbbf","owner":[],"postedDate":"June 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-07-21T16:11:06+00:00","versionOfRecord":{"articleIdentity":"rs-6852734","link":"https://doi.org/10.1007/s11701-025-02564-2","journal":{"identity":"journal-of-robotic-surgery","isVorOnly":false,"title":"Journal of Robotic Surgery"},"publishedOn":"2025-07-16 16:05:40","publishedOnDateReadable":"July 16th, 2025"},"versionCreatedAt":"2025-06-17 09:28:15","video":"","vorDoi":"10.1007/s11701-025-02564-2","vorDoiUrl":"https://doi.org/10.1007/s11701-025-02564-2","workflowStages":[]},"version":"v1","identity":"rs-6852734","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6852734","identity":"rs-6852734","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}