Real-world outcomes of robotic and non-robotic hysterectomy for endometrial cancer: insights from a national cohort.

This was a retrospective, population-based study using 2018–2020 Taiwan Cancer Registry who underwent one of three surgical approaches (open, laparoscopic, or robot-assisted) and outcomes were evaluated using NHIRD with follow-up data available through 2021. The linkage between the Taiwan Cancer Registry and the NHIRD was established using encrypted National identification numbers, which serve as unique identifiers across all healthcare encounters in Taiwan. This linkage achieved a match rate of 98.7%, with unmatched cases primarily due to death, migration, or data entry errors. The NHIRD captures all deaths through integration with the national death registry and records all healthcare events across hospitals, ensuring nearly complete follow-up. This population-based linkage design minimizes selection bias, enables comprehensive tracking of outcomes, and provides robust data validity for evaluating real-world surgical effectiveness and safety. Patients with histologically confirmed endometrial carcinoma (ICD-O-3 code C54.1) and their primary surgical approach were first identified from the Taiwan Cancer Registry, which provides precise topographic and morphological classification. Subsequent information on surgical timing, hospitalization, perioperative complications, transfusion, and readmission was obtained from the NHIRD using ICD-10 clinical codes (C54.1 for malignant neoplasm of the endometrium). This combined use of ICD-O-3 for accurate cancer identification and ICD-10 for outcome verification ensured both diagnostic precision and comprehensive follow-up in the analysis. During the study period (2018–2020), RAH was available as a self-pay procedure in Taiwan, prior to receiving formal National Health Insurance reimbursement approval in September 2024. The use of the Taiwan Cancer Registry as the diagnostic criterion for EC is both stringent and reliable. Information on surgery type (OH, LH or RAH), cancer stage, tumor size (mm), lymphovascular invasion, and smoking status was obtained by linking with the Taiwan Cancer Registry. Approval for the study was granted by the Institutional Review Board of MacKay Memorial Hospital (Taiwan; approval number 22MMHIS360e). Because this was a retrospective data-based study, the requirement for obtaining informed consent was formally waived. Comorbid conditions in patients with EC were determined using the Charlson Comorbidity Index (CCI), with coding adapted according to reference [ 9 ]. The study compared survival distributions of the three surgical modalities using Kaplan–Meier estimation, with the log-rank test applied to assess statistical differences. Overall survival was defined as the interval extending from surgical intervention to death from any cause. To account for baseline imbalances and multiple confounders among the three surgical groups, we applied inverse probability of treatment weighting (IPTW) using propensity scores estimated via multinomial logistic regression. Covariates included age, Charlson Comorbidity Index, cancer stage, body mass index, smoking status, tumor size, lymphovascular invasion, and receipt of neoadjuvant therapy. This approach creates a weighted pseudo-population in which these factors are evenly distributed across treatment arms, thereby mitigating selection bias arising from differences in patient complexity and institutional capability. By balancing these covariates, IPTW reduces the influence of confounding variables that could otherwise worsen observed clinical outcomes in older or more advanced cases predominantly treated with OH. Additionally, inclusion of surgery year and hospital level in the propensity model partially adjusted for temporal adoption effects, since RAH was primarily performed later in the study period at tertiary centers by experienced gynecologic oncologists. After weighting, all standardized mean differences were < 0.1, indicating adequate balance among groups and supporting the validity of comparative outcome estimates. Total medical costs were calculated using reimbursement data from the NHIRD, encompassing all inpatient and outpatient expenses related to the index surgery and follow-up. These included operating room time, anesthesia, surgical and medical equipment, physician and nursing fees, medications, transfusions, ward charges, intensive care, and any subsequent readmissions. This approach captures both procedural and hospitalization-related costs, providing a comprehensive estimate of healthcare expenditures under Taiwan’s National Health Insurance system. Data analysis included descriptive, survival, and regression components. Categorical parameters were summarized as frequencies and percentages, whereas continuous variables were expressed as mean (SD) or median (IQR). Kaplan–Meier plots with log-rank tests assessed survival disparities between surgical strategies, with overall survival defined as the time from surgery to death. To evaluate differences in postoperative outcomes, logistic regression produced odds ratios (ORs) with 95% confidence intervals (CIs). Analyses were performed using both the crude dataset and the IPTW-adjusted cohort to control for confounders. Two-tailed p-values < 0.05 were considered significant.

From 2018 to 2021, the NHIRD identified 3,176 OH, 424 RAH, and 1,760 LH cases (Fig.  1 ). Mean ages were 56.74 ± 10.83 (OH), 55.34 ± 10.60 (RAH), and 56.06 ± 10.86 (LH), with most patients aged 50–69 years (Table  1 ). CCI ≥ 3 showed no group difference. Late-stage disease (stage 3–4) was more frequent in OH (13.28%) than LH (2.84%) or RAH (2.83%, p  < 0.0001). Tumor sizes were larger in OH (47.58 ± 31.73 mm) than LH (30.26 ± 19.98 mm³) and RAH (32.52 ± 20.08 mm³). After IPTW, baseline characteristics were balanced (Table  2 ). Fig. 1 enrollment flow enrollment flow Table 1 Demographic data (OH ( N  = 3,176), RAH ( N  = 424), LH ( N  = 1,760)) OH ( N  = 3176) RAH ( N  = 424) LH ( N  = 1760) P -value SMD N or mean (SD) % or median (IQR) N or mean (SD) % or median (IQR) N or mean (SD) % or median (IQR) OH/RAH OH/LH RAH/LH OH/RAH OH/LH RAH/LH Age 56.74(10.83) 56.50(14) 55.34(10.60) 56(13) 56.06(10.86) 56(14) 0.0122 0.0364 0.2141 −0.13078 0.063 −0.06774 Age Group 18 to < 50 762 23.99% 115 27.12% 441 25.06% 0.1584 0.4041 0.3807 0.07176 −0.0124 0.04702 50 to < 70 2050 64.55% 270 63.68% 1137 64.60% 0.726 0.9688 0.7216 −0.01807 −0.0005 −0.01923 70+ 364 11.46% 39 9.20% 182 10.34% 0.1651 0.2295 0.4836 −0.07436 0.018 −0.03847 BMI 26.66(5.55) 25.70(7.10) 26.55(5.82) 25.20(6.80) 26.59(5.51) 25(7.10) 0.6972 0.659 0.893 0.019 0.013 −0.007 Normal 1086 34.19% 160 37.74% 649 36.88% 0.1499 0.0588 0.7418 0.07381 −0.0281 0.01779 Overweight 2058 64.00% 251 59.20% 1098 62.39% 0.0239 0.0909 0.2252 −0.11549 0.0167 −0.06529 NA 32 1.01% 13 3.07% 13 0.74% 0.0003 0.341 < 0.0001 0.14598 −0.1766 0.17082 Charlson Comorbidity Index 0–1 857 26.98% 106 25.00% 481 27.33% 0.3861 0.7934 0.3314 −0.04521 −0.0039 −0.05298 2 1385 43.61% 207 48.82% 790 44.89% 0.0424 0.3863 0.1443 0.10462 −0.0129 0.07885 3+ 934 29.41% 111 26.18% 489 27.78% 0.1689 0.2276 0.5063 −0.07207 0.018 −0.03614 Cancer stage (CTNM) 1 2406 75.76% 382 90.09% 1632 92.73% < 0.0001 < 0.0001 0.0693 0.38797 −0.02466 −0.09399 2 348 10.96% 30 7.08% 78 4.43% 0.0143 < 0.0001 0.0242 −0.13576 0.126 0.11362 3 385 12.12% 12 2.83% 50 2.84% < 0.0001 < 0.0001 0.8795 −0.37102 −0.1913 −0.00825 4 37 1.16% 0.0787 < 0.0001 0.5419 −0.11155 −0.1913 0.02924 Neoadjuvant therapy Yes 84 2.64% 7 1.65% 35 1.99% 0.2207 0.15 0.6495 −0.06856 0.0217 −0.02525 No 3092 97.36% 417 98.35% 1725 98.01% 0.2207 0.15 0.6495 −0.06856 −0.0217 −0.02525 Tumor size(mm) 47.58(31.73) 40(36) 32.52(20.08) 30(27) 30.26(19.98) 26(24) < 0.0001 < 0.0001 0.0471 0.02689 0.651 −0.20356 Laterality Lymph vascular invasion 1045 32.90% 114 26.89% 348 19.77% 0.0128 < 0.0001 0.0013 −0.13162 0.1512 0.16868 Smoking 125 3.94% 11 2.59% 62 3.52% 0.1736 0.4666 0.3397 −0.07549 0.0111 −0.0539 Operation Pelvic lymphadenectomy 167 5.26 15 < 0.0001 < 0.0001 0.2104 −0.3109 0.26268 −0.07815 Laparoscopic pelvic lymphadenectomy 0 0 0 0 0 0 Paraaortic lymphadenectomy 48 1.51 0 0 18 1.02 0.0108 0.1523 0.0365 −0.17516 0.04369 −0.14372 Laparoscopic paraaortic lymphadenectomy 0 0 0 0 0 0 Hospital level Medical center 1597 50.28 252 59.43 1113 63.24 < 0.0001 0.003 < 0.0001 1.61062 2.00132 0.23837 Other 1055 524 172 40.56 600 34.09 < 0.0001 0.003 < 0.0001 1.61062 2.00132 0.23837 Missing 524 16.5 47 2.67 < 0.0001 0.003 < 0.0001 1.61062 2.00132 0.23837 Specialty Obstetrics and Gynecology 2625 82.65 422 99.53 1702 96.7 < 0.0001 0.0214 < 0.0001 1.57592 1.94375 0.20672 Others 27 0.85 11 0.63 < 0.0001 0.0214 < 0.0001 1.57592 1.94375 0.20672 Missing 524 16.5 2 0.47 47 2.67 < 0.0001 0.0214 < 0.0001 1.57592 1.94375 0.20672 Demographic data (OH ( N  = 3,176), RAH ( N  = 424), LH ( N  = 1,760)) Before IPTW, transfusion rates were 30.32% (OH), 13.92% (RAH), and 11.93% (LH) ( p  < 0.0001) (Table  3 ). Mean hospital stay was 8.09 days (OH), 5.99 (RAH), and 5.44 (LH) ( p  < 0.0001). ICU admission rates were 3.94% (OH), 2.12% (RAH), and 1.48% (LH). Reoperation rates were 0.98% (OH), 4.72% (RAH), and 3.01% (LH); readmissions were 3.31%, 6.37%, and 4.72%, respectively (both p  < 0.01). Costs were NTD 130,281 (OH), 65,992 (RAH), and 121,001 (LH) (all p  < 0.0001), with consistent savings across 30-, 60-, 90-day, and 12-month follow-ups. After IPTW, transfusion remained lower in RAH (15.51%) vs. OH (29.67%, p  < 0.0001); LH was not different from RAH. Mean stay was 8.05 (OH), 5.90 (RAH), and 5.45 (LH) days ( p  < 0.0001). ICU days were shorter in RAH vs. OH ( p  = 0.0097). Reoperation was higher in RAH (3.86%) vs. OH (1.01%, p  < 0.0001) and in LH (4.19%) vs. RAH (3.07%, p  = 0.0489). Costs favored RAH and LH over OH across all time points ( p  < 0.0001). Age Group Table 2 IPTW_Demographic data (A) OH vs. RAH; (B) RAH vs. LH A OH (N = 3600) RAH (N = 3583) P-value SMD N or mean (SD) % or median (IQR) N or mean (SD) % or median (IQR) OH/RAH OH/RAH Age 56.58(11.52) 56(14) 57.16(32.03) 57.00(12) 0.3028 0.05482 Age Group 18 to < 50 877 24.36% 810 22.60% 0.0773 −0.0405 50 to < 70 2320 64.44% 2350 65.60% 0.3037 0.02412 70+ 403 11.20% 423 11.81% 0.4187 0.02001 BMI 26.64(5.92) 25.60(7.20) 26.73(17.32) 25.40(7.20) 0.7697 −0.007 Normal 1247 34.63% 1314 36.67% 0.0719 0.04239 Overweight 2309 64.13% 2231 62.28% 0.1039 −0.03834 NA 44 1.24% 38 1.06% 0.4863 −0.01241 Charlson Comorbidity Index 0–1 963 26.74% 974 27.18% 0.6785 0.00989 2 1592 44.23% 1613 45.04% 0.4902 0.01625 3+ 1045 29.03% 996 27.79% 0.2426 −0.02776 Cancer stage (CTNM) 1 2788 77.45% 2805 78.28% 0.3954 0.02253 2 378 10.50% 344 9.60% 0.2045 −0.03149 3 396 11.00% 408 11.40% 0.5922 0.01552 4 38 1.06% 26 0.72% 0.1354 −0.03973 Neoadjuvant therapy Yes 91 2.53% 63 1.75% 0.0233 −0.05342 No 3509 97.47% 3520 98.25% 0.0233 −0.05342 Tumor size(mm) 46.99(33.32) 40(35) 35.13(63.58) 30(30) <.0001 0.01446 Laterality Lymph vascular invasion 1160 32.22% 1164 32.48% 0.8133 0.0057 Smoking 136 3.78% 175 4.88% 0.0217 0.06207 Operation Pelvic lymphadenectomy 185 5.14 9 0.24 <.0001 Laparoscopic pelvic lymphadenectomy 0 0 0 0 Paraaortic lymphadenectomy 54 1.49 0 0 <.0001 Laparoscopic paraaortic lymphadenectomy 0 0 0 0 Hospital level Medical center 1820 50.56 1983 55.34 <.0001 Other 1780 49.44 1600 44.66 <.0001 Specialty Obstetrics and Gynecology 2998 83.27 3556 99.24 <.0001 Others 602 16.73 27 0.76 <.0001 B LH (N = 2185)  RAH (N = 2168)  P-value  SMD N or mean (SD) % or median (IQR) N or mean (SD) % or median (IQR) OH/RAH OH/RAH Age 55.90(12.18) 56(14) 55.86(24.17) 56(13) 0.9529 −0.0032 Age Group 18 to < 50 559 25.56% 561 25.88% 0.807 0.00737 50 to < 70 1405 64.31% 1384 63.82% 0.7344 −0.01028 70+ 221 10.13% 223 10.30% 0.8533 0.00571 BMI 26.55(6.11) 25.60(7.20) 26.60(13.11) 25.30(6.70) 0.8791 −0.005 Normal 808 37.01% 810 37.36% 0.808 0.00735 Overweight 1349 61.73% 1331 61.40% 0.8248 −0.00669 NA 28 1.27% 27 1.24% 0.9308 −0.00216 Charlson Comorbidity Index 0–1 586 26.82% 580 26.74% 0.9473 −0.01887 2 999 45.71% 1002 46.24% 0.7258 −0.00202 3+ 600 27.47% 586 27.03% 0.7438 0.01062 Cancer stage (CTNM) 1 2015 92.21% 1990 91.76% 0.5872 0.01482 2 108 4.94% 114 5.29% 0.6057 0.00452 3 62 2.85% 64 2.96% 0.883 0.00701 4 38 1.06% 26 0.72% 0.8097 −0.00993 Neoadjuvant therapy Yes 44 1.99% 2119 97.74% 0.5448 0.01978 No 2141 98.01% 49 2.26% 0.5448 0.01978 Tumor size(mm) 30.62(22.65) 27(24) 31.53(44.39) 30(25) 0.4209 −0.0651 Laterality Lymph vascular invasion 465 21.27% 466 21.51% 0.8465 0.0057 Smoking 73 3.35% 77 3.53% 0.7395 0.01066 Operation Pelvic lymphadenectomy 17 0.8 4 0.17 0.0026 Laparoscopic pelvic lymphadenectomy 0 0 0 0 paraaortic lymphadenectomy 22 1.03 0 0 <.0001 Laparoscopic paraaortic lymphadenectomy 0 0 0 0 Hospital level Medical center 1382 63.23 1300 59.97 <.0001 Other 803 36.77 868 40.03 <.0001 Specialty Obstetrics and Gynecology 2112 96.64 2151 99.19 <.0001 Others 73 3.34 17 0.81 <.0001 Table 3 Perioperative outcomes Unmatched group IPTW OH ( N  = 3176) RAH ( N  = 424) LH ( N  = 1760) OH/RAH OH/LH RAH/LH OH ( N  = 3600) RAH ( N  = 3583) P -value LH ( N  = 2185) RAH ( N  = 2168) P -value Transfusion (Y/N) N,% 963,30.32% 59,13.92% 210,11.93% < 0.0001 < 0.0001 0.2646 1068,29.67% 556,15.51% < 0.0001 267,12.22% 289,13.31% 0.2818 Length of Stay (day) Mean (Std) 8.09(5.97) 5.99(3.98) 5.44(3.31) < 0.0001 < 0.0001 0.0081 8.05(6.23) 5.90(11.80) < 0.0001 5.45(3.72) 6.06(9.03) < 0.0001 ICU stay (day) N,% 125,3.94% 9,2.12% 26,1.48% 0.064 < 0.0001 0.3421 134,3.73% 106, 2.96% 0.6163 39,1.51% 48,2.20% 0.089 Mean (Std) 2.56(2.31) 1.67(0.71) 2.32(1.77) 0.0088 0.494 0.3633 2.55(2.40) 1.79(2.44) 0.0097 2.22(1.99) 1.71(1.54) 0.2397 Follow-up time (day) Mean (Std) 808.1(346.1) 889.8.(344.4) 826.1(318.4) < 0.0001 0.065 0.0006 811.7(366.7) 879.6(1019.6) < 0.0001 826.4(354.6) 897.6(779.2) 0.0001 Reoperation N,% 31,0.98% 20,4.72% 53,3.01% < 0.0001 < 0.0001 0.0794 36,1.01% 138,3.86% < 0.0001 67,3.07% 91,4.19% 0.0489 Readmission N,% 105,3.31% 27,6.37% 83,4.72% 0.0016 0.0132 0.1626 119,3.32% 192,5.34% < 0.0001 105,4.79% 116,5.37% 0.386 Total Medical Cost In-hospital Mean (SD) 130,281(62077.9) 65992.2(27333.9) 121,001(32871.4) < 0.0001 < 0.0001 < 0.0001 129,151(64578.1) 67925.3(82609.7) < 0.0001 121,214(36697.5) 660301.9(61351.6) < 0.0001 30-day Mean (SD) 136,147(71901.8) 33956.3(1649.1) 123,351(43350.8) < 0.0001 < 0.0001 < 0.0001 134,943(75097.4) 74544.2(101603) < 0.0001 123,540(48611.9) 70941.0(75041.5) < 0.0001 60-day Mean (SD) 139,444(777283.5) 72486.3(34172.5) 124,629(44393.4) < 0.0001 < 0.0001 < 0.0001 138,117(80558.8) 75677.9(101846) < 0.0001 124,839(49778.6) 72260.9(75613.9) < 0.0001 90-day Mean (SD) 141,557(81445.4) 73129.9(34247.5) 125,459(45777.4) < 0.0001 < 0.0001 < 0.0001 140,173(84988.7) 765455.0(101924) < 0.0001 125,655(51285.8) 72883.7(75843.1) < 0.0001 12 month Mean (SD) 151,001(98790.3) 77314.9(39292.2) 131,557(60252.6) < 0.0001 < 0.0001 < 0.0001 149,516(103780) 80770.4(114676) < 0.0001 131,609(67100.1) 77252.5(87669.0) < 0.0001 IPTW_Demographic data (A) OH vs. RAH; (B) RAH vs. LH Perioperative outcomes At 1 month, overall complication rates were 7.65% (OH), 0.94% (RAH), and 6.08% (LH) ( p  < 0.0001). ORs: RAH vs. OH 0.1150 (95% CI: 0.0426–0.3104, p  < 0.001); RAH vs. LH 0.1471 (0.0539–0.4015, p  < 0.001) (Table  4 ). RAH reduced vascular complications (OR 0.9843, 95% CI: 0.9779–0.9886, p  = 0.0093), cardiac events (OR 0.9858, 95% CI: 0.9817–0.9900, p  = 0.0136), and ileus (OR 0.9871, 95% CI: 0.9832–0.9910, p  = 0.0186). After IPTW, complication rates remained lower: RAH 0.95%, OH 7.52%, LH 6.07% (both p  < 0.0001). Adjusted ORs: RAH vs. OH 0.1176 (95% CI: 0.0820–0.1685, p  < 0.001); RAH vs. LH 0.1401 (95% CI: 0.0867–0.2263, p  < 0.001). Specific reductions with RAH vs. OH included vascular (OR 0.9844, p  < 0.001), cardiac (0.9862, p  < 0.001), stroke (0.9932, p  < 0.001), pneumonia (0.9617, p  < 0.001), acute renal failure (0.9982, p  = 0.0103), peritonitis (0.9951, p  < 0.001), ileus (0.9951, p  < 0.001), genitourinary (0.5551, p  = 0.0340), wound disruption (0.9957, p  < 0.001), and surgical site infection (0.5257, p  = 0.0468). Versus LH, RAH lowered sepsis (OR 0.9948, p  = 0.0008), vascular (0.9909, p  < 0.001), cardiac (0.9883, p  < 0.001), stroke (0.9951, p  = 0.0011), pneumonia (0.9947, p  = 0.0007), peritonitis (0.9944, p  = 0.0005), ileus (0.9950, p  = 0.0009), and wound disruption (0.9972, p  = 0.0134). Table 4 Post-operative complication in one month after discharge form hospital for unmatching groups A Post-operative complications OH (N =3176) RAH (N = 424) LH (N =1760) P-value RAH/OH OH/LH RAH/LH N N N OH/RAH OH/LH RAH/LH OR 95% CI OR 95% CI OR 95% CI All 243,7.65% 4,0.94% 107,6.08% <.0001 0.0394 <.0001 0.115 0.0426 0.3104 1.2799 1.0116 1.6194 0.1471 0.0539 0.4015 Sepsis or disseminated intravascular coagulation 8,0.25% 0 9,0.51% 0.3009 0.1361 0.1401 0.9975 0.9957 0.9992 0.4913 0.1892 1.2757 0.9949 0.9916 0.9982 Pseudomembranous enterocolitis 0 0 0 Pulmonary embolism 4,0.13% 0 18 0.4647 0.9054 0.4874 0.9987 0.9975 1 1.1084 0.2028 6.0578 0.9989 0.9973 1.0004 Vascular complications 50,1.57% 0 0.0093 0.0513 0.0488 0.9843 0.9799 0.9886 1.7434 0.9899 3.0706 0.9909 0.9865 0.9954 Cardiac events 45,1.42% 0 21,1.19% 0.0136 0.5122 0.0238 0.9858 0.9817 0.99 1.1902 0.7067 2.0045 0.9881 0.983 0.9932 Stroke 22,0.69% 0 9,0.51% 0.0856 0.4399 0.1401 0.9931 0.9902 0.996 1.3571 0.6235 2.9537 0.9949 0.9916 0.9982 Pneumonia or flu 11,0.35% 0 14 0.2249 0.382 0.1401 0.9965 0.9945 0.9986 0.6762 0.2797 1.6349 0.9949 0.9916 0.9982 Respiratory complications 9 0 0.5267 0.463 0.3949 0.9991 0.998 1.0001 0.5537 0.1116 2.7464 0.9983 0.9964 1.0002 Acute renal failure 0 0.3704 0.5288 0.4874 0.9981 0.9966 0.9996 1.6637 0.3354 8.2517 0.9989 0.9973 1.0004 Peritonitis or peritoneal abscess 16,0.50% 0 10,0.57% 0.143 0.7646 0.1198 0.995 0.9925 0.9974 0.8861 0.4012 1.9568 0.9943 0.9908 0.9978 Ileus 41,1.29% 0 9,0.51% 0.0186 0.0088 0.1401 0.9871 0.9832 0.991 2.5444 1.2338 5.2475 0.9949 0.9916 0.9982 Genitourinary complications 32,1.01% 4 15,0.85% 0.2839 0.5905 0.4234 0.4656 0.1112 1.95 1.1841 0.6395 2.1925 0.5513 0.1256 2.4202 Disruption of operation wound 17 8 0.1708 0.3944 0.2719 0.9956 0.9933 0.9979 1.5541 0.5588 4.3218 0.9972 0.9947 0.9996 Other intraoperative complications 0.5267 0.463 0.3949 0.9991 0.998 1.0001 0.5537 0.1116 2.7464 0.9983 0.9964 1.0002 Surgical site infection 24,0.76% 10,0.57% 0.5166 0.4456 0.8093 0.6224 0.1466 2.6431 1.3325 0.6357 2.7928 0.8294 0.1811 3.7993 B Post-operative complications OPEN (N = 3600) RAS (N = 3583) P-value OR 95% CI LAP (N = 2185) RAS (N =2168) P-value OR 95% CI All 271,7.52% 34, 0.95% <.0001 0.1176 0.082 0.1685 133,6.07% 19.4591,0.90% <.0001 0.1401 0.0867 0.2263 Sepsis or disseminated intravascular coagulation 9,0.24% 0 0.0032 0.9976 0.996 0.9992 11,0.52% 0 0.0008 0.9948 0.9918 0.9978 Pseudomembranous enterocolitis 0 0 0 Pulmonary embolism 5,0.13% 0 0.0334 0.9987 0.9976 0.9999 2,0.11% 0 0.1156 0.9989 0.9974 1.0003 Vascular complications 56,1.56% 0 <.0001 0.9844 0.9803 0.9884 20,0.91% 0 <.0001 0.9909 0.987 0.9949 Cardiac events 50,1.38% 0 <.0001 0.9862 0.9824 0.99 26,1.17% 0 <.0001 0.9883 0.9838 0.9928 Stroke 25,0.68% 0 <.0001 0.9932 0.9905 0.9959 11,0.49% 0 0.0011 0.9951 0.9922 0.998 Pneumonia or flu 138,3.83% 0 <.0001 0.9617 0.9555 0.968 12,0.53% 0 0.0007 0.9947 0.9917 0.9978 Respiratory complications 3,0.09% 0 0.0671 0.9991 0.9981 1.0001 4,0.18% 0 0.051 0.9982 0.9965 1 Acute renal failure 7,0.18% 0 0.0103 0.9982 0.9968 0.9996 3,0.12% 0 0.1077 0.9988 0.9974 1.0003 Peritonitis or peritoneal abscess 18,0.49% 0 <.0001 0.9951 0.9928 0.9974 12,0.56% 0 0.0005 0.9944 0.9912 0.9975 Ileus 46,1.27% 0 <.0001 0.9873 0.9836 0.9909 11,0.50% 0 0.0009 0.995 0.992 0.9979 Genitourinary complications 36,0.99% 20,0.55% 0.034 0.5551 0.3197 0.964 19,0.86% 9,0.41% 0.0609 0.4731 0.2124 1.0536 Disruption of operation wound 15,0.43% 0 <.0001 0.9957 0.9936 0.9979 6,0.28% 0 0.0134 0.9972 0.995 0.9994 Other intraoperative complications 3,0.09% 0 0.074 0.9991 0.9981 1.0001 4,0.17% 0 0.0563 0.9983 0.9966 1 Surgical site infection 27,0.75% 14,0.40% 0.0468 0.5257 0.2759 0.9994 13,0.61% 11,0.49% 0.601 0.8059 0.3584 1.8123 Patient numbers represent inverse probability treatment-weighted (IPTW) pseudo-populations to balance baseline covariates across groups Post-operative complication in one month after discharge form hospital for unmatching groups Patient numbers represent inverse probability treatment-weighted (IPTW) pseudo-populations to balance baseline covariates across groups All-cause mortality HRs: OH vs. RAH 2.53 (95% CI: 1.64–3.88); LH vs. RAH 0.83 (0.52–1.34) (Fig.  2 A). After IPTW: OH vs. RAH 1.39 (1.20–1.61); LH vs. RAH 0.97 (0.73–1.28). Kaplan-Meier curves confirmed higher mortality for OH vs. RAH and similar survival between RAH and LH ( p  < 0.001) (Fig.  2 B and C). Fig. 2 The all-cause mortality of different surgeries in three years. ( A ) unmatching groups; ( B ) OH vs. RAH; ( C ) LH vs. RAH The all-cause mortality of different surgeries in three years. ( A ) unmatching groups; ( B ) OH vs. RAH; ( C ) LH vs. RAH

To our knowledge, this study represents the first nationwide investigation in Taiwan comparing survival and postoperative outcomes between RAH and conventional OH. The predominance of OH (59.2%) in this cohort reflects real-world clinical and institutional factors. Patients undergoing open surgery generally presented with more advanced disease (stage III–IV), larger tumors, and higher rates of lymphovascular invasion, requiring extensive procedures such as debulking or omentectomy. They also exhibited greater medical complexity, with higher transfusion and ICU utilization rates. Furthermore, during 2018–2020, minimally invasive techniques were not universally available across Taiwan, and variations in surgeon experience and hospital resources further influenced the choice of surgical approach. Since RAH was only approved by the Taiwan Bureau of National Health Insurance in September 2024, clinical experience with this technique remains relatively limited. Nevertheless, utilizing data from a national database, our analysis revealed that RAH was linked to markedly reduced rates of blood transfusion and postoperative complications compared with OH surgery. A 2023 meta-analysis compared RAH, LH, and OH and confirmed that robotic surgery offers better overall survival and lower recurrence rates in EC patients, particularly in early to intermediate stages of the disease [ 9 ]. These advantages have made robotic surgery an increasingly preferred option for both patients and surgeons in managing EC. Some studies suggest that RAH does not compromise long-term survival compared to OH surgery [ 10 , 11 ]. Using real-world NHIRD data, we observed that patients who underwent RAH exhibited lower all-cause mortality rates than those who had OH surgery (Fig. 2 A and B). Further prospective or long-term evaluations of RAH outcomes are warranted. RAH has demonstrated advantages over laparotomic (open) hysterectomy, particularly in reducing short-term complication rates, as observed in previous studies [ 12 ]. RAH is often favored over OH surgery when patient-specific factors favor minimally invasive techniques. These factors often apply to patients presenting with challenging surgical conditions, such as severe endometriosis, dense pelvic adhesions, obesity, advanced age, or multiple coexisting illnesses [ 13 ]. In their cohort of 152 women treated with RAH for technically demanding benign disorders—such as endometriosis, uterine leiomyoma, and previous pelvic or abdominal procedures—Boggess and co-authors found very few complications during or after surgery and reported no cases requiring laparotomic conversion [ 14 ]. Consistent with those findings, our analysis also demonstrated that RAH was linked to a significantly lower incidence of complications, as summarized in Tables 3 and 4 . Patients undergoing RAH experience significantly less blood loss, averaging around 87.5 mL, compared to much higher blood loss in OH surgeries. This reduction is largely due to the precision of robotic instruments, which leads to fewer vascular injuries during surgery [ 15 ]. The length of hospital stay (LOS) in our cohort was longer than that reported in Western literature (1.8 days), primarily reflecting characteristics of Taiwan’s healthcare system and cultural recovery practices. Under the National Health Insurance program, hospitalization costs are low and comprehensive, encouraging prolonged inpatient recovery. Patients and families often prefer extended stays for perceived safety, while limited home care infrastructure and additional inpatient consultations for cancer management further contribute to longer LOS. Nevertheless, the relative reduction of approximately 27% in LOS for robotic-assisted hysterectomy compared with open hysterectomy is consistent with other Asian studies, underscoring the procedural advantages of minimally invasive approaches within the Taiwanese healthcare setting [ 16 ]. ICU admissions for robotic surgery patients are rare due to the minimally invasive nature of the procedure, which results in fewer complications. In contrast, OH surgery patients are more likely to require intensive care postoperatively, especially when significant blood loss or complications occurred [ 15 ]. According to our results, RAH demonstrated significantly lower transfusion rates compared to open surgery, both before and after IPTW adjustment. Conversely, there was no significant difference in transfusion rates between RAS and laparoscopic surgery in hysterectomy. This highlights the potential benefits of minimally invasive techniques such as RAS in reducing complications like transfusions and potentially shortening hospital stays for patients with EC undergoing hysterectomy. In Taiwan, RAH was performed as a self-pay procedure during the study period, so the NHIRD captured only hospitalization and postoperative care costs, excluding out-of-pocket surgical fees. Consequently, the lower total costs observed for RAH (NTD 65,992 vs. 130,281 for OH and 121,001 for LH) mainly reflect shorter hospital stays, fewer complications, reduced ICU use, and lower transfusion rates. Although robotic surgery generally has higher direct procedural costs, these findings suggest that indirect savings from improved perioperative outcomes may offset upfront expenses in the Taiwanese healthcare context. Recent literature also emphasizes two additional aspects relevant to our findings—cost-effectiveness and technical performance in challenging surgical scenarios. From an economic standpoint, several studies have shown that the introduction of RAH does not necessarily increase the total cost of endometrial cancer management once postoperative recovery and complication rates are considered. Two study demonstrated that while the upfront procedural cost of RAH may be higher, the reduction in transfusion, intensive care use, and length of stay often offsets these expenses, resulting in comparable or even lower overall hospital expenditures [ 5 , 11 ]. Our national analysis revealed a similar pattern, with RAH showing consistently lower total medical costs across multiple postoperative timeframes, supporting its cost-efficiency within Taiwan’s healthcare reimbursement system. In technically demanding cases, recent studies have underscored RAH’s value in providing enhanced precision and safety. Several studies demonstrated improved outcomes for endometrial cancer patients with complication [ 17 – 19 ]. These findings align with our observation that RAH yields the lowest postoperative complication rate, reinforcing its potential as a preferred approach for complex or high-risk patient populations. Although the RAH group demonstrated lower rates of perioperative complications overall, the slightly higher reoperation and readmission rates observed in our cohort warrant discussion (Table 3 ). These findings likely reflect the early phase of robotic program implementation in Taiwan, during which complication and reoperation rates are known to decline progressively with increasing surgical experience and program maturation [ 20 ]. Additionally, most RAH procedures in our cohort were performed at tertiary referral centers, which typically manage more complex or high-risk patients and maintain stricter postoperative monitoring protocols [ 21 , 22 ]. Such institutions characteristically maintain lower thresholds for readmission or secondary intervention, leading to higher reported rates despite generally favorable clinical outcomes [ 23 ]. Furthermore, minor surgical revisions or short-term readmissions for wound inspection, pain control, or postoperative imaging were more comprehensively documented in the RAH group due to enhanced surveillance practices during program initiation. As robotic surgical experience accumulates and standardization of care pathways continues to advance, these rates are expected to decline, consistent with trends reported in other healthcare systems implementing robotic surgery programs [ 24 ]. Claims-based research is inherently constrained by several factors, including potential data incompleteness and inaccuracies in diagnostic coding. To enhance data reliability, we linked EC cases to the Taiwan Cancer Registry (ICD-O-3, C54.1). Nevertheless, information on the initial stage of EC was not fully available. Furthermore, details such as self-financed medications, laboratory results, and patient anthropometrics (e.g., height, weight) were absent due to the limitations of the NHIRD. Measures of disease severity were also unavailable. In addition, because the National Health Insurance Administration requires approximately one year to process and release new datasets, access to the most up-to-date information is delayed. We therefore await the latest data release to perform further in-depth analyses. In conclusion, the introduction of RAH in Taiwan has impacted the clinical approach and outcomes for EC surgery. A retrospective population-based analysis revealed that RAH is associated with several favorable safety outcomes and improved oncological results compared to open hysterectomy. These findings may support increased adoption of RAH in appropriate surgical candidates, especially where feasible and economically justified. Patient numbers represent inverse probability treatment-weighted (IPTW) pseudo-populations to balance baseline covariates across groups.

Endometrial cancer (EC) originates from the mucosal layer of the uterus and is recognized as one of the most common malignancies affecting the female reproductive system. Globally, around 417,000 new diagnoses were recorded in 2020, ranking EC as the sixth leading cancer among women [ 1 ]. Although EC can develop across a broad age range, it primarily affects women after menopause. Approximately 3% of women are expected to develop endometrial cancer during their lifetime, and the median age at diagnosis is around 61 [ 1 ]. Over the last three decades, its global incidence has escalated by nearly 132%, mainly due to the growing prevalence of obesity and population aging trends [ 2 ]. Management strategies for endometrial cancer are largely determined by disease stage, patient comorbidities, and tumor-specific features. The primary treatment is surgery, but it may be supplemented with other therapies such as radiation, chemotherapy, and hormone therapy, especially in advanced or high-risk cases [ 3 ]. In recent years, the use of minimally invasive techniques for managing EC has shown a continuous upward trend [ 4 , 5 ]. Among these, robot-assisted surgery (RAS) has been increasingly adopted due to its technical benefits, such as three-dimensional visualization, enhanced instrument dexterity, and reduced dependence on an experienced surgical team. Current evidence generally indicates that short-term complication rates are comparable between RAS and conventional laparoscopic surgery in EC patients [ 6 ]. In a post-hoc analysis, Melamed et al. found that the decline in survival reached statistical significance only within the RAS group, while the hazard ratio for the laparoscopic arm crossed unity, suggesting that the two minimally invasive techniques may not yield identical outcomes [ 7 ]. Although our study focuses on endometrial cancer, we referenced Melamed et al.‘s large-scale cohort analysis on cervical cancer to highlight that robot-assisted or minimally invasive techniques may not universally confer survival benefits across all gynecologic malignancies. Their findings underscore the importance of evaluating oncologic outcomes beyond perioperative advantages, particularly in robot-assisted approaches. Multiple investigations have shown that RAH for endometrial cancer tends to yield superior perioperative outcomes when compared with conventional surgical methods [ 6 , 8 ]. However, real-world comparative data examining RAH, LH, and OH in the Taiwanese clinical setting remain limited. This nationwide retrospective analysis sought to compare oncologic outcomes and postoperative morbidity among patients with endometrial cancer treated by RAH, LH, or OH in real-world clinical settings.