OUTCOME: Overall complications ANALYSIS: primary_all_designs STUDIES: 21 TREATMENTS: Conventional, Retzius, Ultra Original data (with adjusted standard errors for multi-arm studies): treat1 treat2 TE seTE seTE.adj narms multiarm Asimakopoulos_2019 Conventional Retzius 1.1253 1.1776 1.1776 2 Beyatli_2025 Retzius Ultra 0.3051 0.4984 0.4984 2 Dalela_Detroit_RCT_2015_2016 Conventional Retzius -0.5305 0.5225 0.5225 2 Eden_2017 Conventional Retzius 1.4663 1.1417 1.1417 2 Feng_2025 Conventional Retzius 0.0572 0.7270 0.7270 2 Karsiyakali_2022 Conventional Retzius -0.7655 0.5858 0.5858 2 Kowalczyk_Georgetown_series Conventional Ultra 0.8271 0.4355 0.4355 2 Lim_2014 Conventional Retzius 0.9362 0.7216 0.7216 2 Maddox_2013 Conventional Retzius -0.0991 0.2444 0.2444 2 Nagoya_RS_anatomy_OAB Conventional Retzius -0.1907 0.6183 0.6183 2 Nanchang_Deng_2021a Conventional Retzius 0.1018 0.3014 0.3014 2 Puliatti_2019 Conventional Ultra 0.0241 0.6177 0.6177 2 Qian_2024 Conventional Retzius -0.7145 0.6210 0.6210 2 Qiu_2020 Conventional Retzius 0.7526 0.7346 0.7346 2 Ratanapornsompong_2020 Conventional Ultra -0.0880 0.4196 0.4196 2 Sayyid_2017 Conventional Retzius -0.7033 1.2330 1.2330 2 Tahra_2021 Conventional Retzius 0.8109 1.2439 1.2439 2 Vargo_2024 Retzius Ultra 1.4137 0.5599 0.6685 3 * Vargo_2024 Conventional Ultra -0.0000 0.6003 0.7771 3 * Vargo_2024 Conventional Retzius -1.4137 0.5599 0.6685 3 * Wang_2021 Conventional Retzius 0.2716 0.7627 0.7627 2 Yee_2021 Conventional Retzius 0.4477 0.6740 0.6740 2 Yilmaz_2023 Conventional Retzius -0.0000 0.4564 0.4564 2 Number of treatment arms (by study): narms Asimakopoulos_2019 2 Beyatli_2025 2 Dalela_Detroit_RCT_2015_2016 2 Eden_2017 2 Feng_2025 2 Karsiyakali_2022 2 Kowalczyk_Georgetown_series 2 Lim_2014 2 Maddox_2013 2 Nagoya_RS_anatomy_OAB 2 Nanchang_Deng_2021a 2 Puliatti_2019 2 Qian_2024 2 Qiu_2020 2 Ratanapornsompong_2020 2 Sayyid_2017 2 Tahra_2021 2 Vargo_2024 3 Wang_2021 2 Yee_2021 2 Yilmaz_2023 2 Results (random effects model): treat1 treat2 OR 95%-CI Asimakopoulos_2019 Conventional Retzius 0.9177 [0.7175; 1.1739] Beyatli_2025 Retzius Ultra 1.5648 [0.9905; 2.4723] Dalela_Detroit_RCT_2015_2016 Conventional Retzius 0.9177 [0.7175; 1.1739] Eden_2017 Conventional Retzius 0.9177 [0.7175; 1.1739] Feng_2025 Conventional Retzius 0.9177 [0.7175; 1.1739] Karsiyakali_2022 Conventional Retzius 0.9177 [0.7175; 1.1739] Kowalczyk_Georgetown_series Conventional Ultra 1.4361 [0.9353; 2.2050] Lim_2014 Conventional Retzius 0.9177 [0.7175; 1.1739] Maddox_2013 Conventional Retzius 0.9177 [0.7175; 1.1739] Nagoya_RS_anatomy_OAB Conventional Retzius 0.9177 [0.7175; 1.1739] Nanchang_Deng_2021a Conventional Retzius 0.9177 [0.7175; 1.1739] Puliatti_2019 Conventional Ultra 1.4361 [0.9353; 2.2050] Qian_2024 Conventional Retzius 0.9177 [0.7175; 1.1739] Qiu_2020 Conventional Retzius 0.9177 [0.7175; 1.1739] Ratanapornsompong_2020 Conventional Ultra 1.4361 [0.9353; 2.2050] Sayyid_2017 Conventional Retzius 0.9177 [0.7175; 1.1739] Tahra_2021 Conventional Retzius 0.9177 [0.7175; 1.1739] Vargo_2024 Retzius Ultra 1.5648 [0.9905; 2.4723] Vargo_2024 Conventional Ultra 1.4361 [0.9353; 2.2050] Vargo_2024 Conventional Retzius 0.9177 [0.7175; 1.1739] Wang_2021 Conventional Retzius 0.9177 [0.7175; 1.1739] Yee_2021 Conventional Retzius 0.9177 [0.7175; 1.1739] Yilmaz_2023 Conventional Retzius 0.9177 [0.7175; 1.1739] Number of studies: k = 21 Number of pairwise comparisons: m = 23 Number of observations: o = 3381 Number of treatments: n = 3 Number of designs: d = 4 Random effects model Treatment estimate (sm = 'OR', comparison: other treatments vs 'Conventional'): OR 95%-CI z p-value Conventional . . . . Retzius 1.0897 [0.8519; 1.3938] 0.68 0.4942 Ultra 0.6963 [0.4535; 1.0692] -1.65 0.0981 Quantifying heterogeneity / inconsistency: tau^2 < 0.0001; tau < 0.0001; I^2 = 1.7% [0.0%; 47.9%] Tests of heterogeneity (within designs) and inconsistency (between designs): Q d.f. p-value Total 20.34 20 0.4367 Within designs 13.57 17 0.6972 Between designs 6.77 3 0.0796 Details of network meta-analysis methods: - Frequentist graph-theoretical approach - Restricted maximum-likelihood estimator for tau^2 - Calculation of I^2 based on Q --- P-SCORE RANKING --- P-score Ultra 0.9617 Conventional 0.4010 Retzius 0.1373 --- SUCRA RANKING --- SUCRA Ultra 0.9630 Conventional 0.4014 Retzius 0.1355 - based on 20000 simulations --- GLOBAL INCONSISTENCY: DESIGN-BY-TREATMENT --- Q statistics to assess homogeneity / consistency Q df p-value Total 20.34 20 0.4367 Within designs 13.57 17 0.6972 Between designs 6.77 3 0.0796 Design-specific decomposition of within-designs Q statistic Design Q df p-value Conventional:Ultra 2.52 2 0.2840 Conventional:Retzius 11.05 15 0.7488 Between-designs Q statistic after detaching of single designs (influential designs have p-value markedly different from 0.0796) Detached design Q df p-value Conventional:Retzius:Ultra 0.00 1 0.9857 Conventional:Retzius 3.29 2 0.1929 Conventional:Ultra 6.57 2 0.0375 Retzius:Ultra 6.67 2 0.0357 Q statistic to assess consistency under the assumption of a full design-by-treatment interaction random effects model Q df p-value tau.within tau2.within Between designs 6.77 3 0.0796 0 0 --- LOCAL INCONSISTENCY: NODE SPLITTING/SIDE --- Separate indirect from direct evidence (SIDE) using back-calculation method Random effects model: comparison k prop nma direct indir. RoR z p-value Retzius:Conventional 17 0.94 1.0897 1.0838 1.1956 0.9065 -0.18 0.8586 Ultra:Conventional 4 0.78 0.6963 0.7866 0.4491 1.7513 1.06 0.2907 Retzius:Ultra 2 0.39 1.5648 2.2148 1.2498 1.7721 1.20 0.2311 Legend: comparison - Treatment comparison k - Number of studies providing direct evidence prop - Direct evidence proportion nma - Estimated treatment effect (OR) in network meta-analysis direct - Estimated treatment effect (OR) derived from direct evidence indir. - Estimated treatment effect (OR) derived from indirect evidence RoR - Ratio of Ratios (direct versus indirect) z - z-value of test for disagreement (direct versus indirect) p-value - p-value of test for disagreement (direct versus indirect)