Three-row versus two-row circular staplers for left-sided colorectal anastomosis: a propensity score-matched analysis of the iCral 2 and 3 prospective cohorts.

Highlights Since most anastomoses after left-sided colorectal resections are performed with a circular stapler, any technological advancement in stapling devices may influence the incidence of anastomotic leakage and related morbidity. In this propensity score-matched analysis, including 850 left-sided colorectal resections derived from two prospective multicenter studies, the use of a three staple row versus a two staple row circular stapler determined a 4.0% (2.1 vs. 6.1%) absolute risk reduction of anastomotic leakage. The last decade has witnessed a significant evolution in colorectal surgery, due to the diffusion of minimally invasive approaches (either laparoscopic or robotic) and the implementation of enhanced recovery pathways (ERP) 1 , allowing optimal oncological, physiological, and cosmetic results associated with shorter postoperative stay 2 . However, early anastomotic adverse events, such as leakage (AL) and bleeding (AB), remain the Achille’s heel of any colorectal anastomosis, leading to prolonged postoperative stay, increased costs, risk of reoperation, and permanent colostomy, together with an increase in overall morbidity and mortality rates 3 , 4 . Furthermore, AL has been shown to affect long-term outcomes being associated with a higher risk of recurrence and shortened survival in colorectal cancer 5 . Nearly 50 years have passed since the first description of mechanical circular staplers (CS) to facilitate colorectal anastomosis 6 , and the double-stapling technique for colorectal end-to-end anastomosis has rapidly become a standard practice that is still widely used today 7 , having demonstrated safety and efficacy equivalent to the hand-sewn anastomosis with the advantages of shorter anastomotic time, less contamination, and greater reproducibility 8 , 9 . Beyond well-known patient-related, disease-related, and procedure-related risk factors 10 , successful healing of any anastomosis relies on an effective blood supply and micro-perfusion of the anastomotic tissue and on the mechanical strength of the newly formed anastomosis 11 . Consequently, any technological improvement in CS may represent a target for AL risk reduction, and during the last 5 years, two significant developments have been commercially available for this purpose. The powered two-row circular stapler (Ethicon, Somerville, New Jersey, USA) decrease the force needed for firing, improving stability at the anastomotic site, reduces the compressive forces on tissues and their slippage, and utilizes two rows of staples with three-dimensional architecture 12 . The three-row circular stapler (Covidien, New Haven, Connecticut, USA) is based on three circular rows of conventional, B-shaped staples, varying in height: the staples of the inner row, closest to the anastomotic lumen, have the shortest height to provide the greatest occlusion and barrier to leak and to bleed, while the second and third rows, each with incrementally longer staples’ height, contribute strength to the closure line, enhancing both tissue micro-perfusion 13 and pressure resistance 14 . Despite several animal models and clinical and artificial intelligence studies 14 – 17 , the effectiveness of the three-row CS in reducing the AL risk has not been fully examined or demonstrated mainly because of the small sample size, small number of considered conditioning variables, and lack of comparison with two-row CS of different producers. Although highly advisable 18 , administrative, economic, and ethical reasons have hindered, to date, a randomized clinical trial on this issue, and the Italian ColoRectal Anastomotic Leakage (iCral) study group decided to estimate the treatment effects of the three-row CS on data derived from two prospective open-label observational multicenter studies 19 , 20 .

M.C., S.G., G.R., M.G.V., F.B., G.B., and M.S.: concept and design; M.C., S.G., F.M., G.R., M.G.V., F.B, G.B., and M.S.: acquisition, analysis, or interpretation of data; M.C., S.G., and F.M.: drafting of the manuscript; M.C., S.G., F.M., G.R., M.G.V., F.B., G.B., and M.S.: critical revision of the manuscript for important intellectual content; F.M. and S.G.: statistical analysis.

Both studies were conducted in accordance with the Declaration of Helsinki and the guidelines for good clinical practice E6 (R2). The study protocols were approved by the ethics committee of the coordinating center (Marche Regional Ethics Committee – CERM – 2018/334 released on 28 November 2018 for iCral2; CERM – 2020/192 released on 30 July 2020 for iCral3) and registered at ClinicalTrials.gov ( NCT03771456 for iCral2 and NCT04397627 for iCral3).

This was a retrospective analysis of a prospective database of patients who underwent colorectal resection and anastomosis for malignant and benign diseases. The aim of the present study was to evaluate the effectiveness of the three-row CS in reducing the risk of early adverse events in a population of patients who underwent left colectomy and/or anterior resection with end-to-end stapled colorectal anastomosis, compared to a control population in which a two-row CS was used. Propensity score-matching analysis (PSMA) was used to adjust for heterogeneity between the two groups. Patients were enrolled in two consecutive studies: iCral2 19 and iCral3 20 . Both studies, designed to investigate the effects of ERP adherence rates on several outcomes, were based on prospective enrollment on a voluntary basis in Italy, carried out from January 2019 to June 2020 in 38 surgical centers (iCral2) and from October 2020 to September 2021 in 76 surgical centers (iCral3). A total of 78 centers were involved in one (42 centers) or both (36 centers) of the studies. All patients who underwent colorectal resection with anastomosis (laparoscopic, robotic, open, or converted approach, including planned Hartmann’s reversals) were assessed according to explicit inclusion/exclusion criteria shared by both studies. Inclusion criteria were: American Society of Anesthesiologists (ASA) class I, II, or III; elective or delayed urgency setting (defined >48 h from admission in iCral2 and >24 h from admission in iCral3); patient’s written informed consent for inclusion in the study and processing of sensitive data. Exclusion criteria were: pregnancy; hyperthermic chemotherapy (HIPEC) for carcinomatosis; incomplete data. The iCral2 study excluded patients with a protective stoma proximal to the anastomosis; conversely, these cases were included in the iCral3 study. Each center was defined as a high volume (≥4) or low volume (<4) according to the median number of enrolled cases per month. All data of the included patients were prospectively uploaded to a web-based database via an electronic case report form, specifically designed for both studies and protected by access credentials for each center/investigator. Continuous and discrete variables related to biometric data, patient-related risk factors, indications and types of surgical procedures, adherence to ERP items, and outcomes were recorded in all cases. When anastomosis was performed with a CS, complete data regarding the manufacturer, model, and diameter were recorded. Quality control of the data for consistency, plausibility, and completeness was performed on each record by local investigators and subsequently validated by the study coordinator, resolving any discrepancies through strict cooperation. During the perioperative period, patients were examined daily by local investigators, who recorded and graded any adverse event and were free to decide on complementary imaging and any further action according to their local criteria. Both studies were conducted in accordance with the Declaration of Helsinki and the guidelines for good clinical practice E6 (R2). The study protocols were approved by the ethics committee of the coordinating center and registered at ClinicalTrials.gov. Subsequently, all other centers were authorized to participate in their local ethics committees. Both studies followed the Strengthening the Reporting of Cohort Studies in Surgery (STROCSS) guidelines 21 . Individual participant-level anonymized datasets were made available for both studies upon reasonable request by contacting the study coordinator. To control for data heterogeneity derived from several treatment confounders, PSMA included 2799 patients (65.8%) out of 4255 cases in which anastomosis was performed using a CS, based on explicit exclusion criteria: any resection different from left colectomy or anterior resection, neo-adjuvant therapy, any anastomosis different form end-to-end, any anastomosis located less than 5 cm from the external anal verge, any anastomosis performed with a 25 mm diameter CS, and any anastomosis protected by a proximal stoma (Fig. 1 ). The variables and outcomes recorded for the study population are shown in Table 1 . To optimize the effectiveness of the PSMA by reducing the number of unmatched cases, continuous variables were categorized according to their median values. Study flowchart according to the Strengthening the Reporting of Cohort Studies in Surgery (STROCSS) guidelines 21 and to the Reporting and Guidelines in Propensity Score Analysis 22 . iCral, Italian ColoRectal Anastomotic Leakage study group; ERP, enhanced recovery pathway. Descriptive analysis of the study population variables. ASA, American Society of Anesthesiologists; ERP, enhanced recovery pathway. All the enrolled patients were followed-up for 8 weeks after surgery, recording and grading any adverse event according to Clavien–Dindo 23 and the Japanese Clinical Oncology Group (JCOG) extended criteria 24 , as well as any unplanned readmission, reoperation, or death, calculated at 60 days after surgery. AL was defined according to the international consensus 25 , and AB was defined as persistent rectal bleeding associated with at least a 20 g/l decrease in hemoglobin concentration 26 . Adverse events and their grading are reported in Table 2 . Adverse events and grading in the study population. DVT, deep venous thrombosis; JCOG, Japanese Clinical Oncology Group. The primary endpoints were overall AL (any AL), major AL (any AL grade >II), and AB, and the secondary endpoints were overall morbidity (any adverse event), major morbidity (any adverse event grade >II), and mortality (any death). No missing data were observed in the database of 2799 patients. A propensity score-matching model 27 was used for analysis (Fig. 1 ). Based on its original theory 28 , the propensity score is a variable that groups several covariates and represents the conditional probability of receiving a protective treatment effect on the outcomes using or not using the treatment variable. The theory requires the following assumptions: no unmeasured confounding variable; the propensity score should not be exactly 0 or 1; the treatment should be well-defined and homogeneous; sufficient overlap in subgroups of covariates; and balance model specifications. Adjusted logistic regression was used to estimate the propensity scores of the treatment and control groups. Based on the conditioning categorical variables selected, each patient was assigned a propensity score estimated by the standardized mean difference (a standardized mean difference less than 0.1 typically indicates a negligible difference between the means of the groups). The treatment (exposure) variable was end-to-end anastomosis performed using the three-row CS, and 20 confounding variables (covariates), potentially affecting the treatment were selected: age, sex, American Society of Anesthesiologists (ASA) class, body mass index (BMI), diabetes, chronic renal failure, chronic liver disease, surgery for malignancy, left colectomy as a type of resection, operation length (minutes), center volume, open surgery, intracorporeal or extracorporeal anastomosis, the diameter of the circular stapler, preoperative blood transfusion(s), intraoperative and/or postoperative blood transfusion(s), and overall, preoperative, intraoperative, and postoperative ERP items adherence rates. No outcome variable was included 29 . As the balance is the main goal of PSMA, the analysis was performed using the software ‘R’ (Version 4.2.2, The R Foundation for Statistical Computing, Vienna, Austria, 2022) with the following specifications: seed 100 for the reproducibility of the analysis; method for distance metric=nearest, distance=logit, caliper=0.1, replace=false (without sampling replacement), ratio=1; adjusted logistic regression to estimate the association between the exposure/treatment variable and the outcomes. The following R libraries/programs have been used: ‘matchit’, ‘glm’, ‘publish’, ‘Tablone’, ‘Plot’, and ‘cobalt’ 30 . Balance in the matched groups was assessed by calculating the standardized mean difference (SMD) and general variance ratio (a variance ratio close to 1 indicates that variances are equal in the two groups). For outcome modeling, an adjusted logistic regression based on the use of the three-row CS as the treatment variable and on the same 20 covariates selected for the PSMA was performed, presenting odds ratios (OR) and 95% confidence intervals (95% CI). The eventual effect of any unobserved confounder was tested through a sensitivity analysis 31 , using the library ‘SensitivityR5’ of the software ‘R’ (Version 4.2.2, The R Foundation for Statistical Computing, Vienna, Austria, 2022) and presenting the Γ values (each 0.1 increment of Γ values representing a 10%-odds of differential assignment to treatment due to any unobserved variable).

After propensity score-matching, 1949 patients were excluded (63 treated with the three-row CS and 1886 with the two-row CS), and two groups of 425 patients each were generated: group A (treatment, true population of interest), using the new three-row CS, and group B (control population), using a two-row CS. This population of 850 patients includes data deriving from 62 (79.5%) of the original 78 centers: group A included data deriving from 45 (57.7%) centers, and group B from 54 (69.2%) centers. The details of the CS models used in both groups are shown in Table 3 . A good balance between the two groups was achieved (Table 4 and Fig. 2 ), with a model variance ratio of 1.049. AL diagnosis was established by intravenous contrast CT scan in 57 (40.7%), clinical criteria in 49 (35.0%), endoluminal contrast CT scan in 26 (18.6%), endoluminal contrast enema in 4 (2.8%), and gross findings at reoperation in the remaining four cases (2.8%). Details of the circular staplers used for end-to-end colorectal anastomosis in the treatment and control groups. CS, circular stapler. Variables distribution in treatment and control groups before and after propensity score-matching. Student’s test for proportions. AB, anastomotic bleeding; AL, anastomotic leakage; ASA, American Society of Anesthesiologists; BT, blood transfusion(s); CS, circular stapler; ERP, enhanced recovery pathway; SMD, standardized mean difference. (A) Jitter plot distribution of propensity scores in treatment and control groups. (B) Love plot of covariates’ standardized mean differences between treatment and control groups before and after matching; the vertical lines represent the interval of ±0.1 within which balance is considered acceptable. ERP, enhanced recovery pathway. The results of the adjusted logistic regression and sensitivity analyses are reported in Table 5 for the primary endpoints and Table 6 for the secondary endpoints. Adjusted multiple regression analysis for primary endpoints. AB, anastomotic bleeding; AL, anastomotic leakage; ASA, American Society of Anesthesiologists; BT, blood transfusion(s); CS, circular stapler; ERP, enhanced recovery pathway; OR (95% CI), odds ratio and 95% confidence intervals. Adjusted multiple regression analysis for secondary endpoints. ASA, American Society of Anesthesiologists; BT, blood transfusion(s); CS, circular stapler; ERP, enhanced recovery pathway; OR (95% CI), odds ratio and 95% confidence intervals. Group A versus group B showed a significantly lower risk of overall anastomotic leakage [9 (2.1%) vs. 26 (6.1%) events; OR 0.33; 95% CI 0.15–0.73; P =0.006] and major anastomotic leakage [9 (2.1%) vs. 22 (5.2%) events; OR 0.39; 95% CI 0.17–0.87; P =0.022]. Concerning the risk of AB, no difference was recorded between the two groups (Table 5 ), whereas it was significantly lower for adherence to preoperative ERP items above its median value [4/573 (0.7%) vs. 8/277 (2.9%) events; OR 0.10; 95% CI 0.02–0.48, P =0.004]. Regarding secondary endpoints, a significantly lower risk of major morbidity was recorded in group A than in group B [15 (3.5%) vs. 28 (6.6%) events; OR 0.47; 95% CI 0.24–0.91; P =0.026], while no significant difference between the two groups was recorded in terms of the risk of overall morbidity and mortality (Table 6 ). The use of CS diameter at least 30 mm versus less than 30 mm significantly reduced the risk of major morbidity [14/495 (2.8%) vs. 29/355 (8.2%) events; OR 0.33; 95% CI 0.16–0.65; P =0.002] and the risk of overall morbidity [95/495 (19.2%) vs. 93/355 (26.2%) events; OR 0.66; 95% CI 0.47–0.94, P =0.02]. The sensitivity analysis showed Γ=1.5 for overall anastomotic leakage, meaning that 50% of patients should have been treated with a two-row CS instead of a three-row CS because of unknown and/or unmeasured confounding variables to alter the result (association of three-row CS use and lower risk of anastomotic leakage) or to lose statistical significance (30% for major anastomotic leakage and 20% for major morbidity).

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Medtronic SI-Italy provided unconditioned support to the organization of three iCral2 study investigator meetings, held in Rome, Italy – October 2018, Matera, Italy – June 2019, and Bologna, Italy – October 2019.

None.

Marco Catarci, iCral Study Group coordinator, had full access to all the data in the study and took responsibility for the integrity of the data and the accuracy of the data analysis.

To our best knowledge, this is the first clinical study based on a large, prospective, multicenter database, comparing three-row CS with several two-row CS of different producers, evaluating the risk of early anastomotic adverse events after left-sided colorectal resection for malignant and benign diseases with stapled end-to-end colorectal anastomosis. The use of the three-row CS significantly and independently reduced AL and major morbidity rates. From a practical point of view, the use of a three-row CS determined a 4% absolute risk reduction for overall AL (3% for major AL and major morbidity), corresponding to the need to treat 25 patients to avoid one AL (33 patients to avoid one major AL and/or one major adverse event). The finding that a CS diameter of at least 30 mm is linked to a significantly lower risk of overall and major morbidity deserves further investigation since the available evidence is controversial 32 , 33 . The main strength of this study is its methodology: a large database gathered during two prospective multicenter studies was analyzed through a PSMA that perfectly responded to the EQUATOR (Enhancing the QUAlity and Transparency Of Health Research) network reporting guidelines 22 . Although observational studies cannot be regarded as a replacement for randomized studies, data generated from large observational cohorts have been increasingly used to evaluate important clinical questions where data from randomized trials are limited or do not exist 34 , mainly because of the lower barriers and cost to subject recruitment. PSMA offers an alternative approach for estimating treatment effects with observational data when randomized trials are not feasible or unethical or when researchers need to assess treatment effects based on real-life data collected through the observation of systems as they operate in normal practice without any intervention implemented by randomized assignment rules, responding to the frequent need to draw conditioned casual inferences from quasi-experimental studies. To account for the conditional probability of treatment selection, thus reducing confounding bias, PSMA presents analytical and interpretation challenges that need to be addressed to maintain the reproducibility of its results, which in recent years has been recognized as a crucial element of high-quality research 35 . The relevant quality of the PSMA used in the present study is based on: (1) a rigorous patients’ selection from the parent population, performed upon explicit criteria: to limit data heterogeneity, several potential confounders 36 – 39 related to the surgical procedure or to the anastomosis itself (any resection different from left colectomy and/or anterior resection, and any anastomosis different from end-to-end), as well as any variable exclusively impacting on subgroups of patients (anastomosis located <5 cm from external anal verge, neo-adjuvant therapy, CS diameter 25 mm, proximal protective stoma) were excluded; (2) a reasoned inclusion of 20 conditioning variables (covariates): resections for both malignant and benign diseases in consideration that the stapled end-to-end anastomosis is the same, despite different resection and vascular control criteria; center volume to account for the potential heterogeneity of multicenter, clustered data; adherence to the ERP to account for the potential heterogeneity of medical, anesthesiological, and surgical perioperative management and its impact on the measured outcomes 2 ; type of resection in relation to the heterogeneity of the treatment for malignant and benign diseases; intracorporeal or extracorporeal anastomosis, although no difference in the AL risk has recently been reported 40 ; CS diameter because its association with the AL risk is still unclear 32 , 33 ; (3) a clear, sheer and restrictive balance algorithm (Fig. 1 ), particularly regarding caliper=0.1, matching ratio=1:1, complete balance assessment, a covariate to number of patients per treatment arm ratio=1:21; (4) complete description of software package and of its related analytic details; (5) evaluation of the treatment effect through an adjusted multiple regression model including the same 20 covariates used for matching; (6) accounting for unmeasured confounders by a sensitivity analysis. Another strength of this study is the large number of enrolled patients in a well-defined time-lapse in a large number of centers, representing a very wide sample of surgical units performing colorectal resections in Italy. Although the multicenter nature of the considered data may be a definite source of a clustering bias, it is undoubtedly representative of real-life data. However, this study had several limitations, and the results should be interpreted with caution. First, several controversial risk factors for AL were not measured or recorded in the parent studies: disease stage 41 , single surgeon’s experience 42 , 43 , level of vascular control 44 , splenic flexure mobilization 45 , rectal stump management 46 , intraoperative anastomotic testing, and reinforcement 47 . The second limitation is represented by the impact of potential residual, known or unknown, confounding factors which are intrinsic to observational studies. This impact seems to be not relevant in this study. Actually, according to the sensitivity analysis (Tables 5 , 6 ), a potential model deviation from random assignment due to unknown and/or unmeasured confounders, which can alter the results, was estimated at 50% (Γ=1.5) for overall AL risk, 30% (Γ=1.3) for major AL risk, and 20% (Γ=1.2) for major morbidity risk. On the other hand, lower values (Γ=1.0) were recorded for overall morbidity (Table 6 ), indicating that unknown and/or unmeasured confounders may account for the controversial findings regarding CS diameter. Another limitation is the lack of testing for the same hypothesis by using the powered two-row CS as the treatment variable. A PSMA has not been performed because only 172 cases treated with the powered two-row CS were available in the examined population (Table 1 ), and this small number of cases would not have allowed the use of the same PSMA algorithm used for the three-row CS. Moreover, two previous PSMAs were performed on the same topic: the first 48 using the powered two-row CS and the second 16 using the three-row CS in the treatment arm. According to PSMA reporting guidelines 22 , both were deeply biased regarding a limited number of cases, retrospective data, matching ratio=1:2, caliper=0.2, the inclusion of a limited (8–11) number of covariates, and an incomplete description of the matching algorithm. As a consequence, the 10% AL absolute risk reduction using these devices recorded by both studies appears at least unrealistic, considering that any stapling device is not a ‘magic bullet’ and that there is always a human being firing it 9 . Finally, although data quality control was performed and repeated at various levels, potential measurement errors by the participating investigators could not be ruled out.

Not commissioned, externally peer-reviewed.

This study clearly highlighted that technological change in stapling devices might play a definite role in limiting the risk of anastomotic leakage and its related morbidity.

None.

Dr Catarci reports personal fees from Baxter Spa outside the submitted work. Dr Guadagni, Masedu, Ruffo, Viola, Borghi, Baldazzi, and Scatizzi have no competing interests.

Assistance with the study: iCral study group co-investigators are: Felice Pirozzi 12 , MD, Paolo Delrio 13 , MD, Gianluca Garulli 14 , MD, Pierluigi Marini 15 , MD, Roberto Campagnacci 16 , MD, Raffaele De Luca 17 , MD, Ferdinando Ficari 18 , MD, Giuseppe Sica 19 , MD, Stefano Scabini 20 , MD, Andrea Liverani 21 , MD, Marco Caricato 22 , MD, FACS, Alberto Patriti 23 , MD, Stefano Mancini 24 , MD, Gian Luca Baiocchi 25 , MD, FACS, Roberto Santoro 26 , MD, Walter Siquini 27 , MD, Gianluca Guercioni 2 , MD, Massimo Basti 28 , MD, Corrado Pedrazzani 29 , MD, Mauro Totis 30 , MD, Alessandro Carrara 31 , MD, Andrea Lucchi 32 , MD, FACS, Maurizio Pavanello 33 , MD, Andrea Muratore 34 , MD, Stefano D’Ugo 35 , MD, Alberto Di Leo 36 , MD, Giusto Pignata 37 , MD, Ugo Elmore 38 , MD, Gabriele Anania 39 , MD, Massimo Carlini 40 , MD, FACS, Francesco Corcione 41 , MD, Nereo Vettoretto 42 , MD, Graziano Longo 43 , MD, Mario Sorrentino 44 , MD, Antonio Giuliani 45 , MD, Giovanni Ferrari 46 , MD, Lucio Taglietti 47 , MD, Augusto Verzelli 48 , MD, Mariantonietta Di Cosmo 49 , MD, Davide Cavaliere 50 , MD, Marco Milone 51 , MD, Stefano Rausei 52 , MD, Giovanni Ciaccio 53 , MD, Giovanni Tebala 54 , MD, FACS, FRCS, Giuseppe Brisinda 55 , MD, Stefano Berti 56 , MD, Paolo Millo 57 , MD, Luigi Boni 58 , MD, FACS, Mario Guerrieri 59 , MD, Roberto Persiani 60 , MD, Dario Parini 61 , MD, Antonino Spinelli 62 , MD, Michele Genna 63 , MD, Vincenzo Bottino 64 , MD, Andrea Coratti 65 , MD, Dario Scala 66 , MD, Umberto Rivolta 67 , MD, Micaela Piccoli 68 , MD, FACS, Carlo Talarico 69 , MD, Franco Roviello 70 , MD, Alessandro Anastasi 71 , MD, Giuseppe Maria Ettorre 72 , MD, Mauro Montuori 73 , MD, Pierpaolo Mariani 74 , MD, Nicolò de Manzini 75 , MD, Annibale Donini 76 , MD, Mariano Fortunato Armellino 77 , MD, Carlo Feo 78 , MD, Silvio Guerriero 79 , MD, Andrea Costanzi 80 , MD, Federico Marchesi 81 , MD, Moreno Cicetti 82 , MD, Paolo Ciano 1 , MD, Michele Benedetti 1 , MD, Leonardo Antonio Montemurro 1 , MD, Maria Sole Mattei 1 , MD, Elena Belloni 1 , MD, Daniela Apa 1 , MD, Matteo Di Carlo 1 , MD, Marco Clementi 3 , MD, Elisa Bertocchi 5 , MD, Gaia Masini 5 , MD, Amedeo Altamura 6 , MD, Francesco Rubichi 6 , MD, Desirée Cianflocca 8 , MD, Marco Migliore 8 , MD, Diletta Cassini 9,10 , MD, Lorenzo Pandolfini 11 , MD, Alessandro Falsetto 11 , MD, Antonio Sciuto 12 , MD, Ugo Pace 13 , MD, Andrea Fares Bucci 13 , MD, Francesco Monari 14 , MD, Grazia Maria Attinà 15 , MD, Angela Maurizi 16 , MD, Michele Simone 17 , MD, Francesco Giudici 18 , MD, Fabio Cianchi 18 , MD, Gabriele Baldini 18 , MD, Bruno Sensi 19 , MD, Alessandra Aprile 20 , MD, Domenico Soriero 20 , MD, Andrea Scarinci 21 , MD, Gabriella Teresa Capolupo 22 , MD, FACS, Valerio Sisti 23 , MD, Marcella Lodovica Ricci 23 , MD, Andrea Sagnotta 24 , MD, PhD, Sarah Molfino 25 , MD, Pietro Amodio 26 , MD, Alessandro Cardinali 27 , MD, Simone Cicconi 2 , MD, Irene Marziali 2 , MD, Diletta Frazzini 28 , MD, Cristian Conti 29 , MD, Nicolò Tamini 30 , MD, Marco Braga 30 , MD, Michele Motter 31 , MD, Giuseppe Tirone 31 , MD, Giacomo Martorelli 32 , MD, Alban Cacurri 32 , MD, Carlo Di Marco 33 , MD, Patrizia Marsanic 34 , MD, Nicoletta Sveva Pipitone Federico 34 , MD, Marcello Spampinato 35 , MD, PhD, FEBS (HPB), Lorenzo Crepaz 36 , MD, Jacopo Andreuccetti 37 , MD, Ilaria Canfora 37 , MD, Giulia Maggi 38 , MD, Matteo Chiozza 39 , MD, Domenico Spoletini 40 , MD, Rosa Marcellinaro 40 , MD, Giorgio Lisi 40 , MD, Umberto Bracale 41 , MD, Roberto Peltrini 41 , MD, Maria Michela Di Nuzzo 41 , MD, Emanuele Botteri 42 , MD, Simone Santoni 43 , MD, Massimo Stefanoni 44 , MD, Giovanni Del Vecchio 45 , MD, Carmelo Magistro 46 , MD, Silvia Ruggiero 47 , MD, Arianna Birindelli 47 , MD, Andrea Budassi 48 , MD, Daniele Zigiotto 49 , MD, Leonardo Solaini 50 , MD, Giorgio Ercolani 50 , MD, Giovanni Domenico De Palma 51 , MD, Silvia Tenconi 52 , MD, Paolo Locurto 53 , MD, Antonio Di Cintio 54 , MD, Maria Michela Chiarello 55 , MD, Maria Cariati 55 , MD, Andrea Gennai 56 , MD, Manuela Grivon 57 , MD, Elisa Cassinotti 58 , MD, Monica Ortenzi 59 , MD, Alberto Biondi 60 , MD, Maurizio De Luca 61 , MD, Francesco Carrano 62 , MD, Francesca Fior 63 , MD, Antonio Ferronetti 64 , MD, Giuseppe Giuliani 65 , MD, Graziella Marino 66 , MD, Camillo Leonardo Bertoglio 67 , MD, Francesca Pecchini 68 , MD, Vincenzo Greco 69 , MD, Roberto Piagnerelli 70 , MD, Giuseppe Canonico 71 , MD, Marco Colasanti 72 , MD, Enrico Pinotti 73 , MD, Roberta Carminati 74 , MD, Edoardo Osenda 75 , MD, Luigina Graziosi 76 , MD, Ciro De Martino 77 , MD, Giovanna Ioia 77 , MD, Fioralba Pindozzi 78 , MD, Lorenzo Organetti 79 , MD, Michela Monteleone 80 , MD, Giorgio Dalmonte 81 , MD, Gabriele La Gioia 82 , MD. From the 12 General Surgery Unit, ASL Napoli 2 Nord, Pozzuoli (NA); 13 Colorectal Surgical Oncology, Istituto Nazionale per lo Studio e la Cura dei Tumori, “Fondazione Giovanni Pascale IRCCS-Italia”, Napoli; 14 General Surgery Unit, Infermi Hospital, Rimini; 15 General & Emergency Surgery Unit, San Camillo-Forlanini Hospital, Roma; 16 General Surgery Unit, “C. Urbani” Hospital, Jesi (AN); 17 Department of Surgical Oncology, IRCCS Istituto Tumori “Giovanni Paolo II”, Bari; 18 General Surgery and IBD Unit, Careggi University Hospital, Firenze; 19 Minimally Invasive Surgery Unit, Policlinico Tor Vergata University Hospital, Roma; 20 General & Oncologic Surgery Unit, IRCCS “San Martino” National Cancer Center, Genova; 21 General Surgery Unit, Regina Apostolorum Hospital, Albano Laziale (RM); 22 Colorectal Surgery Unit, Policlinico Campus BioMedico, Roma; 23 Department of Surgery, Marche Nord Hospital, Pesaro e Fano (PU); 24 General & Oncologic Surgery Unit, San Filippo Neri Hospital, ASL Roma 1; 25 General Surgery Unit 3, Department of Clinical and Experimental Sciences, University of Brescia; 26 General Oncologic Surgery Unit, Belcolle Hospital, Viterbo; 27 General Surgery Unit, S. Lucia Hospital, Macerata; 28 General Surgery Unit, Spirito Santo Hospital, Pescara; 29 General & HPB Surgery Unit, University Hospital, Verona; 30 Colorectal Surgery Unit, San Gerardo Hospital, ASST Monza; 31 1 st General Surgery Unit, S. Chiara Hospital, Trento; 32 General Surgery Unit, “Ceccarini” Hospital, Riccione (RN); 33 General Surgery Unit, AULSS2 Marca Trevigiana, Conegliano Veneto (TV); 34 General Surgery Unit, “E. Agnelli” Hospital, Pinerolo (TO); 35 General Surgery Unit, “V. Fazzi” Hospital, Lecce; 36 General and Minimally Invasive Surgery Unit, San Camillo Hospital, Trento; 37 2 nd General Surgery Unit 2, Spedali Civili di Brescia; 38 Gastroenterologic Surgery Unit, IRCCS S. Raffaele Hospital, Milano; 39 General & Laparoscopic Surgery Unit, University Hospital, Ferrara; 40 General Surgery Unit, S. Eugenio Hospital, ASL Roma 2; 41 General Oncologic and Mininvasive Surgery Unit, “Federico II” University, Napoli; 42 General Surgery Unit, Spedali Civili of Brescia, Montichiari (BS); 43 General Surgery Unit, Policlinico Casilino, Roma; 44 General Surgery Unit, Latisana-Palmanova Hospital, Friuli Centrale University (UD); 45 General Surgery Unit, S. Carlo Hospital, Potenza; 46 General Oncologic and Mininvasive Surgery Unit, Great Metropolitan Niguarda Hospital, Milano; 47 General Surgery Unit, ASST Valcamonica, Esine (BS); 48 General Surgery Unit, Profili Hospital, Fabriano (AN); 49 General & Upper GI Surgery Unit, University Hospital, Verona; 50 General & Oncologic Surgery Unit, AUSL Romagna, Forlì (FC); 51 General & Endoscopic Surgery Unit, “Federico II” University, Napoli; 52 General Surgery Unit, Gallarate Hospital (VA); 53 General Surgery Unit, S. Elia Hospital, Caltanissetta; 54 General Surgery Unit, S. Maria Hospital, Terni; 55 General Surgery Unit, San Giovanni di Dio Hospital, Crotone; 56 General Surgery Unit, ASL 5 Liguria POLL, La Spezia; 57 General Surgery Unit, “U. Parini” Regional Hospital, Aosta; 58 General Surgery Unit, Fondazione IRCCS Ca’ Granda, Policlinico Maggiore Hospital, Milano; 59 Surgical Clinic, Torrette Hospital, University of Ancona; 60 General Surgery Unit, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Roma; 61 General Surgery Unit, S. Maria della Misericordia Hospital, Rovigo; 62 Colorectal Surgery Unit, Humanitas University, Rozzano (MI); 63 General & Bariatric Surgery Unit, University Hospital, Verona; 64 General & Oncologic Surgery Unit, Evangelico Betania Hospital, Napoli; 65 General Surgery Unit, Misericordia Hospital, Grosseto; 66 Abdominal Oncologic Surgery Unit, Basilicata Oncologic Hospital, Rionero in Vulture (PZ); 67 General Surgery Unit, Fornaroli Hospital, ASST Ovest Milanese, Magenta (MI); 68 General Surgery Unit, Civil Hospital, Baggiovara (MO); 69 General Surgery Unit, Villa dei Gerani Hospital, Vibo Valentia (VV); 70 Surgical Clinic, University of Siena; 71 General Surgery Unit, San Giovanni di Dio Hospital, Firenze; 72 General & Transplant Surgery Unit, San Camillo-Forlanini Hospital, Roma; 73 General & Mininvasive Surgery Unit, S. Pietro Hospital, Ponte San Pietro (BG); 74 General Surgery Unit, Pesenti Fenaroli Hospital, Alzano Lombardo (BG); 75 Surgical Clinic, University of Trieste; 76 General & Emergency Surgery Unit, University of Perugia; 77 General & Emergency Surgery Unit, S. Giovanni di Dio e Ruggi d’Aragona Hospital, Salerno; 78 General Surgery Unit, Delta Hospital, Lagosanto (FE); 79 General Surgery Unit, “F. Murri” Hospital, Fermo; 80 General Surgery Unit, S. Leopoldo Hospital, Merate (LC); 81 Surgical Clinic, University of Parma; 82 General Surgery Unit, S. Maria della Misericordia Hospital, Urbino (PU); Italy.

Individual participant-level anonymized datasets were made available for both parent studies and presented analysis upon reasonable request by contacting the study coordinator.