Bowel preparation for elective colorectal resection: multi-treatment machine learning analysis on 6241 cases from a prospective Italian cohort.

This was a secondary unplanned ad hoc multi-treatment re-analysis of two prospective cohorts of patients who had undergone colorectal surgery for malignant and benign diseases based on machine-learning procedures. A total of 8359 patients who underwent colorectal resection with anastomosis were enrolled in two consecutive studies upon explicit inclusion/exclusion criteria in 78 surgical centers in Italy from January 2019 to September 2021: iCral2 [ 34 ] and iCral3 [ 35 ]. To control for data imbalance derived from several treatment confounders, the present analysis included 6241 patients (74.7%) out of 8359 available in the parent studies, based on explicit exclusion criteria (Fig.  1 ). Any record with missing information regarding preoperative bowel preparation or with MBP performed using anything different from polyethylene glycol (PEG) was excluded; patients treated without PIVAP were excluded considering its significant impact on the risk of SSIs [ 23 ]; delayed urgencies were excluded because this study is focused on elective resections; any anastomosis protected by a proximal stoma and patients treated with neo-adjuvant therapy, perioperative steroids, or dialysis were excluded because these treatments were impacting only on subgroups of subjects; patients treated by anterior resection with anastomosis at less than 6 cm from the anal verge and without protective stoma were excluded in relation to the significant impact of this procedure on the risk of AL. The study adhered to the Strengthening the Reporting of Observational Studies in Epidemiology statement [ 39 ] and checklist (online supplemental material). Fig. 1 Study flowchart. PEG, polyethylene glycol; MNA-SF, mini nutritional assessment–short form [ 38 ]; ERAS, enhanced recovery after surgery; NBP, no bowel preparation; oA, oral antibiotics; MBP, mechanical bowel preparation; MoABP, mechanical bowel preparation and oral antibiotics Study flowchart. PEG, polyethylene glycol; MNA-SF, mini nutritional assessment–short form [ 38 ]; ERAS, enhanced recovery after surgery; NBP, no bowel preparation; oA, oral antibiotics; MBP, mechanical bowel preparation; MoABP, mechanical bowel preparation and oral antibiotics Four different treatment groups were considered: (a) no mechanical bowel preparation and no oral antibiotics (NBP; No. = 3742; 60.0%); (b) oral antibiotics alone (oA; No. = 406; 6.5%); (c) mechanical bowel preparation alone (MBP; No. = 1486; 23.8%); (d) mechanical bowel preparation and oral antibiotics (MoABP; No. = 607; 9.7%). All patients in the MBP and MoABP groups received products containing PEG on the day before surgery. Patients in the oAB and MoABP groups received several different oral antibiotic schedules, the majority of which contained metronidazole (Table  1 ). Table 1 Oral antibiotics schedules in the oA and MoABP groups Oral antibiotic(s) Administration schedule oA (406 pts.) MoABP (607 pts.) p * No % No % Metronidazole (500 mg) Paromomycin (250 mg) Started 2 days preop., TID Started 2 days preop., BID 118 29.1 29 4.8 .006 Metronidazole (500 mg) Cefazolin (2000 mg) Started 1 day preop., TID Started 1 day preop., OD 76 18.7 50 8.2 .102 Metronidazole (500 mg) Trimethoprim (160 mg)/sulfamethoxazole (800 mg) Started 1 day preop., TID Started 1 day preop., TID 68 16.7 61 10.0 .267 Metronidazole (500 mg) Neomicin plus bacitracin (300 mg) Started 1 day preop., TID Started 1 day preop., TID 47 11.6 6 0.9 .419 Metronidazole (500 mg) Amoxicilline (1000 mg) Started 3 days preop., BID Started 3 days preop., BID 25 6.2 5 0.8 .623 Metronidazole (250 mg) Ciprofloxacin (500 mg) Started 1 day preop., TID Started 1 day preop., BID 20 4.9 21 3.5 .823 Metronidazole (500 mg) Rifaximin (400 mg) Started 7 days preop., TID Started 7 days preop., BID 5 1.2 9 1.5 .963 Metronidazole (250 mg) Amoxicilline (1000 mg) Started 1 day preop., BID Started 1 day preop., BID 0 0 50 8.2 n.e Metronidazole (250 mg) Rifaximin (200 mg) Started 1 day preop., TID Started 1 day preop., BID 3 0.8 0 0 n.e Metronidazole (500 mg) Rifaximin (200 mg) Started 1 day preop., BID Started 1 day preop., BID 0 0 68 11.2 n.e Metronidazole (1000 mg) Rifaximin (400 mg) Started 1 day preop., TID Started 1 day preop., TID 0 0 11 1.8 n.e Metronidazole (500 mg) Paromomycin (500 mg) Rifaximin (400 mg) Started 1 day preop., BID Started 1 day preop., BID Started 1 day preop., BID 0 0 126 20.8 n.e Paromomycin (250 mg) Started 4 days preop., QID 44 10.8 0 0 n.e Paromomycin (1000 mg) Started 1 day preop., OD 0 0 37 6.1 n.e Rifaximin (400 mg) Started 1 day preop., TID 0 0 102 16.8 n.e Amoxicillin (1000 mg) Started 3 days preop., TID 0 0 17 2.8 n.e Neomicin plus bacitracin (300 mg) Started 1 day preop., TID 0 0 15 2.5 n.e * OD once daily, BID 2 times per day, TID 3 times per day, ( QID ) 4 times per day, preop ., preoperatively, n.e ., test not executable because there are cells with insufficient values a t test for proportions comparison, oA oral antibiotics b MoABP mechanical bowel preparation plus oral antibiotics Oral antibiotics schedules in the oA and MoABP groups Metronidazole (500 mg) Paromomycin (250 mg) Started 2 days preop., TID Started 2 days preop., BID Metronidazole (500 mg) Cefazolin (2000 mg) Started 1 day preop., TID Started 1 day preop., OD Metronidazole (500 mg) Trimethoprim (160 mg)/sulfamethoxazole (800 mg) Started 1 day preop., TID Started 1 day preop., TID Metronidazole (500 mg) Neomicin plus bacitracin (300 mg) Started 1 day preop., TID Started 1 day preop., TID Metronidazole (500 mg) Amoxicilline (1000 mg) Started 3 days preop., BID Started 3 days preop., BID Metronidazole (250 mg) Ciprofloxacin (500 mg) Started 1 day preop., TID Started 1 day preop., BID Metronidazole (500 mg) Rifaximin (400 mg) Started 7 days preop., TID Started 7 days preop., BID Metronidazole (250 mg) Amoxicilline (1000 mg) Started 1 day preop., BID Started 1 day preop., BID Metronidazole (250 mg) Rifaximin (200 mg) Started 1 day preop., TID Started 1 day preop., BID Metronidazole (500 mg) Rifaximin (200 mg) Started 1 day preop., BID Started 1 day preop., BID Metronidazole (1000 mg) Rifaximin (400 mg) Started 1 day preop., TID Started 1 day preop., TID Metronidazole (500 mg) Paromomycin (500 mg) Rifaximin (400 mg) Started 1 day preop., BID Started 1 day preop., BID Started 1 day preop., BID * OD once daily, BID 2 times per day, TID 3 times per day, ( QID ) 4 times per day, preop ., preoperatively, n.e ., test not executable because there are cells with insufficient values a t test for proportions comparison, oA oral antibiotics b MoABP mechanical bowel preparation plus oral antibiotics The parent studies recorded both continuous and discrete variables related to biometric data, patient information, indication and type of surgical procedure, adherence to ERAS program items, and outcomes. Local investigators ensured data quality control, which was validated by the study coordinator, resolving any discrepancies through strict cooperation. Perioperative care was provided by local investigators, who were left free to decide on any complimentary imaging and/or any further action according to local criteria. The descriptive variables considered in the 6241 patients are shown in Table  2 . Continuous variables were categorized according to their median values to optimize the effectiveness of the analysis by reducing the number of unmatched cases. Table 2 Descriptive analysis of the variables considered in the 6241 patients before matching NBP oA MBP MoABP No. = 3742 No. = 406 No. = 1486 No. = 607 Variable Pattern No % No % No % No % p Age (years)  ≤ 70 1863 49.8 203 50.0 882 59.4 342 56.3   70 1879 50.2 203 50.0 604 40.6 265 43.7 Sex Male 1949 52.1 209 51.5 682 45.9 323 53.2  < .001 Female 1793 47.9 197 48.5 804 54.1 284 46.8 ASA class I–II 2402 64.2 255 62.8 1.028 69.2 407 67.1 .003 III 1340 35.8 151 37.2 458 30.8 200 32.9 Body mass index (Kg/m 2 )  ≤ 25.15 1803 48.2 234 57.6 765 51.5 323 53.2   25.15 1939 51.8 172 42.4 721 48.5 284 46.8 Diabetes Yes 565 15.1 42 10.3 192 12.9 81 13.3 .020 No 3177 84.9 364 89.7 1.294 87.1 526 86.7 Chronic renal failure Yes 154 4.1 18 4.4 65 4.4 27 4.4 .958 No 3588 95.9 388 95.6 1.421 95.6 580 95.6 MNA-SF  ≤ 12 1971 52.7 166 40.9 883 59.4 309 50.9   12 1771 47.3 240 59.1 603 40.1 298 49.1 Surgery for malignancy Yes 2713 72.5 312 76.8 992 66.8 427 70.3  < .001 No 1029 27.5 94 23.2 494 33.2 180 29.7 Diverticular disease 535 52.0 60 63.8 142 28.7 107 59.4 Endometriosis 17 1.6 2 2.1 225 45.5 0 0.0 Polyps 214 20.8 18 19.1 47 9.5 17 9.5 IBD 142 13.8 6 6.4 16 3.3 22 12.2 Other 121 11.8 8 8.6 64 13.0 34 18.9 Mini-invasive surgery No 431 11.5 51 12.6 281 18.9 62 10.2  < .001 Yes 3311 88.5 355 87.4 1.205 81.9 545 89.8 Laparoscopic 2790 84.2 317 89.3 1.006 83.5 509 93.4 Robotic 344 10.4 15 4.2 129 10.7 17 3.1 Converted 177 5.4 23 6.5 70 5.8 19 3.5 Standard procedure Yes 3225 86.2 371 91.4 1.251 84.2 488 80.4  < .001 Right colectomy 1850 57.3 208 56.1 360 28.8 199 40.8 Left colectomy 1080 33.5 133 35.8 435 34.8 223 45.7 Anterior resection 295 9.2 30 8.1 456 36.4 66 13.5 No 517 13.8 35 8.6 235 15.8 119 19.6 Transverse colectomy 78 15.1 10 28.3 37 15.7 18 15.1 Splenic flexure colectomy 125 24.2 12 34.3 50 21.3 14 11.8 Hartmann reversal 84 16.3 4 11.5 63 26.8 12 10.1 (Sub) total colectomy 52 10.1 4 11.5 26 11.1 19 16.0 Other 178 34.3 5 14.4 59 25.1 56 47.0 Anastomosis 1 Intracorporeal 2581 69.0 300 73.9 895 60.2 432 71.2  < .001 Extracorporeal 1161 31.0 106 26.1 591 39.8 175 28.8 Anastomosis 2 Stapled 3400 90.9 354 87.2 1.317 88.6 514 84.7  < .001 Handsewn 342 9.1 52 12.8 169 11.4 93 15.3 Anastomosis 3 End-to-end 1464 39.1 164 40.4 935 62.9 293 48.3  < .001 Other shape 2278 60.9 242 59.6 551 37.1 314 51.7 Operation length (minutes)  ≤ 175 1965 52.5 236 58.1 628 42.3 364 60.0  < .001 ˃ 175 1777 47.5 170 41.9 858 57.7 243 40.0 Hospital type Met./Ac 2267 60.1 257 63.3 769 51.7 516 85.0  < .001 Local/Regional 1475 39.4 149 36.7 717 48.3 91 15.0 Unit type Colorectal/oncologic 470 12.6 22 5.4 490 33.0 144 23.7  < .001 General 3272 87.4 384 94.6 996 67.0 463 76.3 Center volume  < 4 cases/month 887 23.7 136 33.5 449 30.2 221 36.4  < .001  ≥ 4 cases/month 2855 76.3 270 66.5 1.037 69.8 386 63.6 Preoperative BT(s) Yes 234 6.2 17 4.2 68 4.6 26 4.3 .023 No 3508 93.8 389 95.8 1.418 95.4 581 95.7 Intra/postoperative BT(s) Yes 242 6.5 15 3.7 95 6.4 43 7.1 .141 No 3500 93.5 391 96.3 1.391 93.6 564 92.9 Overall ERAS adherence (%)  ≤ 73.68 1271 34.0 88 21.7 1.108 74.6 209 34.4  < .001 ˃ 73.68 2471 66.0 318 78.3 378 25.4 398 65.6   Nutritional screening 2780 74.3 301 74.1 914 61.5 410 67.6   Prehabilitation 1730 46.2 228 56.2 276 18.6 183 30.2   Counseling 2751 73.5 276 68.0 733 49.3 471 77.6   Immune enhancing nutrition 1271 34.0 217 53.5 268 18.0 113 18.6   Antithrombotic prophylaxis 3585 95.8 388 95.6 1.385 93.2 550 90.6   Preoperative carbohydrates load 2505 66.9 256 63.1 517 34.8 326 53.7   No preanesthesia 3265 87.3 293 77.2 867 58.3 448 73.8   Standard anesthesia protocol 3188 85.2 396 97.5 934 62.9 584 96.2   Normothermia 3572 95.5 398 98.0 1.211 81.5 576 94.9   Goal-directed fluid therapy 3084 82.4 359 88.4 900 60.6 539 88.8   PONV prophylaxis 3370 90.1 392 96.6 1.143 76.9 543 89.5   Multimodal analgesia 3448 92.1 402 99.0 1.142 76.9 573 94.4   No nasogastric tube 3376 90.2 391 96.3 1.127 75.8 491 80.9   Minimally invasive surgery 3311 88.5 355 87.4 1.205 81.1 545 89.8   No drains 1525 40.7 242 59.6 171 11.5 178 29.3   Urinary catheter < 24–48 h 3096 82.7 380 93.6 832 56.0 484 79.7   Early mobilization 2391 63.9 373 91.9 391 26.3 469 77.3   Early oral feeding 2286 61.1 352 86.7 431 29.0 374 61.6   Pre-discharge check 3275 87.5 345 85.0 848 57.1 503 82.9 NBP no bowel preparation, oA oral antibiotics alone, MBP mechanical bowel preparation alone, MoABP mechanical bowel preparation and oral antibiotics, ASA American Society of Anesthesiologists, MNA-SF mini nutritional assessment–short form, IBD inflammatory bowel disease, Intracorporeal, anastomosis performed under visual control through the scope, Extracorporeal, anastomosis performed under direct visual control through an open access, Met./Ac ., Metropolitan/Academic, BT blood transfusion, ERAS : Enhanced recovery after surgery, PONV postoperative nausea/vomiting, p chi square independence test with three degrees of freedom Descriptive analysis of the variables considered in the 6241 patients before matching NBP no bowel preparation, oA oral antibiotics alone, MBP mechanical bowel preparation alone, MoABP mechanical bowel preparation and oral antibiotics, ASA American Society of Anesthesiologists, MNA-SF mini nutritional assessment–short form, IBD inflammatory bowel disease, Intracorporeal, anastomosis performed under visual control through the scope, Extracorporeal, anastomosis performed under direct visual control through an open access, Met./Ac ., Metropolitan/Academic, BT blood transfusion, ERAS : Enhanced recovery after surgery, PONV postoperative nausea/vomiting, p chi square independence test with three degrees of freedom All the outcomes were calculated at 60 days after surgery. Any adverse event was recorded and graded [ 40 , 41 ], as well as any reoperation, readmission, or death. The primary endpoints were AL, defined according to the international consensus criteria [ 42 ], SSIs, according to the criteria of the Centers for Disease Control and Prevention/National Healthcare Safety Network (CDC/NHSN) [ 43 ], and overall morbidity (OM; any adverse event). The secondary endpoints were superficial and/or deep incisional surgical site infections (sdiSSIs), defined as specific complications including purulent drainage from superficial incisions, positive culture of fluid or tissue from superficial incisions, pain or tenderness, localized swelling, redness, heat, and/or infections involving deep fascial and muscle layers without fascial dehiscence; deep wound dehiscence; abdominal collection/abscess, defined as any intraperitoneal postoperative collection altering the normal postoperative course, requiring either medical, radiological, endoscopic, or surgical intervention [ 43 ]; major morbidity (any adverse event grade > II); reoperation (any unplanned operation); mortality (any death). Both studies were conducted in accordance with the Declaration of Helsinki and guidelines for good clinical practice E6 (R2). All enrolled patients signed a consent to be included in the studies. The study protocols were approved by the ethics committee of the coordinating center (Marche Regional Ethics Committee (CERM) 2018/334 released on 11/28/2018 for iCral2 and 2020/192 released on 07/30/2020 for iCral3) and registered at ClinicalTrials.gov ( NCT03771456 for iCral2 and NCT04397627 for iCral3). Subsequently, all other centers were authorized to participate in their local ethics committees. Both studies were approved for planned primary and any unplanned secondary analyses; therefore, no further authorization for the current analysis was requested. Individual participant-level anonymized datasets were made available upon reasonable request by contacting the study coordinator. Sample sizes were calculated and reported in the respective core papers [ 34 , 35 ]. Events per variable guideline were followed [ 44 ]. There were no missing data in the database of 6241 patients. The target of estimands was represented by the average treatment effect in the true population of interest (ATT) answering the question “How would the average outcome(s) change if anyone receiving the reference treatment (NBP) had instead received another treatment?” A machine-learning technique, named the Generalized Boosted Model (GBM), was used to estimate the propensity score weights for the binary comparisons between the reference treatment and the other treatment arms. GBM estimation involves an iterative process with multiple regression trees to capture complex and nonlinear relationships between treatment assignment and the covariates without over-fitting the data [ 37 ]. The choice of GBM is due to a better balance of the features [ 37 ] and to an enhanced bias reduction [ 35 ] compared to other multinomial logistic regression models such as inverse probability weighting (IPWT). The analysis was performed using the “twang library” (Toolkit for Weighting and Analysis of Nonequivalent Groups,) of the software “R©” (Version 4.2.2, The R Foundation© for Statistical Computing, Vienna, Austria, 2022). As GBM works iteratively estimating the propensity scores according to the minimization of the distance of the weighted distributions of the covariates given the baseline treatment, balance comparisons have been estimated by performing 10,000 iterations and using the Kolmogorov–Smirnov (KS.mean) metrics with a threshold of 0.2 (a KS-mean difference less than 0.2 typically indicates a negligible difference between the means of the groups) [ 37 ]. The KS.mean was preferred based on the availability of a large sample size allowing comparison of the entire distribution rather than just of the mean. Twenty covariates potentially affecting the four-treatments variable assignments [ 45 ] were included in the model (Fig.  1 ). For the outcome analysis, weighted logistic regression models for both primary and secondary endpoints defined as dichotomous variables, according to the baseline treatment (NBP) and the other three treatment arms (oA, MBP, and MoABP), were estimated using the “svyglm library” (Survey General Linear Models) of the software “R © ” (Version 4.2.2, The R Foundation © for Statistical Computing, Vienna, Austria, 2022). The logistic regression models for the endpoints were adjusted considering the same 20 covariates used in the weight estimation, using a “doubly robust” estimation of the treatment effects [ 37 ]. Considering that the primary endpoints were not independent, having been selected based on available evidence [ 23 ], a Sidak–Bonferroni adjustment for multiple comparisons/outcomes was applied, calculating α  = 0.012. Statistical significance, therefore, was accepted for p values < 0.012. All the instructions used with the software “R © ” are available upon reasonable request to the study coordinator.

The population of 6241 patients included data deriving from 72 (92.3%) of the original 78 centers. NBP group included data deriving from 61 (84.7%), oA from 12 (16.7%), MBP from 52 (72.2%), and MoABP from 18 (25.0%) of the 72 centers. All the 20 covariates included in the model showed an optimal balance among treatment groups (Fig.  2 ). Fig. 2 Love plot of covariates’ Kolmogorov–Smirnov mean differences before and after adjustment using a machine learning technique, comparing the reference treatment (no bowel preparation, named “0” in the figure) with the other 3 treatments (oral antibiotics alone, named “1”; mechanical bowel preparation alone, named “2”; mechanical bowel preparation and oral antibiotics, named “3”); ERAS, enhanced recovery after surgery Love plot of covariates’ Kolmogorov–Smirnov mean differences before and after adjustment using a machine learning technique, comparing the reference treatment (no bowel preparation, named “0” in the figure) with the other 3 treatments (oral antibiotics alone, named “1”; mechanical bowel preparation alone, named “2”; mechanical bowel preparation and oral antibiotics, named “3”); ERAS, enhanced recovery after surgery The multi-treatment weighted logistic regression analysis for primary endpoints (Fig.  3 ) showed the AL risk (3.3% after NBP) to be significantly higher after MBP (5.6%; OR 1.82; 95% CI 1.23–2.71; p  = 0.003) and comparable after oA (3.9%) and MoABP (3.5%). The SSI risk (5.0% after NBP) was significantly lower after MoABP (2.8%; OR 0.42; 95% CI 0.22–0.80; p  = 0.008) and comparable after oA (5.4%) and MBP (6.8%). The OM risk (26.6% after NBP) was significantly higher after MBP (28.9%; OR 1.38; 95% CI 1.10–1.72; p  = 0.005), comparable after oA (25.6%) and MoABP (22.2%). Fig. 3 Multi-treatment weighted logistic regression analysis for primary endpoints (log scale); NBP, no bowel preparation; oA, oral antibiotics alone; MBP, mechanical bowel preparation alone; MoABP, mechanical bowel preparation and oral antibiotics Multi-treatment weighted logistic regression analysis for primary endpoints (log scale); NBP, no bowel preparation; oA, oral antibiotics alone; MBP, mechanical bowel preparation alone; MoABP, mechanical bowel preparation and oral antibiotics Concerning secondary endpoints (Table  3 ), no significant differences were recorded concerning the risk of deep wound dehiscence, abdominal collection/abscess, reoperation, and mortality. The risk of sdiSSI (3.3% after NBP) was significantly reduced after MoABP (1.7%; OR 0.29; 95% CI 0.14–0.60; p  = 0.001), and the risk of major morbidity (5.3% after NBP) was significantly higher after oA (7.6%; OR 2.07; 95% CI 1.31–3.28; p  = 0.002). Table 3 Multi-treatment weighted logistic regression analysis for secondary endpoints Endpoint/treatment NBP (No. = 3742) OR (95% CI); p oA (No. = 406) OR (95% CI); p MBP (No. = 1486) OR (95% CI); p MoABP (No. = 607) OR (95% CI); p sdiSSIs 3.3% Reference 2.5% 0.67 (0.33–1.40); p  = .285 4.9% 1.29 (0.81–2.07); p  = .289 1.7% 0.29 (0.14–0.60); p  = .001 Deep wound dehiscence 0.2% Reference 0.7% 3.08 (0.84–11.2); p  = .089 0.3% 0.75 (0.19–2.96); p  = .678 0.2% 0.50 (0.06–4.13); p  = .521 Abdominal collection/abscess 1.7% Reference 0.7% 0.35 (0.08–1.51); p  = .157 1.8% 1.53 (0.81–2.91); p  = .190 1.0% 0.54 (0.15–1.88); p  = .332 Major morbidity 5.3% Reference 7.6% 2.07 (1.31–3.28); p  = .002 6.7% 1.04 (0.72–1.52); p  = .825 4.9% 0.71 (0.46–1.12); p  = .140 Reoperation 4.6% Reference 5.4% 1.48 (0.86–2.53); p  = .158 6.2% 1.26 (0.86–1.85); p  = .230 4.5% 0.76 (0.47–1.22); p  = .250 Mortality 0.9% Reference 0.5% 0.86 (0.21–3.48); p  = .833 1.0% 1.38 (0.61–3.11); p  = .439 0.3% 0.62 (0.11–3.38); p  = .578 NBP no bowel preparation, oA oral antibiotics alone, MBP mechanical bowel preparation alone, MoABP mechanical bowel preparation and oral antibiotics, sdiSSIs superficial and/or deep incisional surgical site infections Multi-treatment weighted logistic regression analysis for secondary endpoints 3.3% Reference 2.5% 0.67 (0.33–1.40); p  = .285 4.9% 1.29 (0.81–2.07); p  = .289 1.7% 0.29 (0.14–0.60); p  = .001 0.2% Reference 0.7% 3.08 (0.84–11.2); p  = .089 0.3% 0.75 (0.19–2.96); p  = .678 0.2% 0.50 (0.06–4.13); p  = .521 1.7% Reference 0.7% 0.35 (0.08–1.51); p  = .157 1.8% 1.53 (0.81–2.91); p  = .190 1.0% 0.54 (0.15–1.88); p  = .332 5.3% Reference 7.6% 2.07 (1.31–3.28); p  = .002 6.7% 1.04 (0.72–1.52); p  = .825 4.9% 0.71 (0.46–1.12); p  = .140 4.6% Reference 5.4% 1.48 (0.86–2.53); p  = .158 6.2% 1.26 (0.86–1.85); p  = .230 4.5% 0.76 (0.47–1.22); p  = .250 0.9% Reference 0.5% 0.86 (0.21–3.48); p  = .833 1.0% 1.38 (0.61–3.11); p  = .439 0.3% 0.62 (0.11–3.38); p  = .578 NBP no bowel preparation, oA oral antibiotics alone, MBP mechanical bowel preparation alone, MoABP mechanical bowel preparation and oral antibiotics, sdiSSIs superficial and/or deep incisional surgical site infections All the details regarding the multi-treatment machine learning adjusted comparisons are reported in the online supplemental material.

To the best of our knowledge, this is the first multi-treatment propensity score weighting analysis performed using the machine-learning weighted/adjusted regression model to assess different bowel preparation methods before elective colorectal surgery. When conclusive evidence from randomized trials is lacking or when researchers need to assess treatment effects based on real-life data, multiple treatments propensity score weighting analysis based on machine-learning methods performed on data from prospective observational studies offers an alternative approach for estimating treatment effects. The machine learning GBM model adopted in this study provides an improvement in bias reduction and external validity (not reducing the sample size analyzed) in comparison with propensity score-matching analyses between the ATT and the other treatments (three in the present study) and enhances bias reduction in comparison with IPWT [ 36 , 37 ]. The main finding of the present analysis is that MoABP, compared to NBP, showed a significantly lower SSI risk, with no significant difference concerning the AL risk and a borderline reduction of the OM risk (Fig.  3 ). As the severity of complications comprised into OM rates may be skewed between groups and not captured by aggregate analysis, a detailed list of adverse events is reported in Table S4 in online supplemental material. This finding remained consistent with the analysis of secondary endpoints, with a significant reduction of the sdiSSI risk, without any significant difference regarding the risks of major morbidity, mortality, and reoperation (Table  3 ). Although the only available, though largely underpowered, randomized trial comparing NBP with MoABP [ 25 ] failed to detect any significant difference regarding SSI rates in the two arms, our results support the findings of the ACS-NSQIP retrospective series [ 9 – 13 ], the North American societies guidelines [ 14 – 16 ], and the most recent European guideline [ 31 ] towards the recommendation of MoABP in elective colorectal surgery. However, since both oA and MBP determine deep alterations of gut microbiota with possible impact on SSIs and AL rates [ 46 ], and considering that an optimal oral antibiotics administration schedule is far from being established in clinical practice (Table  1 ), the results of ongoing randomized trials comparing oA alone for colon resection [ 28 ] and MBP for rectal resections [ 22 ] with MoABP are eagerly awaited. At the same time, no significant differences were recorded for all the primary endpoints concerning oA (Fig.  3 ), whereas it determined a significantly higher major morbidity risk (Table  3 ), possibly linked to a higher, though not significant, rate of major deep wound dehiscence, sdiSSIs, anastomotic leakage, and cardiac dysfunction events (Table S4 in online supplemental material). Finally, MBP determined significantly higher AL and OM risks (Fig.  3 ), confirming the available evidence from randomized trials [ 1 – 4 ] and the findings of a recent propensity score-matched comparison of NBP vs. MBP alone performed on a more limited number of cases derived by the iCral database [ 5 ]. Considering that MBP alone was still used in nearly one-quarter of our cases, a de-implementation strategy or, according to the preference of some surgeons for a clean colon, a shift towards MoABP is highly advisable. The main strength of the present study is represented by a large number of prospectively enrolled patients in a well-defined time-lapse in a large number of centers, treated by mini-invasive surgery in more than 80% of cases, representing a wide sample of surgical units performing colorectal resections in Italy. Although the multicenter nature of the data may be a definite source of clustering bias, it is undoubtedly representative of real-life clinical practice. Another strength is represented by its methodology (Fig.  1 ): (a) a reasoned selection of patients from the parent database was performed upon explicit criteria, limiting data imbalance; (b) the inclusion of 20 covariates into the model allowed to account for the potential clustering bias of multicenter data, for any confounder due to different perioperative pathways, to surgical approach and techniques, to blood transfusion-related morbidity [ 47 ], and to patient-related factors; (c) evaluation of the treatments effect through a weighted-adjusted regression model including the same 20 covariates [ 48 ]. Although the treatment groups were significantly unbalanced before GBM weighting (Table  2 ) concerning several well-known risk factors for the endpoints (i.e.,: age, sex, ASA class, nutritional status, minimally invasive surgery, type of resection, type and caseload of the recruiting center), the machine-learning generalized boosted model used in this study markedly improves bias reduction minimizing the distance of the weighted distributions of the 20 covariates (Fig.  2 ) compared to alternative methods such as IPWT [ 36 , 37 ]. However, this study has several limitations, and its results should be interpreted with caution: (a) a relevant heterogeneity of oral antibiotic schedules (Table  1 ), as within and between previously published RCT and related meta-analyses [ 33 ]; (b) the exclusion criteria applied to the parent database (Fig.  1 ) practically excluded any resection performed for low rectal cancer, making the results not applicable to this subgroup of patients; (c) several aspects of health-acquired infections preventive bundle (preoperative whole-body bathing, hair removal, and skin decontamination) and single surgeon’s experience [ 49 ] were not measured in the parent studies; (d) finally, further bias from residual unknown factors and potential measurement errors by the participating investigators may have had an impact on the results.

This multi-treatment machine learning analysis, despite the limitations mentioned above, showed that mechanical bowel preparation combined with oral antibiotics significantly reduced the SSI risk after elective colorectal surgery.

Current practice and recommendations regarding bowel preparation before elective colorectal surgery to reduce the incidence of anastomotic leakage (AL) and surgical site infections (SSIs) remain controversial. Mechanical bowel preparation (MBP), once routinely used, may cause preoperative dehydration, electrolyte disturbance, and discomfort, and failed to demonstrate any clear benefit over no bowel preparation (NBP) [ 1 – 5 ]. European [ 6 ] and Italian [ 7 ] enhanced recovery after surgery (ERAS) societies’ guidelines currently recommend NBP, albeit leaving room for oral antibiotics (oA) alone or in combination with MBP [ 8 ]. At the same time, results of large retrospective population-based studies of the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) suggested that MBP combined with oral antibiotics (MoABP) significantly decreased the rates of SSIs and overall morbidity (OM) compared to NBP [ 9 – 13 ], inducing four large North-American societies (The American Society of Colon and Rectal Surgeons, the Society of American Gastrointestinal and Endoscopic Surgeons, the American Society for Enhanced Recovery, and the Perioperative Quality Initiative) to recommend MoABP [ 14 – 16 ]. As a consequence, the use of MoABP is currently reported by 50% of Austrian–German [ 17 ] and by 80% of North American [ 18 ] surgeons. During the last 8 years, one RCT was launched comparing NBP with MoABP [ 19 ], two MoABP with oA [ 20 , 21 ], and one MoABP with MBP for rectal cancer [ 22 ]. To the best of our knowledge, only one [ 22 ] of these trials recently completed the planned enrollment and none published its final results yet [ 23 ]. An interesting four-arm RCT comparing NBP with oA, MBP, and MoABP for colon resections [ 24 ] was recently closed before completion due to poor accrual. Meanwhile, one RCT comparing NBP with MoABP [ 25 ] failed to detect significant differences in SSIs and AL rates but was largely underpowered; oA showed a significant reduction of SSI rates in two RCTs, either alone [ 26 , 27 ] or combined with MBP [ 26 ], and an international multicenter RCT comparing oA with MoABP [ 28 ] is currently still recruiting. Finally, one RCT reported that MoABP significantly reduced SSI rates compared to MBP after colorectal resections [ 29 ], and another that MoABP significantly reduced both SSI and AL rates compared to MBP after rectal resections [ 30 ]. Very recently, the European Association of Endoscopic Surgery, the European Society of ColoProctology, and the Society of American Gastrointestinal and Endoscopic Surgeons published a joint guideline [ 31 ] based on a previous systematic review and network meta-analysis [ 32 ], with a conditional recommendation for MoABP, supported by low-quality evidence due to variable adherence to preoperative intravenous antibiotic prophylaxis (PIVAP) and great heterogeneity regarding oA schedules [ 33 ]. The relevant heterogeneity of the available evidence induced the Italian ColoRectal Anastomotic Leakage (iCral) study group to estimate the effects of NBP in patients treated with PIVAP before elective colorectal surgery (treatment variable) in comparison to three other treatments (oA, MBP, MoABP) on a large dataset derived from two prospective multicenter open-label observational studies [ 34 , 35 ]. Several recent studies of propensity score estimation showed that machine learning methods outperform logistic regression models with iterative variable sections in terms of bias reduction and mean-squared error [ 36 ] and may be advantageous in multiple treatment settings [ 37 ]. Therefore, a multi-treatment analysis based on machine learning procedures was used to compare four bowel preparation modalities before elective colorectal surgery.

Below is the link to the electronic supplementary material. Supplementary file1 (DOCX 60 KB) Supplementary file1 (DOCX 60 KB)