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Machine learning-based prediction models affecting the recovery of postoperative bowel function for patients undergoing colorectal surgeries

BMC Surgery volume 24, Article number: 143 (2024) Cite this article

Abstract

Purpose

The debate surrounding factors influencing postoperative flatus and defecation in patients undergoing colorectal resection prompted this study. Our objective was to identify independent risk factors and develop prediction models for postoperative bowel function in patients undergoing colorectal surgeries.

Methods

A retrospective analysis of medical records was conducted for patients who undergoing colorectal surgeries at Peking University People’s Hospital from January 2015 to October 2021. Machine learning algorithms were employed to identify risk factors and construct prediction models for the time of the first postoperative flatus and defecation. The prediction models were evaluated using sensitivity, specificity, the Youden index, and the area under the receiver operating characteristic curve (AUC) through logistic regression, random forest, Naïve Bayes, and extreme gradient boosting algorithms.

Results

The study included 1358 patients for postoperative flatus timing analysis and 1430 patients for postoperative defecation timing analysis between January 2015 and December 2020 as part of the training phase. Additionally, a validation set comprised 200 patients who undergoing colorectal surgeries from January to October 2021. The logistic regression prediction model exhibited the highest AUC (0.78) for predicting the timing of the first postoperative flatus. Identified independent risk factors influencing the time of first postoperative flatus were Age (p < 0.01), oral laxatives for bowel preparation (p = 0.01), probiotics (p = 0.02), oral antibiotics for bowel preparation (p = 0.02), duration of operation (p = 0.02), postoperative fortified antibiotics (p = 0.02), and time of first postoperative feeding (p < 0.01). Furthermore, logistic regression achieved an AUC of 0.72 for predicting the time of first postoperative defecation, with age (p < 0.01), oral antibiotics for bowel preparation (p = 0.01), probiotics (p = 0.01), and time of first postoperative feeding (p < 0.01) identified as independent risk factors.

Conclusions

The study suggests that he use of probiotics and early recovery of diet may enhance the recovery of bowel function in patients undergoing colorectal surgeries. Among the various analytical methods used, logistic regression emerged as the most effective approach for predicting the timing of the first postoperative flatus and defecation in this patient population.

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Background

The recovery of bowel function after colorectal surgery has been extensively researched. Poor recovery of bowel function can lead to prolonged hospital stays, increased complications rates, higher hospitalization costs, and mortality [1, 2]. While stool form scales offer a straightforward approach to evaluating intestinal transit rate, they are not commonly utilized in clinical setting or research endeavors [3]. Symptoms like nausea and/or vomiting, fecal urgency, and bowel movement are considered indicative signals of postoperative bowel function restoration [4]. Time of first bowel motion, time of first postoperative flatus and defecation are employed to gauge postoperative bowel function for patients undergoing colorectal surgeries [5,6,7,8].

Numerous risk factors influence the recuperation of postoperative bowel function. Previous studies about recovery of bowel function following colorectal surgeries suggested that laparoscopic surgery may enhance postoperative bowel function recovery compared to traditional laparotomy [9]. Mechanical bowel preparation had shown benefits in some studies, yet recent research indicated that it may not consistently improve patients’ recovery and could lead to patients’ discomfort [10]. The outcomes concerning postoperative bowel function recovery remain contentious. Limited studies have delved into multivariate analysis of the time of the first postoperative flatus and defecation for patients undergoing colorectal surgeries. Furthermore, no study had developed a prediction model for postoperative bowel function recovery in this patient population.

This study aimed to establish prediction models using machine learning algorithms to assess the risks and identify independent risk factors associated with the time of first postoperative flatus and defecation for patients undergoing colorectal surgeries.

Methods

Participants

This study was approved by the Ethics committee of Peking University People’s Hospital (2022PHB053-001, Beijing, China). A retrospective study was conducted to develop and internally validate the time of the first postoperative flatus and defecation. Adult patients undergoing colorectal surgeries at Peking University People’s Hospital from January 2015 to October 2021 were enrolled. Exclusion criteria were patients who met one of the following characteristics:

  • Patients who had a history of surgical reconstruction of the digestive tract.

  • Patients who had undergone enterostomies, such as jejunostomy, total proctocolectomy with ileostomy, or colostomy.

  • Patients who were younger than 18 years old.

  • Data on the time to postoperative flatus or/and defecation were incomplete.

Previous studies [11, 12] found that the mean time of the first postoperative flatus was 4 days and the time of the first postoperative defecation was 5 days for patients undergoing colorectal surgeries. Therefore, we defined patients in two groups between the time of the first postoperative flatus within 4 days and more than 5 days. We defined patients in two groups between the time of the first postoperative defecation within 4 days and more than 5 days as well.

Data collection

General data of patients were carefully recorded, including age, gender, body mass index (BMI), history of alcohol, and history of smocking. Underlying disease (hypertension, coronary heart disease [CHD], arrhythmia, cerebral infraction, encephalorrhagia, hypothyroidism, diabetes, chronic obstructive pulmonary disease [COPD], renal inadequacy, hyperlipidemia, hepatic inadequacy, blood disease) diagnosed before admission were entered into excel of case report form (CRF). Data of preoperative chemotherapy, preoperative anemia, preoperative ileus, the American Society of Anesthesiologists (ASA) classification, bowel preparation before surgery (soapsuds enema, oral laxatives, glycerin enema, and oral antibiotics) were adopted in CRF. Data of surgery such as surgical site (right hemicolectomy, transverse colectomy, left hemicolectomy, sigmoid colectomy, and proctectomy), surgical approach (laparotomy or laparoscopic surgery), excision method (local resection or extended radical resection), duration of operation, antibiotics correlation (preoperative, fortified before surgery, fortified after surgery, duration of antibiotics) were entered into CRF. Postoperative data including time of the first postoperative feeding (day), probiotics correlation; postoperative albumin level, postoperative analgesia (no analgesia, opioids, opioids combine non-steroidal anti-inflammatory drugs [NSAIDs], and take NSAIDs alone), duration of analgesia, time to the extraction of a gastric tube and the drainage tube were entered recorded. All data were obtained by medical record such as papery medical records library or electronic medical record system.

Statistical analysis

Univariate analysis

Univariate analysis was performed using SPSS 26.0 software (IBM, Armonk, NY, USA) to identify the relative risk factors affecting the time of first postoperative flatus and defecation by. Quantitative data with normal distribution were expressed as mean ± standard deviations (SD) or medians and interquartile ranges and compared using a one-way analysis of variance. Frequencies and percentages were used for categorical variables. An independent sample t-test was performed according to the homogeneity of variance for continuous variables. The frequency and composition ratio were used for the statistical description of classification data, and the χ2 test or Fisher’s exact test was used for comparison between groups. A p-value < 0.05 was considered to indicate significance.

Model development

Prediction models for the time of the first postoperative flatus and defecation were developed using four machine learning algorithms: logistic regression (LR), random forest (RF), Naïve Bayes (NB), and extreme gradient boosting (XGB). Data from patients undergoing colorectal surgeries from January 2015 to December 2020 were used as training sets, while data from January to October 2021 served as validation sets. We calculated the number of true-positive (TP), true-negative (TN), false-positive (FP), and false-negative (FN) results. Performance and discrimination of the prediction models were evaluated by the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value (TP/[TP + FP]), negative predictive value (TP/[TP + FN]), and Youden index ([sensitivity + specificity]-1). The AUC value greater than 0.6 indicated good predictive value, with closer value to 1 indicating better model performance. Nomograms based on the results of logistic regression were planned if logistic regression outperformed the other three methods. The prediction models were developed using the R software RMS package (version 4.0.3).

Results

Baseline characteristics and related risk factors

A total of 1438 patients undergoing colorectal surgeries from January 2015 to December 2020 were involved, including 856 patients for the time of first postoperative flatus within 4 days and 1052 patients for the time of first postoperative flatus within 5 days in the training set. 200 patients undergoing colorectal surgeries from January to October 2021 were involved in the validation set (Fig. 1). The mean time to postoperative flatus was 4.17 ± 1.45 days and the mean time to postoperative defecation was 4.77 ± 1.89 days.

Fig. 1
figure 1

Study eligibility of patients who undergone colorectal surgeries

Full size image

Time of first postoperative flatus

Clinical characteristics of the patients at the time of the first postoperative flatus were shown in Table 1. Among univariate analysis, age, right colectomy, sigmoid colectomy, malignancy, hypothyroidism, preoperative anemia, preoperative ileus, ASA classification, soapsuds enema, oral laxatives, and oral antibiotic for bowel preparation, laparotomy, duration of operation, preoperative antibiotics, preoperative fortified antibiotics, time of postoperative feeding, probiotics, duration of analgesia, hypoproteinemia, time to the extraction of the gastric tube and drainage tube were associated with the time of first postoperative flatus for patients undergoing colorectal surgeries.

Table 1 Basic characteristics in the time of first postoperative flatus for patients undergoing colorectal surgeries
Full size table

Time of first postoperative defecation

We investigated the variates by univariate analysis and found that 14 indicators including age, right hemicolectomy, proctectomy, encepalorrhagia, preoperative chemotherapy, ASA classification, glycerin enema for bowel preparation, oral antibiotics for bowel preparation, preoperative antibiotics, preoperative fortified antibiotics, time of first postoperative feeding, probiotics, hypoproteinemia and time to the extraction of the drainage tube were associated with that prolong the time of first postoperative defecation (Table 2).

Table 2 Regression coefficients of the time of first postoperative flatus model based on 7 independent variables
Full size table

Development of prediction models

Time of first postoperative flatus

Four prediction models were conducted based on the aforementioned variables by machine learning algorithms. We used the data from January 2015 to December 2020 as a training set and the samples from January to October 2021 as a validation set. The area under the receiver operating characteristic (AUC) was 0.78(0.71–0.84) in the validation of logistic regression analysis, 0.74(0.66–0.83) in the validation of random forest (RF), 0.69(0.61–0.77) in the validation of Naïve Bayes (NB), and was 0.71(0.63–0.79) in the validation of extreme gradient boosting (XGB) for the prediction model of the time to postoperative flatus for patients undergoing colorectal surgeries (Fig. 2). Logistic regression was found to be the best-performing model for predicting the time of the first postoperative flatus comparing with the other three models as the AUC, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and Youden index (sensitivity + specificity-1) were shown in Additional file 1. A nomogram was used to present the data of the time to the first postoperative flatus based on logistic regression for practical use (Fig. 3). A total number of points was calculated with age, probiotics, oral laxatives for bowel preparation, oral antibiotics for bowel preparation, duration of operation, and time of first postoperative feeding. The total score can be attached to the probability of the time to postoperative flatus (Tables 3 and 4).

Fig. 2
figure 2

Receiver operating characteristic (ROC) curves of the prediction model for the time of first postoperative flatus conducted by machine learning algorithms

Full size image
Fig. 3
figure 3

Nomogram for the time of first postoperative flatus. To estimate the probability of the time of postoperative flatus, mark patient value at each axis, draw a straight line perpendicular to the point axis, and calculate the points for all variables. Then mark the sum on the total point axis and the points met the risk axis

Full size image
Table 3 Basic characteristics in the time of first postoperative defecation for patients undergoing colorectal surgeries
Full size table
Table 4 Regression coefficients of the time of first postoperative defecation model based on 4 independent variables
Full size table

Time of first postoperative defecation

In the validation set, AUCs for the LR, RF, NB, and XGB algorithms were 0.72(0.61–0.84), 0.69(0.58–0.80), 0.68(0.57–0.79), and 0.66 (0.54–0.77) (Fig. 4). The performance of AUC, sensitivity, specificity, PPV, NPV, and Youden index was summarized in Additional file 2. We selected the LR algorithm for the final model because the prediction model of the time to the first postoperative defecation performed well by LR. A nomogram for the time of the first postoperative defecation using by LR for patients undergoing colorectal surgeries was created based on the independent risk factors. The value of age, probiotics, oral antibiotics for bowel preparation, and time of postoperative feeding was given a score on the point scale axis in Fig. 5. A total score can be calculated by adding each score of these independent risk factors to estimate the probability of the time to the first postoperative defecation.

Fig. 4
figure 4

Receiver operating characteristic (ROC) curves for the prediction model of the time of first postoperative defecation conducted by machine learning algorithms

Full size image
Fig. 5
figure 5

Nomogram for the time of first postoperative defecation. The value of variable was given a score on the point scale axis. To estimate the risk of the time of first postoperative defecation, a total score could be calculated by each axis and could be projected to the lower total point scale

Full size image

Postoperative complications

The incidence of postoperative complications was shown in Table 5. The symptom of abdominal distension (27.97%) contributed the highest rate of postoperative complications, while the incidence of diarrhea (27.70%) placed second to postoperative complications for patients undergoing colorectal surgeries.

Table 5 Postoperative complications of patients undergoing colorectal surgeries
Full size table

Discussion

In this study, we evaluated the ability of four machine learning algorithms to predict the time of postoperative flatus and defecation for patients undergoing colorectal surgeries. Our final prediction model achieved an AUC value of 0.78(0.71–0.84) for the time of postoperative flatus and 0.72(0.61–0.84) for the time of postoperative defecation according to the best performance of the logistic regression model compared with the other three models. The logistic regression model identified seven variables age, oral laxatives for bowel preparation, oral antibiotics for bowel preparation, probiotics, postoperative fortified antibiotics, duration of operation, and time of postoperative feeding for the time of postoperative flatus and four variables age, oral antibiotics for bowel preparation, probiotics, and time of postoperative feeding for the time of postoperative defecation.

The function of the bowel is to ingest and digest food and fluids, absorb nutrients, and eliminate any waste products, which is important to understand how surgery may alter not just its anatomy, but also its function [13]. Postoperative recovery is a dynamic process in that patients try to regain their independence, but postoperative bowel dysfunction is one of the most common complications among patients who have undergone major abdominal surgery [14]. Urinary and sexual dysfunction are the most common complications for patients undergoing rectal surgery [15]. Bowel dysfunction can manifest as constipation, anal incontinence, or diarrhea.is more likely to occur if there is a large bowel resection such as a colectomy or if most of the rectum is resected [16]. Regaining normal bowel functions after surgery is considered important for patients. Bowel motion, the time of first postoperative flatus, and the time of first postoperative defecation are usually used to assess bowel function during early postoperative recovery [17]. In this study, the time of postoperative flatus and defecation were selected to assess postoperative bowel function.

We found that mechanical bowel preparation with antibiotics and age were strong predictors for the risk of postoperative flatus and defecation. The mean age of the patients was 64.03 years in the study about the time of postoperative flatus and the mean age of patients with the time of postoperative flatus more than 5 days was 67.05 years in this study. Mechanical bowel preparations such as soap enema and oral laxatives can reduce fecal bulk which may decrease bacterial colonization, thereby reducing the risk of postoperative complications such as anastomotic leakage and surgical site infection [18]. The studies told us that mechanical bowel preparation combined with oral antibiotic bowel preparation can reduce the incidence of surgical site infection, anastomotic leakage, and other morbidity compared with mechanical bowel preparation for patients undergoing elective colorectal surgery [19,20,21,22,23]. Therefore, it was recommended that mechanical bowel preparation combined with oral antibiotic preparation for patients undergoing elective colorectal surgery in 2009 US guidelines [24]. In recent years, many studies found that the potential advantages of mechanical bowel preparation combined with an oral antibiotic, such as nausea and dehydration were considered not worthwhile [25] and did not add significant value to reducing the incidence of infectious complications [26].

Probiotics and the time of postoperative feeding were predictors of reducing the time of postoperative flatus and defecation in this study. Many researchers have focused on probiotics because they found that gastrointestinal microflora plays an important role in maintaining human health [27]. Probiotics help to improve the intestinal microecology balance and stimulate immunity, which may inhibit colon cancer and decrease the incidence of postoperative complications including surgical site infection, urinary tract infection, and septicemia [28,29,30]. Early enteral nutrition is recommended for patients after gastrointestinal surgery [31,32,33]. A study about postoperative feeding for patients undergoing colorectal surgeries found that there was no difference between patients who accepted early postoperative feeding and traditional postoperative feeding [34]. Early resume to postoperative feeding helps improve clinical outcomes such as promoting bowel motility, shortening the time of postoperative defecation, and reducing intestinal mucosal hypermetabolism [35, 36].

Previous studies comparing traditional open and laparoscopic surgery for rectal cancer had found that mean time about the time of postoperative flatus was 96.5 h vs 123 h [9]. Recent research showed that there was no difference of time to recovery of postoperative bowel function among different site of colon [37]. Another study suggested that robotic reduced-port surgery for left-sided colorectal cancer was safe and no additional benefit compared with laparoscopic surgery [38]. In our study, there was no difference between laparotomy and laparoscopic surgery of colorectum for patients undergoing colorectal surgery. The Enhanced Recovery after Surgery (ERAS) protocol was well developed especially for patients undergoing the surgery treatment in laparoscopic colorectal tumor resection since the ERAS Study formed in Europe in 2001 [39]. The data varied widely because there were different wards in the department of gastrointestinal surgery. More and more surgeons are following the principle of ERAS protocol for perioperative management. However, there were also someone choose traditional methods for perioperative management in our department.

In recent years, artificial intelligence has mostly narrowed down to machine learning methods. Current machine learning methods include neural networks, support vector machines, or random forests that have been used to develop prediction models and identify risk factors in recent years [40, 41], but statistical models have limitations in processing numerous unrefined variables. In this study, LR showed the best performance among the other three prediction models because the assessment indicator of postoperative bowel function was limited. We believe that machine learning algorithms will be actively used as tools for predicting complex outcomes and have greater potential.

There were several limitations in this study. Firstly, the variables of the models are clinically relevant, but causality cannot be confirmed due to the nature of retrospective data. Secondly, due to retrospective design, possible collection, entry bias, and residual confounding may occur, and we did not collect the medical history of constipation. Furthermore, the risk of the time of postoperative flatus and defecation is complicated. Thirdly, our study is a single-center study due to the lack of data from other surgical centers. We validated our model by different time at the same independent dataset, which is considered to be a kind of controversial external validation. Despite these limitations, ours is the first study to identify independent risk factors for the time of postoperative flatus and defecation in colorectal surgeries using a machine learning algorithm.

Conclusion

By means of machine learning techniques, we selected independent risk factors, as well as evaluated prediction models for the first postoperative flatus and defecation time on adult patients undergoing colorectal surgeries. In addition, probiotics and early recovery of postoperative feeding may improve postoperative bowel function, while oral antibiotics for bowel preparation may affect postoperative bowel function for those patients.

Availability of data and materials

The data are available from the corresponding author on reasonable request. But the datasets are not publicly available due to privacy or ethical restrictions.

Abbreviations

BMI:

Body mass index

CHD:

Coronary heart disease

COPD:

Chronic obstructive pulmonary disease

ASA:

American Society of Anesthesiologists

NSAIDs:

Non-steroidal anti-inflammatory drugs

CRF:

Case report form

OR:

Odds ratios

CI:

Confidence intervals

LR:

Logistic regression

RF:

Random forest

NB:

Naïve Bayes

XGB:

Extreme gradient boosting

TP:

True-positive

TN:

True-negative

FP:

False-positive

FN:

False-negative

ROC:

Receiver operating characteristic

AUC:

Area under the receiver operating characteristic curve

PPV:

Positive predictive value

NPV:

Negative predictive value

References

  1. Vather R, Trivedi S, Bissett I. Defining postoperative ileus: results of a systematic review and global survey. J Gastrointest Surg. 2013;17:962–72.

    Article  PubMed  Google Scholar 

  2. Iyer S, Saunders W, Stemkowski S. Economic burden of postoperative ileus associated with colectomy in the United States. J Manag Care Pharm. 2009;15:485–94.

    PubMed  Google Scholar 

  3. Lewis SJ, Heaton KW. Stool form scale as a useful guide to intestinal transit time. Scand J Gastroenterol. 1997;32:920–4.

    Article  CAS  PubMed  Google Scholar 

  4. Whitcomb EL, Lukacz ES, Lawrence JM, Nager CW, Luber KM. Prevalence and degree of bother from pelvic floor disorders in obese women. Int Urogynecol J. 2008;20:289.

    Article  Google Scholar 

  5. Nanthiphatthanachai A, Insin P. Effect of chewing gum on gastrointestinal function recovery after surgery of gynecological cancer patients at rajavithi hospital: a randomized controlled trial. Asian Pac J Cancer Prev. 2020;21:761–70.

    Article  PubMed  PubMed Central  Google Scholar 

  6. de Leede EM, van Leersum NJ, Kroon HM, van Weel V, van der Sijp JRM, Bonsing BA, et al. Multicentre randomized clinical trial of the effect of chewing gum after abdominal surgery. Br J Surg. 2018;105:820–8.

    Article  PubMed  Google Scholar 

  7. McKay WP, Donais P. Bowel function after bowel surgery: morphine with ketamine or placebo; a randomized controlled trial pilot study. Acta Anaesthesiol Scand. 2007;51:1166–71.

    Article  CAS  PubMed  Google Scholar 

  8. Yuan L, O’Grady G, Milne T, Jaung R, Vather R, Bissett IP. Prospective comparison of return of bowel function after left versus right colectomy. ANZ J Surg. 2018;88:E242–7.

    Article  PubMed  Google Scholar 

  9. Kang SB, Park JW, Jeong SY, Nam BH, Choi HS, Kim DW, et al. Open versus laparoscopic surgery for mid or low rectal cancer after neoadjuvant chemoradiotherapy (COREAN trial): short-term outcomes of an open-label randomised controlled trial. Lancet Oncol. 2010;11:637–45.

    Article  PubMed  Google Scholar 

  10. Klinger AL, Green H, Monlezun DJ, Beck D, Kann B, Vargas HD, et al. The role of bowel preparation in colorectal surgery: results of the 2012–2015 ACS-NSQIP data. Ann Surg. 2019;269:671–7.

    Article  PubMed  Google Scholar 

  11. Liu Z, Efetov S, Guan X, Zhou H, Tulina I, Wang G, et al. A Multicenter study evaluating natural orifice specimen extraction surgery for rectal cancer. J Surg Res. 2019;243:236–41.

    Article  PubMed  Google Scholar 

  12. El Nakeeb A, Fikry A, El Metwally T, Fouda E, Youssef M, Ghazy H, et al. Early oral feeding in patients undergoing elective colonic anastomosis. Int J Surg. 2009;7:206–9.

    Article  PubMed  Google Scholar 

  13. Tortora GJ, Derrickson BH. Principles of anatomy & physiology. Wiley; 2008.

  14. Murphy MM, Tevis SE, Kennedy GD. Independent risk factors for prolonged postoperative ileus development. J Surg Res. 2016;201:279–85.

    Article  PubMed  Google Scholar 

  15. Li K, Pang P, Cheng H, Zeng J, He X, Cao F, et al. Protective effect of laparoscopic functional total mesorectal excision on urinary and sexual functions in male patients with mid-low rectal cancer. Asian J Surg. 2023;46:236–43.

    Article  PubMed  Google Scholar 

  16. National Guideline A. NICE Evidence Reviews Collection. Optimal management of low anterior resection syndrome: Colorectal cancer (update): Evidence review E2. London: National Institute for Health and Care Excellence (NICE) Copyright © NICE 2020; 2020.

    Google Scholar 

  17. Tan SH, Liao YM, Lee KC, Ko YL, Lin PC. Exploring bowel dysfunction of patients following colorectal surgery: a cohort study. J Clin Nurs. 2019;28:1577–84.

    Article  PubMed  Google Scholar 

  18. Nichols RL, Condon RE. Preoperative preparation of the colon. Surg Gynecol Obstet. 1971;132:323–37.

    CAS  PubMed  Google Scholar 

  19. Condon RE, Bartlett JG, Greenlee H, Schulte WJ, Ochi S, Abbe R, et al. Efficacy of oral and systemic antibiotic prophylaxis in colorectal operations. Arch Surg. 1983;118:496–502.

    Article  CAS  PubMed  Google Scholar 

  20. Takesue Y, Yokoyama T, Akagi S, Ohge H, Murakami Y, Sakashita Y, et al. A brief course of colon preparation with oral antibiotics. Surg Today. 2000;30:112–6.

    Article  CAS  PubMed  Google Scholar 

  21. Rollins K, Javanmard-Emamghissi H, Acheson A, Lobo D. The role of oral antibiotic preparation in elective colorectal surgery: a meta-analysis. Ann Surg. 2018;270:1.

    Google Scholar 

  22. Fry DE. Colon preparation and surgical site infection. Am J Surg. 2011;202:225–32.

    Article  PubMed  Google Scholar 

  23. Espin Basany E, Solís-Peña A, Pellino G, Kreisler E, Fraccalvieri D, Muinelo-Lorenzo M, et al. Preoperative oral antibiotics and surgical-site infections in colon surgery (ORALEV): a multicentre, single-blind, pragmatic, randomised controlled trial. Lancet Gastroenterol Hepatol. 2020;5:729–38.

    Article  PubMed  Google Scholar 

  24. Migaly J, Bafford AC, Francone TD, Gaertner WB, Eskicioglu C, Bordeianou L, et al. The American Society of Colon and Rectal Surgeons Clinical Practice Guidelines for the use of bowel preparation in elective colon and rectal surgery. Dis Colon Rectum. 2019;62:3–8.

    Article  PubMed  Google Scholar 

  25. Koskenvuo L, Lehtonen T, Koskensalo S, Rasilainen S, Klintrup K, Ehrlich A, et al. Mechanical and oral antibiotic bowel preparation versus no bowel preparation for elective colectomy (MOBILE): a multicentre, randomised, parallel, single-blinded trial. Lancet. 2019;394:840–8.

    Article  CAS  PubMed  Google Scholar 

  26. Aslan G, Baltaci S, Akdogan B, Kuyumcuoǧlu U, Kaplan M, Cal C, et al. A prospective randomized multicenter study of Turkish Society of Urooncology comparing two different mechanical bowel preparation methods for radical cystectomy. Urol Oncol. 2013;31:664–70.

    Article  PubMed  Google Scholar 

  27. Bernet MF, Brassart D, Neeser JR, Servin AL. Lactobacillus acidophilus LA 1 binds to cultured human intestinal cell lines and inhibits cell attachment and cell invasion by enterovirulent bacteria. Gut. 1994;35:483–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Eslami M, Yousefi B, Kokhaei P, Hemati M, Nejad ZR, Arabkari V, et al. Importance of probiotics in the prevention and treatment of colorectal cancer. J Cell Physiol. 2019;234:17127–43.

    Article  CAS  PubMed  Google Scholar 

  29. Tlaskalová-Hogenová H, Štěpánková R, Hudcovic T, Tučková L, Cukrowska B, Lodinová-Žádníková R, et al. Commensal bacteria (normal microflora), mucosal immunity and chronic inflammatory and autoimmune diseases. Immunol Lett. 2004;93:97–108.

    Article  PubMed  Google Scholar 

  30. Chen C, Wen T, Zhao Q. Probiotics used for postoperative infections in patients undergoing colorectal cancer surgery. Biomed Res Int. 2020;2020:5734718.

    PubMed  PubMed Central  Google Scholar 

  31. Abunnaja S, Cuviello A, Sanchez JA. Enteral and parenteral nutrition in the perioperative period: state of the art. Nutrients. 2013;5:608–23.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Chen W, Zhang Z, Xiong M, Meng X, Dai F, Fang J, et al. Early enteral nutrition after total gastrectomy for gastric cancer. Asia Pac J Clin Nutr. 2014;23:607–11.

    PubMed  Google Scholar 

  33. Ding D, Feng Y, Song B, Gao S, Zhao J. Effects of preoperative and postoperative enteral nutrition on postoperative nutritional status and immune function of gastric cancer patients. Turk J Gastroenterol. 2015;26:181–5.

    Article  PubMed  Google Scholar 

  34. Fujii T, Morita H, Sutoh T, Yajima R, Yamaguchi S, Tsutsumi S, et al. Benefit of oral feeding as early as one day after elective surgery for colorectal cancer: oral feeding on first versus second postoperative day. Int Surg. 2014;99:211–5.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Herbert G, Perry R, Andersen HK, Atkinson C, Penfold C, Lewis SJ, et al. Early enteral nutrition within 24 hours of lower gastrointestinal surgery versus later commencement for length of hospital stay and postoperative complications. Cochrane Database Syst Rev. 2019;7:Cd004080.

    PubMed  Google Scholar 

  36. Wang G, Chen H, Liu J, Ma Y, Jia H. A comparison of postoperative early enteral nutrition with delayed enteral nutrition in patients with esophageal cancer. Nutrients. 2015;7:4308–17.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Pélissier E, Monek O, Cuche F. [Reducing the hospital stay after colorectal resection]. Ann Chir. 2005;130:608–12.

    Article  PubMed  Google Scholar 

  38. Wei PL, Huang YJ, Wang W, Huang YM. Comparison of robotic reduced-port and laparoscopic approaches for left-sided colorectal cancer surgery. Asian J Surg. 2023;46:698–704.

    Article  PubMed  Google Scholar 

  39. Zhang C, Fang R, Gu B. The enhanced recovery after surgery (ERAS) pathway for patients undergoing laparoscopic colorectal tumor resection. Asian J Surg. 2022;45:2556–7.

    Article  PubMed  Google Scholar 

  40. Desai RJ, Wang SV, Vaduganathan M, Evers T, Schneeweiss S. Comparison of machine learning methods with traditional models for use of administrative claims with electronic medical records to predict heart failure outcomes. JAMA Netw Open. 2020;3:e1918962.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Mortazavi BJ, Bucholz EM, Desai NR, Huang C, Curtis JP, Masoudi FA, et al. Comparison of machine learning methods with national cardiovascular data registry models for prediction of risk of bleeding after percutaneous coronary intervention. JAMA Netw Open. 2019;2:e196835.

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

Thank you for all participants for accomplishing this study.

Funding

This work was supported by Peking University People’s Hospital Scientific Research Development Funds (RDL 2020–06).

Author information

Author notes

  1. Shuguang Yang and Fuzheng Guo are co-first authors and contributed equally to this work.

  2. Zhidong Gao and Yingjiang Ye contributed equally to this work.

Authors and Affiliations

  1. Department of Critical Care Medicine, Peking University People’s Hospital, 11 Xizhimen South Street, Beijing, 100044, P.R. China

    Shuguang Yang, Huiying Zhao & Youzhong An

  2. Trauma Center, Peking University People’s Hospital, National Center for Trauma Medicine, Key Laboratory of Trauma and Neural Regeneration (Ministry of Education), Beijing, China

    Fuzheng Guo

  3. Research Center of Clinical Epidemiology, Peking University Third Hospital, Xue Yuan Road, Haidian District, Beijing, 100191, P.R. China

    Hua Zhang

  4. Laboratory of Surgical Oncology, Department of Gastrointestinal Surgery, Peking University People’s Hospital, 11 Xizhimen South Street, Beijing, 100044, P.R. China

    Zhidong Gao & Yingjiang Ye

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  6. Zhidong Gao
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  7. Yingjiang Ye
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Contributions

SY and ZG designed the study. SY and FG participated in the literature search, analysis of data, as well as manuscript writing. YY participated in the literature search and data analysis and YA revised the manuscript. HZ participated in the data analysis and revised the manuscript. HZ had made contributions to the acquisition, analysis of data. SY and FG are equal to the first author. YY and ZG are corresponding authors and are responsible for ensuring that all listed authors have approved the manuscript before submission.

Corresponding authors

Correspondence to Zhidong Gao or Yingjiang Ye.

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Ethics approval and consent to participate

This study was approved by medical ethics committee of Peking University People’s Hospital(2022PHB053-001). Written informed consent was obtained from all participants prior to the enrollment of this study.

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Not applicable.

Competing interests

The authors declare no competing interests.

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Yang, S., Zhao, H., An, Y. et al. Machine learning-based prediction models affecting the recovery of postoperative bowel function for patients undergoing colorectal surgeries. BMC Surg 24, 143 (2024). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12893-024-02437-9

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  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12893-024-02437-9

Keywords

BMC Surgery

ISSN: 1471-2482

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