• Accelerated rehabilitation
• Appropriateness evaluation protocol
• Colorectal cancer
• Ineffective hospital day
• Postoperative length of stay

Colorectal cancer significantly impacts global health, with unplanned reoperations post-surgery being key determinants of patient outcomes. Existing predictive models for these reoperations lack precision in integrating complex clinical data.

To develop and validate a machine learning model for predicting unplanned reoperation risk in colorectal cancer patients.

Data of patients treated for colorectal cancer (n = 2044) at the First Affiliated Hospital of Wenzhou Medical University and Wenzhou Central Hospital from March 2020 to March 2022 were retrospectively collected. Patients were divided into an experimental group (n = 60) and a control group (n = 1984) according to unplanned reoperation occurrence. Patients were also divided into a training group and a validation group (7:3 ratio). We used three different machine learning methods to screen characteristic variables. A nomogram was created based on multifactor logistic regression, and the model performance was assessed using receiver operating characteristic curve, calibration curve, Hosmer-Lemeshow test, and decision curve analysis. The risk scores of the two groups were calculated and compared to validate the model.

More patients in the experimental group were ≥ 60 years old, male, and had a history of hypertension, laparotomy, and hypoproteinemia, compared to the control group. Multiple logistic regression analysis confirmed the following as independent risk factors for unplanned reoperation (P < 0.05): Prognostic Nutritional Index value, history of laparotomy, hypertension, or stroke, hypoproteinemia, age, tumor-node-metastasis staging, surgical time, gender, and American Society of Anesthesiologists classification. Receiver operating characteristic curve analysis showed that the model had good discrimination and clinical utility.

This study used a machine learning approach to build a model that accurately predicts the risk of postoperative unplanned reoperation in patients with colorectal cancer, which can improve treatment decisions and prognosis.

• Colorectal cancer
• Postoperative unplanned reoperation
• Unplanned reoperation
• Clinical validation
• Nomogram
• Machine learning models

• Humans
• Male
• Colorectal Neoplasms/surgery
• Colorectal Neoplasms/pathology
• Female
• Machine Learning
• Middle Aged
• Reoperation/statistics & numerical data
• Retrospective Studies
• Risk Factors
• Risk Assessment/methods
• Risk Assessment/statistics & numerical data
• Aged
• Postoperative Complications/etiology
• Postoperative Complications/epidemiology
• Nomograms
• ROC Curve
• China/epidemiology
• Adult

• gastroenterology
• medical research

• Humans
• Patient Discharge
• Pancreatitis/therapy
• Acute Disease
• Hospitalization
• Cohort Studies

• KA-2019-14, FK131864 János Bolyai Research Scholarship of the Hungarian Academy of Sciences
• NKFIH OTKA grant (K131996) János Bolyai Research Scholarship of the Hungarian Academy of Sciences
• ÚNKP-22-5 New National Excellence Program of the Ministry for Innovation and Technology from the source of the National Research, Development and Innovation Fund
• (BO/00648/21/5) János Bolyai Research Grant
• UNKP-22-5-SZTE-585 New National Excellence Program
• 739593 EU's Horizon 2020 research and innovation program
• ÚNKP-22-4-II New national Excellence Program of the Ministry for Culture and Innovation from the Source of the National Research, Development and Innovation Fund
• TKP2021-EGA-23 Ministry of Innovation and Technology of Hungary
• EU’s Horizon 2020 research and innovation program

Colorectal cancer (CRC) is one of the most common malignancies in the world. Despite significant improvements in surgical technique, postoperative complications still occur in a fair percentage of patients undergoing colorectal surgery. The most feared complication is anastomotic leakage. It negatively affects short-term prognosis, with increased post-operative morbidity and mortality, higher hospitalization time and costs. Moreover, it may require further surgery with the creation of a permanent or temporary stoma. While there is no doubt about the negative impact of anastomotic dehiscence on the short-term prognosis of patients operated on for CRC, still under discussion is its impact on the long-term prognosis. Some authors have described an association between leakage and reduced overall survival, disease-free survival, and increased recurrence, while other Authors have found no real impact of dehiscence on long term prognosis. The purpose of this paper is to review all the literature about the impact of anastomotic dehiscence on long-term prognosis after CRC surgery. The main risk factors of leakage and early detection markers are also summarized.

• Anastomotic leakage
• Colorectal surgery
• Colon cancer
• Rectal cancer
• Long term prognosis
• Long term survival

• Anastomotic leak
• Biomarkers
• Elective colorectal surgery
• Enhanced recovery after surgery
• Predictive value

• Humans
• Anastomotic Leak/diagnosis
• Biomarkers
• C-Reactive Protein/metabolism
• Colectomy/adverse effects
• Colectomy/methods
• Retrospective Studies

• colorectal cancer
• enhanced recovery after surgery
• group practice
• minimally invasive surgery

Colorectal cancer is a common malignant tumor of the digestive system. In order to study the clinical efficacy of ultrasound-guided QLB and TAPB in the treatment and postoperative analgesia of patients undergoing laparoscopic colorectal surgery. A total of 96 patients undergoing laparoscopic colorectal surgery from January 2021 to January 2022 are selected as the study subjects. The results show that ultrasound-guided QLB and TAPB therapy have good analgesic effects in patients undergoing laparoscopic colorectal surgery, and QLB treatment and postoperative analgesic effect are better than TAPB.

This study aimed to investigate the relationship between clinical and laboratory parameters and complication status to predict which patients can be safely discharged from the hospital on the third postoperative day (POD).

Data from a prospectively maintained database of 2,110 consecutive patients with gastric adenocarcinoma who underwent curative surgery were reviewed. The third POD vital signs, laboratory data, and details of the course after surgery were collected. Patients with grade II or higher complications after the third POD were considered unsuitable for early discharge. The performance metrics were calculated for all algorithm parameters. The proposed algorithm was tested using a validation dataset of consecutive patients from the same center.

Of 1,438 patients in the study cohort, 142 (9.9%) were considered unsuitable for early discharge. C-reactive protein level, body temperature, pulse rate, and neutrophil count had good performance metrics and were determined to be independent prognostic factors. An algorithm consisting of these 4 parameters had a negative predictive value (NPV) of 95.9% (95% confidence interval [CI], 94.2–97.3), sensitivity of 80.3% (95% CI, 72.8–86.5), and specificity of 51.1% (95% CI, 48.3–53.8). Only 28 (1.9%) patients in the study cohort were classified as false negatives. In the validation dataset, the NPV was 93.7%, sensitivity was 66%, and 3.3% (17/512) of patients were classified as false negatives.

Simple clinical and laboratory parameters obtained on the third POD can be used when making decisions regarding the safe early discharge of patients who underwent gastrectomy.