ObjectiveTo investigate the risk factors affecting the occurrence of infectious complications after radical gastrectomy for gastric cancer, and to establish a risk prediction Nomogram model. MethodsThe clinicopathologic data of 429 primary gastric cancer patients who underwent radical resection for gastric cancer at the Second Department of General Surgery of Shaanxi Provincial People’s Hospital between January 2018 and December 2020 were retrospectively collected to explore the influencing factors of infectious complications using multivariate logistic regression analyses, and to construct a prediction model based on the results of the multivariate analysis, and then to further validate the differentiation, consistency, and clinical utility of the model. ResultsOf the 429 patients, infectious complications occurred in 86 cases (20.05%), including 53 cases (12.35%) of pulmonary infections, 16 cases (3.73%) of abdominal infections, 7 cases (1.63%) of incision infections, and 10 cases (2.33%) of urinary tract infections. The results of multivariate logistic analysis showed that low prognostic nutritional index [OR=0.951, 95%CI (0.905, 0.999), P=0.044], long surgery time [OR=1.274, 95%CI (1.069, 1.518), P=0.007], American Society of Anesthesiologists physical status classification (ASA) grade Ⅲ–Ⅳ [OR=9.607, 95%CI (4.484, 20.584), P＜0.001] and alcohol use [OR=3.116, 95%CI (1.696, 5.726), P＜0.001] were independent risk factors for the occurrence of infectious complications, and a Nomogram model was established based on these factors, with an area under the ROC of 0.802 [95%CI (0.746, 0.858)]; the calibration curves showed that the probability of occurrence of infectious complications after radical gastrectomy predicted by the Nomogram was in good agreement with the actual results; the decision curve analysis showed that the Nomogram model could obtain clinical benefits in a wide range of thresholds and had good practicality.ConclusionsClinicians need to pay attention to the perioperative management of gastric cancer patients, fully assess the patients’ own conditions through the prediction model established by prognostic nutritional index, surgery time, ASA grade and alcohol use, and take targeted interventions for the patients with higher risks, in order to reduce the risk of postoperative infectious complications.
ObjectiveTo explore the risk factors of perioperative severe complications (Clavien-Dindo grade Ⅲ and above) after laparoscopic radical resection of colorectal cancer (CRC). MethodsThe clinicopathologic data of CRC patients who met the inclusion and exclusion criteria treated in the Shaanxi Provincial People’s Hospital from January 2018 to December 2020 were retrospectively analyzed. The univariate and multivariate logistic analyses were used to explore the risk factors of perioperative severe complications after the laparoscopic radical resection of CRC. ResultsAtotal of 170 eligible patients were included in this study, and the postoperative complications occurred in 45 patients, 24 of whom were severe complications. The univariate analysis results showed that the age (P<0.001), body mass index (BMI, P=0.047), age adjusted Charlson complication index (aCCI) score (P=0.002), American Association of Anesthesiologists (ASA) classification (P<0.001), prognostic nutritional index (PNI, P=0.011), preoperative anemia (P=0.011), operation numbers of surgeon (P=0.003), and operation time (P=0.026) were related to the perioperative severe complications in the patients underwent the laparoscopic radical resection of CRC. The statistic indexes of univariate analysis (P<0.05) combined with indexes of clinical significance were included in the multivariate analysis, the results showed that the ASA classification Ⅲ– Ⅳ (OR=3.536, P=0.027), BMI ≥25 kg/m2 (OR=3.228, P=0.031), preoperative anemia (OR=2.876, P=0.049), operation numbers of surgeon <300 (OR=0.324, P=0.046), and the operation time ≥300 min (OR=3.480, P=0.020) increased the probability of perioperative severe complications in the patients underwent the laparoscopic radical resection of CRC. ConclusionsThe results of this study suggest that clinicians should pay attention to the perioperative management of patients with CRC, such as adequately evaluating the preoperative status of patients by ASA classification, PNI, and aCCI to adjust the malnutrition of patients; after operation, the patients with BMI ≥25 kg/m2 and operation time more than 300 min should be paid more attention. At the same time, the surgeon should continuously accumulate the operation numbers and improve the operation proficiency so as to reduce the occurrence of perioperative severe complications after laparoscopic radical resection of CRC.
ObjectiveTo explore the risk factors affecting operation treatment selection of acute adhesive small bowel obstruction (ASBO), and establish a prediction model of surgical treatment selection to provide a guidance for clinical decision-making. MethodsThe patients with acute ASBO admitted to this hospital and met the inclusion and exclusion criteria, from January 2019 to December 2022, were retrospectively collected, and the patients were assigned into the surgical treatment and conservative treatment according to the treatment selection. The differences in the clinicopathologic factors between the patients with surgical treatment and conservative treatment were compared. Meanwhile, the factors with statistical differences (P<0.05) or the factors with clinical significance judged based on professional knowledge were included to screen the influencing factors of surgical treatment selection using the multivariate logistic regression analysis, and the selected influencing factors were used to construct the logistic regression prediction model equation. The area under the receiver operating characteristic curve (AUC) and its 95% confidence interval (95%CI) was used to evaluate the prediction efficiency of the prediction model equation. ResultsA total of 231 patients with acute ASBO were included, 117 (50.6%) of whom underwent surgical treatment and 114 (49.4%) underwent conservative treatment. In all 16 clinicopathologic factors between the patients with surgical treatment and conservative treatment had statistical differences (P<0.05) including the body mass index (BMI), preopeative high fever, intestinal type, sign of peritonitis, acute physiology and chronic health evaluation Ⅱ (APACHE Ⅱ) score excluded age scoring, abdominal surgery history and times of abdominal surgery history, times of pre-admission seek medical advice and preoperative conservative treatment time, the air-liquid level by X-ray plain film, and severe small bowel obstruction and adhesive bands by CT examination, as well as the white blood cell count (WBC), neutrophil percentage, albumin (ALB), and urea nitrogen. The multivariate logistic regression analysis showed that the acute ASBO accompanied by sign of peritonitis (β=1.778, P=0.028), history of abdominal surgery (β=1.394, P=0.022), and adhesive bands (β=1.321, P=0.010) and severe small bowel obstruction (β=1.183, P=0.018) by CT examination, WBC (β=0.524, P＜0.001), APACHEⅡ score excluded age scoring (β=0.291, P＜0.001), and BMI (β=0.191, P=0.011) had positive impacts on adopting surgical treatment, while preoperative ALB (β=–0.101, P=0.023) and conservative treatment time (β=–0.391, P＜0.001) had negative impacts on adopting surgical treatment. The accuracy, specificity, and sensitivity of the logistic regression prediction model equation constructed according to these 9 influencing factors were 84.8%, 71.1%, and 77.7%, respectively. The AUC (95%CI) of the prediction model equation to distinguish selection of surgical treatment from conservative treatment was 0.942 (0.914, 0.970). ConclusionsAccording to the preliminary results of this study, surgical treatment is recommended for patients with acute ASBO accompanied by signs of peritonitis, history of abdominal surgery, adhesive bands and severe small bowel obstruction by CT, increased preoperative WBC, high APACHEⅡ score excluded age scoring, high BMI, preoperative low ALB level, and shorter preoperative conservative treatment time. And the logistic prediction model equation constructed according to these characteristics in this study has a good discrimination for patients with surgical treatment or conservative treatment selection.