Prediction of the risk of severe small bowel obstruction and effects of Houpu Paiqi mixture in patients undergoing surgery for small bowel obstruction.

AIM: Small bowel obstruction is a common condition that requires emergency surgery. Slow recovery of bowel function after surgery or the occurrence of one or more complications can exacerbate the disease and result in severe small bowel obstruction (SSBO), significantly impacting recovery. It is characterized by a failure to regain enteral nutrition promptly, requiring long-term intensive care. Therefore, it is necessary to identify factors that predict SSBO, to allow early intervention for patients likely to develop this condition. METHODS: Of the 260 patients who underwent emergency or elective surgery for small bowel obstruction between January 2018 and December 2022, 45 developed SSBO. The least absolute shrinkage and selection operator regression model was applied to optimize factor selection and multivariable logistic regression analysis was used to construct a predictive model. The performance and clinical utility of the nomogram were determined and internal validation was conducted. In addition, the effects of the Houpu Paiqi mixture on postoperative recovery were analyzed by comparing the clinical data of 28 patients who were treated with the mixture and 61patients who did not receive it. RESULTS: The predictors included in the prediction nomogram were age, peritonitis, intestinal resection and anastomosis, complications, operation time, Acute Physiology and Chronic Health Evaluation II score, white blood cell count, and procalcitonin level. The model had an area under the receiver operating characteristic curve of 0.948 (95% confidence interval: 0.814-0.956). Decision curve analysis demonstrated that the SSBO risk nomogram had a good net clinical benefit. In addition, treatment with the Houpu Paiqi mixture reduced postoperative exhaust time, postoperative defecation time, time to first postoperative liquid feed, and length of stay in hospital. CONCLUSIONS: We developed a nomogram that can assist clinicians in identifying patients at greater risk of SSBO, which may aid in early diagnosis and intervention. Additionally, we found that the Houpu Paiqi mixture promoted postoperative recovery.

Risk factors and prediction model for inpatient surgical site infection after elective abdominal surgery.

BACKGROUND: Surgical site infections (SSIs) are the commonest healthcare-associated infection. In addition to increasing mortality, it also lengthens the hospital stay and raises healthcare expenses. SSIs are challenging to predict, with most models having poor predictability. Therefore, we developed a prediction model for SSI after elective abdominal surgery by identifying risk factors. AIM: To analyse the data on inpatients undergoing elective abdominal surgery to identify risk factors and develop predictive models that will help clinicians assess patients preoperatively. METHODS: We retrospectively analysed the inpatient records of Shaanxi Provincial People's Hospital from January 1, 2018 to January 1, 2021. We included the demographic data of the patients and their haematological test results in our analysis. The attending physicians provided the Nutritional Risk Screening 2002 (NRS 2002) scores. The surgeons and anaesthesiologists manually calculated the National Nosocomial Infections Surveillance (NNIS) scores. Inpatient SSI risk factors were evaluated using univariate analysis and multivariate logistic regression. Nomograms were used in the predictive models. The receiver operating characteristic and area under the curve values were used to measure the specificity and accuracy of the model. RESULTS: A total of 3018 patients met the inclusion criteria. The surgical sites included the uterus (42.2%), the liver (27.6%), the gastrointestinal tract (19.1%), the appendix (5.9%), the kidney (3.7%), and the groin area (1.4%). SSI occurred in 5% of the patients (n = 150). The risk factors associated with SSI were as follows: Age; gender; marital status; place of residence; history of diabetes; surgical season; surgical site; NRS 2002 score; preoperative white blood cell, procalcitonin (PCT), albumin, and low-density lipoprotein cholesterol (LDL) levels; preoperative antibiotic use; anaesthesia method; incision grade; NNIS score; intraoperative blood loss; intraoperative drainage tube placement; surgical operation items. Multivariate logistic regression revealed the following independent risk factors: A history of diabetes [odds ratio (OR) = 5.698, 95% confidence interval (CI): 3.305-9.825, P = 0.001], antibiotic use (OR = 14.977, 95%CI: 2.865-78.299, P = 0.001), an NRS 2002 score of ≥ 3 (OR = 2.426, 95%CI: 1.199-4.909, P = 0.014), general anaesthesia (OR = 3.334, 95%CI: 1.134-9.806, P = 0.029), an NNIS score of ≥ 2 (OR = 2.362, 95%CI: 1.019-5.476, P = 0.045), PCT ≥ 0.05 µg/L (OR = 1.687, 95%CI: 1.056-2.695, P = 0.029), LDL < 3.37 mmol/L (OR = 1.719, 95%CI: 1.039-2.842, P = 0.035), intraoperative blood loss ≥ 200 mL (OR = 29.026, 95%CI: 13.751-61.266, P < 0.001), surgical season (P < 0.05), surgical site (P < 0.05), and incision grade I or III (P < 0.05). The overall area under the receiver operating characteristic curve of the predictive model was 0.926, which is significantly higher than the NNIS score (0.662). CONCLUSION: The patient's condition and haematological test indicators form the bases of our prediction model. It is a novel, efficient, and highly accurate predictive model for preventing postoperative SSI, thereby improving the prognosis in patients undergoing abdominal surgery.

[The development and changes of pressure of putrefactive gas in cadaveric enterocelia in spring and its forensic application].

OBJECTIVE: To study the development and changes of pressure of putrefactive gas (PPG) in cadaveric enterocelia in spring, and to explore its application in estimation of postmortem interval (EPI). METHODS: 57 goats were divided into 2 groups according to means of death, on land or in water. Celiac PPG were observed timely and systematically. RESULTS: The development of PPG in cadaveric enterocelia, which can be divided into raising phase, peak phase, and declining phase was observed, and a model to estimate postmortern interval by changes of PPG was founded. CONCLUSION: Measuring PPG in cadaveric enterocelia could be used in forensic EPI.