Background: This study aimed to establish machine learning models for preoperative prediction of the pathological types of acute appendicitis.Methods: Based on histopathology, 136 patients with acute appendicitis were included and divided into three types: acute simple appendicitis (SA, n=8), acute purulent appendicitis (PA, n=104), and acute gangrenous or perforated appendicitis (GPA, n=24). Patients with SA/PA and PA/GPA were divided into training (70%) and testing (30%) sets. Statistically significant features (P<0.05) for pathology prediction were selected by univariate analysis. According to clinical and laboratory data, machine learning logistic regression (LR) models were built. Area under receiver operating characteristic curve (AUC) was used for model assessment.Results: Nausea and vomiting, abdominal pain time, neutrophils (NE), CD4 + T cell, helper T cell, B lymphocyte, natural killer (NK) cell counts, and CD4 + /CD8 + ratio were selected features for the SA/ PA group (P<0.05). Nausea and vomiting, abdominal pain time, the highest temperature, CD8 + T cell, procalcitonin (PCT), and C-reactive protein (CRP) were selected features for the PA/GPA group (P<0.05).By using LR models, the blood markers can distinguish SA and PA (training AUC =0.904, testing AUC =0.910). To introduce additional clinical features, the AUC for the testing set increased to 0.926. In the PA/ GPA prediction model, AUC with blood biomarkers was 0.834 for the training and 0.821 for the testing set.Combining with clinical features, the AUC for the testing set increased to 0.854.Conclusions: Peripheral blood biomarkers can predict the pathological type of SA from PA and GPA.Introducing clinical symptoms could further improve the prediction performance.

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BACKGROUND Accurate preoperative prediction of complicated appendicitis (CA) could help selecting optimal treatment and reducing risks of postoperative complications. The study aimed to develop a machine learning model based on clinical symptoms and laboratory data for preoperatively predicting CA.METHODS 136 patients with clinicopathological diagnosis of acute appendicitis were retrospectively included in the study. The dataset was randomly divided (94: 42) into training and testing set. Predictive models using individual and combined selected clinical and laboratory data features were built separately. Three combined models were constructed using logistic regression (LR), support vector machine (SVM) and random forest (RF) algorithms. The CA prediction performance was evaluated with Receiver Operating Characteristic (ROC) analysis, using the area under the curve (AUC), sensitivity, specificity and accuracy factors.RESULTS The features of the abdominal pain time, nausea and vomiting, the highest temperature, high sensitivity-CRP (hs-CRP) and procalcitonin (PCT) had significant differences in the CA prediction (P<0.001). The ability to predict CA by individual feature was low (AUC<0.8). The prediction by combined features was significantly improved. The AUC of the three models (LR, SVM and RF) in the training set and the testing set were 0.805, 0.888, 0.908 and 0.794, 0.895, 0.761, respectively. The SVM-based model showed a better performance for CA prediction. RF had a higher AUC in the training set, but its poor efficiency in the testing set indicated a poor generalization ability.CONCLUSIONS The SVM machine learning model applying clinical and laboratory data can well predict CA preoperatively which could assist diagnosis in resource limited settings.

Objective This study aimed to investigate the value of combining percutaneous transhepatic gallbladder drainage (PTGD) with gallbladder-preserving cholecystolithotomy (GPC) in high-risk patients with acute calculous cholecystitis. Methods Clinical data from 74 high-risk patients with acute calculous cholecystitis, admitted to our hospital between October 2018 and September 2021, were analyzed retrospectively. All the patients underwent PTGD, and 59 of them underwent delayed cholecystectomy, while 14 patients were subjected to GPC 8–12 weeks after the PTGD; one patient, whose life expectancy was fewer than 6 months, was not treated for gallstones after PTGD. Results In all 74 patients, symptom remission was achieved after the PTGD therapy, and the incidence of catheter-related complications was 10.8%. Among the 59 patients who underwent delayed cholecystectomy (DC) after PTGD, there was a complication incidence of 6.8%. Of the 14 patients who underwent GPC after the PTGD, 13 patients were subjected to the removal of drainage tubes, 1 patient received cholecystostomy catheter draining externally, and two patients (14.3%) had complications. There were no perioperative deaths. Conclusion Percutaneous transhepatic gallbladder drainage, combined with GPC, is a safe and effective treatment that is suitable for high-risk patients with acute calculous cholecystitis who cannot receive DC. This combined method allows for early acute cholecystitis to settle, helps to remove gallstones at a later stage, and solves the problem of long-term tube drainage after PTGD.