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Abstract Background Laparoscopic surgery is effective for treating common bile duct (CBD) stones. However, it has high requirements for surgeons and the risk of conversion to laparotomy cannot be ignored. However, when conditions during surgery are not favorable, persisting with laparoscopic procedures blindly can lead to serious complications. Our study aimed to establish a nomogram model for predicting conversion of laparoscopic to laparotomy for choledocholithiasis. Materials and methods A total of 867 patients who were diagnosed with choledocholithiasis and underwent laparoscopic surgery were randomly divided into a training group (70%, n = 607) and a validation group (30%, n = 260). A nomogram was constructed based on the results of logistic regression analysis. The area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA) were used to assess the predictive performance of the nomogram. Results Previous upper abdominal surgery, maximum diameter of stone ≥12 mm, medial wall of the duodenum stone, thickening of the gallbladder wall, thickening of CBD wall, stone size/CBD size ≥0.75, and simultaneous laparoscopic hepatectomy were included in the nomogram. The AUC values were 0.813 (95% CI: 0.766–0.861) and 0.804 (95% CI: 0.737–0.871) in the training and validation groups, respectively. The calibration curve showed excellent consistency between the nomogram predictions and actual observations. DCA showed a positive net benefit for the nomogram. Conclusions We constructed a nomogram with a good ability to predict conversion to open surgery in laparoscopic surgery for choledocholithiasis, which can help surgeons to make a reasonable operation plan before surgery and timely convert to laparotomy during operation to reduce potential harm to the patient.

IntroductionOvarian cancer (OV) is a highly lethal gynecological malignancy with a poor prognosis. Lactate metabolism is crucial for tumor cell survival, proliferation, and immune evasion. Our study aims to investigate the role of lactate metabolism-related genes (LMRGs) in OV and their potential as biomarkers for prognosis, immune microenvironment, and immunotherapy response.MethodsOvarian samples were collected from the TCGA cohort. And 12 lactate-related pathways were identified from the MsigDB database. Differentially expressed genes within these pathways were designated as LMRGs, which undergo unsupervised clustering to identify distinct clusters based on LMRGs. Subsequently, we assessed survival outcomes, immune cell infiltration levels, Hallmaker pathway activation patterns, and chemotaxis among different subtypes. After conducting additional unsupervised clustering based on differentially expressed genes (DEGs), significant differences in the expression of LMRGs between the two clusters were observed. The differentially expressed genes were subjected to subsequent functional enrichment analysis. Furthermore, we construct a model incorporating LMRGs. Subsequently, the lactate score for each tumor sample was calculated based on this model, facilitating the classification of samples into high and low groups according to their respective lactate scores. Distinct groups examined disparities in survival prognosis, copy number variation (CNV), single nucleotide variation (SNV), and immune infiltration. The lactate score served as a quantitative measure of OV's lactate metabolism pattern and an independent prognostic factor.ResultsThis study investigated the potential role of LMRGs in tumor microenvironment diversity and prognosis in OV, suggesting that LMRGs play a crucial role in OV progression and the tumor microenvironment, thus serving as novel indicators for prognosis, immune microenvironment status, and response to immunotherapy.