Objective: Inflammation and malnutrition are common in patients with advanced lung cancer undergoing palliative care, and their survival time is limited. In this study, we created a prognostic model using the Inflam-Nutri score to predict the survival of these patients. Methods: A retrospective cohort study was conducted on 223 patients with advanced, histologically confirmed unresectable lung cancer treated between January 2017 and December 2018. The cutoff values of the neutrophil-albumin ratio (NAR) and Patient-Generated Subjective Global Assessment (PG-SGA) score were determined by the X-tile program. Least absolute shrinkage and selection operator (LASSO) Cox regression and multivariate Cox regression analysis were performed to identify prognostic factors of overall survival (OS). We then established a nomogram model. The model was assessed by a validation cohort of 72 patients treated between January 2019 and December 2019. The predictive accuracy and discriminative ability were assessed by the concordance index (C-index), a plot of the calibration curve and risk group stratification. The clinical usefulness of the nomogram was measured by decision curve analysis (DCA). Results: The nomogram incorporated stage, supportive care treatment, the NAR and the PG-SGA score. The calibration curve presented good performance in the validation cohorts. The model showed discriminability with a C-index of 0.76 in the training cohort and 0.77 in the validation cohort. DCA demonstrated that the nomogram provided a higher net benefit across a wide, reasonable range of threshold probabilities for predicting OS. The survival curves of different risk groups were clearly separated. Conclusions: The NAR and PG-SGA scores were independently related to survival. Our prognostic model based on the Inflam-Nutri score could provide prognostic information for advanced palliative lung cancer patients and physicians.

Stomach cancer (GC) has one of the highest rates of thrombosis among cancers and can lead to considerable morbidity, mortality, and additional costs. However, to date, there is no suitable venous thromboembolism (VTE) prediction model for gastric cancer patients to predict risk. Therefore, there is an urgent need to establish a clinical prediction model for VTE in gastric cancer patients. We collected data on 3092 patients between January 1, 2018 and December 31, 2021. And after feature selection, 11 variables are reserved as predictors to build the model. Five machine learning (ML) algorithms are used to build different VTE predictive models. The accuracy, sensitivity, specificity, and AUC of these five models were compared with traditional logistic regression (LR) to recommend the best VTE prediction model. RF and XGB models have selected the essential characters in the model: Clinical stage, Blood Transfusion History, D-Dimer, AGE, and FDP. The model has an AUC of 0.825, an accuracy of 0.799, a sensitivity of 0.710, and a specificity of 0.802 in the validation set. The model has good performance and high application value in clinical practice, and can identify high-risk groups of gastric cancer patients and prevent venous thromboembolism.

Background: The efficacy and necessity of prophylactic antibiotics in clean and clean-contaminated surgery remains controversial. Methods: The studies were screened and extracted using databases including PubMed, Embase, Cochrane Library, Web of Science, and Clinical Trials.gov according to predefined eligibility criteria. Randomized controlled trials (RCTs) comparing the effect of preoperative and postoperative prophylactic antibiotic use on the incidence of surgical site infections (SSIs) in patients undergoing any clean or clean-contaminated surgery. Results: A total of 16 189 participants in 48 RCTs were included in the primary meta-analysis following the eligibility criteria. The pooled odds ratio (OR) for SSI with antibiotic prophylaxis versus placebo was 0.60 (95% CI: 0.53–0.68). The pooled OR among gastrointestinal, oncology, orthopedics, neurosurgery, oral, and urology surgery was 3.06 (95% CI: 1.05–8.91), 1.16 (95% CI: 0.89–1.50), 2.04 (95% CI: 1.09–3.81), 3.05 (95% CI: 1.25–7.47), 3.55 (95% CI: 1.78–7.06), and 2.26 (95% CI: 1.12–4.55), respectively. Furthermore, the summary mean difference (MD) for patients’ length of hospitalization was −0.91 (95% CI: −1.61, −0.16). The results of sensitivity analyses for all combined effect sizes showed good stability. Conclusion: Antibiotics are both effective, safe, and necessary in preventing surgical wound infections in clean and clean-contaminated procedures, attributed to their reduction in the incidence of surgical site infections as well as the length of patient hospitalization.

Background: Immune checkpoint inhibitors (ICIs) have emerged as a standard treatment for various malignancies. However, research indicates blocking the immune checkpoint pathway may exacerbate atherosclerotic lesions.Objectives: We aimed to investigate whether ICI therapy increases the risk of arterial thromboembolic events (ATEs).Methods: A retrospective cohort study was conducted on patients with histologically confirmed cancer at our institution between 2018 and 2021, using the propensity score matching method. The primary endpoint was ATEs occurrence, comprising acute coronary syndrome, stroke/transient ischemic attack, and peripheral arterial thromboembolism. Subgroup analyses assessed whether the ICI treatment effect on ATEs varied over time by limiting the maximum follow‐up duration. Logistic regression analysis identified ATE risk factors in ICI‐treated patients.Results: Overall, the ICI group (n = 2877) demonstrated an ATEs risk 2.01 times higher than the non‐ICI group (RR, 2.01 [95% CI (1.61–2.51)]; p < 0.001). Subgroup analysis revealed no significant increase in ATEs risk for ICI‐treated patients within 1 year (Limited to a max 9‐month follow‐up, p = 0.075). However, ATEs risk in the ICI group rose by 41% at 1 year (p = 0.010) and 97% at 4 years (p ≤ 0.001). Age, diabetes, hypertension, peripheral atherosclerosis, atrial fibrillation, chronic ischemic heart disease, distant cancer metastasis, and ICI treatment cycles contributed to ATEs risk elevation in ICI‐treated patients.Conclusion: ICI‐treated patients may exhibit a higher risk of ATEs, especially after 1 year of treatment.

Objective: In China, lung cancer ranks first in both incidence and mortality among all malignant tumors. Non-small cell lung cancer (NSCLC) constitutes the vast majority of cases, accounting for 80% to 85% of cases. Immune checkpoint inhibitors (ICIs), either as monotherapies or combined with other treatments, have become the standard first-line therapy for NSCLC patients. This study aimed to establish a nomogram model for NSCLC patients receiving immunotherapy incorporating demographic information, clinical characteristics, and laboratory indicators.Methods: From January 1, 2019, to December 31, 2022, a prospective longitudinal cohort study involving 1321 patients with NSCLC undergoing immunotherapy was conducted at Chongqing University Cancer Hospital. Clinical and pathological characteristics, as well as follow-up data, were collected and analyzed. To explore prognostic factors affecting overall survival (OS), a Cox regression model was used to test the significance of various variables. Independent prognostic indicators were identified through multivariate analysis and then used to construct a nomogram prediction model. To validate the accuracy and practicality of this model, the concordance index (C-index), 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.Result: In the final model, 11 variables from the training cohort were identified as independent risk factors for patients with NSCLC: age, KPS score, BMI, diabetes, targeted therapy, Hb, WBC, LDH, CRP, PLR, and LMR. The C-index for OS in the training cohort was 0.717 (95% CI, 0.689–0.745) and 0.704 (95% CI, 0.660–0.750) in the validation cohort. Calibration curves for survival probability showed good concordance between the nomogram predictions and actual observations. The AUCs for 1-year, 2-year, and 3-year OS in the training cohort were 0.724, 0.764, and 0.79, respectively, and 0.725, 0.736, and 0.818 in the validation cohort. DCA demonstrated that the nomogram model had a greater overall net benefit.Conclusion: A prognostic model for OS in NSCLC patients receiving immunotherapy was established, providing a simple and reliable tool for predicting patient survival (https://icisnsclc.shinyapps.io/DynNomapp/). This model offers valuable guidance for clinicians in making treatment decisions and recommendations.

Objective. We aimed to evaluate the performance of artificial intelligence (AI) system in detecting high-grade precancerous lesions. Methods. A retrospective and diagnostic study was conducted in Chongqing Cancer Hospital. Anonymized medical records with cytology, HPV testing, colposcopy findings with images, and the histopathological results were selected. The sensitivity, specificity, and areas under the curve (AUC) in detecting CIN2+ and CIN3+ were evaluated for the AI system, the AI-assisted colposcopy, and the human colposcopists, respectively. Results. Anonymized medical records from 346 women were obtained. The images captured under colposcopy of 194 women were found positive by the AI system; 245 women were found positive either by human colposcopists or the AI system. In detecting CIN2+, the AI-assisted colposcopy significantly increased the sensitivity (96.6% vs. 88.8%, ). The specificity was significantly lower for AI-assisted colposcopy (38.1%), compared with human colposcopists (59.5%, ) or the AI system (57.6%, ). The AUCs for the human colposcopists, AI system, and AI-assisted colposcopy were 0.741, 0.765, and 0.674, respectively. In detecting CIN3+, the sensitivities of the AI system and AI-assisted colposcopy were not significantly higher than human colposcopists (97.5% vs. 92.6%, ). The specificity was significantly lower for AI-assisted colposcopy (37.4%) compared with human colposcopists (59.2%, ) or compared with the AI system (56.6%, ). The AUCs for the human colposcopists, AI system, and AI-assisted colposcopy were 0.759, 0.674, and 0.771, respectively. Conclusions. The AI system provided equally matched sensitivity to human colposcopists in detecting CIN2+ and CIN3+. The AI-assisted colposcopy significantly improved the sensitivity in detecting CIN2+.