Comparative efficacy and safety of anti-PD-1 therapies used in metastatic colorectal cancer: a systematic review and network meta-analysis

Objective: Systemic studies on anti-PD-1 therapy in patients with metastatic colorectal cancer (mCRC) with microsatellite instability or mismatch repair defects are lacking. We aimed to summarize the evidence regarding the efficacy and safety of pembrolizumab, nivolumab, ipilimumab, and tislelizumab in mCRC. Methods: Network meta-analyses (NMAs) can provide comparative efficacy and safety data for clinical decision-making. In this NMA, eligible publications from PubMed, EMBASE, Web of Science, and Cochrane Library from 2016 to April 2023 were identified through a systematic literature review. Literature screening and data extraction were performed according to established criteria. The quality of the literature was evaluated using the Cochrane risk of bias tool, and statistical analysis was performed using Revman5.4 and R language. The main outcome indicators, DCR, ORR, PFS, and OS, were used to evaluate the effectiveness of the drugs, and the main outcome indicators AE and SAE were used to evaluate the safety of each program. Results: Fifteen studies with a sample size of 798 patients were included. In terms of effectiveness, the disease control rate DCR of PD-1 inhibitors was 0.727[95% CI:0.654-0.794]; objective response rate ORR was 0.448[95% CI:0.382-0.514]; and the 1-year progression-free survival rate was 0.551[95% CI:0.458-0.642]. The 1-year overall survival rate was 0.790[95% CI:0.705-0.865]. The adverse events associated with anti-PD-1 were 0.567[95% CI:0.344-0.778] in terms of safety. The total incidence of grade 3 or higher adverse events was 0.241[95% CI:0.174-0.313]. In the subgroup analysis results, the incidence of DCR in the nivolumab + ipilimumab group was 0.826[95% CI:0.780-0.869], the ORR was 0.512[95% CI:0.377-0.647], and the PFS was 0.668[95% CI:0.516-0.804]. The incidence of AE was 0.319 [95% CI:0.039-0.700] and SAE was 0.294 [95% CI:0.171-0.433]... (AU)

Effect of medication therapy management on discharged patient with Ulcerative Colitis with initial stage biotherapy: a randomized study

Purpose: The aim of this study was to explore the effects of medication therapy management in improving perception, medication adherence, and disease control in UC patients with first-stage of biotherapy. Subjects and Methods: A total of 120 patients with UC who received first-stage biotherapy participated in this study. The patients were divided into MTM group and CFU group. Both groups received three times follow-up, which were carried out at first, third, and sixth discharged month, Group A was followed with the MTM method, and Group B received conventional follow-up. MDRKT was used to assess patient perception, adherence to treatment was assessed by MMSA-8, and we also explored disease control and patient satisfaction. Results: A total of 116 patients completed the survey, the MTM group showed a significant improvement in perception, 84.2% of patients can correctly handle ADEs and 82.5% of patients knew what to do when they leak medication, 87.8% of patients in the MTM group had better adherence than 71.2% in the CPU group (P<0.05). The evaluation of disease control showed that 56.1% of patients in the Group A were in remission which was significantly higher than 32.2% in the Group B (P<0.05). Furthermore, the result of the questionnaire survey showed that perception, ADE, self-management, anxiety, and satisfaction were better in the MTM group than in the CPU group (P<0.05). Conclusion: The MTM group was effective in improving medication adherence, perception, and satisfaction in the patient with ulcerative colitis treated with first-stage biotherapy, and the disease control significantly improved (AU)

Development and validation of machine learning models for postoperative venous thromboembolism prediction in colorectal cancer inpatients: a retrospective study.

Background: Colorectal cancer (CRC) is a heterogeneous group of malignancies distinguished by distinct clinical features. The association of these features with venous thromboembolism (VTE) is yet to be clarified. Machine learning (ML) models are well suited to improve VTE prediction in CRC due to their ability to receive the characteristics of a large number of features and understand the dataset to obtain implicit correlations. Methods: Data were extracted from 4,914 patients with colorectal cancer between August 2019 and August 2022, and 1,191 patients who underwent surgery on the primary tumor site with curative intent were included. The variables analyzed included patient-level factors, cancer-level factors, and laboratory test results. Model training was conducted on 30% of the dataset using a ten-fold cross-validation method and model validation was performed using the total dataset. The primary outcome was VTE occurrence in postoperative 30 days. Six ML algorithms, including logistic regression (LR), random forest (RF), extreme gradient boosting (XGBoost), weighted support vector machine (SVM), a multilayer perception (MLP) network, and a long short-term memory (LSTM) network, were applied for model fitting. The model evaluation was based on six indicators, including receiver operating characteristic curve-area under the curve (ROC-AUC), sensitivity (SEN), specificity (SPE), positive predictive value (PPV), negative predictive value (NPV), and Brier score. Two previous VTE models (Caprini and Khorana) were used as the benchmarks. Results: The incidence of postoperative VTE was 10.8%. The top ten significant predictors included lymph node metastasis, C-reactive protein, tumor grade, anemia, primary tumor location, sex, age, D-dimer level, thrombin time, and tumor stage. In our results, the XGBoost model showed the best performance, with a ROC-AUC of 0.990, a SEN of 96.9%, a SPE of 96.1% in training dataset and a ROC-AUC of 0.908, a SEN of 77.5%, a SPE of 93.7% in validation dataset. All ML models outperformed the previously developed models (Caprini and Khorana). Conclusions: This study developed postoperative VTE predictive models using six ML algorithms. The XGBoost VTE model might supply a complementary tool for clinical VTE prophylaxis decision-making and the proposed risk factors could shed some light on VTE risk stratification in CRC patients.