Development and validation of a nomogram for predicting venous thromboembolism risk in post-surgery patients with cervical cancer.

Objective: Postoperative venous thromboembolism (VTE) is a potentially life-threatening complication. This study aimed to develop a predictive model to identify independent risk factors and estimate the likelihood of VTE in patients undergoing surgery for cervical cancer.

Methods: We conducted a retrospective cohort study involving 1,174 patients who underwent surgery for cervical carcinoma between 2019 and 2022.

Development and validation of machine learning models for predicting venous thromboembolism in colorectal cancer patients: A cohort study in China.

Background: With advancements in healthcare, traditional VTE risk assessment tools are increasingly insufficient to meet the demands of high-quality care, underscoring the need for innovative and specialized assessment methods.

Objective: Owing to the remarkable success of machine learning in supervised learning and disease prediction, our objective is to develop a reliable and efficient model for assessing VTE risk by leveraging the fundamental data and clinical characteristics of colorectal cancer patients within our medical facility.

Methods: Six commonly used machine learning algorithms were utilized in our study to predict the occurrence of VTE in patients with rectal cancer.

Development and validation of a predictive risk tool for VTE in women with breast cancer under chemotherapy: a cohort study in China.

Objective: The incidence of venous thromboembolism (VTE) is significantly elevated in breast cancer patients, with a three-to-fourfold increase, and further escalates to sixfold in those undergoing chemotherapy. This study aims to identify the risk factors for VTE and develop a Nomogram risk prediction model distinct from the traditional Khorana score.

Methods: Univariate Cox regression analysis assessed the impact of each variable on the occurrence of VTE, while stepwise multivariate Cox regression analysis identified independent predictors.

Development and validation of a nomogram model for predicting venous thromboembolism risk in lung cancer patients treated with immune checkpoint inhibitors: A cohort study in China.

Objective: Venous thromboembolism (VTE) poses a significant threat to lung cancer patients, particularly those receiving treatment with immune checkpoint inhibitors (ICIs). We aimed to develop and validate a nomogram model for predicting the occurrence of VTE in lung cancer patients undergoing ICI therapy.

Methods: The data for this retrospective cohort study was collected from cancer patients admitted to Chongqing University Cancer Hospital for ICI treatment between 2019 and 2022.

Short-term reproductive outcomes analysis and prediction of the modified uterine stent treatment for mild to moderate intrauterine adhesions: experience at a single institution.

Background: To evaluate the efficacy of modified uterine stent in the treatment of mild-to-moderate intrauterine adhesions and explore the relative indicators affecting prognosis prediction.

Methods: A total of 115 patients with mild-to-moderate intrauterine adhesions received a modified uterine stent placement after hysteroscopy adhesiolysis. The second-look hysteroscopy operated after 3 months surgery, and the third-look hysteroscopy operated after 6 months surgery if necessary.

The influence of social participation and depressive symptoms on cognition among middle-aged and older adults.

Background: The global aging phenomenon has raised concerns about the cognitive abilities of older individuals. This study aimed to explore the relationship between social participation, depressive symptoms, and cognitive function among middle-aged and older adults.

Methods: This study utilized data from the China Longitudinal Study of Health and Retirement (CHARLS) from wave 1 to wave 4.

Development and validation of nomogram prognostic model for predicting OS in patients with diffuse large B-cell lymphoma: a cohort study in China.

This study comprehensively incorporates pathological parameters and novel clinical prognostic factors from the international prognostic index (IPI) to develop a nomogram prognostic model for overall survival in patients with diffuse large B-cell lymphoma (DLBCL). The aim is to facilitate personalized treatment and management strategies. This study enrolled a total of 783 cases for analysis.

Causal association between major depressive disorder and coronary heart disease: a two-sample bidirectional mendelian randomization study.

Background: Major depressive disorder (MDD) is a highly heterogeneous mental illness and a major public health problem worldwide. A large number of observational studies have demonstrated a clear association between MDD and coronary heart disease (CHD), and some studies have even suggested that the relationship is bidirectional. However, it was unknown whether any causal relationship existed between them and whether causality was bidirectional in such an instance.

Public health insurance and the risk of cancer-specific mortality in patients with cervical cancer: A Chinese prospective cohort study.

Objective: Cervical cancer has one of the highest incidence and mortality rates of any malignant tumor of the female reproductive tract, and its longer treatment period will place significant financial strain on patients and their families. Little is known about how health insurance policies influence cervical cancer prognosis, particularly in developing countries. The relationship between cervical cancer specific death and cervical cancer all-cause mortality with public health insurance, self-payment rate, and the combined effect of public health insurance and self-payment rate was investigated in this study.

Machine learning predicts cancer-associated venous thromboembolism using clinically available variables in gastric cancer patients.

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.