Objective: This study aimed to assess the occurrence of chemotherapy-induced nausea and vomiting (CINV) in acute phase (24 h after chemotherapy) and delayed phase (2–5 days after chemotherapy) after standard antiemetic therapy and to explore the risk factors of CINV in the acute and delayed phases. Methods: This prospective and observational study analyzed the data of 400 breast cancer patients scheduled for chemotherapy in two hospitals. The self-report survey was developed to assess the occurrence of CINV and their associated factors. On day 2 and day 6 of chemotherapy, CINV was evaluated by the Multinational Association of Supportive Care in Cancer Antiemetic Tool (MAT). The incidence of acute and delayed CINV was expressed as frequency and percentage. Results: Among 400 patients, 29.8% and 23.5% experienced acute and delayed CINV, respectively. Logistic regression analysis showed that the risk factors associated with acute CINV included pain/insomnia, history of CINV, and highly emetogenic chemotherapy. The history of motion sickness (MS), history of CINV, number of chemotherapy cycles completed, and the incidence of acute CINV were significant risk factors for delayed CINV (all P < 0.05). Conclusions: The results of this study are helpful for nurses to identify high-risk patients with CINV, formulate effective treatment plans, and reduce the incidence of CINV.
Aim We aim to develop and validate a nomogram including readily available clinical and laboratory indicators to predict the risk of metabolic-associated fatty liver disease (MAFLD) in the Chinese physical examination population. Methods The annual physical examination data of Chinese adults from 2016 to 2020 were retrospectively analyzed. We extracted the clinical data of 138 664 subjects and randomized participants to the development and validation groups (7:3). Significant predictors associated with MAFLD were identified by using univariate and random forest analyses, and a nomogram was constructed to predict the risk of MAFLD based on a Lasso logistic model. Receiver operating characteristic curve analysis, calibration curves, and decision curve analysis were used to verify the discrimination, calibration, and clinical practicability of the nomogram, respectively. Results Ten variables were selected to establish the nomogram for predicting MAFLD risk: sex, age, waist circumference (WC), uric acid (UA), body mass index (BMI), waist-to-hip ratio (WHR), systolic blood pressure (SBP), fasting plasma glucose (FPG), triglycerides (TG), and alanine aminotransferase (ALT). The nomogram built on the nonoverfitting multivariable model showed good prediction of discrimination (AUC 0.914, 95% CI: 0.911–0.917), calibration, and clinical utility. Conclusions This nomogram can be used as a quick screening tool to assess MAFLD risk and identify individuals at high risk of MAFLD, thus contributing to the improved management of MAFLD.
Hospitalized cancer patients are high-risk venous thromboembolism (VTE) population. It is vital to identify the VTE risk of inpatients on admission accurately. Machine learning (ML) has advantages in data processing and model development. The article aims to develop predictive models using four ML methods to evaluate VTE risk for hospitalized cancer patients and select a model with the best performance. A retrospective cohort study was conducted for cancer patients hospitalized at Hunan Cancer Hospital between December 1, 2017 and November 30, 2020. A total of 1,100 inpatients were included in the study. There were 340 patients in the VTE group and 760 patients in the non-VTE group. The XGBoost model had the best performance for the prediction of VTE risk compared to the other three machine learning models. The AUROC value of the XGBoost model was 0.818(95%CI: 0.762, 0.870). The top five important features in the XGBoost model were D-dimer, diabetes, hypertension, pleural metastasis and hematological malignancies. Machine learning models contribute to evaluating the VTE risk in hospitalized cancer patients. They may be the practical risk stratification tools to provide a clinical reference for clinicians and nurses, especially the XGBoost model.
Background: Hospitalized cancer patients suffer a high risk of venous thromboembolism (VTE). Guidelines suggested performing a personalized thromboprophylaxis guided by VTE risk assessment tools. Machine learning (ML) has advantages in data processing and model development. The study aimed to develop predictive models using four different ML methods and to compare their predictive performance.Methods: A retrospective case-control study was conducted from October 1, 2021 to February 30, 2022 in Hunan Cancer Hospital. A total of 1,100 hospitalized cancer patients were included. The outcome variable was the occurrence of VTE during hospitalization. Input variables, including patient, tumor, treatment and laboratory indicators characteristics, were trained for four ML models: Logistic Regression, Support Vector Machine, Random Forest, and extreme gradient boosting (XGBoost). Model performance was compared using the area under the receiver operating characteristic curve (AUROC). Features rankings were achieved according to the permutation scores of selected features in the optimal model.Results: A total of 1,100 patients (mean [SD] age, 54.75[11.08] years; 485[44.09%] male) were included in the study. There were 340 patients in the VTE group and 760 patients in the non-VTE group. XGBoost model showed the best predictive performance among four models, with AUROC value of 0.818 (95%CI: 0.762, 0.870). Performance of other three models were lower with the following AUROCs in the testing set: Logistic Regression, 0.757(95%CI: 0.689,0.816); Support Vector Machine, 0.759(95%CI: 0.697,0.818); and Random Forest, 0.743(95%CI: 0.678,0.808). The most five significant features in XGBoost model were D-dimer, diabetes, hypertension, pleural metastasis and hematological malignancies. Conclusion: Four predictive models were developed using ML algorithms. XGBoost model was the optimal predictive model compared to other three ML models (Logistic Regression, Support Vector Machine, and Random Forest). This study indicates that ML may play an important role in VTE risk estimation among hospitalized cancer patients and provide reference for thromboprophylaxis.
Aim We aim to develop and validate a nomogram including readily available clinical and laboratory indicators to predict the risk of MAFLD in the Chinese physical examination population.Methods The annual physical examination data of Chinese adults from 2016 to 2020 were retrospectively analyzed. We extracted the clinical data of 138 664 subjects and randomized participants to the development and validation groups (7:3). Significant predictors associated with MAFLD were identified by using univariate and random forest, and the nomogram was constructed to predict the risk of MAFLD based on a Lasso-Logistic model. Receiver operating characteristic curve analysis, calibration curves, and decision curve analysis were used to verify the discrimination, calibration, and clinical practicability of the nomogram, respectively.Results Ten variables were selected to establish the nomogram for predicting MAFLD risk: sex, age, waist circumference, uric acid, BMI, WHR, SBP, FPG, TG, and ALT. The nomogram built on the non-overfitting multivariable model showed good prediction of discrimination (AUC 0.914, 95% CI: 0.911–0.917), calibration, and clinical utility.Conclusions This nomogram can be used as a quick screening tool to assess MAFLD risk and identify individuals at high risk of MAFLD, thus contributing to the improved management of MAFLD.
Introduction The aggregation of lifestyle behaviors and their association with metabolic associated fatty liver disease (MAFLD) remains unclear. We identified lifestyle patterns and investigated their association with MAFLD in a sample of Chinese adults who underwent annual physical examinations. Methods Annual physical examination data of Chinese adults from January 2016 to December 2020 was used in this study. We created a scoring system for lifestyle items combining statistical method (Multivariate analysis of variance) and clinical expertise’s opinion (Delphi method). Subsequently, principal components analysis and two-step cluster analysis were implemented to derive lifestyle patterns of men and women. Binary logistic regression analysis was used to explore the prevalence risk of MAFLD among lifestyle patterns stratified by gender. Results A total of 196,515 subjects were included in the analysis. Based on the defined lifestyle scoring system, nine and four lifestyle patterns were identified for men and women, respectively, which included “healthy or unhealthy” patterns and mixed patterns containing a combination of healthy and risky lifestyle behaviors. This study showed that subjects with an unhealthy or mixed pattern had a differentially higher risk of developing MAFLD than subjects with a relatively healthy pattern, especially among men. Conclusions Clusters of unfavorable behaviors are more prominent in men when compared to women. Lifestyle patterns, as the important factors influencing the development of MAFLD, show significant gender differences in the risk of MAFLD. There is a strong need for future research to develop targeted MAFLD interventions based on the identified behavioral clusters by gender stratification.
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