ABSTRACT
Background: Oxidative Balance Score (OBS) is a tool for assessing the oxidative stress-related exposures of diet and lifestyle. The study aimed to investigate the association between OBS and low muscle mass. Methods: Overall, 6,307 individuals over the age of 18 were assessed using data from the 2011 to 2018 National Health and Nutrition Examination Survey (NHANES). Weighted logistic regression and models were used, together with adjusted models. Results: There was a negative relationship between OBS and low muscle mass [odds ratio (OR): 0.96, 95% confidence interval (CI): 0.94-0.97, p< 0.0001] using the first OBS level as reference. The values (all 95% CI) were 0.745 (0.527-1.054) for the second level, 0.650 (0.456-0.927) for the third level, and 0.326 (0.206-0.514) for the fourth level (P for trend <0.0001). Independent links with low muscle mass were found for diet and lifestyle factors. A restricted cubic spline model indicated a non-linear association between OBS and low muscle mass risk (P for non-linearity<0.05). In addition, the inflection points of the nonlinear curves for the relationship between OBS and risk of low muscle mass were 20. Conclusion: OBS and low muscle mass were found to be significantly negatively correlated. By modulating oxidative balance, a healthy lifestyle and antioxidant rich diet could be a preventive strategy for low muscle mass.
ABSTRACT
PURPOSE: The objective of this study is to investigate the associated risk factors of pulmonary infection in individuals diagnosed with chronic kidney disease (CKD). The primary goal is to develop a predictive model that can anticipate the likelihood of pulmonary infection during hospitalization among CKD patients. METHODS: This retrospective cohort study was conducted at two prominent tertiary teaching hospitals. Three distinct models were formulated employing three different approaches: (1) the statistics-driven model, (2) the clinical knowledge-driven model, and (3) the decision tree model. The simplest and most efficient model was obtained by comparing their predictive power, stability, and practicability. RESULTS: This study involved a total of 971 patients, with 388 individuals comprising the modeling group and 583 individuals comprising the validation group. Three different models, namely Models A, B, and C, were utilized, resulting in the identification of seven, four, and eleven predictors, respectively. Ultimately, a statistical knowledge-driven model was selected, which exhibited a C-statistic of 0.891 (0.855-0.927) and a Brier score of 0.012. Furthermore, the Hosmer-Lemeshow test indicated that the model demonstrated good calibration. Additionally, Model A displayed a satisfactory C-statistic of 0.883 (0.856-0.911) during external validation. The statistical-driven model, known as the A-C2GH2S risk score (which incorporates factors such as albumin, C2 [previous COPD history, blood calcium], random venous blood glucose, H2 [hemoglobin, high-density lipoprotein], and smoking), was utilized to determine the risk score for the incidence rate of lung infection in patients with CKD. The findings revealed a gradual increase in the occurrence of pulmonary infections, ranging from 1.84% for individuals with an A-C2GH2S Risk Score ≤ 6, to 93.96% for those with an A-C2GH2S Risk Score ≥ 18.5. CONCLUSION: A predictive model comprising seven predictors was developed to forecast pulmonary infection in patients with CKD. This model is characterized by its simplicity, practicality, and it also has good specificity and sensitivity after verification.
ABSTRACT
Objectives: Acute respiratory failure (ARF) is a common complication of bronchial asthma (BA). ARF onset increases the risk of patient death. This study aims to develop a predictive model for ARF in BA patients during hospitalization. Methods: This was a retrospective cohort study carried out at two large tertiary hospitals. Three models were developed using three different ways: (1) the statistics-driven model, (2) the clinical knowledge-driven model, and (3) the decision tree model. The simplest and most efficient model was obtained by comparing their predictive power, stability, and practicability. Results: This study included 398 patients, with 298 constituting the modeling group and 100 constituting the validation group. Models A, B, and C yielded seven, seven, and eleven predictors, respectively. Finally, we chose the clinical knowledge-driven model, whose C-statistics and Brier scores were 0.862 (0.820-0.904) and 0.1320, respectively. The Hosmer-Lemeshow test revealed that this model had good calibration. The clinical knowledge-driven model demonstrated satisfactory C-statistics during external and internal validation, with values of 0.890 (0.815-0.965) and 0.854 (0.820-0.900), respectively. A risk score for ARF incidence was created: The A2-BEST2 Risk Score (A2 (area of pulmonary infection, albumin), BMI, Economic condition, Smoking, and T2(hormone initiation Time and long-term regular medication Treatment)). ARF incidence increased gradually from 1.37% (The A2-BEST2 Risk Score ≤ 4) to 90.32% (A2-BEST2 Risk Score ≥ 11.5). Conclusion: We constructed a predictive model of seven predictors to predict ARF in BA patients. This predictor's model is simple, practical, and supported by existing clinical knowledge.
