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1.
Front Immunol ; 14: 1224631, 2023.
Article in English | MEDLINE | ID: mdl-37600788

ABSTRACT

Background: Immunoglobulin A nephropathy (IgAN) is one of the leading causes of end-stage kidney disease (ESKD). Many studies have shown the significance of pathological manifestations in predicting the outcome of patients with IgAN, especially T-score of Oxford classification. Evaluating prognosis may be hampered in patients without renal biopsy. Methods: A baseline dataset of 690 patients with IgAN and an independent follow-up dataset of 1,168 patients were used as training and testing sets to develop the pathology T-score prediction (T pre) model based on the stacking algorithm, respectively. The 5-year ESKD prediction models using clinical variables (base model), clinical variables and real pathological T-score (base model plus T bio), and clinical variables and T pre (base model plus T pre) were developed separately in 1,168 patients with regular follow-up to evaluate whether T pre could assist in predicting ESKD. In addition, an external validation set consisting of 355 patients was used to evaluate the performance of the 5-year ESKD prediction model using T pre. Results: The features selected by AUCRF for the T pre model included age, systolic arterial pressure, diastolic arterial pressure, proteinuria, eGFR, serum IgA, and uric acid. The AUC of the T pre was 0.82 (95% CI: 0.80-0.85) in an independent testing set. For the 5-year ESKD prediction model, the AUC of the base model was 0.86 (95% CI: 0.75-0.97). When the T bio was added to the base model, there was an increase in AUC [from 0.86 (95% CI: 0.75-0.97) to 0.92 (95% CI: 0.85-0.98); P = 0.03]. There was no difference in AUC between the base model plus T pre and the base model plus T bio [0.90 (95% CI: 0.82-0.99) vs. 0.92 (95% CI: 0.85-0.98), P = 0.52]. The AUC of the 5-year ESKD prediction model using T pre was 0.93 (95% CI: 0.87-0.99) in the external validation set. Conclusion: A pathology T-score prediction (T pre) model using routine clinical characteristics was constructed, which could predict the pathological severity and assist clinicians to predict the prognosis of IgAN patients lacking kidney pathology scores.


Subject(s)
Glomerulonephritis, IGA , Kidney Failure, Chronic , Humans , Glomerulonephritis, IGA/diagnosis , Kidney , Machine Learning , Kidney Failure, Chronic/etiology , Algorithms
2.
Zhonghua Liu Xing Bing Xue Za Zhi ; 34(11): 1080-2, 2013 Nov.
Article in Chinese | MEDLINE | ID: mdl-24517938

ABSTRACT

OBJECTIVE: Network Scale-Up Method (NSUM) was used to estimate the Personal Network Size (C) of college students in Beijing and the size of men who have sex with men (MSM). METHODS: A survey was conducted on Beijing college students to estimate the personal network size (C) through NSUM and then using the back-estimating and attitude adjustment to correct C value and the size of the target population. RESULTS: The mean of C among Beijing college students was 97 and the differences of C between different majors and different grades were both statistically significant. Among Beijing College students, 3.96% of them identified themselves as MSM, with the number as 16 260. CONCLUSION: Using the NSUM to estimate C of Beijing college students and size of MSM seemed to be credible which worth popularized.


Subject(s)
Homosexuality, Male/statistics & numerical data , Masculinity , Social Networking , Students , Adult , Humans , Male , Regression Analysis
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