Dataset Information

Multi-Factor Analysis of Single-Center Asthma Control in Xiamen, China.

ABSTRACT: We evaluated the effects of air pollutants, age, allergic history, family allergic history, treatment, treatment steps, and compliance on uncontrolled childhood asthma in Xiamen, China. The clinical data of children with asthma in the pediatric outpatient department of the First Affiliated Hospital of Xiamen University from January 2016 to June 2018 were analyzed retrospectively. According to the assessment of the patients' outcome including well-controlled, partly-controlled and uncontrolled, 7,211 cases of 3,268 patients were selected. Rank sum test and ordered multi-class logistic regression analysis were used. In the rank sum test, age, allergic history, family allergic history, season, treatment, treatment steps and compliance were found associated with uncontrolled rate (all P < 0.001). Logistic regression analysis showed that PM₁₀, NO₂, and SO₂ raised uncontrolled-asthma rate (aOR 1.311, aOR 1.281, aOR 1.252, respectively). Older children had lower uncontrolled rate (OR = 0.849, 95% CI: 0.758-0.950), children with higher treatment steps had higher uncontrolled rate (OR = 1.227, 95%CI: 1.040-1.448), and children with better treatment compliance have lower uncontrolled rate (OR = 0.374 95% CI: 0.331-0.424). The order of the uncontrolled rate of asthma from high to low was winter, spring, autumn, and summer. PM₁₀, NO₂, SO₂, age, season, treatment steps, and treatment compliance have significance for predicting the control rate of childhood asthma in Xiamen, China.

SUBMITTER: Zhu Y

PROVIDER: S-EPMC6901658 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Multi-Factor Analysis of Single-Center Asthma Control in Xiamen, China.

Zhu Yu Y Zhong Taoling T Ge Dandan D Li Qiyuan Q Wu Jinzhun J

Frontiers in pediatrics 20191203

We evaluated the effects of air pollutants, age, allergic history, family allergic history, treatment, treatment steps, and compliance on uncontrolled childhood asthma in Xiamen, China. The clinical data of children with asthma in the pediatric outpatient department of the First Affiliated Hospital of Xiamen University from January 2016 to June 2018 were analyzed retrospectively. According to the assessment of the patients' outcome including well-controlled, partly-controlled and uncontrolled, 7 ...[more]

PMID: 31850293

Similar Datasets

Project description:BACKGROUND:Prognostic models based on high-dimensional omics data generated from clinical patient samples, such as tumor tissues or biopsies, are increasingly used for prognosis of radio-therapeutic success. The model development process requires two independent discovery and validation data sets. Each of them may contain samples collected in a single center or a collection of samples from multiple centers. Multi-center data tend to be more heterogeneous than single-center data but are less affected by potential site-specific biases. Optimal use of limited data resources for discovery and validation with respect to the expected success of a study requires dispassionate, objective decision-making. In this work, we addressed the impact of the choice of single-center and multi-center data as discovery and validation data sets, and assessed how this impact depends on the three data characteristics signal strength, number of informative features and sample size. METHODS:We set up a simulation study to quantify the predictive performance of a model trained and validated on different combinations of in silico single-center and multi-center data. The standard bioinformatical analysis workflow of batch correction, feature selection and parameter estimation was emulated. For the determination of model quality, four measures were used: false discovery rate, prediction error, chance of successful validation (significant correlation of predicted and true validation data outcome) and model calibration. RESULTS:In agreement with literature about generalizability of signatures, prognostic models fitted to multi-center data consistently outperformed their single-center counterparts when the prediction error was the quality criterion of interest. However, for low signal strengths and small sample sizes, single-center discovery sets showed superior performance with respect to false discovery rate and chance of successful validation. CONCLUSIONS:With regard to decision making, this simulation study underlines the importance of study aims being defined precisely a priori. Minimization of the prediction error requires multi-center discovery data, whereas single-center data are preferable with respect to false discovery rate and chance of successful validation when the expected signal or sample size is low. In contrast, the choice of validation data solely affects the quality of the estimator of the prediction error, which was more precise on multi-center validation data.

Dataset Information

Multi-Factor Analysis of Single-Center Asthma Control in Xiamen, China.

Publications

Multi-Factor Analysis of Single-Center Asthma Control in Xiamen, China.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets