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Additive effects of coexisting respiratory comorbidities on overall or respiratory mortality in patients with asthma: a national cohort study.

ABSTRACT: Asthmatic patients are generally considered to have an increased risk of mortality compared with subjects without asthma. However, this issue has been less evaluated using nationally representative data. Moreover, it is unclear whether respiratory comorbidities other than chronic obstructive pulmonary disease (COPD) are associated with increased mortality in asthmatic patients compared with subjects without. Using a nationally representative sample database, we performed a retrospective cohort study of patients with asthma and age-sex-matched control cohort. We estimated the hazard ratio (HR) and stratified the asthma cohort based on respiratory comorbidities. During a median 8.9-year follow-up, the overall mortality rate was higher in the asthma cohort than in the control cohort (p < 0.001). The hazard ratio (HR) for overall mortality in the asthma cohort compared with the control cohort was 1.13. The effects of asthma on overall mortality were more evident in males, patients under medical aid, and subjects with COPD. Respiratory comorbidities were significantly associated with increased risk of overall mortality in asthmatic patients compared with controls (adjusted HRs; 1.48 for COPD, 1.40 for bronchiectasis, 4.08 for lung cancer, and 1.59 for pneumonia). While asthma and lung cancer showed an additive effect only on overall mortality, asthma and other respiratory comorbidities (COPD, pneumonia, and bronchiectasis) had additive effects only on respiratory mortality. Patients with asthma had a higher overall mortality rate compared with subjects without asthma. Respiratory comorbidities showed an additive effect on overall or respiratory mortality in patients with asthma.

SUBMITTER: Yeo Y

PROVIDER: S-EPMC9110422 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature

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Additive effects of coexisting respiratory comorbidities on overall or respiratory mortality in patients with asthma: a national cohort study.

Yeo Yoomi Y Lee Hyun H Ryu Jiin J Chung Sung Jun SJ Park Tai Sun TS Park Dong Won DW Kim Sang-Heon SH Kim Tae Hyung TH Sohn Jang Won JW Yoon Ho Joo HJ Min Kyung Hoon KH Moon Ji-Yong JY

Scientific reports 20220516 1

Asthmatic patients are generally considered to have an increased risk of mortality compared with subjects without asthma. However, this issue has been less evaluated using nationally representative data. Moreover, it is unclear whether respiratory comorbidities other than chronic obstructive pulmonary disease (COPD) are associated with increased mortality in asthmatic patients compared with subjects without. Using a nationally representative sample database, we performed a retrospective cohort s ...[more]

PMID: 35577832

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Project description:BackgroundAsthma is a heterogeneous disease that affects millions of children and adults. There is a lack of objective gold standard diagnosis that spans the ages; instead, diagnoses are made by clinician assessment based on a cluster of signs, symptoms and objective tests dependent on age. Yet, there is a clear morbidity associated with chronic asthma symptoms. Machine learning has become a popular tool to improve asthma diagnosis and classification. There is a paucity of literature on the use of Bayesian machine learning algorithms to predict asthma diagnosis in children. This paper develops a prediction model using the Bayesian additive regression trees (BART) and compares its performance to various machine learning algorithms in predicting the diagnosis of childhood asthma.MethodsClinically relevant variables collected at or before 3 years of age from 2794 participants in the CHILD Cohort Study were used to predict physician-diagnosed asthma at age 5. BART and six other commonly used machine learning algorithms, namely adaptive boosting, logistic regression, decision tree, neural network, random forest, and support vector machine were trained. Measures of performance including sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve were calculated. The confidence intervals were calculated using Bootstrapping samples. Important predictors and interaction effects associated with asthma were also identified using BART.ResultsBART, logistic regression and random forest showed the highest area under the ROC curve compared to other machine learning algorithms. Based on BART, recurrent wheeze, respiratory infection and food sensitization at 3 years of age were the most important predictors. The three most important interaction effects were found to be interaction terms of respiratory infection at 3 years and recurrent wheezing at 3 years, maternal asthma and paternal asthma, and maternal wheezing and inhalant sensitization of child at 3 years.ConclusionsBART demonstrated promising prediction performance when compared to other machine learning algorithms. Future research could validate the BART in an external cohort to evaluate its reliability and generalizability.

Dataset Information

Additive effects of coexisting respiratory comorbidities on overall or respiratory mortality in patients with asthma: a national cohort study.

Publications

Additive effects of coexisting respiratory comorbidities on overall or respiratory mortality in patients with asthma: a national cohort study.

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