دورية أكاديمية
Multivariate analysis and data mining help predict asthma exacerbations.
| العنوان: | Multivariate analysis and data mining help predict asthma exacerbations. |
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| المؤلفون: | Mihaicuta S; Center for Research and Innovation in Precision Medicine of Respiratory Diseases, Department of Pulmonology, 'Victor Babes' University of Medicine and Pharmacy Timisoara, Timisoara, Romania., Udrescu L; Department I-Drug Analysis, Faculty of Pharmacy, 'Victor Babes' University of Medicine and Pharmacy Timisoara, Timisoara, Romania., Militaru A; Department of Computer and Information Technology, Politehnica University Timisoara, Timisoara, Romania., Nadasan V; Department of Hygiene, 'G.E. Palade' University of Medicine, Pharmacy, Science and Technology of Targu Mures, Targu Mures, Romania., Tiotiu A; Department of Pulmonology, Nancy University Hospital, Nancy, France., Bikov A; Wythenshawe Hospital, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, United Kingdom.; Division of Infection, Immunity & Respiratory Medicine, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, United Kingdom., Ursoniu S; Department of Public Health and Health Management, 'Victor Babes' University of Medicine and Pharmacy Timisoara, Timisoara, Romania.; Center for Translational Research and Systems Medicine, Timisoara, Romania., Birza R; Center for Research and Innovation in Precision Medicine of Respiratory Diseases, Department of Pulmonology, 'Victor Babes' University of Medicine and Pharmacy Timisoara, Timisoara, Romania., Popa AM; Center for Research and Innovation in Precision Medicine of Respiratory Diseases, Department of Pulmonology, 'Victor Babes' University of Medicine and Pharmacy Timisoara, Timisoara, Romania., Frent S; Center for Research and Innovation in Precision Medicine of Respiratory Diseases, Department of Pulmonology, 'Victor Babes' University of Medicine and Pharmacy Timisoara, Timisoara, Romania. |
| المصدر: | The Journal of asthma : official journal of the Association for the Care of Asthma [J Asthma] 2024 Jun; Vol. 61 (6), pp. 608-618. Date of Electronic Publication: 2024 Jan 01. |
| نوع المنشور: | Journal Article; Research Support, Non-U.S. Gov't |
| اللغة: | English |
| بيانات الدورية: | Publisher: Informa Healthcare Country of Publication: England NLM ID: 8106454 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1532-4303 (Electronic) Linking ISSN: 02770903 NLM ISO Abbreviation: J Asthma Subsets: MEDLINE |
| أسماء مطبوعة: | Publication: London : Informa Healthcare Original Publication: [Ossining, N.Y. : Asthma Publications Society, c1981- |
| مواضيع طبية MeSH: | Data Mining* , Asthma*/physiopathology , Asthma*/diagnosis , Occupational Exposure*/adverse effects , Occupational Exposure*/statistics & numerical data, Humans ; Female ; Male ; Middle Aged ; Adult ; Retrospective Studies ; Aged ; Multivariate Analysis ; Young Adult ; Aged, 80 and over ; Disease Progression ; Asthma, Occupational/diagnosis ; Asthma, Occupational/physiopathology ; Logistic Models |
| مستخلص: | Background: Work-related asthma has become a highly prevalent occupational lung disorder. Objective: Our study aims to evaluate occupational exposure as a predictor for asthma exacerbation. Method: We performed a retrospective evaluation of 584 consecutive patients diagnosed and treated for asthma between October 2017 and December 2019 in four clinics from Western Romania. We evaluated the enrolled patients for their asthma control level by employing the Asthma Control Test (ACT < 20 represents uncontrolled asthma), the medical record of asthma exacerbations, occupational exposure, and lung function (i.e. spirometry). Then, we used statistical and data mining methods to explore the most important predictors for asthma exacerbations. Results: We identified essential predictors by calculating the odds ratios (OR) for the exacerbation in a logistic regression model. The average age was 45.42 ± 11.74 years (19-85 years), and 422 (72.26%) participants were females. 42.97% of participants had exacerbations in the past year, and 31.16% had a history of occupational exposure. In a multivariate model analysis adjusted for age and gender, the most important predictors for exacerbation were uncontrolled asthma (OR 4.79, p < .001), occupational exposure (OR 4.65, p < .001), and lung function impairment (FEV1 < 80%) (OR 1.15, p = .011). The ensemble machine learning experiments on combined patient features harnessed by our data mining approach reveal that the best predictor is professional exposure, followed by ACT. Conclusions: Machine learning ensemble methods and statistical analysis concordantly indicate that occupational exposure and ACT < 20 are strong predictors for asthma exacerbation. |
| فهرسة مساهمة: | Keywords: Occupational exposure; asthma exacerbation; data mining; ensemble learning; predictor; work-related asthma |
| تواريخ الأحداث: | Date Created: 20231219 Date Completed: 20240507 Latest Revision: 20240507 |
| رمز التحديث: | 20240507 |
| DOI: | 10.1080/02770903.2023.2297366 |
| PMID: | 38112563 |
| قاعدة البيانات: | MEDLINE |
PDF full text from Publisher | تدمد: | 1532-4303 |
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| DOI: | 10.1080/02770903.2023.2297366 |