Dataset Information

Machine Learning Methods for the Diagnosis of Chronic Obstructive Pulmonary Disease in Healthy Subjects: Retrospective Observational Cohort Study.

ABSTRACT:

Background

Airflow limitation is a critical physiological feature in chronic obstructive pulmonary disease (COPD), for which long-term exposure to noxious substances, including tobacco smoke, is an established risk. However, not all long-term smokers develop COPD, meaning that other risk factors exist.

Objective

This study aimed to predict the risk factors for COPD diagnosis using machine learning in an annual medical check-up database.

Methods

In this retrospective observational cohort study (ARTDECO [Analysis of Risk Factors to Detect COPD]), annual medical check-up records for all Hitachi Ltd employees in Japan collected from April 1998 to March 2019 were analyzed. Employees who provided informed consent via an opt-out model were screened and those aged 30 to 75 years without a prior diagnosis of COPD/asthma or a history of cancer were included. The database included clinical measurements (eg, pulmonary function tests) and questionnaire responses. To predict the risk factors for COPD diagnosis within a 3-year period, the Gradient Boosting Decision Tree machine learning (XGBoost) method was applied as a primary approach, with logistic regression as a secondary method. A diagnosis of COPD was made when the ratio of the prebronchodilator forced expiratory volume in 1 second (FEV₁) to prebronchodilator forced vital capacity (FVC) was <0.7 during two consecutive examinations.

Results

Of the 26,101 individuals screened, 1213 met the exclusion criteria, and thus, 24,815 individuals were included in the analysis. The top 10 predictors for COPD diagnosis were FEV₁/FVC, smoking status, allergic symptoms, cough, pack years, hemoglobin A_1c, serum albumin, mean corpuscular volume, percent predicted vital capacity, and percent predicted value of FEV₁. The areas under the receiver operating characteristic curves of the XGBoost model and the logistic regression model were 0.956 and 0.943, respectively.

Conclusions

Using a machine learning model in this longitudinal database, we identified a number of parameters as risk factors other than smoking exposure or lung function to support general practitioners and occupational health physicians to predict the development of COPD. Further research to confirm our results is warranted, as our analysis involved a database used only in Japan.

SUBMITTER: Muro S

PROVIDER: S-EPMC8293159 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundRadiomics and artificial intelligence approaches have been developed to predict chronic obstructive pulmonary disease (COPD), but it is still unclear which approach has the best performance. Therefore, we established five prediction models that employed deep-learning (DL) and radiomics-based machine-learning (ML) approaches to identify COPD on low-dose computed tomography (LDCT) images and compared the relative performance of the different models to find the best model for identifying COPD.MethodsThis retrospective analysis included 1,024 subjects (169 COPD patients and 855 control subjects) who underwent LDCT scans from August 2018 to July 2021. Five prediction models, including models that employed computed tomography (CT)-based radiomics features, chest CT images, quantitative lung density parameters, and demographic and clinical characteristics, were established to identify COPD by DL or ML approaches. Model 1 used CT-based radiomics features by ML method. Model 2 used a combination of CT-based radiomics features, lung density parameters, and demographic and clinical characteristics by ML method. Model 3 used CT images only by DL method. Model 4 used a combination of CT images, lung density parameters, and demographic and clinical characteristics by DL method. Model 5 used a combination of CT images, CT-based radiomics features, lung density parameters, and demographic and clinical characteristics by DL method. The accuracy, sensitivity, specificity, highest negative predictive values (NPVs), positive predictive values, and areas under the receiver operating characteristic (AUC) curve of the five prediction models were compared to examine their performance. The DeLong test was used to compare the AUCs of the different models.ResultsIn total, 107 radiomics features were extracted from each subject's CT images, 17 lung density parameters were acquired by quantitative measurement, and 18 selected demographic and clinical characteristics were recorded in this study. Model 2 had the highest AUC [0.73, 95% confidence interval (CI): 0.64-0.82], while model 3 had the lowest AUC (0.65, 95% CI: 0.55-0.75) in the test set. Model 2 also had the highest sensitivity (0.84), the highest accuracy (0.81), and the highest NPV (0.36). In the test set, based on the AUC results, Model 2 significantly outperformed Model 1 (P=0.03).ConclusionsThe results showed that the identification ability of models that employ CT-based radiomics features combined with lung density parameters, and demographic and clinical characteristics using ML methods performed better than the chest CT image-based DL methods. ML methods are more suitable and beneficial for COPD identification.

Project description:BACKGROUND:Heparin is one of the most commonly used medications in intensive care units. In clinical practice, the use of a weight-based heparin dosing nomogram is standard practice for the treatment of thrombosis. Recently, machine learning techniques have dramatically improved the ability of computers to provide clinical decision support and have allowed for the possibility of computer generated, algorithm-based heparin dosing recommendations. OBJECTIVE:The objective of this study was to predict the effects of heparin treatment using machine learning methods to optimize heparin dosing in intensive care units based on the predictions. Patient state predictions were based upon activated partial thromboplastin time in 3 different ranges: subtherapeutic, normal therapeutic, and supratherapeutic, respectively. METHODS:Retrospective data from 2 intensive care unit research databases (Multiparameter Intelligent Monitoring in Intensive Care III, MIMIC-III; e-Intensive Care Unit Collaborative Research Database, eICU) were used for the analysis. Candidate machine learning models (random forest, support vector machine, adaptive boosting, extreme gradient boosting, and shallow neural network) were compared in 3 patient groups to evaluate the classification performance for predicting the subtherapeutic, normal therapeutic, and supratherapeutic patient states. The model results were evaluated using precision, recall, F1 score, and accuracy. RESULTS:Data from the MIMIC-III database (n=2789 patients) and from the eICU database (n=575 patients) were used. In 3-class classification, the shallow neural network algorithm performed the best (F1 scores of 87.26%, 85.98%, and 87.55% for data set 1, 2, and 3, respectively). The shallow neural network algorithm achieved the highest F1 scores within the patient therapeutic state groups: subtherapeutic (data set 1: 79.35%; data set 2: 83.67%; data set 3: 83.33%), normal therapeutic (data set 1: 93.15%; data set 2: 87.76%; data set 3: 84.62%), and supratherapeutic (data set 1: 88.00%; data set 2: 86.54%; data set 3: 95.45%) therapeutic ranges, respectively. CONCLUSIONS:The most appropriate model for predicting the effects of heparin treatment was found by comparing multiple machine learning models and can be used to further guide optimal heparin dosing. Using multicenter intensive care unit data, our study demonstrates the feasibility of predicting the outcomes of heparin treatment using data-driven methods, and thus, how machine learning-based models can be used to optimize and personalize heparin dosing to improve patient safety. Manual analysis and validation suggested that the model outperformed standard practice heparin treatment dosing.

Project description:Background The use of antibiotics in mild to severe acute exacerbations of chronic obstructive pulmonary disease (COPD) remains controversial. Aim To explore in-hospital antibiotic use in severe acute exacerbations of COPD (AECOPD), to analyze determinants of in-hospital antibiotic use, and to investigate its association with hospital length of stay (LOS) and in-hospital mortality. Methods A retrospective, observational study was conducted in Ghent University Hospital. Severe AECOPD were defined as hospitalizations for AECOPD (ICD-10 J44.0 and J44.1) discharged between 2016 and 2021. Patients with a concomitant diagnosis of pneumonia or ‘pure’ asthma were excluded. An alluvial plot was used to describe antibiotic treatment patterns. Logistic regression analyses identified determinants of in-hospital antibiotic use. Cox proportional hazards regression analyses were used to compare time to discharge alive and time to in-hospital death between antibiotic-treated and non-antibiotic-treated AECOPD patients. Results In total, 431 AECOPD patients (mean age 70 years, 63% males) were included. More than two-thirds (68%) of patients were treated with antibiotics, mainly amoxicillin-clavulanic acid. In multivariable analysis, several patient-related variables (age, body mass index (BMI), cancer), treatment-related variables (maintenance azithromycin, theophylline), clinical variables (sputum volume and body temperature) and laboratory results (C-reactive protein (CRP) levels) were associated with in-hospital antibiotic use independent of sputum purulence, neutrophil counts, inhaled corticosteroids and intensive care unit of which CRP level was the strongest determinant. The median hospital LOS was significantly longer in antibiotic-treated patients (6 days [4–10]) compared to non-antibiotic-treated patients (4 days [2–7]) (p < 0.001, Log rank test). This was indicated by a reduced probability of hospital discharge even after adjustment for age, sputum purulence, BMI, in-hospital systemic corticosteroid use and forced expiratory volume in one second (FEV1) (adjusted hazard ratio 0.60; 95% CI 0.43; 0.84). In-hospital antibiotic use was not significantly associated with in-hospital mortality. Conclusions In this observational study in a Belgian tertiary hospital, in-hospital antibiotic use among patients with severe AECOPD was determined by the symptom severity of the exacerbation and the underlying COPD severity as recommended by the guidelines, but also by patient-related variables. Moreover, in-hospital antibiotic use was associated with a longer hospital stay, which may be linked to their disease severity, slower response to treatment or 'harm' due to antibiotics. Trial registration Number: B670201939030; date of registration: March 5, 2019. Supplementary Information The online version contains supplementary material available at 10.1186/s12890-023-02426-3.