Finding etiology of chronic cough is an essential part of treatment. Although guidelines include many laboratory tests for diagnosis, these are not possible in many primary care centers. We aimed to identify the characteristics and the differences associated with its cause to develop a clinical prediction model. Adult subjects with chronic cough who completed both Korean version of the Leicester Cough Questionnaire (K-LCQ) and COugh Assessment Test (COAT) were enrolled. Clinical characteristics of each etiology were compared using features included in questionnaires. Decision tree models were built to classify the causes. A total of 246 subjects were included for analysis. Subjects with asthma including cough variant asthma (CVA) suffered from more severe cough in physical and psychological domains. Subjects with eosinophilic bronchitis (EB) presented less severe cough in physical domain. Those with gastro-esophageal reflux disease (GERD) displayed less severe cough in all 3 domains. In logistic regression, voice hoarseness was an independent feature of upper airway cough syndrome (UACS), whereas female sex, tiredness, and hypersensitivity to irritants were predictors of asthma/CVA; less hoarseness was a significant feature of EB, and feeling fed-up and hoarseness were less common characteristics of GERD. The decision tree was built to classify the causes and the accuracy was relatively high for both K-LCQ and COAT, except for UACS. Voice hoarseness, degree of tiredness, hypersensitivity to irritants and feeling fed-up are important features in determining the etiologies. The decision tree may further assists classifying the causes of chronic cough.
Most of the previous studies on chronic cough have been focused on prevalence, identifying common causes, or development of effective diagnostic flow for laboratory tests2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17. However, a description of the detailed features and comparison of characteristics to distinguish each etiology had been limited, which is a fundamental step in medical practice. Our study revealed the characteristic differences of chronic cough by each etiology. One of interesting findings is the differences between asthma/CVA and EB. Although they share immunopathology of eosinophilic airway inflammation20, the detailed features between them have not been characterized. Patients of chronic cough with EB were not female dominant; less severe features; and specific differences of each item in the cough questionnaire were observed, unlike those with asthma/CVA. Furthermore, present study attempted to make a simple classification algorithm to decide which test should proceed further using clinical features. The decision tree enables physicians to classify causes very fast that can serve as a useful tool in clinical practice. Also, decision trees may provide clues for the pathogenesis of disease due to their own structure. Initially, building a single decision tree for every cause was tried, but accuracy of this classification was unsatisfactory. This supposed to be from multiple causes accompanied by, therefore, separate decision trees for each cause were re-constructed. These decision trees, specific to each cause, produced higher accuracy except for UACS; which suggested that the reason for low accuracy at initial single tree might be attributable to UACS. Since a simple physical examination could enhance the diagnostic accuracy of UACS, further large studies including this information are needed.
Several limitations should be addressed. Detailed results of physical examination, spirometry, bronchoprovocation test, eosinophil count in induced sputum, or exhaled fraction of nitric oxide were not reviewed in this analysis. Therefore, there could be some possibilities of misdiagnosis that underestimate the accuracy of our model. Nevertheless, the clinical diagnosis was decided by pulmonary specialists in respiratory centers, considering their available facilities. Validation of the COAT is tested only in a single country21 and would need to be further generalized. Though we tried to use cross-validation in our analysis, we did not have external validation set to test our model. Since prevalence of chronic cough could be different among different countries, predictive performance of decision tree could be lower if practitioners in different countries use different diagnostic criteria particularly in primary care setting. Therefore, ascertainment of this cohort may limit generalizability to the other races. Further large studies to confirm our findings are needed, especially from different countries. Lastly, information about comorbidities could have enhance the understanding of their relation to symptoms and pathophysiology.
To eliminate the disparity and maldistribution of physicians and medical specialty services, the development of diagnostic support for rare diseases using artificial intelligence is being promoted. Immunoglobulin G4 (IgG4)-related disease (IgG4-RD) is a rare disorder often requiring special knowledge and experience to diagnose. In this study, we investigated the possibility of differential diagnosis of IgG4-RD based on basic patient characteristics and blood test findings using machine learning.
In particular, this problem is a major obstacle for the diagnosis of immunoglobulin (Ig) G4-related disease (IgG4-RD). IgG4-RD is a systemic fibroinflammatory disease characterized by elevated serum levels of IgG4, marked infiltration of IgG4-bearing plasma cells, and fibrosis in the involved organs [1]. It is a rare disease that was newly conceptualized in this century, and it has many differential diagnoses. General physicians do not always recognize IgG4-RD in patients. In addition, delays in diagnosis and treatment can lead to severe organ dysfunction.
The use of artificial intelligence (AI) may help solve this issue. Machine learning, which is a data analysis technique for realizing AI, is a method in which computers automatically analyze data to discover and learn the rules and patterns behind the data. In recent years, there has been an emphasis on making predictions and decisions based on the results of such learning, and the use of AI in the medical field has increased. It has been successful in building models for retrospectively identifying abnormalities in diverse types of images [2]. In particular, systems for detecting colorectal cancer, skin tumors, cerebral aneurysms, and influenza infection, among others, by AI-based imaging diagnosis have been consecutively developed. In rheumatology, many results related to treatment support have been reported. Studies have successfully predicted the response to treatment and the prognosis of rheumatoid arthritis (RA) patients using data on clinical markers and genetic analyses. In 2019, Kim et al. used transcriptome profiling of RA synovium to predict treatment responses from inflammatory signals [3]. Furthermore, Guan et al. used clinical data and single-nucleotide polymorphism sequence data to predict the patient response to antitumor necrosis factor therapy [4]. Subsequently, many prognostic predictors of the patient response to treatment and rehospitalization have been reported for RA, systemic lupus erythematosus (SLE), juvenile idiopathic arthritis, spondyloarthropathy, and osteoarthritis. However, there have not yet been any AI studies for IgG4-RD.
In addition, the therapeutic strategy for rheumatic diseases is decided after carefully considering the distribution and degree of disability. These are the areas in which AI excels the most. Currently, the diagnosis of IgG4-related disease (IgG4-RD) is based on blood test results; findings from imaging examinations such as computed tomography (CT), MRI, and fluorodeoxyglucose positron emission tomography (FDG-PET), and histopathological findings. As a result, the invasiveness to the patients is high, and the high cost of medical care has become a problem. Thus, this study investigated whether AI can be trained to differentiate IgG4-RD from other rheumatic diseases by learning the typical cases of both IgG4-RD and non-IgG4-RD, and whether proper diagnosis is possible. The results of this study are expected to be useful for assisting nonspecialist physicians in the community to make appropriate diagnosis and treatment decisions for patients with IgG4-RD.
In terms of model fitting, when the serum IgG4 values were known, the CART method used a cp (a parameter indicating the complexity of the tree model) value of 0.017, and the random forest method used a mtry (the number of variables to be employed in the model) of 5 and a ntree (the number of decision trees to be tried) of 400, and when the serum IgG4 values were unknown, for the CART method used a cp value of 0.015, and random forest method used a mtry of 3 and a ntree of 300 for optimization. The Shapiro-Wilk test was conducted to confirm that the training, test, and validation samples had a normal distribution (p = 0.62). Intergroup comparisons were performed using a two-tailed t test. The accuracy of the model was retrospectively evaluated by drawing a receiver-operating characteristic (ROC) curve from the sensitivity and specificity of the validation sample, and by calculating the area under the curve (AUC). P values less than 0.05 were considered to denote statistical significance. Values are provided as the mean  standard deviation unless otherwise noted.
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