Prediction of long-term mortality by using machine learning models in Chinese patients with connective tissue disease-associated interstitial lung disease

Diagnostic Approach to Interstitial Lung Diseases Associated with Connective Tissue Diseases

Interstitial lung disorders are a group of respiratory diseases characterized by interstitial compartment infiltration, varying degrees of infiltration, and fibrosis, with or without small airway involvement. Although some are idiopathic (e.g., idiopathic pulmonary fibrosis, idiopathic interstitial pneumonias, and sarcoidosis), the great majority have an underlying etiology, such as systemic autoimmune rheumatic disease (SARD, also called Connective Tissue Diseases or CTD), inhalational exposure to organic matter, medications, and rarely, genetic disorders. This review focuses on diagnostic approaches in interstitial lung diseases associated with SARDs. To make an accurate diagnosis, a multidisciplinary, personalized approach is required, with input from various specialties, including pulmonary, rheumatology, radiology, and pathology, to reach a consensus. In a minority of patients, a definitive diagnosis cannot be established. Their clinical presentations and prognosis can be variable even within subsets of SARDs.

Development of a novel predictive model for interstitial lung disease in ANCA-associated vasculitis prognostications within the Chinese population

Antineutrophil cytoplasmic antibody vasculitis-associated interstitial lung disease (AAV-ILD) is a potentially life-threatening disease. However, very little research has been done on the condition's mortality risk. Hence, our objective is to find out the factors influencing the prognosis of AAV-ILD and employ these findings to create a nomogram model. Patients with AAV-ILD who received treatment at the First Affiliated Hospital of Zhengzhou University during the period from March 1, 2011, to April 1, 2022 were selected for this research. The development of nomogram entailed a synergistic integration of univariate, Lasso, and multivariate Cox regression analyses. Internal validation ensued through bootstrap techniques involving 1000 re-sampling iterations. Discrimination and calibration were assessed utilizing Harrell's C-index, receiver operating characteristic (ROC) curve, and calibration curve. Model performance was evaluated through integrated discrimination improvement (IDI), net reclassification improvement (NRI), and likelihood ratio test. The net benefit of the model was evaluated using decision curve analysis (DCA). A cohort comprising 192 patients was enrolled for analysis. Throughout observation period, 32.29% of the population died. Key factors such as cardiac involvement, albumin, smoking history, and age displayed substantial prognostic relevance in AAV-ILD. These factors were incorporated to craft a predictive nomogram. Impressively, the model exhibited robust performance, boasting a Harrell's C index of 0.826 and an AUC of 0.940 (95% CI 0.904-0.976). The calibration curves depicted a high degree of harmony between predicted outcomes and actual observations. Significantly enhancing discriminative ability compared to the ILD-GAP model, the nomogram was validated through the IDI, NRI, and likelihood ratio test. DCA underscored the superior predictive value of the predictive model over the ILD-GAP model. The internal validation further affirmed this efficacy, with a mean Harrell's C-index of 0.815 for the predictive model. The nomogram model can be employed to predict the prognosis of patients with AAV-ILD. Moreover, the model performance is satisfactory. In the future, external datasets could be utilized for external validation.

Recent Advancements in Computed Tomography Assessment of Fibrotic Interstitial Lung Diseases

Interstitial lung disease (ILD) is a heterogeneous group of disorders with complex and varied imaging manifestations and prognosis. High-resolution computed tomography (HRCT) is the current standard-of-care imaging tool for ILD assessment. However, visual evaluation of HRCT is limited by interobserver variation and poor sensitivity for subtle changes. Such challenges have led to tremendous recent research interest in objective and reproducible methods to examine ILDs. Computer-aided CT analysis to include texture analysis and machine learning methods have recently been shown to be viable supplements to traditional visual assessment through improved characterization and quantification of ILDs. These quantitative tools have not only been shown to correlate well with pulmonary function tests and patient outcomes but are also useful in disease diagnosis, surveillance and management. In this review, we provide an overview of recent computer-aided tools in diagnosis, prognosis, and longitudinal evaluation of fibrotic ILDs, while outlining some of the pitfalls and challenges that have precluded further advancement of these tools as well as potential solutions and further endeavors.

A computed tomography-based radiomics nomogram for predicting overall survival in patients with connective tissue disease-associated interstitial lung disease

OBJECTIVES

Accurate prognostic prediction is beneficial for the management of patients with connective tissue disease-associated interstitial lung disease (CTD-ILD). The purpose of the present study was to develop and validate a nomogram using clinical features and computed tomography (CT) based radiomics features to predict overall survival (OS) in patients with CTD-ILD, and to assess the incremental prognostic value the radiomics might add to clinical risk factors.

MATERIALS & METHODS

Patients from two clinical centers with CTD-ILD were enrolled in the present retrospective study. A radiomics signature, a clinical model and a combined nomogram were developed and assessed in the cohorts. The incremental value of radiomics signature to the clinical independent risk factors in survival prediction was evaluated. The models were externally validated to evaluate the model generalization ability.

RESULTS

A total of 215 patients (mean age, 53 years ± 14 [standard deviation], 45 men) were evaluated. Patients with higher radiomics scores had higher mortality risk than those with lower radiomics scores (Hazard ratio, 12.396; 95% CI, 3.364-45.680; P < 0.001). The combined nomogram showed better predictive capability than the clinical model did with higher C-indices (0.800, 0.738, 0.742 vs. 0.747, 0.631, 0.587 in the training, internal- and external-validation cohort, respectively), time-AUCs and overall net-benefit.

CONCLUSION

The radiomics signature is a potential prognostic biomarker of CTD-ILD and add incremental value to the clinical independent risk factors. The combined nomogram can provide a more accurate estimation of OS than the clinical model for CTD-ILD patients.

CLINICAL RELEVANCE STATEMENT

The developed combined nomogram showed accurate prognostic prediction performance, which is beneficial for the management of CTD-ILD patients. It also proved radiomics could extract prognostic information from CT images.

Seeing through a glass darkly: Uncertainties about palliative care for patients with interstitial lung disease

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Prediction of mortality in pneumonia patients with connective tissue disease treated with glucocorticoids or/and immunosuppressants by machine learning

Objectives

The assessment of accurate mortality risk is essential for managing pneumonia patients with connective tissue disease (CTD) treated with glucocorticoids or/and immunosuppressants. This study aimed to construct a nomogram for predicting 90-day mortality in pneumonia patients using machine learning.

Methods

Data were obtained from the DRYAD database. Pneumonia patients with CTD were screened. The samples were randomly divided into a training cohort (70%) and a validation cohort (30%). A univariate Cox regression analysis was used to screen for prognostic variables in the training cohort. Prognostic variables were entered into the least absolute shrinkage and selection operator (Lasso) and a random survival forest (RSF) analysis was used to screen important prognostic variables. The overlapping prognostic variables of the two algorithms were entered into the stepwise Cox regression analysis to screen the main prognostic variables and construct a model. Model predictive power was assessed using the C-index, the calibration curve, and the clinical subgroup analysis (age, gender, interstitial lung disease, diabetes mellitus). The clinical benefits of the model were assessed using a decision curve analysis (DCA). Similarly, the C-index was calculated and the calibration curve was plotted to verify the model stability in the validation cohort.

Results

A total of 368 pneumonia patients with CTD (training cohort: 247; validation cohort: 121) treated with glucocorticoids or/and immunosuppressants were included. The univariate Cox regression analysis obtained 19 prognostic variables. Lasso and RSF algorithms obtained eight overlapping variables. The overlapping variables were entered into a stepwise Cox regression to obtain five variables (fever, cyanosis, blood urea nitrogen, ganciclovir treatment, and anti-pseudomonas treatment), and a prognostic model was constructed based on the five variables. The C-index of the construction nomogram of the training cohort was 0.808. The calibration curve, DCA results, and clinical subgroup analysis showed that the model also had good predictive power. Similarly, the C-index of the model in the validation cohort was 0.762 and the calibration curve had good predictive value.

Conclusion

In this study, the nomogram developed performed well in predicting the 90-day risk of death in pneumonia patients with CTD treated with glucocorticoids or/and immunosuppressants.

The current status and future of FDA-approved artificial intelligence tools in chest radiology in the United States

Artificial intelligence (AI) is becoming more widespread within radiology. Capabilities that AI algorithms currently provide include detection, segmentation, classification, and quantification of pathological findings. Artificial intelligence software have created challenges for the traditional United States Food and Drug Administration (FDA) approval process for medical devices given their abilities to evolve over time with incremental data input. Currently, there are 190 FDA-approved radiology AI-based software devices, 42 of which pertain specifically to thoracic radiology. The majority of these algorithms are approved for the detection and/or analysis of pulmonary nodules, for monitoring placement of endotracheal tubes and indwelling catheters, for detection of emergent findings, and for assessment of pulmonary parenchyma; however, as technology evolves, there are many other potential applications that can be explored. For example, evaluation of non-idiopathic pulmonary fibrosis interstitial lung diseases, synthesis of imaging, clinical and/or laboratory data to yield comprehensive diagnoses, and survival or prognosis prediction of certain pathologies. With increasing physician and developer engagement, transparency and frequent communication between developers and regulatory agencies, such as the FDA, AI medical devices will be able to provide a critical supplement to patient management and ultimately enhance physicians' ability to improve patient care.

Analysis of Clinical Characteristics of Connective Tissue Disease-Associated Interstitial Lung Disease in 161 Patients: A Retrospective Study

Objective

This study was conducted to retrospectively analyze the clinical characteristics of CTD-ILD patients to provide strategies for clinical management.

Methods

This study collected and analyzed the clinical data and relevant examination results of 161 patients diagnosed with CTD-ILD between 01 January 2018 and 01 January 2021.

Results

A total of 161 CTD-ILD patients, 74.53% were females and 25.47% were males, 32.92% were elderly and 67.08% were non-elderly. The main clinical symptoms of CTD-ILD patients were cough (44.72%), decreased activity tolerance (40.37%). RA-ILD was the most common one in the non-elderly and the elderly CTD-ILD patients (48.15% and 50.94%, respectively). Compared with non-elderly, elderly patients with CTD-ILD had a longer duration of CTD (p=0.04). However, fatigue (p=0.005), activity tolerance (p=0.029), the incidence of pulmonary diffusion dysfunction (p=0.047), and systemic immunoinflammatory index (SII, p=0.014) (platelet × NLR) were all decreased. The standard deviation of red blood cell distribution width (RDW) (p=0.024) and immunoglobulin (IgA) (p=0.033) was significantly increased. The smoking index was significantly higher in men than in women with CTD-ILD (p=0.000), but symptoms of reduced activity tolerance were less pronounced than in women (p<0.05). Elderly CTD-ILD patients (p=0.003) and women from non-elderly patients were prone to lower hemoglobin (p=0.000). Among the elderly, the lymphocyte ratio was more significantly elevated in female CTD-ILD patients than in males (p=0.018). In contrast, neutrophil to lymphocyte ratio (NLR) and SII were lower in female (p=0.038) than in male CTD-ILD patients (p=0.043).

Conclusion

CTD-ILD mainly affects non-elderly and women. Age may not be involved with decreased activity tolerance and increased lung function impairment in CTD-ILD patients. However, the elderly patients with CTD-ILD, especially the elderly female patients with low inflammation levels and high immune disorders, have a poor prognosis.