Identification of Idiopathic Pulmonary Fibrosis and Prediction of Disease Severity via Machine Learning Analysis of Comprehensive Metabolic Panel and Complete Blood Count Data.

BACKGROUND: Diagnosis of idiopathic pulmonary fibrosis (IPF) typically relies on high-resolution computed tomography imaging (HRCT) or histopathology, while monitoring disease severity is done via frequent pulmonary function testing (PFT). More reliable and convenient methods of diagnosing fibrotic interstitial lung disease (ILD) type and monitoring severity would allow for early identification and enhance current therapeutic interventions. This study tested the hypothesis that a machine learning (ML) ensemble analysis of comprehensive metabolic panel (CMP) and complete blood count (CBC) data can accurately distinguish IPF from connective tissue disease ILD (CTD-ILD) and predict disease severity as seen with PFT. METHODS: Outpatient data with diagnosis of IPF or CTD-ILD (n = 103 visits by 53 patients) were analyzed via ML methodology to evaluate (1) IPF vs CTD-ILD diagnosis; (2) %predicted Diffusing Capacity of Lung for Carbon Monoxide (DLCO) moderate or mild vs severe; (3) %predicted Forced Vital Capacity (FVC) moderate or mild vs severe; and (4) %predicted FVC mild vs moderate or severe. RESULTS: ML methodology identified IPF from CTD-ILD with AUCTEST = 0.893, while PFT was classified as DLCO moderate or mild vs severe with AUCTEST = 0.749, FVC moderate or mild vs severe with AUCTEST = 0.741, and FVC mild vs moderate or severe with AUCTEST = 0.739. Key features included albumin, alanine transaminase, %lymphocytes, hemoglobin, %eosinophils, white blood cell count, %monocytes, and %neutrophils. CONCLUSION: Analysis of CMP and CBC data via proposed ML methodology offers the potential to distinguish IPF from CTD-ILD and predict severity on associated PFT with accuracy that meets or exceeds current clinical practice.

Disease diagnosis and severity classification in pulmonary fibrosis using carbonyl volatile organic compounds in exhaled breath.

BACKGROUND: Pathophysiological conditions underlying pulmonary fibrosis remain poorly understood. Exhaled breath volatile organic compounds (VOCs) have shown promise for lung disease diagnosis and classification. In particular, carbonyls are a byproduct of oxidative stress, associated with fibrosis in the lungs. To explore the potential of exhaled carbonyl VOCs to reflect underlying pathophysiological conditions in pulmonary fibrosis, this proof-of-concept study tested the hypothesis that volatile and low abundance carbonyl compounds could be linked to diagnosis and associated disease severity. METHODS: Exhaled breath samples were collected from outpatients with a diagnosis of Idiopathic Pulmonary Fibrosis (IPF) or Connective Tissue related Interstitial Lung Disease (CTD-ILD) with stable lung function for 3 months before enrollment, as measured by pulmonary function testing (PFT) DLCO (%), FVC (%) and FEV1 (%). A novel microreactor was used to capture carbonyl compounds in the breath as direct output products. A machine learning workflow was implemented with the captured carbonyl compounds as input features for classification of diagnosis and disease severity based on PFT (DLCO and FVC normal/mild vs. moderate/severe; FEV1 normal/mild/moderate vs. moderately severe/severe). RESULTS: The proposed approach classified diagnosis with AUROC=0.877 ± 0.047 in the validation subsets. The AUROC was 0.820 ± 0.064, 0.898 ± 0.040, and 0.873 ± 0.051 for disease severity based on DLCO, FEV1, and FVC measurements, respectively. Eleven key carbonyl VOCs were identified with the potential to differentiate diagnosis and to classify severity. CONCLUSIONS: Exhaled breath carbonyl compounds can be linked to pulmonary function and fibrotic ILD diagnosis, moving towards improved pathophysiological understanding of pulmonary fibrosis.

Detection and Monitoring of Interstitial Lung Disease in Patients with Systemic Sclerosis.

PURPOSE OF REVIEW: Interstitial lung disease (ILD) is a common manifestation of systemic sclerosis (SSc). We explore the importance of early detection, monitoring, and management of SSc-ILD. RECENT FINDINGS: All patients with SSc are at risk of ILD and should be screened for ILD at diagnosis using a high-resolution computed tomography (HRCT) scan. Some patients with SSc-ILD develop a progressive phenotype characterized by worsening fibrosis on HRCT, decline in lung function, and early mortality. To evaluate progression and inform treatment decisions, regular monitoring is important and should include pulmonary function testing, evaluation of symptoms and quality of life, and, where indicated, repeat HRCT. Multidisciplinary discussion enables comprehensive evaluation of the available information and its implications for management. The first-line treatment for SSc-ILD is usually immunosuppression. The antifibrotic drug nintedanib has been approved for slowing lung function decline in patients with SSc-ILD. Optimal management of patients with SSc-ILD requires a multidisciplinary and patient-centered approach.

Intracranial hemorrhage in a young COVID-19 patient.

COVID-19 patients are increasingly understood to develop multisystem manifestations, including neurologic involvement. We report the case of a 42-year old COVID-19 positive patient with a fatal intracerebral hemorrhage (ICH). The patient presented with fever and dyspnea, requiring intubation due to medical complications. After prolonged sedation and anticoagulation, the patient suddenly developed bilaterally fixed and dilated pupils, caused by a right-sided intracranial hemorrhage with uncal herniation. The course of this case illustrates the delicate balance between hypercoagulability and coagulation factor depletion; especially in the intubated and sedated patient, in whom regular neurological assessments are impeded. As we expand our understanding of the neurological ramifications of COVID-19, clinicians need to be increasingly aware of the precarious coagulation balance.