interstitial fibrosis lung :: Article CreatorSpotlight On Interstitial Lung Disease Diagnosis And Treatment On ILD Day, Sept. 18
CHICAGO, Sept. 9, 2024 /PRNewswire/ -- Nine leading patient organizations are uniting to present the fourth annual ILD Day on Wednesday, Sept. 18, to raise awareness and understanding of interstitial lung disease (ILD) and pulmonary fibrosis (PF). ILD encompasses a large group of more than 200 diseases characterized by inflammation and/or scarring in the lungs, making it difficult to breathe and get oxygen to the bloodstream.
(PRNewsfoto/The Pulmonary Fibrosis Foundation)
"Interstitial lung disease is a debilitating condition that's often difficult to diagnose, making early detection crucial," said Scott Staszak, President and CEO of the Pulmonary Fibrosis Foundation. "If you or a loved one experiences a persistent dry cough, shortness of breath and fatigue, see a pulmonologist immediately. Treatments to slow the progression, along with resources and support, are available."
More than 250,000 Americans are living with ILD and 50,000 new cases are diagnosed annually. Pulmonary fibrosis can be seen in many types of ILD, and the damage caused by ILD can be irreversible and worsen over time.
Symptoms and Risk Factors of ILDThe most common symptoms of ILD include shortness of breath, dry cough and fatigue. Other symptoms include chest discomfort, "clubbing" of the fingertips, loss of appetite and unexplained weight loss.
Causes of ILD include the use of certain medications, radiation to the chest, and environmental and occupational exposures. In addition, patients with some diseases, such as rheumatoid arthritis, scleroderma, myositis, including dermatomyositis and polymyositis (DM and PM), sarcoidosis and Sjögren's, may develop ILD. A specific form of ILD, namely idiopathic pulmonary fibrosis (IPF), primarily occurs in older individuals.
Educational WebinarAn educational webinar, "The journey to diagnosis: Process, evaluation, and your care team," will be held at 12 p.M. CDT on Wednesday, Sept. 18. The presentation will address how doctors recognize ILD and find the right diagnosis for each individual. Important information about the roles of your care team and how to advocate for yourself will be provided. The webinar will be presented by Dr. Sonye Danoff, pulmonologist with Johns Hopkins Medicine and Senior Medical Advisor, PFF Care Center Network. Registration is available here. The webinar will be recorded and published on the PFF YouTube channel.
ILD Day is a collaboration between the Pulmonary Fibrosis Foundation, Arthritis Foundation, Foundation for Sarcoidosis Research, The Myositis Association, PF Warriors, National Scleroderma Foundation, Scleroderma Research Foundation, Sjögren's Foundation and Wescoe Foundation for Pulmonary Fibrosis.
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To register for the ILD Day webinar or for more information about ILD, visit ILDDay.Org.
About ILD DayILD Day was created to drive awareness of interstitial lung disease (ILD) and is recognized annually in September. It is presented by a collaboration of nine organizations representing patients affected by interstitial lung disease: Pulmonary Fibrosis Foundation, Arthritis Foundation, Foundation for Sarcoidosis Research, The Myositis Association, PF Warriors, National Scleroderma Foundation, Scleroderma Research Foundation, Sjögren's Foundation, and Wescoe Foundation for Pulmonary Fibrosis. For more information, visit ILDDay.Org.
About the Pulmonary Fibrosis FoundationThe mission of the Pulmonary Fibrosis Foundation is to accelerate the development of new treatments and ultimately a cure for pulmonary fibrosis. Until this goal is achieved, the PFF is committed to advancing improved care of patients with PF and providing unequaled support and education resources for patients, caregivers, family members, and health care providers. The PFF has a four-star rating from Charity Navigator and is an accredited charity by the Better Business Bureau (BBB) Wise Giving Alliance. The Foundation has met all of the requirements of the National Health Council Standards of Excellence Certification Program® and has earned the Guidestar Platinum Seal of Transparency. For more information, visit pulmonaryfibrosis.Org or call 844.TalkPFF (844.825.5733).
Contact: Dorothy Coyle 773-332-6201
Interstitial lung disease (ILD) affects more than 250,000 Americans. The fourth annual ILD Day will take place on September 18 to drive awareness of ILD. This year's ILD Day webinar, The Journey to Diagnosis: Process, Evaluation and Your Care Team , will take place from 12-1 p.M. CT. Visit ildday.Org for information and to register for the webinar.
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SOURCE The Pulmonary Fibrosis Foundation
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Identifying Risk Factors For Progressive Pulmonary Fibrosis In Interstitial Lung Disease: A Prognostic Nomogram Approach
A nomogram based on four pulmonary factors is helpful but not a substitute for clinical judgment, say the authors.
Reliable prognostic tools to help shed light on the critical clinical characteristics associated with progressive pulmonary fibrosis in interstitial lung disease patients would help guide treatment
A recent study aimed to identify these clinical characteristics, with the goal of developing a prognostic nomogram model for clinical application.
A group of China-based researchers conducted the study, which was published in Frontiers in Medicine earlier this year. Jia-Jia Fin of the Department of Pulmonary Disease at Sunsimiao Hospital in Shanxi, China, is listed as the first author.
The researchers enrolled interstitial lung disease patients with relatively comprehensive clinical data in the retrospective study and assessed the incidence of progressive pulmonary fibrosis over the course of a year by utilizing the collected demographics, laboratory data, high-resolution computed tomography and pulmonary function test results.
In all, 307 patients from January 2015 to December 2022 were included in the study.
"We used a training cohort of interstitial lung disease patients to identify early predictors of progressive pulmonary fibrosis and then validated them in an internal validation cohort and subsets of interstitial lung disease patients using a multivariable logistic regression analysis," one of study authors said. "A prognostic nomogram was formulated based on these predictors, and the accuracy and efficiency were evaluated using the area under the receiver operating characteristic curve, calibration plot and decision curve analysis."
Through the data, the authors developed and validated a prognostic nomogram model for predicting the risk of progressive pulmonary fibrosis in interstitial lung disease patients based on four factors: diffusing capacity of the lungs for carbon monoxide, complicated pneumonia, the Medical Research Council dyspnea score and high-resolution computed tomography score. They found that combined pneumonia, low baseline diffusing capacity of the lungs for carbon monoxide, a high Medical Research Council dyspnea score and a high-resolution computed tomography score were significant predictors of progressive pulmonary fibrosis.
Almost 40% of the patients in their study experienced progression of pulmonary fibrosis despite existing therapies.
"The nomogram showed good discrimination and calibration in both the training and validation cohorts, and it outperformed every single predictor in terms of accuracy and efficiency," the authors wrote in their paper. "We believe the nomogram could help clinicians stratify lung disease patients into different risk groups and tailor their management accordingly."
The study also identified certain features of lung disease patients who may develop a progressive fibrosis phenotype, including those with connective tissue disease-related interstitial lung disease and non-IPF idiopathic interstitial pneumonia.
The authors cautioned that there are some limitations to their findings, including a retrospective design, a single-center setting and the potential selection bias. With that in mind, they suggest longitudinal studies should be run to confirm and optimize the predictive model in different populations and settings.
"The nomogram is a useful tool for risk assessment and decision-making, but it is not a substitute for clinical judgment or individualized care," Fin and the co-authors authors wrote.
Machine Learning Model Accurately Predicts Interstitial Lung Abnormalities On CT Scans, Claims Study
Researchers have found that machine learning models can correctly predict the probability of interstitial lung abnormalities (ILAs) from computed tomography scans, a breakthrough that could help in devising early detection and treatment strategies for lung diseases. Despite the clinical importance of ILAs, there has been a difficulty in their automated identification. This study was a development and performance test of machine learning models in predicting the probabilities of ILA based on CT images from a large dataset provided by the Boston Lung Cancer Study. The study was recently published in the journal Radiology by Akinori H. And colleagues.
Interstitial lung abnormalities, often incidentally found in computed tomography scans, emerge due to their significant clinical implications, including a relation to higher susceptibility for pulmonary fibrosis and other diseases affecting the lungs. However, fully automated detection of ILAs has not yet been realized, and therefore the development of reliable predictive models becomes necessary. Presently, this study was a key attempt at devising and evaluating machine learning models that could predict the probability of the occurrence of ILAs in CT scans for assisting diagnosis in a clinical setting with efficiency.
It includes 1,382 CTs of the Boston Lung Cancer Study, collected from February 2004 to June 2017. The cohort consisted of cases with an average patient age of 67 years and 759 females. Two radiologists and one pulmonologist visually assessed the truth about the presence of ILAs. The proposed system is based on a stepwise methodology in the development of automated ILA probability prediction models.
The two key components of this system were; the model generated an ILA probability for each section of the CT scan and the case inference model combined probabilities from the section inference model to output a case-level ILA probability. This study tested the machine learning classifiers on SVM, RF, and CNN. These undetermined sections and cases were assessed by both two- and three-label methods. Receiver operating characteristic analysis was used to assess the performance of the model. The AUC of the ROC-the accuracy metric of this model-is depicted below.
• Among the 1,382 CT scans evaluated:
• Of these, 8% or 104 scans were labeled to contain ILAs.
• 36% or 492 scans were labeled as indeterminate for the presence of ILAs.
• 57%, or 786 scans, were labeled without evidence of ILAs.
• Datasets Training data 96 scans; 48 with ILA
• Validation set: 24 scans; 12 with ILA
• Testing data 1,262 scans; 44 with ILA
• Among the evaluated models, the section inference model with a three-label method combined with the case inference model with an RF classifier using the two-label method reached the highest AUC of 0.87.
• The result shows substantial performance in predicting the probability of ILAs from CT scans, therefore suggesting that this model can be highly useful in a clinical setting.
These findings suggest that machine learning may be useful for automated ILA detection, which would be very important for the early diagnosis and management of lung diseases. The high AUC generated by the model further underlines its accuracy and reliability; it therefore will be worth employing in routine clinical practice. This should hopefully translate into the capability for timely interventions hence improving the outcomes of patients who are at risk of developing more serious lung conditions.
Reference:
Hata, A., Aoyagi, K., Hino, T., Kawagishi, M., Wada, N., Song, J., Wang, X., Valtchinov, V. I., Nishino, M., Muraguchi, Y., Nakatsugawa, M., Koga, A., Sugihara, N., Ozaki, M., Hunninghake, G. M., Tomiyama, N., Li, Y., Christiani, D. C., Hatabu, H., & Weintraub, E. (2024). Automated interstitial lung abnormality probability prediction at CT: A stepwise machine learning approach in the Boston lung cancer study. Radiology, 312(3). Https://doi.Org/10.1148/radiol.233435
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