A PHP Error was encountered

Severity: Warning

Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests

Filename: helpers/my_audit_helper.php

Line Number: 176

Backtrace:

File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents

File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url

File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3122
Function: getPubMedXML

File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global

File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword

File: /var/www/html/index.php
Line: 316
Function: require_once

U-Net-based computed tomography quantification of viral pneumonia can predict fibrotic interstitial lung abnormalities at 3-month follow-up. | LitMetric

AI Article Synopsis

  • A study was conducted to evaluate how well quantitative CT features can predict fibrotic interstitial lung abnormalities (ILAs) in patients three months after COVID-19 infection.
  • Data was gathered from two groups: one from a fever clinic/emergency department and the other from patients hospitalized with COVID-19 pneumonia, and machine learning techniques were used for analysis.
  • Results showed that factors such as pneumonia volume, consolidation volume, ground-glass opacity volume, and CT scores were significant predictors of fibrotic ILAs, displaying reliable predictive validity in both training and validation datasets.

Article Abstract

Background: Given the high prevalence of fibrotic interstitial lung abnormalities (ILAs) post-COVID-19, this study aims to evaluate the effectiveness of quantitative CT features in predicting fibrotic ILAs at 3-month follow-up.

Methods: This retrospective study utilized cohorts from distinct clinical settings: the training dataset comprised individuals presenting at the fever clinic and emergency department, while the validation dataset included patients hospitalized with COVID-19 pneumonia. They were classified into fibrotic group and nonfibrotic group based on whether the fibrotic ILAs were present at follow-up. A U-Net-based AI tool was used for quantification of both pneumonia lesions and pulmonary blood volumes. Receiver operating characteristic (ROC) curve analysis and multivariate analysis were used to assess their predictive abilities for fibrotic ILAs.

Results: Among the training dataset, 122 patients (mean age of 68 years ±16 [standard deviation], 73 men), 55.74% showed fibrotic ILAs at 3-month follow-up. The multivariate analysis identified the pneumonia volume [PV, odd ratio (OR) 3.28, 95% confidence interval (CI): 1.20-9.31,  = 0.02], consolidation volume (CV, OR 3.77, 95% CI: 1.37-10.75,  = 0.01), ground-glass opacity volume (GV, OR 3.38, 95% CI: 1.26-9.38,  = 0.02), pneumonia mass (PM, OR 3.58, 95% CI: 1.28-10.46,  = 0.02), and the CT score (OR 12.06, 95% CI: 3.15-58.89,  < 0.001) as independent predictors of fibrotic ILAs, and all quantitative parameters were as effective as CT score (all  > 0.05). And the area under the curve (AUC) values were PV (0.79), GV (0.78), PM (0.79), CV (0.80), and the CT score (0.77). The validation dataset, comprising 45 patients (mean age 67.29 ± 14.29 years, 25 males) with 57.78% showing fibrotic ILAs at follow-up, confirmed the predictive validity of these parameters with AUC values for PV (0.86), CV (0.90), GV (0.83), PM (0.88), and the CT score (0.85). Additionally, the percentage of blood volume in vessels <5mm relative to the total pulmonary blood volume (BV5%) was significantly lower in patients with fibrotic ILAs ( = 0.048) compared to those without.

Conclusion: U-Net based quantification of pneumonia lesion and BV5% on baseline CT scan has the potential to predict fibrotic ILAs at follow-up in COVID-19 patients.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11471527PMC
http://dx.doi.org/10.3389/fmed.2024.1435337DOI Listing

Publication Analysis

Top Keywords

fibrotic ilas
16
fibrotic
8
fibrotic interstitial
8
interstitial lung
8
lung abnormalities
8
3-month follow-up
8
ilas 3-month
8
training dataset
8
validation dataset
8
ilas follow-up
8

Similar Publications

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!