Literature DB >> 33778502

Predicting Outcome in Idiopathic Pulmonary Fibrosis: Addition of Fibrotic Score at Thin-Section CT of the Chest to Gender, Age, and Physiology Score Improves the Prediction Model.

Anurag Chahal1, Roozbeh Sharif1, Jubal Watts1, Joao de Andrade1, Tracy Luckhardt1, Young-Il Kim1, Rekha Ramchandran1, Sushilkumar Sonavane1.   

Abstract

PURPOSE: To assess the impact of adding thin-section CT-derived semiquantitative fibrotic score to gender, age, and physiology (GAP) model for predicting survival in idiopathic pulmonary fibrosis (IPF).
MATERIALS AND METHODS: In this retrospective study of 194 patients with IPF, primary outcome was transplant-free survival. Two thoracic radiologists visually estimated the percentage of reticulation and honeycombing at baseline thin-section CT, which were added to give fibrotic score. For analysis, fibrotic score cutoff (x) determined by using receiver operating characteristic analysis categorized patients into group A (<x) and group B (≥x). Another categorization based on GAP score created group 1 (score 0-3) and group 2 (score >3). Combining the above categories gave four groups (A1, A2, B1, B2). Kaplan-Meier survival analysis was performed with comparison statistics (log-rank test), and hazard ratios were calculated by using the Cox model.
RESULTS: The study patients included 141 men (72.7%), with average age of 66.1 years ± 9.1 (standard deviation). Eighty-four patients (43.3%) has stage I disease with a median follow up of 3.3 years. The interobserver agreement for thin-section CT fibrotic score was substantial (83.3%; κ = 0.64). The optimal cutoff for fibrotic score was 25% (x), with area under the curve of 0.654 (95% confidence interval [CI]: 0.569, 0.74). Survival for group A1 was significantly better than in the other three groups (P < .001). The hazard ratios for respective groups were as follows: B1 was 4.03 (95% CI: 2.02, 8.07), A2 was 4.10 (95% CI: 1.89, 8.87), and B2 was 5.62 (95% CI: 2.86, 11.06) (P < .001 for all). Within the group with GAP score less than or equal to 3 (A1, B1), participants with higher fibrotic score (B1) had four times the increased risk of death or transplantation (P < .001).
CONCLUSION: Incorporating semiquantitative fibrotic score from thin-section CT to GAP score provides an improved prediction model for survival in idiopathic pulmonary fibrosis.© RSNA, 2019See also the commentary by Chung in this issue. 2019 by the Radiological Society of North America, Inc.

Entities:  

Year:  2019        PMID: 33778502      PMCID: PMC7970098          DOI: 10.1148/ryct.2019180029

Source DB:  PubMed          Journal:  Radiol Cardiothorac Imaging        ISSN: 2638-6135


  33 in total

1.  Ascertainment of individual risk of mortality for patients with idiopathic pulmonary fibrosis.

Authors:  Roland M du Bois; Derek Weycker; Carlo Albera; Williamson Z Bradford; Ulrich Costabel; Alex Kartashov; Lisa Lancaster; Paul W Noble; Ganesh Raghu; Steven A Sahn; Javier Szwarcberg; Michiel Thomeer; Dominique Valeyre; Talmadge E King
Journal:  Am J Respir Crit Care Med       Date:  2011-08-15       Impact factor: 21.405

2.  Predicting survival across chronic interstitial lung disease: the ILD-GAP model.

Authors:  Christopher J Ryerson; Eric Vittinghoff; Brett Ley; Joyce S Lee; Joshua J Mooney; Kirk D Jones; Brett M Elicker; Paul J Wolters; Laura L Koth; Talmadge E King; Harold R Collard
Journal:  Chest       Date:  2014-04       Impact factor: 9.410

Review 3.  Physiology of the lung in idiopathic pulmonary fibrosis.

Authors:  Laurent Plantier; Aurélie Cazes; Anh-Tuan Dinh-Xuan; Catherine Bancal; Sylvain Marchand-Adam; Bruno Crestani
Journal:  Eur Respir Rev       Date:  2018-01-24

4.  Quantitative CT analysis of honeycombing area in idiopathic pulmonary fibrosis: Correlations with pulmonary function tests.

Authors:  Hiroaki Nakagawa; Yukihiro Nagatani; Masashi Takahashi; Emiko Ogawa; Nguyen Van Tho; Yasushi Ryujin; Taishi Nagao; Yasutaka Nakano
Journal:  Eur J Radiol       Date:  2015-11-07       Impact factor: 3.528

5.  High-resolution CT findings in fibrotic idiopathic interstitial pneumonias with little honeycombing: serial changes and prognostic implications.

Authors:  Ho Yun Lee; Kyung Soo Lee; Yeon Joo Jeong; Jung Hwa Hwang; Hyo Jin Kim; Man Pyo Chung; Joungho Han
Journal:  AJR Am J Roentgenol       Date:  2012-11       Impact factor: 3.959

6.  Assessment of prognosis of patients with idiopathic pulmonary fibrosis by computer-aided analysis of CT images.

Authors:  Tae Iwasawa; Akira Asakura; Fumikazu Sakai; Tetu Kanauchi; Toshiyuki Gotoh; Takashi Ogura; Takuya Yazawa; Junichi Nishimura; Tomio Inoue
Journal:  J Thorac Imaging       Date:  2009-08       Impact factor: 3.000

7.  Idiopathic pulmonary fibrosis: physiologic tests, quantitative CT indexes, and CT visual scores as predictors of mortality.

Authors:  Alan C Best; Jiangfeng Meng; Anne M Lynch; Carmen M Bozic; David Miller; Gary K Grunwald; David A Lynch
Journal:  Radiology       Date:  2008-01-30       Impact factor: 11.105

8.  Prognostic determinants among clinical, thin-section CT, and histopathologic findings for fibrotic idiopathic interstitial pneumonias: tertiary hospital study.

Authors:  Kyung Min Shin; Kyung Soo Lee; Man Pyo Chung; Joungho Han; Young A Bae; Tae Sung Kim; Myung Jin Chung
Journal:  Radiology       Date:  2008-08-05       Impact factor: 11.105

9.  High-resolution CT scoring system-based grading scale predicts the clinical outcomes in patients with idiopathic pulmonary fibrosis.

Authors:  Keishi Oda; Hiroshi Ishimoto; Kazuhiro Yatera; Keisuke Naito; Takaaki Ogoshi; Kei Yamasaki; Tomotoshi Imanaga; Toru Tsuda; Hiroyuki Nakao; Toshinori Kawanami; Hiroshi Mukae
Journal:  Respir Res       Date:  2014-01-30

10.  Clinical Course and Changes in High-Resolution Computed Tomography Findings in Patients with Idiopathic Pulmonary Fibrosis without Honeycombing.

Authors:  Hiroyoshi Yamauchi; Masashi Bando; Tomohisa Baba; Kensuke Kataoka; Yoshihito Yamada; Hiroshi Yamamoto; Atsushi Miyamoto; Soichiro Ikushima; Takeshi Johkoh; Fumikazu Sakai; Yasuhiro Terasaki; Akira Hebisawa; Yoshinori Kawabata; Yukihiko Sugiyama; Takashi Ogura
Journal:  PLoS One       Date:  2016-11-09       Impact factor: 3.240

View more
  3 in total

1.  Traction Bronchiectasis/Bronchiolectasis on CT Scans in Relationship to Clinical Outcomes and Mortality: The COPDGene Study.

Authors:  Akinori Hata; Takuya Hino; Rachel K Putman; Masahiro Yanagawa; Tomoyuki Hida; Aravind A Menon; Osamu Honda; Yoshitake Yamada; Mizuki Nishino; Tetsuro Araki; Vladimir I Valtchinov; Masahiro Jinzaki; Hiroshi Honda; Kousei Ishigami; Takeshi Johkoh; Noriyuki Tomiyama; David C Christiani; David A Lynch; Raúl San José Estépar; George R Washko; Michael H Cho; Edwin K Silverman; Gary M Hunninghake; Hiroto Hatabu
Journal:  Radiology       Date:  2022-05-31       Impact factor: 29.146

2.  CT quantification of the heterogeneity of fibrosis boundaries in idiopathic pulmonary fibrosis.

Authors:  Junghoan Park; Julip Jung; Soon Ho Yoon; Helen Hong; Hyungjin Kim; Heekyung Kim; Jeong-Hwa Yoon; Jin Mo Goo
Journal:  Eur Radiol       Date:  2021-01-13       Impact factor: 7.034

3.  Interstitial lung disease is a dominant feature in patients with circulating myositis-specific antibodies.

Authors:  Abhinav K Misra; Nathan L Wong; Terrance T Healey; Edward V Lally; Barry S Shea
Journal:  BMC Pulm Med       Date:  2021-11-14       Impact factor: 3.317

  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.