Literature DB >> 22222134

The future of pulmonary function testing.

Neil R MacIntyre1.   

Abstract

The pulmonary function lab of today is heavily focused on describing pathophysiology and quantifying the extent of disease. As we move forward, it is important that the results of pulmonary function tests go beyond this and be linked to important outcomes that truly affect clinical decision making. To get there, improvements in device performance are required, high quality technicians are critical, and properly trained interpreting clinicians with good reference standards are mandatory. Moreover, as accessibility to these tests is increased, it is important that quality metrics remain intact. There is a wide array of novel tests that might be performed by pulmonary function labs in the future. These range from modification of current technologies to brand new technologies that are still in early development. Examples include exhaled breath analysis, sophisticated analyses of lung mechanics and gas exchange, cardiac and tissue oxygenation assessments, and imaging technologies. Adoption of any new technology will require, even more than today, clear evidence that the new technology is a real adjunct to clinical decision making.

Mesh:

Substances:

Year:  2012        PMID: 22222134     DOI: 10.4187/respcare.01422

Source DB:  PubMed          Journal:  Respir Care        ISSN: 0020-1324            Impact factor:   2.258


  6 in total

1.  Respiratory Oscillometry in Chronic Obstructive Pulmonary Disease: Association with Functional Capacity as Evaluated by Adl Glittre Test and Hand Grip Strength Test.

Authors:  Caroline Oliveira Ribeiro; Agnaldo José Lopes; Pedro Lopes de Melo
Journal:  Int J Chron Obstruct Pulmon Dis       Date:  2022-05-04

2.  Combined forced oscillation and fractional-order modeling in patients with work-related asthma: a case-control study analyzing respiratory biomechanics and diagnostic accuracy.

Authors:  Fábio Augusto D Alegria Tuza; Paula Morisco de Sá; Hermano A Castro; Agnaldo José Lopes; Pedro Lopes de Melo
Journal:  Biomed Eng Online       Date:  2020-12-09       Impact factor: 2.819

3.  Machine learning associated with respiratory oscillometry: a computer-aided diagnosis system for the detection of respiratory abnormalities in systemic sclerosis.

Authors:  Domingos S M Andrade; Luigi Maciel Ribeiro; Agnaldo J Lopes; Jorge L M Amaral; Pedro L Melo
Journal:  Biomed Eng Online       Date:  2021-03-25       Impact factor: 2.819

Review 4.  The Use and Utility of Machine Learning in Achieving Precision Medicine in Systemic Sclerosis: A Narrative Review.

Authors:  Francesco Bonomi; Silvia Peretti; Gemma Lepri; Vincenzo Venerito; Edda Russo; Cosimo Bruni; Florenzo Iannone; Sabina Tangaro; Amedeo Amedei; Serena Guiducci; Marco Matucci Cerinic; Silvia Bellando Randone
Journal:  J Pers Med       Date:  2022-07-23

5.  Application of Machine Learning in Pulmonary Function Assessment Where Are We Now and Where Are We Going?

Authors:  Paresh C Giri; Anand M Chowdhury; Armando Bedoya; Hengji Chen; Hyun Suk Lee; Patty Lee; Craig Henriquez; Neil R MacIntyre; Yuh-Chin T Huang
Journal:  Front Physiol       Date:  2021-06-24       Impact factor: 4.566

6.  Safety and use of pulmonary function tests: a retrospective study from a single center over seven years' clinical practice.

Authors:  Fei Li; Zhi-Wen Huang; Xiao-Fei Wang; Hui-Wen Xu; Hua Yu; Yan-Bin Chen; Jian-An Huang; Jia-Jia Wang; Wei Lei
Journal:  BMC Pulm Med       Date:  2019-12-21       Impact factor: 3.317

  6 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.