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<!-- Epidemiology and prognosis -->
About 5 million people are affected globally.<ref name=Melt2008>{{cite journal |vauthors=Meltzer EB, Noble PW | title = Idiopathic pulmonary fibrosis | journal = Orphanet Journal of Rare Diseases| volume = 3 | issue = 1 | pages = 8| year = 2008 | pmid = 18366757 | pmc = 2330030 | doi = 10.1186/1750-1172-3-8 }}</ref> The disease newly occurs in about 12 per 100,000 people per year.<ref name=Fer2018/> Those in their 60s and 70s are most commonly affected.<ref name=Fer2018/> Males are affected more often than females.<ref name=Fer2018/> Average [[life expectancy]] following diagnosis is about four years.<ref name=NIH2018/> Updated international guidelines were published in 2022, which some simplification in diagnosis and the removal of antacids as a possible adjunct therapy.<ref>{{Cite journal |
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[[Stem cell therapy|Stem cell therapies]] for IPF are an area of research.<ref>{{cite journal | vauthors = Liu M, Ren D, Wu D, Zheng J, Tu W | title = Stem Cell and Idiopathic Pulmonary Fibrosis: Mechanisms and Treatment | journal = Current Stem Cell Research & Therapy | volume = 10 | issue = 6 | pages = 466–76 | year = 2015 | pmid = 25986617 | doi = 10.2174/1574888X10666150519092639 }}</ref><ref>{{cite web|title=Stem cell therapy for lung fibrosis conditions |url= https://www.sciencedaily.com/releases/2017/08/170803091928.htm |website=Sciencedaily.com |access-date=8 June 2018}}</ref>
A [[machine learning]] algorithm has been proposed that discovers subtle patterns in individual history of medical encounters to reliably estimate the risk of a future IPF diagnosis, up to four years before current medical practice.<ref name=onishenko2022>{{cite journal | vauthors=Onishchenko D, Marlowe RJ, Ngufor, Faust LJ, Limper AH, Hunninghake GM, Martinez FJ, Chattopadhyay I | title = Screening for idiopathic pulmonary fibrosis using comorbidity signatures in electronic health records. | journal = Nature Medicine | year = 2022 | volume = 28 | issue = 10 | pages = 2107–2116 | pmid = 36175678 | doi = 10.1038/s41591-022-02010-y | s2cid = 252622515 | url=https://www.nature.com/articles/s41591-022-02010-y}}</ref> The algorithm outputs a score (ZCoR) using medical history on file with no new tests, and might be deployable as a universal IPF screening tool in primary care. ZCoR has been trained and validated on nearly 3 million patients across multiple databases, achieving high predictive performance in out-of-sample data (positive likelihood ratio > 30 with 99% specificity). The authors conclude that past respiratory disorders maximally contribute to IPF risk, followed by known IPF comorbidities, metabolic diseases, cardiovascular abnormalities, and diseases of the eye, with the overall pattern of the importance ranking substantially invariant across the sexes.
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