Title |
Precision Medicine In Action: The Impact Of Ivacaftor On Cystic Fibrosis–Related Hospitalizations
|
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Published in |
Health Affairs, May 2018
|
DOI | 10.1377/hlthaff.2017.1554 |
Pubmed ID | |
Authors |
Lisa B Feng, Scott D Grosse, Ridgely Fisk Green, Aliza K Fink, Gregory S Sawicki |
Abstract |
Cystic fibrosis is a life-threatening genetic disease that causes severe damage to the lungs. Ivacaftor, the first drug that targeted the underlying defect of the disease caused by specific mutations, is a sterling example of the potential of precision medicine. Clinical trial and registry studies showed that ivacaftor improved outcomes and reduced hospitalizations. Our study used US administrative claims data to assess the real-world effectiveness of ivacaftor. Comparing twelve-month rates before and after starting the use of ivacaftor among people who initiated therapy during 2012-2015, we found that overall and cystic fibrosis-related inpatient admissions fell by 55 percent and 81 percent, respectively. There was a comparable reduction in inpatient spending. Ivacaftor appears to be effective for multiple mutations that cause the disease, as suggested by the fact that during the study period, ivacaftor's use was extended to nine additional mutations in 2014. Examination of evidence from clinical trial, clinical care, and administrative data sources is important for understanding the real-world effectiveness of precision medicines such as ivacaftor. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 12 | 41% |
United Kingdom | 5 | 17% |
Netherlands | 1 | 3% |
Congo, The Democratic Republic of the | 1 | 3% |
Canada | 1 | 3% |
Unknown | 9 | 31% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 19 | 66% |
Scientists | 6 | 21% |
Practitioners (doctors, other healthcare professionals) | 2 | 7% |
Science communicators (journalists, bloggers, editors) | 1 | 3% |
Unknown | 1 | 3% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 64 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Bachelor | 8 | 13% |
Student > Ph. D. Student | 6 | 9% |
Researcher | 6 | 9% |
Other | 5 | 8% |
Student > Master | 5 | 8% |
Other | 8 | 13% |
Unknown | 26 | 41% |
Readers by discipline | Count | As % |
---|---|---|
Medicine and Dentistry | 12 | 19% |
Biochemistry, Genetics and Molecular Biology | 7 | 11% |
Social Sciences | 3 | 5% |
Pharmacology, Toxicology and Pharmaceutical Science | 2 | 3% |
Nursing and Health Professions | 2 | 3% |
Other | 11 | 17% |
Unknown | 27 | 42% |