3 research outputs found

    Transcatheter Aortic Valve Replacement in Oncology Patients With Severe Aortic Stenosis.

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    OBJECTIVES The authors sought to collect data on contemporary practice and outcome of transcatheter aortic valve replacement (TAVR) in oncology patients with severe aortic stenosis (AS). BACKGROUND Oncology patients with severe AS are often denied valve replacement. TAVR may be an emerging treatment option. METHODS A worldwide registry was designed to collect data on patients who undergo TAVR while having active malignancy. Data from 222 cancer patients from 18 TAVR centers were compared versus 2,522 "no-cancer" patients from 5 participating centers. Propensity-score matching was performed to further adjust for bias. RESULTS Cancer patients' age was 78.8 ± 7.5 years, STS score 4.9 ± 3.4%, 62% men. Most frequent cancers were gastrointestinal (22%), prostate (16%), breast (15%), hematologic (15%), and lung (11%). At the time of TAVR, 40% had stage 4 cancer. Periprocedural complications were comparable between the groups. Although 30-day mortality was similar, 1-year mortality was higher in cancer patients (15% vs. 9%; p < 0.001); one-half of the deaths were due to neoplasm. Among patients who survived 1 year after the TAVR, one-third were in remission/cured from cancer. Progressive malignancy (stage III to IV) was a strong mortality predictor (hazard ratio: 2.37; 95% confidence interval: 1.74 to 3.23; p < 0.001), whereas stage I to II cancer was not associated with higher mortality compared with no-cancer patients. CONCLUSIONS TAVR in cancer patients is associated with similar short-term but worse long-term prognosis compared with patients without cancer. Among this cohort, mortality is largely driven by cancer, and progressive malignancy is a strong mortality predictor. Importantly, 85% of the patients were alive at 1 year, one-third were in remission/cured from cancer. (Outcomes of Transcatheter Aortic Valve Implantation in Oncology Patients With Severe Aortic Stenosis [TOP-AS]; NCT03181997)

    A Review of Recent Trends and Challenges in Computational Modeling of Paper and Paperboard at Different Scales

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