James Pitcavage
Geisinger Health System
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Publication
Featured researches published by James Pitcavage.
Public Health Genomics | 2014
Jonathan Bock; Kimberly J. Fairley; Robert E. Smith; Daniel D. Maeng; James Pitcavage; Nicholas A. Inverso; Marc S. Williams
Background/Aims: Triple therapy [adding protease inhibitors to standard of care (SOC)] dramatically increases treatment response in selected patients with hepatitis C virus (HCV). Interleukin 28B (IL28Β) genotyping helps predict responsiveness in these patients; however, the economic implications of IL28Β genotyping in HCV genotype 2 or 3 infected patients are unknown. Short- and long-term costs and outcomes of SOC therapy were calculated and used to determine the cost-effectiveness thresholds for using triple therapy in HCV genotype 2 or 3 infected patients. Methods: Costs and outcomes were calculated by conducting cohort simulations on decision trees modeling SOC and triple therapy. Quality-adjusted life expectancies and long-term costs were predicted through Markov modeling. Results: For triple therapy to be cost-effective, sustained virologic response (SVR) rates must improve (depending on age) by 7.91-11.11 and 9.06-12.8% for HCV genotype 2 and 3 cohorts, respectively. When triple therapy is guided by 2 IL28Β variants, a 2.63-3.72% improvement in SVR is needed for cost-effectiveness, and when guided by only one variant, a 1.4-8.91% improvement is needed. Conclusions: Markov modeling revealed that modest increases in SVR rates from IL28Β-guided triple therapy can lead to both lower costs and better health outcomes than SOC therapy in the long run.
Public Health Genomics | 2014
George P. Patrinos; Eleni Dalabira; Emmanouil Viennas; Elisavet Daki; Angeliki Komianou; Marina Bartsakoulia; Konstantinos Poulas; Theodora Katsila; Giannis Tzimas; Christina Mitropoulou; Yuan Mai; Ron H.N. van Schaik; Athanassios Vozikis; Claudia Pisanu; Evangelia-Eirini Tsermpini; Eirini Mavroidi; Alessio Squassina; Denis Horgan; Marleen Jansen; Lada Leyens; Jonathan A Lal; Ralf Sudbrak; Erica Hackenitz; Ulrike Bußhoff; Wolfgang Ballensiefen; Angela Brand; Sotiria Kechagia; Takis Vidalis; Effy Vayena; Susan R Snyder
R. Adany, Debrecen, Hungary A. Aaro, Odense, Denmark D. Avard, Montréal, Qué., Canada I. Blancquaert, Montréal, Qué., Canada J.-J. Cassiman, Leuven, Belgium E.E. Castilla, Rio de Janeiro, Brazil S. Grosse, Atlanta, Ga., USA J. Harris, Oslo, Norway A. Haslberger, Vienna, Austria D. Ibarreta, Sevilla, Spain M. Karmali, Toronto, Ont., Canada H. Lehrach, Berlin, Germany J. Little, Ottawa, Ont., Canada N. Malats, Madrid, Spain C. McBride, Bethesda, Md., USA S.A. Morré, Amsterdam, Th e Netherlands P. O’Leary, Perth, W.A., Australia F. Paccaud, Epalinges, Switzerland B. Peterlin, Ljubljana, Slovenia Editor-in-Chief
Clinical Medicine & Research | 2013
Jonathan Bock; Kimberly J. Fairley; Robert Smith; Daniel Maeng; James Pitcavage; Nicholas A. Inverso; Marc S. Williams
Background/Aims The addition of protease inhibitors to standard of care (SOC) dramatically increases treatment response in Hepatitis C Virus (HCV) genotype 1 patients. Moreover, Interleukin 28B (IL28B) genotyping helps predict responsiveness for these patients. However, the economic implications of incorporating IL28B genotyping in HCV genotype 2 or 3 infected patients are unknown. This study used a treatment algorithm that included IL28B genotype-guided therapy to examine the short and long-term cost-effectiveness of utilizing these single-nucleotide polymorphisms in treatment-naïve HCV genotype 2 or 3 infected patients. Methods A treatment algorithm was constructed to reflect a therapy regimen for treatment-naïve patients with HCV genotype 2 or 3 infection using pegylated-interferon, ribavirin, and telaprevir. To examine the role of the IL28B gene in affecting costs and health outcomes, a decision tree was derived from the treatment algorithm in order to populate a predictive cost model for therapy using our treatment algorithm. Results Expected short-term costs of therapy following our algorithm were
AJOB empirical bioethics | 2016
Nancy E. Kass; Ruth R. Faden; Rachel Fabi; Stephanie R. Morain; Kristina Hallez; Danielle Whicher; Sean Tunis; Rachael Moloney; Donna A. Messner; James Pitcavage
21,648.92 and
Journal of the American College of Cardiology | 2017
Shikhar Agarwal; James Pitcavage; Karan Sud; Badal Thakkar
47,972.84 for the CC and TT genotypes at rs12979860, respectively, and
Harm Reduction Journal | 2015
Matthew C. Rousu; Richard J. O’Connor; Maansi Bansal-Travers; James Pitcavage; James F. Thrasher
47,972.84 and
Value in Health | 2016
J. Hao; S.R. Snyder; James Pitcavage; R. Critchley-Thorne
21,648.92 for patients with the CT genotype at rs12979860 and the TG/GG and TT genotypes at rs8099917, respectively. Predicted costs among patients undergoing SOC therapy were
Journal of Patient-Centered Research and Reviews | 2017
Daniel Maeng; James Pitcavage; George Rohrer; John Bulger
20,758.92. Sustained virologic response (SVR) rates for genotypes 2/3 were predicted to occur in 82.2% (8,220 of 10,000) of patients overall—88.83% (8,883 of 10,000) and 65.91% (6,591 of 10,000) for the CC and TT genotypes at rs12979860 and 81.01% (8,101 of 10,000) overall for patients with the CT genotype at rs12979860 [72.08% (7,208 of 10,000) and 86.78% (8,678 of 10,000) for the TG/GG and TT genotypes at rs8099917]. Markov modeling predicted a 27.29 quality-adjusted life-expectancy (QALE) after following our treatment algorithm while adding
Journal of Patient-Centered Research and Reviews | 2016
James Pitcavage; Wen Feng; Daniel D. Maeng
7,766.51 in long-term costs. The model predicted only a 26.65 QALE after SOC therapy (while adding
Journal of Patient-Centered Research and Reviews | 2016
Susan R Snyder; Rosemary Leeming; Alanna Kulchak Rahm; Jing Hao; James Pitcavage
9,599.05 in long-term costs). Conclusions Although short-term treatment costs of an IL28B genotype-guided approach exceed those of SOC for treatment-naïve HCV genotype 2/3 infected patients, Markov modeling suggests that lower long-term costs and improved health outcomes may be achieved by the proposed algorithm and provides a dominant cost-effective strategy for treating this population of HCV infected patients.