Janet Hegland
National Marrow Donor Program
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Annals of Internal Medicine | 1997
Stephanie J. Lee; Karen M. Kuntz; Mary M. Horowitz; Philip B. McGlave; John M. Goldman; Kathleen A. Sobocinski; Janet Hegland; Craig Kollman; Susan K. Parsons; Milton C. Weinstein; Jane C. Weeks; Joseph H. Antin
Each year, approximately 4300 people in the United States receive a diagnosis of chronic myelogenous leukemia (CML) at a median age of 50 years [1]. Median survival is 3 to 6 years, and death usually results from progression to acute leukemia [2]. Although several studies [3-6] have shown improved survival with the use of certain chemotherapeutic agents, bone marrow transplantation is the only proven curative therapy. For patients younger than 50 years of age who have an HLA-identical related donor, transplantation within the first year after diagnosis is recommended [7-11]. The use of HLA-compatible unrelated donor transplantation has been advocated for patients without a related donor, but this recommendation is controversial because unrelated donor transplantation is associated with high morbidity and mortality rates [12, 13]. In contrast to patients with many other diseases for which transplantation is considered, patients with chronic-phase CML generally feel well, continue to work, and require few medications or medical interventions. Treatment is necessary only to control symptoms and blood counts. Hydroxyurea is an inexpensive, relatively nontoxic, effective oral medication. Interferon- is more expensive, is toxic, and must be administered by subcutaneous injection, but it is also effective and may prolong survival. Patients may be stratified into broad prognostic groups on the basis of their clinical characteristics at the time of diagnosis [2, 14-19]. However, no clinical features accurately predict an individual patients progression to acute leukemia [20]. Once progression occurs, both standard chemotherapy and transplantation have minimal success in prolonging survival [7, 21-24]. The process of deciding whether and when to undergo unrelated donor transplantation is complicated by the extreme unpredictability of outcomes. The risk for unsalvageable progression of CML must be weighed against the substantial risk for illness and death that may result from the acute and chronic side effects of transplantation [25]. Some physicians advise waiting until disease progression is evident or interferon- therapy has failed before proceeding to transplantation, accepting the risks of the delay in exchange for the possibility of postponing or avoiding transplantation. Others advise performing a transplantation as soon as possible to afford the best chance of a successful transplantation outcome and long-term survival. We used decision analytic techniques to combine historical data on risk for CML progression with data from the International Bone Marrow Transplant Registry (IBMTR) and the National Marrow Donor Program (NMDP) on transplantation outcomes. A Markov model [26] was constructed to allow comparison of the treatment options available to a patient with a new diagnosis of chronic-phase CML. This approach allows the simultaneous and quantitative consideration of patient age, quality of life, risk aversion, risk for CML progression, and likelihood of transplantation success to help guide decision making. Methods Markov Model A Markov model is an analytic structure that tracks the clinical events occurring in a hypothetical cohort of patients in various scenarios. We constructed a model to analyze the decision faced by a patient with a new diagnosis of chronic-phase CML who is considering having unrelated donor bone marrow transplantation. Five strategies were compared: no transplantation; transplantation within the first year; transplantation 1 to 2 years after diagnosis; transplantation delayed until 2 to 3 years after diagnosis; and transplantation delayed until more than 3 years after diagnosis. At any time point, the model considered a patient to be in one of the following clinical states: alive with chronic-phase CML; alive without chronic graft-versus-host disease after transplantation; alive with chronic graft-versus-host disease after transplantation; or dead from progressive CML, complications of transplantation, or other causes (Figure 1). Time spent in each state was adjusted for the quality of life experienced while in that state, and a discount factor was applied. Using the model, we calculated discounted, quality-adjusted life expectancy for each strategy, considering competing risks for illness and death from CML and transplantation. A cycle length of 6 months was chosen to match the available clinical data. All analyses were done with DATA (TreeAge Software, Inc., Williamstown, Massachusetts), a decision analysis program. Figure 1. Structure of the Markov model. Data Sources Data from the medical literature, transplant registries, and physician assessments were used in the model. Prognosis of Patients with Chronic Myelogenous Leukemia Who Do Not Receive Transplants The life expectancy of patients with CML who do not undergo transplantation was calculated by using the survival curves from six published studies, including interferon trials and prognostic staging studies [4-615, 19, 26, 27]. After published information ended, the survival curves were extrapolated by using a function fitted to the clinical data until the entire cohort had died. Life expectancy estimates for 35-year-old patients with CML who do not undergo transplantation are shown in Table 1. Similar analyses were performed for 25- and 45-year-old patients (data not shown). For the 15 survival curves analyzed, the mean undiscounted expected survival was 5.15 years (range, 3.66 to 7.58 years). A survival curve derived from patients with intermediate-prognosis CML (Table 1, rank 6) who had an expected survival of 5.31 years was chosen as the baseline curve [15]. Sensitivity analysis was performed by using data from the best (Table 1, rank 1) [15] and worst (Table 1, rank 15) [19] survival curves to illustrate results for patients with the best and worst prognoses. The life expectancy of the Sokal low-risk group (Table 1, rank 1) is superior to that in the groups randomly assigned to receive interferon in the major studies [4-6, 27] and thus was chosen to represent the patients with the best prognosis. Table 1. Life Expectancy of a 35-Year-Old Patient with Chronic Myelogenous Leukemia Who Does Not Have Bone Marrow Transplantation, Based on Published Studies Outcomes of Unrelated Donor Bone Marrow Transplantation Data from 778 unrelated donor transplantations performed between 1987 and 1994 for chronic-phase CML were provided by the IBMTR and the NMDP. To eliminate overlap from patients reported to both registries, we combined patients reported to the NMDP (transplantations done in the United States; n = 465) with those reported to the IBMTR (transplantations done outside of the United States; n = 313). The final population included both 5/6 and 6/6 serologic matches. Each Kaplan-Meier curve is based on data from time of transplantation to death, and a separate Kaplan-Meier curve was calculated for each stratum. Kaplan-Meier survival curves through 5 years after transplantation were stratified by patient age (15 to 29 years of age, 30 to 39 years of age, 40 years of age) and time from diagnosis to transplantation (<1 year, 1 to 2 years, 2 to 3 years, >3 years) to generate 12 strata containing 40 to 125 patients each. Finer stratification was not possible because of the size of the entire population. Fifty-eight percent of patients were male, median age at the time of transplantation was 36 years, and median time from diagnosis to transplantation was 20 months. No modeling assumptions were made for time periods for which clinical data were available. Data were not stratified by CML prognostic groups because these groups are not predictive once a patient proceeds to transplantation ([28]; IBMTR. Unpublished data). When registry data ended, we estimated a 2% annual excess mortality rate for patients who had transplantation and did not have chronic graft-versus-host disease [10] and a 3% annual excess mortality rate for patients who had chronic graft-versus-host disease. These figures were extrapolated from analyses of large related-donor cohorts and are due to late relapses and treatment-related death [10, 29, 30]. The incidence of chronic graft-versus-host disease in this cohort was 55% to 100%. On the basis of published literature [25], we modeled a 59% cumulative incidence of this complication and then performed sensitivity analyses. Patients with chronic graft-versus-host disease tend to have worse quality of life and decreased survival [28, 29]. Although chronic graft-versus-host disease often resolves in practice, our model considers patients with this complication to have ongoing compromised quality of life and increased mortality. This assumption causes underestimation of the value of transplantation. Adjustments for Quality of Life Estimates of the quality of life in different health states (utilities) were derived from standard gamble questions [31] posed to 12 physicians who were familiar with transplantation outcomes. This technique assigns a utility between 0.0 (death) and 1.0 (perfect health) to quality of life by identifying the maximum gamble between perfect health and death that a person is willing to accept to avoid a compromised health state. For example, a utility of 0.9 means that a person gives equal value to remaining in a compromised health state and accepting a gamble with a 90% chance of perfect health and a 10% chance of immediate death. The mean utility for life without chronic graft-versus-host disease after transplantation was 0.979 (range, 0.95 to 1.0), and the mean utility for life with chronic graft-versus-host disease after transplantation was 0.9 (range, 0.75 to 1.0). These estimates were tested by using sensitivity analyses. Time Discount Rate: Time Preference and Risk Aversion The value of future years of life relative to the present were discounted on the basis of two assumptions. Time preference for life-years assumes that persons value present time more than they do distant time. Risk aversion,
Bone Marrow Transplantation | 2000
Carolyn Katovich Hurley; La Baxter-Lowe; Ann B. Begovich; M.A. Fernández-Viña; Harriet Noreen; Barbara Schmeckpeper; Z Awdeh; M. Chopek; Marcela Salazar; Tm Williams; Edmond J. Yunis; D Kitajima; K Shipp; J Splett; T Winden; Craig Kollman; David Johnson; J Ng; Robert J. Hartzman; Janet Hegland
A comprehensive analysis of the HLA-D region loci, DRB1, DRB3, DRB5, DQA1, DQB1, DPA1 and DPB1, was performed to determine allelic diversity and underlying HLA disparity in 1259 bone marrow recipients and their unrelated donors transplanted through the National Marrow Donor Program. Although 43.0% of DRB1 alleles known to exist at the beginning of the study were found in this predominantly Caucasian transplant population, a few alleles predominated at each locus. In recipients, 67.1% of DRB1 alleles identified were one or two of six common DRB1 alleles. Only 118 (9.4%) donor–recipient pairs were matched for all alleles of DRB1, DQA1, DQB1, DPA1 and DPB1. While 79.4% of the pairs were matched for DRB1, only 13.2% were matched for DPB1 alleles. Almost 66% of pairs differed by more than one allele mismatch and 59.0% differed at more than one HLA-D locus. DQB1 was matched in 85.9% of DRB1-matched pairs. In contrast, only 13.9% of the pairs matched for DRB1, DQA1 and DQB1 were also matched for DPA1 and DPB1. This database, highlighting the underlying HLA disparity within the pairs, forms the foundation of an ongoing study to establish the relationship between HLA matching and successful outcome in unrelated allogeneic stem cell transplant. Bone Marrow Transplantation (2000) 25, 385–393.
Bone Marrow Transplantation | 2001
Craig Kollman; T Weis; Galen E. Switzer; M Halet; D Kitajima; Janet Hegland; Dennis L. Confer
A prospective survey involving 544 searches of the US National Marrow Donor Program (NMDP) Registry was conducted to identify reasons why many patients who have apparent HLA-matched donors do not proceed to transplant. Coordinators at NMDP transplant centers, patients and referring physicians were surveyed shortly after the initial search, and follow-up surveys were sent to the coordinators as the search was ongoing. The death of the patient, worsening of the patients medical condition and length of the search process were the most commonly cited barriers to transplantation. Other times a decision was made not to transplant through the NMDP due to the use of a donor from another source, a preference for chemotherapy or immunotherapy, hesitancy on the part of the transplant physician or patient, or because the patient did not require a transplant. Responses differed between U.S. and international cases. An unrelated donor outside the NMDP was the most common reason cited by international coordinators (46%), whereas the death of the patient was the most common reason among US coordinators (13%). The death of the patient was the second most common reason cited by international coordinators at 9%. Financial problems were listed by 41% of US coordinators as a potential barrier at the time of initial search, but only 5% indicated this as an actual barrier on a follow-up survey. Finances were cited as the most important reason 3% of the time overall, and 6% for African Americans and Asian/Pacific Islanders. Bone Marrow Transplantation (2001) 27, 581–587.
Blood | 2001
Craig Kollman; Craig W. S. Howe; Claudio Anasetti; Joseph H. Antin; Stella M. Davies; Alexandra H. Filipovich; Janet Hegland; Naynesh Kamani; Nancy A. Kernan; Roberta J. King; Voravit Ratanatharathorn; Daniel J. Weisdorf; Dennis L. Confer
Blood | 1996
Charles Peters; Michael Balthazor; Elsa Shapiro; Roberta J. King; Craig Kollman; Janet Hegland; Jean Henslee-Downey; Michael E. Trigg; Morton J. Cowan; Jean E. Sanders; Nancy J. Bunin; Howard J. Weinstein; Carl Lenarsky; Peter Falk; Richard R. Harris; Tom Bowen; Thomas Williams; Guy H. Grayson; Phyllis I. Warkentin; Leonard Sender; Valerie A. Cool; Mary Grillenden; Seymour Packman; Paige Kaplan; Lawrence A. Lockman; James Anderson; William Krivit; Kathryn E. Dusenbery; John E Wagner
Blood | 2001
Alexandra H. Filipovich; Judy V. Stone; Sandra C. Tomany; Michele Ireland; Craig Kollman; Corey J. Pelz; James T. Casper; Morton J. Cowan; John R. Edwards; Anders Fasth; Robert Peter Gale; Anne K. Junker; Naynesh Kamani; Brett Loechelt; Daniel W. Pietryga; Olle Ringdén; Marcus Vowels; Janet Hegland; Aronica V. Williams; John P. Klein; Kathleen A. Sobocinski; Philip A. Rowlings; Mary M. Horowitz
Tissue Antigens | 2001
Harriet Noreen; Neng Yu; Michelle Setterholm; M. Ohashi; J. Baisch; R. Endres; M. Fernandez-Vina; U. Heine; Susan Hsu; Malek Kamoun; Y. Mitsuishi; Dimitri Monos; L. Perlee; S. Rodriguez-Marino; Anajane G. Smith; Soo Young Yang; K. Shipp; Janet Hegland; Carolyn Katovich Hurley
Human Immunology | 2007
Carolyn Katovich Hurley; Marcelo Fernandez-Vina; William H. Hildebrand; Harriet Noreen; Elizabeth Trachtenberg; Thomas M. Williams; Lee Ann Baxter-Lowe; Ann B. Begovich; Effie W. Petersdorf; Annamalai Selvakumar; Peter Stastny; Janet Hegland; R.J. Hartzman; Michael Carston; Sharavi Gandham; Craig Kollman; Gene Nelson; Stephen Spellman; Michelle Setterholm
Tissue Antigens | 1999
Carolyn Katovich Hurley; Judith A. Wade; Machteld Oudshoorn; Derek Middleton; Debra Kukuruga; Cristina Navarrete; Frank T. Christiansen; Janet Hegland; Ee Chee Ren; Irene Andersen; S.A. Cleaver; Chaim Brautbar; Colette Raffoux
Human Immunology | 1999
Carolyn Katovich Hurley; Judith A. Wade; Machteld Oudshoorn; Derek Middleton; Debra Kukuruga; Cristina Navarrete; Frank T. Christiansen; Janet Hegland; Ee Chee Ren; Irene Andersen; S.A. Cleaver; Chaim Brautbar; Colette Raffoux