Directrices para presentación de informes de ensayos clínicos sobre intervenciones con inteligencia artificial: extensión CONSORT-AI / Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension / Diretrizes para relatórios de ensaios clínicos com intervenções que utilizam inteligência artificial: a extensão CONSORT-AI

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ABSTRACT The CONSORT 2010 statement provides minimum guidelines for reporting randomized trials. Its widespread use has been instrumental in ensuring transparency in the evaluation of new interventions. More recently, there has been a growing recognition that interventions involving artificial intelligence (AI) need to undergo rigorous, prospective evaluation to demonstrate impact on health outcomes. The CONSORT-AI (Consolidated Standards of Reporting Trials-Artificial Intelligence) extension is a new reporting guideline for clinical trials evaluating interventions with an AI component. It was developed in parallel with its companion statement for clinical trial protocols: SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials-Artificial Intelligence). Both guidelines were developed through a staged consensus process involving literature review and expert consultation to generate 29 candidate items, which were assessed by an international multi-stakeholder group in a two-stage Delphi survey (103 stakeholders), agreed upon in a two-day consensus meeting (31 stakeholders) and refined through a checklist pilot (34 participants). The CONSORT-AI extension includes 14 new items that were considered sufficiently important for AI interventions that they should be routinely reported in addition to the core CONSORT 2010 items. CONSORT-AI recommends that investigators provide clear descriptions of the AI intervention, including instructions and skills required for use, the setting in which the AI intervention is integrated, the handling of inputs and outputs of the AI intervention, the human-AI interaction and provision of an analysis of error cases. CONSORT-AI will help promote transparency and completeness in reporting clinical trials for AI interventions. It will assist editors and peer reviewers, as well as the general readership, to understand, interpret and critically appraise the quality of clinical trial design and risk of bias in the reported outcomes.

RESUMO A declaração CONSORT 2010 apresenta diretrizes mínimas para relatórios de ensaios clínicos randomizados. Seu uso generalizado tem sido fundamental para garantir a transparência na avaliação de novas intervenções. Recentemente, tem-se reconhecido cada vez mais que intervenções que incluem inteligência artificial (IA) precisam ser submetidas a uma avaliação rigorosa e prospectiva para demonstrar seus impactos sobre os resultados de saúde. A extensão CONSORT-AI (Consolidated Standards of Reporting Trials - Artificial Intelligence) é uma nova diretriz para relatórios de ensaios clínicos que avaliam intervenções com um componente de IA. Ela foi desenvolvida em paralelo à sua declaração complementar para protocolos de ensaios clínicos, a SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials - Artificial Intelligence). Ambas as diretrizes foram desenvolvidas por meio de um processo de consenso em etapas que incluiu revisão da literatura e consultas a especialistas para gerar 29 itens candidatos. Foram feitas consultas sobre esses itens a um grupo internacional composto por 103 interessados diretos, que participaram de uma pesquisa Delphi em duas etapas. Chegou-se a um acordo sobre os itens em uma reunião de consenso que incluiu 31 interessados diretos, e os itens foram refinados por meio de uma lista de verificação piloto que envolveu 34 participantes. A extensão CONSORT-AI inclui 14 itens novos que, devido à sua importância para as intervenções de IA, devem ser informados rotineiramente juntamente com os itens básicos da CONSORT 2010. A CONSORT-AI preconiza que os pesquisadores descrevam claramente a intervenção de IA, incluindo instruções e as habilidades necessárias para seu uso, o contexto no qual a intervenção de IA está inserida, considerações sobre o manuseio dos dados de entrada e saída da intervenção de IA, a interação humano-IA e uma análise dos casos de erro. A CONSORT-AI ajudará a promover a transparência e a integralidade nos relatórios de ensaios clínicos com intervenções que utilizam IA. Seu uso ajudará editores e revisores, bem como leitores em geral, a entender, interpretar e avaliar criticamente a qualidade do desenho do ensaio clínico e o risco de viés nos resultados relatados.

Directrices para los protocolos de ensayos clínicos de intervenciones con inteligencia artificial: la extensión SPIRIT-AI / Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension / Diretrizes para protocolos de ensaios clínicos com intervenções que utilizam inteligência artificial: a extensão SPIRIT-AI

resumen está disponible en el texto completo

ABSTRACT The SPIRIT 2013 statement aims to improve the completeness of clinical trial protocol reporting by providing evidence-based recommendations for the minimum set of items to be addressed. This guidance has been instrumental in promoting transparent evaluation of new interventions. More recently, there has been a growing recognition that interventions involving artificial intelligence (AI) need to undergo rigorous, prospective evaluation to demonstrate their impact on health outcomes. The SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials-Artificial Intelligence) extension is a new reporting guideline for clinical trial protocols evaluating interventions with an AI component. It was developed in parallel with its companion statement for trial reports: CONSORT-AI (Consolidated Standards of Reporting Trials-Artificial Intelligence). Both guidelines were developed through a staged consensus process involving literature review and expert consultation to generate 26 candidate items, which were consulted upon by an international multi-stakeholder group in a two-stage Delphi survey (103 stakeholders), agreed upon in a consensus meeting (31 stakeholders) and refined through a checklist pilot (34 participants). The SPIRIT-AI extension includes 15 new items that were considered sufficiently important for clinical trial protocols of AI interventions. These new items should be routinely reported in addition to the core SPIRIT 2013 items. SPIRIT-AI recommends that investigators provide clear descriptions of the AI intervention, including instructions and skills required for use, the setting in which the AI intervention will be integrated, considerations for the handling of input and output data, the human-AI interaction and analysis of error cases. SPIRIT-AI will help promote transparency and completeness for clinical trial protocols for AI interventions. Its use will assist editors and peer reviewers, as well as the general readership, to understand, interpret and critically appraise the design and risk of bias for a planned clinical trial.

RESUMO A declaração SPIRIT 2013 tem como objetivo melhorar a integralidade dos relatórios dos protocolos de ensaios clínicos, fornecendo recomendações baseadas em evidências para o conjunto mínimo de itens que devem ser abordados. Essas orientações têm sido fundamentais para promover uma avaliação transparente de novas intervenções. Recentemente, tem-se reconhecido cada vez mais que intervenções que incluem inteligência artificial (IA) precisam ser submetidas a uma avaliação rigorosa e prospectiva para demonstrar seus impactos sobre os resultados de saúde. A extensão SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials - Artificial Intelligence) é uma nova diretriz de relatório para protocolos de ensaios clínicos que avaliam intervenções com um componente de IA. Essa diretriz foi desenvolvida em paralelo à sua declaração complementar para relatórios de ensaios clínicos, CONSORT-AI (Consolidated Standards of Reporting Trials - Artificial Intelligence). Ambas as diretrizes foram desenvolvidas por meio de um processo de consenso em etapas que incluiu revisão da literatura e consultas a especialistas para gerar 26 itens candidatos. Foram feitas consultas sobre esses itens a um grupo internacional composto por 103 interessados diretos, que participaram de uma pesquisa Delphi em duas etapas. Chegou-se a um acordo sobre os itens em uma reunião de consenso que incluiu 31 interessados diretos, e os itens foram refinados por meio de uma lista de verificação piloto que envolveu 34 participantes. A extensão SPIRIT-AI inclui 15 itens novos que foram considerados suficientemente importantes para os protocolos de ensaios clínicos com intervenções que utilizam IA. Esses itens novos devem constar dos relatórios de rotina, juntamente com os itens básicos da SPIRIT 2013. A SPIRIT-AI preconiza que os pesquisadores descrevam claramente a intervenção de IA, incluindo instruções e as habilidades necessárias para seu uso, o contexto no qual a intervenção de IA será integrada, considerações sobre o manuseio dos dados de entrada e saída, a interação humano-IA e a análise de casos de erro. A SPIRIT-AI ajudará a promover a transparência e a integralidade nos protocolos de ensaios clínicos com intervenções que utilizam IA. Seu uso ajudará editores e revisores, bem como leitores em geral, a entender, interpretar e avaliar criticamente o delineamento e o risco de viés de um futuro estudo clínico.

Directrices para los protocolos de ensayos clínicos de intervenciones con inteligencia artificial: la extensión SPIRIT-AI / Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension / Diretrizes para protocolos de ensaios clínicos com intervenções que utilizam inteligência artificial: a extensão SPIRIT-AI

resumen está disponible en el texto completo

ABSTRACT The SPIRIT 2013 statement aims to improve the completeness of clinical trial protocol reporting by providing evidence-based recommendations for the minimum set of items to be addressed. This guidance has been instrumental in promoting transparent evaluation of new interventions. More recently, there has been a growing recognition that interventions involving artificial intelligence (AI) need to undergo rigorous, prospective evaluation to demonstrate their impact on health outcomes. The SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials-Artificial Intelligence) extension is a new reporting guideline for clinical trial protocols evaluating interventions with an AI component. It was developed in parallel with its companion statement for trial reports: CONSORT-AI (Consolidated Standards of Reporting Trials-Artificial Intelligence). Both guidelines were developed through a staged consensus process involving literature review and expert consultation to generate 26 candidate items, which were consulted upon by an international multi-stakeholder group in a two-stage Delphi survey (103 stakeholders), agreed upon in a consensus meeting (31 stakeholders) and refined through a checklist pilot (34 participants). The SPIRIT-AI extension includes 15 new items that were considered sufficiently important for clinical trial protocols of AI interventions. These new items should be routinely reported in addition to the core SPIRIT 2013 items. SPIRIT-AI recommends that investigators provide clear descriptions of the AI intervention, including instructions and skills required for use, the setting in which the AI intervention will be integrated, considerations for the handling of input and output data, the human-AI interaction and analysis of error cases. SPIRIT-AI will help promote transparency and completeness for clinical trial protocols for AI interventions. Its use will assist editors and peer reviewers, as well as the general readership, to understand, interpret and critically appraise the design and risk of bias for a planned clinical trial.

RESUMO A declaração SPIRIT 2013 tem como objetivo melhorar a integralidade dos relatórios dos protocolos de ensaios clínicos, fornecendo recomendações baseadas em evidências para o conjunto mínimo de itens que devem ser abordados. Essas orientações têm sido fundamentais para promover uma avaliação transparente de novas intervenções. Recentemente, tem-se reconhecido cada vez mais que intervenções que incluem inteligência artificial (IA) precisam ser submetidas a uma avaliação rigorosa e prospectiva para demonstrar seus impactos sobre os resultados de saúde. A extensão SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials - Artificial Intelligence) é uma nova diretriz de relatório para protocolos de ensaios clínicos que avaliam intervenções com um componente de IA. Essa diretriz foi desenvolvida em paralelo à sua declaração complementar para relatórios de ensaios clínicos, CONSORT-AI (Consolidated Standards of Reporting Trials - Artificial Intelligence). Ambas as diretrizes foram desenvolvidas por meio de um processo de consenso em etapas que incluiu revisão da literatura e consultas a especialistas para gerar 26 itens candidatos. Foram feitas consultas sobre esses itens a um grupo internacional composto por 103 interessados diretos, que participaram de uma pesquisa Delphi em duas etapas. Chegou-se a um acordo sobre os itens em uma reunião de consenso que incluiu 31 interessados diretos, e os itens foram refinados por meio de uma lista de verificação piloto que envolveu 34 participantes. A extensão SPIRIT-AI inclui 15 itens novos que foram considerados suficientemente importantes para os protocolos de ensaios clínicos com intervenções que utilizam IA. Esses itens novos devem constar dos relatórios de rotina, juntamente com os itens básicos da SPIRIT 2013. A SPIRIT-AI preconiza que os pesquisadores descrevam claramente a intervenção de IA, incluindo instruções e as habilidades necessárias para seu uso, o contexto no qual a intervenção de IA será integrada, considerações sobre o manuseio dos dados de entrada e saída, a interação humano-IA e a análise de casos de erro. A SPIRIT-AI ajudará a promover a transparência e a integralidade nos protocolos de ensaios clínicos com intervenções que utilizam IA. Seu uso ajudará editores e revisores, bem como leitores em geral, a entender, interpretar e avaliar criticamente o delineamento e o risco de viés de um futuro estudo clínico.

Impact of a five-dimensional framework on R&D productivity at AstraZeneca.

In 2011, AstraZeneca embarked on a major revision of its research and development (R&D) strategy with the aim of improving R&D productivity, which was below industry averages in 2005-2010. A cornerstone of the revised strategy was to focus decision-making on five technical determinants (the right target, right tissue, right safety, right patient and right commercial potential). In this article, we describe the progress made using this '5R framework' in the hope that our experience could be useful to other companies tackling R&D productivity issues. We focus on the evolution of our approach to target validation, hit and lead optimization, pharmacokinetic/pharmacodynamic modelling and drug safety testing, which have helped improve the quality of candidate drug nomination, as well as the development of the right culture, where 'truth seeking' is encouraged by more rigorous and quantitative decision-making. We also discuss where the approach has failed and the lessons learned. Overall, the continued evolution and application of the 5R framework are beginning to have an impact, with success rates from candidate drug nomination to phase III completion improving from 4% in 2005-2010 to 19% in 2012-2016.

Integrating dose estimation into a decision-making framework for model-based drug development.

Model-informed drug discovery and development offers the promise of more efficient clinical development, with increased productivity and reduced cost through scientific decision making and risk management. Go/no-go development decisions in the pharmaceutical industry are often driven by effect size estimates, with the goal of meeting commercially generated target profiles. Sufficient efficacy is critical for eventual success, but the decision to advance development phase is also dependent on adequate knowledge of appropriate dose and dose-response. Doses which are too high or low pose risk of clinical or commercial failure. This paper addresses this issue and continues the evolution of formal decision frameworks in drug development. Here, we consider the integration of both efficacy and dose-response estimation accuracy into the go/no-go decision process, using a model-based approach. Using prespecified target and lower reference values associated with both efficacy and dose accuracy, we build a decision framework to more completely characterize development risk. Given the limited knowledge of dose response in early development, our approach incorporates a set of dose-response models and uses model averaging. The approach and its operating characteristics are illustrated through simulation. Finally, we demonstrate the decision approach on a post hoc analysis of the phase 2 data for naloxegol (a drug approved for opioid-induced constipation).

Embracing model-based designs for dose-finding trials.

BACKGROUND: Dose-finding trials are essential to drug development as they establish recommended doses for later-phase testing. We aim to motivate wider use of model-based designs for dose finding, such as the continual reassessment method (CRM). METHODS: We carried out a literature review of dose-finding designs and conducted a survey to identify perceived barriers to their implementation. RESULTS: We describe the benefits of model-based designs (flexibility, superior operating characteristics, extended scope), their current uptake, and existing resources. The most prominent barriers to implementation of a model-based design were lack of suitable training, chief investigators' preference for algorithm-based designs (e.g., 3+3), and limited resources for study design before funding. We use a real-world example to illustrate how these barriers can be overcome. CONCLUSIONS: There is overwhelming evidence for the benefits of CRM. Many leading pharmaceutical companies routinely implement model-based designs. Our analysis identified barriers for academic statisticians and clinical academics in mirroring the progress industry has made in trial design. Unified support from funders, regulators, and journal editors could result in more accurate doses for later-phase testing, and increase the efficiency and success of clinical drug development. We give recommendations for increasing the uptake of model-based designs for dose-finding trials in academia.

Decision-making in early clinical drug development.

This paper illustrates an approach to setting the decision framework for a study in early clinical drug development. It shows how the criteria for a go and a stop decision are calculated based on pre-specified target and lower reference values. The framework can lead to a three-outcome approach by including a consider zone; this could enable smaller studies to be performed in early development, with other information either external to or within the study used to reach a go or stop decision. In this way, Phase I/II trials can be geared towards providing actionable decision-making rather than the traditional focus on statistical significance. The example provided illustrates how the decision criteria were calculated for a Phase II study, including an interim analysis, and how the operating characteristics were assessed to ensure the decision criteria were robust. Copyright © 2016 John Wiley & Sons, Ltd.

A Quantitative Process for Enhancing End of Phase 2 Decisions.

The objectives of the phase 2 stage in a drug development program are to evaluate the safety and tolerability of different doses, select a promising dose range, and look for early signs of activity. At the end of phase 2, a decision to initiate phase 3 studies is made that involves the commitment of considerable resources. This multifactorial decision, generally made by balancing the current condition of a development organization's portfolio, the future cost of development, the competitive landscape, and the expected safety and efficacy benefits of a new therapy, needs to be a good one. In this article, we present a practical quantitative process that has been implemented for drugs entering phase 2 at Amgen Ltd. to ensure a consistent and explicit evidence-based approach is used to contribute to decisions for new drug candidates. Broadly following this process will also help statisticians increase their strategic influence in drug development programs. The process is illustrated using an example from the pancreatic cancer indication. Embedded within the process is a predominantly Bayesian approach to predicting the probability of efficacy success in a future (frequentist) phase 3 program.

A statistician's perspective on biomarkers in drug development.

Biomarkers play an increasingly important role in many aspects of pharmaceutical discovery and development, including personalized medicine and the assessment of safety data, with heavy reliance being placed on their delivery. Statisticians have a fundamental role to play in ensuring that biomarkers and the data they generate are used appropriately and to address relevant objectives such as the estimation of biological effects or the forecast of outcomes so that claims of predictivity or surrogacy are only made based upon sound scientific arguments. This includes ensuring that studies are designed to answer specific and pertinent questions, that the analyses performed account for all levels and sources of variability and that the conclusions drawn are robust in the presence of multiplicity and confounding factors, especially as many biomarkers are multidimensional or may be an indirect measure of the clinical outcome. In all of these areas, as in any area of drug development, statistical best practice incorporating both scientific rigor and a practical understanding of the situation should be followed. This article is intended as an introduction for statisticians embarking upon biomarker-based work and discusses these issues from a practising statistician's perspective with reference to examples.

Making available information from studies sponsored by the pharmaceutical industry: some current practices.

Since the web-based registry ClinicalTrials.gov was launched on 29 February 2000, the pharmaceutical industry has made available an increasing amount of information about the clinical trials that it sponsors. The process has been spurred on by a number of factors including a wish by the industry to provide greater transparency regarding clinical trial data; and has been both aided and complicated by the number of institutions that have a legitimate interest in guiding and defining what should be made available. This article reviews the history of this process of making information about clinical trials publicly available. It provides a reader's guide to the study registries and the databases of results; and looks at some indicators of consistency in the posting of study information.

The potential for bias in reporting of industry-sponsored clinical trials.

Concerns about potentially misleading reporting of pharmaceutical industry research have surfaced many times. The potential for duality (and thereby conflict) of interest is only too clear when you consider the sums of money required for the discovery, development and commercialization of new medicines. As the ability of major, mid-size and small pharmaceutical companies to innovate has waned, as evidenced by the seemingly relentless decline in the numbers of new medicines approved by Food and Drug Administration and European Medicines Agency year-on-year, not only has the cost per new approved medicine risen: so too has the public and media concern about the extent to which the pharmaceutical industry is open and honest about the efficacy, safety and quality of the drugs we manufacture and sell. In 2005 an Editorial in Journal of the American Medical Association made clear that, so great was their concern about misleading reporting of industry-sponsored studies, henceforth no article would be published that was not also guaranteed by independent statistical analysis. We examine the precursors to this Editorial, as well as its immediate and lasting effects for statisticians, for the manner in which statistical analysis is carried out, and for the industry more generally.

Proposed best practice for statisticians in the reporting and publication of pharmaceutical industry-sponsored clinical trials.

In this paper we set out what we consider to be a set of best practices for statisticians in the reporting of pharmaceutical industry-sponsored clinical trials. We make eight recommendations covering: author responsibilities and recognition; publication timing; conflicts of interest; freedom to act; full author access to data; trial registration and independent review. These recommendations are made in the context of the prominent role played by statisticians in the design, conduct, analysis and reporting of pharmaceutical sponsored trials and the perception of the reporting of these trials in the wider community.

Missing data: discussion points from the PSI missing data expert group.

The Points to Consider Document on Missing Data was adopted by the Committee of Health and Medicinal Products (CHMP) in December 2001. In September 2007 the CHMP issued a recommendation to review the document, with particular emphasis on summarizing and critically appraising the pattern of drop-outs, explaining the role and limitations of the 'last observation carried forward' method and describing the CHMP's cautionary stance on the use of mixed models. In preparation for the release of the updated guidance document, statisticians in the Pharmaceutical Industry held a one-day expert group meeting in September 2008. Topics that were debated included minimizing the extent of missing data and understanding the missing data mechanism, defining the principles for handling missing data and understanding the assumptions underlying different analysis methods. A clear message from the meeting was that at present, biostatisticians tend only to react to missing data. Limited pro-active planning is undertaken when designing clinical trials. Missing data mechanisms for a trial need to be considered during the planning phase and the impact on the objectives assessed. Another area for improvement is in the understanding of the pattern of missing data observed during a trial and thus the missing data mechanism via the plotting of data; for example, use of Kaplan-Meier curves looking at time to withdrawal.

GDNF in Parkinson's disease: the perils of post-hoc power.

The practice of performing post-hoc power calculations for studies that do not demonstrate statistically significant results has been widely recognized in the scientific literature as being unhelpful and potentially misleading. However, this practice continues to cause confusion in the interpretation of results from clinical trials and other studies. Here, we examine the re-interpretation of a recent randomized, double-blind, placebo-controlled study of intraputamenally administered GDNF in late-stage Parkinson's disease patients [Hutchinson M, Gurney S, Newson R. GDNF in Parkinson disease: an object lesson in the tyranny of type II. J Neurosci Methods 2007;163:190-2]. Their main criticism is that the study was not large enough to detect clinically worthwhile effects and that the observed non-significant result does not contradict the promising results observed in two previous, small, open-label studies. We have carefully assessed the re-analysis of the data performed by Hutchinson et al. and found their conclusions to be flawed, in part because they are based on post-hoc power calculations. We have reaffirmed that the confidence interval for the treatment effect in the placebo-controlled study of GDNF shows that the trial is capable of excluding effects of GDNF of the magnitudes that were observed in the open-label studies and that the conclusions drawn in the original paper remain scientifically sound.

Randomized controlled trial of intraputamenal glial cell line-derived neurotrophic factor infusion in Parkinson disease.

OBJECTIVE: Glial cell line-derived neurotrophic factor (GDNF) exerts potent trophic influence on midbrain dopaminergic neurons. This randomized controlled clinical trial was designed to confirm initial clinical benefits observed in a small, open-label trial using intraputamenal (Ipu) infusion of recombinant human GDNF (liatermin). METHODS: Thirty-four PD patients were randomized 1 to 1 to receive bilateral continuous Ipu infusion of liatermin 15 microg/putamen/day or placebo. The primary end point was the change in Unified Parkinson Disease Rating Scale (UPDRS) motor score in the practically defined off condition at 6 months. Secondary end points included other UPDRS scores, motor tests, dyskinesia ratings, patient diaries, and (18)F-dopa uptake. RESULTS: At 6 months, mean percentage changes in "off" UPDRS motor score were -10.0% and -4.5% in the liatermin and placebo groups, respectively. This treatment difference was not significant (95% confidence interval, -23.0 to 12.0, p = 0.53). Secondary end point results were similar between the groups. A 32.5% treatment difference favoring liatermin in mean (18)F-dopa influx constant (p = 0.019) was observed. Serious, device-related adverse events required surgical repositioning of catheters in two patients and removal of devices in another. Neutralizing antiliatermin antibodies were detected in three patients (one on-study and two in the open-label extension). INTERPRETATION: Liatermin did not confer the predetermined level of clinical benefit to patients with PD despite increased (18)F-dopa uptake. It is uncertain whether technical differences between this trial and positive open-label studies contributed in any way this negative outcome.

Filgrastim-mobilized peripheral blood progenitor cells versus bone marrow transplantation for treating leukemia: 3-year results from the EBMT randomized trial.

BACKGROUND AND OBJECTIVES: Allogeneic peripheral blood progenitor cells (PBPC) are now widely used as the source of hematopoietic stem cells for transplantation. However, it is still not clear which patients should receive mobilized PBPC or bone marrow cells to reconstitute hematopoiesis after myeloablative conditioning. The aim of this study is to present 3-year-follow-up data on outcome (incidence and severity of chronic graft-versus-host disease (GVHD), overall survival (OS) and leukemia-free survival (LFS) after a PBPC transplant (PBPCT) or a bone marrow transplant (BMT). DESIGN AND METHODS: Data on 350 patients with leukemia were collected in a multicenter, randomized study initiated by the EBMT. The patients were randomized to receive filgrastim-mobilized PBSCT or BMT from an HLA-identical donor. RESULTS: At a median follow-up of 3 years, significantly more patients transplanted with PBPC than with bone marrow developed chronic GVHD (73% vs 55%, p=0.003) and extensive chronic GvHD (36% vs 19%, p=0.002). The higher incidence and greater severity of chronic GvHD had little impact on the patient's performance status or survival. OS was 58% for PBPCT recipients versus 65% among those undergoing BMT. LFS was 56% for PBPCT recipients versus 60% for BMT recipients. INTERPRETATION AND CONCLUSIONS: Patients transplanted with PBPC from an HLA-identical sibling develop more chronic GvHD than those transplanted with bone marrow, but the final impact of this difference is unclear. Longer follow-up is necessary to characterize the impact of chronic GvHD on quality of life, leukemia-free survival and overall survival.

Efficacy and safety of darbepoetin alfa in anaemic patients with lymphoproliferative malignancies: a randomized, double-blind, placebo-controlled study.

This phase 3, randomized, double-blind, placebo-controlled study was designed to evaluate the efficacy and safety of darbepoetin alfa in anaemic patients with lymphoproliferative malignancies. Patients (n = 344) with lymphoma or myeloma received darbepoetin alfa 2.25 microg/kg or placebo s.c., once weekly for 12 weeks. The percentage of patients achieving a haemoglobin response was significantly higher in the darbepoetin alfa group (60%) than in the placebo group (18%) (P < 0.001), regardless of baseline endogenous erythropoietin level. However, increased responsiveness was observed in patients with lower baseline erythropoietin levels. Darbepoetin alfa also resulted in higher mean changes in haemoglobin than placebo from baseline to the last value during the treatment phase (1.80 g/dl vs 0.19 g/dl) and after 12 weeks of treatment (2.66 g/dl vs 0.69 g/dl). A significantly lower percentage of patients in the darbepoetin alfa group received red blood cell transfusions than in the placebo group (P < 0.001). The efficacy of darbepoetin alfa was consistent for patients with lymphoma or myeloma. Improvements in quality of life were also observed with darbepoetin alfa. The overall safety profile of darbepoetin alfa was consistent with that expected for this patient population. Darbepoetin alfa significantly increased haemoglobin and reduced red blood cell transfusions in patients with lymphoproliferative malignancies receiving chemotherapy.