BACKGROUND: A study was undertaken to investigate whether granulocyte-colony-stimulating factor (G-CSF) injection in lower adipose tissue-containing sites (arms and legs) would result in a lower exposure and reduced stem cell collection efficiency compared with injection into abdominal skin.
STUDY DESIGN AND METHODS: We completed a prospective randomized study to determine the efficacy and tolerability of different injection sites for patients with multiple myeloma or lymphoma undergoing stem cell mobilization and apheresis. Primary endpoints were the number of CD34+ cells collected and the number of days of apheresis. Forty patients were randomly assigned to receive cytokine injections in their abdomen (Group A) or extremities (Group B). Randomization was stratified based on diagnosis (myeloma, n = 29 vs. lymphoma, n = 11), age, and mobilization strategy and balanced across demographic factors and body mass index.
RESULTS: Thirty-five subjects were evaluable for the primary endpoint: 18 in Group A and 17 in Group B. One evaluable subject in each group failed to collect a minimum dose of at least 2.0 × 106 CD34+ cells/kg. The mean numbers of CD34+ cells (±SD) collected were not different between Groups A and B (9.15 × 106 ± 4.7 × 106/kg vs. 9.85 × 106 ± 5 × 106/kg, respectively; p = NS) after a median of 2 days of apheresis. Adverse events were not different between the two groups.
CONCLUSION: The site of G-CSF administration does not affect the number of CD34+ cells collected by apheresis or the duration of apheresis needed to reach the target cell dose.
Background: Large linked databases (LLDB) represent a novel resource for cancer outcomes research. However, accurate means of identifying a patient population of interest within these LLDBs can be challenging. Our research group developed a fully integrated platform that provides a means of combining independent legacy databases into a single cancer-focused LLDB system. We compared the sensitivity and specifi city of several SQL-based query strategies for identifying a histologic lymphoma subtype in this LLDB to determine the most accurate legacy data source for identifying a specifi c cancer patient population.
Methods: Query strategies were developed to identify patients with follicular lymphoma from a LLDB of cancer registry data, electronic medical records (EMR), laboratory, administrative, pharmacy, and other clinical data. Queries were performed using common diagnostic codes (ICD-9), cancer registry histology codes (ICD-O), and text searches of EMRs. We reviewed medical records and pathology reports to confirm each diagnosis and calculated the sensitivity and specificity for each query strategy.
Results: Together the queries identified 1538 potential cases of follicular lymphoma. Review of pathology and other medical reports confirmed 415 cases of follicular lymphoma, 300 pathology-verifi ed and 115 verified from other medical reports. The query using ICD-O codes was highly specific (96%). Queries using text strings varied in sensitivity (range 7–92%) and specifi city (range 86–99%). Queries using ICD-9 codes were both less sensitive (34–44%) and specific (35–87%).
Conclusions: Queries of linked-cancer databases that include cancer registry data should utilize ICD-O codes or employ structured free-text searches to identify patient populations with a precise histologic diagnosis.
Lymphomas are the fifth most common cancer in United States with numerous histological subtypes. Integrating existing clinical information on lymphoma patients provides a platform for understanding biological variability in presentation and treatment response and aids development of novel therapies. We developed a cancer Biomedical Informatics Grid™ (caBIG™) Silver level compliant lymphoma database, called the Lymphoma Enterprise Architecture Data-system™ (LEAD™), which integrates the pathology, pharmacy, laboratory, cancer registry, clinical trials, and clinical data from institutional databases. We utilized the Cancer Common Ontological Representation Environment Software Development Kit (caCORE SDK) provided by National Cancer Institute's Center for Bioinformatics to establish the LEAD™ platform for data management. The caCORE SDK generated system utilizes an n-tier architecture with open Application Programming Interfaces, controlled vocabularies, and registered metadata to achieve semantic integration across multiple cancer databases. We demonstrated that the data elements and structures within LEAD™ could be used to manage clinical research data from phase 1 clinical trials, cohort studies, and registry data from the Surveillance Epidemiology and End Results database. This work provides a clear example of how semantic technologies from caBIG™ can be applied to support a wide range of clinical and research tasks, and integrate data from disparate systems into a single architecture. This illustrates the central importance of caBIG™ to the management of clinical and biological data.
by
Gilles Salles;
Stephen J. Schuster;
Sven de Vos;
Nina D. Wagner-Johnston;
Andreas Viardot;
Kristie Blum;
Christopher Flowers;
Wojciech J. Jurczak;
Ian W. Flinn;
Brad S. Kahl;
Peter Martin;
Yeonhee Kim;
Sanatan Shreay;
MAtthias Will;
Bess Sorensen;
Madlaina Breuleux;
Pier Luigi Zinzani;
Ajay K. Gopal