Background: Management and traceability of biospecimen preanalytical variations are necessary to provide effective and efficient interconnectivity and interoperability between Biobanks. Methods: Therefore, the International Society for Biological and Environmental Repositories Biospecimen Science Working Group developed a “Standard PREanalytical Code” (SPREC) that identifies the main preanalytical factors of clinical fluid and solid biospecimens and their simple derivatives. Results: The SPREC is easy to implement and can be integrated into Biobank quality management systems and databases. It can also be extended to nonhuman biorepository areas. Its flexibility allows integration of new novel technological developments in future versions. SPREC version 01 is presented in this article. Conclusions and Impact: Implementation of the SPREC is expected to facilitate and consolidate international multicenter biomarker identification research and biospecimen research in the clinical Biobank environment. Cancer Epidemiol Biomarkers Prev; 19(4); 1004–11. ©2010 AACR.

The first version of the Standard PREanalytical Code (SPREC) was developed in 2009 by the International Society for Biological and Environmental Repositories (ISBER) Biospecimen Science Working Group to facilitate documentation and communication of the most important preanalytical quality parameters of different types of biospecimens used for research. This same Working Group has now updated the SPREC to version 2.0, presented here, so that it contains more options to allow for recent technological developments. Existing elements have been fine tuned. An interface to the Biospecimen Reporting for Improved Study Quality (BRISQ) has been defined, and informatics solutions for SPREC implementation have been developed. A glossary with SPREC-related definitions has also been added.

Objective. To design a precision medicine approach aimed at exploiting significant patterns in data, in order to produce venous thromboembolism (VTE) risk predictors for cancer outpatients that might be of advantage over the currently recommended model (Khorana score). Design: Multiple kernel learning (MKL) based on support vector machines and random optimization (RO) models were used to produce VTE risk predictors (referred to as machine learning [ML]-RO) yielding the best classification performance over a training (3-fold cross-validation) and testing set. Results. Attributes of the patient data set (n = 1179) were clustered into 9 groups according to clinical significance. Our analysis produced 6 ML-RO models in the training set, which yielded better likelihood ratios (LRs) than baseline models. Of interest, the most significant LRs were observed in 2 ML-RO approaches not including the Khorana score (ML-RO-2: positive likelihood ratio [+LR] = 1.68, negative likelihood ratio [–LR] = 0.24; ML-RO-3: +LR = 1.64, –LR = 0.37). The enhanced performance of ML-RO approaches over the Khorana score was further confirmed by the analysis of the areas under the Precision-Recall curve (AUCPR), and the approaches were superior in the ML-RO approaches (best performances: ML-RO-2: AUCPR = 0.212; ML-RO-3-K: AUCPR = 0.146) compared with the Khorana score (AUCPR = 0.096). Of interest, the best-fitting model was ML-RO-2, in which blood lipids and body mass index/performance status retained the strongest weights, with a weaker association with tumor site/stage and drugs. Conclusions. Although the monocentric validation of the presented predictors might represent a limitation, these results demonstrate that a model based on MKL and RO may represent a novel methodological approach to derive VTE risk classifiers. Moreover, this study highlights the advantages of optimizing the relative importance of groups of clinical attributes in the selection of VTE risk predictors.