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81.

Introduction

Malignant pleural mesothelioma (MPM) is an uncommon cancer with a poor prognosis and heterogeneous survival. Surgery for MPM is offered in some specialist centers to highly selected patients. A previously described classification and regression tree (CART) model stratified survival in unselected MPM patients using routinely collected clinical data. This study aimed to examine the performance of this CART model on a highly selected surgical population.

Methods

Data were collected from subjects undergoing cytoreductive surgery for MPM from specialist centers in Hyõgo, Japan, and Sydney, Australia, between 1991 and 2016. The CART model was applied using the combination of clinical variables to stratify subjects into risk groups (1 through 4); survival characteristics were then compared.

Results

Two hundred eighty-nine cases were included (205 from Australia, 84 from Japan). Overall median survival was 34.6 (interquartile range: 17.5–56.1) months; median age was 63.0 (interquartile range: 57.0–67.8) years, and 83.0% (n = 240) were male. There were no clinically meaningful differences between the two cohorts. Survival across the four risk groups was significantly different (p < 0.0001); the model stratified survival well with a Harrell's concordance statistic of 0.62 (95% confidence interval: 0.57–0.66) at 36 months. The group with the longest survival (median, 82.5 months) had: no weight loss, hemoglobin > 153 g/L and serum albumin > 43 g/L at time of referral to the surgical center.

Conclusions

Using routinely available clinical variables, the CART model was able to stratify surgical patients into risk groups with statistically different survival characteristics with fair to good performance. Presence of weight loss, anemia, and low albumin should confer caution when considering surgical therapy for MPM.  相似文献   
82.
In a prospective study, we sought to determine acceptability of linkage of administrative and clinical trial data among Canadian patients and Research Ethics Boards (REBs). The goal is to develop a more harmonized approach to data, with potential to improve clinical trial conduct through enhanced data quality collected at reduced cost and inconvenience for patients. On completion of the original LY.12 randomized clinical trial in lymphoma (NCT00078949), participants were invited to enrol in the Long-term Innovative Follow-up Extension (LIFE) component. Those consenting to do so provided comprehensive identifying information to facilitate linkage with their administrative data. We prospectively designed a global assessment of this innovative approach to clinical trial follow-up including rates of REB approval and patient consent. The pre-specified benchmark for patient acceptability was 80%. Of 16 REBs who reviewed the research protocol, 14 (89%) provided approval; two in Quebec declined due to small patient numbers. Of 140 patients invited to participate, 115 (82%, 95% CI 76 to 88%) from across 9 Canadian provinces provided consent and their full name, date of birth, health insurance number and postal code to facilitate linkage with their administrative data for long-term follow-up. Linkage of clinical trial and administrative data is feasible and acceptable. Further collaborative work including many stakeholders is required to develop an optimized secure approach to research. A more coordinated national approach to health data could facilitate more rapid testing and identification of new effective treatments across multiple jurisdictions and diseases from diabetes to COVID-19.  相似文献   
83.
locStra is an ‐package for the analysis of regional and global population stratification in whole‐genome sequencing (WGS) studies, where regional stratification refers to the substructure defined by the loci in a particular region on the genome. Population substructure can be assessed based on the genetic covariance matrix, the genomic relationship matrix, and the unweighted/weighted genetic Jaccard similarity matrix. Using a sliding window approach, the regional similarity matrices are compared with the global ones, based on user‐defined window sizes and metrics, for example, the correlation between regional and global eigenvectors. An algorithm for the specification of the window size is provided. As the implementation fully exploits sparse matrix algebra and is written in C++, the analysis is highly efficient. Even on single cores, for realistic study sizes (several thousand subjects, several million rare variants per subject), the runtime for the genome‐wide computation of all regional similarity matrices does typically not exceed one hour, enabling an unprecedented investigation of regional stratification across the entire genome. The package is applied to three WGS studies, illustrating the varying patterns of regional substructure across the genome and its beneficial effects on association testing.  相似文献   
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