Reclassification is observed even when there is no or minimal improvement in the area under the receiver operating characteristic curve (AUC), and it is unclear whether it indicates improved clinical utility. The authors investigated total reclassification, net reclassification improvement, and integrated discrimination improvement for different DeltaAUC using empirical and simulated data. Empirical analyses compared prediction of type 2 diabetes risk based on age, sex, and body mass index with prediction updated with 18 established genetic risk factors. Simulated data were used to investigate measures of reclassification against DeltaAUCs of 0.005, 0.05, and 0.10. Total reclassification and net reclassification improvement were calculated for all possible cutoff values. The AUC of type 2 diabetes risk prediction improved from 0.63 to 0.66 when 18 polymorphisms were added, whereas total reclassification ranged from 0% to 22.5% depending on the cutoff value chosen. In the simulation study, total reclassification, net reclassification improvement, and integrated discrimination improvement increased with higher DeltaAUC. When DeltaAUC was low (0.005), net reclassification improvement values were close to zero, integrated discrimination improvement was 0.08% (P > 0.05), but total reclassification ranged from 0 to 6.7%. Reclassification increases with increasing AUC but predominantly varies with the cutoff values chosen. Reclassification observed in the absence of AUC increase is unlikely to improve clinical utility.

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BackgroundFew studies have examined gender differences in health status and cardiovascular outcomes in patients with peripheral artery disease (PAD). This study assessed (1) self‐reported health status at PAD diagnosis and 12‐months later, and explored (2) whether outcomes in women with PAD differ with regard to long‐term major adverse events.Methods and ResultsA total of 816 patients (285 women) with PAD were enrolled from 2 vascular clinics in the Netherlands. Baseline clinical data and subsequent adverse events were recorded and patients completed the Short Form‐12 (SF‐12, Physical Component Score [PCS] and Mental Component Score [MCS]) upon PAD diagnosis and 12‐months later. Women had similar ages and clinical characteristics, but poorer socio‐economic status and more depressive symptoms at initial diagnosis, as compared with men. Women also had poorer physical (PCS: 37±10 versus 40±10, P=0.004) and mental (MCS: 47±12 versus 49±11, P=0.005) health status at the time of presentation. At 12‐months, women still reported a poorer overall PCS score (41±12 versus 46±11, P=0.006) and MCS score (42±14 versus 49±12, P=0.002). Female gender was an independent determinant of a poorer baseline and 12‐month PCS and MCS scores. However, there were no significant differences by gender on either mortality (unadjusted hazard ratio [HR]=0.93, 95% CI 0.60;1.44, P=0.74) or major adverse events (unadjusted HR=0.90, 95% CI 0.63;1.29, P=0.57), after a median follow‐up of 3.2 years.ConclusionsWomen's physical and mental health status is compromised both at initial PAD diagnosis and at 12‐month follow‐up, despite experiencing a similar magnitude of change in their health scores throughout the first 12‐months after diagnosis.

Background: Genome-wide association studies identified novel breast cancer susceptibility variants that could be used to predict breast cancer in asymptomatic women. This review and modeling study aimed to investigate the current and potential predictive performance of genetic risk models.Methods: Genotypes and disease status were simulated for a population of 10,000 women. Genetic risk models were constructed from polymorphisms from meta-analysis including, in separate scenarios, all polymorphisms or statistically significant polymorphisms only. We additionally investigated the magnitude of the odds ratios (OR) for 1 to 100 hypothetical polymorphisms that would be needed to achieve similar discriminative accuracy as available prediction models [modeled range of area under the receiver operating characteristic curve (AUC) 0.70-0.80].Results: Of the 96 polymorphisms that had been investigated in meta-analyses, 41 showed significant associations. AUC was 0.68 for the genetic risk model based on all 96 polymorphisms and 0.67 for the 41 significant polymorphisms. Addition of 50 additional variants, each with risk allele frequencies of 0.30, requires per-allele ORs of 1.2 to increase this AUC to 0.70, 1.3 to increase AUC to 0.75, and 1.5 to increase AUC to 0.80. To achieve AUC of 0.80, even 100 additional variants would need per-allele ORs of 1.3 to 1.7, depending on risk allele frequencies.Conclusion: The predictive ability of genetic risk models in breast cancer has the potential to become comparable to that of current breast cancer risk models.Impact: Risk prediction based on low susceptibility variants becomes a realistic tool in prevention of nonfamilial breast cancer. Cancer Epidemiol Biomarkers Prev; 20(1); 9-22. Ó2011 AACR.

Two distinctive PAD phenotypes-each with its own characteristics and risk factors-emerged by anatomic lesion location; however, PAD-specific leg symptoms did not always reflect the anatomic lesion location. These findings may open new opportunities to better tailor PAD management to these two PAD subgroups and may raise awareness about not relying on self-reported symptoms to guide further diagnostic imaging and peripheral lesion management.