Dataset Information

Quantifying survival in patients with Proteus syndrome.

ABSTRACT: PurposeProteus syndrome is a rare mosaic overgrowth disorder that is associated with severe complications. While anecdotal data have suggested that the life span of affected patients is reduced, this has not been measured. Mortality data on rare diseases is critical for assessing treatments and other interventions.MethodsTo address this we used the clinical research records of 64 patients in a longitudinal natural history cohort at the National Institutes of Health to ascertain the data in an organized manner and estimate survival using a Kaplan-Meier approach.ResultsThe median age of diagnosis was 19 months. Based on this analysis, there was 25% probability of death by 22 years of age. Ten of the 11 patients who died were younger than 22 years of age, and there was only a single death after this age.ConclusionThese data quantify the risk of premature death in Proteus syndrome, which can be used to support interventions and trials. Although the risk of death is substantial, the fact that only one patient died after 22 years of age supports anecdotal evidence that the disease process moderates after the end of adolescence. Interventions to reduce mortality should be targeted to the pediatric age range.

SUBMITTER: Sapp JC

PROVIDER: S-EPMC5720922 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Quantifying survival in patients with Proteus syndrome.

Sapp Julie C JC Hu Lian L Zhao Jean J Gruber Ashlyn A Schwartz Brian B Ferrari Dora D Biesecker Leslie G LG

Genetics in medicine : official journal of the American College of Medical Genetics 20170629 12

PurposeProteus syndrome is a rare mosaic overgrowth disorder that is associated with severe complications. While anecdotal data have suggested that the life span of affected patients is reduced, this has not been measured. Mortality data on rare diseases is critical for assessing treatments and other interventions.MethodsTo address this we used the clinical research records of 64 patients in a longitudinal natural history cohort at the National Institutes of Health to ascertain the data in an or ...[more]

PMID: 28661492

Similar Datasets

Project description:Meta-analyses play an important role in synthesizing evidence from diverse studies and datasets that address similar questions. A major obstacle for meta-analyses arises from biases in reporting. In particular, it is speculated that findings which do not achieve formal statistical significance are less likely reported than statistically significant findings. Moreover, the patterns of bias can be complex and may also depend on the timing of the research results and their relationship with previously published work. In this paper, we present an approach that is specifically designed to analyze large-scale datasets on published results. Such datasets are currently emerging in diverse research fields, particularly in molecular medicine. We use our approach to investigate a dataset on Alzheimer's disease (AD) that covers 1167 results from case-control studies on 102 genetic markers. We observe that initial studies on a genetic marker tend to be substantially more biased than subsequent replications. The chances for initial, statistically non-significant results to be published are estimated to be about 44% (95% CI, 32% to 63%) relative to statistically significant results, while statistically non-significant replications have almost the same chance to be published as statistically significant replications (84%; 95% CI, 66% to 107%). Early replications tend to be biased against initial findings, an observation previously termed Proteus phenomenon: The chances for non-significant studies going in the same direction as the initial result are estimated to be lower than the chances for non-significant studies opposing the initial result (73%; 95% CI, 55% to 96%). Such dynamic patterns in bias are difficult to capture by conventional methods, where typically simple publication bias is assumed to operate. Our approach captures and corrects for complex dynamic patterns of bias, and thereby helps generating conclusions from published results that are more robust against the presence of different coexisting types of selective reporting.

Dataset Information

Quantifying survival in patients with Proteus syndrome.

Publications

Quantifying survival in patients with Proteus syndrome.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets