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Analysis of publically available skin sensitization data from REACH registrations 2008-2014.

ABSTRACT: The public data on skin sensitization from REACH registrations already included 19,111 studies on skin sensitization in December 2014, making it the largest repository of such data so far (1,470 substances with mouse LLNA, 2,787 with GPMT, 762 with both in vivo and in vitro and 139 with only in vitro data). 21% were classified as sensitizers. The extracted skin sensitization data was analyzed to identify relationships in skin sensitization guidelines, visualize structural relationships of sensitizers, and build models to predict sensitization. A chemical with molecular weight > 500 Da is generally considered non-sensitizing owing to low bioavailability, but 49 sensitizing chemicals with a molecular weight > 500 Da were found. A chemical similarity map was produced using PubChem's 2D Tanimoto similarity metric and Gephi force layout visualization. Nine clusters of chemicals were identified by Blondel's module recognition algorithm revealing wide module-dependent variation. Approximately 31% of mapped chemicals are Michaell's acceptors but alone this does not imply skin sensitization. A simple sensitization model using molecular weight and five ToxTree structural alerts showed a balanced accuracy of 65.8% (specificity 80.4%, sensitivity 51.4%), demonstrating that structural alerts have information value. A simple variant of k-nearest neighbors outperformed the ToxTree approach even at 75% similarity threshold (82% balanced accuracy at 0.95 threshold). At higher thresholds, the balanced accuracy increased. Lower similarity thresholds decrease sensitivity faster than specificity. This analysis scopes the landscape of chemical skin sensitization, demonstrating the value of large public datasets for health hazard prediction.

SUBMITTER: Luechtefeld T

PROVIDER: S-EPMC5546098 | biostudies-other | 2016

REPOSITORIES: biostudies-other

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Project description:OBJECTIVES:We examine the volume-outcome relationship in isolated transcatheter aortic valve implantations (TAVI). Our interest was whether the volume-outcome relationship for TAVI exists on the centre level, whether it occurs equally for different outcomes and how it develops over time. DESIGN:Secondary data analysis of electronic health records. The comprehensive German Federal Bureau of Statistics Diagnosis Related Groups database was queried for data on all isolated TAVI procedures performed in Germany between 2008 and 2014. Logistic and linear regression analyses were carried out. Risk adjustment was applied using a predefined set of patient characteristics to account for differences in the risk factor composition of the patient populations between centres and over time. Centres performing TAVI were stratified into groups performing <50, 50-99 and ?100 procedures per year. SETTING:Germany 2008-2014. PARTICIPANTS:All patients undergoing isolated TAVI in the observation period. INTERVENTIONS:None. PRIMARY AND SECONDARY OUTCOME MEASURES:In-hospital mortality, bleeding, stroke, probability of ventilation >48?hours, length of hospital stay and reimbursement. RESULTS:Between 2008 and 2014, a total of 43 996 TAVI procedures were performed in 113 different centres in Germany with a total of 2532 cases of in-hospital mortality. Risk-adjusted in-hospital mortality decreases over the years and is lower the higher the annual procedure volume at the centre is. The magnitude of the latter effect declines over the observation period. Our results indicate a ceiling effect in the volume-outcome relationship: the volume-outcome relationship is eminent in circumstances of relatively unfavourable outcomes. Alongside improving outcomes, however, the volume-outcome relationship decreases. Also, a volume-outcome relationship seems to be absent in circumstances of constantly low event rates. CONCLUSIONS:The hypothesised volume-outcome relationship for TAVI exists but diminishes and may disappear over time. This should be taken into account when considering mandatory minimum thresholds.

Dataset Information

Analysis of publically available skin sensitization data from REACH registrations 2008-2014.

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