ArticleDer Ophthalmologe : Zeitschrift der Deutschen Ophthalmologischen Gesellschaft2016
[Freiburg keratoconus registry : Example of application of smart data for clinical research and inititial results].
Article in Der Ophthalmologe : Zeitschrift der Deutschen Ophthalmologischen Gesellschaft, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed, 1 citations in OpenAlex.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundKeratoconus is a progressive corneal disease with thinning and scarring of the cornea. Diagnostic and treatment options are usually evaluated in large prospective or retrospective trials. Big data and smart data provide the possibility to analyze routine data for clinical research. In this article we report the generation of a monocentric keratoconus registry by means of computerized data analysis of routine data. This demonstrates the potential of clinical research by means of routine data.
methodsA "clinical data warehouse" was created from all available routine electronic data. At the time of first presentation, each eye was classified into one out of four categories: suspected, early disease, late disease and status postkeratoplasty. Through integration of multiple data sources the clinical course for each patient was documented in the registry.
resultsA total of 3681 eyes from 1841 patients were included. The median follow-up time was 0.54 years. Patient age was higher in the groups with more severe stages of keratoconus, the proportion of female patients was higher in the group of suspected keratoconus and patient age and male to female ratios showed statistically significant differences between the groups (p < 0.001).
conclusionWe were able to create a "clinical data warehouse" by linking multiple data sources and normalizing the data. With this tool we established a novel, monocentric keratoconus registry. Only the grading of disease severity and the exclusion of false positive results were carried out manually. In our opinion establishing a structured clinical data warehouse has a huge potential for clinical and retrospective studies and proves the value of the Smart Data concept.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.