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01.12.2012 | Research article | Ausgabe 1/2012 Open Access

BMC Medical Informatics and Decision Making 1/2012

An electronic health record-enabled obesity database

Zeitschrift:
BMC Medical Informatics and Decision Making > Ausgabe 1/2012
Autoren:
G Craig Wood, Xin Chu, Christina Manney, William Strodel, Anthony Petrick, Jon Gabrielsen, Jamie Seiler, David Carey, George Argyropoulos, Peter Benotti, Christopher D Still, Glenn S Gerhard
Wichtige Hinweise

Electronic supplementary material

The online version of this article (doi:10.​1186/​1472-6947-12-45) contains supplementary material, which is available to authorized users.

Competing interest

Co-author Christopher Still receives grant and consulting support from Ethicon-Endosurgery. Co-author Anthony Petrick receives educational grants from Covidien and Ethicon-Endosurgery. Other authors declare no conflict of interest.

Authors’ contributions

GCW, GSG, PB, GA, and CDS designed the study, directed the data analysis, performed data analysis, and contributed to writing the paper. XC, CM, and JS prepared the data set for analysis. WS, AP, JG, and DC directed the data analysis and contributed to writing the paper. All of the authors have read and approved the final manuscript.

Abstract

Background

The effectiveness of weight loss therapies is commonly measured using body mass index and other obesity-related variables. Although these data are often stored in electronic health records (EHRs) and potentially very accessible, few studies on obesity and weight loss have used data derived from EHRs. We developed processes for obtaining data from the EHR in order to construct a database on patients undergoing Roux-en-Y gastric bypass (RYGB) surgery.

Methods

Clinical data obtained as part of standard of care in a bariatric surgery program at an integrated health delivery system were extracted from the EHR and deposited into a data warehouse. Data files were extracted, cleaned, and stored in research datasets. To illustrate the utility of the data, Kaplan-Meier analysis was used to estimate length of post-operative follow-up.

Results

Demographic, laboratory, medication, co-morbidity, and survey data were obtained from 2028 patients who had undergone RYGB at the same institution since 2004. Pre-and post-operative diagnostic and prescribing information were available on all patients, while survey laboratory data were available on a majority of patients. The number of patients with post-operative laboratory test results varied by test. Based on Kaplan-Meier estimates, over 74% of patients had post-operative weight data available at 4 years.

Conclusion

A variety of EHR-derived data related to obesity can be efficiently obtained and used to study important outcomes following RYGB.
Zusatzmaterial
Additional file 1:Supplementary materials.(DOC 408 KB)
12911_2011_563_MOESM1_ESM.doc
Authors’ original file for figure 1
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Authors’ original file for figure 2
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Literatur
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