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20 years of DIEAP flap breast reconstruction : a big data analysis

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Abstract
With every hospital admission, a vast amount of data is collected from every patient. Big data can help in data mining and processing of this volume of data. The goal of this study is to investigate the potential of big data analyses by analyzing clinically relevant data from the immediate postoperative phase using big data mining techniques. A second aim is to understand the importance of different postoperative parameters. We analyzed all data generated during the admission of 739 women undergoing a free DIEAP flap breast reconstruction. The patients' complete midcare nursing report, laboratory data, operative reports and drug schedule were examined (7,405,359 data points). The duration of anesthesia does not predict the need for revision. Low Red Blood cell Counts (3.53 x 10(6)/mu L versus 3.79 x 10(6)/mu L, p < 0.001) and a low MAP (MAP = 73.37 versus 76.62; p < 0.001) postoperatively are correlated with significantly more revisions. Different drugs (asthma/COPD medication, Butyrophenones) can also play a significant role in the success of the free flap. In a world that is becoming more data driven, there is a clear need for electronic medical records which are easy to use for the practitioner, nursing staff, and the researcher. Very large datasets can be used, and big data analysis allows a relatively easy and fast interpretation all this information.

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MLA
Depypere, Bernard, et al. “20 Years of DIEAP Flap Breast Reconstruction : A Big Data Analysis.” SCIENTIFIC REPORTS, vol. 9, 2019, doi:10.1038/s41598-019-49125-w.
APA
Depypere, B., Herregods, S., Denolf, J., Kerkhove, L.-P., Mainil, L., Vyncke, T., … Depypere, H. (2019). 20 years of DIEAP flap breast reconstruction : a big data analysis. SCIENTIFIC REPORTS, 9. https://doi.org/10.1038/s41598-019-49125-w
Chicago author-date
Depypere, Bernard, Sofie Herregods, Jacob Denolf, Louis-Philippe Kerkhove, Laurent Mainil, Tom Vyncke, Phillip Blondeel, and Herman Depypere. 2019. “20 Years of DIEAP Flap Breast Reconstruction : A Big Data Analysis.” SCIENTIFIC REPORTS 9. https://doi.org/10.1038/s41598-019-49125-w.
Chicago author-date (all authors)
Depypere, Bernard, Sofie Herregods, Jacob Denolf, Louis-Philippe Kerkhove, Laurent Mainil, Tom Vyncke, Phillip Blondeel, and Herman Depypere. 2019. “20 Years of DIEAP Flap Breast Reconstruction : A Big Data Analysis.” SCIENTIFIC REPORTS 9. doi:10.1038/s41598-019-49125-w.
Vancouver
1.
Depypere B, Herregods S, Denolf J, Kerkhove L-P, Mainil L, Vyncke T, et al. 20 years of DIEAP flap breast reconstruction : a big data analysis. SCIENTIFIC REPORTS. 2019;9.
IEEE
[1]
B. Depypere et al., “20 years of DIEAP flap breast reconstruction : a big data analysis,” SCIENTIFIC REPORTS, vol. 9, 2019.
@article{8644528,
  abstract     = {{With every hospital admission, a vast amount of data is collected from every patient. Big data can help in data mining and processing of this volume of data. The goal of this study is to investigate the potential of big data analyses by analyzing clinically relevant data from the immediate postoperative phase using big data mining techniques. A second aim is to understand the importance of different postoperative parameters. We analyzed all data generated during the admission of 739 women undergoing a free DIEAP flap breast reconstruction. The patients' complete midcare nursing report, laboratory data, operative reports and drug schedule were examined (7,405,359 data points). The duration of anesthesia does not predict the need for revision. Low Red Blood cell Counts (3.53 x 10(6)/mu L versus 3.79 x 10(6)/mu L, p < 0.001) and a low MAP (MAP = 73.37 versus 76.62; p < 0.001) postoperatively are correlated with significantly more revisions. Different drugs (asthma/COPD medication, Butyrophenones) can also play a significant role in the success of the free flap. In a world that is becoming more data driven, there is a clear need for electronic medical records which are easy to use for the practitioner, nursing staff, and the researcher. Very large datasets can be used, and big data analysis allows a relatively easy and fast interpretation all this information.}},
  articleno    = {{12899}},
  author       = {{Depypere, Bernard and Herregods, Sofie and Denolf, Jacob and Kerkhove, Louis-Philippe and Mainil, Laurent and Vyncke, Tom and Blondeel, Phillip and Depypere, Herman}},
  issn         = {{2045-2322}},
  journal      = {{SCIENTIFIC REPORTS}},
  language     = {{eng}},
  pages        = {{7}},
  title        = {{20 years of DIEAP flap breast reconstruction : a big data analysis}},
  url          = {{http://dx.doi.org/10.1038/s41598-019-49125-w}},
  volume       = {{9}},
  year         = {{2019}},
}

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