Imperial College London
Alternate

Prof Marc Chadeau-Hyam

Faculty of Medicine, School of Public Health

Professor of Computational Epidemiology and Biostatistics
 
 
 
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Contact

 

+44 (0)20 7594 1637m.chadeau

 
 
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Location

 

520Medical SchoolSt Mary's Campus

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Summary

 

Publications

Citation

BibTex format

@article{Garmendia:2023:10.1101/2023.01.20.23284432,
author = {Garmendia, AT and Gkouzionis, I and Triantafyllidis, CP and Dimakopoulos, V and Liliopoulos, S and Vuckovic, D and Paseiro-Garcia, L and Chadeau-Hyam, M},
doi = {10.1101/2023.01.20.23284432},
title = {Towards personalised early prediction of Intra-Operative Hypotension following anesthesia using Deep Learning and phenotypic heterogeneity},
url = {http://dx.doi.org/10.1101/2023.01.20.23284432},
year = {2023}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - <jats:title>Abstract</jats:title><jats:p>Intra-Operative Hypotension (IOH) is a haemodynamic abnormality that is commonly observed in operating theatres following general anesthesia and associates with life-threatening post-operative complications. Using Long Short Term Memory (LSTM) models applied to Electronic Health Records (EHR) and time-series intra-operative data in 604 patients that underwent colorectal surgery we predicted the instant risk of IOH events within the next five minutes. K-means clustering was used to group patients based on pre-clinical data. As part of a sensitivity analysis, the model was also trained on patients clustered according to Mean artelial Blood Pressure (MBP) time-series trends at the start of the operation using K-means with Dynamic Time Warping. The baseline LSTM model trained on all patients yielded a test set Area Under the Curve (AUC) value of 0.83. In contrast, training the model on smaller sized clusters (grouped by EHR) improved the AUC value (0.85). Similarly, the AUC was increased by 4.8% (0.87) when training the model on clusters grouped by MBP. The encouraging results of the baseline model demonstrate the applicability of the approach in a clinical setting. Furthermore, the increased predictive performance of the model after being trained using a clustering approach first, paves the way for a more personalised patient stratification approach to IOH prediction using clinical data.</jats:p>
AU  - Garmendia,AT
AU  - Gkouzionis,I
AU  - Triantafyllidis,CP
AU  - Dimakopoulos,V
AU  - Liliopoulos,S
AU  - Vuckovic,D
AU  - Paseiro-Garcia,L
AU  - Chadeau-Hyam,M
DO  - 10.1101/2023.01.20.23284432
PY  - 2023///
TI  - Towards personalised early prediction of Intra-Operative Hypotension following anesthesia using Deep Learning and phenotypic heterogeneity
UR  - http://dx.doi.org/10.1101/2023.01.20.23284432
ER  -
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