Model-driven survival prediction after congenital heart surgery
The objective of the study was to improve postoperative risk assessment in congenital heart surgery by developing a machine-learning model based on readily available peri- and postoperative parameters.Our bicentric retrospective data analysis from January 2014 to December 2019 of established risk pa...
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
September 2023
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| In: |
Interdisciplinary cardiovascular and thoracic surgery
Year: 2023, Volume: 37, Issue: 3, Pages: 1-7 |
| ISSN: | 2753-670X |
| DOI: | 10.1093/icvts/ivad089 |
| Online Access: | Verlag, kostenfrei, Volltext: https://doi.org/10.1093/icvts/ivad089
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| Author Notes: | Christoph Zürn, David Hübner, Victoria C. Ziesenitz, René Höhn, Lena Schuler, Tim Schlange, Matthias Gorenflo, Fabian A. Kari, Johannes Kroll, Tsvetomir Loukanov, Rolf Klemm and Brigitte Stiller |
| Summary: | The objective of the study was to improve postoperative risk assessment in congenital heart surgery by developing a machine-learning model based on readily available peri- and postoperative parameters.Our bicentric retrospective data analysis from January 2014 to December 2019 of established risk parameters for dismal outcome was used to train and test a model to predict postoperative survival within the first 30 days. The Freiburg training data consisted of 780 procedures; the Heidelberg test data comprised 985 procedures. STAT mortality score, age, aortic cross-clamp time and postoperative lactate values over 24 h were considered.Our model showed an area under the curve (AUC) of 94.86%, specificity of 89.48% and sensitivity of 85.00%, resulting in 3 false negatives and 99 false positives.The STAT mortality score and the aortic cross-clamp time each showed a statistically highly significant impact on postoperative mortality. Interestingly, a child’s age was barely statistically significant. Postoperative lactate values indicated an increased mortality risk if they were either constantly at a high level or low during the first 8 h postoperatively with an increase afterwards.When considering parameters available before, at the end of and 24 h after surgery, the predictive power of the complete model achieved the highest AUC. This, compared to the already high predictive power alone (AUC 88.9%) of the STAT mortality score, translates to an error reduction of 53.5%.Our model predicts postoperative survival after congenital heart surgery with great accuracy. Compared with preoperative risk assessments, our postoperative risk assessment reduces prediction error by half. Heightened awareness of high-risk patients should improve preventive measures and thus patient safety. |
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| Item Description: | Online verfügbar: 05. Juni 2023, Artikelversion: 10. September 2023 Gesehen am 14.11.2023 |
| Physical Description: | Online Resource |
| ISSN: | 2753-670X |
| DOI: | 10.1093/icvts/ivad089 |