Impact of Inflammation After Cardiac Surgery on 30-Day Mortality and Machine Learning Risk Prediction
SIRS is significantly associated with increased 30-day mortality following cardiac surgery. Machine learning models effectively predict SIRS, paving the way for future investigations on potential targeted interventions that may mitigate adverse outcomes.
Authors
- Domenico Paparella (ORCID: https://orcid.org/0000-0003-2261-7098)
- Anna Lange-Consiglio (ORCID: https://orcid.org/0000-0002-7748-3413)
- Felice Piancone
- Richard Paul Whitlock (ORCID: https://orcid.org/0000-0002-6863-5884)
- Renard Gerhardus Haumann (ORCID: https://orcid.org/0009-0001-2151-4452)
- Enrico Squiccimarro (ORCID: https://orcid.org/0000-0002-6887-747X)
- Cataldo Labriola (ORCID: https://orcid.org/0000-0002-8148-2640)
- Giuseppe Speziale (ORCID: https://orcid.org/0000-0003-1149-1151)
- Roberto Lorusso (ORCID: https://orcid.org/0000-0002-5805-7777)
Institutions
- University of Foggia (IT)
- Maastricht University Medical Centre (NL)
- Medisch Spectrum Twente (NL)
- Population Health Research Institute (CA)
- Woodlands Hospital (GB)
- University of Twente (NL)
Publication Details
- Journal
- Journal of Cardiothoracic and Vascular Anesthesia
- Published
- 2024-12-10
- DOI
- https://doi.org/10.1053/j.jvca.2024.12.013
- Citations
- 9
- Primary Topic
- Cardiac Health and Mental Health
- Type
- article
- Field-Weighted Citation Impact
- 3.62