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Identifying and preliminary validating patient clusters in coronary artery bypass grafting: integrating autonomic
Pavandeep Singh1, Alberto Porta1,2, Marco Ranucci1
1Department of Cardiothoracic, Vascular Anesthesia and Intensive Care, IRCCS Policlinico San Donato, 20097 San Donato Milanese, Italy.
Insights
This study identified two patient clusters undergoing coronary artery bypass grafting (CABG) based on risk and autonomic response. Lower-risk patients experienced fewer complications, highlighting the value of autonomic function in risk stratification.
Area of Science:
- Cardiology and Anesthesiology
- Autonomic Nervous System Function
- Patient Risk Stratification
Background:
- Coronary artery bypass grafting (CABG) patient outcomes can vary significantly.
- Identifying distinct patient subgroups is crucial for personalized care and improved outcomes.
- Autonomic function plays a role in perioperative stress and recovery.
Purpose of the Study:
- To identify and validate distinct patient clusters undergoing CABG.
- To analyze clusters based on demographic, clinical, and autonomic function characteristics.
- To determine the predictive value of these clusters for postoperative complications.
Main Methods:
- A cohort of 154 adult patients undergoing CABG in Italy was prospectively studied.
- Clustering was performed using t-distributed stochastic neighbor embedding (t-SNE) and hierarchical clustering on 23 variables.
- Included variables were pre- and post-anesthesia autonomic function indices, and demographic/clinical data.
Main Results:
- Two distinct clusters were identified: 'Higher Risk-Responsive Group' and 'Lower Risk-Responsive Group'.
- The 'Higher Risk-Responsive Group' comprised older patients with higher comorbidity and poorer autonomic function.
- The 'Lower Risk-Responsive Group' experienced significantly fewer complications (IRR = 0.441, p=0.004).
Conclusions:
- Autonomic function measures, combined with clinical data, can improve patient monitoring and risk stratification.
- Integrating these factors into early warning scores may enhance postoperative outcomes.
- Post-anesthesia autonomic function, particularly systolic arterial pressure variability, is a significant predictor of complications.
Aims:
This study aims to identify distinct clusters of patients undergoing coronary artery bypass grafting (CABG) based on demographic, clinical, and autonomic function characteristics and to validate these clusters.
Methods And Results:
Our cohort study included 154 subjects aged 18 years and older undergoing CABG, enrolled in Italy, from April 2017 to January 2020. Data were prospectively collected from pre-anaesthesia induction to hospital discharge. Clustering was performed using t-distributed stochastic neighbour embedding (t-SNE) on 23 variables and hierarchical clustering, including pre- and post-anaesthesia autonomic function indices and demographic and clinical data. Two distinct clusters were identified: 'higher risk-responsive group' and 'lower risk-responsive group'. The higher risk-responsive group cluster consisted of older patients with higher co-morbidity rates and worse autonomic function. Validation of clusters through multiple correspondence analysis and Poisson regression demonstrated significant differences in post-operative outcomes. Patients in the lower risk-responsive group cluster had fewer complications (IRR = 0.441, P = 0.004). The analysis indicated that intensive care unit (ICU) stay duration and the power of systolic arterial pressure (SAP) series in low-frequency band derived in the post-anaesthesia phase were significant predictors of complications above and beyond the expected contributions of age and comorbidities, with longer ICU stays and lower low-frequency power of SAP post-anaesthesia induction being associated with higher complication rates.
Conclusion:
Integrating autonomic function measures and demographic and clinical data could enhance patient monitoring and intervention, improving outcomes if included in future risk stratification tools and early warning score systems.
Registration:
ClinicalTrials.gov: NCT03169608.
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