What is the clinical evidence from this study?

Study design: Cohort. Population: Coronary artery bypass grafting (CABG) (n=154). Intervention: Lower risk-responsive group cluster vs. Higher risk-responsive group cluster. Primary outcome: Post-operative complications (IRR 0.441, p=0.004).

What does this research mean for the field?

Integrating autonomic function indices with demographic and clinical data identifies distinct patient clusters that significantly predict post-operative complications in patients undergoing coronary artery bypass grafting. Novelty: ClaimNovelty.INCREMENTAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

April 3, 2025Open Access

Identifying and preliminary validating patient clusters in coronary artery bypass grafting: integrating autonomic function with clinical and demographic data for personalized care

Key Result

Patients classified in the lower risk-responsive group cluster had significantly fewer post-operative complications compared to the higher risk-responsive group (IRR = 0.441, P = 0.004).

Study Design

Type

Cohort (n=154)

Structured PICO

Does integrating autonomic function with clinical and demographic data identify distinct risk clusters for post-operative complications in patients undergoing CABG?

Population

154 subjects aged 18 years and older undergoing coronary artery bypass grafting (CABG) in Italy

Intervention

Risk stratification using clustering (t-SNE and hierarchical) based on 23 variables including pre- and post-anaesthesia autonomic function indices, demographic, and clinical data

Outcome

Post-operative complicationshard clinical

Integrating autonomic function measures with demographic and clinical data identifies distinct patient clusters that predict post-operative complications in CABG patients.

Main Result

Effect estimate: IRR 0.441

p-value: p=0.004

Abstract

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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