Abstract

Incidence of postoperative atrial fibrillation (POAF) after cardiac surgery remains high and is associated with adverse patient outcomes. Risk scoring tools have been developed to predict POAF, yet discrimination performance remains moderate. Machine learning (ML) models can achieve better performance but may exhibit performance heterogeneity across race and sex subpopulations. We evaluate 8 risk scoring tools and 6 ML models on a heterogeneous cohort derived from electronic health records. Our results suggest that ML models achieve higher discrimination yet are less fair, especially with respect to race. Our findings highlight the need for building accurate and fair ML models to facilitate consistent and equitable assessment of POAF risk.

Competing Interest Statement

The authors have declared no competing interest.

Funding Statement

This study was funded by NIH Award 1R21HL156184.

Author Declarations

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

Yes

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

This study on de-identified data was approved by the Emory University Institutional Review Board.

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

Yes

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

Yes

I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

Yes

Footnotes

↵† sj3261cumc.columbia.edu

↵‡ evalverde3gatech.edu

↵§ kathryn.woodemory.edu

↵¶ kendra.janel.grubbemory.edu

↵⊠ miguel.a.lealemory.edu

↵** vhertzbemory.edu

Data Availability

Data provided in the present study are not available but the code used is available.

AbbreviationsPOAFpost-operative atrial fibrillationAUROCarea under the receiver operating characteristic curveMLmachine learningSVMsupport vector machinesGBMgradient boosting machinesRFrandom forestsCABGcoronary artery bypass graft