Random effects adjustment in machine learning models for cardiac surgery risk prediction: a benchmarking study

Objectives There is an ongoing debate over whether a procedural specific (e.g. Society of Thoracic Surgeons (STS)) or universal model (e.g. EuroSCORE II (ES II)) should be used for patient selection in cardiac surgery. Recently, we showed that ES II suffers from severe performance drift across several important metrics and that ML approaches such as Xgboost and Random Forest are substantially more resistant to dataset drift. With the growing interest in big data and its leverage through the use of ML approaches that are not limited by linear statistical assumptions, the number of clinical variables can theoretically increase exponentially. In addition, the variations and residual confounding that historically hindered the usefulness of cardiac risk stratification scores can potentially be taken into account. Here, we assess these possibilities on a large United Kingdom (UK) database. Methods A retrospective analysis of prospectively routinely gathered data on adult patients undergoing cardiac surgery in the UK between 2012-2019. We temporally split the data 70:30 into a training and validation subset. Two sets of seven ML mortality prediction models, with and without variable selection were assessed for consensus Clinical Effective Metric (CEM) overall performance and performance within each of CEM’s consistuent metrics. Confounding and potential causal relationships between covariates and outcomes were evaluated using bayesian network analysis. Results A total of 227,087 adults underwent cardiac surgery during the study period with a mortality rate of 2.76%. For non-variable selected (NVS) risk scores with 102 variables, Xgboost with adjustment for hospital variation was superior to the Xgboost without adjustment (p < 2e-16). Both NVS and the 18 variables selected (VS) Xgboost with adjustment for hospital variation risk scores were superior to the Xgboost (ES II 18 variables) model (p < 6.3e-15), with NVS Xgboost with adjustment for hospital variation having the best performance, followed by the VS Xgboost with adjustment for hospital variation (CEM Difference: 0.0150 and 0.0023, respectively). Conclusions We have identified an ML adjusted risk score comprising 102 variables that increases risk stratification performance on hold out dataset, removing the need to perform variable selection and reduction. This paves the way for further research that utilises this new set of variables with hospital-based adjustments for the safer selection of patients undergoing cardiac surgery. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This work was supported by a grant from the BHF-Turing Institute and the NIHR Biomedical Research Centre at University Hospitals Bristol and Weston NHS Foundation Trust and the University of Bristol. ### 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: The register-based cohort study is part of a research approved by the Health Research Authority (HRA) and Health and Care Research Wales and a waiver for patients' consent was waived (HCRW) (IRAS ID: 278171). 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 All data used in this study are from the National Adult Cardiac Surgery Audit (NACSA) dataset. These data may be requested from Healthcare Quality Improvement Partnership (HQIP), . Code for deriving training, update, and hold-out datasets is available on GitHub and authors can provide confirmatory de-identified record IDs for each set upon reasonable request.