{
  "abstract": "Background Current risk scores inadequately predict long-term mortality after transcatheter aortic valve replacement (TAVR), limiting their ability to guide decisions around procedural futility. We aimed to develop and externally validate a machine learning (ML) model using only preprocedural variables to predict 1-year all-cause mortality.Methods An ML model was trained on a retrospective cohort of 1025 TAVR patients using 52 clinical and echocardiographic variables. Feature selection and model tuning were performed via a multiobjective evolutionary algorithm to optimise predictive performance and model simplicity. The final model used 13 preprocedural variables and was externally validated in an independent cohort of 270 patients. Performance was compared with European System for Cardiac Operative Risk Evaluation II (EuroSCORE II), FRANCE-2 and TAVI2-SCORE using the area under the curve (AUC), calibration and net reclassification improvement (NRI).Results The ML model demonstrated excellent discrimination, with AUCs of 0.81 in the discovery cohort and 0.84 in the external validation cohort. This exceeded the performance of EuroSCORE II (AUC: 0.61 and 0.70), FRANCE-2 (0.52 and 0.58) and TAVI2-SCORE (0.56 and 0.64). Calibration plots showed strong agreement between predicted and observed risks. NRI in the test set compared with FRANCE-2 was 0.62 (95% CI: 0.49 to 0.75); compared with TAVI2-SCORE, it was 0.36 (95% CI: 0.14 to 0.61). The final model incorporated age, atrial fibrillation, creatinine, haemoglobin, pulmonary function, frailty markers (Katz Index, poor mobility) and tricuspid regurgitation. Misclassification analysis revealed that most errors were clustered near the decision threshold, with no evidence of systematic bias. Performance was consistent across subgroups and robust to temporal and institutional variation.Conclusion This externally validated ML model, using 13 routinely available variables, significantly outperforms existing risk scores in predicting 1-year mortality post-TAVR. Its simplicity and generalisability support its potential use in real-world clinical decision-making to identify patients at high risk of procedural futility.",
  "authors": [
    {
      "affiliations": [
        "Department of Cardiology, King’s College Hospital NHS Foundation Trust, London, UK"
      ],
      "name": "Apurva H Bharucha"
    },
    {
      "affiliations": [
        "Department of Cardiology, King’s College Hospital NHS Foundation Trust, London, UK"
      ],
      "name": "Samuel Brown"
    },
    {
      "affiliations": [
        "King's College London British Heart Foundation Centre of Research Excellence, School of Cardiovascular Medicine and Sciences, King's College London, London, UK"
      ],
      "name": "Konstantinos Theofilatos"
    },
    {
      "affiliations": [
        "Department of Cardiology, King’s College Hospital NHS Foundation Trust, London, UK"
      ],
      "name": "Bhawana Singh"
    },
    {
      "affiliations": [
        "Department of Cardiology, King’s College Hospital NHS Foundation Trust, London, UK"
      ],
      "name": "Victor Galusko"
    },
    {
      "affiliations": [
        "Cardiovascular Department, Guy’s and St Thomas’ NHS Foundation Trust, London, UK",
        "Victorian Heart Institute, Monash University, Melbourne, Victoria, Australia"
      ],
      "name": "Hashrul Rashid"
    },
    {
      "affiliations": [
        "Department of Cardiology, King’s College Hospital NHS Foundation Trust, London, UK"
      ],
      "name": "Hoda Abdelgawad"
    },
    {
      "affiliations": [
        "Department of Cardiology, King’s College Hospital NHS Foundation Trust, London, UK"
      ],
      "name": "Rafal Dworakowski"
    },
    {
      "affiliations": [
        "Department of Cardiology, King’s College Hospital NHS Foundation Trust, London, UK"
      ],
      "name": "Alexandros Papachristidis"
    },
    {
      "affiliations": [
        "Cardiovascular Department, Guy’s and St Thomas’ NHS Foundation Trust, London, UK"
      ],
      "name": "Tiffany Patterson"
    },
    {
      "affiliations": [
        "Cardiovascular Department, Guy’s and St Thomas’ NHS Foundation Trust, London, UK"
      ],
      "name": "Simon R Redwood"
    },
    {
      "affiliations": [
        "Cardiovascular Department, Guy’s and St Thomas’ NHS Foundation Trust, London, UK"
      ],
      "name": "Bernard Prendergast"
    },
    {
      "affiliations": [
        "Department of Cardiology, King’s College Hospital NHS Foundation Trust, London, UK"
      ],
      "name": "Jonathan A Byrne"
    },
    {
      "affiliations": [
        "Department of Cardiology, King’s College Hospital NHS Foundation Trust, London, UK"
      ],
      "name": "Philip A MacCarthy"
    },
    {
      "affiliations": [
        "Department of Cardiology, King’s College Hospital NHS Foundation Trust, London, UK"
      ],
      "name": "Kevin O’Gallagher"
    },
    {
      "affiliations": [
        "Department of Cardiology, King’s College Hospital NHS Foundation Trust, London, UK"
      ],
      "name": "Mehdi Eskandari"
    }
  ],
  "title": "Parsimonious machine learning model to predict 1-year mortality and procedural futility after transcatheter aortic valve replacement",
  "uid": "f8ca4e6f-7ceb-5f1f-8db9-b7c28c680d02"
}