Treatment response to spironolactone in patients with heart failure with preserved ejection fraction: a machine learning- based analysis of two randomized controlled trials

被引:14
|
作者
Kresoja, Karl-Patrik [1 ,2 ]
Unterhuber, Matthias [1 ,2 ]
Wachter, Rolf [3 ,4 ,5 ]
Rommel, Karl-Philipp [1 ,2 ]
Besler, Christian [1 ,2 ]
Shah, Sanjiv [6 ]
Thiele, Holger [1 ,2 ]
Edelmann, Frank [7 ,8 ]
Lurz, Philipp [1 ,2 ,9 ]
机构
[1] Univ Leipzig, Heart Ctr Leipzig, Dept Cardiol, Leipzig, Germany
[2] Heart Ctr Leipzig, Leipzig Heart Inst, Leipzig, Germany
[3] Univ Med Gottingen, Univ Hosp Leipzig, Dept Cardiol, Gottingen, Germany
[4] Univ Med Gottingen, Clin Cardiol & Pneumol, Gottingen, Germany
[5] German Cardiovasc Res Ctr DZHK, Partner Site Gottingen, Gottingen, Germany
[6] Northwestern Univ, Feinberg Sch Med, Dept Med, Div Cardiol, Evanston, IL USA
[7] Charite Univ Med Berlin, Dept Internal Med & Cardiol, Campus Virchow Klinikum, Berlin, Germany
[8] German Cardiovasc Res Ctr DZHK, Partner Site Berlin, Berlin, Germany
[9] Univ Leipzig, Dept Internal Med Cardiol, Heart Ctr Leipzig, Struempellstr 39, D-04289 Leipzig, Germany
来源
EBIOMEDICINE | 2023年 / 96卷
关键词
Machine learning; Heart failure with preserved ejection fraction; Spironolactone; PULMONARY-ARTERY PRESSURE; DIASTOLIC FUNCTION; EXERCISE CAPACITY; ECHOCARDIOGRAPHY;
D O I
10.1016/j.ebiom.2023.104795
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Background Whether there is a subset of patients with heart failure with preserved ejection fraction (HFpEF) that benefit from spironolactone therapy is unclear. We applied a machine learning approach to identify responders and non-responders to spironolactone among patients with HFpEF in two large randomized clinical trials.Methods Using a reiterative cluster allocating permutation approach, patients from the derivation cohort (Aldo-DHF) were identified according to their treatment response to spironolactone with respect to improvement in E/e'. Heterogenous features of response ('responders' and 'non-responders') were characterized by an extreme gradient boosting (XGBoost) algorithm. XGBoost was used to predict treatment response in the validation cohort (TOPCAT). The primary endpoint of the validation cohort was a combined endpoint of cardiovascular mortality, aborted cardiac arrest, or heart failure hospitalization. Patients with missing variables for the XGboost model were excluded from the validation analysis.Findings Out of 422 patients from the derivation cohort, reiterative cluster allocating permutation identified 159 patients (38%) as spironolactone responders, in whom E/e' significantly improved (p = 0.005). Within the validation cohort (n = 525) spironolactone treatment significantly reduced the occurrence of the primary outcome among re-sponders (n = 185, p log rank = 0.008), but not among patients in the non-responder group (n = 340, p log rank = 0.52).Interpretation Machine learning approaches might aid in identifying HFpEF patients who are likely to show a favorable therapeutic response to spironolactone.
引用
收藏
页数:12
相关论文
共 50 条
  • [1] Effects of spironolactone in heart failure with preserved ejection fraction A meta-analysis of randomized controlled trials
    Li, Shuai
    Zhang, Xinling
    Dong, Mei
    Gong, Shu
    Shang, Zhi
    Jia, Xu
    Chen, Wenqiang
    Yang, Jianmin
    Li, Jifu
    MEDICINE, 2018, 97 (35)
  • [2] Spironolactone in patients with heart failure and preserved ejection fraction
    Sanchez-Sanchez, C.
    Mendoza-Ruiz de Zuazu, H. F.
    Formiga, F.
    Manzano, L.
    Ceresuela, L. M.
    Carrera-Izquierdo, M.
    Gonzalez Franco, A.
    Epelde-Gonzalo, F.
    Cerqueiro-Gonzalez, J. M.
    Montero-Perez-Barquero, M.
    REVISTA CLINICA ESPANOLA, 2015, 215 (06): : 301 - 307
  • [3] Efficacy and safety of spironolactone in the heart failure with mid-range ejection fraction and heart failure with preserved ejection fraction A meta-analysis of randomized clinical trials
    Xiang, Yajie
    Shi, Wenhai
    Li, Zhuolin
    Yang, Yunjing
    Wang, Stephen Yishu
    Xiang, Rui
    Feng, Panpan
    Wen, Li
    Huang, Wei
    MEDICINE, 2019, 98 (13)
  • [4] Spironolactone effect on cardiac structure and function of patients with heart failure and preserved ejection fraction: a pooled analysis of three randomized trials
    Ferreira, Joao Pedro
    Cleland, John G.
    Girerd, Nicolas
    Bozec, Erwan
    Rossignol, Patrick
    Pellicori, Pierpaolo
    Cosmi, Franco
    Mariottoni, Beatrice
    Solomon, Scott D.
    Pitt, Bertram
    Pfeffer, Marc A.
    Shah, Amil M.
    Petutschnigg, Johannes
    Pieske, Burkert
    Edelmann, Frank
    Zannad, Faiez
    EUROPEAN JOURNAL OF HEART FAILURE, 2023, 25 (01) : 108 - 113
  • [5] Pharmacotherapies in Heart Failure With Preserved Ejection Fraction: A Systematic Review and Meta-Analysis of Randomized Controlled Trials
    Baral, Nischit
    Gautam, Swotantra
    Yadav, Saroj A.
    Poudel, Sangeeta
    Adhikari, Govinda
    Rauniyar, Rohit
    Savarapu, Pramod
    Katel, Anjan
    Paudel, Anish C.
    Parajuli, Prem R.
    CUREUS JOURNAL OF MEDICAL SCIENCE, 2021, 13 (02)
  • [6] Phenotypes of heart failure with preserved ejection fraction and effect of spironolactone treatment
    Choy, Manting
    Liang, Weihao
    He, Jiangui
    Fu, Michael
    Dong, Yugang
    He, Xin
    Liu, Chen
    ESC HEART FAILURE, 2022, 9 (04): : 2567 - 2575
  • [7] Influence of ejection fraction on outcomes and efficacy of spironolactone in patients with heart failure with preserved ejection fraction
    Solomon, Scott D.
    Claggett, Brian
    Lewis, Eldrin F.
    Desai, Akshay
    Anand, Inder
    Sweitzer, Nancy K.
    O'Meara, Eileen
    Shah, Sanjiv J.
    McKinlay, Sonja
    Fleg, Jerome L.
    Sopko, George
    Pitt, Bertram
    Pfeffer, Marc A.
    EUROPEAN HEART JOURNAL, 2016, 37 (05) : 455 - 462
  • [8] Role of spironolactone in the treatment of heart failure with preserved ejection fraction
    Kosmas, Constantine E.
    Silverio, Delia
    Sourlas, Andreas
    Montan, Peter D.
    Guzman, Eliscer
    ANNALS OF TRANSLATIONAL MEDICINE, 2018, 6 (23)
  • [9] Phenomapping of patients with heart failure with preserved ejection fraction using machine learning-based unsupervised cluster analysis
    Segar, Matthew W.
    Patel, Kershaw V.
    Ayers, Colby
    Basit, Mujeeb
    Tang, W. H. Wilson
    Willett, Duwayne
    Berry, Jarett
    Grodin, Justin L.
    Pandey, Ambarish
    EUROPEAN JOURNAL OF HEART FAILURE, 2020, 22 (01) : 148 - 158
  • [10] Targeting efficacy of spironolactone in patients with heart failure with preserved ejection fraction: the TOPCAT study
    Zhou, Hui-min
    Zhan, Rong-jian
    Chen, Xuanyu
    Lin, Yi-fen
    Zhang, Shao-zhao
    Zheng, Huigan
    Wang, Xueqin
    Huang, Meng-ting
    Xu, Chao-guang
    Liao, Xin-xue
    Tian, Ting
    Zhuang, Xiao-dong
    ESC HEART FAILURE, 2023, 10 (01): : 322 - 333