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Machine learning based risk stratification of patients undergoing cardiac resynchronization therapy

Session Rapid Fire 6 - Risk stratification and prognosis

Speaker Marton Tokodi

Congress : Heart Failure 2019

  • Topic : arrhythmias and device therapy
  • Sub-topic : Cardiac Resynchronization Therapy
  • Session type : Rapid Fire Abstracts
  • FP Number : 1474

Authors : M Tokodi (Budapest,HU), Z Toser (Budapest,HU), AM Boros (Budapest,HU), W Schwertner (Budapest,HU), A Kovacs (Budapest,HU), P Perge (Budapest,HU), G Szeplaki (Budapest,HU), L Geller (Budapest,HU), A Kosztin (Budapest,HU), B Merkely (Budapest,HU)

M Tokodi1 , Z Toser1 , AM Boros1 , W Schwertner1 , A Kovacs1 , P Perge1 , G Szeplaki1 , L Geller1 , A Kosztin1 , B Merkely1 , 1Semmelweis University Heart Center - Budapest - Hungary ,


Background: Cardiac Resynchronization Therapy (CRT) is a cornerstone in the management of patients with advanced heart failure, reduced ejection fraction and wide QRS complex. Despite its well-known beneficial effects, mortality rates still remain high in this patient population. Therefore, accurate risk stratification of these patients would be essential, however, the currently available risk scores have several shortcomings which limit their utilization in the everyday clinical practice. 
Purpose: Accordingly, our aim was to design and validate a machine learning based risk stratification system to predict 2-year and 5-year mortality from pre-implant parameters of patients undergoing CRT implantation. 
Methods: We trained two models separately to predict 2-year (model 1) and 5-year mortality (model 2). As training cohort of model 1 we used 2098 patients (67±10 years, 1574 [75%] males, 1026 [49%] CRT-P, 1072 [51%] CRT-D) undergoing CRT implantation. Out of this population 1650 patients (66±10 years, 1258 [76%] males, 899 [54%] CRT-P, 751 [46%] CRT-D) also had 5-year follow-up data available and they served as the training cohort for model 2. At the time of implantation, demographics, cardiovascular risk factors, medication and laboratory test results were assessed. Forty-seven pre-implant parameters were used to train the models. Our models were designed in a way to tolerate missing values. Among non-linear classifiers, random forest (number of trees: 200) demonstrated the best performance. We validated our models, along with the Seattle Heart Failure Model (SHFM), VALID-CRT risk score and EAARN score on an independent cohort of 136 patients (66±10 years, 110 [81%] males, 114 [84%] CRT-P, 22 [16%] CRT-D).
Results: There were 458 (22%) deaths in the 2-year, 879 (53%) deaths in the 5-year training cohort. In the validation cohort, there were 30 (22%) deaths at 2 years and 58 (43%) deaths at 5 years after CRT implantation. For the prediction of 2-year mortality, the Area Under the Receiver-Operating Characteristic Curve (AUC) for model 1 was 0.77 (95% Confidence Interval [CI]: 0.67-0.87; p=0.002), for SHFM was 0.54 (95% CI: 0.39-0.69; p=0.006), for EAARN was 0.57 (95% CI: 0.46-0.68, p=0.002), and for VALID-CRT was 0.62 (95% CI: 0.52-0.71; p=0.002). To predict 5-year mortality the AUC for model 2 was 0.85 (95% CI: 0.78-0.91; p=0.001), for SHFM was 0.62 (95% CI: 0.51-0.74; p=0.003), for EAARN was 0.61 (95% CI: 0.51-0.70, p=0.002), for VALID-CRT was 0.65 (95% CI: 0.56-0.74; p=0.002). The AUCs of the machine learning based models were significantly higher than the AUCs of the pre-existing scores (DeLong test, all p<0.05).
Conclusion: Our results indicate that machine learning algorithms can outperform the already existing linear model based scores. By capturing the non-linear association of predictors, the utilization of these state-of-the-art approaches may facilitate optimal candidate selection and prognostication of patients undergoing CRT implantation.

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