Our aim was to develop a machine learning (ML)-based risk stratification system to
predict 1-, 2-, 3-, 4-, and 5-year all-cause mortality from pre-implant parameters
of patients undergoing cardiac resynchronization therapy (CRT).Multiple ML models
were trained on a retrospective database of 1510 patients undergoing CRT implantation
to predict 1- to 5-year all-cause mortality. Thirty-three pre-implant clinical features
were selected to train the models. The best performing model [SEMMELWEIS-CRT score
(perSonalizEd assessMent of estiMatEd risk of mortaLity With machinE learnIng in patientS
undergoing CRT implantation)], along with pre-existing scores (Seattle Heart Failure
Model, VALID-CRT, EAARN, ScREEN, and CRT-score), was tested on an independent cohort
of 158 patients. There were 805 (53%) deaths in the training cohort and 80 (51%) deaths
in the test cohort during the 5-year follow-up period. Among the trained classifiers,
random forest demonstrated the best performance. For the prediction of 1-, 2-, 3-,
4-, and 5-year mortality, the areas under the receiver operating characteristic curves
of the SEMMELWEIS-CRT score were 0.768 (95% CI: 0.674-0.861; P < 0.001), 0.793 (95%
CI: 0.718-0.867; P < 0.001), 0.785 (95% CI: 0.711-0.859; P < 0.001), 0.776 (95% CI:
0.703-0.849; P < 0.001), and 0.803 (95% CI: 0.733-0.872; P < 0.001), respectively.
The discriminative ability of our model was superior to other evaluated scores.The
SEMMELWEIS-CRT score (available at semmelweiscrtscore.com) exhibited good discriminative
capabilities for the prediction of all-cause death in CRT patients and outperformed
the already existing risk scores. By capturing the non-linear association of predictors,
the utilization of ML approaches may facilitate optimal candidate selection and prognostication
of patients undergoing CRT implantation.
