In:
Circulation, Ovid Technologies (Wolters Kluwer Health), Vol. 142, No. Suppl_3 ( 2020-11-17)
Abstract:
Introduction: Dual-isotope (201TlCl and 123I-β-methyl-P-iodophenyl-pentadecanoic acid (BMIPP) ) single photon emission computed tomography (SPECT) is utilized to estimate not only in patients with ischemic heart disease but with congestive heart failure (CHF). We tried to construct predictive model for cardiac prognosis on the SPECT for cardiac death by machine learning. Hypothesis: Machine learning is a powerful tool to predict cardiac prognosis in patients with CHF Methods: Consecutive 310 patients who admitted with CHF (77.1±3.1 years, 164 males) were enrolled. After initial treatment, they underwent electrocardiography gated SPECT and observed in median 507 days [IQR: 165, 1032] . Multivariate Cox regression analysis for cardiac death was performed, and predictive model was constructed by ROC curve analysis and machine learning (Random Forest and Deep Learning). The accuracies (= [True positive + True negative] / Total) of the prediction models were compared with ROC curve model. Results: Thirty-six patients fell into cardiac death. Cox analysis showed Age, left ventricular ejection fraction (LVEF), summed rest score (SRS) of BMIPP, and mismatch score were significant predictors (Hazard ratio: 1.068, 0.970, 1.032, 1.092, P value: 〈 0.001, 0.014, 0.002, 〈 0.001, respectively). ROC curve analysis of them revealed the accuracy of the cut-off value was 0.479-0.773. Conversely, machine learning model demonstrated higher accuracy for cardiac death (Random Forest: 0.895, Deep Learning: 0.935). The top 4 feature importance of the random forest were LVEF (0.299), SRS BMIPP (0.263), Age (0.262), and mismatch score (0.160). Conclusions: Machine learning model on SPECT had powerful predictive value for predicting cardiac death in patients with CHF.
Type of Medium:
Online Resource
ISSN:
0009-7322
,
1524-4539
DOI:
10.1161/circ.142.suppl_3.12872
Language:
English
Publisher:
Ovid Technologies (Wolters Kluwer Health)
Publication Date:
2020
detail.hit.zdb_id:
1466401-X