Author + information
- Mouaz H. Al-Mallaha,b,
- Amjad Ahmeda,b,
- Waqas Qureshia,b,
- Radwa Elshawia,b,
- Clinton Brawnera,b,
- Michael Blahaa,b,
- Haitham Ahmeda,b,
- Jonathan Ehrmana,b,
- Steven Keteyiana,b and
- Sherif Sakra,b
Background: Prior studies have demonstrated that cardiorespiratory fitness (CRF) is a strong marker of cardiovascular health. Machine learning (ML) can enhance the prediction of outcomes through classification technique that classifies the data into predetermined categories. The aim of the analysis is to assess the relation between CRF and all-cause mortality (ACM) using ML approaches.
Methods: We included 34,212 patients (55% males, mean age 54±13years) not known to have coronary artery disease or heart failure who underwent clinician-referred exercise treadmill stress testing at Henry Ford Health Systems Between 1991 and 2009 and had complete 10-year follow-up. The primary outcome of this analysis was ACM at 10 years. The probability of 10 year ACM was calculated using logistic regression (LR) and ML and the accuracy of these methods were calculated and compared.
Results: A total of 3,921 patients experienced ACM at ten years. Using LR, the sensitivity to predict ACM was 44.9% (95%CI 43.3%-46.5%) while the specificity was 93.4% (95%CI 93.1%-93.7%). The sensitivity of ML to predict ACM was 87.40% (86.32%-88.42%) while the specificity was 97.21% (97.02%-97.39%). ML approach was associated with improved model discrimination, (area under the curve for ML (0.923 (95%CI 0.917-0.928)) compared to LR (0.836 (95%CI 0.829-0.846)), p<0.0001)(Figure 1).
Conclusions: Our analysis demonstrates that ML provides better accuracy and discrimination of the prediction of ACM among patients undergoing stress testing.
Poster Hall, Hall C
Sunday, March 19, 2017, 9:45 a.m.-10:30 a.m.
Session Title: Non Invasive Imaging: Role of Exercise Testing
Abstract Category: 31. Non Invasive Imaging: Sports and Exercise
Presentation Number: 1289-202
- 2017 American College of Cardiology Foundation