| Title |
Artificial neural network modeling enhances risk stratification and can reduce downstream testing for patients with suspected acute coronary syndromes, negative cardiac biomarkers, and normal ECGs
|
|---|---|
| Published in |
The International Journal of Cardiovascular Imaging, December 2015
|
| DOI | 10.1007/s10554-015-0821-9 |
| Pubmed ID | |
| Authors |
Hussain A. Isma’eel, Paul C. Cremer, Shaden Khalaf, Mohamad M. Almedawar, Imad H. Elhajj, George E. Sakr, Wael A. Jaber |
| Abstract |
Despite uncertain yield, guidelines endorse routine stress myocardial perfusion imaging (MPI) for patients with suspected acute coronary syndromes, unremarkable serial electrocardiograms, and negative troponin measurements. In these patients, outcome prediction and risk stratification models could spare unnecessary testing. This study therefore investigated the use of artificial neural networks (ANN) to improve risk stratification and prediction of MPI and angiographic results. We retrospectively identified 5354 consecutive patients referred from the emergency department for rest-stress MPI after serial negative troponins and normal ECGs. Patients were risk stratified according to thrombolysis in myocardial infarction (TIMI) scores, ischemia was defined as >5 % reversible perfusion defect, and obstructive coronary artery disease was defined as >50 % angiographic obstruction. For ANN, the network architecture employed a systematic method where the number of neurons is changed incrementally, and bootstrapping was performed to evaluate the accuracy of the models. Compared to TIMI scores, ANN models provided improved discriminatory power. With regards to MPI, an ANN model could reduce testing by 59 % and maintain a 96 % negative predictive value (NPV) for ruling out ischemia. Application of an ANN model could also avoid 73 % of invasive coronary angiograms while maintaining a 98 % NPV for detecting obstructive CAD. An online calculator for clinical use was created using these models. The ANN models improved risk stratification when compared to the TIMI score. Our calculator could also reduce downstream testing while maintaining an excellent NPV, though further study is needed before the calculator can be used clinically. |
X Demographics
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 66 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 66 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 11 | 17% |
| Student > Ph. D. Student | 10 | 15% |
| Student > Master | 9 | 14% |
| Student > Doctoral Student | 6 | 9% |
| Student > Bachelor | 5 | 8% |
| Other | 13 | 20% |
| Unknown | 12 | 18% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 21 | 32% |
| Computer Science | 7 | 11% |
| Biochemistry, Genetics and Molecular Biology | 4 | 6% |
| Pharmacology, Toxicology and Pharmaceutical Science | 3 | 5% |
| Engineering | 3 | 5% |
| Other | 10 | 15% |
| Unknown | 18 | 27% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 03 December 2015.
All research outputs
#22,759,452
of 25,374,647 outputs
Outputs from The International Journal of Cardiovascular Imaging
#1,460
of 2,012 outputs
Outputs of similar age
#337,485
of 395,408 outputs
Outputs of similar age from The International Journal of Cardiovascular Imaging
#19
of 37 outputs
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