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Published on: October 22, 2014
An efficient approach to estimate the risk of coronary artery disease for people living with HIV using
Grace Lui1,2, Ho Sang Leung3, Jack Lee4
1Department of Medicine and Therapeutics, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, Hong Kong SAR.
Insights
Retinal image analysis using machine learning significantly improves cardiovascular disease risk assessment in people living with HIV (PLWH). This advanced tool offers higher accuracy for coronary artery disease (CAD) detection compared to traditional methods.
Area of Science:
- Ophthalmology
- Cardiology
- Infectious Diseases
- Medical Imaging
- Machine Learning
Background:
- People living with HIV (PLWH) face elevated risks of non-communicable diseases, particularly cardiovascular diseases (CVD).
- Current HIV management guidelines suggest cardiovascular risk screening, but existing risk models lack specificity for PLWH.
- There is a critical need for improved tools to accurately assess cardiovascular risk in PLWH.
Purpose of the Study:
- To evaluate the efficacy of automatic retinal image analysis in assessing coronary artery disease (CAD) among PLWH.
- To compare the performance of models utilizing traditional cardiovascular risk factors, retinal characteristics, and a combination of both.
Main Methods:
- A prospective study enrolled PLWH with at least one cardiovascular risk factor.
- Participants underwent computerized tomography (CT) coronary angiogram and digital fundus photography.
- Performance was assessed by comparing the area under the curve (AUC) of receiver operating characteristic curves for different risk assessment models.
Main Results:
- Machine-learning models incorporating retinal characteristics demonstrated superior performance (AUCs of 0.987 and 0.979) compared to traditional risk factors alone (AUC 0.746) for coronary atherosclerosis.
- For obstructive CAD, retinal analysis models (AUCs 0.986-0.991) significantly outperformed traditional risk factors (AUC 0.777).
- The combined model achieved high sensitivity (93.0%-95.7%) and specificity (93.2%-97.8%) for detecting coronary atherosclerosis and obstructive CAD.
Conclusions:
- Retinal characteristics, alone or combined with traditional factors, show superior performance in assessing coronary atherosclerosis and obstructive CAD in Asian PLWH.
- Machine-learning-based retinal image analysis can enhance the accuracy of cardiovascular risk assessment in this population.
Background:
People living with HIV (PLWH) have increased risks of non-communicable diseases, especially cardiovascular diseases. Current HIV clinical management guidelines recommend regular cardiovascular risk screening, but the risk equation models are not specific for PLWH. Better tools are needed to assess cardiovascular risk among PLWH accurately.
Methods:
We performed a prospective study to determine the performance of automatic retinal image analysis in assessing coronary artery disease (CAD) in PLWH. We enrolled PLWH with ≥1 cardiovascular risk factor. All participants had computerized tomography (CT) coronary angiogram and digital fundus photographs. The primary outcome was coronary atherosclerosis; secondary outcomes included obstructive CAD. In addition, we compared the performances of three models (traditional cardiovascular risk factors alone; retinal characteristics alone; and both traditional and retinal characteristics) by comparing the area under the curve (AUC) of receiver operating characteristic curves.
Results:
Among the 115 participants included in the analyses, with a mean age of 54 years, 89% were male, 95% had undetectable HIV RNA, 45% had hypertension, 40% had diabetes, 45% had dyslipidemia, and 55% had obesity, 71 (61.7%) had coronary atherosclerosis, and 23 (20.0%) had obstructive CAD. The machine-learning models, including retinal characteristics with and without traditional cardiovascular risk factors, had AUC of 0.987 and 0.979, respectively and had significantly better performance than the model including traditional cardiovascular risk factors alone (AUC 0.746) in assessing coronary artery disease atherosclerosis. The sensitivity and specificity for risk of coronary atherosclerosis in the combined model were 93.0% and 93.2%, respectively. For the assessment of obstructive CAD, models using retinal characteristics alone (AUC 0.986) or in combination with traditional risk factors (AUC 0.991) performed significantly better than traditional risk factors alone (AUC 0.777). The sensitivity and specificity for risk of obstructive CAD in the combined model were 95.7% and 97.8%, respectively.
Conclusion:
In this cohort of Asian PLWH at risk of cardiovascular diseases, retinal characteristics, either alone or combined with traditional risk factors, had superior performance in assessing coronary atherosclerosis and obstructive CAD.
Summary:
People living with HIV in an Asian cohort with risk factors for cardiovascular disease had a high prevalence of coronary artery disease (CAD). A machine-learning-based retinal image analysis could increase the accuracy in assessing the risk of coronary atherosclerosis and obstructive CAD.
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