An efficient approach to estimate the risk of coronary artery disease for people living with HIV using machine-learning-based retinal image analysis
Copyright: © 2023 Lui et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited..
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.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2023 |
|---|---|
| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:18 |
|---|---|
| Enthalten in: |
PloS one - 18(2023), 2 vom: 28., Seite e0281701 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Lui, Grace [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
|---|
| Anmerkungen: |
Date Completed 28.02.2023 Date Revised 07.04.2023 published: Electronic-eCollection Citation Status MEDLINE |
|---|
| doi: |
10.1371/journal.pone.0281701 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM35333491X |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM35333491X | ||
| 003 | DE-627 | ||
| 005 | 20231226055744.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231226s2023 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1371/journal.pone.0281701 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1177.xml |
| 035 | |a (DE-627)NLM35333491X | ||
| 035 | |a (NLM)36827291 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Lui, Grace |e verfasserin |4 aut | |
| 245 | 1 | 3 | |a An efficient approach to estimate the risk of coronary artery disease for people living with HIV using machine-learning-based retinal image analysis |
| 264 | 1 | |c 2023 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Completed 28.02.2023 | ||
| 500 | |a Date Revised 07.04.2023 | ||
| 500 | |a published: Electronic-eCollection | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright: © 2023 Lui et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 700 | 1 | |a Leung, Ho Sang |e verfasserin |4 aut | |
| 700 | 1 | |a Lee, Jack |e verfasserin |4 aut | |
| 700 | 1 | |a Wong, Chun Kwok |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Xinxin |e verfasserin |4 aut | |
| 700 | 1 | |a Ho, Mary |e verfasserin |4 aut | |
| 700 | 1 | |a Wong, Vivian |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Timothy |e verfasserin |4 aut | |
| 700 | 1 | |a Ho, Tracy |e verfasserin |4 aut | |
| 700 | 1 | |a Chan, Yin Yan |e verfasserin |4 aut | |
| 700 | 1 | |a Lee, Shui Shan |e verfasserin |4 aut | |
| 700 | 1 | |a Lee, Alex Pw |e verfasserin |4 aut | |
| 700 | 1 | |a Wong, Ka Tak |e verfasserin |4 aut | |
| 700 | 1 | |a Zee, Benny |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t PloS one |d 2006 |g 18(2023), 2 vom: 28., Seite e0281701 |w (DE-627)NLM167327399 |x 1932-6203 |7 nnns |
| 773 | 1 | 8 | |g volume:18 |g year:2023 |g number:2 |g day:28 |g pages:e0281701 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1371/journal.pone.0281701 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 18 |j 2023 |e 2 |b 28 |h e0281701 | ||