TY - JOUR
T1 - Ocular blood flow as a clinical observation
T2 - Value, limitations and data analysis
AU - Harris, Alon
AU - Guidoboni, Giovanna
AU - Siesky, Brent
AU - Mathew, Sunu
AU - Verticchio Vercellin, Alice C.
AU - Rowe, Lucas
AU - Arciero, Julia
N1 - Funding Information:
Alon Harris would like to disclose that he receives remuneration from AdOM for serving as a consultant and a board member, and from Thea for a speaking engagement. Alon Harris also holds an ownership interest in AdOM, Luseed, Oxymap, and QuLent. Giovanna Guidoboni would like to disclose that she receives remuneration from Foresite Healthcare LLC for serving as a consultant. The contribution of the author Alice C. Verticchio Vercellin was supported by Fondazione Roma and by the Italian Ministry of Health.
Funding Information:
Supported by NIH grant ( NIH 1 R01 EY030851-01 ), NSF-DMS 1853222 / 1853303 , NSF DMS - 1654019 , and NSF DMS - 1852146 .
Funding Information:
A higher TPG may lead to optic nerve damage due to alterations in axonal transportation, lamina deformation, altered blood flow, or a combination of these, ultimately resulting in glaucomatous damage. In fact, TPG is hypothesized to be the primary pressure-related parameter for glaucoma (Burgoyne et al., 2005) since the ONH is located at the junction between the intraocular space and the orbital retrobulbar space. Siaudvytyte et al. and Ren et al. both have studied the effect of TPG on neuroretinal rim area (NRA) in OAG. In their prospective study, Siaudvytyte et al. demonstrated that IOP and NRA was significantly lower in NTG compared to OAG and healthy controls (IOP = 13.7 mmHg, 24.7 mmHg, and 15.9 mmHg, respectively; P < 0.001) and NRA was 0.97 mm2 in NTG, 1.32mm2 in OAG, and 1.79 mm2 in healthy controls; P = 0.003). Additionally, the TPG was found to be highest in OAG (15.7 mmHg) when compared to NTG (6.3 mmHg) and healthy controls (5.4 mmHg; P < 0.001), and ICP was found to be lower in NTG (7.4 mmHg) when compared with OAG (8.9 mmHg) and healthy subjects (10.5 mmHg). Importantly, however, the difference between groups was not statistically significant (P > 0.05) (Siaudvytyte et al., 2014). Ren et al. in their study noted that there was a significant association between NRA and visual field defect with the translaminar cribrosa pressure difference (NRA r = ?0.38, p = 0.006; visual field r = 0.38, p = 0.008) (Ren et al., 2011). In addition, 9 out of 22 patients undergoing surgery to reduce ICP for normal pressure hydrocephalus have been shown to develop NTG, which is a 40-fold increase compared to the rate of NTG development in a general elderly population without hydrocephalus (p < 0.001), supporting the theory that an imbalance between IOP and ICP is critical in the development of optic nerve damage (Gallina et al., 2018).Supported by NIH grant (NIH 1 R01 EY030851-01), NSF-DMS 1853222/1853303, NSF DMS-1654019, and NSF DMS-1852146.
Publisher Copyright:
© 2020 Elsevier Ltd
PY - 2020/9
Y1 - 2020/9
N2 - Alterations in ocular blood flow have been identified as important risk factors for the onset and progression of numerous diseases of the eye. In particular, several population-based and longitudinal-based studies have provided compelling evidence of hemodynamic biomarkers as independent risk factors for ocular disease throughout several different geographic regions. Despite this evidence, the relative contribution of blood flow to ocular physiology and pathology in synergy with other risk factors and comorbidities (e.g., age, gender, race, diabetes and hypertension) remains uncertain. There is currently no gold standard for assessing all relevant vascular beds in the eye, and the heterogeneous vascular biomarkers derived from multiple ocular imaging technologies are non-interchangeable and difficult to interpret as a whole. As a result of these disease complexities and imaging limitations, standard statistical methods often yield inconsistent results across studies and are unable to quantify or explain a patient's overall risk for ocular disease. Combining mathematical modeling with artificial intelligence holds great promise for advancing data analysis in ophthalmology and enabling individualized risk assessment from diverse, multi-input clinical and demographic biomarkers. Mechanism-driven mathematical modeling makes virtual laboratories available to investigate pathogenic mechanisms, advance diagnostic ability and improve disease management. Artificial intelligence provides a novel method for utilizing a vast amount of data from a wide range of patient types to diagnose and monitor ocular disease. This article reviews the state of the art and major unanswered questions related to ocular vascular anatomy and physiology, ocular imaging techniques, clinical findings in glaucoma and other eye diseases, and mechanistic modeling predictions, while laying a path for integrating clinical observations with mathematical models and artificial intelligence. Viable alternatives for integrated data analysis are proposed that aim to overcome the limitations of standard statistical approaches and enable individually tailored precision medicine in ophthalmology.
AB - Alterations in ocular blood flow have been identified as important risk factors for the onset and progression of numerous diseases of the eye. In particular, several population-based and longitudinal-based studies have provided compelling evidence of hemodynamic biomarkers as independent risk factors for ocular disease throughout several different geographic regions. Despite this evidence, the relative contribution of blood flow to ocular physiology and pathology in synergy with other risk factors and comorbidities (e.g., age, gender, race, diabetes and hypertension) remains uncertain. There is currently no gold standard for assessing all relevant vascular beds in the eye, and the heterogeneous vascular biomarkers derived from multiple ocular imaging technologies are non-interchangeable and difficult to interpret as a whole. As a result of these disease complexities and imaging limitations, standard statistical methods often yield inconsistent results across studies and are unable to quantify or explain a patient's overall risk for ocular disease. Combining mathematical modeling with artificial intelligence holds great promise for advancing data analysis in ophthalmology and enabling individualized risk assessment from diverse, multi-input clinical and demographic biomarkers. Mechanism-driven mathematical modeling makes virtual laboratories available to investigate pathogenic mechanisms, advance diagnostic ability and improve disease management. Artificial intelligence provides a novel method for utilizing a vast amount of data from a wide range of patient types to diagnose and monitor ocular disease. This article reviews the state of the art and major unanswered questions related to ocular vascular anatomy and physiology, ocular imaging techniques, clinical findings in glaucoma and other eye diseases, and mechanistic modeling predictions, while laying a path for integrating clinical observations with mathematical models and artificial intelligence. Viable alternatives for integrated data analysis are proposed that aim to overcome the limitations of standard statistical approaches and enable individually tailored precision medicine in ophthalmology.
KW - Artificial intelligence
KW - Glaucoma
KW - Mathematical models
KW - Ocular blood flow
KW - Vascular risk factors
UR - http://www.scopus.com/inward/record.url?scp=85079056659&partnerID=8YFLogxK
U2 - 10.1016/j.preteyeres.2020.100841
DO - 10.1016/j.preteyeres.2020.100841
M3 - Review article
C2 - 31987983
AN - SCOPUS:85079056659
VL - 78
JO - Progress in Retinal and Eye Research
JF - Progress in Retinal and Eye Research
SN - 1350-9462
M1 - 100841
ER -