Cardiovascular disease is a leading cause of global death. Prospective population-based studies have found that changes in retinal microvasculature are associated with the development of coronary artery disease. Recently, artificial intelligence deep learning (DL) algorithms have been developed for the fully automated assessment of retinal vessel calibres.

In this study, we validate the association between retinal vessel calibres measured by a DL system (Singapore I Vessel Assessment) and incident myocardial infarction (MI) and assess its incremental performance in discriminating patients with and without MI when added to risk prediction models, using a large UK Biobank cohort.

Retinal arteriolar narrowing was significantly associated with incident MI in both the age, gender and fellow calibre-adjusted (HR=1.67 (95% CI: 1.19 to 2.36)) and multivariable models (HR=1.64 (95% CI: 1.16 to 2.32)) adjusted for age, gender and other cardiovascular risk factors such as blood pressure, diabetes mellitus (DM) and cholesterol status. The area under the receiver operating characteristic curve increased from 0.738 to 0.745 (p=0.018) in the age–gender-adjusted model and from 0.782 to 0.787 (p=0.010) in the multivariable model. The continuous net reclassification improvements (NRIs) were significant in the age and gender-adjusted (NRI=21.56 (95% CI: 3.33 to 33.42)) and the multivariable models (NRI=18.35 (95% CI: 6.27 to 32.61)). In the subgroup analysis, similar associations between retinal arteriolar narrowing and incident MI were observed, particularly for men (HR=1.62 (95% CI: 1.07 to 2.46)), non-smokers (HR=1.65 (95% CI: 1.13 to 2.42)), patients without DM (HR=1.73 (95% CI: 1.19 to 2.51)) and hypertensive patients (HR=1.95 (95% CI: 1.30 to 2.93)) in the multivariable models.

Our results support DL-based retinal vessel measurements as markers of incident MI in a predominantly Caucasian population.

Patient-reported outcome measures (PROMs) are required for patient-centred care. There are limited PROMs with good psychometric properties, and limitations to any language-based scale are often constrained by the written words or numerals used. Therefore, we developed the Functional Activity Scoring Tool (FAST), a self-reporting pictorial scale. FAST measures the impact of knee osteoarthritis on essential activities of daily living (ADL) and the significant changes in the self-perceived functional status over time.

This study aims to (1) develop FAST with adaptation from the Wong-Baker FACES pain rating scale, (2) validate FAST against the Patient-Specific Functional Scale (PSFS) and Knee Injury and Osteoarthritis Outcome Score (KOOS) and (3) establish the reliability, validity and responsiveness of FAST in individuals with knee osteoarthritis.

The prospective study protocol investigates the validity, responsiveness and reliability of FAST. The PSFS and KOOS will be gold standard comparisons. Participant recruitment will occur at four public polyclinics that offer physiotherapy outpatient services in Singapore. Onsite physiotherapists familiar with the study eligibilities will refer potential participants to the investigators after the routine physiotherapy assessment. After providing written consent, eligible participants will complete outcome measurements with FAST, the PSFS and KOOS during baseline and follow-up assessments. The Global Rating of Change (GROC) scale will determine how the participant’s knee status was changed compared with the beginning of the physiotherapy intervention.

SingHealth Centralised Institutional Review Board approved the study (CIRB reference number: 2022/2602). The final results will be published via scientific publication. FAST will benefit the evaluation and management of those who suffer knee osteoarthritis regardless of English proficiency or language barriers.

NCT05590663