Model Structure Uncertainty in the Characterization and Growth of Geographic Atrophy

Progression rate of macular retinal pigment epithelium atrophy in geographic atrophy and selected inherited retinal dystrophies. A systematic review and meta-analysis

PURPOSE

To compare the macular retinal pigment epithelium (RPE) atrophy progression rate of selected degenerative and macular inherited retinal diseases (IRD).

DESIGN

Systematic review and meta-analysis.

METHODS

The protocol was registered on the PROSPERO database. Medline, Embase, Web of Science, Cochrane Central Register of Controlled Trials, and Google Scholar were searched up to 15/Sep/2023 for articles reporting the RPE atrophy growth rate in treatment-naïve eyes with geographic atrophy (GA), Stargardt disease (STGD1), Best disease, pseudoxanthoma elasticum (PXE), central areolar choroidal dystrophy (CACD), or pattern dystrophies with no previous or current macular neovascularization and a minimum follow-up time of 12 months. Meta-analyses determined mean RPE atrophy growth rates per disease, imaging modality (fundus autofluorescence [FAF], optical coherence tomography [OCT], or color fundus photography [CFP]) and metric (mm2/year or mm/year). The Newcastle-Ottawa scale and the Cochrane Risk-of-Bias tool assessed the risk of bias, and funnel plots were used to evaluate small-study effects.

RESULTS

From 4354 publications, 85 were included for meta-analysis: 69 studies (7815 eyes) on GA, 15 (1367 eyes) on STGD1, and one on both. Two studies on PXE were only eligible for review. No studies for other diseases met our eligibility criteria. The overall mean RPE atrophy growth rate for GA using FAF was 1.65 mm2/year (95% confidence interval [CI], 1.49-1.81) and 0.35 mm/year (95% CI, 0.28-0.41); using OCT, it was 1.46 mm2/year (95% CI, 1.28-1.64) and 0.34 mm/year (95% CI, 0.28-0.40); and on CFP it was 1.76 mm2/year (95% CI, 1.56-1.97) and 0.30 mm/year (95% CI, 0.28-0.31). For STGD1, using FAF it was 1.0 mm2/year (95% CI, 0.77-1.23) and 0.20 mm/year (95% CI, 0.17-0.23); on OCT, it was 0.80 mm2/year (95% CI, 0.72-0.88). No studies on STGD1 reported the growth rate with other imaging modalities or metrics. Growth rates in GA were faster than in STGD1 (p<0.05). A larger baseline area of atrophy was generally associated with faster growth rates.

CONCLUSION

The RPE atrophy growth rate in GA is faster than in STGD1 but with great variation between studies and imaging modalities. Limited information was available for other macular IRD, suggesting further research is needed.

Progression Dynamics of Early versus Later Stage Atrophic Lesions in Nonneovascular Age-Related Macular Degeneration Using Quantitative OCT Biomarker Segmentation

PURPOSE

To investigate the progression of geographic atrophy secondary to nonneovascular age-related macular degeneration in early and later stage lesions using artificial intelligence-based precision tools.

DESIGN

Retrospective analysis of an observational cohort study.

SUBJECTS

Seventy-four eyes of 49 patients with ≥ 1 complete retinal pigment epithelial and outer retinal atrophy (cRORA) lesion secondary to age-related macular degeneration were included. Patients were divided between recently developed cRORA and lesions with advanced disease status.

METHODS

Patients were prospectively imaged by spectral-domain OCT volume scans. The study period encompassed 18 months with scheduled visits every 6 months. Growth rates of recent cRORA-converted lesions were compared with lesions in an advanced disease status using mixed effect models.

MAIN OUTCOME MEASURES

The progression of retinal pigment epithelial loss (RPEL) was considered the primary end point. Secondary end points consisted of external limiting membrane disruption and ellipsoid zone loss. These pathognomonic imaging biomarkers were quantified using validated deep-learning algorithms. Further, the ellipsoid zone/RPEL ratio was analyzed in both study cohorts.

RESULTS

Mean (95% confidence interval [CI]) square root progression of recently converted lesions was 79.68 (95% CI, -77.14 to 236.49), 68.22 (95% CI, -101.21 to 237.65), and 84.825 (95% CI, -124.82 to 294.47) mm/half year for RPEL, external limiting membrane loss, and ellipsoid zone loss respectively. Mean square root progression of advanced lesions was 131.74 (95% CI, -22.57 to 286.05), 129.96 (95% CI, -36.67 to 296.59), and 116.84 (95% CI, -90.56 to 324.3) mm/half year for RPEL, external limiting membrane loss, and ellipsoid zone loss, respectively. RPEL (P = 0.038) and external limiting membrane disruption (P = 0.026) progression showed significant differences between the 2 study cohorts. Further recent converters had significantly (P < 0.001) higher ellipsoid zone/RPEL ratios at all time points compared with patients in an advanced disease status (1.71 95% CI, 1.12-2.28 vs. 1.14; 95% CI, 0.56-1.71).

CONCLUSION

Early cRORA lesions have slower growth rates in comparison to atrophic lesions in advanced disease stages. Differences in growth dynamics may play a crucial role in understanding the pathophysiology of nonneovascular age-related macular degeneration and for the interpretation of clinical trials in geographic atrophy. Individual disease monitoring using artificial intelligence-based quantification paves the way toward optimized geographic atrophy management.

FINANCIAL DISCLOSURE(S)

The author(s) have no proprietary or commercial interest in any materials discussed in this article.

Progression of Geographic Atrophy: Epistemic Uncertainties Affecting Mathematical Models and Machine Learning

Purpose

The purpose of this study was to identify a taxonomy of epistemic uncertainties that affect results for geographic atrophy (GA) assessment and progression.

Methods

An important source of variability is called "epistemic uncertainty," which is due to incomplete system knowledge (i.e. limitations in measurement devices, artifacts, and human subjective evaluation, including annotation errors). In this study, different epistemic uncertainties affecting the analysis of GA were identified and organized into a taxonomy. The uncertainties were discussed and analyzed, and an example was provided in the case of model structure uncertainty by characterizing progression of GA by mathematical modelling and machine learning. It was hypothesized that GA growth follows a logistic (sigmoidal) function. Using case studies, the GA growth data were used to test the sigmoidal hypothesis.

Results

Epistemic uncertainties were identified, including measurement error (imperfect outcomes from measuring tools), subjective judgment (grading affected by grader's vision and experience), model input uncertainties (data corruption or entry errors), and model structure uncertainties (elucidating the right progression pattern). Using GA growth data from case studies, it was demonstrated that GA growth can be represented by a sigmoidal function, where growth eventually approaches an upper limit.

Conclusion

Epistemic uncertainties contribute to errors in study results and are reducible if identified and addressed. By prior identification of epistemic uncertainties, it is possible to (a) quantify uncertainty not accounted for by natural statistical variability, and (b) reduce the presence of these uncertainties in future studies.

Translational Relevance

Lowering epistemic uncertainty will reduce experimental error, improve consistency and reproducibility, and increase confidence in diagnostics.