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- Title
Archetypal analysis of longitudinal visual fields for idiopathic intracranial hypertension patients presenting in a clinic setting.
- Authors
Branco, Joseph; Elze, Tobias; Wang, Jui-Kai; Pasquale, Louis R.; Garvin, Mona K.; Kardon, Randy; Kupersmith, Mark J.
- Abstract
We previously applied archetypal analysis (AA) using visual fields (VF) from the Idiopathic Intracranial Hypertension Treatment Trial (IIHTT) to derive a model, which quantified patterns (or archetypes [ATs] of VF loss), anticipated recovery, and identified residual VF deficits. We hypothesized that AA could produce similar results using IIH VFs collected in clinical practice. We applied AA to 803 VFs from 235 eyes with IIH from an outpatient neuro-ophthalmology clinic and created a clinic-derived model of ATs, with the relative weight (RW) and average total deviation (TD) for each AT. We also created a combined-derived model from an input dataset containing the clinic VFs and 2862 VFs from the IIHTT. We used both models to decompose clinic VF into ATs of varying percent weight (PW), correlated presentation AT PW with mean deviation (MD), and evaluated final visit VFs considered "normal" by MD ≥ -2.00 dB for residual abnormal ATs. The 14-AT clinic-derived and combined-derived models revealed similar patterns of VF loss previously identified in the IIHTT model. AT1 (a normal pattern) was most prevalent in both models (RW = 51.8% for clinic-derived; 35.4% for combined-derived). Presentation AT1 PW correlated with final visit MD (r = 0.82, p < 0.001 for the clinic-derived model; r = 0.59, p < 0.001 for the combined-derived model). Both models showed ATs with similar patterns of regional VF loss. The most common patterns of VF loss in "normal" final visit VFs using each model were clinic-derived AT2 (mild global depression with enlarged blind spot; 44/125 VFs; 34%) and combined-derived AT2 (near-normal; 93/149 VFs; 62%). AA provides quantitative values for IIH-related patterns of VF loss that can be used to monitor VF changes in a clinic setting. Presentation AT1 PW is associated with the degree of VF recovery. AA identifies residual VF deficits not otherwise indicated by MD. Author summary: Archetypal analysis is a type of machine learning that can extract common patterns from a dataset. In our work, we applied archetypal analysis to visual fields from eyes with papilledema due to idiopathic intracranial hypertension. We used archetypal analysis to create a model of the most common patterns of visual field loss and quantify the amount of representation of each pattern within the entire dataset. We then decomposed each visual field into a weighted linear combination of these patterns. We found that the weight of the pattern representing normal vision at presentation was associated with better visual field recovery. We also found that of 98 visual fields considered to be normal by the mean deviation, a commonly-used global measure of visual field loss, archetypal analysis detected a regional deficit in 58 (59%) of them. The mean deviation cannot account for minor or regional visual field deficits that archetypal analysis can detect. Therefore, archetypal analysis is a more powerful method to analyze visual field loss.
- Subjects
VISUAL fields; CONFIDENCE intervals; MACHINE learning; VISUAL perception; DESCRIPTIVE statistics; DATA analysis software; INTRACRANIAL hypertension
- Publication
PLoS Digital Health, 2023, Vol 1, Issue 5, p1
- ISSN
2767-3170
- Publication type
Article
- DOI
10.1371/journal.pdig.0000240