Patient Personality and Illness Perceptions in Relation to Follow-Up Appointment Adherence in Neuro-Ophthalmology.

BACKGROUND: Improving patient attendance at medical follow-up visits may have a notable impact on disease and overall health outcomes. Understanding factors contributing to poor attendance is important for identifying at-risk patients and designing interventions to improve clinical outcomes. Our objective was to identify personality and illness perception factors associated with attendance at recommended follow-up visits in a neuro-ophthalmology practice. METHODS: New or established patients (≥18 years) with scheduled neuro-ophthalmology (study) or glaucoma (comparison) appointments at a tertiary care academic medical center completed the Brief Illness Perception Questionnaire and Ten-Item Personality Inventory. Physician recommendations made during the visit were recorded (medications, referrals, follow-up, testing, and procedures). A chart review was performed 18 months after enrollment to assess attendance at follow-up appointment and adherence with other physician recommendations. Multiple variable logistic regression models studied associations between follow-up appointment attendance and demographic factors, appointment factors, and survey responses. RESULTS: Among 152 respondents (97% response rate (152 of 157), aged 19-97 years, 58% female, 34% new, 80 neuro-ophthalmology, 72 glaucoma), neuro-ophthalmology subjects were younger, more likely to be White, non-Hispanic, female and new to the practice than subjects with glaucoma. They reported higher emotional impact, identity, and consequences related to their illness (P = 0.001-0.03). Neuro-ophthalmology physician recommendations included more referrals to other services (17.5% vs 1.4%, P = 0.001, chi-square) and more radiology studies (15% vs 0%, P = 0.001, chi-square), but fewer follow-up visits (75% vs 97%, P < 0.0005, chi-square). Among those with recommended follow-up visits, neuro-ophthalmology subjects had lower rates of on-time appointment attendance (55% vs 77%, P = 0.009, chi-square). In a multiple variable model, on-time follow-up attendance was associated with shorter recommended follow-up interval (≤90 days, P < 0.0005), established (vs new) patient status at enrollment visit (P = 0.04), and glaucoma (P = 0.08), but not subject demographics, illness perception, or personality factors. CONCLUSIONS: Patient demographics, illness perception, and personality traits were not associated with follow-up appointment attendance and therefore unlikely to be useful for identifying patients at risk of being lost to follow-up. New neuro-ophthalmology patients with a follow-up recommended ≥90 days in advance may benefit from targeted interventions to improve follow-up appointment adherence.

Development and Implementation of a Handheld Pupillometer for Detection of Optic Neuropathies.

Purpose: Quantitative pupillometry has utility in research settings for measuring optic nerve and autonomic function. We configured a portable device to perform quantitative pupillometry with application to detecting unilateral optic neuropathies in the clinical setting.Materials & methods: Light stimuli were delivered, and pupil diameter responses recorded using customized software implemented on a commercial portable electroretinography device. Increasing pupillary constriction occurred with increasing duration and intensity of full field blue light (470 nm) stimuli in healthy subjects. Flashes of 1 s dim (50 cd/m2) and bright (316 cd/m2) blue light were administered to both eyes of subjects with unilateral optic neuropathies (n = 10) and controls (n = 5). Maximum pupillary constriction (Cmax) for each stimulus was compared between control eyes and optic neuropathy eyes. Cmax for the inter-eye difference curve (Cdiffmax) was compared between control and optic neuropathy subjects.Results: The pupil protocol lasted 15 minutes and was well tolerated by subjects. Cmax for bright and dim stimuli was reduced in eyes with optic neuropathy compared to fellow and control eyes (p < .0005 for all). Inter-eye Cdiffmax was larger in optic neuropathy subjects than control subjects for both dim and bright stimuli (p = .002, <0.0005). There was no overlap between groups for Cmax and Cdiffmax for either stimulus.Conclusions: A portable pupillometer was implemented on a commercial portable electroretinography platform and applied in a pilot manner to subjects with and without unilateral optic neuropathies. Optic neuropathy eyes were distinguished from non-optic neuropathy eyes both within and between subjects.

Comparison of Machine Learning Approaches to Improve Diagnosis of Optic Neuropathy Using Photopic Negative Response Measured Using a Handheld Device.

The photopic negative response of the full-field electroretinogram (ERG) is reduced in optic neuropathies. However, technical requirements for measurement and poor classification performance have limited widespread clinical application. Recent advances in hardware facilitate efficient clinic-based recording of the full-field ERG. Time series classification, a machine learning approach, may improve classification by using the entire ERG waveform as the input. In this study, full-field ERGs were recorded in 217 eyes (109 optic neuropathy and 108 controls) of 155 subjects. User-defined ERG features including photopic negative response were reduced in optic neuropathy eyes (p < 0.0005, generalized estimating equation models accounting for age). However, classification of optic neuropathy based on user-defined features was only fair with receiver operating characteristic area under the curve ranging between 0.62 and 0.68 and F1 score at the optimal cutoff ranging between 0.30 and 0.33. In comparison, machine learning classifiers using a variety of time series analysis approaches had F1 scores of 0.58-0.76 on a test data set. Time series classifications are promising for improving optic neuropathy diagnosis using ERG waveforms. Larger sample sizes will be important to refine the models.