Dataset Information

Factors Predicting a Greater Likelihood of Poor Visual Field Reliability in Glaucoma Patients and Suspects.

ABSTRACT:

Purpose

Identify factors predicting worse or better than expected visual field (VF) performance.

Methods

A total of 10,262 VFs from 1538 eyes of 909 subjects with manifest or suspected glaucoma were analyzed. Linear mixed-effects models predicted mean deviation (MD) at each timepoint. Differences between observed and predicted MD (?MD) were calculated and logistic regression identified factors predicting lower than expected (?MD <-1 dB) or higher than expected (?MD >1 dB) sensitivity.

Results

Both higher and lower than expected sensitivity were more likely in VFs with severe compared with mild damage (relative risk [RR] >1.3, P < 0.05). Higher than expected sensitivity was more likely in VFs with moderate damage (RR = 2.57, P < 0.001). False-positive (FP) errors increased the likelihood of higher than expected sensitivity at all disease stages (RR >2.1 per 10% increase, P < 0.001), whereas false-negative (FN) errors increased the likelihood of lower than expected sensitivity in mild and moderate disease (RR >1.19 per 10% increase, P < 0.05). Fixation loss errors slightly increased the likelihood of higher than expected VF sensitivity in moderate and severe disease (RR >1.1 per 10% increase, P < 0.01). Longer test duration increased likelihood of lower than expected sensitivity at all disease stages (RR >1.36 per minute increase, P < 0.001). Lower than expected sensitivity was more likely in late afternoon tests (RR = 1.27, P < 0.01). A total of 26.6% of VFs had higher or lower than expected sensitivity in the absence of FPs, FNs, or fixation losses.

Conclusions

FPs, test duration, and FNs are the primary measures predicting if a VF is likely to be reliable, although tests with normal reliability measures may still be unreliable. Our results help clinicians judge VF reliability and highlight the need to integrate reliability measures with other clinical data when making treatment decisions.

Translational relevance

This likelihood model derived from a large dataset helps clinicians identify VFs that may either falsely suggest disease progression or mask true worsening, thereby improving the utility of VFs in clinical practice.

SUBMITTER: Aboobakar IF

PROVIDER: S-EPMC7255630 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Factors Predicting a Greater Likelihood of Poor Visual Field Reliability in Glaucoma Patients and Suspects.

Aboobakar Inas F IF Wang Jiangxia J Chauhan Balwantray C BC Boland Michael V MV Friedman David S DS Ramulu Pradeep Y PY Yohannan Jithin J

Translational vision science & technology 20200130 1

<h4>Purpose</h4>Identify factors predicting worse or better than expected visual field (VF) performance.<h4>Methods</h4>A total of 10,262 VFs from 1538 eyes of 909 subjects with manifest or suspected glaucoma were analyzed. Linear mixed-effects models predicted mean deviation (MD) at each timepoint. Differences between observed and predicted MD (ΔMD) were calculated and logistic regression identified factors predicting lower than expected (ΔMD <-1 dB) or higher than expected (ΔMD >1 dB) sensitiv ...[more]

PMID: 32509439

Similar Datasets

Project description:PURPOSE:To develop a visual field (VF) feature model to predict the reversal of glaucoma hemifield test (GHT) results to within normal limits (WNL) after 2 consecutive outside normal limits (ONL) results. DESIGN:Retrospective cohort study. PARTICIPANTS:Visual fields of 44?503 eyes from 26?130 participants. METHODS:Eyes with 3 or more consecutive reliable VFs measured with the Humphrey Field Analyzer (Swedish interactive threshold algorithm standard 24-2) were included. Eyes with ONL GHT results for the 2 baseline VFs were selected. We extracted 3 categories of VF features from the baseline tests: (1) VF global indices (mean deviation [MD] and pattern standard deviation), (2) mismatch between baseline VFs, and (3) VF loss patterns (archetypes). Logistic regression was applied to predict the GHT results reversal. Cross-validation was applied to evaluate the model on testing data by the area under the receiver operating characteristic curve (AUC). We ascertained clinical glaucoma status on a patient subset (n = 97) to determine the usefulness of our model. MAIN OUTCOME MEASURES:Predictive models for GHT results reversal using VF features. RESULTS:For the 16?604 eyes with 2 initial ONL results, the prevalence of a subsequent WNL result increased from 0.1% for MD < -12 dB to 13.8% for MD ?-3 dB. Compared with models with VF global indices, the AUC of predictive models increased from 0.669 (MD ?-3 dB) and 0.697 (-6 dB ? MD < -3 dB) to 0.770 and 0.820, respectively, by adding VF mismatch features and computationally derived VF archetypes (P < 0.001 for both). The GHT results reversal was associated with a large mismatch between baseline VFs. Moreover, the GHT results reversal was associated more with VF archetypes of nonglaucomatous loss, severe widespread loss, and lens rim artifacts. For a subset of 97 eyes, using our model to predict absence of glaucoma based on clinical evidence after 2 ONL results yielded significantly better prediction accuracy (87.7%; P < 0.001) than predicting GHT results reversal (68.8%) with a prescribed specificity 67.7%. CONCLUSIONS:Using VF features may predict the GHT results reversal to WNL after 2 consecutive ONL results.

Project description:PURPOSE:Assess the impact of false-positives (FP), false-negatives (FN), fixation losses (FL), and test duration (TD) on visual field (VF) reliability at different stages of glaucoma severity. DESIGN:Retrospective. PARTICIPANTS:A total of 10?262 VFs from 1538 eyes of 909 subjects with suspect or manifest glaucoma and ?5 VF examinations. METHODS:Predicted mean deviation (MD) was calculated with multilevel modeling of longitudinal data. Differences between predicted and observed MD (?MD) were calculated as a reliability measure. The impact of FP, FN, FL, and TD on ?MD was assessed using multilevel modeling. MAIN OUTCOME MEASURES:?MD associated with a 10% increment in FP, FN, and FL, or a 1-minute increase in TD. RESULTS:FL had little impact on ?MD (<0.2 decibels [dB] per 10% abnormal catch trials), and no level of FL produced ?1 dB of ?MD at any disease stage. FP yielded greater than expected MD, with a 10% increment in abnormal catch trials associated with a ?MD = 0.42, 0.73, and 0.66 dB in mild (MD >-6 dB), moderate (-6 ?MD <-12 dB), and severe (-12 ?MD ?-20 dB) disease, respectively, up to 20% abnormal catch trials, and a ?MD = 1.57, 2.06, and 3.53 dB beyond 20% abnormal catch trials. FNs generally produced observed MDs below expected MDs. FN were minimally impactful up to 20% abnormal catch trials (?MD per 10% increment >-0.14 dB at all levels of severity). Beyond 20% abnormal catch trials, each 10% increment in abnormal catch trials was associated with a ?MD = -1.27, -0.53, and -0.51 dB in mild, moderate, and severe disease, respectively. |?MD| ?1 dB occurred with 22% FP and 26% FN in early, 14% FP and 34% FN in moderate, and 16% FP and 51% FN in severe disease. A 1-minute increment in TD produced ?MDs between -0.35 and -0.40 dB. CONCLUSIONS:FL have little impact on reliability in patients with established glaucoma. FP, and to a lesser extent FNs and TD, significantly affect reliability. The impact of FP and FN varies with disease severity and over the range of abnormal catch trials. On the basis of our findings, we present evidence-based, severity-specific standards for classifying VF reliability for clinical or research applications.

Dataset Information

Factors Predicting a Greater Likelihood of Poor Visual Field Reliability in Glaucoma Patients and Suspects.

Purpose

Methods

Results

Conclusions

Translational relevance

Publications

Factors Predicting a Greater Likelihood of Poor Visual Field Reliability in Glaucoma Patients and Suspects.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets