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

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 (ΔMD) were calculated and logistic regression identified factors predicting lower than expected (ΔMD <−1 dB) or higher than expected (ΔMD >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.