Nomograms for Converting Perimetric Sensitivity From Full Threshold and SITA Fast to SITA Standard in Patients With Glaucoma and Healthy Subjects

Giammaria, Sara; Vianna, Jayme R; Ohno, Yuko; Iwase, Aiko; Chauhan, Balwantray C.

doi:10.1167/tvst.10.9.2

Cited by 5 publications

(4 citation statements)

References 30 publications

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“…Sensitivity values from the full-threshold and SITA strategies are not interchangeable (the full-threshold strategy yields lower sensitivity values compared with the SITA standard strategy) . Therefore, we converted values obtained using the full-threshold strategy to SITA standard equivalents using recently described nomograms . In brief, for each visual field test, we converted the pointwise sensitivity values using the appropriate nomogram for either patients with glaucoma or healthy participants.…”

Section: Methodsmentioning

confidence: 99%

Rates of Visual Field Change in Patients With Glaucoma and Healthy Individuals

et al. 2022

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IMPORTANCEEstimating the rate of glaucomatous visual field change provides practical assessment of disease progression and has implications for management decisions.OBJECTIVE To assess the rates of visual field change in patients receiving treatment for glaucoma compared with healthy individuals over an extensive follow-up period and to quantify the impact of important covariates for these rates. DESIGN, SETTING, AND PARTICIPANTSThis prospective longitudinal cohort study was conducted in a hospital-based setting from January 1991 to February 2020. The study included 40 patients receiving treatment for open-angle glaucoma and 29 healthy participants. One eye of each participant was randomly selected as the study eye.EXPOSURES Patients with glaucoma and healthy participants received testing with standard automated perimetry every 6 months. Individual rates of mean sensitivity change were computed using ordinary least-squares regression analysis, and linear mixed-effects modeling was used to estimate the mean rates of mean sensitivity change in the 2 groups and the impact of baseline mean sensitivity, baseline age, and follow-up intraocular pressure for rate estimates.MAIN OUTCOMES AND MEASURES Rate of mean sensitivity change in patients with glaucoma and healthy participants. RESULTS A total of 40 patients with glaucoma (median age, 53.07 years [IQR, 48.34-57.97 years]; 21 men [52%]) and 29 healthy participants (median age, 48.80 years [IQR, 40.40-59.07 years], 17 women [59%]) were followed up for a median of 25.65 years (IQR, 22.49-27.02 years) and 19.56 years (IQR, 16.19-26.21 years), respectively. Most participants (65 individuals [94%]) self-identified as White, with the exception of 2 patients with glaucoma (1 self-identified as Black and 1 as South Asian) and 2 healthy participants (both self-identified as South Asian). The mean follow-up intraocular pressure of patients with glaucoma (median, 15.83 mm Hg [IQR,) was similar to that of healthy participants (median, 14.94 mm Hg [IQR, mm Hg]; P = .25). In an ordinary least-squares regression analysis, 31 patients (78%) with glaucoma had rates of mean sensitivity change within the range of healthy participants (ie, between −0.20 dB/y and 0.15 dB/y). Linear mixed-effects modeling revealed that the mean (SE) rate of mean sensitivity change in healthy participants was 0.003 (0.033) dB/y (95% CI, −0.062 to 0.068; P = .93). In comparison, patients with glaucoma had a mean (SE) rate of mean sensitivity change that was −0.032 (0.052) dB/y faster, but this difference was not statistically significant (95% CI, −0.134 to 0.070; P = .53). Among covariates, only baseline mean sensitivity was associated with the rate of mean sensitivity change (mean [SE], 0.021 [0.010] dB/y/dB; 95% CI, 0.002-0.041; P = .03). CONCLUSIONS AND RELEVANCEThe results of this cohort study suggest that over a median follow-up of more than 25 years, the rate of visual field change in patients receiving treatment for glaucoma was comparable to that of healthy individuals. These findings could guide pra...

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Section: Methodsmentioning

confidence: 99%

Rates of Visual Field Change in Patients With Glaucoma and Healthy Individuals

et al. 2022

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“…Finally, both statistically significant variables and clinically significant variables were included in the multivariate logistic regression analysis, and the nomogram model was constructed using the statistically significant variables selected at last (P < 0.05). 15 , 24 , 25…”

Section: Methodsmentioning

confidence: 99%

“…At present, more and more researchers have made a lot of efforts to build prediction models or improve existing prediction models. 15–17 among which building nomogram is one of the hottest research directions. In the latest research, 15 , 18 , 19 the use of nomogram prediction has shown good performance.…”

Section: Introductionmentioning

confidence: 99%

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Nomogram for Prediction of Diabetic Retinopathy Among Type 2 Diabetes Population in Xinjiang, China

Li¹,

Li²,

Zhao³

et al. 2022

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Purpose To establish an accurate risk prediction model of diabetic retinopathy (DR) using cost effective and easily available patients’ characteristics and clinical biomarkers. Patients and Methods Totally 18,904 cases diagnosed type 2 diabetes mellitus (T2DM) were collected, among which 13,980 cases were selected after quality screening. The least absolute shrinkage and selection operator (LASSO) regression models were used for univariate analysis and factors selection, and the multi-factor logistic regression analysis was used to establish the prediction model. Discrimination, calibration, and clinical usefulness of the prediction model were assessed using AUC/ Harrell’s C statistic, calibration plot, and decision curve analysis. Both the development group and validation group were assessed. Results Candidate variables were selected by Lasso regression and multivariate logistic regression analysis. Finally, the candidate predictive variables were included diabetic peripheral neuropathy (DPN), age, neutrophilic granulocyte (NE), high-density lipoprotein (HDL), hemoglobin A1c (HbA1C), duration of T2DM, and glycosylated serum protein (GSP) were used to establish a nomogram model for predicting the risk of DR. In the development group, the area under the receiver operating characteristic curve (AUC) was 0.882 (95% CI, 0.875–0.888). In the validation group, the AUC was 0.870 (95% CI, 0.856–0.881). Meanwhile, the optimism-corrected Harrell’s C statistic were 0.878 and 0.867 in the development group and the validation group, respectively. Decision curve analysis demonstrated that the nomogram was clinically useful. Conclusion We constructed and verified nomograms that could accurately predict the risk of DR in T2DM patients, which could be used to predict the personalized risk of DR patients in Xinjiang, China.

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