scholarly journals Association between the number of visual fields and the accuracy of future prediction in eyes with retinitis pigmentosa

2021 ◽  
Vol 6 (1) ◽  
pp. e000900
Author(s):  
Ryo Asaoka ◽  
Akio Oishi ◽  
Yuri Fujino ◽  
Hiroshi Murata ◽  
Keiko Azuma ◽  
...  
Keyword(s):  
Linear Regression ◽  
Retinitis Pigmentosa ◽  
Prediction Error ◽  
Visual Fields ◽  
Absolute Error ◽  
Mean Deviation ◽  
Humphrey Field Analyzer ◽  
Minimum Number ◽  
Future Prediction ◽  
The Mean

PurposeTo evaluate the minimum number of visual fields (VFs) required to precisely predict future VFs in eyes with retinitis pigmentosa (RP).MethodsA series of 12 VFs (Humphrey Field Analyzer 10–2 test (8.9 years in average) were analysed from 102 eyes of 52 patients with RP. The absolute error to predict the 12th VF using the prior 11 VFs was calculated in a pointwise manner, using the linear regression, and the 95% CI range was determined. Then, using 3–10 initial VFs, next VFs (4th to 11th VFs, respectively) were also predicted. The minimum number of VFs required for the mean absolute prediction error to reach the 95% CI was identified. Similar analyses were iterated for the second and third next VF predictions. Similar analyses were conducted using mean deviation (MD).ResultsIn the pointwise analysis, the minimum number of VFs required to reach the 95% CI for the 12th VF was five (first and second next VF predictions) and six (third next VF prediction). For the MD analysis, three (first and second next VF predictions) and four (third next VF prediction) VFs were required to reach 95% CI for the 12th VF.ConclusionsThe minimum number of VFs required to obtain accurate predictions of the future VF was five or six in the pointwise analysis and three or four in the analysis with MD.

scholarly journals The Association Between the Number of Visual Fields and the Accuracy of Future Prediction in Eyes With Retinitis Pigmentosa

2021 ◽  
Author(s):  
Ryo Asaoka ◽  
Akio Oishi ◽  
Yuri Fujino ◽  
Hiroshi Murata ◽  
Keiko Azuma ◽  
...  
Keyword(s):  
Confidence Interval ◽  
Retinitis Pigmentosa ◽  
Prediction Error ◽  
Visual Fields ◽  
Absolute Error ◽  
Mean Deviation ◽  
Humphrey Field Analyzer ◽  
Minimum Number ◽  
Future Prediction ◽  
The Mean

Abstract The purpose of the study was to evaluate the minimum number of visual fields (VFs) required to precisely predict future VFs in eyes with retinitis pigmentosa (RP). A series of 12 VFs (Humphrey Field Analyzer 10-2 test, (8.9 years in average) were analyzed from 102 eyes of 52 RP patients. The absolute error to predict the 12th VF using the prior 11 VFs was calculated in a pointwise manner, using the linear regression, and the 95% confidence interval (CI) range was determined. Then, using 3 to 10 initial VFs, next VFs (4th to 11th VFs, respectively) were also predicted. The minimum number of VFs required for the mean absolute prediction error to reach the 95% CI was identified. Similar analyses were iterated for the second and third next VF predictions. Similar analyses were conducted using mean deviation (MD). In the pointwise analysis, the minimum number of VFs required to reach the 95% CI for the 12th VF was 5 (first and second next VF predictions) and 6 (third next VF prediction). For the MD analysis, 3 (first and second next VF predictions) and 4 (third next VF prediction) VFs were required to reach 95% CI for the 12th VF.

scholarly journals Comparison of Perimetric Outcomes from Melbourne Rapid Fields Tablet Perimeter Software and Humphrey Field Analyzer in Glaucoma Patients

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Harsh Kumar ◽  
Mithun Thulasidas
Keyword(s):  
Standard Deviation ◽  
Visual Field ◽  
Rural Areas ◽  
Visual Fields ◽  
Test Time ◽  
Mean Deviation ◽  
Time Saving ◽  
Humphrey Field Analyzer ◽  
Probability Plot ◽  
The Mean

Purpose. To compare visual field results obtained using Melbourne Rapid Fields (MRF) iPad-based perimeter software and Humphrey Field Analyzer (HFA) 24-2 Swedish Interactive Threshold Algorithm (SITA) standard program in glaucoma patients. Design. A cross-sectional observational study. Methods. In this single-centre study involving patients diagnosed with glaucoma, the perimetric outcomes of MRF were compared against those returned from the HFA 24-2 SITA standard. Outcomes included mean deviation (MD), pattern standard deviation (PSD), visual field index (VFI)/visual capacity (VC), foveal threshold, test time, number of points depressed at P<5% on PSD probability plot, and glaucoma hemifield test/color coded indicator. Results. The study included 28 eyes of 28 glaucoma patients. Mean (standard deviation) test times were 342.07 (56.70) seconds for MRF and 375.11 (88.95) for HFA 24-2 SITA standard P=0.046. Mean MD was significantly lower for MRF (Δ = 3.09, P<0.001), and mean PSD was significantly higher for MRF (Δ = 1.40, P=0.005) compared with HFA. The mean foveal threshold for the MRF was significantly lower than the mean HFA foveal threshold ((Δ = 9.25, P<0.001). The number of points depressed at P<5% on the PSD probability plot was significantly less for MRF P<0.001. Other perimetric outcomes showed no significant differences between both. Bland–Altman plots showed that considerable variability existed between the programs. Conclusion. MRF is a good cost-effective, time-saving, user-friendly tool for monitoring visual fields in settings where access to traditional perimetry is limited. The lack of Internet strength in rural areas and questionable detection of early cases may be two points in MRF fields requiring an upgrade.

scholarly journals Estimating Rates of Progression and Predicting Future Visual Fields in Glaucoma Using a Deep Variational Autoencoder

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Samuel I. Berchuck ◽  
Sayan Mukherjee ◽  
Felipe A. Medeiros
Keyword(s):  
Visual Field ◽  
Learning Algorithm ◽  
Visual Fields ◽  
Visual Field Loss ◽  
Absolute Error ◽  
Rate Of Change ◽  
Mean Deviation ◽  
Automated Perimetry ◽  
Standard Automated Perimetry ◽  
Deep Learning Algorithm

AbstractIn this manuscript we develop a deep learning algorithm to improve estimation of rates of progression and prediction of future patterns of visual field loss in glaucoma. A generalized variational auto-encoder (VAE) was trained to learn a low-dimensional representation of standard automated perimetry (SAP) visual fields using 29,161 fields from 3,832 patients. The VAE was trained on a 90% sample of the data, with randomization at the patient level. Using the remaining 10%, rates of progression and predictions were generated, with comparisons to SAP mean deviation (MD) rates and point-wise (PW) regression predictions, respectively. The longitudinal rate of change through the VAE latent space (e.g., with eight dimensions) detected a significantly higher proportion of progression than MD at two (25% vs. 9%) and four (35% vs 15%) years from baseline. Early on, VAE improved prediction over PW, with significantly smaller mean absolute error in predicting the 4th, 6th and 8th visits from the first three (e.g., visit eight: VAE8: 5.14 dB vs. PW: 8.07 dB; P < 0.001). A deep VAE can be used for assessing both rates and trajectories of progression in glaucoma, with the additional benefit of being a generative technique capable of predicting future patterns of visual field damage.

scholarly journals Jump or kink: on super-efficiency in segmented linear regression breakpoint estimation

Biometrika ◽  
2020 ◽  
Author(s):  
Yining Chen
Keyword(s):  
Structural Change ◽  
Linear Regression ◽  
Convergence Rate ◽  
Pointwise Convergence ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
The Other ◽  
Fixed Parameter ◽  
The Mean ◽  
Potential Remedy

Summary We consider the problem of segmented linear regression with a single breakpoint, with the focus on estimating the location of the breakpoint. If $n$ is the sample size, we show that the global minimax convergence rate for this problem in terms of the mean absolute error is $O(n^{-1/3})$. On the other hand, we demonstrate the construction of a super-efficient estimator that achieves the pointwise convergence rate of either $O(n^{-1})$ or $O(n^{-1/2})$ for every fixed parameter value, depending on whether the structural change is a jump or a kink. The implications of this example and a potential remedy are discussed.

scholarly journals Long-term outcomes of prediction error after combined phacoemulsification and trabeculectomy in glaucoma patients

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yeon Soo Kang ◽  
Mi Sun Sung ◽  
Hwan Heo ◽  
Yong Sok Ji ◽  
Sang Woo Park
Keyword(s):  
Risk Factors ◽  
Prediction Error ◽  
Anterior Segment ◽  
Anterior Chamber Depth ◽  
Absolute Error ◽  
Long Term Outcomes ◽  
Corrected Visual Acuity ◽  
Before And After ◽  
The Mean

Abstract Background To investigate long-term outcomes of prediction error after phacotrabeculectomy and to determine risk factors that may cause unstable prediction error after phacotrabeculectomy in glaucoma patients. Methods A total 120 eyes of 120 patients who had underwent uncomplicated phacotrabeculectomy (combined group) or phacoemulsification (phaco-only group) were included. Best-corrected visual acuity (BCVA), intraocular pressure (IOP) were measured before and after surgery, and anterior segment parameters including anterior chamber depth (ACD), lens vault (LV), and anterior vault (AV) measured using anterior-segment optical coherence tomography were compared between the two groups. The mean absolute error (MAE) at 3, 6, 12, and 24 months postoperatively were compared. Risk factors associated with unstable prediction error (MAE ≥ 0.5) were investigated in the combined group. Results In both groups, BCVA was improved and IOP was decreased significantly. MAE at 3, 6, 12, 24 months postoperatively were not significantly different between two groups. The risk factors for unstable prediction error after 12 months of phacotrabeculectomy were old age and LV. Whereas, the only factor predicting unstable prediction error after 24 months of phacotrabeculectomy was LV. The cut-off value of LV for predicting unstable refractive error analyzed by the ROC curve was 0.855 mm. Conclusions Phacotrabeculectomy may be an effective treatment with stable long-term outcomes of prediction error similar to phacoemulsification in patients with glaucoma. However, elderly patients or patients with large LV may be predisposed to unstable prediction error after phacotrabeculectomy.

scholarly journals Relationship between structural and functional changes in glaucomatous eyes: a multifocal electroretinogram study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Hiroki Tanaka ◽  
Kyoko Ishida ◽  
Kenji Ozawa ◽  
Takuma Ishihara ◽  
Akira Sawada ◽  
...  
Keyword(s):  
Functional Assessment ◽  
Multifocal Electroretinogram ◽  
Visual Fields ◽  
Negative Response ◽  
Photopic Negative Response ◽  
Mean Deviation ◽  
Ganglion Cell Complex ◽  
Functional Changes ◽  
The Mean ◽  
Optimal Measure

Abstract Background The nasal to temporal amplitudes ratio (N/T) of multifocal electroretinography (mfERG) scans measured within 5° of the macula can be used to detect glaucomatous change. The photopic negative response (PhNR) of mfERG elicited by a circular stimulus centered on the fovea was significantly reduced in eyes with glaucoma. The PhNR to B-wave ratio (PhNR/B) is the optimal measure of the PhNR. However, clinical superiority for evaluating glaucoma patients has not been determined between N/T and PhNR/B yet. Methods For morphological assessments, ganglion cell complex (GCC) in six regions and the average were measured by optical coherence tomography (OCT). For functional assessment, Humphrey visual fields (VF) with mean sensitivities (MT) and mfERG scans with parameters of N/T and the multifocal photopic negative response to B-wave ratio (mfPhNR/B) were measured. Sixty-nine eyes of 44 glaucoma patients were included and correlations between mfERG parameters and OCT or VF parameters were evaluated. Results The mean age of patients was 59.4 years. The mean deviation for all eyes obtained with the VF 30–2 and VF 10–2 was − 7.00 and − 6.31 dB, respectively. Significant correlations between GCC thickness or VF parameter and the N/T were found, especially in the inferior and inforotemporal retinal areas corresponding to superior and superonasal VF sectors (GCC vs N/T; coefficient = − 7.916 and − 7.857, and MT vs N/T; coefficient = − 4.302 and − 4.437, in the inferior and inforotemporal retinal areas, respectively, all p values < 0.05). However, similar associations were not obtained between mfPhNR/B and OCT or VF parameters. The mfPhNR/B only in the inferotemporal sector was significantly correlated with the average thickness of GCC (coefficient = 4.823, P = 0.012). Conclusions The N/T was correlated with GCC and VF in more numbers of measurement areas than the mfPhNR/B in the current study, however, a future study modifying the stimuli and amplitudes to obtain the spatial correspondence to OCT and VF measurement will be required to evaluate the value of mfERG.

scholarly journals Accuracy of partial coherence interferometry in patients with large inter-eye axial length difference

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0246721
Author(s):  
Christine A. Petersen ◽  
Daniel C. Terveen ◽  
Tyler Quist ◽  
Parisa Taravati ◽  
Leona Ding ◽  
...  
Keyword(s):  
Prediction Error ◽  
Absolute Error ◽  
Partial Coherence ◽  
Partial Coherence Interferometry ◽  
Refractive Outcomes ◽  
Academic Medical ◽  
Significant Difference ◽  
Study Population ◽  
The Mean ◽  
Axial Eye Length

Background To determine accuracy of partial coherence interferometry (PCI) in patients with large inter-eye axial eye length (AEL) difference. Methods Patients undergoing cataract surgery at two academic medical centers with an inter-eye axial eye length (AEL) difference of > 0.30 mm were identified and were matched to control patients without inter-eye AEL difference > 0.30 mm on the basis of age, sex, and AEL. The expected post-operative refraction for the implanted IOL was calculated using SRK/T, Holladay II, and Hoffer Q formulae. The main outcome measures were the refractive prediction error and the equivalence of the refractive outcomes between the subjects and controls. Results Review of 2212 eyes from 1617 patients found 131 eyes of 93 patients which met inclusion criteria. These were matched to 131 control eyes of 115 patients. The mean AEL was 24.92 ± 1.50 mm. The mean absolute error (MAE) ranged from 0.47 D to 0.69 D, and was not statistically different between subjects and controls. The refractive prediction error was equivalent between the cases and controls, with no significant difference between the MAE for any formula, nor in the number of cases vs. controls with a refractive prediction error of at least 0.50 D or 1.00 D. Conclusions Among eyes in our study population, good-quality PCI data was equally accurate in patients with or without an inter-eye AEL difference > 0.30 mm. Confirmatory AEL measurements using different AEL measuring modalities in patients with a large inter-eye AEL difference may not be necessary.

Achieving low target intraocular pressures in severe glaucoma

2020 ◽  
pp. 112067212097990
Author(s):  
Gazella Bruce Warjri ◽  
Talvir Sidhu ◽  
Azmira Kishan ◽  
Aswini Kumar Behera ◽  
Jyoti Shakrawal ◽  
...  
Keyword(s):  
Open Angle Glaucoma ◽  
Angle Closure ◽  
Mean Deviation ◽  
Age Group ◽  
Open Angle ◽  
Primary Angle Closure ◽  
Primary Angle Closure Glaucoma ◽  
Humphrey Field Analyzer ◽  
The Mean

Purpose: This study aimed to assess details of therapy required to achieve an intraocular pressure (IOP) of ⩽12 mmHg in patients with severe glaucoma of different etiologies. Methods: Patients with a follow-up of at least 1 year, who fulfilled inclusion criteria were selected, and data analyzed with respect to baseline IOP, number of medications, and/or surgeries required to achieve an IOP of ⩽12 mmHg and ⩾6 mmHg. Final IOP, visual field status and medications/surgery required were noted. Results: About 127 eyes of 85 patients met all criteria. There were 48 eyes having primary angle closure glaucoma (PACG), 16 eyes with primary open angle glaucoma (POAG), 17 eyes of juvenile open angle glaucoma (JOAG), and 46 eyes of secondary glaucoma. The mean baseline IOP was 33.14 ± 11.07 mmHg and final IOP 10.25 ± 1.81 mmHg. In the age group >40 years, 29.63% of patients were controlled on medication as compared to 9.09% and 5.71% in the age group of <20 years and 20–40 years ( p = 0.007). With a baseline IOP of <25 mmHg, 48.65% required a trabeculectomy, whereas for 25–30, and >30 mmHg, 62.5% and 97.29% respectively, required surgery ( p < 0.001).66.67% of patients having mean deviation of −12 to −16 dB (decibels) on Humphrey field analyzer (HFA) underwent surgery to achieve target IOP in comparison to 83.52% who had mean deviation greater than −16 dB ( p = 0.036). Conclusion: Only 21.26% of severe glaucoma eyes could be controlled on medications alone. A baseline IOP of >25 mmHg, age <40 years and mean deviation worse than −16 dB, had a higher frequency of trabeculectomies to achieve an IOP of ⩽12 mmHg.

scholarly journals Prediction of Unbound Ceftriaxone Concentration in Children: Simple Bioanalysis Method and Basic Mathematical Equation

2020 ◽  
Vol 65 (1) ◽  
pp. e00779-20
Author(s):  
Min Kan ◽  
Hai-Yan Shi ◽  
Bing Han ◽  
Yue-E Wu ◽  
Qian Li ◽  
...  
Keyword(s):  
Linear Regression ◽  
Prediction Error ◽  
Predictive Performance ◽  
Prediction Equation ◽  
Individual Variability ◽  
Unbound Fraction ◽  
One Step ◽  
The Mean ◽  
Unbound Concentration

ABSTRACTThe pharmacological activity of ceftriaxone depends on the unbound concentration. However, direct measurement of unbound concentrations is obstructive, and high individual variability of the unbound fraction of ceftriaxone was shown in children. We aim to evaluate and validate a method to predict unbound ceftriaxone concentrations in pediatric patients. Ninety-five pairs of concentrations (total and unbound) from 92 patients were measured by the bioanalysis method that we developed. The predictive performance of the three equations (empirical in vivo equation, disease-adapted equation, and multiple linear regression equation) was assessed by the mean absolute prediction error (MAPE), the mean prediction error (MPE), the proportions of the prediction error within ±30% (P30) and ±50% (P50), and linear regression of predicted versus actual unbound levels (R2). The average total and unbound ceftriaxone concentrations were 126.18 ± 81.46 μg/ml and 18.82 ± 21.75 μg/ml, and the unbound fraction varied greatly from 4.75% to 39.97%. The MPE, MAPE, P30, P50, and R2 of the empirical in vivo equation, disease equation, and multiple linear equation were 0.17 versus 0.00 versus 0.06, 0.24 versus 0.15 versus 0.27, 63.2% versus 89.5% versus 74.7%, 96.8% versus 97.9% versus 86.3%, and 0.8730 versus 0.9342 versus 0.9315, respectively. The disease-adapted equation showed the best predictive performance. We have developed and validated a bioanalysis method with one-step extraction pretreatment for the determination of total ceftriaxone concentrations, and a prediction equation of the unbound concentration is recommended. The proposed method can facilitate clinical practice and research on unbound ceftriaxone in children. (This study has been registered at ClinicalTrials.gov under identifier NCT03113344.)

scholarly journals A new hybrid of Fuzzy C-Means Method and Fuzzy Linear Regression Model in Predicting Manufacturing Income

2018 ◽  
Vol 7 (4.30) ◽  
pp. 473
Author(s):  
Nurfarawahida Ramly ◽  
Mohd Saifullah Rusiman ◽  
Norziha Che Him ◽  
Maria Elena Nor ◽  
Supar Man ◽  
...  
Keyword(s):  
Linear Regression ◽  
Mean Absolute Error ◽  
Human Perception ◽  
Absolute Error ◽  
Percentage Error ◽  
Fuzzy Linear Regression ◽  
Fuzzy C Means ◽  
Fuzzy Linear Regression Model ◽  
The Mean ◽  
Ambiguous Data

Analysis by human perception could not be solved using traditional method since uncertainty within the data have to be dealt with first. Thus, fuzzy structure system is considered. The objectives of this study are to determine suitable cluster by using fuzzy c-means (FCM) method, to apply existing methods such as multiple linear regression (MLR) and fuzzy linear regression (FLR) as proposed by Tanaka and Ni and to improve the FCM method and FLR model proposed by Zolfaghari to predict manufacturing income. This study focused on FLR which is suitable for ambiguous data in modelling. Clustering is used to cluster or group the data according to its similarity where FCM is the best method. The performance of models will measure by using the mean square error (MSE), the mean absolute error (MAE) and the mean absolute percentage error (MAPE). Results shows that the improvisation of FCM method and FLR model obtained the lowest value of error measurement with MSE=1.825 , MAE=115932.702 and MAPE=95.0366. Therefore, as the conclusion, a new hybrid of FCM method and FLR model are the best model for predicting manufacturing income compared to the other models.

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