UNASSIGNED: To determine the prevalence of subclinical keratoconus (SKCN) among individuals undergoing routine, uncomplicated age-related cataract surgery and its impact on visual and refractive outcomes.
UNASSIGNED: At a major academic ophthalmology department in the United States, we reviewed records of patients aged 50 years and older who underwent surgery from January 2011 to June 2022. We excluded patients who had poor-quality or unreliable tomographic data, previous corneal surgery, keratorefractive procedures, and significant vision-limiting ocular pathology. We defined SKCN if an eye had a Belin-Ambrósio enhanced ectasia index (BAD-D) ≥1.7, which was based on the results of a meta-analysis of large studies. In addition to the BAD-D cutoff, the eye had to deviate significantly on at least one of seven additional parameters: 1) posterior elevation at thinnest point, 2) index of vertical asymmetry, 3) index of surface variation, 4) total front higher order aberrations, 5) front vertical coma, 6) front secondary vertical coma, 7) back vertical coma. An individual had SKCN if at least one eye met the tomography-based classification and did not have manifest KCN in either eye. Visual and refractive outcomes data were acquired from patients of one experienced cataract surgeon with cases done from July 2021 to June 2022. Statistical significance was set at p < 0.05.
UNASSIGNED: Among 5592 eyes from 3828 individuals, the prevalence of SKCN was 24.7% (95% CI, 23.4 - 26.1, 945 individuals), and the prevalence of KCN was 1.9% (95% CI, 1.6 - 2.4, 87 individuals). The prevalence of SKCN did not increase with age and was more prevalent among females and non-white races. Median post-operative month one distance-corrected visual acuity (DCVA) and proportion of eyes with improvement in DCVA were similar between normal and SKCN eyes. The proportion of eyes reaching ±0.5 and ±1.0 diopter within the refractive target were similar between normal and SKCN eyes.
UNASSIGNED: SKCN is highly prevalent and should be detected but is unlikely to have a significant deleterious effect on outcomes in routine, uncomplicated cataract surgery.

BACKGROUND: To evaluate the corneal biomechanics of stable keratoconus suspects (Stable-KCS) at 1-year follow-up and compare them with those of subclinical keratoconus (SKC).
METHODS: This prospective case-control study included the eyes of 144 patients. Biomechanical and tomographic parameters were recorded (Corvis ST and Pentacam). Patients without clinical signs of keratoconus in both eyes but suspicious tomography findings were included in the Stable-KCS group (n = 72). Longitudinal follow-up was used to evaluate Stable-KCS changes. Unilateral keratoconus contralateral eyes with suspicious tomography were included in the SKC group (n = 72). T-tests and non-parametric tests were used for comparison. Multivariate general linear models were used to adjust for confounding factors for further analysis. Receiver operating characteristic (ROC) curves were used to analyze the distinguishability.
RESULTS: The biomechanical and tomographic parameters of Stable-KCS showed no progression during the follow-up time (13.19 ± 2.41 months, p > 0.05). Fifteen biomechanical parameters and the Stress-Strain Index (SSI) differed between the two groups (p < 0.016). The A1 dArc length showed the strongest distinguishing ability (area under the ROC = 0.888) between Stable-KCS and SKC, with 90.28% sensitivity and 77.78% specificity at the cut-off value of -0.0175.
CONCLUSIONS: The A1 dArc length could distinguish between Stable-KCS and SKC, indicating the need to focus on changes in the A1 dArc length for keratoconus suspects during the follow-up period. Although both have abnormalities on tomography, the corneal biomechanics and SSI of Stable-KCS were stronger than those of SKC, which may explain the lack of progression of Stable-KCS.

To analyze the corneal biomechanical properties in patients with abnormal corneal tomography (ACT) and predict their stability using the biomechanical stability index (BSI).
Prospective cohort study.
Setting: Multicenter study.
This study included 385 eyes of 278 patients with stable ACT (n = 70), subclinical keratoconus (SKC, n = 65), keratoconus (n = 65), normal controls (NL, n = 142). Forty-three eyes with first-visit ACT were included in a separate cohort (follow-up ACT group).
Tomographical and biomechanical parameters (Pentacam and Corvis ST) were recorded.
Nonparametric tests were used for comparison. Logistic regression was employed to introduce BSI to separate stable ACT and SKC accurately. An independent dataset of 43 first-visit ACT eyes was followed up for 1 year to validate BSI\'s accuracy and positive and negative predictive values (PPV, NPV).
The tomographical and biomechanical parameters in patients with Stable ACT remained stable over the follow-up period (12.73 ± 2.57 months, P > .05). Stable ACT had 12/14 biomechanical parameters different (P < .05) from SKC but not different from NL (P > .05). With a cut-off value of 0.585, BSI demonstrated the strongest ability to distinguish between stable ACT and SKC (area under the receiver operating characteristic curve = 0.991), with 93.85% sensitivity and 97.14% specificity. During the 1-year follow-up of 43 eyes (follow-up ACT group), 30 remained stable. The accuracy, PPV, and NPV of the BSI were 95.35%, 100%, and 93.75%, respectively.
Biomechanical properties of patients with stable abnormal tomography corneas were stronger than SKC and close to normal corneas, which may explain the reason for tomographic stability. The BSI may be useful for predicting disease progression in patients with ACT and the possible management of corneal cross-linking at the first visit.

Background: Keratoconus (KC) occurs at puberty but diagnosis is focused on adults. The early diagnosis of pediatric KC can prevent its progression and improve the quality of life of patients. This study aimed to evaluate the ability of corneal tomographic and biomechanical variables through machine learning analysis to detect subclinical keratoconus (SKC) in a pediatric population. Methods: Fifty-two KC, 52 SKC, and 52 control pediatric eyes matched by age and gender were recruited in a case-control study. The corneal tomographic and biomechanical parameters were measured by professionals. A linear mixed-effects test was used to compare the differences among the three groups and a least significant difference analysis was used to conduct pairwise comparisons. The receiver operating characteristic (ROC) curve and the Delong test were used to evaluate diagnostic ability. Variables were used in a multivariate logistic regression in the machine learning analysis, using a stepwise variable selection to decrease overfitting, and comprehensive indices for detecting pediatric SKC eyes were produced in each step. Results: PE, BAD-D, and TBI had the highest area under the curve (AUC) values in identifying pediatric KC eyes, and the corresponding cutoff values were 12 μm, 2.48, and 0.6, respectively. For discriminating SKC eyes, the highest AUC (95% CI) was found in SP A1 with a value of 0.84 (0.765, 0.915), and BAD-D was the best parameter among the corneal tomographic parameters with an AUC (95% CI) value of 0.817 (0.729, 0.886). Three models were generated in the machine learning analysis, and Model 3 (y = 0.400*PE + 1.982* DA ratio max [2 mm]-0.072 * SP A1-3.245) had the highest AUC (95% CI) value, with 90.4% sensitivity and 76.9% specificity, and the cutoff value providing the best Youden index was 0.19. Conclusion: The criteria of parameters for diagnosing pediatric KC and SKC eyes were inconsistent with the adult population. Combined corneal tomographic and biomechanical parameters could enhance the early diagnosis of young patients and improve the inadequate representation of pediatric KC research.

To retest the performance of Pentacam parameters in the detection of eyes with subclinical keratoconus (KC) and mild KC based on different definitions from the Amsler-Krumeich (AK), Collaborative Longitudinal Evaluation of Keratoconus (CLEK), and ABCD systems.
This cross-sectional university-based study comprised 24 eyes with subclinical KC, 144 eyes with mild KC (based on AK in 101 eyes, CLEK in 28 eyes, and ABCD in 15 eyes), and 70 controls. Diagnostic ability of the thinnest point (TP) pachymetry, KISA% index, inferior-superior asymmetry, corneal aberrations, Pentacam indices, front/back elevations, pachymetric progression index, Ambrósio-Relational Thickness (ARTmax), and Belin/Ambrósio Enhanced Ectasia Display scores (Df, Db, Dp, Dt, Da, and D-final) were evaluated.
ARTmax (83.3% sensitivity/74.3% specificity) had the highest ability in distinguishing subclinical KC from normal, followed by TP pachymetry, Dt, and Da. D-final showed excellent sensitivity/specificity in mild KC diagnosis based on AK (98%/100%) and CLEK (97.4%/100%) descriptions. In the mild KC-ABCD group, index of vertical asymmetry accurately detected all eyes with mild KC and 97.1% of the controls.
This study points out the gray zone in the detection of eyes with subclinical and mild KC due to overlapping terminology and grading criteria. Pentacam parameters seem to have modest capability in subclinical KC detection, indicating the necessity for additional diagnostic modalities. However, eyes with mild KC can be diagnosed with high accuracy using Pentacam parameters, although the strongest parameters may vary according to the definition of “mild KC.” Nevertheless, uniform and definitive criteria for subclinical and clinical KC classification are required for a diagnostic and therapeutic consensus in KC.

BACKGROUND: The diagnosis of keratoconus, as the most prevalent corneal ectatic disorder, at the subclinical stage gained great attention due to the increased acceptance of refractive surgeries. This study aimed to assess the pattern of the corneal biomechanical properties derived from Corneal Visualization Scheimpflug Technology (Corvis ST) and evaluate the diagnostic value of these parameters in distinguishing subclinical keratoconus (SKC) from normal eyes.
METHODS: This prospective study was conducted on 73 SKC and 69 normal eyes. Subclinical keratoconus eyes were defined as corneas with no clinical evidence of keratoconus and suspicious topographic and tomographic features. Following a complete ophthalmic examination, topographic and tomographic corneal assessment via Pentacam HR, and corneal biomechanical evaluation utilizing Corvis ST were done.
RESULTS: Subclinical keratoconus eyes presented significantly higher Deformation Amplitude (DA) ratio, Tomographic Biomechanical Index (TBI), and Corvis Biomechanical Index (CBI) rates than the control group. Conversely, Ambrósio Relational Thickness to the Horizontal profile (ARTh), and Stiffness Parameter at the first Applanation (SPA1) showed significantly lower rates in SKC eyes. In diagnosing SKC from normal eyes, TBI (AUC: 0.858, Cut-off value: > 0.33, Youden index: 0.55), ARTh (AUC: 0.813, Cut-off value: ≤ 488.1, Youden index: 0.58), and CBI (AUC: 0.804, Cut-off value: > 0.47, Youden index: 0.49) appeared as good indicators.
CONCLUSIONS: TBI, CBI, and ARTh parameters could be valuable in distinguishing SKC eyes from normal ones.

Purpose: To evaluate the interocular consistency of biomechanical properties in normal, keratoconus (KC) and subclinical keratoconus (SKC) populations and explore the application of interocular asymmetry values in KC and SKC diagnoses. Methods: This was a retrospective chart-review study of 331 ametropic subjects (control group) and 207 KC patients (KC group, including 94 SKC patients). Interocular consistency was evaluated using the intraclass correlation coefficient (ICC). Interocular asymmetry was compared between the control and KC groups and its correlation with disease severity was analyzed. Three logistic models were constructed using biomechanical monocular parameters and interocular asymmetry values. The diagnostic ability of interocular asymmetry values and the newly established models were evaluated using receiver operating characteristic curves and calibration curves. Net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were also estimated. Results: The interocular consistency significantly decreased and the interocular asymmetry values increased in KC patients compared with those in control individuals. In addition, the interocular asymmetry values increased with respect to the severity of KC. The binocular assisted biomechanical index (BaBI) had an area under the curve (AUC) of 0.998 (97.8% sensitivity, 99.2% specificity; cutoff 0.401), which was statistically higher than that of the Corvis biomechanical index [CBI; AUC = 0.935, p < 0.001 (DeLong\'s test), 85.6% sensitivity]. The optimized cutoff of 0.163 provided an AUC of 0.996 for SKC with 97.8% sensitivity, which was higher than that of CBI [AUC = 0.925, p < 0.001 (DeLong\'s test), 82.8% sensitivity]. Conclusion: Biomechanical interocular asymmetry values can reduce the false-negative rate and improve the performance in KC and SKC diagnoses.

OBJECTIVE: To characterize focal biomechanical alterations in subclinical keratoconus (SKC) using motion-tracking (MT) Brillouin microscopy and evaluate the ability of MT Brillouin metrics to differentiate eyes with SKC from normal control eyes.
METHODS: Prospective cross-sectional study.
METHODS: Thirty eyes from 30 patients were evaluated, including 15 eyes from 15 bilaterally normal patients and 15 eyes with SKC from 15 patients.
METHODS: All patients underwent Scheimpflug tomography and MT Brillouin microscopy using a custom-built device. Mean and minimum MT Brillouin values within the anterior plateau region and anterior 150 μm were generated. Scheimpflug metrics evaluated included inferior-superior (IS) value, maximum keratometry (Kmax), thinnest corneal thickness, asymmetry indices, Belin/Ambrosio display total deviation, and Ambrosio relational thickness. Receiver operating characteristic (ROC) curves were generated for all Scheimpflug and MT Brillouin metrics evaluated to determine the area under the ROC curve (AUC), sensitivity, and specificity for each variable.
METHODS: Discriminative performance based on AUC, sensitivity, and specificity.
RESULTS: No significant differences were found between groups for age, sex, manifest refraction spherical equivalent, corrected distance visual acuity, Kmax, or KISA% index. Among Scheimpflug metrics, significant differences were found between groups for thinnest corneal thickness (556 μm vs. 522 μm; P < 0.001), IS value (0.29 diopter [D] vs. 1.05 D; P < 0.001), index of vertical asymmetry (IVA; 0.10 vs. 0.19; P < 0.001), and keratoconus index (1.01 vs. 1.05; P < 0.001), and no significant differences were found for any other Scheimpflug metric. Among MT Brillouin metrics, clear differences were found between control eyes and eyes with SKC for mean plateau (5.71 GHz vs. 5.68 GHz; P < 0.0001), minimum plateau (5.69 GHz vs. 5.65 GHz; P < 0.0001), mean anterior 150 μm (5.72 GHz vs. 5.68 GHz; P < 0.0001), and minimum anterior 150 μm (5.70 GHz vs. 5.66 GHz; P < 0.001). All MT Brillouin plateau and anterior 150 μm mean and minimum metrics fully differentiated groups (AUC, 1.0 for each), whereas the best performing Scheimpflug metrics were keratoconus index (AUC, 0.91), IS value (AUC, 0.89), and IVA (AUC, 0.88).
CONCLUSIONS: Motion-tracking Brillouin microscopy metrics effectively characterize focal corneal biomechanical alterations in eyes with SKC and clearly differentiated these eyes from control eyes, including eyes that were not differentiated accurately using Scheimpflug metrics.
BACKGROUND: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

UNASSIGNED: The objective of this study is to investigate the changes in topometry, tomography, and corneal densitometry in subclinical keratoconus (SK) at the 6-month interval.
UNASSIGNED: The clinical keratoconus and SK groups included 25 eyes; the control group included 22 eyes from 22 patients. Corneal topographic, tomographic, topometric, and densitometric values obtained using the Pentacam HR imaging system were analyzed.
UNASSIGNED: Posterior elevation (PE), Keratoconus index (KI), index of height asymmetry (IHA), index of height decentration (IHD), Dp, Da, Final D, maximum pachymetric progression index (PPImax), and maximum Ambrósio relational thickness parameters showed significant changes between the baseline and the 6th-month follow-up in SK group (p<0.05 for all values). There were significant changes in all zones except a central layer of 6-10 zone, anterior, and central layer of 10-12 zone between the baseline and the 6th-month follow-up in the SK group (p<0.05, for all values). The changes in mean±standard deviation of KI, IHA, IHD, PPImax parameters, and corneal densitometry values of the posterior layer of 0-2 mm and 2-6 mm zones were significant in the SK group compared to the controls (p<0.05, for all values).
UNASSIGNED: PE, KI, IHA, IHD, and PPImax parameters as well as increasing corneal light backscatter of the posterior central layer might be useful for follow-up of progression of SK. New multimeric parameters created by combinations of topometric, tomographic, and corneal densitometry parameters could be the future of SK follow-up.

OBJECTIVE: The aim of the study was to evaluate the utility of epithelial mapping, Corvis biomechanical index (CBI), and tomographic biomechanical index (TBI) in diagnosing the spectrum of keratoconus (KC).
METHODS: This was a retrospective study where KC subjects with an age between 11 and 50 years were enrolled. Subjects with ocular diseases, history of previous corneal surgery, corneal scars or hydrops, ocular trauma, ocular surface disorder, systemic disease, and poor-quality scans were excluded. KC was classified using Belin ABCD classification system. Epithelial thickness, corneal tomography, and corneal biomechanical measurements were recorded using Fourier-domain optical coherence tomography Avanti with corneal adaptor module, Pentacam HR, and Corvis® ST, respectively. To understand the utility of various corneal parameters in diagnosing spectrum of keratoconus, cutoff values for epithelial thickness at the thinnest location and its standard deviation (SD) were considered 45 and 3 microns, respectively, CBI of 0.5 and TBI of 0.29 was considered.
RESULTS: Sixty-five eyes (45 - KC, 10 - subclinical KC (SBKC), and 10 - forme fruste [FF]) of 34 patients with a mean ± SD age of 30.73 ± 5.71 were included. In our keratoconic sample, epithelial mapping alone helped diagnose the 77.77% of cases, however, when combined with CBI, it helped diagnose 95.5% cases and when combined with TBI, it was useful in diagnosing 100% of cases. In SBKC group, 40% of cases were detected by epithelial mapping alone, and when combined with CBI, it helped diagnose 70% of cases and TBI further helped diagnose 90% of cases. We did not see any role of epithelial mapping in detecting FFKC cases whereas CBI and TBI helped diagnose 60% and 90% of cases, respectively.
CONCLUSIONS: The utility of epithelial mapping as a solitary tool is limited in detecting the spectrum of KC, especially SB and FFKC. However, combining it with corneal biomechanical parameters could help improve the efficacy of diagnosis of KC.