Baseline factors predicting the need for corneal crosslinking in patients with keratoconus

Kato, Naoko; Negishi, Kazuno; Sakai, Chikako; Ohta, Michio

doi:10.1371/journal.pone.0231439

Cited by 9 publications

(13 citation statements)

References 24 publications

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“…Age is an inevitable factor in keratoconus progression, partly because keratoconus is a disorder that tends to progress depend on the patients’ age; its progression tends to slow during middle age but young onset keratoconus has been shown to progress much faster [ 33 , 34 ]. We had previously investigated the condition of keratoconus patients who were followed-up twice or more after the initial visit and found that the patients’ age was the most relevant factor with respect to keratoconus progression, followed by Rmin (the minimum sagittal curvature evaluated by Pentacam HR) of the corneal frontal plane [ 35 ]. The disproportionate influence of age between progression and non-progression groups was ineluctable.…”

Section: Discussionmentioning

confidence: 99%

Predicting Keratoconus Progression and Need for Corneal Crosslinking Using Deep Learning

Kato

Masumoto

Tanabe

et al. 2021

JCM

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We aimed to predict keratoconus progression and the need for corneal crosslinking (CXL) using deep learning (DL). Two hundred and seventy-four corneal tomography images taken by Pentacam HR® (Oculus, Wetzlar, Germany) of 158 keratoconus patients were examined. All patients were examined two times or more, and divided into two groups; the progression group and the non-progression group. An axial map of the frontal corneal plane, a pachymetry map, and a combination of these two maps at the initial examination were assessed according to the patients’ age. Training with a convolutional neural network on these learning data objects was conducted. Ninety eyes showed progression and 184 eyes showed no progression. The axial map, the pachymetry map, and their combination combined with patients’ age showed mean AUC values of 0.783, 0.784, and 0.814 (95% confidence interval (0.721–0.845) (0.722–0.846), and (0.755–0.872), respectively), with sensitivities of 87.8%, 77.8%, and 77.8% ((79.2–93.7), (67.8–85.9), and (67.8–85.9)) and specificities of 59.8%, 65.8%, and 69.6% ((52.3–66.9), (58.4–72.6), and (62.4–76.1)), respectively. Using the proposed DL neural network model, keratoconus progression can be predicted on corneal tomography maps combined with patients’ age.

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Section: Discussionmentioning

confidence: 99%

Predicting Keratoconus Progression and Need for Corneal Crosslinking Using Deep Learning

Kato

Masumoto

Tanabe

et al. 2021

JCM

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“…In the literature, parameters reported to have predictive value in KC progression include young age, 20 , 21 , 22 , 23 low TCT, 24 high Kmean, 24 high anterior Kmax 18 , 23 and posterior Kmax, 25 high central posterior 24 or anterior 20 elevation, index of surface variance (ISV), 26 high index of height decentration (IHD), 26 and vertical coma. 25 The fact that none of the initial corneal tomography parameters had predictive value for progression in this study whereas some initial biomechanical parameters showed significant differences between the groups supports the argument that the primary derangement in KC is biomechanical and that tomographic changes follow biomechanical disruption.…”

Section: Discussionmentioning

confidence: 99%

Superiority of Baseline Biomechanical Properties over Corneal Tomography in Predicting Keratoconus Progression

Erol¹,

Atalay²,

Özalp³

et al. 2021

tjo

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Objectives: To determine corneal biomechanical and tomographic factors associated with keratoconus (KC) progression. Materials and Methods: This study included 111 eyes of 111 KC patients who were followed-up for at least 1 year. Progression was defined as the presence of progressive change between the first two consecutive baseline visits in any single parameter (A, B, or C) ≥95% confidence interval or two parameters ≥80% confidence interval for the KC population evaluated by the Belin ABCD progression display. The eye with better initial tomographic findings was chosen as the study eye. Analyzed Pentacam parameters were maximum keratometry (Kmax), minimum pachymetry (Kmin), central corneal thickness, thinnest corneal thickness, 90° vertical anterior and posterior coma data in Zernike analysis, and Belin Ambrosio Enhanced Ectasia Display Final D value. Corneal hysteresis (CH) and corneal resistance factor (CRF) were analyzed together with the waveform parameters obtained with Ocular Response Analyzer (ORA). Factors related to KC progression were evaluated using t-tests and logistic regression tests. Statistical significance was accepted as p<0.05. Results: There were 44 (mean age: 27.1±8.5 years, female: 25) and 67 (mean age: 31.1±9.1 years, female: 36) patients in the progressive and non-progressive groups, respectively. Although Pentacam parameters along with CH and CRF were similar between the two groups, ORA waveform parameter derived from the second applanation signal p2area was statistically significantly lower in the progressive group (p=0.02). Each 100-unit decrease in p2area increased the likelihood of keratoconus progression by approximately 30% in the logistic regression analysis (β=0.707, p=0.001, model r2=0.27). Conclusion: Parameters derived from the second applanation signal of ORA may be superior to conventional ORA parameters and corneal tomography in predicting KC progression.

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“…In the age group 1, group 2 and group 3, there were 97, 445 and 342 eyes from 884 patients and the mean age was 15.5±1.5 (10)(11)(12)(13)(14)(15)(16)(17)(18) No difference was found for eye rubbing (RUB), presence of allergies (ALLRG), contact lens use (CL), presence of family member with KC (Family KC), coexistence of thyroid disorders (Thyroid) and presence of Neurodermitis (NRDRM) among age groups. However, there was a difference in some of the aforementioned parameters among progressive and nonprogressive eyes (table 1).…”

Section: Resultsmentioning

confidence: 99%

“…A young age below 18 years, is an independent risk factor for progression of KC [3,4,[12][13][14], even though progression may also occur beyond the age of 30 [5,13,15]. Sherman et al reported that in multivariate analysis, age 30 or younger conferred a sevenfold increased risk of transplantation compared with ages of over 40 [13].…”

Section: Discussionmentioning

confidence: 99%

Risk Factors for Keratoconus Progression in Children Compared with Young and Middle-aged Adults

Sideroudi

Flockerzi

Jullien

et al. 2023

Klin Monbl Augenheilkd

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Background: To investigate the risk factors for keratoconus progression in children (10-18 years old) compared to young adults (19-35 years old) and middle-aged adults (36-55 years old). Patients/Methods: 97 children, 445 young adults and 342 middle-aged adults underwent total ophthalmic examination including clinical refraction, slit lamp examination, corneal tomography, eye biometry and biomechanical properties measurements. Comparison among three age groups and between progressive and non-progressive eyes, were assessed. Logistic regression was applied to determine the potential prognostic factors for keratoconus progression in three age groups. Results: Univariate logistic regression analysis show that the most prominent factors associated with progression were corneal posterior vertical radius (RVP), eye rubbing (RUB), slit lamp corneal thinning (SLT), contact lens use (CL) and central corneal thickness (CCT) in all age groups. Additionally, the anterior chamber volume (ACV) and keratoconus match index (KMI) were associated with progression in age group 1. Location of thinnest corneal thickness at the vertical axis (TCTy), distance from apex to the thinnest point (BADISTAPEX), Scissor reflection in retinoscopy (SKIAREFLEX), and Vogt striae were associated with progression in age group 2, and TCTy, anterior and posterior asphericity (ASPA and ASPP, respectively), BADISTAPEX, SKIAREFLEX and Vogt Striae were associated with progression in age group 3. The multivariate model with the highest predictability indicated RVP, ACV and SLT as independent determinants of progression in age group 1 (AUC:90%, sensitivity: 88.9%, specificity: 90.9%), RVP, ACV, SLT and SKIAREFLEX in group 2 (AUC: 81.6%, sensitivity 88.5%, specificity: 70.3%) and RVP, SLT, Vogt Striae and CL in age group 3 (AUC: 80%, sensitivity 82.8%, specificity: 73%). Conclusion: Anterior chamber volume and keratoconus match index seem to play a major role in the progression of pediatric KC, compared to adults. This is probably due to different anatomical and biomechanical characteristics of a child’s eye globe. Abstrakt Hintergrund: Diese Studie untersucht die Risikofaktoren für das Fortschreiten des Keratokonus bei Kindern (10-18 Jahre) im Vergleich zu jungen Erwachsenen (19-35 Jahre) und Erwachsenen mittleren Alters (36-55 Jahre). Patienten und Methoden: 97 Kinder, 445 junge Erwachsene und 342 Erwachsene mittleren Alters unterzogen sich einer umfassenden augenärztlichen Untersuchung, die eine klinische Refraktion, eine Spaltlampenuntersuchung, eine Hornhauttomographie, eine Augenbiometrie und eine Messung der biomechanischen Eigenschaften umfasste. Es wurde ein Vergleich zwischen den drei Altersgruppen sowie zwischen progressiven und nicht-progressiven Augen vorgenommen. Mit Hilfe einer logistischen Regression wurden die potenziellen prognostischen Faktoren für das Fortschreiten des Keratokonus in den drei Altersgruppen verglichen. Ergebnisse: Eine univariate logistische Regressionsanalyse zeigt, dass die folgenden wichtigsten Faktoren mit dem Fortschreiten des Keratokonus in allen Altersgruppen assoziiert sind: vertikaler Krümmungsradius der hinteren Hornhaut (RVP), Augenreiben (RUB), Spaltlampen-Hornhautausdünnung (SLT), Tragen von Kontaktlinsen (CL) und zentrale Hornhaut dicke (CCT). Zusätzlich wurden das Vorderkammervolumen (ACV) und der Keratokonus-Match-Index (KMI) mit der Progression in der Altersgruppe 1 in Verbindung gebracht. Die Stelle der dünnsten Hornhautdicke an der vertikalen Achse (TCTy), der Abstand vom Apex zur dünnsten Stelle (BADISTAPEX), die helle Reflexion des nasalen Bereichs des Limbus (SKIAREFLEX) und die Vogt-Linien wurden mit der Progression in der Altersgruppe 2 assoziiert, und TCTy, vordere und hintere Asphärizität (ASPA bzw. ASPP), BADISTAPEX, SKIAREFLEX und Vogt-Linien wurden mit der Progression in der Altersgruppe 3 assoziiert. Das Multivarianzmodell mit der höchsten Vorhersagbarkeit zeigte RVP, ACV und SLT als unabhängige Determinanten der Progression in Altersgruppe 1 (AUC: 90%, Sensitivität: 88,9%, Spezifität: 90,9%), RVP, ACV, SLT und SKIAREFLEX in Gruppe 2 (AUC: 81,6%, Sensitivität 88,5%, Spezifität: 70,3%) und RVP, SLT, Vogt-Linien und CL in Altersgruppe 3 (AUC: 80%, Sensitivität 82,8%, Spezifität: 73%). Schlussfolgerungen: Das Volumen der Vorderkammer und der Keratokonus-Match-Index scheinen beim Fortschreiten des KC bei Kindern im Vergleich zu Erwachsenen eine größere Rolle zu spielen. Dies ist wahrscheinlich auf die unterschiedlichen anatomischen und biomechanischen Merkmale des kindlichen Augapfels zurückzuführen.

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Baseline factors predicting the need for corneal crosslinking in patients with keratoconus

Cited by 9 publications

References 24 publications

Predicting Keratoconus Progression and Need for Corneal Crosslinking Using Deep Learning

Predicting Keratoconus Progression and Need for Corneal Crosslinking Using Deep Learning

Superiority of Baseline Biomechanical Properties over Corneal Tomography in Predicting Keratoconus Progression

Risk Factors for Keratoconus Progression in Children Compared with Young and Middle-aged Adults

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