See the published version of this article at BMC Ophthalmology.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research article
A predictive model for early diagnosis of keratoconus
GRACIA CASTRO-LUNA
Universidad de Almería
Corresponding Author
ORCiD: https://orcid.org/0000-0001-6928-9728
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published version at BMC Ophthalmology.
Background: The diagnosis of keratoconus in the early stages of the disease is necessary to initiate an early treatment of keratoconus. Furthermore, to avoid possible refractive surgery that could produce ectasias. This study aims to describe the topographic, pachymetric and aberrometry characteristics in patients with keratoconus, subclinical keratoconus and normal corneas. Additionally to propose a diagnostic model of subclinical keratoconus based in binary logistic regression models
Methods: The design was a cross-sectional study. It included 205 eyes from 205 patients distributed in 82 normal corneas, 40 early-stage keratoconus and 83 established keratoconus. The rotary Scheimpflug camera (Pentacam® type) analyzed the topographic, pachymetric and aberrometry variables. It performed a descriptive and bivariate analysis of the recorded data. A diagnostic and predictive model of early-stage keratoconus was calculated with the statistically significant variables
Results: Statistically significant differences were observed when comparing normal corneas with early-stage keratoconus/ in variables of the vertical asymmetry to 90º and the central corneal thickness. The binary logistic regression model included the minimal corneal thickness, the anterior coma to 90º and posterior coma to 90º. The model properly diagnosed 92% of cases with a sensitivity of 97.59%, specificity 98.78%, accuracy 98.18% and precision 98.78%
Conclusions: The differential diagnosis between normal cases and subclinical keratoconus depends on the mínimum corneal thickness, the anterior coma to 90º and the posterior coma to 90º.
Keywords
keratoconus, corneal topography, high order aberrations, coma.
Figure 1
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
Version 3
Posted 19 May, 2020
Published
On 02 Jul, 2020
No community comments so far
Review #2 received
Received 29 May, 2020
Review #1 received
Received 25 May, 2020
Reviewer #2 agreed
On 12 May, 2020
Editor assigned
On 07 May, 2020
Reviewers invited
Invitations sent on 07 May, 2020
Reviewer #1 agreed
On 07 May, 2020
Editor invited
On 06 May, 2020
Submission checks complete
On 14 Jan, 2020
No comments provided
Editorial decision: Major revision
On 29 Apr, 2020
Review #2 received
Received 14 Apr, 2020
Review #1 received
Received 01 Apr, 2020
Reviewer #2 agreed
On 24 Mar, 2020
Reviewer #1 agreed
On 20 Mar, 2020
Reviewers invited
Invitations sent on 28 Feb, 2020
Editor assigned
On 20 Jan, 2020
Submission checks complete
On 19 Jan, 2020
Editor invited
On 19 Jan, 2020
No comments provided
Editor assigned
On 14 Jan, 2020
Editorial decision: Revise before sending to peer reviewers
On 14 Jan, 2020
Submission checks complete
On 13 Jan, 2020
Editor invited
On 13 Jan, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Ophthalmology
Learn more about our company.
This preprint is under consideration at BMC Ophthalmology. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research article
A predictive model for early diagnosis of keratoconus
GRACIA CASTRO-LUNA
Universidad de Almería
Corresponding Author
ORCiD: https://orcid.org/0000-0001-6928-9728
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
Version 3
Posted 19 May, 2020
Published
On 02 Jul, 2020
No community comments so far
Review #2 received
Received 29 May, 2020
Review #1 received
Received 25 May, 2020
Reviewer #2 agreed
On 12 May, 2020
Editor assigned
On 07 May, 2020
Reviewers invited
Invitations sent on 07 May, 2020
Reviewer #1 agreed
On 07 May, 2020
Editor invited
On 06 May, 2020
Submission checks complete
On 14 Jan, 2020
No comments provided
Editorial decision: Major revision
On 29 Apr, 2020
Review #2 received
Received 14 Apr, 2020
Review #1 received
Received 01 Apr, 2020
Reviewer #2 agreed
On 24 Mar, 2020
Reviewer #1 agreed
On 20 Mar, 2020
Reviewers invited
Invitations sent on 28 Feb, 2020
Editor assigned
On 20 Jan, 2020
Submission checks complete
On 19 Jan, 2020
Editor invited
On 19 Jan, 2020
No comments provided
Editor assigned
On 14 Jan, 2020
Editorial decision: Revise before sending to peer reviewers
On 14 Jan, 2020
Submission checks complete
On 13 Jan, 2020
Editor invited
On 13 Jan, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Ophthalmology
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published version at BMC Ophthalmology.
Background: The diagnosis of keratoconus in the early stages of the disease is necessary to initiate an early treatment of keratoconus. Furthermore, to avoid possible refractive surgery that could produce ectasias. This study aims to describe the topographic, pachymetric and aberrometry characteristics in patients with keratoconus, subclinical keratoconus and normal corneas. Additionally to propose a diagnostic model of subclinical keratoconus based in binary logistic regression models
Methods: The design was a cross-sectional study. It included 205 eyes from 205 patients distributed in 82 normal corneas, 40 early-stage keratoconus and 83 established keratoconus. The rotary Scheimpflug camera (Pentacam® type) analyzed the topographic, pachymetric and aberrometry variables. It performed a descriptive and bivariate analysis of the recorded data. A diagnostic and predictive model of early-stage keratoconus was calculated with the statistically significant variables
Results: Statistically significant differences were observed when comparing normal corneas with early-stage keratoconus/ in variables of the vertical asymmetry to 90º and the central corneal thickness. The binary logistic regression model included the minimal corneal thickness, the anterior coma to 90º and posterior coma to 90º. The model properly diagnosed 92% of cases with a sensitivity of 97.59%, specificity 98.78%, accuracy 98.18% and precision 98.78%
Conclusions: The differential diagnosis between normal cases and subclinical keratoconus depends on the mínimum corneal thickness, the anterior coma to 90º and the posterior coma to 90º.
Figure 1