Early diagnosis of keratoconus using test value by auto keratometer.

Early diagnosis of keratoconus using test value by auto keratometer. - Early diagnosis of keratoconus using auto keratometer.

Status

Active, not recruiting

Phases

Unknown

Study type

Observational

Source

JPRN

Registry ID

JPRN-UMIN000036372

Enrollment

200

Registered

2019-04-01

Start date

2018-08-01

Completion date

Unknown

Last updated

2026-06-29

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Keratoconus

Interventions

None listed

Eligibility

Sex/Gender

All

Inclusion criteria

Inclusion criteria: Patient diagnosed to be keratoconus by corneal shape analysis device (corneal topography or cornea tomography)

Exclusion criteria

Exclusion criteria: Patient who also have corneal disease other than keratoconus.

Design outcomes

Primary

Measure	Time frame
Choose keratoconus which was examined during the target period from insurance names of diseases, confirm that it is keratoconus by using corneal shape analysis, enter the parameters of age, sex, measured value of keratometer and corneal shape analysis (corneal topography or anterior segment OCT) into the excel data. For normal subject select the patient with normal corneal shape who visited Nagoya Eye Clinic or Sato Yuya Eye Clinic for the purpose of refraction surgery, then enter the excel data as well. Data will be accumulated at Nagoya Eye Clinic. When data from all facility were collected patients data will be randomly divided into two, and a group will be regression formula group for prediction probability of keratoconus. The other group will assess the fidelity of regression formula. For group one analysis dependent variable will be early keratoconus or normal, independent variable will be keratometer value (average K value, flat K value, steep K value, astigmatic power, astigmatic axis), do logistic regression analysis, then calculate the cut-off value. To calculate the cut-off value determine the parameter value when the value of [sensitivity-(1- specificity)] is greatest(Youden Index).By using the determined regression formula assess the fidelity of regression formula determined in group one, by group two to calculate sensitivity and specificity.	—

Measure

Time frame

Choose keratoconus which was examined during the target period from insurance names of diseases, confirm that it is keratoconus by using corneal shape analysis, enter the parameters of age, sex, measured value of keratometer and corneal shape analysis (corneal topography or anterior segment OCT) into the excel data. For normal subject select the patient with normal corneal shape who visited Nagoya Eye Clinic or Sato Yuya Eye Clinic for the purpose of refraction surgery, then enter the excel data as well. Data will be accumulated at Nagoya Eye Clinic. When data from all facility were collected patients data will be randomly divided into two, and a group will be regression formula group for prediction probability of keratoconus. The other group will assess the fidelity of regression formula. For group one analysis dependent variable will be early keratoconus or normal, independent variable will be keratometer value (average K value, flat K value, steep K value, astigmatic power, astigmatic axis), do logistic regression analysis, then calculate the cut-off value. To calculate the cut-off value determine the parameter value when the value of [sensitivity-(1- specificity)] is greatest(Youden Index).By using the determined regression formula assess the fidelity of regression formula determined in group one, by group two to calculate sensitivity and specificity.

—

Countries

Japan

Contacts

Public ContactTakashi Kojima

Nagoya Eye Clinic Ophthalmology

sawada@lasik.ne.jp0120-758-0490

Outcome results