Unique ID issued by UMIN | UMIN000036372 |
---|---|
Receipt number | R000038054 |
Scientific Title | Early diagnosis of keratoconus using test value by auto keratometer. |
Date of disclosure of the study information | 2019/04/01 |
Last modified on | 2021/04/05 10:32:39 |
Early diagnosis of keratoconus using test value by auto keratometer.
Early diagnosis of keratoconus using auto keratometer.
Early diagnosis of keratoconus using test value by auto keratometer.
Early diagnosis of keratoconus using auto keratometer.
Japan |
Keratoconus
Ophthalmology |
Others
NO
To evaluate possibility of diagnosing early keratoconus using test value from auto keratometer.
Efficacy
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.
Observational
10 | years-old | <= |
Not applicable |
Male and Female
Patient diagnosed to be keratoconus by corneal shape analysis device (corneal topography or cornea tomography)
Patient who also have corneal disease other than keratoconus.
200
1st name | Takashi |
Middle name | |
Last name | Kojima |
Nagoya Eye Clinic
Ophthalmology
456-0003
3F Meitetsu Kanayama Daiichi Bldg 25-1 Namiyose cho, Atsuta-ku, Nagoya, Aichi
0120-758-0490
kojima@chukyogroup.jp
1st name | Takashi |
Middle name | |
Last name | Kojima |
Nagoya Eye Clinic
Ophthalmology
456-0003
3F Meitetsu Kanayama Daiichi Bldg 25-1 Namiyose cho, Atsuta-ku, Nagoya, Aichi
0120-758-0490
sawada@lasik.ne.jp
Nagoya Eye Clinic
none
Other
a
a
a
aa
NO
JCHO中京病院(愛知県)、名古屋アイクリニック(愛知県)、飯田市立病院(長野県)、佐藤裕也眼科医院(宮城県)、岐阜赤十字病院(岐阜県)
2019 | Year | 04 | Month | 01 | Day |
https://pubmed.ncbi.nlm.nih.gov/32114181/
Published
https://pubmed.ncbi.nlm.nih.gov/32114181/
328
Sensitivity and specificity of KKI were 85.0% and 86.7%, respectively.
2021 | Year | 04 | Month | 05 | Day |
123 patients/eyes in the keratoconus group: average age 26.36 +- 8.68 years, 84 men, 39 women, 205 patients/eyes in the control group: 26.20 +- 7.34 years, 139 men, 66 women
123 patients/eyes in the keratoconus group and 205 patients/eyes in the control group were enrolled. Since this is a retrospective study, there is no random allocation.
none
As a result of logistic regression analysis, the selected dependent variables were steeper meridian K value (partial regression coefficient 1.284, odds ratio 3.610), flatter meridian K value (partial regression coefficient -0.618, odds ratio 0.539), and direct astigmatism (partial regression coefficient -3.163 and the odds ratio 0.042). AUROC was 0.90, which was significantly higher than when each parameter was used alone (P <0.001). The sensitivity and specificity of the Keratometer Keratoconus Index (KKI) prepared from the test values of the autokeratometer created by the results of logistic regression analysis were 85.0% and 86.7%, respectively.
Completed
2018 | Year | 06 | Month | 01 | Day |
2018 | Year | 07 | Month | 11 | Day |
2018 | Year | 08 | Month | 01 | Day |
2019 | Year | 05 | Month | 31 | Day |
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.
2019 | Year | 04 | Month | 01 | Day |
2021 | Year | 04 | Month | 05 | Day |
Value
https://center6.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000038054