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10. Noninferiority of Microhook to Trabectome: Trabectome versus Ab Interno Microhook Trabeculotomy Comparative Study (Tram Trac Study)

Author

Mori, Sotaro, Tanito, Masaki, Shoji, Nobuyuki, Yokoyama, Yu, Kameda, Takanori, Shoji, Takuhei, Mizoue, Shiro, Saito, Yuta, Ishida, Kyoko, Ueda, Toshihiko, Nakamura, Makoto, Namiguchi, Koji, Inoue, Kenji, Kono, Yusuke, Kasahara, Masayuki, Wataru, Matsumiya, Takayuki, Nagai, Tadamichi, Akagi, Makita, Jun, Kono, Michihiro, Yokoyama, Kota, and Nakazawa, Toru

Published
2022
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16. Validating Variational Bayes Linear Regression Method With Multi-Central Datasets

Author

Murata, Hiroshi, Zangwill, Linda M, Fujino, Yuri, Matsuura, Masato, Miki, Atsuya, Hirasawa, Kazunori, Tanito, Masaki, Mizoue, Shiro, Mori, Kazuhiko, Suzuki, Katsuyoshi, Yamashita, Takehiro, Kashiwagi, Kenji, Shoji, Nobuyuki, and Asaoka, Ryo

Subjects
Aging, 4.2 Evaluation of markers and technologies, Detection, screening and diagnosis, Aged, Bayes Theorem, Databases, Factual, Datasets as Topic, Disease Progression, Female, Glaucoma, Open-Angle, Humans, Linear Models, Low Tension Glaucoma, Male, Middle Aged, Retrospective Studies, Scotoma, Visual Field Tests, Visual Fields, Biological Sciences, Medical and Health Sciences, Ophthalmology & Optometry
Abstract

PurposeTo validate the prediction accuracy of variational Bayes linear regression (VBLR) with two datasets external to the training dataset.MethodThe training dataset consisted of 7268 eyes of 4278 subjects from the University of Tokyo Hospital. The Japanese Archive of Multicentral Databases in Glaucoma (JAMDIG) dataset consisted of 271 eyes of 177 patients, and the Diagnostic Innovations in Glaucoma Study (DIGS) dataset includes 248 eyes of 173 patients, which were used for validation. Prediction accuracy was compared between the VBLR and ordinary least squared linear regression (OLSLR). First, OLSLR and VBLR were carried out using total deviation (TD) values at each of the 52 test points from the second to fourth visual fields (VFs) (VF2-4) to 2nd to 10th VF (VF2-10) of each patient in JAMDIG and DIGS datasets, and the TD values of the 11th VF test were predicted every time. The predictive accuracy of each method was compared through the root mean squared error (RMSE) statistic.ResultsOLSLR RMSEs with the JAMDIG and DIGS datasets were between 31 and 4.3 dB, and between 19.5 and 3.9 dB. On the other hand, VBLR RMSEs with JAMDIG and DIGS datasets were between 5.0 and 3.7, and between 4.6 and 3.6 dB. There was statistically significant difference between VBLR and OLSLR for both datasets at every series (VF2-4 to VF2-10) (P < 0.01 for all tests). However, there was no statistically significant difference in VBLR RMSEs between JAMDIG and DIGS datasets at any series of VFs (VF2-2 to VF2-10) (P > 0.05).ConclusionsVBLR outperformed OLSLR to predict future VF progression, and the VBLR has a potential to be a helpful tool at clinical settings.

Published
2018

41. Accuracy of pattern deviation in estimating the glaucomatous damage in the central 10° visual field in eyes with glaucoma and cataract

Author

Tomita, Ryo, Asaoka, Ryo, Hirasawa, Kazunori, Fujino, Yuri, Nakakura, Shunsuke, Murata, Hiroshi, Omura, Tetsuo, Shoji, Nobuyuki, Obana, Akira, Nishiguchi, Koji M, and Tanito, Masaki

Abstract

Background/aimsThe accuracy of pattern deviation (PD) in estimating the damage to the glaucomatous visual field (VF) in the central 10° in eyes with glaucoma and cataract is unclear.MethodsThis retrospective study includes 63 eyes of 52 glaucoma patients who successfully underwent cataract surgery or cataract surgery plus iStent implantation. Using the Humphrey Field Analyser 10–2 test, VF was measured within 6 months preoperatively and postoperatively (VFpreand VFpost, respectively). The mean total deviation values in VFpost(mTDpost) indicates glaucomatous damage without cataract and the difference between this value and mean PD values in VFpre(mPDpre) was evaluated (εmPD). The effect of cataract was then evaluated as the difference between mTDpostand mTDpre(ΔmTD), while the effects of mTDpostand ΔmTD on εmPD were also assessed. In addition, based on preoperative visual acuity (VApre) and VFpre, the optimal model for predicting mTDpostwas identified. The error of this method (εOptimalModel) was estimated as the difference against mTDpost, which was compared with εmPD.ResultsCompared with mTDpre, there was a significant improvement in mTDpost(p=0.028). A significant difference was observed between mPDpreand mTDpost(p<0.001). Further, εmPD significantly increased with the increase of mTDpostor ΔmTD (p<0.001 and p=0.0444, respectively). The absolute εOptimalModel was significantly smaller than the absolute εmPD (p<0.001).ConclusionsThis study warns clinicians that PD of the central 10° VF might underestimate the glaucomatous VF damage with the progression of glaucoma and overestimate it as a cataract progresses.

Published
2024
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46. Accuracy of pattern deviation in estimating the glaucomatous damage in the central 10° visual field in eyes with glaucoma and cataract

Author

Tomita, Ryo, primary, Asaoka, Ryo, additional, Hirasawa, Kazunori, additional, Fujino, Yuri, additional, Nakakura, Shunsuke, additional, Murata, Hiroshi, additional, Omura, Tetsuo, additional, Shoji, Nobuyuki, additional, Obana, Akira, additional, Nishiguchi, Koji M, additional, and Tanito, Masaki, additional

Published
2022
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47. Efficacy of predetermined therapeutic measures against bleb‐related infection in the Collaborative Bleb‐related Infection Incidence and Treatment Study

Author

Shoji, Nobuyuki, Arakaki, Yoshikuni, Nakamoto, Kenji, Yamamoto, Tetsuya, Kuwayama, Yasuaki, Aoyama, Yumiko, Kawase, Kazuhide, Ban, Takahiro, Sawada, Akira, Mochizuki, Kiyofumi, Futa, Ryusuke, Yasuda, Noriko, Nakajima, Masayuki, Sugiyama, Tetsuya, Kojima, Shota, Hirotsuji, Norihiko, Kano, Kiyoshi, Ohura, Atsushi, Kawai, Eriko, Nakata, Atsuko, Kuwamura, Rika, Okuno, Kensuke, Yawata, Kenji, Matsumoto, Shun, Abe, Haruki, Fukuchi, Takeo, Shirakashi, Motohiro, Ueda, Jun, Tanaka, Takayuki, Negi, Akira, Nakamura, Makoto, Kanamori, Akiyasu, Yamada, Yuko, Tawara, Akihiko, Kubota, Toshiaki, Tou, Norihiko, Kimura, Tairo, Furusawa, Chiaki, Inoue, Naoki, Kawabata, Kiho, Tachibana, Nobuhiko, Matsuda, Akira, Seto, Takahiko, Kimura, Satoshi, Ueno, Satoki, Tokuda, Naoto, Inoue, Jun, Sugiyama, Kazuhisa, Ohkubo, Shinji, Higashide, Tomomi, Saito, Yoshiaki, Takeda, Hisashi, Nagata, Atsushi, Uji, Yukitaka, Tsukitome, Hideyuki, Ito, Kunio, Ikesugi, Kengo, Matsumura, Miyo, Nambu, Hiroyuki, Hamanaka, Teruhiko, Omata, Takayasu, Watanabe, Yukiko, Sakuma, Keiko, Amari, Yoko, Takahashi, Genichiro, Noro, Takahiko, Miyata, Hiroshi, Hirasawa, Manabu, Ohtake, Yuichiro, Yuki, Kenya, Araie, Makoto, Tomita, Goji, Aihara, Makoto, Tomidokoro, Atsuo, Matsuo, Hiroshi, Atarashi, Takuya, Mayama, Chihiro, Yamagami, Junkichi, Okinami, Satoshi, Ishikawa, Shinichiro, Morita, Tetsuya, Otsuki, Tomohiro, Suzuki, Hiromasa, Naruse, Ryoko, Yamakawa, Yayoi, Kasahara, Masayuki, Yoshitomi, Takeshi, Kashiwagi, Kenji, Mabuchi, Fumihiko, Chiba, Tatsuya, Suzuki, Yasuyuki, Shimizu, Satoko, Mishima, Hiromu K, Kiuchi, Yoshiaki, Mochizuki, Hideki, Sone, Takashi, Tsukamoto, Hidetoshi, Okada, Koji, Sawaguchi, Shoichi, Sakai, Hiroshi, Ishikawa, Shusaku, Tanihara, Hidenobu, Inatani, Masaru, Inoue, Toshihiro, Koga, Takahisa, Aoyama, Akira, Yamazaki, Yoshio, Okajima, Yasuhiko, Mizuki, Kenji, Hayamizu, Fukuko, Nakagami, Takako, Mori, Kazuhiko, Ikeda, Yoko, Ueno, Morio, Imai, Kojiro, Suzumura, Hirotaka, Kawai, Kenji, Sueno, Toshiharu, Saito, Nobuo, Otori, Yasumasa, Iwase, Aiko, Katsushima, Harumi, Kitazawa, Yoshiaki, Shimizu, Hiroshi, Shirato, Shiroaki, Matsumoto, Chota, Yoshikawa, Keiji, Inoue, Yoshitsugu, Kondo, Akira, and Yamamoto, Kimiko

Published
2018
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