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Start Over Author "Jabs A" Journal american journal of ophthalmology
314 results on '"Jabs A"'

1. Incidence and Remission of Post‐Surgical Cystoid Macular Edema Following Cataract Surgery in Eyes With Intraocular Inflammation

Author

Gangaputra, Sapna, Newcomb, Craig, Ying, Gui-Shuang, Groth, Sylvia, Fitzgerald, Tonetta D., Artornsombudh, Pichaporn, Kothari, Srishti, Pujari, Siddharth S., Jabs, Douglas A., Levy-Clarke, Grace A., Nussenblatt, Robert B., Rosenbaum, James T., Sen, H. Nida, Suhler, Eric B., Thorne, Jennifer E., Bhatt, Nirali P., Foster, C. Stephen, Dreger, Kurt A., Buchanich, Jeanine M., and Kempen, John H.

Published
2024
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3. Incidence of and Risk Factors for Cataract in Anterior Uveitis

Author

Papaliodis, George N., Rosner, Bernard A., Dreger, Kurt A., Fitzgerald, Tonetta D., Artornsombudh, Pichaporn, Kothari, Srishti, Gangaputra, Sapna S., Levy-Clarke, Grace A., Nussenblatt, Robert B., Rosenbaum, James T., Sen, H. Nida, Suhler, Eric B., Thorne, Jennifer E., Bhatt, Nirali P., Foster, C. Stephen, Jabs, Douglas A., Pak, Clara M., Ying, Gui-shuang, and Kempen, John H.

Published
2023
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5. Corneal Endothelial Transplantation in Uveitis: Incidence and Risk Factors

Author

Roldan, Ana M., Zebardast, Nazlee, Pistilli, Maxwell, Khachatryan, Naira, Payal, Abhishek, Begum, Hosne, Artornsombudh, Pichaporn, Pujari, Siddharth S., Rosenbaum, James T., Sen, H. Nida, Suhler, Eric B., Thorne, Jennifer E., Bhatt, Nirali P., Foster, C. Stephen, Jabs, Douglas A., Levy-Clarke, Grace A., Nussenblatt, Robert B., Buchanich, Jeanine M., and Kempen, John H.

Published
2022
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6. Risk of Cataract in Intermediate Uveitis

Author

Minkus, Caroline L., Pistilli, Maxwell, Dreger, Kurt A., Fitzgerald, Tonetta D., Payal, Abhishek R., Begum, Hosne, Kaçmaz, R. Oktay, Jabs, Douglas A., Nussenblatt, Robert B., Rosenbaum, James T., Levy-Clarke, Grace A., Sen, H. Nida, Suhler, Eric B., Thorne, Jennifer E., Bhatt, Nirali P., Foster, C. Stephen, Buchanich, Jeanine M., and Kempen, John H.

Published
2021
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10. Factors Predicting Visual Acuity Outcome in Intermediate, Posterior, and Panuveitis: The Multicenter Uveitis Steroid Treatment (MUST) Trial

Author

Kempen, John H, Van Natta, Mark L, Altaweel, Michael M, Dunn, James P, Jabs, Douglas A, Lightman, Susan L, Thorne, Jennifer E, Holbrook, Janet T, Jaffe, Glenn J, Branchaud, Brenda, Hahn, Paul, Koreen, Larry, Lad, Eleonora M, Lin, Phoebe, Martel, Joseph Nissim, Serrano, Neha, Skalak, Cindy, Vajzovic, Lejla, Baer, Claxton, Bryant, Joyce, Chavala, Sai, Cusick, Michael, Day, Shelley, Dayani, Pouya, Ehlers, Justis, Kesen, Muge, Lee, Annie, Melamud, Alex, Qureshi, Jawad A, Scott, Adrienne Williams, See, Robert F, Shuler, Robert K, Wood, Megan, Yeh, Steven, Fernandes, Alcides, Gibbs, Deborah, Leef, Donna, Martin, Daniel F, Srivastava, Sunil, Begum, Hosne, Boring, Jeff, Brotherson, Kristen L, Burkholder, Bryn, Butler, Nicholas J, Cain, Dennis, Cook, Mary A, Emmert, David, Graul, Janis R, Herring, Mark, Laing, Ashley, Leung, Theresa G, Mahon, Michael C, Moradi, Ahmafreza, Nwankwo, Antonia, Ostheimer, Trucian L, Reed, Terry, Arnold, Ellen, Barnabie, Patricia M, Belair, Marie-Lynn, Bolton, Stephen G, Brodine, Joseph B, Brown, Diane M, Brune, Lisa M, Galor, Anat, Gan, Theresa, Jacobowitz, Adam, Kapoor, Meera, Kedhar, Sanjay, Kim, Stephen, Leder, Henry A, Livingston, Alison G, Morton, Yavette, Nolan, Kisten, Peters, George B, Soto, Priscilla, Stevenson, Ricardo, Tarver-Carr, Michelle, Wang, Yue, Foster, C Stephen, Anesi, Stephen D, Bruner, Linda, Ceron, Olga, Hinkle, David M, Persons, Nancy, Wentworth, Bailey, Acevedo, Sarah, Anzaar, Fahd, Cesca, Tom, Contero, Angelica, Fitzpatrick, Kayleigh, Goronga, Faith, Johnson, Jyothir, Lebron, Karina Q, Marvell, Danielle, Morgan, Chandra, Patel, Nita, Pinto, Jennifer, Siddique, Sana S, and Sprague, Janet

Subjects
Clinical Trials and Supportive Activities, Clinical Research, Eye Disease and Disorders of Vision, 6.1 Pharmaceuticals, Evaluation of treatments and therapeutic interventions, Eye, Aged, Drug Implants, Female, Follow-Up Studies, Glucocorticoids, Humans, Male, Middle Aged, Panuveitis, Time Factors, Tomography, Optical Coherence, Treatment Outcome, Visual Acuity, Multicenter Uveitis Steroid Treatment (MUST) Trial Research Group, Clinical Sciences, Opthalmology and Optometry, Public Health and Health Services, Ophthalmology & Optometry
Abstract

PurposeTo identify factors associated with best-corrected visual acuity (BCVA) presentation and 2-year outcome in 479 intermediate, posterior, and panuveitic eyes.DesignCohort study using randomized controlled trial data.MethodsMulticenter Uveitis Steroid Treatment (MUST) Trial masked BCVA measurements at baseline and at 2 years follow-up used gold-standard methods. Twenty-three clinical centers documented characteristics per protocol, which were evaluated as potential predictive factors for baseline BCVA and 2-year change in BCVA.ResultsBaseline factors significantly associated with reduced BCVA included age ≥50 vs 10 vs grade 0; cataract; macular thickening; and exudative retinal detachment. Over 2 years, eyes better than 20/50 and 20/50 or worse at baseline improved, on average, by 1 letter (P = .52) and 10 letters (P < .001), respectively. Both treatment groups and all sites of uveitis improved similarly. Factors associated with improved BCVA included resolution of active AC cells, resolution of macular thickening, and cataract surgery in an initially cataractous eye. Factors associated with worsening BCVA included longer duration of uveitis (6-10 or >10 vs

Published
2015

11. Comparison Between Methotrexate and Mycophenolate Mofetil Monotherapy for the Control of Noninfectious Ocular Inflammatory Diseases

Author

Gangaputra, Sapna S., Newcomb, Craig W., Joffe, Marshall M., Dreger, Kurt, Begum, Hosne, Artornsombudh, Pichaporn, Pujari, Siddharth S., Daniel, Ebenezer, Sen, H. Nida, Suhler, Eric B., Thorne, Jennifer E., Bhatt, Nirali P., Foster, C. Stephen, Jabs, Douglas A., Nussenblatt, Robert B., Rosenbaum, James T., Levy-Clarke, Grace A., and Kempen, John H.

Published
2019
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16. Scleritis in Lyme Disease

Author

Meghan K. Berkenstock, Kayla Long, John B. Miller, Bryn B. Burkholder, John N. Aucott, and Douglas A. Jabs

Subjects
Lyme Disease, Ophthalmology, Recurrence, Incidence, Humans, Anti-Bacterial Agents, Scleritis
Abstract

To estimate the incidence of scleritis in Lyme disease and report clinical features.Incidence rate estimate and case series.Data were collected from an electronic medical record on patients with scleritis presenting to the Wilmer Eye Institute between January 1, 2012 and December 31, 2020. A diagnosis of Lyme disease was made using the Infectious Diseases Society of America, American Academy of Neurology, and the American College of Rheumatology 2020 joint criteria plus a response to antibiotic therapy. After identifying all new-onset cases of scleritis in the database, the proportion of new-onset scleritis with Lyme disease was calculated. The proportion of Lyme disease cases with scleritis was estimated using the number of cases with Lyme disease from the Baltimore metropolitan area reported to the Centers for Disease Control and Prevention. After querying other major eye centers in the area for any cases of Lyme disease scleritis, none were identified, and the incidence of Lyme disease scleritis was estimated using published U.S. Census data for the greater Baltimore metropolitan area.Six cases of Lyme disease scleritis were identified in the 8-year time frame; 1 additional case was identified in the following year. Lyme disease scleritis accounted for 0.6% of all cases of scleritis, and 0.052% of patients with Lyme disease had scleritis. The estimated incidence of Lyme scleritis was 0.2 per 1,000,000 population per year (95% confidence interval 0-0.4), whereas the estimated incidence of Lyme disease in the area was 3 per 10,000 population per year (95% confidence interval 2.9-3.1). All scleritis cases were anterior, unilateral, without necrosis, and resolved with antibiotic use without relapse in a median of 39.5 days (range 29-57 days). Other features of Lyme disease were present in 4 of 7 patients, including a history of erythema migrans in 2 of 7 patients.Lyme disease is an uncommon cause of scleritis in endemic areas.

Published
2022
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17. Dissociations of the Fluocinolone Acetonide Implant: The Multicenter Uveitis Steroid Treatment (MUST) Trial and Follow-up Study

Author

Jaffe, Glenn J., Branchaud, Brenda, Hahn, Paul, Koreen, Larry, Lad, Eleonora (Nora) M., Lin, Phoebe, Martel, Joseph Nissim, (Shah) Serrano, Neha, Skalak, Cindy, Vajzovic, Lejla, Baer, Claxton, Bryant, Joyce, Chavala, Sai, Cusick, Michael, Day, Shelley, Dayani, Pouya, Ehlers, Justis, Kesen, Muge, Lee, Annie, Melamud, Alex, Qureshi, Jawad A., Scott, Adrienne Williams, See, Robert F., Shuler, Robert K., Wood, Megan, Yeh, Steven, Fernandes, Alcides, Gibbs, Deborah, Leef, Donna, Martin, Daniel F., Srivastava, Sunil, Dunn, James P., Begum, Hosne, Boring, Jeff, Brotherson, Kristen L., Burkholder, Bryn, Butler, Nicholas J., Cain, Dennis, Cook, Mary A., Emmert, David, Graul, Janis R., Herring, Mark, Laing, Ashley, Leung, Theresa G., Mahon, Michael C., Moradi, Ahmafreza, Nwankwo, Antonia, Ostheimer, Trucian L., Reed, Terry, Arnold, Ellen, Barnabie, Patricia M., Belair, Marie-Lynn, Bolton, Stephen G., Brodine, Joseph B., Brown, Diane M., Brune, Lisa M., Galor, Anat, Gan, Theresa, Jacobowitz, Adam, Kapoor, Meera, Kedhar, Sanjay, Kim, Stephen, Leder, Henry A., Livingston, Alison G., Morton, Yavette, Nolan, Kisten, Peters, George B., Soto, Priscilla, Stevenson, Ricardo, Tarver-Carr, Michelle, Wang, Yue, Foster, C. Stephen, Anesi, Stephen D., Linda Bruner, Ceron, Olga, Hinkle, David M., Persons, Nancy, Wentworth, Bailey, Acevedo, Sarah, Anzaar, Fahd, Cesca, Tom, Contero, Angelica, Fitzpatrick, Kayleigh, Goronga, Faith, Johnson, Jyothir, Lebron, Karina Q., Marvell, Danielle, Morgan, Chandra, Patel, Nita, Pinto, Jennifer, Siddique, Sana S., Sprague, Janet, Yilmaz, Taygan, Sen, H. Nida, Bono, Michael, Cunningham, Denise, Hayes, Darryl, Koutsandreas, Dessie, Nussenblatt, Robert B., Sherry, Patti R., Short, Gregory L., Smith, Wendy, Temple, Alana, Bamji, Allison, Coleman, Hanna, Davuluri, Geetaniali, Faia, Lisa, Gottlieb, Chloe, Jirawuthiworavong, Guy V., Lew, Julie C., Mercer, Richard, Obiyor, Dominic, Perry, Cheryl H., Potapova, Natalia, Weichel, Eric, Wroblewski, Keith J., Latkany, Paul A., Coonan, Corinne, Honda, Andrea, Lorenzo-Latkany, Monica, Masini, Robert, Morell, Susan, Nguyen, Angela, Badamo, Jason, Boyd, Kenneth M., Enos, Matthew, Gallardo, Jenny, Jarczynski, Jacek, Lee, Ji Yun, McGrosky, Mirjana, Nour, Ann, Sanchez, Meredith, Steinberg, Kate, Stawell, Richard J., Breayley, Lisa, D'Sylva, Carly, Glatz, Elizabeth, Hodgson, Lauren, Lim, Lyndell, Ling, Cecilia, McIntosh, Rachel, Morrison (Ewing), Julie, Newton, Andrew, Sanmugasundram, Sutha, Smallwood, Richard, Zamir, Ehud, Hunt, Nicola, Jones, Lisa, Koukouras, Ignatios, Williams, Suzanne, Merrill, Pauline T., Carns, Danielle, Richine, Len, Voskuil-Marre, Denise L., Woo, Kisung, Gaynes, Bruce, Giannoulis, Christina, Hulvey, Pam, Kernbauer, Elaine, Khan, Heena S., Levine, Sarah J., Toennessen, Scott, Tonner, Eileen, Wang, Robert C., Aguado, Hank, Arceneaux, Sally, Duignan, Karen, Fish, Gary E., Hesse, Nick, Jaramillo, Diana, Mackens, Michael, Arnwine, Jean, Callanan, David, Cummings, Kimberly, Gray, Keith, Howden, Susie, Mutz, Karin, Sanchez, Brenda, Lightman, Susan, Ismetova, Filis, Prytherch, Ashley, Seguin-Greenstein, Sophie, Tomkins, Oren, Bar, Asat, Edwards, Kate, Joshi, Lavanish, Moraji, Jiten, Samy, Ahmed, Stubbs, Timothy, Taylor, Simon, Towler, Hamish, Tronnberg, Rebecca, Holland, Gary N., Almanzor, Robert D., Castellanos, Jose, Hubschman, Jean Pierre, Johiro, Ann K., Kukuyev, Alla, Levinson, Ralph D., McCannel, Colin A., Ransome, Susan S., Gonzales, Christine R., Gupta, Anurag, Kalyani, Partho S., Kapamajian, Michael A., Kappel, Peter J., Arcinue, Cheryl, Chuang, Janne, Barteselli, Giulio, Currie, Glenn, Mendoza, Veronica, Powell, Debbie, Clark, Tom, Cochran, Denine E., Freeman, William R., Hedaya, Joshua, Kemper, Tiara, Kozak, Igor, LeMoine, Jacqueline M., Loughran, Megan E., Magana, Luzandra, Mojana, Francesca, Morrison, Victoria, Nguyen, Vivian, Oster, Stephen F., Acharya, Nisha, Clay, David, Lee, Salena, Lew, Mary, Margolis, Todd P., Stewart, Jay, Wong, Ira G., Brown, Debra, Khouri, Claire M., Goldstein, Debra A., Birnbaum, Andrea, Degillio, Andrea, Rosa, Gemma De la, Ramirez, Carmen, Simjanowski, Evica, Skelly, Mariner, Castro-Malek, Anna L., Crooke, Catherine E., Huntley, Melody, Nash, Katrina, Niec, Marcia, Pyatetsky, Dimitry, Ramirez, Misel, Rozenbajgier, Zuzanna, Tessler, Howard H., Davis, Janet L., Albini, Thomas A., Chin, Marie, Castaño, Daniela, Elizondo, Ariana, Ho, Macy, Kovach, Jaclyn L., Lin, Richard C-S., Mandelcorn, Efrem, Nguyen, Jackie K-D., Pacini, Aura, Pineda, Susan, Pinto, David A., Rebimbas, Jose, Stepien, Kimberly E., Teran, Claudia, Elner, Susan G., Bernard, Hillary, Fournier, Linda, Godsey, Lindsay, Goings, Linda, Hackel, Richard, Hesselgrave, Moella, Jayasundera, K. Thiran, Prusak, Robert, Titus, Pamela, Bergeron, Melissa, Blosser, Reneé, Brown, Rebecca, Chrisman-McClure, Carrie, Gothrup, Julie R., Saxe, Stephen J., Sizemore, Deanna, Kempen, John H., Berger, James, Drossner, Sheri, DuPont, Joan C., Maguire, Albert M., Petner, Janice, Engelhard, Stephanie, Hopkins, Tim, McCall, Dawn, McRay, Monique, Will, Daniel, Xu, Wei, Lo, Jonathan, Salvo, Rebecca, Windsor, Elizabeth, Weeney, Laurel, Pavan, Peter R., Albritton, Ken, Leto, JoAnn, Madow, Brian, Mayor, Lori, Pautler, Scott E., Saxon, Wyatt, Soto, Judy, Goldstein, Burton, Klukoff, Amy, Lambright, Lucy, McDonald, Kim, Ortiz, Maria, Scymanky, Susan, Szalay, Dee Dee, Rao, Narsing, Davis, Tamara, Douglass, Jackie, Linton, Judith, Padilla, Margaret, Ramos, Sylvia, Aguirre, Alexia, Chong, Lawrence, Cisneros, Lupe, Corona, Elizabeth, Eliott, Dean, Fawzi, Amani, Garcia, Jesse, Khurana, Rahul, Lim, Jennifer, Mead, Rachel, Tsai, Julie H., Vitale, Albert, Bernstein, Paul S., Carlstrom, Bonnie, Gilman, James, Hanseen, Sandra, Morris, Paula, Ramirez, Diana, Wegner, Kimberley, Sheppard, John D., Anthony, Brianne, Casper, Amber, Felix-Kent, Lisa, Fernandez, Jeanette, Johnson, Tari, Scoper, Stephen V., Cole, R. Denise, Crawford, Nancy, Franklin, Lisa, Hamelin, Krista, Martin, Jen, Marx, Rebecca, Schultz, Gregory, Webb, Joseph, Yeager, Pamela, Kim, Rosa Y., Benz, Matthew S., Brown, David M., Chen, Eric, Fish, Richard H., Kegley, Eric, Shawver, Laura, Wong, Tien P., De La Garza, Rebecca, Friday (Hay), Shayla, Rao, P. Kumar, Adcock, Eve, Apte, Rajendra S., Baladenski, Amy, Curtis, Rhonda, Gould, Sarah, Hebden, Amanda, Kambarian, Jamie, Meyer, Charla, Pistorius, Sam, Quinn, Melanie, Rathert, Greg, Blinder, Kevin J., Hartz, Ashley, Light, Pam, Shah, Gaurav K., VanGelder, Russell, Jabs, Douglas A., Altaweel, Michael M., Kurinij, Natalie, Brown, Diane, Prusakowski, Nancy, Thorne, Jennifer E., Hubbard, Larry, Wittes, Janet, Barlow, William E., Hochberg, Marc, Lyon, Alice T., Palestine, Alan G., Simon, Lee S., Rosenbaum, James T., Smith, Harmon, Davis, Janet, Thorne, Jennifer, Acharya, Nisha R., Vitale, Albert T., Boring, Jeffrey A., Alexander, Judith, Ng, Wai Ping, Friedman, David S., Adler, Anna, Burke, Alyce, Katz, Joanne, Reed, Susan, Ansari, Husam, Cohen, Nicholas, Modak, Sanjukta, Sugar, Elizabeth A., Burke, Alyce E., Drye, Lea T., Van Natta, Mark L., Frick, Kevin, Louis, Thomas A., Shade, David, Pascual, Karen, Slutsky-Sanon, Jill S., Glomp, Colby, Nieves, Melissa A., Stevens, Maria, Allen, Amanda, Hilal, Yasmin, Holbrook, Janet T., Abreu, Francis, Casper, Anne Shanklin, Ewing, Cathleen, Hart, Adante, Lears, Andrea, Li, Shirley, Meinert, Jill, Morrison, Vinnette, Nowakowski, Deborah, Reyes, Girlie, Shade, Dave M., Smith, Jacqueline, Steuernagle, Karen, Van Natta, Mark, Venugopal, Vidya, Yu, Tsung, Chen, Paul, Collins, Karen, Dodge, John, Frick, Kevin D., Jackson, Rosetta, Jimenez, Christian, Landers, Ariel, Livingston, Hope, Meinert, Curtis L., Rayapudi, Sobharani, Shen, Weijiang, Shiflett, Charles, Smith, Rochelle, Tieman, Ada, Tonascia, James A., Zheng, Richard, Allan, James, Benz, Wendy K., Domalpally, Amitha, Johnson, Kristine A., Myers, Dawn J., Pak, Jeong Won, Reimers, James L., Christianson, Debra J., Chambers, Geoffrey, Fleischli, Margaret A., Freund, Jacquelyn, Glander, Kathleen E., Goulding, Anne, Gama, Vonnie, Gangaputra, Sapna, Hafford, Dennis, Harris, Susan E., Hubbard, Larry D., Joyce, Jeffrey M., Kruse, Christina N., Nagle, Lauren, Remm, Amy, Padden-Lechten, Gwyn E., Pohlman, Alyson, Shaw, Ruth A., Sivesind, Peggy, Thayer, Dennis, Treichel, Erika, Warren, Kelly J., Watson, Sheila M., Webster, Mary K., White, James K., Wilhelmson, Tara, and Zhang, Grace

Published
2016
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18. Remission of Intermediate Uveitis: Incidence and Predictive Factors

Author

Kempen, John H., Joffe, Marshall M., Dreger, Kurt A., Fitzgerald, Tonetta D., Newcomb, Craig W., Pistilli, Maxwell, Kothari, Srishti, Khacharyan, Naira, Artornsombudh, Pichaporn, Hanish, Asaf, Payal, Abhishek, Gangaputra, Sapna S., Thorne, Jennifer E., Begum, Hosne, Daniel, Ebenezer, Dunn, James P., Jabs, Douglas A., Helzlsouer, Kathy J., Foster, C. Stephen, Kaçmaz, R. Oktay, Pujari, Siddharth S., Sen, H. Nida, Nussenblatt, Robert B., Levy-Clarke, Grace A., Suhler, Eric B., Rosenbaum, James T., Liesegang, Teresa, Buchanich, Jeanine, Washington, Terri L., Gewaily, Dina Y., and Liesegang, Teresa L.

Published
2016
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20. Factors Predicting Visual Acuity Outcome in Intermediate, Posterior, and Panuveitis: The Multicenter Uveitis Steroid Treatment (MUST) Trial

Author

Jaffe, Glenn J., Branchaud, Brenda, Hahn, Paul, Koreen, Larry, Lad, Eleonora (Nora) M., Lin, Phoebe, Martel, Joseph Nissim, Serrano, Neha (Shah), Skalak, Cindy, Vajzovic, Lejla, Baer, Claxton, Bryant, Joyce, Chavala, Sai, Cusick, Michael, Day, Shelley, Dayani, Pouya, Ehlers, Justis, Kesen, Muge, Lee, Annie, Melamud, Alex, Qureshi, Jawad A., Scott, Adrienne Williams, See, Robert F., Shuler, Robert K., Wood, Megan, Yeh, Steven, Fernandes, Alcides, Gibbs, Deborah, Leef, Donna, Martin, Daniel F., Srivastava, Sunil, Dunn, James P., Begum, Hosne, Boring, Jeff, Brotherson, Kristen L., Burkholder, Bryn, Butler, Nicholas J., Cain, Dennis, Cook, Mary A., Emmert, David, Graul, Janis R., Herring, Mark, Laing, Ashley, Leung, Theresa G., Mahon, Michael C., Moradi, Ahmafreza, Nwankwo, Antonia, Ostheimer, Trucian L., Reed, Terry, Arnold, Ellen, Barnabie, Patricia M., Belair, Marie-Lynn, Bolton, Stephen G., Brodine, Joseph B., Brown, Diane M., Brune, Lisa M., Galor, Anat, Gan, Theresa, Jacobowitz, Adam, Kapoor, Meera, Kedhar, Sanjay, Kim, Stephen, Leder, Henry A., Livingston, Alison G., Morton, Yavette, Nolan, Kisten, Peters, George B., Soto, Priscilla, Stevenson, Ricardo, Tarver-Carr, Michelle, Wang, Yue, Foster, C. Stephen, Anesi, Stephen D., Bruner, Linda, Ceron, Olga, Hinkle, David M., Persons, Nancy, Wentworth, Bailey, Acevedo, Sarah, Anzaar, Fahd, Cesca, Tom, Contero, Angelica, Fitzpatrick, Kayleigh, Goronga, Faith, Johnson, Jyothir, Lebron, Karina Q., Marvell, Danielle, Morgan, Chandra, Patel, Nita, Pinto, Jennifer, Siddique, Sana S., Sprague, Janet, Yilmaz, Taygan, Sen, H. Nida, Bono, Michael, Cunningham, Denise, Hayes, Darryl, Koutsandreas, Dessie, Nussenblatt, Robert B., Sherry, Patti R., Short, Gregory L., Smith, Wendy, Temple, Alana, Bamji, Allison, Coleman, Hanna, Davuluri, Geetaniali, Faia, Lisa, Gottlieb, Chloe, Jirawuthiworavong, Guy V., Lew, Julie C., Mercer, Richard, Obiyor, Dominic, Perry, Cheryl H., Potapova, Natalia, Weichel, Eric, Wroblewski, Keith J., Latkany, Paul A., Coonan, Corinne, Honda, Andrea, Lorenzo-Latkany, Monica, Masini, Robert, Morell, Susan, Nguyen, Angela, Badamo, Jason, Boyd, Kenneth M., Enos, Matthew, Gallardo, Jenny, Jarczynski, Jacek, Lee, Ji Yun, McGrosky, Mirjana, Nour, Ann, Sanchez, Meredith, Steinberg, Kate, Stawell, Richard J., Breayley, Lisa, D'Sylva, Carly, Glatz, Elizabeth, Hodgson, Lauren, Lim, Lyndell, Ling, Cecilia, McIntosh, Rachel, Morrison (Ewing), Julie, Newton, Andrew, Sanmugasundram, Sutha, Smallwood, Richard, Zamir, Ehud, Hunt, Nicola, Jones, Lisa, Koukouras, Ignatios, Williams, Suzanne, Merrill, Pauline T., Carns, Danielle, Richine, Len, Voskuil-Marre, Denise L., Woo, Kisung, Gaynes, Bruce, Giannoulis, Christina, Hulvey, Pam, Kernbauer, Elaine, Khan, Heena S., Levine, Sarah J., Toennessen, Scott, Tonner, Eileen, Wang, Robert C., Aguado, Hank, Arceneaux, Sally, Duignan, Karen, Fish, Gary E., Hesse, Nick, Jaramillo, Diana, Mackens, Michael, Arnwine, Jean, Callanan, David, Cummings, Kimberly, Gray, Keith, Howden, Susie, Mutz, Karin, Sanchez, Brenda, Lightman, Susan, Ismetova, Filis, Prytherch, Ashley, Seguin-Greenstein, Sophie, Tomkins, Oren, Bar, Asat, Edwards, Kate, Joshi, Lavanish, Moraji, Jiten, Samy, Ahmed, Stubbs, Timothy, Taylor, Simon, Towler, Hamish, Tronnberg, Rebecca, Holland, Gary N., Almanzor, Robert D., Castellanos, Jose, Hubschman, Jean Pierre, Johiro, Ann K., Kukuyev, Alla, Levinson, Ralph D., McCannel, Colin A., Ransome, Susan S., Gonzales, Christine R., Gupta, Anurag, Kalyani, Partho S., Kapamajian, Michael A., Kappel, Peter J., Arcinue, Cheryl, Chuang, Janne, Barteselli, Giulio, Currie, Glenn, Mendoza, Veronica, Powell, Debbie, Clark, Tom, Cochran, Denine E., Freeman, William R., Hedaya, Joshua, Kemper, Tiara, Kozak, Igor, LeMoine, Jacqueline M., Loughran, Megan E., Magana, Luzandra, Mojana, Francesca, Morrison, Victoria, Nguyen, Vivian, Oster, Stephen F., Acharya, Nisha, Clay, David, Lee, Salena, Lew, Mary, Margolis, Todd P., Stewart, Jay, Wong, Ira G., Brown, Debra, Khouri, Claire M., Goldstein, Debra A., Birnbaum, Andrea, Degillio, Andrea, Rosa, Gemma De la, Ramirez, Carmen, Simjanowski, Evica, Skelly, Mariner, Castro-Malek, Anna L., Crooke, Catherine E., Huntley, Melody, Nash, Katrina, Niec, Marcia, Pyatetsky, Dimitry, Ramirez, Misel, Rozenbajgier, Zuzanna, Tessler, Howard H., Davis, Janet L., Albini, Thomas A., Chin, Marie, Castaño, Daniela, Elizondo, Ariana, Ho, Macy, Kovach, Jaclyn L., Lin, Richard C-S., Mandelcorn, Efrem, Nguyen, Jackie K-D., Pacini, Aura, Pineda, Susan, Pinto, David A., Rebimbas, Jose, Stepien, Kimberly E., Teran, Claudia, Elner, Susan G., Bernard, Hillary, Fournier, Linda, Godsey, Lindsay, Goings, Linda, Hackel, Richard, Hesselgrave, Moella, Jayasundera, K. Thiran, Prusak, Robert, Titus, Pamela, Bergeron, Melissa, Blosser, Reneé, Brown, Rebecca, Chrisman-McClure, Carrie, Gothrup, Julie R., Saxe, Stephen J., Sizemore, Deanna, Kempen, John H., Berger, James, Drossner, Sheri, DuPont, Joan C., Maguire, Albert M., Petner, Janice, Engelhard, Stephanie, Hopkins, Tim, Lo, Jonathan, McCall, Dawn, McRay, Monique, Salvo, Rebecca, Will, Daniel, Xu, Wei, Windsor, Elizabeth, Weeney, Laurel, Pavan, Peter R., Albritton, Ken, Leto, JoAnn, Madow, Brian, Mayor, Lori, Pautler, Scott E., Saxon, Wyatt, Soto, Judy, Goldstein, Burton, Klukoff, Amy, Lambright, Lucy, McDonald, Kim, Ortiz, Maria, Scymanky, Susan, Szalay, Dee Dee, Rao, Narsing, Davis, Tamara, Douglass, Jackie, Linton, Judith, Padilla, Margaret, Ramos, Sylvia, Rao, Narsing A., Aguirre, Alexia, Chong, Lawrence, Cisneros, Lupe, Corona, Elizabeth, Eliott, Dean, Fawzi, Amani, Garcia, Jesse, Khurana, Rahul, Lim, Jennifer, Mead, Rachel, Tsai, Julie H., Vitale, Albert, Bernstein, Paul S., Carlstrom, Bonnie, Gilman, James, Hanseen, Sandra, Morris, Paula, Ramirez, Diana, Wegner, Kimberley, Sheppard, John D., Anthony, Brianne, Casper, Amber, Felix-Kent, Lisa, Fernandez, Jeanette, Johnson, Tari, Scoper, Stephen V., Cole, R. Denise, Crawford, Nancy, Franklin, Lisa, Hamelin, Krista, Martin, Jen, Marx, Rebecca, Schultz, Gregory, Webb, Joseph, Yeager, Pamela, Kim, Rosa Y., Benz, Matthew S., Brown, David M., Chen, Eric, Fish, Richard H., Kegley, Eric, Shawver, Laura, Wong, Tien P., De La Garza, Rebecca, Friday (Hay), Shayla, Rao, P. Kumar, Adcock, Eve, Apte, Rajendra S., Baladenski, Amy, Curtis, Rhonda, Gould, Sarah, Hebden, Amanda, Kambarian, Jamie, Meyer, Charla, Pistorius, Sam, Quinn, Melanie, Rathert, Greg, Blinder, Kevin J., Hartz, Ashley, Light, Pam, Shah, Gaurav K., VanGelder, Russell, Jabs, Douglas A., Altaweel, Michael M., Holbrook, Janet T., Kurinij, Natalie, Brown, Diane, Prusakowski, Nancy, Thorne, Jennifer E., Hubbard, Larry, Wittes, Janet, Barlow, William E., Hochberg, Marc, Lyon, Alice T., Palestine, Alan G., Simon, Lee S., Rosenbaum, James T., Smith, Harmon, Davis, Janet, Thorne, Jennifer, Acharya, Nisha R., Vitale, Albert T., Boring, Jeffrey A., Alexander, Judith, Ng, Wai Ping, Friedman, David S., Adler, Anna, Burke, Alyce, Katz, Joanne, Reed, Susan, Ansari, Husam, Cohen, Nicholas, Modak, Sanjukta, Sugar, Elizabeth A., Burke, Alyce E., Drye, Lea T., Van Natta, Mark L., Frick, Kevin, Katz, JoAnn, Louis, Thomas A., Shade, David, Pascual, Karen, Slutsky-Sanon, Jill S., Allen, Amanda, Glomp, Colby, Hilal, Yasmin, Nieves, Melissa A., Stevens, Maria, Abreu, Francis, Casper, Anne Shanklin, Ewing, Cathleen, Hart, Adante, Lears, Andrea, Li, Shirley, Meinert, Jill, Morrison, Vinnette, Nowakowski, Deborah, Reyes, Girlie, Shade, Dave M., Smith, Jacqueline, Steuernagle, Karen, Van Natta, Mark, Venugopal, Vidya, Yu, Tsung, Chen, Paul, Collins, Karen, Dodge, John, Frick, Kevin D., Jackson, Rosetta, Jimenez, Christian, Landers, Ariel, Livingston, Hope, Meinert, Curtis L., Rayapudi, Sobharani, Shen, Weijiang, Shiflett, Charles, Smith, Rochelle, Tieman, Ada, Tonascia, James A., Zheng, Richard, Allan, James, Benz, Wendy K., Domalpally, Amitha, Johnson, Kristine A., Myers, Dawn J., Won Pak, Jeong, Reimers, James L., Chambers, Geoffrey, Christianson, Debra J., Fleischli, Margaret A., Freund, Jacquelyn, Gama, Vonnie, Gangaputra, Sapna, Glander, Kathleen E., Goulding, Anne, Hafford, Dennis, Harris, Susan E., Hubbard, Larry D., Joyce, Jeffrey M., Kruse, Christina N., Nagle, Lauren, Padden-Lechten, Gwyn E., Pohlman, Alyson, Remm, Amy, Shaw, Ruth A., Sivesind, Peggy, Thayer, Dennis, Treichel, Erika, Warren, Kelly J., Watson, Sheila M., Webster, Mary K., White, James K., Wilhelmson, Tara, Zhang, Grace, and Lightman, Susan L.

Published
2015
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25. Classification Criteria for Fuchs Uveitis Syndrome

Author

Douglas A. Jabs, Jennifer E. Thorne, Peter McCluskey, Soon-Phaik Chee, Debra A. Goldstein, Philip I. Murray, Nisha R. Acharya, Neal Oden, Brett Trusko, James T. Rosenbaum, and Alan G. Palestine

Subjects
Adult, Male, medicine.medical_specialty, Training set, Adolescent, Fundus Oculi, business.industry, Iris, Middle Aged, Fuchs uveitis syndrome, Article, Confidence interval, Uveitis, Young Adult, Ophthalmology, Humans, Medicine, Female, Anterior uveitis, Fluorescein Angiography, business
Abstract

Purpose To determine classification criteria for Fuchs uveitis syndrome. Design Machine learning of cases with Fuchs uveitis syndrome and 8 other anterior uveitides. Methods Cases of anterior uveitides were collected in an informatics-designed preliminary database, and a final database was constructed of cases achieving supermajority agreement on the diagnosis, using formal consensus techniques. Cases were split into a training set and a validation set. Machine learning using multinomial logistic regression was used on the training set to determine a parsimonious set of criteria that minimized the misclassification rate among the anterior uveitides. The resulting criteria were evaluated on the validation set. Results One thousand eighty-three cases of anterior uveitides, including 146 cases of Fuchs uveitis syndrome, were evaluated by machine learning. The overall accuracy for anterior uveitides was 97.5% in the training set and 96.7% in the validation set (95% confidence interval 92.4, 98.6). Key criteria for Fuchs uveitis syndrome included unilateral anterior uveitis with or without vitritis and either: 1) heterochromia or 2) unilateral diffuse iris atrophy and stellate keratic precipitates. The overall accuracy for anterior uveitides was 97.5% in the training set (95% confidence interval [CI] 96.3, 98.4) and 96.7% in the validation set (95% CI 92.4, 98.6). The misclassification rates for FUS were 4.7% in the training set and 5.5% in the validation set, respectively. Conclusions The criteria for Fuchs uveitis syndrome had a low misclassification rate and appeared to perform well enough for use in clinical and translational research.

Published
2021
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26. Classification Criteria for Vogt-Koyanagi-Harada Disease

Author

Annabelle A. Okada, Alastair K Denniston, Andrew D. Dick, Peter McCluskey, Russell W. Read, Alan G. Palestine, Brett Trusko, Jennifer E. Thorne, Douglas A. Jabs, Michal Kramer, Neal Oden, and James P. Dunn

Subjects
Adult, Male, Vogt–Koyanagi–Harada disease, Pediatrics, medicine.medical_specialty, Training set, Fundus Oculi, business.industry, Middle Aged, Fundus (eye), medicine.disease, Article, eye diseases, Confidence interval, Ophthalmology, medicine, Humans, Fluorescein angiogram, Female, Fluorescein Angiography, Uveomeningoencephalitic Syndrome, business, Tomography, Optical Coherence
Abstract

Purpose To determine classification criteria for Vogt-Koyanagi-Harada (VKH) disease Design Machine learning of cases with VKH disease and 5 other panuveitides. Methods Cases of panuveitides were collected in an informatics-designed preliminary database, and a final database was constructed of cases achieving supermajority agreement on the diagnosis, using formal consensus techniques. Cases were split into a training set and a validation set. Machine learning using multinomial logistic regression was used on the training set to determine a parsimonious set of criteria that minimized the misclassification rate among the panuveitides. The resulting criteria were evaluated on the validation set. Results One thousand twelve cases of panuveitides, including 156 cases of early-stage VKH and 103 cases of late-stage VKH, were evaluated. Overall accuracy for panuveitides was 96.3% in the training set and 94.0% in the validation set (95% confidence interval 89.0, 96.8). Key criteria for early-stage VKH included: 1) exudative retinal detachment with characteristic appearance on fluorescein angiogram or optical coherence tomography or 2) panuveitis with ≥2 of 5 neurologic symptoms/signs. Key criteria for late-stage VKH included history of early-stage VKH and either: 1) sunset glow fundus or 2) uveitis and ≥1 of 3 cutaneous signs. The misclassification rates in the learning and validation sets for early-stage VKH were 8.0% and 7.7%, respectively, and for late-stage VKH 1.0% and 12%, respectively. Conclusions The criteria for VKH had a reasonably low misclassification rate and appeared to perform sufficiently well for use in clinical and translational research.

Published
2021
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27. Classification Criteria for Juvenile Idiopathic Arthritis–Associated Chronic Anterior Uveitis

Author

Philip I. Murray, Peter McCluskey, Nisha R. Acharya, Debra A. Goldstein, Jennifer E. Thorne, Soon-Phaik Chee, Alan G. Palestine, Douglas A. Jabs, James T. Rosenbaum, Brett Trusko, and Neal Oden

Subjects
Adult, Male, Pediatrics, medicine.medical_specialty, Consensus, Arthritis, Newly diagnosed, Article, Young Adult, 03 medical and health sciences, 0302 clinical medicine, Humans, Medicine, 030304 developmental biology, 0303 health sciences, Training set, business.industry, Middle Aged, Enthesitis-Related Arthritis, medicine.disease, Uveitis, Anterior, Insidious onset, Arthritis, Juvenile, Confidence interval, Ophthalmology, Chronic Disease, 030221 ophthalmology & optometry, Chronic anterior uveitis, Female, Polyarthritis, business
Abstract

Purpose To determine classification criteria for juvenile idiopathic arthritis (JIA)-associated chronic anterior uveitis (CAU). Design Machine learning of cases with JIA CAU and 8 other anterior uveitides. Methods Cases of anterior uveitides were collected in an informatics-designed preliminary database, and a final database was constructed of cases achieving supermajority agreement on the diagnosis, using formal consensus techniques. Cases were split into a training set and a validation set. Machine learning using multinomial logistic regression was used on the training set to determine a parsimonious set of criteria that minimized the misclassification rate among the anterior uveitides. The resulting criteria were evaluated on the validation set. Results One thousand eighty-three cases of anterior uveitides, including 202 cases of JIA CAU, were evaluated by machine learning. The overall accuracy for anterior uveitides was 97.5% in the training set and 96.7% in the validation set (95% confidence interval 92.4, 98.6). Key criteria for JIA CAU included 1) chronic anterior uveitis (or if newly diagnosed insidious onset) and 2) JIA, except for the systemic, rheumatoid factor-positive polyarthritis, and enthesitis related arthritis variants. The misclassification rates for JIA CAU were 2.4% in the training set and 0% in the validation set, respectively. Conclusions The criteria for JIA CAU had a low misclassification rate and appeared to perform well enough for use in clinical and translational research.

Published
2021
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28. Classification Criteria for Tubercular Uveitis

Author

Susan Lightman, Jennifer E. Thorne, Vishali Gupta, Gary N. Holland, Douglas A. Jabs, Neal Oden, Bahram Bodaghi, Elizabeth M. Graham, Brett Trusko, Rubens Belfort, Alan G. Palestine, and Justine R. Smith

Subjects
Adult, Male, medicine.medical_specialty, Tuberculosis, Tuberculosis, Ocular, Article, Multifocal choroiditis, Machine Learning, Uveitis, Young Adult, 03 medical and health sciences, 0302 clinical medicine, medicine, Humans, Retrospective Studies, 030304 developmental biology, 0303 health sciences, Training set, Tuberculin Test, Retinal vasculitis, business.industry, Panuveitis, Middle Aged, medicine.disease, Dermatology, Ophthalmology, Choroiditis, 030221 ophthalmology & optometry, Female, Tuberculoma, business
Abstract

Purpose To determine classification criteria for tubercular uveitis DESIGN: Machine learning of cases with tubercular uveitis and 14 other uveitides. Methods Cases of non-infectious posterior or panuveitis, and of infectious posterior or panuveitis were collected in an informatics-designed preliminary database, and a final database was constructed of cases achieving supermajority agreement on the diagnosis, using formal consensus techniques. Cases were analyzed by anatomic class, and each class was split into a training set and a validation set. Machine learning using multinomial logistic regression was used on the training set to determine a parsimonious set of criteria that minimized the misclassification rate among the intermediate uveitides. The resulting criteria were evaluated on the validation sets. Results Two hundred seventy-seven cases of tubercular uveitis were evaluated by machine learning against other uveitides. Key criteria for tubercular uveitis were a compatible uveitic syndrome, including: 1) anterior uveitis with iris nodules, 2) serpiginous-like tubercular choroiditis, 3) choroidal nodule (tuberculoma), 4) occlusive retinal vasculitis, and 5) in hosts with evidence of active systemic tuberculosis, multifocal choroiditis; and evidence of tuberculosis, including: 1) histologically- or microbiologically-confirmed infection, 2) positive interferon-Ɣ release assay test, or 3) positive tuberculin skin test. The overall accuracy of the diagnosis of tubercular uveitis versus other uveitides in the validation set was 98.2% (95% CI 96.5, 99.1). The misclassification rates for tubercular uveitis were: training set 3.4%; and validation set 3.6%. Conclusions The criteria for tubercular uveitis had a low misclassification rate and appeared to perform sufficiently well for use in clinical and translational research.

Published
2021
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29. Classification Criteria for Varicella Zoster Virus Anterior Uveitis

Author

Alan G. Palestine, Debra A. Goldstein, Nisha R. Acharya, Douglas A. Jabs, Philip I. Murray, Soon-Phaik Chee, Jennifer E. Thorne, James T. Rosenbaum, Neal Oden, Peter McCluskey, Laure Caspers, and Brett Trusko

Subjects
Adult, Male, Herpesvirus 3, Human, medicine.medical_specialty, Adolescent, viruses, Eye Infections, Viral, Aqueous humor, medicine.disease_cause, Article, Aqueous Humor, Machine Learning, Young Adult, 03 medical and health sciences, 0302 clinical medicine, Ophthalmology, medicine, Humans, 030304 developmental biology, 0303 health sciences, Training set, business.industry, Varicella zoster virus, virus diseases, Middle Aged, Uveitis, Anterior, Confidence interval, DNA, Viral, Herpes Zoster Ophthalmicus, 030221 ophthalmology & optometry, Female, Anterior uveitis, business
Abstract

Purpose To determine classification criteria for varicella zoster virus (VZV) anterior uveitis DESIGN: Machine learning of cases with VZV anterior uveitis and 8 other anterior uveitides. Methods Cases of anterior uveitides were collected in an informatics-designed preliminary database, and a final database was constructed of cases achieving supermajority agreement on the diagnosis, using formal consensus techniques. Cases were split into a training set and a validation set. Machine learning using multinomial logistic regression was used on the training set to determine a parsimonious set of criteria that minimized the misclassification rate among the anterior uveitides. The resulting criteria were evaluated on the validation set. Results One thousand eighty-three cases of anterior uveitides, including 123 cases of VZV anterior uveitis, were evaluated by machine learning. The overall accuracy for anterior uveitides was 97.5% in the training set and 96.7% in the validation set (95% confidence interval 92.4, 98.6). Key criteria for VZV anterior uveitis included unilateral anterior uveitis with either 1) positive aqueous humor polymerase chain reaction assay for VZV; 2) sectoral iris atrophy in a patient ≥60 years of age; or 3) concurrent or recent dermatomal herpes zoster. The misclassification rates for VZV anterior uveitis were 0.9% in the training set and 0% in the validation set, respectively. Conclusions The criteria for VZV anterior uveitis had a low misclassification rate and appeared to perform sufficiently well for use in clinical and translational research.

Published
2021
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30. Classification Criteria for Multifocal Choroiditis With Panuveitis

Author

Susan E Wittenberg, Douglas A. Jabs, Peter McCluskey, Albert T. Vitale, Antoine P. Brézin, Alan G. Palestine, Jennifer E. Thorne, Brett Trusko, Russell W. Read, Neal Oden, and Ralph D. Levinson

Subjects
Adult, Male, medicine.medical_specialty, Anterior Chamber, Posterior pole, Visual Acuity, Article, Multifocal choroiditis, Machine Learning, Set (abstract data type), 03 medical and health sciences, 0302 clinical medicine, medicine, Humans, 030304 developmental biology, 0303 health sciences, Training set, business.industry, Multifocal Choroiditis, Panuveitis, Middle Aged, Confidence interval, Ophthalmology, 030221 ophthalmology & optometry, Chorioretinal scars, Female, Radiology, business
Abstract

Purpose To determine classification criteria for multifocal choroiditis with panuveitis (MFCPU) DESIGN: : Machine learning of cases with MFCPU and 8 other posterior uveitides. Methods Cases of posterior uveitides were collected in an informatics-designed preliminary database, and a final database was constructed of cases achieving supermajority agreement on diagnosis, using formal consensus techniques. Cases were split into a training set and a validation set. Machine learning using multinomial logistic regression was used on the training set to determine a parsimonious set of criteria that minimized the misclassification rate among the posterior uveitides. The resulting criteria were evaluated on the validation set. Results One thousand sixty-eight cases of posterior uveitides, including 138 cases of MFCPU, were evaluated by machine learning. Key criteria for MFCPU included: 1) multifocal choroiditis with the predominant lesions size >125 µm in diameter; 2) lesions outside the posterior pole (with or without posterior involvement); and either 3) punched-out atrophic chorioretinal scars or 4) more than minimal mild anterior chamber and/or vitreous inflammation. Overall accuracy for posterior uveitides was 93.9% in the training set and 98.0% (95% confidence interval 94.3, 99.3) in the validation set. The misclassification rates for MFCPU were 15% in the training set and 0% in the validation set. Conclusions The criteria for MFCPU had a reasonably low misclassification rate and appeared to perform sufficiently well for use in clinical and translational research.

Published
2021
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31. Classification Criteria for Acute Retinal Necrosis Syndrome

Author

Neal Oden, Russell N. Van Gelder, Douglas A. Jabs, Todd P. Margolis, Elizabeth M. Graham, Gary N. Holland, Susan Lightman, Rubens Belfort, Brett Trusko, Alan G. Palestine, Jennifer E. Thorne, Bahram Bodaghi, and Justine R. Smith

Subjects
Adult, Male, medicine.medical_specialty, Fundus Oculi, Retinitis, Acute retinal necrosis syndrome, medicine.disease_cause, Retina, Article, Machine Learning, Occlusion, Humans, Medicine, Fluorescein Angiography, business.industry, Varicella zoster virus, Retinal Necrosis Syndrome, Acute, Clinical appearance, Middle Aged, medicine.disease, Confidence interval, Ophthalmology, Intraocular fluid, Female, Acute retinal necrosis, Radiology, business, Tomography, Optical Coherence
Abstract

To determine classification criteria for acute retinal necrosis (ARN).Machine learning of cases with ARN and 4 other infectious posterior uveitides / panuveitides.Cases of infectious posterior uveitides / panuveitides were collected in an informatics-designed preliminary database, and a final database was constructed of cases achieving supermajority agreement on diagnosis, using formal consensus techniques. Cases were split into a training set and a validation set. Machine learning using multinomial logistic regression was used on the training set to determine a parsimonious set of criteria that minimized the misclassification rate among the infectious posterior uveitides / panuveitides. The resulting criteria were evaluated on the validation set.Eight hundred three cases of infectious posterior uveitides / panuveitides, including 186 cases of ARN, were evaluated by machine learning. Key criteria for ARN included (1) peripheral necrotizing retinitis and either (2) polymerase chain reaction assay of an intraocular fluid specimen positive for either herpes simplex virus or varicella zoster virus or (3) a characteristic clinical appearance with circumferential or confluent retinitis, retinal vascular sheathing and/or occlusion, and more than minimal vitritis. Overall accuracy for infectious posterior uveitides / panuveitides was 92.1% in the training set and 93.3% (95% confidence interval 88.2, 96.3) in the validation set. The misclassification rates for ARN were 15% in the training set and 11.5% in the validation set.The criteria for ARN had a reasonably low misclassification rate and seemed to perform sufficiently well for use in clinical and translational research.

Published
2021
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32. Classification Criteria for Behçet Disease Uveitis

Author

Russell W. Read, Douglas A. Jabs, Annabelle A. Okada, Steven Yeh, Jennifer E. Thorne, Michal Kramer, Andrew D. Dick, Alan G. Palestine, Neal Oden, Peter McCluskey, James P. Dunn, and Brett Trusko

Subjects
Adult, Male, medicine.medical_specialty, Consensus, Article, Machine Learning, Uveitis, Young Adult, 03 medical and health sciences, 0302 clinical medicine, medicine, Humans, Retrospective Studies, 030304 developmental biology, 0303 health sciences, Training set, business.industry, Behcet disease, Retinal vasculitis, Behcet Syndrome, Panuveitis, medicine.disease, Dermatology, Confidence interval, stomatognathic diseases, Ophthalmology, Posterior uveitis, 030221 ophthalmology & optometry, Female, Anterior uveitis, business
Abstract

Purpose To determine classification criteria for Behcet disease uveitis. Design Machine learning of cases with Behcet disease and 5 other panuveitides. Methods Cases of panuveitides were collected in an informatics-designed preliminary database, and a final database was constructed of cases achieving supermajority agreement on the diagnosis, using formal consensus techniques. Cases were split into a training set and a validation set. Machine learning using multinomial logistic regression was used on the training set to determine a parsimonious set of criteria that minimized the misclassification rate among the intermediate uveitides. The resulting criteria were evaluated on the validation set. Results One thousand twelve of cases panuveitides, including 194 cases of Behcet disease with uveitis, were evaluated by machine learning. The overall accuracy for panuveitides was 96.3% in the training set and 94.0% in the validation set (95% confidence interval 89.0, 96.8). Key criteria for Behcet disease uveitis were a diagnosis of Behcet disease using the International Study Group for Behcet Disease criteria and a compatible uveitis, including: 1) anterior uveitis; 2) anterior chamber and vitreous inflammation; 3) posterior uveitis with retinal vasculitis and/or focal infiltrates; or 4) panuveitis with retinal vasculitis and/or focal infiltrates. The misclassification rates for Behcet disease uveitis were 0.6 % in the training set and 0% in the validation set, respectively. Conclusions The criteria for Behcet disease uveitis had a low misclassification rate and appeared to perform sufficiently well for use in clinical and translational research.

Published
2021
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33. Classification Criteria for Cytomegalovirus Retinitis

Author

Douglas A. Jabs, Rubens Belfort, Alan G. Palestine, Neal Oden, Bahram Bodaghi, Elizabeth M. Graham, Russell N. Van Gelder, Susan Lightman, Jennifer E. Thorne, Gary N. Holland, Brett Trusko, and Justine R. Smith

Subjects
Adult, Male, Pediatrics, medicine.medical_specialty, Cytomegalovirus, Eye Infections, Viral, Article, Machine Learning, 03 medical and health sciences, 0302 clinical medicine, Text mining, medicine, Humans, 030304 developmental biology, Multinomial logistic regression, 0303 health sciences, business.industry, Middle Aged, medicine.disease, Ophthalmology, Cytomegalovirus Retinitis, DNA, Viral, 030221 ophthalmology & optometry, Female, Cytomegalovirus retinitis, business
Abstract

PURPOSE: To determine classification criteria for cytomegalovirus (CMV) retinitis. DESIGN: Machine learning of cases with CMV retinitis and 4 other infectious posterior/panuveitides. METHODS: Cases of infectious posterior/panuveitides were collected in an informatics-designed preliminary database, and a final database was constructed of cases achieving supermajority agreement on diagnosis, using formal consensus techniques. Cases were split into a training set and a validation set. Machine learning using multinomial logistic regression was used on the training set to determine a parsimonious set of criteria that minimized the misclassification rate among the infectious posterior/panuveitides. The resulting criteria were evaluated on the validation set. RESULTS: Eight hundred three cases of infectious posterior/panuveitides, including 211 cases of CMV retinitis, were evaluated by machine learning. Key criteria for CMV retinitis included: 1) necrotizing retinitis with indistinct borders due to numerous small satellites; 2) evidence of immune compromise; and either 3) a characteristic clinical appearance or 4) positive polymerase chain assay for CMV from an intraocular specimen. Characteristic appearances for CMV retinitis included: 1) wedge-shaped area of retinitis; 2) hemorrhagic retinitis; or 3) granular retinitis. Overall accuracy for infectious posterior/panuveitides was 92.1% in the training set and 93.3% (95% confidence interval 88.2, 96.3) in the validation set. The misclassification rates for CMV retinitis were 6.9% in the training set and 6.3% in the validation set. CONCLUSIONS: The criteria for CMV retinitis had a low misclassification rate and appeared to perform sufficiently well for use in clinical and translational research.

Published
2021
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34. Classification Criteria for Sarcoidosis-Associated Uveitis

Author

Alastair K Denniston, Douglas A. Jabs, Brett Trusko, Annabelle A. Okada, Alan G. Palestine, Neal Oden, Peter McCluskey, Nisha R. Acharya, Susan Lightman, Jennifer E. Thorne, and Albert T. Vitale

Subjects
Adult, Male, Bilateral hilar adenopathy, medicine.medical_specialty, Sarcoidosis, Biopsy, Article, Uveitis, 03 medical and health sciences, 0302 clinical medicine, medicine, Humans, Uvea, 030304 developmental biology, 0303 health sciences, Training set, business.industry, Panuveitis, Middle Aged, medicine.disease, Confidence interval, Ophthalmology, 030221 ophthalmology & optometry, Intermediate uveitis, Female, Anterior uveitis, Radiology, business
Abstract

Purpose To determine classification criteria for sarcoidosis-associated uveitis DESIGN: Machine learning of cases with sarcoid uveitis and 15 other uveitides. Methods Cases of anterior, intermediate, and panuveitides were collected in an informatics-designed preliminary database, and a final database was constructed of cases achieving supermajority agreement on the diagnosis, using formal consensus techniques. Cases were analyzed by anatomic class, and each class was split into a training set and a validation set. Machine learning using multinomial logistic regression was used on the training sets to determine a parsimonious set of criteria that minimized the misclassification rate among the intermediate uveitides. The resulting criteria were evaluated on the validation sets. Results One thousand eighty-three anterior uveitides, 589 intermediate uveitides, and 1012 panuveitides, including 278 cases of sarcoidosis-associated uveitis, were evaluated by machine learning. Key criteria for sarcoidosis-associated uveitis included a compatible uveitic syndrome of any anatomic class and evidence of sarcoidosis, either 1) a tissue biopsy demonstrating non-caseating granulomata or 2) bilateral hilar adenopathy on chest imaging. The overall accuracy of the diagnosis of sarcoidosis-associated uveitis in the validation set was 99.7% (95% confidence interval 98.8, 99.9).The misclassification rates for sarcoidosis-associated uveitis in the training sets were: anterior uveitis 3.2%, intermediate uveitis 2.6%, and panuveitis 1.2%; in the validation sets the misclassification rates were: anterior uveitis 0%, intermediate uveitis 0%, and panuveitis 0%, respectively. Conclusions The criteria for sarcoidosis-associated uveitis had a low misclassification rate and appeared to perform sufficiently well for use in clinical and translational research.

Published
2021
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35. Classification Criteria for Birdshot Chorioretinitis

Author

Russell W. Read, Jennifer E. Thorne, Antoine P. Brézin, Neal Oden, Douglas A. Jabs, Albert T. Vitale, Alan G. Palestine, Brett Trusko, Peter McCluskey, Ralph D. Levinson, and Susan E Wittenberg

Subjects
Male, medicine.medical_specialty, Consensus, Training set, Choroid, Fundus Oculi, business.industry, Indocyanine green angiography, Birdshot Chorioretinopathy, Birdshot chorioretinitis, Middle Aged, Retina, Article, Confidence interval, Multifocal choroiditis, Machine Learning, Ophthalmology, medicine, Humans, Female, Fluorescein Angiography, business
Abstract

Purpose To determine classification criteria for birdshot chorioretinitis. Design Machine learning of cases with birdshot chorioretinitis and 8 other posterior uveitides. Methods Cases of posterior uveitides were collected in an informatics-designed preliminary database, and a final database was constructed of cases achieving supermajority agreement on diagnosis, using formal consensus techniques. Cases were split into a training set and a validation set. Machine learning using multinomial logistic regression was used on the training set to determine a parsimonious set of criteria that minimized the misclassification rate among the infectious posterior/panuveitides. The resulting criteria were evaluated on the validation set. Results One thousand sixty-eight cases of posterior uveitides, including 207 cases of birdshot chorioretinitis, were evaluated by machine learning. Key criteria for birdshot chorioretinitis included a multifocal choroiditis with: 1) the characteristic appearance a bilateral multifocal choroiditis with cream-colored or yellow-orange, oval or round choroidal spots ("birdshot" spots); 2) absent to mild anterior chamber inflammation; and 3) absent to moderate vitreous inflammation; or multifocal choroiditis with positive HLA-A29 testing and either: 1) classic "birdshot spots" or 2) characteristic imaging on indocyanine green angiography. Overall accuracy for posterior uveitides was 93.9% in the training set and 98.0% (95% confidence interval 94.3, 99.3) in the validation set. The misclassification rates for birdshot chorioretinitis were 10% in the training set and 0% in the validation set. Conclusions The criteria for birdshot chorioretinitis had a low misclassification rate and appeared to perform sufficiently well for use in clinical and translational research.

Published
2021
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36. Classification Criteria for Syphilitic Uveitis

Author

Douglas A. Jabs, Brett Trusko, Bahram Bodaghi, Justine R. Smith, Susan Lightman, Elizabeth M. Graham, Jennifer E. Thorne, Neal Oden, Gary N. Holland, Rubens Belfort, and Alan G. Palestine

Subjects
Adult, Male, 0303 health sciences, medicine.medical_specialty, business.industry, Middle Aged, medicine.disease, Dermatology, Article, Eye Infections, Bacterial, Machine Learning, Uveitis, 03 medical and health sciences, Ophthalmology, 0302 clinical medicine, 030221 ophthalmology & optometry, Humans, Medicine, Female, Syphilis, business, 030304 developmental biology, Multinomial logistic regression
Abstract

PURPOSE: To determine classification criteria for syphilitic uveitis DESIGN: Machine learning of cases with syphilitic uveitis and 24 other uveitides. METHODS: Cases of anterior, intermediate, posterior, and panuveitides were collected in an informatics-designed preliminary database, and a final database was constructed of cases achieving supermajority agreement on the diagnosis, using formal consensus techniques. Cases were analyzed by anatomic class, and each class was split into a training set and a validation set. Machine learning using multinomial logistic regression was used on the training set to determine a parsimonious set of criteria that minimized the misclassification rate among the different uveitic classes. The resulting criteria were evaluated on the validation set. RESULTS: Two hundred twenty-two cases of syphilitic uveitis were evaluated by machine learning with cases evaluated against other uveitides in the relevant uveitic class. Key criteria for syphilitic uveitis included a compatible uveitic presentation, (1) anterior uveitis, 2) intermediate uveitis, or 3) posterior or panuveitis with retinal, retinal pigment epithelial, or retinal vascular inflammation) and evidence of syphilis infection with a positive treponemal test. The Centers for Disease Control and Prevention reverse screening algorithm for syphilis testing is recommended. The misclassification rates for syphilitic uveitis in the training sets were: anterior uveitides 0%, intermediate uveitides 6.0%, posterior uveitides 0%, panuveitides 0%, and infectious posterior/panuveitides 8.6%. The overall accuracy of the diagnosis of syphilitic uveitis in the validation set was 100% (99% CI 99.5, 100) – i.e. the validation sets misclassification rates were 0% for each uveitic class. CONCLUSIONS: The criteria for syphilitic uveitis had a low misclassification rate and appeared to perform sufficiently well for use in clinical and translational research.

Published
2021
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43. Incidence and Outcome of Uveitic Glaucoma in Eyes With Intermediate, Posterior, or Panuveitis Followed up to 10 Years After Randomization to Fluocinolone Acetonide Implant or Systemic Therapy

Author

Multicenter Uveitis Steroid Treatment (Must) Trial, Michael M. Altaweel, Douglas A. Jabs, David S. Friedman, Elizabeth A. Sugar, Mark L. Van Natta, Lyndell L Lim, Susan G. Elner, Janet T. Holbrook, Husam Ansari, James P. Dunn, and John H. Kempen

Subjects
Adult, Male, medicine.medical_specialty, Intraocular pressure, genetic structures, Visual Acuity, Glaucoma, Article, 03 medical and health sciences, 0302 clinical medicine, Fluocinolone acetonide, Risk Factors, Ophthalmology, Panuveitis, medicine, Humans, Prospective Studies, Prospective cohort study, Glucocorticoids, Intraocular Pressure, 030304 developmental biology, Drug Implants, 0303 health sciences, business.industry, Incidence, Hazard ratio, Uveitis, Posterior, Middle Aged, medicine.disease, eye diseases, Vitreous Body, Treatment Outcome, Fluocinolone Acetonide, 030221 ophthalmology & optometry, Female, sense organs, Implant, business, Uveitis, Intermediate, Uveitis, Follow-Up Studies, medicine.drug
Abstract

PURPOSE: To evaluate long-term risk and outcomes of glaucoma in eyes with intermediate, posterior, and panuveitis managed with systemic or fluocinolone acetonide (0.59 mg, “implant”) therapy. DESIGN: Prospective Follow-up of the Multicenter Uveitis Steroid Treatment (MUST) Clinical Trial Cohort METHODS: Patients with intermediate, posterior or panuveitis randomized to implant or systemic therapy (corticosteroid plus immunosuppression in >90%) were followed prospectively for glaucoma incidence and outcome. RESULTS: Among 405 uveitic at-risk eyes of 232 patients (median follow-up=6.9 years), 40% (79/196) of eyes assigned and treated with implant and 8% (17/209) of eyes assigned and treated with systemic therapy (censoring eyes receiving an implant upon implantation) developed glaucoma (Hazard Ratio (HR)=5.9 (95% CI: 3.2, 10.8); p

Published
2020
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50. Mycophenolate Mofetil for Ocular Inflammation

Author

Daniel, Ebenezer, Thorne, Jennifer E., Newcomb, Craig W., Pujari, Siddharth S., Kaçmaz, R. Oktay, Levy-Clarke, Grace A., Nussenblatt, Robert B., Rosenbaum, James T., Suhler, Eric B., Foster, C. Stephen, Jabs, Douglas A., and Kempen, John H.

Published
2010
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