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Using polygenic scores and clinical data for bipolar disorder patient stratification and lithium response prediction: machine learning approach – CORRIGENDUM
- Micah Cearns, Azmeraw T. Amare, Klaus Oliver Schubert, Anbupalam Thalamuthu, Joseph Frank, Fabian Streit, Mazda Adli, Nirmala Akula, Kazufumi Akiyama, Raffaella Ardau, Bárbara Arias, JeanMichel Aubry, Lena Backlund, Abesh Kumar Bhattacharjee, Frank Bellivier, Antonio Benabarre, Susanne Bengesser, Joanna M. Biernacka, Armin Birner, Clara Brichant-Petitjean, Pablo Cervantes, HsiChung Chen, Caterina Chillotti, Sven Cichon, Cristiana Cruceanu, Piotr M. Czerski, Nina Dalkner, Alexandre Dayer, Franziska Degenhardt, Maria Del Zompo, J. Raymond DePaulo, Bruno Étain, Peter Falkai, Andreas J. Forstner, Louise Frisen, Mark A. Frye, Janice M. Fullerton, Sébastien Gard, Julie S. Garnham, Fernando S. Goes, Maria Grigoroiu-Serbanescu, Paul Grof, Ryota Hashimoto, Joanna Hauser, Urs Heilbronner, Stefan Herms, Per Hoffmann, Andrea Hofmann, Liping Hou, Yi-Hsiang Hsu, Stephane Jamain, Esther Jiménez, Jean-Pierre Kahn, Layla Kassem, Po-Hsiu Kuo, Tadafumi Kato, John Kelsoe, Sarah Kittel-Schneider, Sebastian Kliwicki, Barbara König, Ichiro Kusumi, Gonzalo Laje, Mikael Landén, Catharina Lavebratt, Marion Leboyer, Susan G. Leckband, Mario Maj, the Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium, Mirko Manchia, Lina Martinsson, Michael J. McCarthy, Susan McElroy, Francesc Colom, Marina Mitjans, Francis M. Mondimore, Palmiero Monteleone, Caroline M. Nievergelt, Markus M. Nöthen, Tomas Novák, Claire O'Donovan, Norio Ozaki, Vincent Millischer, Sergi Papiol, Andrea Pfennig, Claudia Pisanu, James B. Potash, Andreas Reif, Eva Reininghaus, Guy A. Rouleau, Janusz K. Rybakowski, Martin Schalling, Peter R. Schofield, Barbara W. Schweizer, Giovanni Severino, Tatyana Shekhtman, Paul D. Shilling, Katzutaka Shimoda, Christian Simhandl, Claire M. Slaney, Alessio Squassina, Thomas Stamm, Pavla Stopkova, Fasil TekolaAyele, Alfonso Tortorella, Gustavo Turecki, Julia Veeh, Eduard Vieta, Stephanie H. Witt, Gloria Roberts, Peter P. Zandi, Martin Alda, Michael Bauer, Francis J. McMahon, Philip B. Mitchell, Thomas G. Schulze, Marcella Rietschel, Scott R. Clark, Bernhard T. Baune
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- Journal:
- The British Journal of Psychiatry / Volume 221 / Issue 2 / August 2022
- Published online by Cambridge University Press:
- 04 May 2022, p. 494
- Print publication:
- August 2022
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Using polygenic scores and clinical data for bipolar disorder patient stratification and lithium response prediction: machine learning approach
- Micah Cearns, Azmeraw T. Amare, Klaus Oliver Schubert, Anbupalam Thalamuthu, Joseph Frank, Fabian Streit, Mazda Adli, Nirmala Akula, Kazufumi Akiyama, Raffaella Ardau, Bárbara Arias, Jean-Michel Aubry, Lena Backlund, Abesh Kumar Bhattacharjee, Frank Bellivier, Antonio Benabarre, Susanne Bengesser, Joanna M. Biernacka, Armin Birner, Clara Brichant-Petitjean, Pablo Cervantes, Hsi-Chung Chen, Caterina Chillotti, Sven Cichon, Cristiana Cruceanu, Piotr M. Czerski, Nina Dalkner, Alexandre Dayer, Franziska Degenhardt, Maria Del Zompo, J. Raymond DePaulo, Bruno Étain, Peter Falkai, Andreas J. Forstner, Louise Frisen, Mark A. Frye, Janice M. Fullerton, Sébastien Gard, Julie S. Garnham, Fernando S. Goes, Maria Grigoroiu-Serbanescu, Paul Grof, Ryota Hashimoto, Joanna Hauser, Urs Heilbronner, Stefan Herms, Per Hoffmann, Andrea Hofmann, Liping Hou, Yi-Hsiang Hsu, Stephane Jamain, Esther Jiménez, Jean-Pierre Kahn, Layla Kassem, Po-Hsiu Kuo, Tadafumi Kato, John Kelsoe, Sarah Kittel-Schneider, Sebastian Kliwicki, Barbara König, Ichiro Kusumi, Gonzalo Laje, Mikael Landén, Catharina Lavebratt, Marion Leboyer, Susan G. Leckband, Mario Maj, the Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium, Mirko Manchia, Lina Martinsson, Michael J. McCarthy, Susan McElroy, Francesc Colom, Marina Mitjans, Francis M. Mondimore, Palmiero Monteleone, Caroline M. Nievergelt, Markus M. Nöthen, Tomas Novák, Claire O'Donovan, Norio Ozaki, Vincent Millischer, Sergi Papiol, Andrea Pfennig, Claudia Pisanu, James B. Potash, Andreas Reif, Eva Reininghaus, Guy A. Rouleau, Janusz K. Rybakowski, Martin Schalling, Peter R. Schofield, Barbara W. Schweizer, Giovanni Severino, Tatyana Shekhtman, Paul D. Shilling, Katzutaka Shimoda, Christian Simhandl, Claire M. Slaney, Alessio Squassina, Thomas Stamm, Pavla Stopkova, Fasil Tekola-Ayele, Alfonso Tortorella, Gustavo Turecki, Julia Veeh, Eduard Vieta, Stephanie H. Witt, Gloria Roberts, Peter P. Zandi, Martin Alda, Michael Bauer, Francis J. McMahon, Philip B. Mitchell, Thomas G. Schulze, Marcella Rietschel, Scott R. Clark, Bernhard T. Baune
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- Journal:
- The British Journal of Psychiatry / Volume 220 / Issue 4 / April 2022
- Published online by Cambridge University Press:
- 28 February 2022, pp. 219-228
- Print publication:
- April 2022
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Background
Response to lithium in patients with bipolar disorder is associated with clinical and transdiagnostic genetic factors. The predictive combination of these variables might help clinicians better predict which patients will respond to lithium treatment.
AimsTo use a combination of transdiagnostic genetic and clinical factors to predict lithium response in patients with bipolar disorder.
MethodThis study utilised genetic and clinical data (n = 1034) collected as part of the International Consortium on Lithium Genetics (ConLi+Gen) project. Polygenic risk scores (PRS) were computed for schizophrenia and major depressive disorder, and then combined with clinical variables using a cross-validated machine-learning regression approach. Unimodal, multimodal and genetically stratified models were trained and validated using ridge, elastic net and random forest regression on 692 patients with bipolar disorder from ten study sites using leave-site-out cross-validation. All models were then tested on an independent test set of 342 patients. The best performing models were then tested in a classification framework.
ResultsThe best performing linear model explained 5.1% (P = 0.0001) of variance in lithium response and was composed of clinical variables, PRS variables and interaction terms between them. The best performing non-linear model used only clinical variables and explained 8.1% (P = 0.0001) of variance in lithium response. A priori genomic stratification improved non-linear model performance to 13.7% (P = 0.0001) and improved the binary classification of lithium response. This model stratified patients based on their meta-polygenic loadings for major depressive disorder and schizophrenia and was then trained using clinical data.
ConclusionsUsing PRS to first stratify patients genetically and then train machine-learning models with clinical predictors led to large improvements in lithium response prediction. When used with other PRS and biological markers in the future this approach may help inform which patients are most likely to respond to lithium treatment.
Contributors
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- By Linda S. Aglio, Cyrus Ahmadi Yazdi, Syed Irfan Qasim Ali, Caryn Barnet, Jessica Bauerle, Felicity Billings, Evan Blaney, Beverly Chang, Christopher Chen, Zinaida Chepurny, Hyung Sun Choi, Allison Clark, Lauren J. Cornella, Lisa Crossley, Michael D’Ambra, Galina Davidyuk, Whitney de Luna, Manisha S. Desai, Sukumar P. Desai, Kelly G. Elterman, Michaela K. Farber, Iuliu Fat, Jaida Fitzgerald, Devon Flaherty, John A. Fox, Gyorgy Frendl, Rejean Gareau, Joseph M. Garfield, Andrea Girnius, Laverne D. Gugino, J. Tasker Gundy, Carly C. Guthrie, Lisa M. Hammond, M. Tariq Hanifi, James Hardy, Philip M. Hartigan, Thomas Hickey, Richard Hsu, Mohab Ibrahim, David Janfaza, Yuka Kiyota, Suzanne Klainer, Benjamin Kloesel, Hanjo Ko, Bhavani Kodali, Vesela Kovacheva, J. Matthew Kynes, Robert W. Lekowski, Joyce Lo, Jeffrey Lu, Alvaro A. Macias, Zahra M. Malik, Erich N. Marks, Brendan McGinn, Jonathan R. Meserve, Annette Mizuguchi, Srdjan S. Nedeljkovic, Ju-Mei Ng, Michael Nguyen, Olutoyin Okanlawon, Jennifer Oliver, Krishna Parekh, Jessica Patterson, Christian Peccora, Pete Pelletier, Sujatha Pentakota, James H. Philip, Marc Philip T. Pimentel, Timothy D. Quinn, Elizabeth M. Rickerson, Susan L. Sager, Julia Serber, Shaheen Shaikh, Stanton Shernan, David Silver, Alissa Sodickson, Pingping Song, George P. Topulos, Agnieszka Trzcinka, Richard D. Urman, Rosemary Uzomba, Joshua Vacanti, Assia Valovska, Michael Vaninetti, Scott W. Vaughan, Kamen Vlassakov, Christopher Voscopoulos, Emily L. Wang, Laura Westfall, Zhiling Xiong, Stephanie Yacoubian, Dongdong Yao, Martin Zammert, Maksim Zayaruzny, Jose Luis Zeballos, Natthasorn Zinboonyahgoon, Jie Zhou
- Edited by Linda S. Aglio, Robert W. Lekowski, Richard D. Urman
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- Book:
- Essential Clinical Anesthesia Review
- Published online:
- 05 February 2015
- Print publication:
- 08 January 2015, pp xi-xvi
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Contributors
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- By Shamsuddin Akhtar, Greg Albert, Sidney Allison, Muhammad Anwar, Haruo Arita, Amanda Barker, Mary Hanna Bekhit, Jeanna Blitz, Tyson Bolinske, David Burbulys, Asokumar Buvanendran, Gregory Cain, Keith A. Candiotti, Daniel B. Carr, Derek Chalmers, John Charney, Rex Cheng, Roger Chou, Keun Sam Chung, Anna Clebone, Frederick Conlin, Susan Dabu-Bondoc, Tiffany Denepitiya-Balicki, Jeanette Derdemezi, Anahat Kaur Dhillon, Ho Dzung, Juan Jose Egas, Stephen M. Eskaros, Zhuang T. Fang, Claudia R. Fernandez Robles, Victor A. Filadora, Ellen Flanagan, Dan Froicu, Allison Gandey, Nehal Gatha, Boris Gelman, Christopher Gharibo, Muhammad K. Ghori, Brian Ginsberg, Michael E. Goldberg, Jeff Gudin, Thomas Halaszynski, Martin Hale, Dorothea Hall, Craig T. Hartrick, Justin Hata, Lars E. Helgeson, Joe C. Hong, Richard W. Hong, Balazs Horvath, Eric S. Hsu, Gabriel Jacobs, Jonathan S. Jahr, Rongjie Jaing, Inderjeet Singh Julka, Zeev N. Kain, Clinton Kakazu, Kianusch Kiai, Mary Keyes, Michael M. Kim, Peter G. Lacouture, Ryan Lanier, Vivian K. Lee, Mark J. Lema, Oscar A. de Leon-Casasola, Imanuel Lerman, Philip Levin, Steven Levin, JinLei Li, Eric C. Lin, Sharon Lin, David A. Lindley, Ana M. Lobo, Marisa Lomanto, Mirjana Lovrincevic, Brenda C. McClain, Tariq Malik, Jure Marijic, Joseph Marino, Laura Mechtler, Alan Miller, Carly Miller, Amit Mirchandani, Sukanya Mitra, Fleurise Montecillo, James M. Moore, Debra E. Morrison, Philip F. Morway, Carsten Nadjat-Haiem, Hamid Nourmand, Dana Oprea, Sunil J. Panchal, Edward J. Park, Kathleen Ji Park, Kellie Park, Parisa Partownavid, Akta Patel, Bijal Patel, Komal D. Patel, Neesa Patel, Swati Patel, Paul M. Peloso, Danielle Perret, Anthony DePlato, Marjorie Podraza Stiegler, Despina Psillides, Mamatha Punjala, Johan Raeder, Siamak Rahman, Aziz M. Razzuk, Maggy G. Riad, Kristin L. Richards, R. Todd Rinnier, Ian W. Rodger, Joseph Rosa, Abraham Rosenbaum, Alireza Sadoughi, Veena Salgar, Leslie Schechter, Michael Seneca, Yasser F. Shaheen, James H. Shull, Elizabeth Sinatra, Raymond S. Sinatra, Neil Singla, Neil Sinha, Denis V. Snegovskikh, Dmitri Souzdalnitski, Julie Sramcik, Zoreh Steffens, Alexander Timchenko, Vadim Tokhner, Marc C. Torjman, Co T. Truong, Nalini Vadivelu, Ashley Vaughn, Anjali Vira, Eugene R. Viscusi, Dajie Wang, Shu-ming Wang, J. Michael Watkins-Pitchford, Steven J. Weisman, Ira Whitten, Bryan S. Williams, Jeremy M. Wong, Thomas Wong, Christopher Wray, Yaw Wu, Anthony T. Yarussi, Laurie Yonemoto, Bita H. Zadeh, Jill Zafar, Martha Zegarra, Keren Ziv
- Edited by Raymond S. Sinatra, Jonathan S. Jahr, University of California, Los Angeles, School of Medicine, J. Michael Watkins-Pitchford
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- Book:
- The Essence of Analgesia and Analgesics
- Published online:
- 06 December 2010
- Print publication:
- 14 October 2010, pp xi-xviii
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20 - COMPLICATIONS IN LAPAROSCOPY
- Camran Nezhat, Stanford University School of Medicine, California, Farr Nezhat, Mount Sinai School of Medicine, New York, Ceana Nezhat
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- Book:
- Nezhat's Operative Gynecologic Laparoscopy and Hysteroscopy
- Published online:
- 23 December 2009
- Print publication:
- 07 July 2008, pp 520-536
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Summary
INTRODUCTION
Laparoscopy is an accepted method of treatment in gynecology, general surgery, urology, and pediatric surgery. It is generally safe, is usually well tolerated by patients, and, when compared to its open surgical counterpart, offers the advantages of less postoperative pain, reduced surgical trauma, and a shortened postoperative hospital stay. However, as with any surgical procedure, laparoscopy has technique-related complications. One of these complications is major vascular injury (MVI), of which consequences can be quite serious. Injuries to the large vessels (aorta, vena cava, iliac vessels, and mesenteric vessels) are commonly referred to as MVI and occur in a variety of laparoscopic fields (see Figure 20.1.1, Table 20.1.1). Many of these injuries occur while inserting the Veress needle and/or trocars through the abdominal wall and, as a result, do not occur in conventional procedures. While the reported incidence may be low, ranging from 0.05% to 0.14%, the mortality arising from these injuries is substantially higher and has been reported to reach up to 17% (see Table 20.1.2). Therefore, the rare occurrence of MVI carries with it the risk of a potentially catastrophic outcome.
INCIDENCE
MVI can occur in laparoscopic surgery during the early maneuvers required to enter the peritoneal cavity, or during the surgical dissection required for the specific procedure.
10 - ENDOMETRIOSIS
- Camran Nezhat, Stanford University School of Medicine, California, Farr Nezhat, Mount Sinai School of Medicine, New York, Ceana Nezhat
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- Book:
- Nezhat's Operative Gynecologic Laparoscopy and Hysteroscopy
- Published online:
- 23 December 2009
- Print publication:
- 07 July 2008, pp 251-303
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Summary
Endometriosis is classically defined as the presence of endometrial glands and stroma in ectopic locations. Affecting from 6% to 10% of reproductive-aged women, endometriosis may result in dysmenorrhea, dyspareunia, chronic pelvic pain, and/or subfertility. The prevalence of this condition in women experiencing pain, infertility, or both is as high as 50%. Endometriosis is a debilitating condition, posing quality-of-life issues for the individual patient. The disorder represents a major cause of gynecologic hospitalization in the United States, estimated to have exceeded $3 billion in inpatient health care costs in 2004 alone. The significant individual and public health concerns associated with endometriosis underscore the importance of understanding its pathogenesis. The first recorded description of pathology consistent with endometriosis was provided by Shroen in 1690. Despite the passage of time and extensive investigation, the exact pathogenesis of this enigmatic disorder remains unknown.
THEORIES REGARDING PATHOGENESIS
Numerous theories detailing the development of endometriosis have been described. For purposes of review, these theories can generally be classified into those that propose that implants arise from tissues other than the endometrium and those that propose that implants arise from uterine endometrium (Table 10.1.1).
Nonendometrial Origin
Metaplasia of coelomic epithelium represents a distinct pathogenic mechanism for the establishment of endometriotic implants.