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: This article describes the rationale, aims, and methodology of the Accelerating Medicines Partnership® Schizophrenia (AMP® SCZ). This is the largest international collaboration to date that will develop algorithms to predict trajectories and outcomes of individuals at clinical high risk (CHR) for psychosis and to advance the development and use of novel pharmacological interventions for CHR individuals. We present a description of the participating research networks and the data processing analysis and coordination center, their processes for data harmonization across 43 sites from 13 participating countries (recruitment across North America, Australia, Europe, Asia, and South America), data flow and quality assessment processes, data analyses, and the transfer of data to the National Institute of Mental Health (NIMH) Data Archive (NDA) for use by the research community. In an expected sample of approximately 2000 CHR individuals and 640 matched healthy controls, AMP SCZ will collect clinical, environmental, and cognitive data along with multimodal biomarkers, including neuroimaging, electrophysiology, fluid biospecimens, speech and facial expression samples, novel measures derived from digital health technologies including smartphone-based daily surveys, and passive sensing as well as actigraphy. The study will investigate a range of clinical outcomes over a 2-year period, including transition to psychosis, remission or persistence of CHR status, attenuated positive symptoms, persistent negative symptoms, mood and anxiety symptoms, and psychosocial functioning. The global reach of AMP SCZ and its harmonized innovative methods promise to catalyze the development of new treatments to address critical unmet clinical and public health needs in CHR individuals.
Accelerating Medicines Partnership® Schizophrenia (AMP® SCZ): Rationale and Study Design of the Largest Global Prospective Cohort Study of Clinical High Risk for Psychosis
Wannan, Cassandra M J;Nelson, Barnaby
;Addington, Jean;Allott, Kelly;Anticevic, Alan;Arango, Celso;Baker, Justin T;Bearden, Carrie E;Billah, Tashrif;Bouix, Sylvain;Broome, Matthew R;Buccilli, Kate;Cadenhead, Kristin S;Calkins, Monica E;Cannon, Tyrone D;Cecci, Guillermo;Chen, Eric Yu Hai;Cho, Kang Ik K;Choi, Jimmy;Clark, Scott R;Coleman, Michael J;Conus, Philippe;Corcoran, Cheryl M;Cornblatt, Barbara A;Diaz-Caneja, Covadonga M;Dwyer, Dominic;Ebdrup, Bjørn H;Ellman, Lauren M;Fusar-Poli, Paolo;Galindo, Liliana;Gaspar, Pablo A;Gerber, Carla;Glenthøj, Louise Birkedal;Glynn, Robert;Harms, Michael P;Horton, Leslie E;Kahn, René S;Kambeitz, Joseph;Kambeitz-Ilankovic, Lana;Kane, John M;Kapur, Tina;Keshavan, Matcheri S;Kim, Sung-Wan;Koutsouleris, Nikolaos;Kubicki, Marek;Kwon, Jun Soo;Langbein, Kerstin;Lewandowski, Kathryn E;Light, Gregory A;Mamah, Daniel;Marcy, Patricia J;Mathalon, Daniel H;McGorry, Patrick D;Mittal, Vijay A;Nordentoft, Merete;Nunez, Angela;Pasternak, Ofer;Pearlson, Godfrey D;Perez, Jesus;Perkins, Diana O;Powers, Albert R;Roalf, David R;Sabb, Fred W;Schiffman, Jason;Shah, Jai L;Smesny, Stefan;Spark, Jessica;Stone, William S;Strauss, Gregory P;Tamayo, Zailyn;Torous, John;Upthegrove, Rachel;Vangel, Mark;Verma, Swapna;Wang, Jijun;Rossum, Inge Winter-van;Wolf, Daniel H;Wolff, Phillip;Wood, Stephen J;Yung, Alison R;Agurto, Carla;Alvarez-Jimenez, Mario;Amminger, Paul;Armando, Marco;Asgari-Targhi, Ameneh;Cahill, John;Carrión, Ricardo E;Castro, Eduardo;Cetin-Karayumak, Suheyla;Mallar Chakravarty, M;Cho, Youngsun T;Cotter, David;D'Alfonso, Simon;Ennis, Michaela;Fadnavis, Shreyas;Fonteneau, Clara;Gao, Caroline;Gupta, Tina;Gur, Raquel E;Gur, Ruben C;Hamilton, Holly K;Hoftman, Gil D;Jacobs, Grace R;Jarcho, Johanna;Ji, Jie Lisa;Kohler, Christian G;Lalousis, Paris Alexandros;Lavoie, Suzie;Lepage, Martin;Liebenthal, Einat;Mervis, Josh;Murty, Vishnu;Nicholas, Spero C;Ning, Lipeng;Penzel, Nora;Poldrack, Russell;Polosecki, Pablo;Pratt, Danielle N;Rabin, Rachel;Rahimi Eichi, Habiballah;Rathi, Yogesh;Reichenberg, Avraham;Reinen, Jenna;Rogers, Jack;Ruiz-Yu, Bernalyn;Scott, Isabelle;Seitz-Holland, Johanna;Srihari, Vinod H;Srivastava, Agrima;Thompson, Andrew;Turetsky, Bruce I;Walsh, Barbara C;Whitford, Thomas;Wigman, Johanna T W;Yao, Beier;Yuen, Hok Pan;Ahmed, Uzair;Byun, Andrew Jin Soo;Chung, Yoonho;Do, Kim;Hendricks, Larry;Huynh, Kevin;Jeffries, Clark;Lane, Erlend;Langholm, Carsten;Lin, Eric;Mantua, Valentina;Santorelli, Gennarina;Ruparel, Kosha;Zoupou, Eirini;Adasme, Tatiana;Addamo, Lauren;Adery, Laura;Ali, Munaza;Auther, Andrea;Aversa, Samantha;Baek, Seon-Hwa;Bates, Kelly;Bathery, Alyssa;Bayer, Johanna M M;Beedham, Rebecca;Bilgrami, Zarina;Birch, Sonia;Bonoldi, Ilaria;Borders, Owen;Borgatti, Renato;Brown, Lisa;Bruna, Alejandro;Carrington, Holly;Castillo-Passi, Rolando I;Chen, Justine;Cheng, Nicholas;Ching, Ann Ee;Clifford, Chloe;Colton, Beau-Luke;Contreras, Pamela;Corral, Sebastián;Damiani, Stefano;Done, Monica;Estradé, Andrés;Etuka, Brandon Asika;Formica, Melanie;Furlan, Rachel;Geljic, Mia;Germano, Carmela;Getachew, Ruth;Goncalves, Mathias;Haidar, Anastasia;Hartmann, Jessica;Jo, Anna;John, Omar;Kerins, Sarah;Kerr, Melissa;Kesselring, Irena;Kim, Honey;Kim, Nicholas;Kinney, Kyle;Krcmar, Marija;Kotler, Elana;Lafanechere, Melanie;Lee, Clarice;Llerena, Joshua;Markiewicz, Christopher;Matnejl, Priya;Maturana, Alejandro;Mavambu, Aissata;Mayol-Troncoso, Rocío;McDonnell, Amelia;McGowan, Alessia;McLaughlin, Danielle;McIlhenny, Rebecca;McQueen, Brittany;Mebrahtu, Yohannes;Mensi, Martina;Hui, Christy Lai Ming;Suen, Yi Nam;Wong, Stephanie Ming Yin;Morrell, Neal;Omar, Mariam;Partridge, Alice;Phassouliotis, Christina;Pichiecchio, Anna;Politi, Pierluigi;Porter, Christian;Provenzani, Umberto;Prunier, Nicholas;Raj, Jasmine;Ray, Susan;Rayner, Victoria;Reyes, Manuel;Reynolds, Kate;Rush, Sage;Salinas, Cesar;Shetty, Jashmina;Snowball, Callum;Tod, Sophie;Turra-Fariña, Gabriel;Valle, Daniela;Veale, Simone;Whitson, Sarah;Wickham, Alana;Youn, Sarah;Zamorano, Francisco;Zavaglia, Elissa;Zinberg, Jamie;Woods, Scott W;Shenton, Martha E
2024-01-01
Abstract
: This article describes the rationale, aims, and methodology of the Accelerating Medicines Partnership® Schizophrenia (AMP® SCZ). This is the largest international collaboration to date that will develop algorithms to predict trajectories and outcomes of individuals at clinical high risk (CHR) for psychosis and to advance the development and use of novel pharmacological interventions for CHR individuals. We present a description of the participating research networks and the data processing analysis and coordination center, their processes for data harmonization across 43 sites from 13 participating countries (recruitment across North America, Australia, Europe, Asia, and South America), data flow and quality assessment processes, data analyses, and the transfer of data to the National Institute of Mental Health (NIMH) Data Archive (NDA) for use by the research community. In an expected sample of approximately 2000 CHR individuals and 640 matched healthy controls, AMP SCZ will collect clinical, environmental, and cognitive data along with multimodal biomarkers, including neuroimaging, electrophysiology, fluid biospecimens, speech and facial expression samples, novel measures derived from digital health technologies including smartphone-based daily surveys, and passive sensing as well as actigraphy. The study will investigate a range of clinical outcomes over a 2-year period, including transition to psychosis, remission or persistence of CHR status, attenuated positive symptoms, persistent negative symptoms, mood and anxiety symptoms, and psychosocial functioning. The global reach of AMP SCZ and its harmonized innovative methods promise to catalyze the development of new treatments to address critical unmet clinical and public health needs in CHR individuals.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11571/1492975
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simulazione ASN
Il report seguente simula gli indicatori relativi alla propria produzione scientifica in relazione alle soglie ASN 2023-2025 del proprio SC/SSD. Si ricorda che il superamento dei valori soglia (almeno 2 su 3) è requisito necessario ma non sufficiente al conseguimento dell'abilitazione. La simulazione si basa sui dati IRIS e sugli indicatori bibliometrici alla data indicata e non tiene conto di eventuali periodi di congedo obbligatorio, che in sede di domanda ASN danno diritto a incrementi percentuali dei valori. La simulazione può differire dall'esito di un’eventuale domanda ASN sia per errori di catalogazione e/o dati mancanti in IRIS, sia per la variabilità dei dati bibliometrici nel tempo. Si consideri che Anvur calcola i valori degli indicatori all'ultima data utile per la presentazione delle domande.
La presente simulazione è stata realizzata sulla base delle specifiche raccolte sul tavolo ER del Focus Group IRIS coordinato dall’Università di Modena e Reggio Emilia e delle regole riportate nel DM 589/2018 e allegata Tabella A. Cineca, l’Università di Modena e Reggio Emilia e il Focus Group IRIS non si assumono alcuna responsabilità in merito all’uso che il diretto interessato o terzi faranno della simulazione. Si specifica inoltre che la simulazione contiene calcoli effettuati con dati e algoritmi di pubblico dominio e deve quindi essere considerata come un mero ausilio al calcolo svolgibile manualmente o con strumenti equivalenti.