Psychotic disorders as a framework for precision psychiatry

被引:19
作者
Coutts, Fiona [1 ]
Koutsouleris, Nikolaos [1 ,2 ,3 ]
McGuire, Philip [4 ,5 ]
机构
[1] Kings Coll London, Inst Psychiat Psychol & Neurosci, London, England
[2] Ludwig Maximilians Univ Munchen, Dept Psychiat & Psychotherapy, Munich, Germany
[3] Max Planck Inst Psychiat, Munich, Germany
[4] Univ Oxford, Dept Psychiat, Oxford, England
[5] NIHR Oxford Hlth Biomed Res Ctr, Oxford, England
关键词
CLINICAL HIGH-RISK; TREATMENT-RESISTANT SCHIZOPHRENIA; ULTRA-HIGH-RISK; DOPAMINE SYNTHESIS CAPACITY; 1ST EPISODE PSYCHOSIS; FUNCTIONAL CONNECTIVITY; ANTIPSYCHOTIC TREATMENT; FOLLOW-UP; 1ST-EPISODE PSYCHOSIS; PREDICTIVE-VALIDITY;
D O I
10.1038/s41582-023-00779-1
中图分类号
R74 [神经病学与精神病学];
学科分类号
摘要
People with psychotic disorders can show marked interindividual variations in the onset of illness, responses to treatment and relapse, but they receive broadly similar clinical care. Precision psychiatry is an approach that aims to stratify people with a given disorder according to different clinical outcomes and tailor treatment to their individual needs. At present, interindividual differences in outcomes of psychotic disorders are difficult to predict on the basis of clinical assessment alone. Therefore, current research in psychosis seeks to build models that predict outcomes by integrating clinical information with a range of biological measures. Here, we review recent progress in the application of precision psychiatry to psychotic disorders and consider the challenges associated with implementing this approach in clinical practice. In this Review, the authors discuss recent efforts to predict disease onset, treatment response and disease outcome in individuals with psychosis. They cover genetic, biological, clinical and environmental predictive factors and assess whether the variation in outcomes is attributable to differences in the pathophysiology of psychosis.
引用
收藏
页码:221 / 234
页数:14
相关论文
共 178 条
  • [1] Addington J, 2003, J PSYCHIATR NEUROSCI, V28, P93
  • [2] Predicting Early Warning Signs of Psychotic Relapse From Passive Sensing Data: An Approach Using Encoder-Decoder Neural Networks
    Adler, Daniel A.
    Ben-Zeev, Dror
    Tseng, Vincent W-S
    Kane, John M.
    Brian, Rachel
    Campbell, Andrew T.
    Hauser, Marta
    Scherer, Emily A.
    Choudhury, Tanzeem
    [J]. JMIR MHEALTH AND UHEALTH, 2020, 8 (08):
  • [3] Negative symptoms and impaired social functioning predict later psychosis in Latino youth at clinical high risk in the North American prodromal longitudinal studies consortium
    Alderman, Tracy
    Addington, Jean
    Bearden, Carrie
    Cannon, Tyrone D.
    Cornblatt, Barbara A.
    McGlashan, Thomas H.
    Perkins, Diana O.
    Seidman, Larry J.
    Tsuang, Ming T.
    Walker, Elaine F.
    Woods, Scott W.
    Cadenhead, Kristin S.
    [J]. EARLY INTERVENTION IN PSYCHIATRY, 2015, 9 (06) : 467 - 475
  • [4] Transition to Psychosis Associated With Prefrontal and Subcortical Dysfunction in Ultra High-Risk Individuals
    Allen, Paul
    Luigjes, Judy
    Howes, Oliver D.
    Egerton, Alice
    Hirao, Kazuyuki
    Valli, Isabel
    Kambeitz, Joseph
    Fusar-Poli, Paolo
    Broome, Matthew
    McGuire, Philip
    [J]. SCHIZOPHRENIA BULLETIN, 2012, 38 (06) : 1268 - 1276
  • [5] Heterogeneity in psychiatric diagnostic classification
    Allsopp, Kate
    Read, John
    Corcoran, Rhiannon
    Kinderrnan, Peter
    [J]. PSYCHIATRY RESEARCH, 2019, 279 : 15 - 22
  • [6] A Comparison of a Machine Learning Model with EuroSCORE II in Predicting Mortality after Elective Cardiac Surgery: A Decision Curve Analysis
    Allyn, Jerome
    Allou, Nicolas
    Augustin, Pascal
    Philip, Ivan
    Martinet, Olivier
    Belghiti, Myriem
    Provenchere, Sophie
    Montravers, Philippe
    Ferdynus, Cyril
    [J]. PLOS ONE, 2017, 12 (01):
  • [7] Risk factors for relapse following treatment for first episode psychosis: A systematic review and meta-analysis of longitudinal studies
    Alvarez-Jimenez, M.
    Priede, A.
    Hetrick, S. E.
    Bendall, S.
    Killackey, E.
    Parker, A. G.
    McGorry, P. D.
    Gleeson, J. F.
    [J]. SCHIZOPHRENIA RESEARCH, 2012, 139 (1-3) : 116 - 128
  • [8] A machine-learning framework for robust and reliable prediction of short- and long-term treatment response in initially antipsychotic-naive schizophrenia patients based on multimodal neuropsychiatric data
    Ambrosen, Karen S.
    Skjerbaek, Martin W.
    Foldager, Jonathan
    Axelsen, Martin C.
    Bak, Nikolaj
    Arvastson, Lars
    Christensen, Soren R.
    Johansen, Louise B.
    Raghava, Jayachandra M.
    Oranje, Bob
    Rostrup, Egill
    Nielsen, Mette O.
    Osler, Merete
    Fagerlund, Birgitte
    Pantelis, Christos
    Kinon, Bruce J.
    Glenthoj, Birte Y.
    Hansen, Lars K.
    Ebdrup, Bjorn H.
    [J]. TRANSLATIONAL PSYCHIATRY, 2020, 10 (01)
  • [9] American Psychiatric Association A, 1994, DIAGNOSTIC STAT MANU, DOI [10.1176/appi.books.9780890425596, DOI 10.1176/APPI.BOOKS.9780890425596]
  • [10] Factors affecting implementation of digital health interventions for people with psychosis or bipolar disorder, and their family and friends: a systematic review
    Aref-Adib, Golnar
    McCloud, Tayla
    Ross, Jamie
    O'Hanlon, Puffin
    Appleton, Victoria
    Rowe, Sarah
    Murray, Elizabeth
    Johnson, Sonia
    Lobban, Fiona
    [J]. LANCET PSYCHIATRY, 2019, 6 (03): : 257 - 266