Improving Prognostic Accuracy in Subjects at Clinical High Risk for Psychosis: Systematic Review of Predictive Models and Meta-analytical Sequential Testing Simulation

Schizophrenia Bulletin

Volumn 43, Issue 2, 2017, Pages 375-388

  Schmidt, André ^a   Cappucciati, Marco ^a,b   Radua, Joaquim ^a,c,d   Rutigliano, Grazia ^a,e   Rocchetti, Matteo ^a,b   Dell'Osso, Liliana ^e   Politi, Pierluigi ^b   Borgwardt, Stefan ^a,f   Reilly, Thomas ^a   Valmaggia, Lucia ^a   McGuire, Philip ^a,g   Fusar Poli, Paolo ^a,g  

^a KING'S COLLEGE LONDON   (United Kingdom)

^b UNIVERSITY OF PAVIA   (Italy)

^c CENTRO DE INVESTIGACIÓN BIOMÉDICA EN RED DE SALUD MENTAL CIBERSAM   (Spain)

^d KAROLINSKA INSTITUTE   (Sweden)

^e UNIVERSITY OF PISA   (Italy)

^f UNIVERSITY OF BASEL   (Switzerland)

^g SOUTH LONDON AND MAUDSLEY NHS FOUNDATION TRUST   (United Kingdom)

Author keywords

clinical high risk; early interventions; prediction; prognostic accuracy; psychosis; treatment prognosis

Indexed keywords

DIAGNOSTIC IMAGING; HUMAN; META ANALYSIS; PATHOPHYSIOLOGY; PROGNOSIS; PSYCHOTIC DISORDERS; THEORETICAL MODEL;

HUMANS; MODELS, THEORETICAL; PROGNOSIS; PSYCHOTIC DISORDERS;

EID: 85018281114     PISSN: 05867614     EISSN: 17451701     Source Type: Journal    
DOI: 10.1093/schbul/sbw098     Document Type: Article

References (68)

1. Fusar-Poli P, Borgwardt S, Bechdolf A, et al. The psychosis high-risk state: A comprehensive state-of-The-Art review. JAMA Psychiatry. 2013;70:107 120.
2. van der Gaag M, Smit F, Bechdolf A, et al. Preventing a first episode of psychosis: meta-Analysis of randomized controlled prevention trials of 12 month and longer-Term followups. Schizophr Res. 2013;149:56 62.
3. Stafford MR, Jackson H, Mayo-Wilson E, Morrison AP, Kendall T. Early interventions to prevent psychosis: systematic review and meta-Analysis. BMJ. 2013;346:f185.
4. Fusar-Poli P, Cappucciati M, Rutigliano G, et al. At risk or not at risk? A meta-Analysis of the prognostic accuracy of psychometric interviews for psychosis prediction. World Psychiatry. 2015;14:322 332.
5. Fusar-Poli P, Carpenter WT, Woods SW, McGlashan TH. Attenuated psychosis syndrome: ready for DSM-5.1? Annu Rev Clin Psychol. 2014;10:155 192.
6. Fusar-Poli P, Bechdolf A, Taylor MJ, et al. At risk for schizophrenic or affective psychoses? A meta-Analysis of DSM/ ICD diagnostic outcomes in individuals at high clinical risk. Schizophr Bull. 2013;39:923 932.
7. Fusar-Poli P, Schultze-Lutter F. Predicting the onset of psychosis in patients at clinical high risk: practical guide to probabilistic prognostic reasoning. Evid Based Ment Health. 2016;19:10 15.
8. Fusar-Poli P, Schultze-Lutter F, Cappucciati M, et al. The dark side of the moon: Meta-Analytical impact of recruitment strategies on risk enrichment in the clinical high risk state for psychosis. Schizophr Bull. 2016;42:732 743.
9. Carpenter WT, Tandon R. Psychotic disorders in DSM-5: summary of changes. Asian J Psychiatr. 2013;6:266 268.
10. Arbyn M, Verdoodt F, Snijders PJ, et al. Accuracy of human papillomavirus testing on self-collected versus clinician-collected samples: A meta-Analysis. Lancet Oncol. 2014;15:172 183.
11. Ma MK, Leung AY, Lo KY, et al. Screening algorithm for BK virus-Associated nephropathy using sequential testing of urinary cytology: A probabilistic model analysis. Am J Nephrol. 2015;42:410 417.
12. Millan MJ, Andrieux A, Bartzokis G, et al. Altering the course of schizophrenia: progress and perspectives. Nat Rev Drug Discov. 2016; 15:485 515.
13. van der Ploeg T, Austin PC, Steyerberg EW. Modern modelling techniques are data hungry: A simulation study for predicting dichotomous endpoints. BMC Med Res Methodol. 2014;14:137.
14. Musoro JZ, Zwinderman AH, Puhan MA, ter Riet G, Geskus RB. Validation of prediction models based on lasso regression with multiply imputed data. BMC Med Res Methodol. 2014;14:116.
15. George B, Seals S, Aban I. Survival analysis and regression models. J Nucl Cardiol. 2014;21:686 694.
16. Tuffery P, Guyon F, Derreumaux P. Improved greedy algorithm for protein structure reconstruction. J Comput Chem. 2005;26:506 513.
17. Moher D, Liberati A, Tetzlaff J, Altman DG, Group P. Preferred reporting items for systematic reviews and metaanalyses: The PRISMA statement. BMJ (Clinical research ed). 2009;339:b2535.
18. Clark SR, Schubert KO, Baune BT. Towards indicated prevention of psychosis: using probabilistic assessments of transition risk in psychosis prodrome. J Neural Transm (Vienna). 2015;122:155 169.
19. Justice AC, Covinsky KE, Berlin JA. Assessing the generalizability of prognostic information. Ann Intern Med. 1999;130:515 524.
20. Altman DG, Royston P. What do we mean by validating a prognostic model? Stat Med. 2000;19:453 473.
21. R Development Core Team. R: A Language and Environment for Statistical Computing. Version 3.0 for Linux. Vienna, Austria: R Foundation for Statistical Computing. 22. Nieman DH, Ruhrmann S, Dragt S, et al. Psychosis prediction: stratification of risk estimation with information-processing and premorbid functioning variables. Schizophr Bull. 2014;40:1482 1490.
22. Koutsouleris N, Riecher-Rossler A, Meisenzahl EM, et al. Detecting the psychosis prodrome across high-risk populations using neuroanatomical biomarkers. Schizophr Bull. 2015;41:471 482.
23. Perkins DO, Jeffries CD, Addington J, et al. Towards a psychosis risk blood diagnostic for persons experiencing highrisk symptoms: preliminary results from the NAPLS project. Schizophr Bull. 2015;41:419 428.
24. Fusar-Poli P, Cappucciati M, Bonoldi I, et al. Prognosis of brief psychotic episodes: A meta-Analysis. JAMA Psychiatry. 2016;73:211 220.
25. Fusar-Poli P, Cappucciati M, Borgwardt S, et al. Heterogeneity of psychosis risk within individuals at clinical high risk: A meta-Analytical stratification. JAMA Psychiatry. 2016;73:113 120.
26. Mason O, Startup M, Halpin S, Schall U, Conrad A, Carr V. Risk factors for transition to first episode psychosis among individuals with at-risk mental states . Schizophr Res. 2004;71:227 237.
27. Lencz T, Smith CW, McLaughlin D, et al. Generalized and specific neurocognitive deficits in prodromal schizophrenia. Biol Psychiatry. 2006;59:863 871.
28. Hoffman RE, Woods SW, Hawkins KA, et al. Extracting spurious messages from noise and risk of schizophrenia-spectrum disorders in a prodromal population. Br J Psychiatry. 2007;191:355 356.
29. Pukrop R, Ruhrmann S, Schultze-Lutter F, Bechdolf A, Brockhaus-Dumke A, Klosterkotter J. Neurocognitive indicators for a conversion to psychosis: comparison of patients in a potentially initial prodromal state who did or did not convert to a psychosis. Schizophr Res. 2007;92:116 125.
30. Cannon TD, Cadenhead K, Cornblatt B, et al. Prediction of psychosis in youth at high clinical risk: A multisite longitudinal study in North America. Arch Gen Psychiatry. 2008;65:28 37.
31. Riecher-Rossler A, Pflueger MO, Aston J, et al. Efficacy of using cognitive status in predicting psychosis: A 7-year followup. Biol Psychiatry. 2009;66:1023 1030.
32. Fusar-Poli P, Byrne M, Valmaggia L, et al. Social dysfunction predicts two years clinical outcome in people at ultra high risk for psychosis. J Psychiatr Res. 2010;44:294 301.
33. Dragt S, Nieman DH, Veltman D, et al. Environmental factors and social adjustment as predictors of a first psychosis in subjects at ultra high risk. Schizophr Res. 2011;125:69 76.
34. Koutsouleris N, Davatzikos C, Bottlender R, et al. Early recognition and disease prediction in the at-risk mental states for psychosis using neurocognitive pattern classification. Schizophr Bull. 2012;38:1200 1215.
35. Nelson B, Yuen HP, Wood SJ, et al. Long-Term follow-up of a group at ultra high risk ("prodromal") for psychosis: The PACE 400 study. JAMA Psychiatry. 2013;70:793 802.
36. Nieman DH, Velthorst E, Becker HE, et al. The Strauss and Carpenter Prognostic Scale in subjects clinically at high risk of psychosis. Acta Psychiatr Scand. 2013;127:53 61.
37. Tarbox SI, Addington J, Cadenhead KS, et al. Premorbid functional development and conversion to psychosis in clinical high-risk youths. Dev Psychopathol. 2013;25:1171 1186.
38. van Tricht MJ, Ruhrmann S, Arns M, et al. Can quantitative EEG measures predict clinical outcome in subjects at Clinical High Risk for psychosis? A prospective multicenter study. Schizophr Res. 2014;153:42 47.
39. Ziermans T, de Wit S, Schothorst P, et al. Neurocognitive and clinical predictors of long-Term outcome in adolescents at ultra-high risk for psychosis: A 6-year follow-up. PLoS One. 2014;9:e93994.
40. Michel C, Ruhrmann S, Schimmelmann BG, Klosterkotter J, Schultze-Lutter F. A stratified model for psychosis prediction in clinical practice. Schizophr Bull. 2014;40:1533 1542.
41. DeVylder JE, Muchomba FM, Gill KE, et al. Symptom trajectories and psychosis onset in a clinical high-risk cohort: The relevance of subthreshold thought disorder. Schizophr Res. 2014;159:278 283.
42. Buchy L, Perkins D, Woods SW, Liu L, Addington J. Impact of substance use on conversion to psychosis in youth at clinical high risk of psychosis. Schizophr Res. 2014;156:277 280.
43. van Tricht MJ, Nieman DH, Koelman JH, et al. Reduced parietal P300 amplitude is associated with an increased risk for a first psychotic episode. Biol Psychiatry. 2010;68:642 648.
44. Cornblatt BA, Carrion RE, Auther A, et al. Psychosis prevention: A modified clinical high risk perspective from the recognition and prevention (RAP) program. Am J Psychiatry. 2015;172:986 994.
45. Ruhrmann S, Schultze-Lutter F, Salokangas RK, et al. Prediction of psychosis in adolescents and young adults at high risk: results from the prospective European prediction of psychosis study. Arch Gen Psychiatry. 2010;67:241 251.
46. Ramyead A, Studerus E, Kometer M, et al. Prediction of psychosis using neural oscillations and machine learning in neuroleptic-naive at-risk patients. World J Biol Psychiatry. 2016;17:285 295.
47. Bearden CE, Wu KN, Caplan R, Cannon TD. Thought disorder and communication deviance as predictors of outcome in youth at clinical high risk for psychosis. J Am Acad Child Adolesc Psychiatry. 2011;50:669 680.
48. Stephan BC, Kurth T, Matthews FE, Brayne C, Dufouil C. Dementia risk prediction in the population: Are screening models accurate? Nat Rev Neurol. 2010;6:318 326.
49. Pettersson-Yeo W, Benetti S, Marquand AF, et al. Using genetic, cognitive and multi-modal neuroimaging data to identify ultra-high-risk and first-episode psychosis at the individual level. Psychol Med. 2013;43:2547 2562.
50. Slaets S, Le Bastard N, Martin JJ, et al. Cerebrospinal fluid A 1-40 improves differential dementia diagnosis in patients with intermediate P-Tau181P levels. J Alzheimers Dis. 2013;36:759 767.
51. Schmidt A, Bachmann R, Kometer M, et al. Mismatch negativity encoding of prediction errors predicts S-ketamineinduced cognitive impairments. Neuropsychopharmacology. 2012;37:865 875.
52. Bodatsch M, Brockhaus-Dumke A, Klosterkotter J, Ruhrmann S. Forecasting psychosis by event-related potentials-systematic review and specific meta-Analysis. Biol Psychiatry. 2015;77:951 958.
53. Owen AM, McMillan KM, Laird AR, Bullmore E. N-back working memory paradigm: A meta-Analysis of normative functional neuroimaging studies. Hum Brain Mapp. 2005;25:46 59.
54. Pflueger MO, Gschwandtner U, Stieglitz RD, Riecher-Rossler A. Neuropsychological deficits in individuals with an at risk mental state for psychosis-working memory as a potential trait marker. Schizophr Res. 2007;97:14 24.
55. Leeson VC, Barnes TR, Harrison M, et al. The relationship between IQ, memory, executive function, and processing speed in recent-onset psychosis: 1-year stability and clinical outcome. Schizophr Bull. 2010;36:400 409.
56. Flatow J, Buckley P, Miller BJ. Meta-Analysis of oxidative stress in schizophrenia. Biol Psychiatry. 2013;74:400 409.
57. Kirkpatrick B, Miller BJ. Inflammation and schizophrenia. Schizophr Bull. 2013;39:1174 1179.
58. Anderson G, Berk M, Dodd S, et al. Immuno-inflammatory, oxidative and nitrosative stress, and neuroprogressive pathways in the etiology, course and treatment of schizophrenia. Prog Neuropsychopharmacol Biol Psychiatry. 2013;42:1 4.
59. Walker EF, Trotman HD, Pearce BD, et al. Cortisol levels and risk for psychosis: initial findings from the North American prodrome longitudinal study. Biol Psychiatry. 2013;74:410 417.
60. Goldstein JM, Seidman LJ, Makris N, et al. Hypothalamic abnormalities in schizophrenia: sex effects and genetic vulnerability. Biol Psychiatry. 2007;61:935 945.
61. Nordholm D, Krogh J, Mondelli V, Dazzan P, Pariante C, Nordentoft M. Pituitary gland volume in patients with schizophrenia, subjects at ultra high-risk of developing psychosis and healthy controls: A systematic review and meta-Analysis. Psychoneuroendocrinology. 2013;38:2394 2404.
62. Fusar-Poli P, Nelson B, Valmaggia L, Yung AR, McGuire PK. Comorbid depressive and anxiety disorders in 509 individuals with an at-risk mental state: impact on psychopathology and transition to psychosis. Schizophr Bull. 2014;40:120 131.
63. Modinos G, Allen P, Frascarelli M, et al. Are we really mapping psychosis risk? Neuroanatomical signature of affective disorders in subjects at ultra high risk. Psychol Med. 2014;44:3491 3501.
64. Kempton MJ, Bonoldi I, Valmaggia L, McGuire P, Fusar-Poli P. Speed of psychosis progression in people at ultrahigh clinical risk: A complementary meta-Analysis. JAMA Psychiatry. 2015;72:622 623.
65. Carrion RE, McLaughlin D, Goldberg TE, et al. Prediction of functional outcome in individuals at clinical high risk for psychosis. JAMA Psychiatry. 2013;70:1133 1142.
66. Addington J, Cornblatt BA, Cadenhead KS, et al. At clinical high risk for psychosis: outcome for nonconverters. Am J Psychiatry. 2011;168:800 805.
67. Mechelli A, Prata D, Kefford C, Kapur S. Predicting clinical response in people at ultra-high risk of psychosis: A systematic and quantitative review. Drug Discov Today. 2015;20:924 927.
68. Macqueen GM. Will there be a role for neuroimaging in clinical psychiatry? J Psychiatry Neurosci. 2010;35:291 293.