Personalized Prediction of Psychosis: External Validation of the NAPLS-2 Psychosis Risk Calculator With the EDIPPP Project
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OBJECTIVE: As part of the second phase of the North American Prodrome Longitudinal Study (NAPLS-2), Cannon and colleagues report, concurrently with the present article, on a risk calculator for the individualized prediction of a psychotic disorder in a 2-year period. The present study represents an external validation of the NAPLS-2 psychosis risk calculator using an independent sample of patients at clinical high risk for psychosis collected as part of the Early Detection, Intervention, and Prevention of Psychosis Program (EDIPPP). METHOD: Of the total EDIPPP sample of 210 subjects rated as being at clinical high risk based on the Structured Interview for Prodromal Syndromes, 176 had at least one follow-up assessment and were included in the construction of a new prediction model with six predictor variables in the NAPLS-2 psychosis risk calculator (unusual thoughts and suspiciousness, symbol coding test performance, verbal learning test performance, decline in social functioning, baseline age, and family history). Discrimination performance was assessed with the area under the receiver operating characteristic curve (AUC). The NAPLS-2 risk calculator was then used to generate a psychosis risk estimate for each case in the external validation sample. RESULTS: The external validation model showed good discrimination, with an AUC of 0.790 (95% CI=0.644-0.937). In addition, the personalized risk generated by the risk calculator provided a solid estimation of the actual conversion outcome in the validation sample. CONCLUSIONS: Two independent samples of clinical high-risk patients converge to validate the NAPLS-2 psychosis risk calculator. This prediction calculator represents a meaningful step toward early intervention and the personalized treatment of psychotic disorders.
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The Cannon et al. model has external validation AUCs of 0.79 (n=176, mean age 16.6, US), 0.63 (n=199, mean age 19.1, China), and 0.71 (n=98, mean age 18.59, US).
“176 (16.6, US); 0.79 (AUC) 292 199 (19.1, China); 0.63 (AUC) 293 98 (18.59, US); 0.71 (AUC) 294”
From Preventive psychiatry: a blueprint for improving the mental health of young people (2021) · cites this paper as [292]
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“This is a substantial advantage compared with the current CHR-P strategy, which is limited by group-level prediction, at risk or not at risk, with few exceptions such as the risk calculator by Cannon et al (58, 59).”
MethodResultZhang, T., Xu, L., Tang, Y., Li, H., Tang, X., Cui, H., Wei, Y., Wang, Y., Hu, Q., Liu, X., et al. (2019) · Psychological Medicine · doi:10.1017/s0033291718002738 · Cited by 82 · see citing works
“Moreover, the accuracy of the SIPS-RC reported in this study (AUC = 0.74) is comparable with the results of previous studies developing and evaluating the performance of the NAPLS-2 calculator (0.71 and 0.79, respectively) (Cannon et al., 2016; Carrion et al., 2016).”
MethodResultInfluentialWannan, C., Nelson, B., Addington, J., Allott, K., Anticevic, A., Arango, C., Baker, J. T., Bearden, C. E., Billah, T., Sylvain Bouix, A., et al. (2024) · Schizophrenia Bulletin · doi:10.1093/schbul/sbae011 · Cited by 90 · see citing works
Self-citationBirmaher, B., Merranko, J. A., Goldstein, T. R., Gill, M. K., Goldstein, B. I., Hower, H., Yen, S., Hafeman, D., Strober, M., Diler, R. S., et al. (2018) · Journal of the American Academy of Child & Adolescent Psychiatry · doi:10.1016/j.jaac.2018.05.023 · Cited by 81 · see citing works
‘At risk mental state’ clinics for psychosis – an idea whose time has come – and gone!
Ajnakina, O., David, A. S., & Murray, R. M. (2019) · Psychological Medicine · doi:10.1017/s0033291718003859 · Cited by 82 · see citing works
“Some more recent reports, however, have suggested that the transition rates from the ARMS phase to clinical psychosis are as low as 8–17% within a 2-year period (Morrison et al., 2012; Carrion et al., 2016; Conrad et al., 2017; Malla et al., 2017).”
Sanfelici, R., Dwyer, D. B., Antonucci, L. A., & Koutsouleris, N. (2020) · Biological Psychiatry · doi:10.1016/j.biopsych.2020.02.009 · Cited by 82 · see citing works
“(74) UHR Multimodal: symptoms, environment,”
The Maudsley environmental risk score for psychosis
Vassos, E., Sham, P. C., Kempton, M. J., Trotta, A., Stilo, S. A., Gayer-Anderson, C. E., Forti, M. D., Lewis, C. M., Murray, R. M., & Morgan, C. (2019) · Psychological Medicine · doi:10.1017/s0033291719002319 · Cited by 82 · see citing works
“Other existing risk calculators focus on the prediction of psychosis among individuals who have already developed a prodromal risk syndrome (Cannon et al., 2016; Carrion et al., 2016) or patients accessing secondary mental health care (Fusar-Poli et al.”
Ho, N. F., Holt, D. J., Cheung, M. W. ‐., Iglesias, J. E., Goh, A., Wang, M., Lim, J. K. W., de Souza, J., Poh, J. S., See, Y. M., et al. (2017) · Neuropsychopharmacology · doi:10.1038/npp.2017.5 · Cited by 80 · see citing works
“(Cannon et al, 2008; Yung et al, 2003), although they are similar to recent at-risk cohorts across multiple worldwide sites (Cannon et al, 2016; Carrion et al, 2016; Ruhrmann et al, 2010); indeed, rates of conversion have been steadily declining over the past decade to ~ 7–15% (FusarPoli et al,…”
ResultBrucato, G., Masucci, M. D., Arndt, L. Y., Ben‐David, S., Colibazzi, T., Corcoran, C. M., Crumbley, A. H., Crump, F. M., Gill, K. E., Kimhy, D., et al. (2017) · Psychological Medicine · doi:10.1017/s0033291717000319 · Cited by 78 · see citing works
“Some recent studies (Cannon et al. 2016; Carrion et al. 2016) have proposed combining current CHR criteria with specific demographic factors, as well as various neurocognitive, psychosocial and/or biometric assessments, to develop algorithms with improved predictive power.”
BackgroundZhang, T., Li, H., Tang, Y., Niznikiewicz, M. A., Shenton, M. E., Keshavan, M. S., Stone, W. S., McCarley, R. W., Seidman, L. J., & Wang, J. (2018) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2018.18010036 · Cited by 71 · see citing works
Hamilton, H., Roach, B. J., Bachman, P., Belger, A., Carrión, R. E., Duncan, E., Johannesen, J., Light, G. A., Niznikiewicz, M. A., Addington, J., et al. (2022) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2022.1417 · Cited by 72 · see citing works
Self-citationCollins, M. A., Ji, J. L., Chung, Y., Lympus, C. A., Afriyie-Agyemang, Y., Addington, J., Goodyear, B. G., Bearden, C. E., Cadenhead, K. S., Mirzakhanian, H., et al. (2022) · Molecular Psychiatry · doi:10.1038/s41380-022-01870-7 · Cited by 70 · see citing works
Self-citationPredictors of Transition to Psychosis in Individuals at Clinical High Risk
Addington, J., Farris, M. S., Stowkowy, J., Santesteban‐Echarri, O., Metzak, P. D., & Kalathil, M. S. (2019) · Current Psychiatry Reports · doi:10.1007/s11920-019-1027-y · Cited by 69 · see citing works
Sydnor, V. J., & Roalf, D. R. (2020) · Schizophrenia Research · doi:10.1016/j.schres.2020.06.028 · Cited by 68 · see citing works
Labad, J. (2018) · Psychoneuroendocrinology · doi:10.1016/j.psyneuen.2018.11.028 · Cited by 67 · see citing works
Bolt, L. K., Amminger, G. P., Farhall, J., McGorry, P. D., Nelson, B., Markulev, C., Yuen, H. P., Schäfer, M. R., Mossaheb, N., Schlögelhofer, M., et al. (2018) · Schizophrenia Research · doi:10.1016/j.schres.2018.12.013 · Cited by 66 · see citing works
Woods, S. W., Walsh, B. C., Powers, A. R., & McGlashan, T. H. (2019) · Springer eBooks · doi:10.1007/978-3-030-17336-4_5 · Cited by 65 · see citing works
Peh, O. H., Rapisarda, A., & Lee, J. (2019) · Psychological Medicine · doi:10.1017/s003329171800394x · Cited by 61 · see citing works
“This is because 6–17% of UHR individuals develop psychosis during the observational window (Cannon et al., 2016; Carrión et al., 2016), and this risk is significantly higher when individuals are not identified early to receive intervention services (Simon and Umbricht, 2010; Fusar-Poli et al.,…”
BackgroundKoutsouleris, N., Worthington, M., Dwyer, D., Kambeitz‐Ilankovic, L., Sanfelici, R., Fusar‐Poli, P., Rosen, M., Ruhrmann, S., Anticevic, A., Addington, J., et al. (2021) · Biological Psychiatry · doi:10.1016/j.biopsych.2021.06.023 · Cited by 58 · see citing works
Self-citationDean, D. J., Walther, S., Bernard, J. A., & Mittal, V. A. (2018) · Clinical Psychological Science · doi:10.1177/2167702618773759 · Cited by 56 · see citing works
“Each CHR participant was also rated on a recently developed calculation of probability for psychosis conversion (Cannon et al., 2016; Carrion et al., 2016) at the same time point as the motor assessment.”
Methodde Pablo, G. S., Besana, F., Arienti, V., Catalán, A., Vaquerizo‐Serrano, J., Cabras, A., Pereira, J. F., Soardo, L., Coronelli, F., Kaur, S., et al. (2021) · EClinicalMedicine · doi:10.1016/j.eclinm.2021.100909 · Cited by 57 · see citing works
“While a plethora of prediction models have been developed and validated on the transition to psychosis and psychosis onset in CHR-P [64-72], only a few have evaluated non-psychotic outcomes [73,74].”
BackgroundProgression from being at-risk to psychosis: next steps
Addington, J., Farris, M., Devoe, D., & Metzak, P. D. (2020) · npj Schizophrenia · doi:10.1038/s41537-020-00117-0 · Cited by 50 · see citing works
Chan, V. (2017) · Child and Adolescent Psychiatric Clinics of North America · doi:10.1016/j.chc.2016.12.014 · Cited by 55 · see citing works
Rosen, M., Betz, L. T., Schultze‐Lutter, F., Chisholm, K., Haidl, T. K., Kambeitz‐Ilankovic, L., Bertolino, A., Borgwardt, S., Brambilla, P., Lencer, R., et al. (2021) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2021.02.032 · Cited by 54 · see citing works
Prediction of conversion to psychosis in individuals with an at-risk mental state
Riecher‐Rössler, A., & Studerus, E. (2017) · Current Opinion in Psychiatry · doi:10.1097/yco.0000000000000320 · Cited by 53 · see citing works
Kline, E. R., Seidman, L. J., Cornblatt, B. A., Woodberry, K. A., Bryant, C., Bearden, C. E., Cadenhead, K. S., Cannon, T. D., Mathalon, D. H., McGlashan, T. H., et al. (2017) · Schizophrenia Research · doi:10.1016/j.schres.2017.05.032 · Cited by 55 · see citing works
“This work complements recent research establishing individualized risk prediction from clinical and neuropsychological factors that can easily be assessed in individuals meeting SIPS criteria for high risk states (see Cannon et al., 2016; Carrión et al., 2016).”
BackgroundSelf-citationA predictive model for conversion to psychosis in clinical high-risk patients
Ciarleglio, A., Brucato, G., Masucci, M. D., Altschuler, R., Colibazzi, T., Corcoran, C. M., Crump, F. M., Horga, G., Lehembre-Shiah, E., Leong, W., et al. (2018) · Psychological Medicine · doi:10.1017/s003329171800171x · Cited by 53 · see citing works
“developed a risk calculator that was published online and allows a clinician to obtain individualized 2-year conversion risks by way of an assessment battery that includes the SIPS, neurocognitive tests, and several clinical measures inclusive of stressful life events (Carrión et al., 2016).”
MethodOliver, D., Spada, G., Colling, C., Broadbent, M., Baldwin, H., Patel, R., Stewart, R., Stahl, D., Dobson, R., McGuire, P., et al. (2021) · Schizophrenia Research · doi:10.1016/j.schres.2020.05.007 · Cited by 49 · see citing works
Kafadar, E., Mittal, V. A., Strauss, G. P., Chapman, H. C., Ellman, L. M., Bansal, S., Gold, J. M., Alderson‐Day, B., Evans, S., Moffatt, J. A., et al. (2020) · Schizophrenia Research · doi:10.1016/j.schres.2020.04.017 · Cited by 48 · see citing works
“, 2016) and the use of clinical measures alone, while promising, is nonetheless limited inpredicting course and triggering treatment initiation (Cannon et al., 2016; Carrion et al., 2016).”
BackgroundLongitudinal perspectives on the psychosis spectrum in 22q11.2 deletion syndrome
Tang, S. X., & Gur, R. E. (2017) · American Journal of Medical Genetics Part A · doi:10.1002/ajmg.a.38500 · Cited by 48 · see citing works
“…to psychosis among individuals at elevated clinical risk by higher levels of unusual thought content and suspiciousness, greater decline in social functioning, younger baseline age, and impairments in verbal learning, memory, and processing speed (Cannon et al., 2016; Carrión et al., 2016).”
BackgroundCollin, G., Nieto-Castañón, A., Shenton, M. E., Pasternak, O., Kelly, S., Keshavan, M. S., Seidman, L. J., McCarley, R. W., Niznikiewicz, M. A., Li, H., et al. (2019) · NeuroImage Clinical · doi:10.1016/j.nicl.2019.102108 · Cited by 46 · see citing works
“This analysis showed that while the association between DMN hyperconnectivity and poor outcome is indeed driven by the converter group, the association between FPN hyperconnectivity and poor outcome seems to be driven mainly by the treatment-refractory group, who show significantly higher FPN within-network connectivi…”
BackgroundInfluentialPredictors of transition in patients with clinical high risk for psychosis: an umbrella review
Andreou, C., Eickhoff, S., Heide, M., de Bock, R., Obleser, J., & Borgwardt, S. (2023) · Translational Psychiatry · doi:10.1038/s41398-023-02586-0 · Cited by 37 · see citing works
“Large samples and harmonization of methods are necessary to overcome the limitations of a currently still very heterogeneous fi eld of research.”
BackgroundAlterations in facial expressivity in youth at clinical high-risk for psychosis.
Gupta, T., Haase, C. M., Strauss, G. P., Cohen, A. S., & Mittal, V. A. (2019) · Journal of Abnormal Psychology · doi:10.1037/abn0000413 · Cited by 44 · see citing works
“Carrión and colleagues (2016) also replicated these results in an independent sample using the same calculation procedure (Carrión et al., 2016).”
Worthington, M., & Cannon, T. D. (2021) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2021.770774 · Cited by 43 · see citing works
Neurocognitive profiles in the prodrome to psychosis in NAPLS-1
Velthorst, E., Meyer, E. C., Giuliano, A. J., Addington, J., Cadenhead, K. S., Cannon, T. D., Cornblatt, B. A., McGlashan, T. H., Perkins, D. O., Tsuang, M. T., et al. (2018) · Schizophrenia Research · doi:10.1016/j.schres.2018.07.038 · Cited by 44 · see citing works
“Advances have been made in developing “risk calculators” for the prediction of conversion to psychosis using combinations of symptoms, neurocognitive scores, social and role function measures and environmental variables (Cannon et al., 2016; Carrion et al., 2016; Michel et al., 2014), and some have begun to incorporat…”
BackgroundSelf-citationCui, H., Giuliano, A. J., Zhang, T., Xu, L., Wei, Y., Tang, Y., Qian, Z., Stone, L., Li, H., Whitfield, S., et al. (2020) · Schizophrenia Research · doi:10.1016/j.schres.2020.06.018 · Cited by 45 · see citing works
Psychosis superspectrum I: Nosology, etiology, and lifespan development
Jonas, K. G., Cannon, T. D., Docherty, A. R., Dwyer, D., Gur, R. C., Gur, R. E., Nelson, B., Reininghaus, U., & Kotov, R. (2024) · Molecular Psychiatry · doi:10.1038/s41380-023-02388-2 · Cited by 42 · see citing works
“Reality distortion symptoms are the strongest predictor of transition to psychotic disorder in CHR-P [166, 183], but personality disorders do not consistently predict transition [174].”
Woods, S. W., Bearden, C. E., Sabb, F. W., Stone, W. S., Torous, J. B., Cornblatt, B. A., Perkins, D. O., Cadenhead, K. S., Addington, J., Powers, A. R., et al. (2020) · Schizophrenia Research · doi:10.1016/j.schres.2020.04.020 · Cited by 40 · see citing works
Self-citationMalda, A., Boonstra, N., Barf, H., de Jong, S., Aleman, A., Addington, J., Pruessner, M., Nieman, D. H., de Haan, L., Morrison, A. P., et al. (2019) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2019.00345 · Cited by 39 · see citing works
“(16) [which has been externally validated (17)], the pretest risk enrichment stratification algorithm by Fusar-Poli et al.”
MethodUsing Electronic Health Records to Facilitate Precision Psychiatry
Oliver, D., Arribas, M., Perry, B. I., Whiting, D., Blackman, G., Krakowski, K., Seyedsalehi, A., Osimo, E. F., Griffiths, S. L., Stahl, D. R., et al. (2024) · Biological Psychiatry · doi:10.1016/j.biopsych.2024.02.1006 · Cited by 38 · see citing works
Hatzimanolis, A., Avramopoulos, D., Arking, D. E., Moes, A., Bhatnagar, P., Lencz, T., Malhotra, A. K., Giakoumaki, S. G., Roussos, P., Smyrnis, N., et al. (2017) · Schizophrenia Bulletin · doi:10.1093/schbul/sbx074 · Cited by 38 · see citing works
Youth at-risk for serious mental illness: methods of the PROCAN study
Addington, J., Goldstein, B. I., Wang, J., Kennedy, S. H., Bray, S., Lebel, C. A., Hassel, S., Marshall, C., & MacQUEEN, G. M. (2018) · BMC Psychiatry · doi:10.1186/s12888-018-1801-0 · Cited by 36 · see citing works
Fusar‐Poli, P., Stringer, D., Durieux, A. M. S., Rutigliano, G., Bonoldi, I., Micheli, A. D., & Stahl, D. R. (2019) · Translational Psychiatry · doi:10.1038/s41398-019-0600-9 · Cited by 37 · see citing works
Social decline in the psychosis prodrome: Predictor potential and heterogeneity of outcome
Carrión, R. E., Auther, A. M., McLaughlin, D., Addington, J., Bearden, C. E., Cadenhead, K. S., Cannon, T. D., Keshavan, M., Mathalon, D. H., McGlashan, T. H., et al. (2021) · Schizophrenia Research · doi:10.1016/j.schres.2020.09.006 · Cited by 36 · see citing works
“We propose that CHR heterogeneity can be used to refine risk profiles, point to false positive identifications, and potentially provide additional information about long-term functioning in addition to conversion to psychosis (Cornblatt and Carrión, 2016).”
BackgroundSelf-citationOsborne, K. J., & Mittal, V. A. (2019) · Psychiatry Research · doi:10.1016/j.psychres.2019.06.034 · Cited by 31 · see citing works
“On the basis of previous work showing good discrimination performance for both the NAPLS-2 and SIPS-RC in CHR samples (Cannon et al., 2016; Carrión et al., 2016; Zhang et al., 2018b), and discrimination performance less than chance (AUC < .”
MethodResultInfluentialLee, J., Jang, H. G., Kim, J.-K., & Min, S. (2019) · Scientific Reports · doi:10.1038/s41598-019-38886-z · Cited by 33 · see citing works
Zhang, T., Xu, L., Li, H., Woodberry, K. A., Kline, E. R., Jiang, J., Cui, H., Tang, Y., Tang, X., Wei, Y., et al. (2019) · Psychological Medicine · doi:10.1017/s003329171900360x · Cited by 34 · see citing works
“Consistent with the NAPLS-2-RC (Cannon et al., 2016; Carrion et al., 2016) and our previous findings (Li et al., 2018; Zhang et al., 2017), the baseline severity level of positive symptoms, global function decline, and the BACS neurocognitive tests were significant predictors of psychosis in the…”
BackgroundResultStuderus, E., Beck, K., Fusar‐Poli, P., & Riecher‐Rössler, A. (2019) · Schizophrenia Bulletin · doi:10.1093/schbul/sbz059 · Cited by 32 · see citing works
An Update on Promising Biomarkers in Schizophrenia
Goldsmith, D. R., Crooks, C. L., Walker, E. F., & Cotes, R. O. (2018) · FOCUS The Journal of Lifelong Learning in Psychiatry · doi:10.1176/appi.focus.20170046 · Cited by 30 · see citing works
Incorporating cortisol into the NAPLS2 individualized risk calculator for prediction of psychosis
Worthington, M., Walker, E. F., Addington, J., Bearden, C. E., Cadenhead, K. S., Cornblatt, B. A., Mathalon, D. H., McGlashan, T. H., Perkins, D. O., Seidman, L. J., et al. (2020) · Schizophrenia Research · doi:10.1016/j.schres.2020.09.022 · Cited by 31 · see citing works
“Although the original model has already been validated in an external sample and validity has been established (Carrión et al., 2016), in the present study, the models were evaluated with and without the same bootstrapping method for consistency.”
MethodSelf-citationOliver, D., Wong, C. M. J., Bøg, M., Jönsson, L., Kinon, B. J., Wehnert, A., Jørgensen, K. T., Irving, J., Stahl, D. R., McGuire, P. K., et al. (2020) · Translational Psychiatry · doi:10.1038/s41398-020-01032-9 · Cited by 27 · see citing works
Fortea, A., Batalla, A., Raduà, J., van Eijndhoven, P., Baeza, I., Albajes‐Eizagirre, A., Fusar‐Poli, P., Castro‐Fornieles, J., de la Serna, E., Luna, L., et al. (2021) · Schizophrenia Research · doi:10.1016/j.schres.2021.05.008 · Cited by 27 · see citing works
Enhancing Psychosis Risk Prediction Through Computational Cognitive Neuroscience
Gold, J. M., Corlett, P. R., Strauss, G. P., Schiffman, J. E., Ellman, L. M., Walker, E. F., Powers, A. R., Woods, S. W., Waltz, J. A., Silverstein, S. M., et al. (2020) · Schizophrenia Bulletin · doi:10.1093/schbul/sbaa091 · Cited by 26 · see citing works
MacNeill, L. A., Allen, N. B., Poleon, R. B., Vargas, T., Osborne, K. J., Damme, K. S. F., Barch, D. M., Krogh‐Jespersen, S., Nielsen, A. N., Norton, E. S., et al. (2021) · Development and Psychopathology · doi:10.1017/s0954579421000651 · Cited by 24 · see citing works
“…of the research on mental health risk calculators, albeit small, is on the conversion to psychosis from prodromal stages (Cannon et al., 2008, 2016; Carrión et al., 2016; Ciarleglio et al., 2019; Fusar-Poli, Davies et al., 2019; Fusar-Poli et al., 2017; Fusar-Poli, Werbeloff et al., 2019; Irving…”
BackgroundConrad, A. M., Lewin, T. J., Sly, K. A., Schall, U., Halpin, S. A., Hunter, M., & Carr, V. J. (2016) · Psychiatry Research · doi:10.1016/j.psychres.2016.12.005 · Cited by 25 · see citing works
“, 2015); personalised calculation of psychosis risk (Cannon et al., 2016; Carrión et al., 2016); non-transition and symptom remission (Lin et al.”
BackgroundHengartner, M. P., Heekeren, K., Dvorsky, D. N., Walitza, S., Wulf Rössler, A., & Theodoridou, A. (2017) · European Psychiatry · doi:10.1016/j.eurpsy.2017.05.026 · Cited by 26 · see citing works
“On a descriptive level, the prognostic accuracy of this model is markedly better than the 71% [17] and 79% [18] reported”
ResultPrediction of psychosis: model development and internal validation of a personalized risk calculator
Lee, T. Y., Hwang, W. J., Kim, N. S., Park, I., Lho, S. K., Moon, S.-Y., Oh, S., Lee, J., Kim, M., Woo, C.-W., et al. (2020) · Psychological Medicine · doi:10.1017/s0033291720004675 · Cited by 24 · see citing works
Community Psychosis Risk Screening: An Instrument Development Investigation
Ellman, L. M., Schiffman, J. E., & Mittal, V. A. (2020) · Journal of Psychiatry and Brain Science · doi:10.20900/jpbs.20200019 · Cited by 23 · see citing works
“Additionally, there are a number of other symptom domains and risk factors that are commonly found in both the prodromal and premorbid periods of psychosis, that likely can improve prediction of CHR for psychosis when assessed along with APPS (see Preliminary Studies) and have been demonstrated to improve prediction o…”
BackgroundReilly, T. J., Mechelli, A., McGuire, P. K., Fusar‐Poli, P., & Uhlhaas, P. J. (2019) · JMIR Mental Health · doi:10.2196/14581 · Cited by 21 · see citing works
“Carrión et al 2016 [54]”
BackgroundPersonalized medicine for schizophrenia
Buckley, P. F., & Miller, B. J. (2017) · Schizophrenia · doi:10.1038/s41537-016-0001-5 · Cited by 21 · see citing works
Puntis, S., Oliver, D., & Fusar-Poli, P. (2021) · Schizophrenia Research · doi:10.1016/j.schres.2021.01.005 · Cited by 21 · see citing works
Chung, Y., Addington, J., Bearden, C. E., Cadenhead, K. S., Cornblatt, B. A., Mathalon, D. H., McGlashan, T. H., Perkins, D. O., Seidman, L. J., Tsuang, M. T., et al. (2019) · Schizophrenia Research · doi:10.1016/j.schres.2019.01.026 · Cited by 20 · see citing works
Self-citationDiscovery and Validation of Prediction Algorithms for Psychosis in Youths at Clinical High Risk
Worthington, M., Cao, H., & Cannon, T. D. (2019) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2019.10.006 · Cited by 20 · see citing works
Osborne, K. J., Vargas, T., & Mittal, V. A. (2019) · Development and Psychopathology · doi:10.1017/s0954579419000385 · Cited by 18 · see citing works
“Discriminant validity for classifying CHR individuals who did and did not convert was demonstrated in a well-powered study (Cannon et al., 2016) that was replicated in an independent sample (Carrión et al., 2016).”
MethodResultKotlicka‐Antczak, M., Karbownik, M. S., Stawiski, K., Pawełczyk, A. M., Żurner, N., Pawełczyk, T., Strzelecki, D., & Fusar‐Poli, P. (2019) · European Psychiatry · doi:10.1016/j.eurpsy.2019.02.007 · Cited by 19 · see citing works
Zhang, T., Yang, S., Xu, L., Tang, X., Wei, Y., Cui, H., Li, H., Tang, Y., Hui, L., Li, C., et al. (2019) · Psychological Medicine · doi:10.1017/s0033291719002174 · Cited by 17 · see citing works
“Finally, as emphasized by Cannon (Cannon et al., 2016) and Carrion (Carrion et al., 2016), the risk calculator remains experimental.”
BackgroundWorthington, M., Miklowitz, D. J., O’Brien, M. P., Addington, J., Bearden, C. E., Cadenhead, K. S., Cornblatt, B. A., Mathalon, D. H., McGlashan, T. H., Perkins, D. O., et al. (2020) · Early Intervention in Psychiatry · doi:10.1111/eip.12914 · Cited by 17 · see citing works
“, 2016) which has been validated in external samples (Carrión et al., 2016; Zhang et al., 2018).”
BackgroundMethodSelf-citationGawęda, Ł., Pionke‐Ubych, R., Arciszewska, A., Prochwicz, K., Frydecka, D., Misiak, B., Cechnicki, A., Cicero, D. C., & Nelson, B. (2019) · Psychiatry Research · doi:10.1016/j.psychres.2019.01.044 · Cited by 17 · see citing works
“In the clinical context, it was shown that considering a combination of different risk factors increase the predictive power for the development of full-blown psychosis above the presence of subclinical psychotic symptoms (Cannon et al., 2016; Carrión et al., 2016).”
BackgroundMoore, T. M., Calkins, M. E., Rosen, A. F. G., Butler, E. R., Ruparel, K., Fusar‐Poli, P., Koutsouleris, N., McGuire, P. K., Cannon, T. D., Gur, R. C., et al. (2021) · Psychological Medicine · doi:10.1017/s0033291720005231 · Cited by 16 · see citing works
“This calculator was later replicated and extended by Carrión et al. (2016), Zhang et al. (2018) and Osborne and Mittal (2019).”
MethodCrouse, J. J., Ho, N., Scott, J., Parker, R., Park, S. H., Couvy-Duchesne, B., Mitchell, B. L., Byrne, E. M., Hermens, D. F., Medland, S. E., et al. (2022) · European Psychiatry · doi:10.1192/j.eurpsy.2022.23 · Cited by 14 · see citing works
“First, feeling threatened by others at 19Up was associated with progression to a psychotic-like subthreshold syndrome, consistent with reports from the USA [7, 40] and China [41] showing that higher levels of suspiciousness predict conversion from a clinical high risk state to a full-threshold psychotic disorder; it i…”
ResultInfluentialComputerized Assessment of Psychosis Risk
Mittal, V. A., Ellman, L. M., Strauss, G. P., Walker, E. F., Corlett, P. R., Schiffman, J. E., Woods, S. W., Powers, A. R., Silverstein, S. M., Waltz, J. A., et al. (2021) · Journal of Psychiatry and Brain Science · doi:10.20900/jpbs.20210011 · Cited by 15 · see citing works
“One important product from this effort is the North American Prodromal Longitudinal Study (NAPLS) risk-calculator [17,18], which is a tool that shows Mittal et al.”
MethodThe many roads to psychosis: recent advances in understanding risk and mechanisms
Bearden, C. E., & Forsyth, J. K. (2018) · F1000Research · doi:10.12688/f1000research.16574.1 · Cited by 15 · see citing works
Zhang, T., Xu, L., Tang, Y., Cui, H., Wei, Y., Tang, X., Hu, Q., Wang, Y., Zhu, Y., Jiang, L., et al. (2018) · Scientific Reports · doi:10.1038/s41598-018-32215-6 · Cited by 15 · see citing works
Guo, J. Y., Niendam, T. A., Auther, A. M., Carrión, R. E., Cornblatt, B. A., Ragland, J. D., Adelsheim, S. N., Calkins, R., Sale, T. G., Taylor, S. F., et al. (2019) · Psychological Medicine · doi:10.1017/s0033291719002332 · Cited by 17 · see citing works
Self-citationTarbox-Berry, S. I., Perkins, D. O., Woods, S. W., & Addington, J. (2018) · Psychological Medicine · doi:10.1017/s0033291717002343 · Cited by 14 · see citing works
“…prediction of unusual thought content and suspiciousness is particularly noteworthy given that these twopositive symptomsare central to theAPSSdiagnosis and are key predictors of psychosis in APSS youth (Cannon et al. 2008, 2016; Addington et al. 2015; Carrion et al. 2016; Brucato et al. 2017).”
BackgroundBhattacharyya, S., Schoeler, T., Patel, R., Forti, M. D., Murray, R. M., & McGuire, P. K. (2020) · Schizophrenia Research · doi:10.1016/j.schres.2020.09.016 · Cited by 14 · see citing works
Addington, J., Liu, L., Braun, A., Brummitt, K., Cadenhead, K. S., Cornblatt, B. A., Holden, J. L., & Granholm, E. (2023) · Schizophrenia Bulletin Open · doi:10.1093/schizbullopen/sgad020 · Cited by 14 · see citing works
Self-citationA genetics-guided approach to the clinical management of schizophrenia
Besterman, A. D. (2023) · Schizophrenia Research · doi:10.1016/j.schres.2023.09.042 · Cited by 15 · see citing works
Limited generalizability of multivariate brain-based dimensions of child psychiatric symptoms
Xu, B., Dall’Aglio, L., Flournoy, J. C., Bortsova, G., Tervo‐Clemmens, B., Collins, P. F., de Bruijne, M., Luciana, M. M., Marquand, A. F., Wang, H., et al. (2024) · Communications Psychology · doi:10.1038/s44271-024-00063-y · Cited by 14 · see citing works
“Thishasbeen widelyimplementedinthevalidationofpredictionmodelsinmedical research 22,23 and recommended as a necessary step in prediction models 24 .”
Mamah, D. T., Mutiso, V., & Ndetei, D. M. (2022) · Schizophrenia Research · doi:10.1016/j.schres.2022.01.031 · Cited by 13 · see citing works
“Other symptom domains such as negative symptoms or cognition are likely relevant and may increase the effectiveness of psychosis-risk screening tools (Cannon et al., 2008; Cannon et al., 2016; Carrion et al., 2016; Ellman et al., 2020; Woods et al., 2009).”
BackgroundLoch, A. A., Gondim, J. M., Argôlo, F. C., Lopes-Rocha, A. C., Andrade, J. C., van de Bilt, M. T., de Jesus, L. P., Haddad, N. M., Cecchi, G., Mota, N. B., et al. (2023) · Schizophrenia Research · doi:10.1016/j.schres.2023.07.011 · Cited by 14 · see citing works
“There are several studies addressing risk of conversion within ARMS samples — including the development of several risk calculators (Carri ´ on et al., 2016; Lee et al., 2020; Oliver et al., 2021; Zhang et al., 2019) — but those aiming to distinguish between ARMS and control individuals are…”
BackgroundResultZhang, T., Wei, Y., Cui, H., Tang, X., Xu, L., Hu, Y., Tang, Y., Liu, H., Chen, T., Li, C., et al. (2023) · Psychiatry Research · doi:10.1016/j.psychres.2023.115385 · Cited by 13 · see citing works
- Authors
- Carrión, R. E., Cornblatt, B. A., Burton, C. Z., Tso, I. F., Auther, A. M., Adelsheim, S., Calkins, R., Carter, C. S., Niendam, T., Sale, T. G., Taylor, S. F., & McFarlane, W. R.
- Affiliations
- Northwell Health (US)
- ORCID
- Venue
- American Journal of Psychiatry
- Published
- 2016-10-01
- Volume
- 173
- Issue
- 10
- Pages
- 989-996
- ISSN
- 0002-953X (ISSN-L)
- 1535-7228
- PMC
- PMC5048503
- OpenAlex
- W2462155653
- Cited by
- 170
- References
- 47
- Citations by year
- 2026: 4 · 2025: 15 · 2024: 7 · 2023: 13 · 2022: 12 · 2021: 16 · 2020: 24 · 2019: 30 · 2018: 21 · 2017: 21 · 2016: 7
- FWCI
- 23.39
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- American Journal of Psychiatry (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- PDXScholar (Portland State University) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- National Institutes of Health (KL2TR000434)
- National Institute of Mental Health (R01MH061523, P30MH074543, R01MH059883, K23MH087708, R21MH101676, K23MH108823, U01MH081857)
- National Center for Advancing Translational Sciences
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Schizophrenia research and treatment
- Social Sciences › Psychology › Clinical Psychology › Psychopathy, Forensic Psychiatry, Sexual Offending
- Health Sciences › Medicine › Psychiatry and Mental health › Bipolar Disorder and Treatment
- Keywords
- psychosis prediction, clinical high risk for psychosis, external validation, early detection and intervention, prodromal psychosis, transition to psychosis, suspicion, symbol coding, verbal learning, family history (medicine), personalized treatment
- MeSH terms
- Adolescent, Adult, Child, Female, Humans, Longitudinal Studies, Male, Models, Psychological, Predictive Value of Tests, Psychiatric Status Rating Scales, Psychotic Disorders, Risk Factors, Early Diagnosis, Young Adult, Prodromal Symptoms
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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