BACKGROUND: Despite extensive early detection research in schizophrenic psychoses, methods for identifying at-risk individuals and predicting their transition to psychosis are still unreliable. Moreover, there are sparse data on long-term prediction. We therefore investigated long-term psychosis transition in individuals with an At Risk Mental State (ARMS) and examined the relative efficacy of clinical and neuropsychological status in optimizing the prediction of transition. METHODS: Sixty-four individuals with ARMS for psychosis were identified from all referrals to our early detection clinic between March 1, 2000 and February 29, 2004. Fifty-three (83%) were followed up for up to 7 (mean 5.4) years. RESULTS: Twenty-one of the 53 staying in follow-up developed psychosis, corresponding to a transition rate of .34 (Kaplan-Meier estimates). Median time to transition was 10 months (range <1-55). Six of all transitions (29%) occurred only after 12 months from referral. Best transition predictors within this population were selected attenuated psychotic symptoms (suspiciousness), negative symptoms (anhedonia/asociality), and cognitive deficits (reduced speed of information processing). With these predictors in an integrated model for predicting transition to psychosis, the overall predictive accuracy was 80.9% with a sensitivity of 83.3% and a specificity of 79.3%. CONCLUSIONS: Follow-up of ARMS subjects should exceed the usual 12 months. Prediction of transitions could be improved by a stronger weighting of certain early symptoms and by introducing neurocognitive tests into a stepwise risk assessment. Confirmatory research will hopefully further improve risk algorithm, including psychopathology and neuropsychological performance, for clinical application in early detection clinics.
BACKGROUND: Despite extensive early detection research in schizophrenic psychoses, methods for identifying at-risk individuals and predicting their transition to psychosis are still unreliable. Moreover, there are sparse data on long-term prediction. We therefore investigated long-term psychosis transition in individuals with an At Risk Mental State (ARMS) and examined the relative efficacy of clinical and neuropsychological status in optimizing the prediction of transition. METHODS: Sixty-four individuals with ARMS for psychosis were identified from all referrals to our early detection clinic between March 1, 2000 and February 29, 2004. Fifty-three (83%) were followed up for up to 7 (mean 5.4) years. RESULTS: Twenty-one of the 53 staying in follow-up developed psychosis, corresponding to a transition rate of .34 (Kaplan-Meier estimates). Median time to transition was 10 months (range <1-55). Six of all transitions (29%) occurred only after 12 months from referral. Best transition predictors within this population were selected attenuated psychotic symptoms (suspiciousness), negative symptoms (anhedonia/asociality), and cognitive deficits (reduced speed of information processing). With these predictors in an integrated model for predicting transition to psychosis, the overall predictive accuracy was 80.9% with a sensitivity of 83.3% and a specificity of 79.3%. CONCLUSIONS: Follow-up of ARMS subjects should exceed the usual 12 months. Prediction of transitions could be improved by a stronger weighting of certain early symptoms and by introducing neurocognitive tests into a stepwise risk assessment. Confirmatory research will hopefully further improve risk algorithm, including psychopathology and neuropsychological performance, for clinical application in early detection clinics.
Authors: Barbara A Cornblatt; Ricardo E Carrión; Jean Addington; Larry Seidman; Elaine F Walker; Tyronne D Cannon; Kristin S Cadenhead; Thomas H McGlashan; Diana O Perkins; Ming T Tsuang; Scott W Woods; Robert Heinssen; Todd Lencz Journal: Schizophr Bull Date: 2011-11-10 Impact factor: 9.306
Authors: Barnaby Nelson; G Paul Amminger; Hok Pan Yuen; Nicky Wallis; Melissa J Kerr; Lisa Dixon; Cameron Carter; Rachel Loewy; Tara A Niendam; Martha Shumway; Sarah Morris; Julie Blasioli; Patrick D McGorry Journal: Early Interv Psychiatry Date: 2017-07-18 Impact factor: 2.732
Authors: Barbara A Cornblatt; Ricardo E Carrión; Andrea Auther; Danielle McLaughlin; Ruth H Olsen; Majnu John; Christoph U Correll Journal: Am J Psychiatry Date: 2015-06-05 Impact factor: 18.112
Authors: Andrew D Thompson; Barnaby Nelson; Hok Pan Yuen; Ashleigh Lin; Günter Paul Amminger; Patrick D McGorry; Stephen J Wood; Alison R Yung Journal: Schizophr Bull Date: 2013-03-02 Impact factor: 9.306