Predicting the individual risk of psychosis conversion in at-risk mental state (ARMS): a multivariate model reveals the influence of nonpsychotic prodromal symptoms.
Journal:
European child & adolescent psychiatry
PMID:
31872289
Abstract
To improve the prediction of the individual risk of conversion to psychosis in UHR subjects, by considering all CAARMS' symptoms at first presentation and using a multivariate machine learning method known as logistic regression with Elastic-net shrinkage. 46 young individuals who sought help from the specialized outpatient unit at Sainte-Anne hospital and who met CAARMS criteria for UHR were assessed, among whom 27 were reassessed at follow-up (22.4 ± 6.54 months) and included in the analysis. Elastic net logistic regression was trained, using CAARMS items at baseline to predict individual evolution between converters (UHR-P) and non-converters (UHR-NP). Elastic-net was used to select the few CAARMS items that best predict the clinical evolution. All validations and significances of predictive models were computed with non-parametric re-sampling strategies that provide robust estimators even when the distributional assumption cannot be guaranteed. Among the 25 CAARMS items, the Elastic net selected 'obsessive-compulsive symptoms' and 'aggression/dangerous behavior' as risk factors for conversion while 'anhedonia' and 'mood swings/lability' were associated with non-conversion at follow-up. In the ten-fold stratified cross-validation, the classification achieved 81.8% of sensitivity (P = 0.035) and 93.7% of specificity (P = 0.0016). Non-psychotic prodromal symptoms bring valuable information to improve the prediction of conversion to psychosis. Elastic net logistic regression applied to clinical data is a promising way to switch from group prediction to an individualized prediction.