G Fond1, E Bulzacka2, M Boucekine3, F Schürhoff2, F Berna4, O Godin2, B Aouizerate5, D Capdevielle6, I Chereau7, T D'Amato8, C Dubertret9, J Dubreucq10, C Faget11, S Leignier10, C Lançon11, J Mallet9, D Misdrahi12, C Passerieux13, R Rey8, A Schandrin6, M Urbach13, P Vidailhet14, M Leboyer2, L Boyer15, P M Llorca7. 1. Fondation FondaMental, Créteil, France; Faculté de Médecine - Secteur Timone, Aix-Marseille Univ, d'Etude et de Recherche sur les Services de Santé et la Qualité de vie, 27 Boulevard Jean Moulin, 13005 Marseille, France. Electronic address: guillaume.fond@ap-hm.fr. 2. Fondation FondaMental, Créteil, France; INSERM U955, équipe de psychiatrie translationnelle, Créteil, France, Université Paris-Est Créteil, DHU Pe-PSY, Pôle de Psychiatrie des Hôpitaux Universitaires H Mondor, Créteil, France. 3. Faculté de Médecine - Secteur Timone, Aix-Marseille Univ, d'Etude et de Recherche sur les Services de Santé et la Qualité de vie, 27 Boulevard Jean Moulin, 13005 Marseille, France. 4. Fondation FondaMental, Créteil, France; Hôpitaux Universitaires de Strasbourg, Université de Strasbourg, INSERM U1114, Fédération de Médecine Translationnelle de Strasbourg, Strasbourg, France. 5. Fondation FondaMental, Créteil, France; Centre Hospitalier Charles Perrens, Université de Bordeaux, Bordeaux F-33076, France; INRA, NutriNeuro, University of Bordeaux, U1286, Bordeaux F-33076, France. 6. Fondation FondaMental, Créteil, France; Hôpital la Colombière, CHRU Montpellier, Service Universitaire de Psychiatrie Adulte, Université Montpellier 1, Montpellier 1061, France. 7. Fondation FondaMental, Créteil, France; Faculté de Médecine, Université d'Auvergne, CMP B, CHU, EA 7280, Clermont-Ferrand Cedex 69 63003, France. 8. Fondation FondaMental, Créteil, France; Centre de Recherche en Neurosciences de Lyon, Université Claude Bernard Lyon, Equipe PSYR2, Centre Hospitalier Le Vinatier, Pole Est, 95 bd Pinel, Bron Cedex 69678, France. 9. Fondation FondaMental, Créteil, France; AP-HP, Department of Psychiatry, Faculté de médecine, Louis Mourier Hospital, Université Paris Diderot, Colombes U894, France. 10. Fondation FondaMental, Créteil, France; Centre Référent de Réhabilitation Psychosociale, Alpes Isère, Grenoble, France. 11. Fondation FondaMental, Créteil, France; Assistance Publique des Hôpitaux de Marseille (AP-HM), pôle universitaire de psychiatrie, Marseille, France. 12. Fondation FondaMental, Créteil, France; Centre Hospitalier Charles Perrens, Université de Bordeaux, Bordeaux F-33076, France; CNRS UMR 5287-INCIA, France. 13. Fondation FondaMental, Créteil, France; Service de psychiatrie d'adulte, Centre Hospitalier de Versailles, UFR des Sciences de la Santé Simone Veil, Université Versailles Saint-Quentin en Yvelines, Versailles, France. 14. Hôpitaux Universitaires de Strasbourg, Université de Strasbourg, INSERM U1114, Fédération de Médecine Translationnelle de Strasbourg, Strasbourg, France. 15. Fondation FondaMental, Créteil, France; Faculté de Médecine - Secteur Timone, Aix-Marseille Univ, d'Etude et de Recherche sur les Services de Santé et la Qualité de vie, 27 Boulevard Jean Moulin, 13005 Marseille, France.
Abstract
BACKGROUND: Predicting psychotic relapse is one of the major challenges in the daily care of schizophrenia. OBJECTIVES: To determine the predictors of psychotic relapse and follow-up withdrawal in a non-selected national sample of stabilized community-dwelling SZ subjects with a machine learning approach. METHODS: Participants were consecutively included in the network of the FondaMental Expert Centers for Schizophrenia and received a thorough clinical and cognitive assessment, including recording of current treatment. Relapse was defined by at least one acute psychotic episode of at least 7 days, reported by the patient, her/his relatives or by the treating psychiatrist, within the 2-year follow-up. A classification and regression tree (CART) was used to construct a predictive decision tree of relapse and follow-up withdrawal. RESULTS: Overall, 549 patients were evaluated in the expert centers at baseline and 315 (57.4%) (mean age = 32.6 years, 24% female gender) were followed-up at 2 years. On the 315 patients who received a visit at 2 years, 125(39.7%) patients had experienced psychotic relapse at least once within the 2 years of follow-up. High anger (Buss&Perry subscore), high physical aggressiveness (Buss&Perry scale subscore), high lifetime number of hospitalization in psychiatry, low education level, and high positive symptomatology at baseline (PANSS positive subscore) were found to be the best predictors of relapse at 2 years, with a percentage of correct prediction of 63.8%, sensitivity 71.0% and specificity 44.8%. High PANSS excited score, illness duration <2 years, low Buss&Perry hostility score, high CTQ score, low premorbid IQ and low medication adherence (BARS) score were found to be the best predictors of follow-up withdrawal with a percentage of correct prediction of 52.4%, sensitivity 62%, specificity 38.7%. CONCLUSION: Machine learning can help constructing predictive score. In the present sample, aggressiveness appears to be a good early warning sign of psychotic relapse and follow-up withdrawal and should be systematically assessed in SZ subjects. The other above-mentioned clinical variables may help clinicians to improve the prediction of psychotic relapse at 2 years.
BACKGROUND: Predicting psychotic relapse is one of the major challenges in the daily care of schizophrenia. OBJECTIVES: To determine the predictors of psychotic relapse and follow-up withdrawal in a non-selected national sample of stabilized community-dwelling SZ subjects with a machine learning approach. METHODS:Participants were consecutively included in the network of the FondaMental Expert Centers for Schizophrenia and received a thorough clinical and cognitive assessment, including recording of current treatment. Relapse was defined by at least one acute psychotic episode of at least 7 days, reported by the patient, her/his relatives or by the treating psychiatrist, within the 2-year follow-up. A classification and regression tree (CART) was used to construct a predictive decision tree of relapse and follow-up withdrawal. RESULTS: Overall, 549 patients were evaluated in the expert centers at baseline and 315 (57.4%) (mean age = 32.6 years, 24% female gender) were followed-up at 2 years. On the 315 patients who received a visit at 2 years, 125(39.7%) patients had experienced psychotic relapse at least once within the 2 years of follow-up. High anger (Buss&Perry subscore), high physical aggressiveness (Buss&Perry scale subscore), high lifetime number of hospitalization in psychiatry, low education level, and high positive symptomatology at baseline (PANSS positive subscore) were found to be the best predictors of relapse at 2 years, with a percentage of correct prediction of 63.8%, sensitivity 71.0% and specificity 44.8%. High PANSS excited score, illness duration <2 years, low Buss&Perry hostility score, high CTQ score, low premorbid IQ and low medication adherence (BARS) score were found to be the best predictors of follow-up withdrawal with a percentage of correct prediction of 52.4%, sensitivity 62%, specificity 38.7%. CONCLUSION: Machine learning can help constructing predictive score. In the present sample, aggressiveness appears to be a good early warning sign of psychotic relapse and follow-up withdrawal and should be systematically assessed in SZ subjects. The other above-mentioned clinical variables may help clinicians to improve the prediction of psychotic relapse at 2 years.
Authors: Gonzalo Salazar de Pablo; Erich Studerus; Julio Vaquerizo-Serrano; Jessica Irving; Ana Catalan; Dominic Oliver; Helen Baldwin; Andrea Danese; Seena Fazel; Ewout W Steyerberg; Daniel Stahl; Paolo Fusar-Poli Journal: Schizophr Bull Date: 2021-03-16 Impact factor: 9.306