{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T13:36:33Z","timestamp":1791380193784,"version":"4.3.3"},"reference-count":6,"publisher":"MedWorks Media","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Psychopharmacol Bull"],"abstract":"<jats:p>Background: Clozapine and olanzapine are associated with clinically relevant weight gain and metabolic risk. Objective: To compare the risk of obesity (BMI \u2a7e 30) between clozapine and olanzapine and to develop a parsimonious prediction model with performance and clinical-utility evaluation. Methods: We fit a multivariable logistic regression on complete cases using index drug, daily dose, prolactin (PRL), cortisol, free thyroxine (FT4), and HDL. Discrimination (AUC), probability accuracy (Brier score), calibration (intercept\/slope and deciles), and decision-curve analysis (DCA) were reported. Results: In the classification cohort, obesity counts were equal for clozapine (9\/19) and olanzapine (9\/19). The model achieved strong internal discrimination (AUC 0.869) with good calibration (slope 1.00; intercept \u22120.00) and Brier 0.123. DCA indicated net benefit at practical thresholds (\u223c5\u201315%), supporting low-harm preventive actions. Conclusions: Drug-related risk can be individualized with a lean model using routine variables. External validation and unit harmonization\u2014especially for HDL\u2014are recommended before broad deployment. Psychopharmacology Bulletin. 2026;56(1):71\u201381.<\/jats:p>","DOI":"10.64719\/pb.15423","type":"journal-article","created":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T16:38:16Z","timestamp":1767976696000},"page":"71-81","source":"Crossref","is-referenced-by-count":0,"title":["Risk of Obesity in Patients Treated with Clozapine or Olanzapine: A Simple, Clinically Useful Prediction Tool"],"prefix":"10.64719","volume":"56","author":[{"given":"Roberto","family":"Lozano","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carina","family":"Bona","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"54175","published-online":{"date-parts":[[2026,1,2]]},"reference":[{"key":"ref1","doi-asserted-by":"crossref","unstructured":"De Hert M, Detraux J, van Winkel R, Yu W, Correll CU. Metabolic and cardiovascular adverse effects associated with antipsychotic drugs. World Psychiatry. 2012;11(2):106\u2013119.","DOI":"10.1038\/nrendo.2011.156"},{"key":"ref2","doi-asserted-by":"crossref","unstructured":"Bak M, Fransen A, Janssen J, van Os J, Drukker M. Almost all antipsychotics result in weight gain: a meta-analysis. PLoS One. 2014;9(4):e94112.","DOI":"10.1371\/journal.pone.0094112"},{"key":"ref3","doi-asserted-by":"crossref","unstructured":"Collins GS, Reitsma JB, Altman DG, Moons KGM. TRIPOD: transparent reporting of a multivariable prediction model for individual prognosis or diagnosis. Ann Intern Med. 2015;162(1):55\u201363.","DOI":"10.7326\/M14-0697"},{"key":"ref4","doi-asserted-by":"crossref","unstructured":"Steyerberg EW. Clinical Prediction Models. 2nd. New York: Springer; 2019.","DOI":"10.1007\/978-3-030-16399-0"},{"key":"ref5","doi-asserted-by":"crossref","unstructured":"Vickers AJ, Elkin EB. Decision curve analysis: a novel method for evaluating prediction models. Med Decis Making. 2006;26(6):565\u2013574.","DOI":"10.1177\/0272989X06295361"},{"key":"ref6","doi-asserted-by":"crossref","unstructured":"Virtanen P, Gommers R, Oliphant TE et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat Methods. 2020;17:261\u2013272.","DOI":"10.1038\/s41592-020-0772-5"}],"container-title":["Psychopharmacology Bulletin"],"original-title":[],"deposited":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T19:36:10Z","timestamp":1791315370000},"score":1,"resource":{"primary":{"URL":"https:\/\/medworksmedia.com\/pb-article\/risk-of-obesity-in-patients-treated-with-clozapine-or-olanzapine-a-simple-clinically-useful-prediction-tool\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,2]]},"references-count":6,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,1,2]]}},"URL":"https:\/\/doi.org\/10.64719\/pb.15423","relation":{},"ISSN":["2472-2448"],"issn-type":[{"value":"2472-2448","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,2]]}}}