LAUSR

Early identification of symptoms that can predict treatment-resistant schizophrenia (TRS) could help clinicians to avoid delays in clozapine therapy. This study aims to investigate symptom patterns that could predict TRS using a discovery/replication study design. First, we followed a cohort of inpatients with schizophrenia (n = 164) in which the most discriminative items at baseline of the Positive and Negative Syndrome Scale (PANSS) were determined using logistic regression with TRS status as an outcome. Using Receiver Operating Characteristic (ROC) curves, we tested the prediction performance of multiple combinations of the identified items. The same items' combination was tested in an independent replication sample of (n = 207) outpatients with schizophrenia. In the discovery sample, the best combination to predict TRS at the discharge was the sum of three baseline PANSS items - conceptual disorganization (P2), difficulty in abstract thinking (N5), and unusual thought content (G9). The P2 + N5 + G9 model yielded an area under the curve (AUC) of 0.881, a sensitivity of 77.8%, and a specificity of 83.3%. In the outpatient sample, the model P2 + N5 + G9 predictive accuracy for TRS was only in the range of "acceptable" with an AUC of 0.756 and sensitivity of 72.3% and a specificity of 74.4%. Overall, the P2 + N5 + G9 model corresponds to the construct of formal thought disorder composed of disorganized thinking, concrete thinking, and bizarre-idiosyncratic thinking. Pronounced levels of these symptoms are easily identifiable in clinical practice and may be a feasible strategy in TRS. Replicating in first-episode cohorts is desirable to understand the likely clinical utility.