Sensitivity and specificity of a facial emotion recognition test in classifying patients with schizophrenia

Abstract

BackgroundSchizophrenia is a debilitating mental illness that causes significant disability. However, the lack of evidence for functional decline yields difficulty in distinguishing patients with schizophrenia from healthy adults. Since patients with schizophrenia demonstrate severe facial emotion recognition deficit (FERD), FERD measurement appears to be a promising solution for the aforementioned challenge.We aimed to develop a FERD-based screening tool to differentiates patients with schizophrenia from healthy adults. MethodsPatients' responses were extracted from a previous study. The most discriminative index was determined by comparing the area under the receiver operating characteristic curve (AUC) of patients’ FER scores in 7 domains individually and collectively. The best cut-off score was selected only for the most discriminative index to provide both high sensitivity and specificity (≥ 0.90). ResultsThe “number of domains failed” showed the highest discriminative value (AUC = 0.92). Since high sensitivity and specificity could not be achieved simultaneously, two sub-optimal cut-off scores were recommended for prospective users. For users prioritizing sensitivity, the “≥ 2 domains failed” index yields high sensitivity (0.96) with modest specificity (0.66). For users targeting specificity, the “≥ 4 domains failed” indexachieves high specificity (0.92) with acceptable sensitivity (0.72). LimitationsConvenience sampling with mild clinical severity and younger healthy adults (< 20 years old) may limit the generalizability. ConclusionThe FERD screener seems to be a discriminative tool with changeable cut-off scores achieving high sensitivity or specificity. Therefore, it may be useful in detecting patients and ruling out adults erroneously suspected of having schizophrenia.