No abstract

OBJECTIVE Diagnostic shifts have been prospectively examined in the short-term, but the long-term stability of initial and follow-up diagnoses have rarely been evaluated. METHOD A cohort of 470 first-admission patients with psychotic disorders was systematically assessed at baseline, 6-month, 2-year, and 10-year follow-up. Longitudinal best-estimate consensus diagnoses were formulated after each assessment. RESULTS At baseline, the diagnostic distribution was: schizophrenia spectrum disorders 29.6%, bipolar disorder with psychotic features 21.1%, major depression with psychotic features 17.0%, substance-induced psychosis 2.4%, and other psychoses 27.9%. At year 10, the distribution changed to 49.8%, 24.0%, 11.1%, 7.0%, and 8.1%, respectively. Overall, 50.7% changed diagnoses at some point during the study. Most participants who were initially diagnosed with schizophrenia or bipolar disorder retained the diagnosis at year 10 (89.2% and 77.8%, respectively). However, 32.0% of participants (N=98) originally given a non-schizophrenia diagnosis gradually shifted into schizophrenia at year 10. The second biggest shift was to bipolar disorder (10.7% of those not given this diagnosis at baseline). Changes in the clinical picture explained many diagnostic shifts. In particular, poorer functioning and greater negative and psychotic symptoms predicted a subsequent shift to schizophrenia. Better functioning and lower negative and depressive symptoms predicted the shift to bipolar disorder. CONCLUSIONS First-admission patients run the risk of being misclassified at early stages in the illness course, including more than 2 years after first hospitalization. Diagnosis should be reassessed at all follow-up points.

Most cases of oral squamous cell carcinoma (OSCC) develop from visible oral potentially malignant disorders (OPMDs). The latter exhibit heterogeneous subtypes with different transformation potentials, complicating the early detection of OSCC during routine visual oral cancer screenings. To develop clinically applicable biomarkers, we collected saliva samples from 96 healthy controls, 103 low-risk OPMDs, 130 high-risk OPMDs, and 131 OSCC subjects. These individuals were enrolled in Taiwan’s Oral Cancer Screening Program. We identified 302 protein biomarkers reported in the literature and/or through in-house studies and prioritized 49 proteins for quantification in the saliva samples using multiple reaction monitoring-MS. Twenty-eight proteins were successfully quantified with high confidence. The quantification data from non-OSCC subjects (healthy controls + low-risk OPMDs) and OSCC subjects in the training set were subjected to classification and regression tree analyses, through which we generated a four-protein panel consisting of MMP1, KNG1, ANXA2, and HSPA5. A risk-score scheme was established, and the panel showed high sensitivity (87.5%) and specificity (80.5%) in the test set to distinguish OSCC samples from non-OSCC samples. The risk score >0.4 detected 84% (42/50) of the stage I OSCCs and a significant portion (42%) of the high-risk OPMDs. Moreover, among 88 high-risk OPMD patients with available follow-up results, 18 developed OSCC within 5 y; of them, 77.8% (14/18) had risk scores >0.4. Our four-protein panel may therefore offer a clinically effective tool for detecting OSCC and monitoring high-risk OPMDs through a readily available biofluid.