ABSTRACT
OBJECTIVE: To employ machine learning algorithms to examine patterns of rumination from RDoC perspective and to determine which variables predict high levels of maladaptive rumination across a transdiagnostic sample. METHOD: Sample of 200 consecutive, consenting outpatient referrals with clinical diagnoses of schizophrenia, schizoaffective, bipolar, depression, anxiety disorders, obsessive compulsive and post-traumatic stress. Machine learning algorithms used a range of variables including sociodemographics, serum levels of immune markers (IL-6, IL-1ß, IL-10, TNF-α and CCL11) and BDNF, psychiatric symptoms and disorders, history of suicide and hospitalizations, functionality, medication use and comorbidities. RESULTS: The best model (with recursive feature elimination) included the following variables: socioeconomic status, illness severity, worry, generalized anxiety and depressive symptoms, and current diagnosis of panic disorder. Linear support vector machine learning differentiated individuals with high levels of rumination from those ones with low (AUC = 0.83, sensitivity = 75, specificity = 71). CONCLUSIONS: Rumination is known to be associated with poor prognosis in mental health. This study suggests that rumination is a maladaptive coping style associated not only with worry, distress and illness severity, but also with socioeconomic status. Also, rumination demonstrated a specific association with panic disorder.
Subject(s)
Anxiety Disorders , Models, Theoretical , Mood Disorders , Psychotic Disorders , Rumination, Cognitive , Social Class , Support Vector Machine , Adaptation, Psychological/physiology , Adult , Anxiety Disorders/classification , Anxiety Disorders/immunology , Anxiety Disorders/physiopathology , Cytokines/blood , Female , Humans , Male , Middle Aged , Mood Disorders/classification , Mood Disorders/immunology , Mood Disorders/physiopathology , Psychotic Disorders/classification , Psychotic Disorders/immunology , Psychotic Disorders/physiopathology , Rumination, Cognitive/physiology , Severity of Illness IndexABSTRACT
OBJECTIVE: To compare the degree of sensorineural hearing loss in patients with Ménière's disease (MD) with and without hyperinsulinism by different methods of assessment. STUDY DESIGN: Historical cohort study. SETTING: Ménière's Disease Care and Research Clinics of Hospital de Clinicas de Porto Alegre, a tertiary care university hospital in Southern Brazil. PATIENTS: Patients with a definite diagnosis of MD based on the American Academy of Otolaryngology-Head and Neck Surgery (AAO-HNS) guidelines. INTERVENTION: Patients were assessed by glucose overload tests (5-h glucose and insulin curves) and under baseline physiological conditions (Homeostasis Model Assessment/Insulin Resistance [HOMA-IR], Quantitative Insulin Sensitivity Check Index [QUICKI], and glucose/insulin ratio). These patients underwent annual pure-tone audiometry and were analyzed using 4-tone average (FTA), that is, arithmetic mean of 500, 1,000, 2,000, and 3,000 Hz, during the third, fourth, and fifth years of disease progression. MAIN OUTCOME MEASURE: Hearing loss assessed by FTA and classified in Stages I to IV (AAO-HNS). RESULTS: Forty-nine (76.6%) patients were defined as hyperinsulinemic and 15 (23.4%) as normoinsulinemic. Impairment on FTA was higher in the hyperinsulinemic group (52.04 ± 17.5 versus 39.75 ± 9.20, p = 0.027) when assessed by the 5-hour insulin curve. Hyperinsulinemic subjects were 3.5 times more likely to develop hearing damage greater than 40 dB (i.e., Stages III and IV) than normoinsulinemic subjects (OR = 3.52; 95% CI, 1.05-11.76). A moderate correlation between the insulin curve and the HOMA-IR was found (r = 0.524, p = 0.001). CONCLUSION: Hyperinsulinism in MD is associated with greater clinical hearing damage.
