Long-Acting Injectable Second-Generation Antipsychotics Improve Negative Symptoms and Suicidal Ideation in Recent Diagnosed Schizophrenia Patients: A 1-Year Follow-up Pilot Study.
Valentina CoriglianoAnna ComparelliIginia MancinelliBenedetta MontalbaniDorian A LamisAntonella De CarolisDenise ErbutoPaolo GirardiMaurizio PompiliPublished in: Schizophrenia research and treatment (2018)
Long-acting injectable second-generation antipsychotics (LAI-SGA) are typically used to maintain treatment adherence in patients with chronic schizophrenia. Recent research suggests that they may also provide an effective treatment strategy for patients with early-phase disease. The aim of this study is to evaluate clinical and psychosocial outcomes among recent and long-term diagnosed schizophrenia outpatients treated with LAI-SGA during a follow-up period of 12 months. Stable schizophrenia patients receiving LAI-SGA with 5 or less years of illness duration (n = 10) were compared to those with more than 5 years of illness duration (n = 15). Clinical data was assessed through the Positive and Negative Syndrome Scale (PANSS), the Global Assessment of Functioning (GAF), the Columbia Suicide Severity Rating Scale (C-SSRS), the Recovery Style Questionnaire (RSQ), and the Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT) Managing Emotion branch. Recently diagnosed patients showed greater improvement versus patients diagnosed for more than 5 years in adjusted mean GAF score, in PANSS factor score for negative and depressive symptoms, and in severity and intensity of suicidal ideation. Our preliminary findings support the hypothesis that LAI-SGA may influence the course of the illness if administered at the early phase of the illness. However, replicate studies are needed, possibly with larger samples.
Keyphrases
- end stage renal disease
- depressive symptoms
- bipolar disorder
- newly diagnosed
- ejection fraction
- chronic kidney disease
- prognostic factors
- mental health
- peritoneal dialysis
- physical activity
- adipose tissue
- machine learning
- electronic health record
- weight loss
- artificial intelligence
- big data
- data analysis
- smoking cessation