Annals of General Psychiatry最新文献

Background: Being one of the most widespread, pervasive, and troublesome illnesses in the world, depression causes dysfunction in various spheres of individual and social life. Regrettably, despite obtaining evidence-based antidepressant medication, up to 70% of people are going to continue to experience troublesome symptoms. Quetiapine, as one of the most commonly prescribed antipsychotic medication worldwide, has been reported as an effective augmentation strategy to antidepressants. The right quetiapine dose and personalized quetiapine treatment are frequently challenging for clinicians. This study aimed to identify important influencing variables for quetiapine dose by maximizing the use of data from real world, and develop a predictive model of quetiapine dose through machine learning techniques to support selections for treatment regimens.

Methods: The study comprised 308 depressed patients who were medicated with quetiapine and hospitalized in the First Hospital of Hebei Medical University, from November 1, 2019, to August 31, 2022. To identify the important variables influencing the dose of quetiapine, a univariate analysis was applied. The prediction abilities of nine machine learning models (XGBoost, LightGBM, RF, GBDT, SVM, LR, ANN, DT) were compared. Algorithm with the optimal model performance was chosen to develop the prediction model.

Results: Four predictors were selected from 38 variables by the univariate analysis (p < 0.05), including quetiapine TDM value, age, mean corpuscular hemoglobin concentration, and total bile acid. Ultimately, the XGBoost algorithm was used to create a prediction model for quetiapine dose that had the greatest predictive performance (accuracy = 0.69) out of nine models. In the testing cohort (62 cases), a total of 43 cases were correctly predicted of the quetiapine dose regimen. In dose subgroup analysis, AUROC for patients with daily dose of 100 mg, 200 mg, 300 mg and 400 mg were 0.99, 0.75, 0.93 and 0.86, respectively.

Conclusions: In this work, machine learning techniques are used for the first time to estimate the dose of quetiapine for patients with depression, which is valuable for the clinical drug recommendations.

Purpose: The present study aimed at assessing the prevalences of post-traumatic stress disorder (PTSD) (main objective), anxiety, depression, and burnout syndrome (BOS) and their associated factors in intensive care unit (ICU) staff workers in the second year of the COVID-19 pandemic.

Materials and methods: An international cross-sectional multicenter ICU-based online survey was carried out among the ICU staff workers in 20 ICUs across 3 continents. ICUs staff workers (both caregivers and non-caregivers) were invited to complete PCL-5, HADS, and MBI questionnaires for assessing PTSD, anxiety, depression, and the different components of BOS, respectively. A personal questionnaire was used to isolate independent associated factors with these disorders.

Results: PCL-5, HADS, and MBI questionnaires were completed by 585, 570, and 539 responders, respectively (525 completed all questionnaires). PTSD was diagnosed in 98/585 responders (16.8%). Changing familial environment, being a non-caregiver staff worker, having not being involved in a COVID-19 patient admission, having not been provided with COVID-19-related information were associated with PTSD. Anxiety was reported in 130/570 responders (22.8%). Working in a public hospital, being a woman, being financially impacted, being a non-clinical healthcare staff member, having no theoretical or practical training on individual preventive measures, and fear of managing COVID-19 patients were associated with anxiety. Depression was reported in 50/570 responders (8.8%). Comorbidity at risk of severe COVID-19, working in a public hospital, looking after a child, being a non-caregiver staff member, having no information, and a request for moving from the unit were associated with depression. Having received no information and no adequate training for COVID-19 patient management were associated with all 3 dimensions of BOS.

Conclusion: The present study confirmed that ICU staff workers, whether they treated COVID-19 patients or not, have a substantial prevalence of psychological disorders.

Background: Bipolar disorder is one of the most burdensome severe mental disorders, characterized by high levels of personal and social disability. Patients often need an integrated pharmacological and non-pharmacological approach. Lithium is one of the most effective treatments available not only in psychiatry, but in the whole medicine, and its clinical efficacy is superior to that of other mood stabilizers. However, a declining trend on lithium prescriptions has been observed worldwide in the last 20 years, supporting the notion that lithium is a 'forgotten drug' and highlighting that the majority of patients with bipolar disorder are missing out the best available pharmacological option. Based on such premises, a narrative review has been carried out on the most common "misconceptions" and "stereotypes" associated with lithium treatment; we also provide a list of "good reasons" for using lithium in ordinary clinical practice to overcome those false myths.

Main text: A narrative search of the available literature has been performed entering the following keywords: "bipolar disorder", "lithium", "myth", "mythology", "pharmacological treatment", and "misunderstanding". The most common false myths have been critically revised and the following statements have been proposed: (1) Lithium should represent the first choice for the treatment of patients with bipolar disorder; (2) lithium treatment is effective in different patients' groups suffering from bipolar disorder; (3) Drug-drug interaction risk can be easily managed during lithium treatment; (4) The optimal management of lithium treatment includes periodical laboratory tests; (5) Slow-release lithium formulation has advantages compared to immediate release formulation; (6) Lithium treatment has antisuicidal properties; (7) Lithium can be carefully managed during pregnancy.

Conclusions: In recent years, a discrepancy between evidence-based recommendations and clinical practice in using lithium treatment for patients with bipolar disorder has been highlighted. It is time to disseminate clear and unbiased information on the clinical efficacy, effectiveness, tolerability and easiness to use of lithium treatment in patients with bipolar disorder. It is necessary to reinvigorate the clinical and academic discussion about the efficacy of lithium, to counteract the decreasing prescription trend of one of the most effective drugs available in the whole medicine.