scholarly journals Real-World Patterns of Utilization and Costs Associated with Second-Generation Oral Antipsychotic Medication for the Treatment of Bipolar Disorder: A Literature Review

2021 ◽  
Vol Volume 17 ◽  
pp. 515-531
Author(s):  
Michael J Doane ◽  
Kristine Ogden ◽  
Leona Bessonova ◽  
Amy K O'Sullivan ◽  
Mauricio Tohen
Keyword(s):  
Bipolar Disorder ◽  
Literature Review ◽  
Antipsychotic Medication ◽  
Real World ◽  
Second Generation ◽  
Patterns Of Utilization ◽  
Oral Antipsychotic

Old drugs vs. new drugs: Treating bipolar disorder in children and adolescents

2011 ◽  
Vol 26 (S2) ◽  
pp. 277-277
Author(s):  
P.T. Dineen ◽  
A.L. Malizia
Keyword(s):  
Bipolar Disorder ◽  
Literature Review ◽  
Children And Adolescents ◽  
Pharmacological Treatment ◽  
Antipsychotic Drugs ◽  
Second Generation ◽  
New Drugs ◽  
Limited Evidence ◽  
Second Generation Antipsychotics ◽  
Old Drugs

This is a literature review of the evidence for pharmacological treatment for bipolar disorder in children and adolescents. The review covers drugs that have controlled studies and is divided into older, classical antimanic drugs and the newer mood-stabliser/antipsychotic drugs. The drugs that have had controlled studies are aripiprazole, lithium, olanzapine, oxcarbazepine, quetiapine, risperidone, topiramate, valproate/divalproex drugs and ziprasidone. Carbamazepine, lamotrigine and nefazodone (which is included for completion) were not found to have any evidence from controlled studies. Of the 9 drugs that have evidence, it was found that the newer second-generation antipsychotics had better evidence for efficacy for management of acute mania in children and adolescents: aripiprazole, risperidone, quetiapine and olanzapine. Classical antimanic agents such as lithium and valproate-based drugs had limited evidence.

scholarly journals Antipsychotic long-acting injections: Mind the gap

2009 ◽  
Vol 195 (S52) ◽  
pp. s1-s4 ◽  
Author(s):  
Maxine X. Patel ◽  
Mark Taylor ◽  
Anthony S. David
Keyword(s):  
Treatment Adherence ◽  
Patient Group ◽  
Antipsychotic Medication ◽  
Antipsychotic Drugs ◽  
Second Generation ◽  
Maintenance Medication ◽  
Oral Antipsychotic ◽  
Long Acting

SummaryLong-acting injections of antipsychotic medication (or depots) were developed specifically to promote treatment adherence and are a valuable option for maintenance medication in psychotic illnesses. Approximately 40–60% of patients with schizophrenia are partially or totally non-adherent to their antipsychotic regimen, but only 30% or less are prescribed a long-acting injection. The use of such injections has declined in recent years after the introduction of second-generation (atypical) oral antipsychotic drugs. Research shows that possible reasons for this decline include concerns that may be based on suboptimal knowledge, as well as an erroneous assumption that one's own patient group is more adherent than those of one's colleagues. Research on attitudes has also revealed that psychiatrists feel that long-acting injections have an ‘image’ problem. This editorial addresses the gaps in knowledge and behaviour associated with possible underutilisation of these formulations, highlighting the role of stigma and the need for more research.

scholarly journals Systematic literature review of machine learning methods used in the analysis of real-world data for patient-provider decision making

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Alan Brnabic ◽  
Lisa M. Hess
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Real World ◽  
Learning Algorithms ◽  
External Validation ◽  
Machine Learning Algorithms ◽  
Learning Methods ◽  
Machine Learning Methods

Abstract Background Machine learning is a broad term encompassing a number of methods that allow the investigator to learn from the data. These methods may permit large real-world databases to be more rapidly translated to applications to inform patient-provider decision making. Methods This systematic literature review was conducted to identify published observational research of employed machine learning to inform decision making at the patient-provider level. The search strategy was implemented and studies meeting eligibility criteria were evaluated by two independent reviewers. Relevant data related to study design, statistical methods and strengths and limitations were identified; study quality was assessed using a modified version of the Luo checklist. Results A total of 34 publications from January 2014 to September 2020 were identified and evaluated for this review. There were diverse methods, statistical packages and approaches used across identified studies. The most common methods included decision tree and random forest approaches. Most studies applied internal validation but only two conducted external validation. Most studies utilized one algorithm, and only eight studies applied multiple machine learning algorithms to the data. Seven items on the Luo checklist failed to be met by more than 50% of published studies. Conclusions A wide variety of approaches, algorithms, statistical software, and validation strategies were employed in the application of machine learning methods to inform patient-provider decision making. There is a need to ensure that multiple machine learning approaches are used, the model selection strategy is clearly defined, and both internal and external validation are necessary to be sure that decisions for patient care are being made with the highest quality evidence. Future work should routinely employ ensemble methods incorporating multiple machine learning algorithms.

scholarly journals S107. HEALTHCARE UTILIZATION AND COST IN SCHIZOPHRENIA AND BIPOLAR DISORDER: REAL-WORLD EVIDENCE FROM US CLAIMS DATABASES

2018 ◽  
Vol 44 (suppl_1) ◽  
pp. S366-S367
Author(s):  
Mallik Greene ◽  
Tingjian Yan ◽  
Eunice Chang ◽  
Ann Hartry ◽  
Jennifer Munday ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Healthcare Utilization ◽  
Real World ◽  
Real World Evidence
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