Integrative analysis to identify shared mechanisms between schizophrenia and bipolar disorder and their comorbidities.

Schizophrenia and bipolar disorder are characterized by highly similar neuropsychological signatures, implying shared neurobiological mechanisms between these two disorders. These disorders also have comorbidities, such as type 2 diabetes mellitus (T2DM). To date, an understanding of the mechanisms that mediate the link between these two disorders remains incomplete. In this work, we identify and investigate shared patterns across multiple schizophrenia, bipolar disorder and T2DM gene expression datasets through multiple strategies. Firstly, we investigate dysregulation patterns at the gene-level and compare our findings against disease-specific knowledge graphs (KGs). Secondly, we analyze the concordance of co-expression patterns across datasets to identify disease-specific as well as common pathways. Thirdly, we examine enriched pathways across datasets and disorders to identify common biological mechanisms between them. Lastly, we investigate the correspondence of shared genetic variants between these two disorders and T2DM as well as the disease-specific KGs. In conclusion, our work reveals several shared candidate genes and pathways, particularly those related to the immune system, such as TNF signaling pathway, IL-17 signaling pathway and NF-kappa B signaling pathway and nervous system, such as dopaminergic synapse and GABAergic synapse, which we propose mediate the link between schizophrenia and bipolar disorder and its shared comorbidity, T2DM.

Strategies for reducing potentially avoidable hospitalizations for ambulatory care-sensitive conditions.

PURPOSE: Hospitalizations for ambulatory care-sensitive conditions (ACSCs) are seen as potentially avoidable with optimal primary care. Little is known, however, about how primary care physicians rate these hospitalizations and whether and how they could be avoided. This study explores the complex causality of such hospitalizations from the perspective of primary care physicians. METHODS: We conducted semistructured interviews with 12 primary care physicians from 10 primary care clinics in Germany regarding 104 hospitalizations of 81 patients with ACSCs at high risk of rehospitalization. RESULTS: Participating physicians rated 43 (41%) of the 104 hospitalizations to be potentially avoidable. During the interviews the cause of hospitalization fell into 5 principal categories: system related (eg, unavailability of ambulatory services), physician related (eg, suboptimal monitoring), medical (eg, medication side effects), patient related (eg, delayed help-seeking), and social (eg, lack of social support). Subcategories frequently associated with physicians' rating of hospitalizations for ACSCs as potentially avoidable were after-hours absence of the treating physician, failure to use ambulatory services, suboptimal monitoring, patients' fearfulness, cultural background and insufficient language skills of patients, medication errors, medication nonadherence, and overprotective caregivers. Comorbidities and medical emergencies were frequent causes attributed to ACSC-based hospitalizations that were rated as being unavoidable. CONCLUSIONS: Primary care physicians rated a significant proportion of hospitalizations for ACSCs to be potentially avoidable. Strategies to avoid these hospitalizations may target after-hours care, optimal use of ambulatory services, intensified monitoring of high-risk patients, and initiatives to improve patients' willingness and ability to seek timely help, as well as patients' medication adherence.

Primary care physicians' experiences with case finding for practice-based care management.

OBJECTIVES: The identification of patients most likely to benefit from care management programs-case finding-is a crucial determinant of their effectiveness regarding improved health outcomes and reduced costs. Until now, research has mainly focused on claims data-based case finding. This study aimed to explore how primary care physicians (PCPs) select patients for practice based care management and how risk prediction may complement their case finding. STUDY DESIGN: Qualitative study. METHODS: We performed 12 semi-structured interviews with PCPs from 10 small- to middle-sized primary care practices in Germany. The interviews focused on their criteria for selecting patients as potential participants in an on-site care management program and how PCPs evaluate claims data-based risk prediction as a case-finding tool. All interviews were transcribed verbatim. We performed qualitative content analysis using the ATLAS.ti software. RESULTS: Three major categories emerged from the physicians interviewed: 1) the physicians' interpretation of the program's eligibility criteria, 2) physician-related criteria, and 3) patient-related criteria. The physician-related criteria included "sympathy/aversion" and "knowing the patient." Patient-related criteria concerned care sensitivity in terms of "willingness to participate," "ability to participate (eg, sufficient language skills, cognitive status)," and "manageable care needs." PCPs believed that their case finding could be supported by additional information from claims data-based risk prediction. CONCLUSIONS: Case finding for care management programs in primary care may benefit from a structured approach combining clinical judgment by PCPs and claims data-based risk modeling. However, further research is needed to identify the optimal case-finding strategy for practice based care management.