66 research outputs found
Risk of hospitalization for heart failure in patients with type 2 diabetes newly treated with DPP-4 inhibitors or other oral glucose-lowering medications: A retrospective registry study on 127,555 patients from the Nationwide OsMed Health-DB Database
Aims Oral glucose-lowering medications are associated with excess risk of heart failure (HF). Given the absence of comparative data among drug classes, we performed a retrospective study in 32 Health Services of 16 Italian regions accounting for a population of 18 million individuals, to assess the association between HF risk and use of sulphonylureas, DPP-4i, and glitazones. Methods and results We extracted data on patients with type 2 diabetes who initiated treatment with DPP-4i, thiazolidinediones, or sulphonylureas alone or in combination with metformin during an accrual time of 2 years. The endpoint was hospitalization for HF (HHF) occurring after the first 6 months of therapy, and the observation was extended for up to 4 years. A total of 127 555 patients were included, of whom 14.3% were on DPP-4i, 72.5% on sulphonylurea, 13.2% on thiazolidinediones, with average 70.7% being on metformin as combination therapy. Patients in the three groups differed significantly for baseline characteristics: age, sex, Charlson index, concurrent medications, and previous cardiovascular events. During an average 2.6-year follow-up, after adjusting for measured confounders, use of DPP-4i was associated with a reduced risk of HHF compared with sulphonylureas [hazard ratio (HR) 0.78; 95% confidence interval (CI) 0.62-0.97; P = 0.026]. After propensity matching, the analysis was restricted to 39 465 patients, and the use of DPP-4i was still associated with a lower risk of HHF (HR 0.70; 95% CI 0.52-0.94; P = 0.018). Conclusion In a very large observational study, the use of DPP-4i was associated with a reduced risk of HHF when compared with sulphonylureas
Association between age of cannabis initiation and gray matter covariance networks in recent onset psychosis
Cannabis use during adolescence is associated with an increased risk of developing psychosis. According to a current hypothesis, this results from detrimental effects of early cannabis use on brain maturation during this vulnerable period. However, studies investigating the interaction between early cannabis use and brain structural alterations hitherto reported inconclusive findings. We investigated effects of age of cannabis initiation on psychosis using data from the multicentric Personalized Prognostic Tools for Early Psychosis Management (PRONIA) and the Cannabis Induced Psychosis (CIP) studies, yielding a total sample of 102 clinically-relevant cannabis users with recent onset psychosis. GM covariance underlies shared maturational processes. Therefore, we performed source-based morphometry analysis with spatial constraints on structural brain networks showing significant alterations in schizophrenia in a previous multisite study, thus testing associations of these networks with the age of cannabis initiation and with confounding factors. Earlier cannabis initiation was associated with more severe positive symptoms in our cohort. Greater gray matter volume (GMV) in the previously identified cerebellar schizophrenia-related network had a significant association with early cannabis use, independent of several possibly confounding factors. Moreover, GMV in the cerebellar network was associated with lower volume in another network previously associated with schizophrenia, comprising the insula, superior temporal, and inferior frontal gyrus. These findings are in line with previous investigations in healthy cannabis users, and suggest that early initiation of cannabis perturbs the developmental trajectory of certain structural brain networks in a manner imparting risk for psychosis later in life
Multimodal Machine Learning Workflows for Prediction of Psychosis in Patients With Clinical High-Risk Syndromes and Recent-Onset Depression
Importance Diverse models have been developed to predict psychosis in patients with clinical high-risk (CHR) states. Whether prediction can be improved by efficiently combining clinical and biological models and by broadening the risk spectrum to young patients with depressive syndromes remains unclear. Objectives To evaluate whether psychosis transition can be predicted in patients with CHR or recent-onset depression (ROD) using multimodal machine learning that optimally integrates clinical and neurocognitive data, structural magnetic resonance imaging (sMRI), and polygenic risk scores (PRS) for schizophrenia; to assess models' geographic generalizability; to test and integrate clinicians' predictions; and to maximize clinical utility by building a sequential prognostic system. Design, Setting, and Participants This multisite, longitudinal prognostic study performed in 7 academic early recognition services in 5 European countries followed up patients with CHR syndromes or ROD and healthy volunteers. The referred sample of 167 patients with CHR syndromes and 167 with ROD was recruited from February 1, 2014, to May 31, 2017, of whom 26 (23 with CHR syndromes and 3 with ROD) developed psychosis. Patients with 18-month follow-up (n = 246) were used for model training and leave-one-site-out cross-validation. The remaining 88 patients with nontransition served as the validation of model specificity. Three hundred thirty-four healthy volunteers provided a normative sample for prognostic signature evaluation. Three independent Swiss projects contributed a further 45 cases with psychosis transition and 600 with nontransition for the external validation of clinical-neurocognitive, sMRI-based, and combined models. Data were analyzed from January 1, 2019, to March 31, 2020. Main Outcomes and Measures Accuracy and generalizability of prognostic systems. Results A total of 668 individuals (334 patients and 334 controls) were included in the analysis (mean [SD] age, 25.1 [5.8] years; 354 [53.0%] female and 314 [47.0%] male). Clinicians attained a balanced accuracy of 73.2% by effectively ruling out (specificity, 84.9%) but ineffectively ruling in (sensitivity, 61.5%) psychosis transition. In contrast, algorithms showed high sensitivity (76.0%-88.0%) but low specificity (53.5%-66.8%). A cybernetic risk calculator combining all algorithmic and human components predicted psychosis with a balanced accuracy of 85.5% (sensitivity, 84.6%; specificity, 86.4%). In comparison, an optimal prognostic workflow produced a balanced accuracy of 85.9% (sensitivity, 84.6%; specificity, 87.3%) at a much lower diagnostic burden by sequentially integrating clinical-neurocognitive, expert-based, PRS-based, and sMRI-based risk estimates as needed for the given patient. Findings were supported by good external validation results. Conclusions and RelevanceThese findings suggest that psychosis transition can be predicted in a broader risk spectrum by sequentially integrating algorithms' and clinicians' risk estimates. For clinical translation, the proposed workflow should undergo large-scale international validation.Question Can a transition to psychosis be predicted in patients with clinical high-risk states or recent-onset depression by optimally integrating clinical, neurocognitive, neuroimaging, and genetic information with clinicians' prognostic estimates? Findings In this prognostic study of 334 patients and 334 control individuals, machine learning models sequentially combining clinical and biological data with clinicians' estimates correctly predicted disease transitions in 85.9% of cases across geographically distinct patient populations. The clinicians' lack of prognostic sensitivity, as measured by a false-negative rate of 38.5%, was reduced to 15.4% by the sequential prognostic model. Meaning These findings suggest that an individualized prognostic workflow integrating artificial and human intelligence may facilitate the personalized prevention of psychosis in young patients with clinical high-risk syndromes or recent-onset depression.</p
Numerical Study of Activation in Anisotropic Myocardium Using Hodgkin-Huxley-Type Models
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