Long-Term Risk of Arterial Thrombosis After Intracerebral Hemorrhage: MUCH-Italy.

BACKGROUND: The identification of patients surviving an acute intracerebral hemorrhage who are at a long-term risk of arterial thrombosis is a poorly defined, crucial issue for clinicians. METHODS: In the setting of the MUCH-Italy (Multicenter Study on Cerebral Haemorrhage in Italy) prospective observational cohort, we enrolled and followed up consecutive 30-day intracerebral hemorrhage survivors to assess the long-term incidence of arterial thrombotic events, to assess the impact of clinical and radiological variables on the risk of these events, and to develop a tool for estimating such a risk at the individual level. Primary end point was a composite of ischemic stroke, myocardial infarction, or other arterial thrombotic events. A point-scoring system was generated by the ß-coefficients of the variables independently associated with the long-term risk of arterial thrombosis, and the predictive MUCH score was calculated as the sum of the weighted scores. RESULTS: Overall, 1729 patients (median follow-up time, 43 months [25th to 75th percentile, 69.0]) qualified for inclusion. Arterial thrombotic events occurred in 169 (9.7%) patients. Male sex, diabetes, hypercholesterolemia, atrial fibrillation, and personal history of coronary artery disease were associated with increased long-term risk of arterial thrombosis, whereas the use of statins and antithrombotic medications after the acute intracerebral hemorrhage was associated with a reduced risk. The area under the receiver operating characteristic curve of the MUCH score predictive validity was 0.716 (95% CI, 0.56-0.81) for the 0- to 1-year score, 0.672 (95% CI, 0.58-0.73) for the 0- to 5-year score, and 0.744 (95% CI, 0.65-0.81) for the 0- to 10-year score. C statistic for the prediction of events that occur from 0 to 10 years was 0.69 (95% CI, 0.64-0.74). CONCLUSIONS: Intracerebral hemorrhage survivors are at high long-term risk of arterial thrombosis. The MUCH score may serve as a simple tool for risk estimation.

SEMtree: tree-based structure learning methods with structural equation models.

MOTIVATION: With the exponential growth of expression and protein-protein interaction (PPI) data, the identification of functional modules in PPI networks that show striking changes in molecular activity or phenotypic signatures becomes of particular interest to reveal process-specific information that is correlated with cellular or disease states. This requires both the identification of network nodes with reliability scores and the availability of an efficient technique to locate the network regions with the highest scores. In the literature, a number of heuristic methods have been suggested. We propose SEMtree(), a set of tree-based structure discovery algorithms, combining graph and statistically interpretable parameters together with a user-friendly R package based on structural equation models framework. RESULTS: Condition-specific changes from differential expression and gene-gene co-expression are recovered with statistical testing of node, directed edge, and directed path difference between groups. In the end, from a list of seed (i.e. disease) genes or gene P-values, the perturbed modules with undirected edges are generated with five state-of-the-art active subnetwork detection methods. The latter are supplied to causal additive trees based on Chu-Liu-Edmonds' algorithm (Chow and Liu, Approximating discrete probability distributions with dependence trees. IEEE Trans Inform Theory 1968;14:462-7) in SEMtree() to be converted in directed trees. This conversion allows to compare the methods in terms of directed active subnetworks. We applied SEMtree() to both Coronavirus disease (COVID-19) RNA-seq dataset (GEO accession: GSE172114) and simulated datasets with various differential expression patterns. Compared to existing methods, SEMtree() is able to capture biologically relevant subnetworks with simple visualization of directed paths, good perturbation extraction, and classifier performance. AVAILABILITY AND IMPLEMENTATION: SEMtree() function is implemented in the R package SEMgraph, easily available at https://CRAN.R-project.org/package=SEMgraph.

Statin use and long-term risk of recurrent intracerebral haemorrhage: the MUCH-Italy.

BACKGROUND: Whether statin use after spontaneous intracerebral haemorrhage (ICH) increases the risk of recurrent ICH is uncertain. METHODS: In the setting of the Multicentric Study on Cerebral Haemorrhage in Italy we followed up a cohort of 30-day ICH survivors, consecutively admitted from January 2002 to July 2014, to assess whether the use of statins after the acute event is associated with recurrent cerebral bleeding. RESULTS: 1623 patients (mean age, 73.9±10.3 years; males, 55.9%) qualified for the analysis. After a median follow-up of 40.5 months (25th to 75th percentile, 67.7) statin use was not associated with increased risk of recurrent ICH either in the whole study group (adjusted HR, 0.99; 95% CI 0.64 to 1.53) or in the subgroups defined by haematoma location (deep ICH, adjusted HR, 0.74; 95% CI 0.35 to 1.57; lobar ICH, adjusted HR, 1.09; 95% CI 0.62 to 1.90), intensity of statins (low-moderate intensity statins, adjusted HR, 0.93; 95% CI 0.58 to 1.49; high-intensity statins, adjusted HR, 1.48; 95% CI 0.66 to 3.31) and use of statins before the index event (adjusted HR, 0.66; 95% CI 0.38 to 1.17). CONCLUSIONS: Statin use appears to be unrelated to the risk of ICH recurrence.

SEMgraph: an R package for causal network inference of high-throughput data with structural equation models.

MOTIVATION: With the advent of high-throughput sequencing in molecular biology and medicine, the need for scalable statistical solutions for modeling complex biological systems has become of critical importance. The increasing number of platforms and possible experimental scenarios raised the problem of integrating large amounts of new heterogeneous data and current knowledge, to test novel hypotheses and improve our comprehension of physiological processes and diseases. RESULTS: Combining network analysis and causal inference within the framework of structural equation modeling (SEM), we developed the R package SEMgraph. It provides a fully automated toolkit, managing complex biological systems as multivariate networks, ensuring robustness and reproducibility through data-driven evaluation of model architecture and perturbation, which is readily interpretable in terms of causal effects among system components. AVAILABILITY AND IMPLEMENTATION: SEMgraph package is available at https://cran.r-project.org/web/packages/SEMgraph. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Bayesian time-varying autoregressive models of COVID-19 epidemics.

The COVID-19 pandemic has highlighted the importance of reliable statistical models which, based on the available data, can provide accurate forecasts and impact analysis of alternative policy measures. Here we propose Bayesian time-dependent Poisson autoregressive models that include time-varying coefficients to estimate the effect of policy covariates on disease counts. The model is applied to the observed series of new positive cases in Italy and in the United States. The results suggest that our proposed models are capable of capturing nonlinear growth of disease counts. We also find that policy measures and, in particular, closure policies and the distribution of vaccines, lead to a significant reduction in disease counts in both countries.

SEMgsa: topology-based pathway enrichment analysis with structural equation models.

BACKGROUND: Pathway enrichment analysis is extensively used in high-throughput experimental studies to gain insight into the functional roles of pre-defined subsets of genes, proteins and metabolites. Methods that leverages information on the topology of the underlying pathways outperform simpler methods that only consider pathway membership, leading to improved performance. Among all the proposed software tools, there's the need to combine high statistical power together with a user-friendly framework, making it difficult to choose the best method for a particular experimental environment. RESULTS: We propose SEMgsa, a topology-based algorithm developed into the framework of structural equation models. SEMgsa combine the SEM p values regarding node-specific group effect estimates in terms of activation or inhibition, after statistically controlling biological relations among genes within pathways. We used SEMgsa to identify biologically relevant results in a Coronavirus disease (COVID-19) RNA-seq dataset (GEO accession: GSE172114) together with a frontotemporal dementia (FTD) DNA methylation dataset (GEO accession: GSE53740) and compared its performance with some existing methods. SEMgsa is highly sensitive to the pathways designed for the specific disease, showing low p values ([Formula: see text]) and ranking in high positions, outperforming existing software tools. Three pathway dysregulation mechanisms were used to generate simulated expression data and evaluate the performance of methods in terms of type I error followed by their statistical power. Simulation results confirm best overall performance of SEMgsa. CONCLUSIONS: SEMgsa is a novel yet powerful method for identifying enrichment with regard to gene expression data. It takes into account topological information and exploits pathway perturbation statistics to reveal biological information. SEMgsa is implemented in the R package SEMgraph, easily available at https://CRAN.R-project.org/package=SEMgraph .

Early seizures and risk of epilepsy and death after intracerebral haemorrhage: The MUCH Italy.

INTRODUCTION: It is unclear which patients with non-traumatic (spontaneous) intracerebral haemorrhage (ICH) are at risk of developing acute symptomatic seizures (provoked seizures occurring within the first week after stroke onset; early seizures, ES) and whether ES predispose to the occurrence of remote symptomatic seizures (unprovoked seizures occurring more than 1 week after stroke; post-stroke epilepsy, PSE) and long-term mortality. PATIENTS AND METHODS: In the setting of the Multicenter Study on Cerebral Haemorrhage in Italy (MUCH-Italy) we examined the risk of ES and whether they predict the occurrence of PSE and all-cause mortality in a cohort of patients with first-ever spontaneous ICH and no previous history of epilepsy, consecutively hospitalized in 12 Italian neurological centers from 2002 to 2014. RESULTS: Among 2570 patients (mean age, 73.4 ± 12.5 years; males, 55.4%) 228 (8.9%) had acute ES (183 (7.1%) short seizures and 45 (1.8%) status epilepticus (SE)). Lobar location of the hematoma (OR, 1.49; 95% CI, 1.06-2.08) was independently associated with the occurrence of ES. Of the 2,037 patients who were followed-up (median follow-up time, 68.0 months (25th-75th percentile, 77.0)), 155 (7.6%) developed PSE. ES (aHR, 2.34; 95% CI, 1.42-3.85), especially when presenting as short seizures (aHR, 2.35; 95% CI, 1.38-4.00) were associated to PSE occurrence. Unlike short seizures, SE was an independent predictor of all-cause mortality (aHR, 1.50; 95% CI, 1.005-2.26). DISCUSSION AND CONCLUSION: The long-term risk of PSE and death after an ICH vary according to ES subtype. This might have implications for the design of future clinical trials targeting post-ICH epileptic seizures.

Survival in Incident Cases with Frontotemporal Lobar Degeneration: A Registry-Based Study.

Population-based registries represent a unique sample to estimate survival. The aim of the present study was to assess survival rates and predictors of outcome in incidental frontotemporal lobar degeneration (FTLD). Incident cases with FTLD, included between January 1, 2017 to December 31, 2017, have been followed for five years. Median survival was 8.16 years from disease onset and 5.38 years from diagnosis. Survival rates did not differ between phenotypes. Shorter disease duration from onset to diagnosis was associated with poorer outcome (p = 0.01). FTLD is a relatively homogeneous disease in terms of survival. Future multinational population-based studies are needed to confirm these findings.

Cortico-spinal tDCS in amyotrophic lateral sclerosis: A randomized, double-blind, sham-controlled trial followed by an open-label phase.

BACKGROUND: Amyotrophic lateral sclerosis (ALS) is a progressive disease for which no curative treatment is currently available. OBJECTIVE: This study aimed to investigate whether cortico-spinal transcranial direct current stimulation (tDCS) could mitigate symptoms in ALS patients via a randomized, double-blind, sham-controlled trial, followed by an open-label phase. METHODS: Thirty-one participants were randomized into two groups for the initial controlled phase. At baseline (T0), Group 1 received placebo stimulation (sham tDCS), while Group 2 received cortico-spinal stimulation (real tDCS) for five days/week for two weeks (T1), with an 8-week (T2) follow-up (randomized, double-blind, sham-controlled phase). At the 24-week follow-up (T3), all participants (Groups 1 and 2) received a second treatment of anodal bilateral motor cortex and cathodal spinal stimulation (real tDCS) for five days/week for two weeks (T4). Follow-up evaluations were performed at 32-weeks (T5) and 48-weeks (T6) (open-label phase). At each time point, clinical assessment, blood sampling, and intracortical connectivity measures using transcranial magnetic stimulation (TMS) were evaluated. Additionally, we evaluated survival rates. RESULTS: Compared to sham stimulation, cortico-spinal tDCS significantly improved global strength, caregiver burden, and quality of life scores, which correlated with the restoration of intracortical connectivity measures. Serum neurofilament light levels decreased among patients who underwent real tDCS but not in those receiving sham tDCS. The number of completed 2-week tDCS treatments significantly influenced patient survival. CONCLUSIONS: Cortico-spinal tDCS may represent a promising therapeutic and rehabilitative approach for patients with ALS. Further larger-scale studies are necessary to evaluate whether tDCS could potentially impact patient survival. CLINICAL TRIAL REGISTRATION: NCT04293484.

Differential impact of the COVID-19 pandemic on primary care utilization related to common mental disorders in four European countries: A retrospective observational study.

Background: The COVID-19 pandemic is commonly believed to have increased common mental disorders (CMD, i.e., depression and anxiety), either directly due to COVID-19 contractions (death of near ones or residual conditions), or indirectly by increasing stress, economic uncertainty, and disruptions in daily life resulting from containment measure. Whereas studies reporting on initial changes in self-reported data frequently have reported increases in CMD, pandemic related changes in CMD related to primary care utilization are less well known. Analyzing time series of routinely and continuously sampled primary healthcare data from Sweden, Norway, Netherlands, and Latvia, we aimed to characterize the impact of the pandemic on CMD recorded prevalence in primary care. Furthermore, by relating these changes to country specific time-trajectories of two classes of containment measures, we evaluated the differential impact of containment strategies on CMD rates. Specifically, we wanted to test whether school restrictions would preferentially affect age groups corresponding to those of school children or their parents. Methods: For the four investigated countries, we collected time-series of monthly counts of unique CMD patients in primary healthcare from the year 2015 (or 2017) until 2021. Using pre-pandemic timepoints to train seasonal Auto Regressive Integrated Moving Average (ARIMA) models, we predicted healthcare utilization during the pandemic. Discrepancies between observed and expected time series were quantified to infer pandemic related changes. To evaluate the effects of COVID-19 measures on CMD related primary care utilization, the predicted time series were related to country specific time series of levels of social distancing and school restrictions. Results: In all countries except Latvia there was an initial (April 2020) decrease in CMD care prevalence, where largest drops were found in Sweden (Prevalence Ratio, PR = 0.85; 95% CI 0.81-0.90), followed by Netherlands (0.86; 95% CI 0.76-1.02) and Norway (0.90; 95% CI 0.83-0.98). Latvia on the other hand experienced increased rates (1.25; 95% CI 1.08-1.49). Whereas PRs in Norway and Netherlands normalized during the latter half of 2020, PRs stayed low in Sweden and elevated in Latvia. The overall changes in PR during the pandemic year 2020 was significantly changed only for Sweden (0.91; 95% CI 0.90-0.93) and Latvia (1.20; 95% CI 1.14-1.26). Overall, the relationship between containment measures and CMD care prevalence were weak and non-significant. In particular, we could not observe any relationship of school restriction to CMD care prevalence for the age groups best corresponding to school children or their parents. Conclusion: Common mental disorders prevalence in primary care decreased during the initial phase of the COVID-19 pandemic in all countries except from Latvia, but normalized in Norway and Netherlands by the latter half of 2020. The onset of the pandemic and the containment strategies were highly correlated within each country, limiting strong conclusions on whether restriction policy had any effects on mental health. Specifically, we found no evidence of associations between school restrictions and CMD care prevalence. Overall, current results lend no support to the common belief that the pandemic severely impacted the mental health of the general population as indicated by healthcare utilization, apart from in Latvia. However, since healthcare utilization is affected by multiple factors in addition to actual need, future studies should combine complementary types of data to better understand the mental health impacts of the pandemic.