MorbidGCN: prediction of multimorbidity with a graph convolutional network based on integration of population phenotypes and disease network.

Exploring multimorbidity relationships among diseases is of great importance for understanding their shared mechanisms, precise diagnosis and treatment. However, the landscape of multimorbidities is still far from complete due to the complex nature of multimorbidity. Although various types of biological data, such as biomolecules and clinical symptoms, have been used to identify multimorbidities, the population phenotype information (e.g. physical activity and diet) remains less explored for multimorbidity. Here, we present a graph convolutional network (GCN) model, named MorbidGCN, for multimorbidity prediction by integrating population phenotypes and disease network. Specifically, MorbidGCN treats the multimorbidity prediction as a missing link prediction problem in the disease network, where a novel feature selection method is embedded to select important phenotypes. Benchmarking results on two large-scale multimorbidity data sets, i.e. the UK Biobank (UKB) and Human Disease Network (HuDiNe) data sets, demonstrate that MorbidGCN outperforms other competitive methods. With MorbidGCN, 9742 and 14 010 novel multimorbidities are identified in the UKB and HuDiNe data sets, respectively. Moreover, we notice that the selected phenotypes that are generally differentially distributed between multimorbidity patients and single-disease patients can help interpret multimorbidities and show potential for prognosis of multimorbidities.

Undiagnosed Disease Network collaborative approach in diagnosing rare disease in a patient with a mosaic CACNA1D variant.

The Undiagnosed Disease Network (UDN) is comprised of clinical and research experts collaborating to diagnose rare disease. The UDN is funded by the National Institutes of Health and includes 12 different clinical sites (About Us, 2022). Here we highlight the success of collaborative efforts within the UDN Clinical Site at Vanderbilt University Medical Center (VUMC) in utilizing a cohort of experts in bioinformatics, structural biology, and genetics specialists in diagnosing rare disease. Our UDN team identified a de novo mosaic CACNA1D variant c.2299T>C in a 5-year-old female with a history of global developmental delay, dystonia, dyskinesis, and seizures. Using a collaborative multidisciplinary approach, our VUMC UDN team diagnosed the participant with Primary Aldosteronism, Seizures, and Neurologic abnormalities (PASNA) OMIM: 615474 due to a rare mosaic CACNA1D variant (O'Neill, 2013). Interestingly, this patient was mosaic, a phenotypic trait previously unreported in PASNA cases. This report highlights the importance of a multidisciplinary approach in diagnosing rare disease.

Standardised reporting of burden of disease studies: the STROBOD statement.

BACKGROUND: The burden of disease (BOD) approach, originating with the Global Burden of Disease (GBD) study in the 1990s, has become a cornerstone for population health monitoring. Despite the widespread use of the Disability-Adjusted Life Year (DALY) metric, variations in methodological approaches and reporting inconsistencies hinder comparability across studies. To tackle this issue, we set out to develop guidelines for reporting DALY calculation studies to improve the transparency and comparability of BOD estimates. METHODS AND FINDINGS: The development of the STROBOD statement began within the European Burden of Disease Network, evolving from initial concepts discussed in workshops and training sessions focused on critical analysis of BOD studies. In 2021, a working group was formed to refine the preliminary version into the final Standardised Reporting of Burden of Disease studies (STROBOD) statement, consisting of 28 items structured across six main sections. These sections cover the title, abstract, introduction, methods, results, discussion, and open science, aiming to ensure transparency and standardization in reporting BOD studies. Notably, the methods section of the STROBOD checklist encompasses aspects such as study setting, data inputs and adjustments, DALY calculation methods, uncertainty analyses, and recommendations for reproducibility and transparency. A pilot phase was conducted to test the efficacy of the STROBOD statement, highlighting the importance of providing clear explanations and examples for each reporting item. CONCLUSIONS: The inaugural STROBOD statement offers a crucial framework for standardizing reporting in BOD research, with plans for ongoing evaluation and potential revisions based on user feedback. While the current version focuses on general BOD methodology, future iterations may include specialized checklists for distinct applications such as injury or risk factor estimation, reflecting the dynamic nature of this field.

Vector-borne disease models with Lagrangian approach.

We develop a multi-group and multi-patch model to study the effects of population dispersal on the spatial spread of vector-borne diseases across a heterogeneous environment. The movement of host and/or vector is described by Lagrangian approach in which the origin or identity of each individual stays unchanged regardless of movement. The basic reproduction number [Formula: see text] of the model is defined and the strong connectivity of the host-vector network is succinctly characterized by the residence times matrices of hosts and vectors. Furthermore, the definition and criterion of the strong connectivity of general infectious disease networks are given and applied to establish the global stability of the disease-free equilibrium. The global dynamics of the model system are shown to be entirely determined by its basic reproduction number. We then obtain several biologically meaningful upper and lower bounds on the basic reproduction number which are independent or dependent of the residence times matrices. In particular, the heterogeneous mixing of hosts and vectors in a homogeneous environment always increases the basic reproduction number. There is a substantial difference on the upper bound of [Formula: see text] between Lagrangian and Eulerian modeling approaches. When only host movement between two patches is concerned, the subdivision of hosts (more host groups) can lead to a larger basic reproduction number. In addition, we numerically investigate the dependence of the basic reproduction number and the total number of infected hosts on the residence times matrix of hosts, and compare the impact of different vector control strategies on disease transmission.

The state of health in the European Union (EU-27) in 2019: a systematic analysis for the Global Burden of Disease study 2019.

BACKGROUND: The European Union (EU) faces many health-related challenges. Burden of diseases information and the resulting trends over time are essential for health planning. This paper reports estimates of disease burden in the EU and individual 27 EU countries in 2019, and compares them with those in 2010. METHODS: We used the Global Burden of Disease 2019 study estimates and 95% uncertainty intervals for the whole EU and each country to evaluate age-standardised death, years of life lost (YLLs), years lived with disability (YLDs) and disability-adjusted life years (DALYs) rates for Level 2 causes, as well as life expectancy and healthy life expectancy (HALE). RESULTS: In 2019, the age-standardised death and DALY rates in the EU were 465.8 deaths and 20,251.0 DALYs per 100,000 inhabitants, respectively. Between 2010 and 2019, there were significant decreases in age-standardised death and YLL rates across EU countries. However, YLD rates remained mainly unchanged. The largest decreases in age-standardised DALY rates were observed for "HIV/AIDS and sexually transmitted diseases" and "transport injuries" (each -19%). "Diabetes and kidney diseases" showed a significant increase for age-standardised DALY rates across the EU (3.5%). In addition, "mental disorders" showed an increasing age-standardised YLL rate (14.5%). CONCLUSIONS: There was a clear trend towards improvement in the overall health status of the EU but with differences between countries. EU health policymakers need to address the burden of diseases, paying specific attention to causes such as mental disorders. There are many opportunities for mutual learning among otherwise similar countries with different patterns of disease.

Acute kidney injury comorbidity analysis based on international classification of diseases-10 codes.

OBJECTIVE: Acute kidney injury (AKI) is a clinical syndrome that occurs as a result of a dramatic decline in kidney function caused by a variety of etiological factors. Its main biomarkers, serum creatinine and urine output, are not effective in diagnosing early AKI. For this reason, this study provides insight into this syndrome by exploring the comorbidities of AKI, which may facilitate the early diagnosis of AKI. In addition, organ crosstalk in AKI was systematically explored based on comorbidities to obtain clinically reliable results. METHODS: We collected data from the Medical Information Mart for Intensive Care-IV database on patients aged [Formula: see text] 18 years in intensive care units (ICU) who were diagnosed with AKI using the criteria proposed by Kidney Disease: Improving Global Outcomes. The Apriori algorithm was used to mine association rules on the diagnoses of 55,486 AKI and non-AKI patients in the ICU. The comorbidities of AKI mined were validated through the Electronic Intensive Care Unit database, the Colombian Open Health Database, and medical literature, after which comorbidity results were visualized using a disease network. Finally, organ diseases were identified and classified from comorbidities to investigate renal crosstalk with other distant organs in AKI. RESULTS: We found 579 AKI comorbidities, and the main ones were disorders of lipoprotein metabolism, essential hypertension, and disorders of fluid, electrolyte, and acid-base balance. Of the 579 comorbidities, 554 were verifiable and 25 were new and not previously reported. In addition, crosstalk between the kidneys and distant non-renal organs including the liver, heart, brain, lungs, and gut was observed in AKI with the strongest heart-kidney crosstalk, followed by lung-kidney crosstalk. CONCLUSION: The comorbidities mined in this study using association rules are scientific and may be used for the early diagnosis of AKI and the construction of AKI predictive models. Furthermore, the organ crosstalk results obtained through comorbidities may provide supporting information for the management of short- and long-term treatment practices for organ dysfunction.

Recent advances in characterization of citrullination and its implication in human disease research: From method development to network integration.

Post-translational modifications (PTM) of proteins increase the functional diversity of the proteome and have been implicated in the pathogenesis of numerous diseases. The most widely understood modifications include phosphorylation, methylation, acetylation, O-linked/N-linked glycosylation, and ubiquitination, all of which have been extensively studied and documented. Citrullination is a historically less explored, yet increasingly studied, protein PTM which has profound effects on protein conformation and protein-protein interactions. Dysregulation of protein citrullination has been associated with disease development and progression. Identification and characterization of citrullinated proteins is highly challenging, complicated by the low cellular abundance of citrullinated proteins, making it difficult to identify and quantify the extent of citrullination in samples, coupled with challenges associated with development of mass spectrometry (MS)-based methods, as the corresponding mass shift is relatively small, +0.984 Da, and identical to the mass shift of deamidation. The focus of this review is to discuss recent advancements of citrullination-specific MS approaches and integration of the potential methodology for improved citrullination identification and characterization. In addition, the association of citrullination in disease networks is also highlighted.

Human disease clinical treatment network for the elderly: analysis of the medicare inpatient length of stay and readmission data.

Clinical treatment outcomes are the quality and cost targets that health-care providers aim to improve. Most existing outcome analysis focuses on a single disease or all diseases combined. Motivated by the success of molecular and phenotypic human disease networks (HDNs), this article develops a clinical treatment network that describes the interconnections among diseases in terms of inpatient length of stay (LOS) and readmission. Here one node represents one disease, and two nodes are linked with an edge if their LOS and number of readmissions are conditionally dependent. This is the very first HDN that jointly analyzes multiple clinical treatment outcomes at the pan-disease level. To accommodate the unique data characteristics, we propose a modeling approach based on two-part generalized linear models and estimation based on penalized integrative analysis. Analysis is conducted on the Medicare inpatient data of 100,000 randomly selected subjects for the period of January 2010 to December 2018. The resulted network has 1008 edges for 106 nodes. We analyze key network properties including connectivity, module/hub, and temporal variation. The findings are biomedically sensible. For example, high connectivity and hub conditions, such as disorders of lipid metabolism and essential hypertension, are identified. There are also findings that are less/not investigated in the literature. Overall, this study can provide additional insight into diseases' properties and their interconnections and assist more efficient disease management and health-care resources allocation.

The increasing significance of disease severity in a burden of disease framework.

Recent estimates have reiterated that non-fatal causes of disease, such as low back pain, headaches and depressive disorders, are amongst the leading causes of disability-adjusted life years (DALYs). For these causes, the contribution of years lived with disability (YLD) - put simply, ill-health - is what drives DALYs, not mortality. Being able to monitor trends in YLD closely is particularly relevant for countries that sit high on the socio-demographic spectrum of development, as it contributes more than half of all DALYs. There is a paucity of data on how the population-level occurrence of disease is distributed according to severity, and as such, the majority of global and national efforts in monitoring YLD lack the ability to differentiate changes in severity across time and location. This raises uncertainties in interpreting these findings without triangulation with other relevant data sources. Our commentary aims to bring this issue to the forefront for users of burden of disease estimates, as its impact is often easily overlooked as part of the fundamental process of generating DALY estimates. Moreover, the wider health harms of the COVID-19 pandemic have underlined the likelihood of latent and delayed demand in accessing vital health and care services that will ultimately lead to exacerbated disease severity and health outcomes. This places increased importance on attempts to be able to differentiate by both the occurrence and severity of disease.

Using a Network-Based Analysis Approach to Investigate the Involvement of S. aureus in the Pathogenesis of Granulomatosis with Polyangiitis.

Chronic nasal carriage of Staphylococcus aureus (SA) has been shown to be significantly higher in GPA patients when compared to healthy subjects, as well as being associated with increased endonasal activity and disease relapse. The aim of this study was to investigate SA involvement in GPA by applying a network-based analysis (NBA) approach to publicly available nasal transcriptomic data. Using these data, our NBA pipeline generated a proteinase 3 (PR3) positive ANCA associated vasculitis (AAV) disease network integrating differentially expressed genes, dysregulated transcription factors (TFs), disease-specific genes derived from GWAS studies, drug-target and protein-protein interactions. The PR3+ AAV disease network captured genes previously reported to be dysregulated in AAV associated. A subnetwork focussing on interactions between SA virulence factors and enriched biological processes revealed potential mechanisms for SA's involvement in PR3+ AAV. Immunosuppressant treatment reduced differential expression and absolute TF activities in this subnetwork for patients with inactive nasal disease but not active nasal disease symptoms at the time of sampling. The disease network generated identified the key molecular signatures and highlighted the associated biological processes in PR3+ AAV and revealed potential mechanisms for SA to affect these processes.

Computationally repurposing drugs for breast cancer subtypes using a network-based approach.

'De novo' drug discovery is costly, slow, and with high risk. Repurposing known drugs for treatment of other diseases offers a fast, low-cost/risk and highly-efficient method toward development of efficacious treatments. The emergence of large-scale heterogeneous biomolecular networks, molecular, chemical and bioactivity data, and genomic and phenotypic data of pharmacological compounds is enabling the development of new area of drug repurposing called 'in silico' drug repurposing, i.e., computational drug repurposing (CDR). The aim of CDR is to discover new indications for an existing drug (drug-centric) or to identify effective drugs for a disease (disease-centric). Both drug-centric and disease-centric approaches have the common challenge of either assessing the similarity or connections between drugs and diseases. However, traditional CDR is fraught with many challenges due to the underlying complex pharmacology and biology of diseases, genes, and drugs, as well as the complexity of their associations. As such, capturing highly non-linear associations among drugs, genes, diseases by most existing CDR methods has been challenging. We propose a network-based integration approach that can best capture knowledge (and complex relationships) contained within and between drugs, genes and disease data. A network-based machine learning approach is applied thereafter by using the extracted knowledge and relationships in order to identify single and pair of approved or experimental drugs with potential therapeutic effects on different breast cancer subtypes. Indeed, further clinical analysis is needed to confirm the therapeutic effects of identified drugs on each breast cancer subtype.

A disease network-based deep learning approach for characterizing melanoma.

Multiple types of genomic variations are present in cutaneous melanoma and some of the genomic features may have an impact on the prognosis of the disease. The access to genomics data via public repositories such as The Cancer Genome Atlas (TCGA) allows for a better understanding of melanoma at the molecular level, therefore making characterization of substantial heterogeneity in melanoma patients possible. Here, we proposed an approach that integrates genomics data, a disease network, and a deep learning model to classify melanoma patients for prognosis, assess the impact of genomic features on the classification and provide interpretation to the impactful features. We integrated genomics data into a melanoma network and applied an autoencoder model to identify subgroups in TCGA melanoma patients. The model utilizes communities identified in the network to effectively reduce the dimensionality of genomics data into a patient score profile. Based on the score profile, we identified three patient subtypes that show different survival times. Furthermore, we quantified and ranked the impact of genomic features on the patient score profile using a machine-learning technique. Follow-up analysis of the top-ranking features provided us with the biological interpretation of them at both pathway and molecular levels, such as their mutation and interactome profiles in melanoma and their involvement in pathways associated with signaling transduction, immune system and cell cycle. Taken together, we demonstrated the ability of the approach to identify disease subgroups using a deep learning model that captures the most relevant information of genomics data in the melanoma network.

Human-Disease Phenotype Map Derived from PheWAS across 38,682 Individuals.

Phenome-wide association studies (PheWASs) have been a useful tool for testing associations between genetic variations and multiple complex traits or diagnoses. Linking PheWAS-based associations between phenotypes and a variant or a genomic region into a network provides a new way to investigate cross-phenotype associations, and it might broaden the understanding of genetic architecture that exists between diagnoses, genes, and pleiotropy. We created a network of associations from one of the largest PheWASs on electronic health record (EHR)-derived phenotypes across 38,682 unrelated samples from the Geisinger's biobank; the samples were genotyped through the DiscovEHR project. We computed associations between 632,574 common variants and 541 diagnosis codes. Using these associations, we constructed a "disease-disease" network (DDN) wherein pairs of diseases were connected on the basis of shared associations with a given genetic variant. The DDN provides a landscape of intra-connections within the same disease classes, as well as inter-connections across disease classes. We identified clusters of diseases with known biological connections, such as autoimmune disorders (type 1 diabetes, rheumatoid arthritis, and multiple sclerosis) and cardiovascular disorders. Previously unreported relationships between multiple diseases were identified on the basis of genetic associations as well. The network approach applied in this study can be used to uncover interactions between diseases as a result of their shared, potentially pleiotropic SNPs. Additionally, this approach might advance clinical research and even clinical practice by accelerating our understanding of disease mechanisms on the basis of similar underlying genetic associations.

High-quality gene/disease embedding in a multi-relational heterogeneous graph after a joint matrix/tensor decomposition.

MOTIVATION: Node embedding of biological entity network has been widely investigated for the downstream application scenarios. To embed full semantics of gene and disease, a multi-relational heterogeneous graph is considered in a scenario where uni-relation between gene/disease and other heterogeneous entities are abundant while multi-relation between gene and disease is relatively sparse. After introducing this novel graph format, it is illuminative to design a specific data integration algorithm to fully capture the graph information and bring embeddings with high quality. RESULTS: First, a typical multi-relational triple dataset was introduced, which carried significant association between gene and disease. Second, we curated all human genes and diseases in seven mainstream datasets and constructed a large-scale gene-disease network, which compromising 163,024 nodes and 25,265,607 edges, and relates to 27,165 genes, 2,665 diseases, 15,067 chemicals, 108,023 mutations, 2,363 pathways, and 7.732 phenotypes. Third, we proposed a Joint Decomposition of Heterogeneous Matrix and Tensor (JDHMT) model, which integrated all heterogeneous data resources and obtained embedding for each gene or disease. Forth, a visualized intrinsic evaluation was performed, which investigated the embeddings in terms of interpretable data clustering. Furthermore, an extrinsic evaluation was performed in the form of linking prediction. Both intrinsic and extrinsic evaluation results showed that JDHMT model outperformed other eleven state-of-the-art (SOTA) methods which are under relation-learning, proximity-preserving or message-passing paradigms. Finally, the constructed gene-disease network, embedding results and codes were made available. DATA AND CODES AVAILABILITY: The constructed massive gene-disease network is available at: https://hzaubionlp.com/heterogeneous-biological-network/. The codes are available at: https://github.com/bionlp-hzau/JDHMT.

Maternal disease factors associated with neonatal jaundice: a case-control study.

BACKGROUND: Neonatal jaundice is common, and despite the considerable medical costs associated with it, there are still few studies on the maternal factors associated with it. Identification of maternal factors associated with neonatal jaundice is very important in terms of prevention, screening and management of neonatal jaundice. The current study aimed to identify maternal disease factors associated with neonatal jaundice. METHODS: We compared the maternal disease diagnostic codes during pregnancy (study A) and 1 year before conception (study B) in mothers whose insurance claims data included newborns treated for neonatal jaundice before birth registration via the National Health Insurance Service-National Sample Cohort (control group). To decrease the effect of confounding variables, the neonatal jaundice and control groups were matched at a ratio of 1:10 via propensity score matching using covariates including age and income. RESULTS: The matched samples for studies A and B included 4,026 and 3,278 (jaundice group: 366 and 298) delivery cases, respectively. In both studies, the jaundice group had a higher proportion of patients who underwent cesarean section than the control group. In study A, other diseases of the digestive system had the highest odds ratio (OR) (K92; adjusted OR: 14.12, 95% confidence interval [CI]: 2.70-82.26). Meanwhile, gastritis and duodenitis had the lowest OR (K29; adjusted OR: 0.39, 95% CI: 0.22-0.69). In study B, salpingitis and oophoritis had the highest OR (N70; adjusted OR: 3.33, 95% CI: 1.59-6.94). Heartburn had the lowest OR (R12; adjusted OR: 0.29, 95% CI:0.12-0.71). CONCLUSIONS: This study identified maternal disease factors correlated with neonatal jaundice during pregnancy and 1 year before conception. Maternal risk factors for neonatal jaundice included syphilis and leiomyoma during pregnancy, and salpingo-oophoritis before pregnancy. The protective factors included infection, inflammatory diseases, and dyspepsia.

Disability adjusted life years associated with COVID-19 in Denmark in the first year of the pandemic.

BACKGROUND: Burden of disease studies measure the public health impact of a disease in a society. The aim of this study was to quantify the direct burden of COVID-19 in the first 12 months of the epidemic in Denmark. METHODS: We collected national surveillance data on positive individuals for SARS-CoV-2 with RT-PCR, hospitalization data, and COVID-19 mortality reported in the period between 26th of February, 2020 to 25th of February, 2021. We calculated disability adjusted life years (DALYs) based on the European Burden of Disease Network consensus COVID-19 model, which considers mild, severe, critical health states, and premature death. We conducted sensitivity analyses for two different death-registration scenarios, within 30 and 60 days after first positive test, respectively. RESULTS: We estimated that of the 211,823 individuals who tested positive to SARS-CoV-2 by RT-PCR in the one-year period, 124,163 (59%; 95% uncertainty interval (UI) 112,782-133,857) had at least mild symptoms of disease. The total estimated disease burden was 30,180 DALYs (95% UI 30,126; 30,242), corresponding to 520 DALYs/100,000. The disease burden was higher in the age groups above 70 years of age, particularly in men. Years of life lost (YLL) contributed with more than 99% of total DALYs. The results of the scenario analysis showed that defining COVID-19-related fatalities as deaths registered up to 30 days after the first positive test led to a lower YLL estimate than when using a 60-days window. CONCLUSION: COVID-19 led to a substantial public health impact in Denmark in the first full year of the epidemic. Our estimates suggest that it was the the sixth most frequent cause of YLL in Denmark in 2020. This impact will be higher when including the post-acute consequences of COVID-19 and indirect health outcomes.

Identifying the biomarkers and pathways associated with hepatocellular carcinoma based on an integrated analysis approach.

BACKGROUND AND AIMS: Hepatocellular carcinoma (HCC) is one of the most common causes of cancer-related death worldwide. The molecular mechanism underlying HCC is still unclear. In this study, we conducted a comprehensive analysis to explore the genes, pathways and their interactions involved in HCC. METHODS: We analysed the gene expression datasets corresponding to 488 samples from 10 studies on HCC and identified the genes differentially expressed in HCC samples. Then, the genes were compared against Phenolyzer and GeneCards to screen those potentially associated with HCC. The features of the selected genes were explored by mapping them onto the human protein-protein interaction network, and a subnetwork related to HCC was constructed. Hub genes in this HCC specific subnetwork were identified, and their relevance with HCC was investigated by survival analysis. RESULTS: We identified 444 differentially expressed genes (177 upregulated and 267 downregulated) related to HCC. Functional enrichment analysis revealed that pathways like p53 signalling and chemical carcinogenesis were eriched in HCC genes. In the subnetwork related to HCC, five disease modules were detected. Further analysis identified six hub genes from the HCC specific subnetwork. Survival analysis showed that the expression levels of these genes were negatively correlated with survival rate of HCC patients. CONCLUSIONS: Based on a systems biology framework, we identified the genes, pathways, as well as the disease specific network related to HCC. We also found novel biomarkers whose expression patterns were correlated with progression of HCC, and they could be candidates for further investigation.

Human disease clinical treatment network for the elderly: The analysis of medicare inpatient length of stay data.

Disease clinical treatment measures, such as inpatient length of stay (LOS), have been examined for most if not all diseases. Such analysis has important implications for the management and planning of health care, financial, and human resources. In addition, clinical treatment measures can also informatively reflect intrinsic disease properties such as severity. The existing studies mostly focus on either a single disease (or a few pre-selected and closely related diseases) or all diseases combined. In this study, we take a new and innovative perspective, examine the interconnections in length of stay (LOS) among diseases, and construct the very first disease clinical treatment network on LOS. To accommodate uniquely challenging data distributions, a new conditional network construction approach is developed. Based on the constructed network, the analysis of important network properties is conducted. The Medicare data on 100 000 randomly selected subjects for the period of January 2008 to December 2018 is analyzed. The network structure and key properties are found to have sensible biomedical interpretations. Being the very first of its kind, this study can be informative to disease clinical management, advance our understanding of disease interconnections, and foster complex network analysis.

Disease network delineates the disease progression profile of cardiovascular diseases.

OBJECTIVE: As Electronic Health Records (EHR) data accumulated explosively in recent years, the tremendous amount of patient clinical data provided opportunities to discover real world evidence. In this study, a graphical disease network, named progressive cardiovascular disease network (progCDN), was built to delineate the progression profiles of cardiovascular diseases (CVD). MATERIALS AND METHODS: The EHR data of 14.3 million patients with CVD diagnoses were collected for building disease network and further analysis. We applied a new designed method, progression rates (PR), to calculate the progression relationship among different diagnoses. Based on the disease network outcome, 23 disease progression pair were selected to screen for salient features. RESULTS: The network depicted the dominant diseases in CVD development, such as the heart failure and coronary arteriosclerosis. Novel progression relationships were also discovered, such as the progression path from long QT syndrome to major depression. In addition, three age-group progCDNs identified a series of age-associated disease progression paths and important successor diseases with age bias. Furthermore, a list of important features with sufficient abundance and high correlation was extracted for building disease risk models. DISCUSSION: The PR method designed for identifying the progression relationship could be widely applied in any EHR database due to its flexibility and robust functionality. Meanwhile, researchers could use the progCDN network to validate or explore novel disease relationships in real world data. CONCLUSION: The first-time interrogation of such a huge CVD patients cohort enabled us to explore the general and age-specific disease progression patterns in CVD development.

Estimating the direct Covid-19 disability-adjusted life years impact on the Malta population for the first full year.

BACKGROUND: Disability-adjusted life years (DALYs) combine the impact of morbidity and mortality, allowing for comprehensive comparisons of the population. The aim was to estimate the DALYs due to Covid-19 in Malta (March 2020-21) and investigate its impact in relation to other causes of disease at a population level. METHODS: Mortality and weekly hospital admission data were used to calculate DALYs, based on the European Burden of Disease Network consensus Covid-19 model. Covid-19 infection duration of 14 days was considered. Sensitivity analyses for different morbidity scenarios, including post-acute consequences were presented. RESULTS: An estimated 70,421 people were infected (with and without symptoms) by Covid-19 in Malta (March 2020-1), out of which 1636 required hospitalisation and 331 deaths, contributing to 5478 DALYs. These DALYs positioned Covid-19 as the fourth leading cause of disease in Malta. Mortality contributed to 95% of DALYs, while post-acute consequences contributed to 60% of morbidity. CONCLUSIONS: Covid-19 over 1 year has impacted substantially the population health in Malta. Post-acute consequences are the leading morbidity factors that require urgent targeted action to ensure timely multidisciplinary care. It is recommended that DALY estimations in 2021 and beyond are calculated to assess the impact of vaccine roll-out and emergence of new variants.