An Epidemiological Human Disease Network Derived from Disease Co-occurrence in Taiwan

Complex networks approach to study comorbidities in patients with unruptured intracranial aneurysms

The role of complex network analysis in patients with diagnosis of unruptured intracranial aneurysm is unexplored. The objective of this study is to assess the applicability of this methodology in aneurysm patients. We retrospectively analyze comprehensive unbiased local digital data of a large number of patients treated for any reason between January 2004 and July 2019. We apply an age-cohort approach to a total of 628,831 patients and construct the diagnostic history of each patient-and include the information how old the patient was when diagnosed for the first time with each diagnosis coded according to International Classification of Diseases. For each cohort of age within a 10 year interval and for each gender, we construct a statistically validated comorbidity network and focused on crucial comorbidity links that the aneurysm code has to other disease codes within the whole network. For all cohorts of different age and gender, the analysis shows that 267 diagnose codes have nearest neighbour statistically validated links to unruptured aneurysm ICD code. Among the 267 comorbidities, 204 (76%) were found in patients aged from 40 to 69-years old. Patterns of connectivity with aneurysms were found for smoking, hypertension, chronic obstructive pulmonary disease, dyslipidemia, and mood disorders. A few uncommon connections are also detected in cohorts of female patients. Our study explored the applicability of network analysis and statistical validation in aneurysm observational study.

Defining the distance between diseases using SNOMED CT embeddings

Characterizing disease relationships is essential to biomedical research to understand disease etiology and improve clinical decision-making. Measurements of distance between disease pairs enable valuable research tasks, such as subgrouping patients and identifying common time courses of disease onset. Distance metrics developed in prior work focused on smaller, targeted disease sets. Distance metrics covering all diseases have not yet been defined, which limits the applications to a broader disease spectrum. Our current study defines disease distances for all disease pairs within the International Classification of Diseases, version 10 (ICD-10), the diagnostic classification system universally used in electronic health records. Our proposed distance is computed based on a biomedical ontology, SNOMED CT (Systemized Nomenclature of Medicine, Clinical Terms), which can also be viewed as a structured knowledge graph. We compared the knowledge graph-based metric to three other distance metrics based on the hierarchical structure of ICD, clinical comorbidity, and genetic correlation, to evaluate how each may capture similar or unique aspects of disease relationships. We show that our knowledge graph-based distance metric captures known phenotypic, clinical, and molecular characteristics at a finer granularity than the other three. With the continued growth of using electronic health records data for research, we believe that our distance metric will play an important role in subgrouping patients for precision health, and enabling individualized disease prevention and treatments.

Patterns of Outpatient Phecodes Predating the Diagnosis of Systemic Lupus Erythematosus in Taiwanese Women

Shortening the time to diagnosis and initiating early treatment are imperative to improve outcomes in patients with systemic lupus erythematosus (SLE). The aim of this case-control study, based on the data from the Taiwan’s National Health Insurance Research Database (NHIRD), was to investigate the patterns of diagnoses of disease phenotypes in female patients with SLE up to eight years prior to its definitive diagnosis. The 547 cases were selected from the 2000–2012 NHIRD catastrophic illness datafile and frequency-matched with 2188 controls. The primary diagnosis based on the first ICD-9-CM code for each outpatient visit was converted to Phecodes. Separate regression models, based on least absolute shrinkage and selection operator (lasso) regularization, with seven different lag periods from 1–2 to 7–8 years, were conducted. Results showed that SLE was associated with 46 disease phenotypes in a lag period of 2–3 years, but fewer in other lag periods. A number of SLE-associated disease phenotypes, such as primary thrombocytopenia, thyroid diseases, Raynaud’s syndrome, renal disease, and several infectious diseases, occurred mainly in the first few years prior to SLE diagnosis. In conclusion, SLE should be suspected when the disease phenotypes identified in the present study occurred concomitantly.

Nature and Extent of Physical Comorbidities Among Korean Patients With Mental Illnesses: Pairwise and Network Analysis Based on Health Insurance Claims Data

OBJECTIVE

The nature of physical comorbidities in patients with mental illness may differ according to diagnosis and personal characteristics. We investigated this complexity by conventional logistic regression and network analysis.

METHODS

A health insurance claims data in Korea was analyzed. For every combination of psychiatric and physical diagnoses, odds ratios were calculated adjusting age and sex. From the patient-diagnosis data, a network of diagnoses was constructed using Jaccard coefficient as the index of comorbidity.

RESULTS

In 1,017,024 individuals, 77,447 (7.6%) were diagnosed with mental illnesses. The number of physical diagnoses among them was 11.2, which was 1.6 times higher than non-psychiatric groups. The most noticeable associations were 1) neurotic illnesses with gastrointestinal/pain disorders and 2) dementia with fracture, Parkinson's disease, and cerebrovascular accidents. Unexpectedly, the diagnosis of metabolic syndrome was only scarcely found in patients with severe mental illnesses (SMIs). However, implicit associations between metabolic syndrome and SMIs were suggested in comorbidity networks.

CONCLUSION

Physical comorbidities in patients with mental illnesses were more extensive than those with other disease categories. However, the result raised questions as to whether the medical resources were being diverted to less serious conditions than more urgent conditions in patients with SMIs.

Age- and Sex-Specific Differences in Multimorbidity Patterns and Temporal Trends on Assessing Hospital Discharge Records in Southwest China: Network-Based Study

Background

Multimorbidity represents a global health challenge, which requires a more global understanding of multimorbidity patterns and trends. However, the majority of studies completed to date have often relied on self-reported conditions, and a simultaneous assessment of the entire spectrum of chronic disease co-occurrence, especially in developing regions, has not yet been performed.

Objective

We attempted to provide a multidimensional approach to understand the full spectrum of chronic disease co-occurrence among general inpatients in southwest China, in order to investigate multimorbidity patterns and temporal trends, and assess their age and sex differences.

Methods

We conducted a retrospective cohort analysis based on 8.8 million hospital discharge records of about 5.0 million individuals of all ages from 2015 to 2019 in a megacity in southwest China. We examined all chronic diagnoses using the ICD-10 (International Classification of Diseases, 10th revision) codes at 3 digits and focused on chronic diseases with ≥1% prevalence for each of the age and sex strata, which resulted in a total of 149 and 145 chronic diseases in males and females, respectively. We constructed multimorbidity networks in the general population based on sex and age, and used the cosine index to measure the co-occurrence of chronic diseases. Then, we divided the networks into communities and assessed their temporal trends.

Results

The results showed complex interactions among chronic diseases, with more intensive connections among males and inpatients ≥40 years old. A total of 9 chronic diseases were simultaneously classified as central diseases, hubs, and bursts in the multimorbidity networks. Among them, 5 diseases were common to both males and females, including hypertension, chronic ischemic heart disease, cerebral infarction, other cerebrovascular diseases, and atherosclerosis. The earliest leaps (degree leaps ≥6) appeared at a disorder of glycoprotein metabolism that happened at 25-29 years in males, about 15 years earlier than in females. The number of chronic diseases in the community increased over time, but the new entrants did not replace the root of the community.

Conclusions

Our multimorbidity network analysis identified specific differences in the co-occurrence of chronic diagnoses by sex and age, which could help in the design of clinical interventions for inpatient multimorbidity.

Characterizing chronological accumulation of comorbidities in healthy veterans: a computational approach

Understanding patient accumulation of comorbidities can facilitate healthcare strategy and personalized preventative care. We applied a directed network graph to electronic health record (EHR) data and characterized comorbidities in a cohort of healthy veterans undergoing screening colonoscopy. The Veterans Affairs Cooperative Studies Program #380 was a prospective longitudinal study of screening and surveillance colonoscopy. We identified initial instances of three-digit ICD-9 diagnoses for participants with at least 5 years of linked EHR history (October 1999 to December 2015). For diagnoses affecting at least 10% of patients, we calculated pairwise chronological relative risk (RR). iGraph was used to produce directed graphs of comorbidities with RR > 1, as well as summary statistics, key diseases, and communities. A directed graph based on 2210 patients visualized longitudinal development of comorbidities. Top hub (preceding) diseases included ischemic heart disease, inflammatory and toxic neuropathy, and diabetes. Top authority (subsequent) diagnoses were acute kidney failure and hypertensive chronic kidney failure. Four communities of correlated comorbidities were identified. Close analysis of top hub and authority diagnoses demonstrated known relationships, correlated sequelae, and novel hypotheses. Directed network graphs portray chronologic comorbidity relationships. We identified relationships between comorbid diagnoses in this aging veteran cohort. This may direct healthcare prioritization and personalized care.

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.

Association of fine-particulate and acidic-gas air pollution with premenstrual syndrome risk

OBJECTIVE

Air pollution had been reported to be associated with the reproductive health of women. However, the association of particulate matter (PM) and acid gases air pollution with premenstrual syndrome (PMS) warrants investigation. This study investigated the effects of air pollution on PMS risk.

POPULATION

We combined data from the Taiwan Air Quality-Monitoring Database and the Longitudinal Health Insurance Database. In total, an observational cohort of 85 078 Taiwanese women not diagnosed as having PMS.

METHODS

Air pollutant concentrations were grouped into four levels based on the concentration quartiles of several types of air pollutants.

MAIN OUTCOME MEASURES

We then applied univariable and multivariable Cox proportional hazard regression models to assess PMS risk in association with each pollutant type.

RESULTS

Women exposed to Q4-level SO2 exhibited a 7.77 times higher PMS risk compared with those to Q1-level SO2 (95% confidence interval [CI] = 6.22-9.71). Women exposed to Q4-level NOx exhibited a 2.86 times higher PMS risk compared with those exposed to Q1-level NOx (95% CI = 2.39-3.43). Women exposed to Q4-level NO exhibited a 3.17 times higher PMS risk compared with women exposed to Q1-level NO (95% CI = 2.68-3.75). Finally, women exposed to Q4-level PM with a ≤2.5-µm diameter (PM2.5) exhibited a 3.41 times higher PMS risk compared with those exposed to Q1-level PM2.5 (95% CI = 2.88-4.04).

CONCLUSIONS

High incidences of PMS were noted in women who lived in areas with higher concentrations of SO2, NOx, NO, NO2 and PM2.5.

Visualizing COVID-19 pandemic risk through network connectedness

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This study presents a novel application of network analysis in healthcare.

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This analysis provides a direct visualization of the COVID-19 pandemic risk.

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The pandemic risk is visualized through the degree of connectedness.

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We present a general network concept for the continuous assessment of pandemic risk.

With the domestic and international spread of the coronavirus disease 2019 (COVID-19), much attention has been given to estimating pandemic risk. We propose the novel application of a well-established scientific approach – the network analysis – to provide a direct visualization of the COVID-19 pandemic risk; infographics are provided in the figures. By showing visually the degree of connectedness between different regions based on reported confirmed cases of COVID-19, we demonstrate that network analysis provides a relatively simple yet powerful way to estimate the pandemic risk.

PedMap: a pediatric diseases map generated from clinical big data from Hangzhou, China

Epidemiological knowledge of pediatric diseases may improve professionals' understanding of the pathophysiology of and risk factors for diseases and is also crucial for decision making related to workforce and resource planning in pediatric departments. In this study, a pediatric disease epidemiology knowledgebase called PedMap (http://pedmap.nbscn.org) was constructed from the clinical data from 5 447 202 outpatient visits of 2 189 868 unique patients at a children's hospital (Hangzhou, China) from 2013 to 2016. The top 100 most-reported pediatric diseases were identified and visualized. These common pediatric diseases were clustered into 4 age groups and 4 seasons. The prevalence, age distribution and co-occurrence diseases for each disease were also visualized. Furthermore, an online prediction tool based on Gaussian regression models was developed to predict pediatric disease incidence based on weather information. PedMap is the first comprehensive epidemiological resource to show the full view of age-related, seasonal, climate-related variations in and co-occurrence patterns of pediatric diseases.

Clustering of immune-mediated diseases in sarcoidosis

PURPOSE OF REVIEW

Sarcoidosis is an immune-mediated disease of unknown cause. Immune-mediated diseases appear to cluster in patients and in families. We review what is known on this topic for sarcoidosis, and what factors may underlie disease clustering.

RECENT FINDINGS

In populations of patients with sarcoidosis, relative risk estimates of Sjögren's syndrome, systemic lupus erythematosus, autoimmune hepatitis, ankylosing spondylitis, multiple sclerosis (MS), celiac disease, autoimmune thyroid disease, and ulcerative colitis, varied between 2.1 and 11.6. In relatives of patients with sarcoidosis, relative risk estimates varied between 1.3 and 5.8 for sarcoidosis, MS, celiac disease, type 1 diabetes, Graves' disease, rheumatoid arthritis, Crohn's disease, and ulcerative colitis. Shared risk loci in key immunological pathways provide evidence for a contribution to development of multiple diseases. Identical changes in the immune status, epigenetic alterations, and environmental triggers have been detected in several diseases, and drug-induced disease is likely responsible for a small portion of co-occurring disease.

SUMMARY

Clustering of sarcoidosis and other immune-mediated diseases in patients and in their relatives occurs for sarcoidosis, MS, celiac disease, Graves' disease, and ulcerative colitis. Further research is needed to substantiate causal links and risk estimates in patients and their relatives.

Air pollution as cause of mental disease: Appraisal of the evidence

A causal association of air pollution with mental diseases is an intriguing possibility raised in a Short Report just published in PLOS Biology. Despite analyses involving large data sets, the available evidence has substantial shortcomings, and a long series of potential biases may invalidate the observed associations. Only bipolar disorder shows consistent results, with similar effects across United States and Denmark data sets, but the effect has modest magnitude, appropriate temporality is not fully secured, and biological gradient, plausibility, coherence, and analogy offer weak support. The signal seems to persist in some robustness analyses, but more analyses by multiple investigators, including contrarians, are necessary. Broader public sharing of data sets would also enhance transparency.