Disease trajectory browser for exploring temporal, population-wide disease progression patterns in 7.2 million Danish patients

Analysing disease trajectories in a cohort of 71,849 Patients: A visual analytics and statistical approach

BACKGROUND

Disease trajectories have become increasingly relevant within the context of an aging population and the rising prevalence of chronic illnesses. Understanding the temporal progression of diseases is crucial for enhancing patient care, preventive measures, and effective management.

OBJECTIVE

The objective of this study is to propose and validate a novel methodology for trajectory impact analysis and interactive visualization of disease trajectories over a cohort of 71,849 patients.

METHODS

This article introduces an innovative comprehensive approach for analysis and interactive visualization of disease trajectories. First, Risk Increase (RI) index is defined that assesses the impact of the initial disease diagnosis on the development of subsequent illnesses. Secondly, visual graphics methods are used to represent cohort trajectories, ensuring a clear and semantically rich presentation that facilitates easy data interpretation.

RESULTS

The proposed approach is demonstrated over the disease trajectories of a cohort comprising 71,849 patients from Tolosaldea, Spain. The study finds several clinically relevant trajectories in this cohort, such as that after suffering a cerebral ischemic stroke, the probability of suffering dementia increases 10.77 times. The clinical relevance of the study outcomes have been assessed by an in-depth analysis conducted by expert clinicians. The identified disease trajectories are in agreement with the latest advancements in the field.

CONCLUSION

The proposed approach for trajectory impact analysis and interactive visualization offers valuable graphs for the comprehensive study of disease trajectories for improved clinical decision-making. The simplicity and interpretability of our methods make them valuable approach for healthcare professionals.

Mapping multimorbidity progression among 190 diseases

BACKGROUND

Current clustering of multimorbidity based on the frequency of common disease combinations is inadequate. We estimated the causal relationships among prevalent diseases and mapped out the clusters of multimorbidity progression among them.

METHODS

In this cohort study, we examined the progression of multimorbidity among 190 diseases among over 500,000 UK Biobank participants over 12.7 years of follow-up. Using a machine learning method for causal inference, we analyzed patterns of how diseases influenced and were influenced by others in females and males. We used clustering analysis and visualization algorithms to identify multimorbidity progress constellations.

RESULTS

We show the top influential and influenced diseases largely overlap between sexes in chronic diseases, with sex-specific ones tending to be acute diseases. Patterns of diseases that influence and are influenced by other diseases also emerged (clustering significance Pau > 0.87), with the top influential diseases affecting many clusters and the top influenced diseases concentrating on a few, suggesting that complex mechanisms are at play for the diseases that increase the development of other diseases while share underlying causes exist among the diseases whose development are increased by others. Bi-directional multimorbidity progress presents substantial clustering tendencies both within and across International Classification Disease chapters, compared to uni-directional ones, which can inform future studies for developing cross-specialty strategies for multimorbidity. Finally, we identify 10 multimorbidity progress constellations for females and 9 for males (clustering stability, adjusted Rand index >0.75), showing interesting differences between sexes.

CONCLUSION

Our findings could inform the future development of targeted interventions and provide an essential foundation for future studies seeking to improve the prevention and management of multimorbidity.

Young adults with a 22q11.2 microdeletion and the cost of aging with complexity in a population-based context

PURPOSE

Information about the impact on the adult health care system is limited for complex rare pediatric diseases, despite their increasing collective prevalence that has paralleled advances in clinical care of children. Within a population-based health care context, we examined costs and multimorbidity in adults with an exemplar of contemporary genetic diagnostics.

METHODS

We estimated direct health care costs over an 18-year period for adults with molecularly confirmed 22q11.2 microdeletion (cases) and matched controls (total 60,459 person-years of data) by linking the case cohort to health administrative data for the Ontario population (∼15 million people). We used linear regression to compare the relative ratio (RR) of costs and to identify baseline predictors of higher costs.

RESULTS

Total adult (age ≥ 18) health care costs were significantly higher for cases compared with population-based (RR 8.5, 95% CI 6.5-11.1) controls, and involved all health care sectors. At study end, when median age was <30 years, case costs were comparable to population-based individuals aged 72 years, likelihood of being within the top 1st percentile of health care costs for the entire (any age) population was significantly greater for cases than controls (odds ratio [OR], for adults 17.90, 95% CI 7.43-43.14), and just 8 (2.19%) cases had a multimorbidity score of zero (vs 1483 (40.63%) controls). The 22q11.2 microdeletion was a significant predictor of higher overall health care costs after adjustment for baseline variables (RR 6.9, 95% CI 4.6-10.5).

CONCLUSION

The findings support the possible extension of integrative models of complex care used in pediatrics to adult medicine and the potential value of genetic diagnostics in adult clinical medicine.

Prevalence and differences in the co-administration of drugs known to interact: an analysis of three distinct and large populations

BACKGROUND

The co-administration of drugs known to interact greatly impacts morbidity, mortality, and health economics. This study aims to examine the drug-drug interaction (DDI) phenomenon with a large-scale longitudinal analysis of age and gender differences found in drug administration data from three distinct healthcare systems.

METHODS

This study analyzes drug administrations from population-wide electronic health records in Blumenau (Brazil; 133 K individuals), Catalonia (Spain; 5.5 M individuals), and Indianapolis (USA; 264 K individuals). The stratified prevalences of DDI for multiple severity levels per patient gender and age at the time of administration are computed, and null models are used to estimate the expected impact of polypharmacy on DDI prevalence. Finally, to study actionable strategies to reduce DDI prevalence, alternative polypharmacy regimens using drugs with fewer known interactions are simulated.

RESULTS

A large prevalence of co-administration of drugs known to interact is found in all populations, affecting 12.51%, 12.12%, and 10.06% of individuals in Blumenau, Indianapolis, and Catalonia, respectively. Despite very different healthcare systems and drug availability, the increasing prevalence of DDI as patients age is very similar across all three populations and is not explained solely by higher co-administration rates in the elderly. In general, the prevalence of DDI is significantly higher in women - with the exception of men over 50 years old in Indianapolis. Finally, we show that using proton pump inhibitor alternatives to omeprazole (the drug involved in more co-administrations in Catalonia and Blumenau), the proportion of patients that are administered known DDI can be reduced by up to 21% in both Blumenau and Catalonia and 2% in Indianapolis.

CONCLUSIONS

DDI administration has a high incidence in society, regardless of geographic, population, and healthcare management differences. Although DDI prevalence increases with age, our analysis points to a complex phenomenon that is much more prevalent than expected, suggesting comorbidities as key drivers of the increase. Furthermore, the gender differences observed in most age groups across populations are concerning in regard to gender equity in healthcare. Finally, our study exemplifies how electronic health records' analysis can lead to actionable interventions that significantly reduce the administration of known DDI and its associated human and economic costs.

Multimorbidity analysis with low condition counts: a robust Bayesian approach for small but important subgroups

BACKGROUND

Robustly examining associations between long-term conditions may be important in identifying opportunities for intervention in multimorbidity but is challenging when evidence is limited. We have developed a Bayesian inference framework that is robust to sparse data and used it to quantify morbidity associations in the oldest old, a population with limited available data.

METHODS

We conducted a retrospective cross-sectional study of a representative dataset of primary care patients in Scotland as of March 2007. We included 40 long-term conditions and studied their associations in 12,009 individuals aged 90 and older, stratified by sex (3039 men, 8970 women). We analysed associations obtained with Relative Risk (RR), a standard measure in the literature, and compared them with our proposed measure, Associations Beyond Chance (ABC). To enable a broad exploration of interactions between long-term conditions, we built networks of association and assessed differences in their analysis when associations are estimated by RR or ABC.

FINDINGS

Our Bayesian framework was appropriately more cautious in attributing association when evidence is lacking, particularly in uncommon conditions. This caution in reporting association was also present in reporting differences in associations between sex and affected the aggregated measures of multimorbidity and network representations.

INTERPRETATION

Incorporating uncertainty into multimorbidity research is crucial to avoid misleading findings when evidence is limited, a problem that particularly affects small but important subgroups. Our proposed framework improves the reliability of estimations of associations and, more in general, of research into disease mechanisms and multimorbidity.

FUNDING

National Institute for Health and Care Research.

Unraveling cradle-to-grave disease trajectories from multilayer comorbidity networks

We aim to comprehensively identify typical life-spanning trajectories and critical events that impact patients' hospital utilization and mortality. We use a unique dataset containing 44 million records of almost all inpatient stays from 2003 to 2014 in Austria to investigate disease trajectories. We develop a new, multilayer disease network approach to quantitatively analyze how cooccurrences of two or more diagnoses form and evolve over the life course of patients. Nodes represent diagnoses in age groups of ten years; each age group makes up a layer of the comorbidity multilayer network. Inter-layer links encode a significant correlation between diagnoses (p < 0.001, relative risk > 1.5), while intra-layers links encode correlations between diagnoses across different age groups. We use an unsupervised clustering algorithm for detecting typical disease trajectories as overlapping clusters in the multilayer comorbidity network. We identify critical events in a patient's career as points where initially overlapping trajectories start to diverge towards different states. We identified 1260 distinct disease trajectories (618 for females, 642 for males) that on average contain 9 (IQR 2-6) different diagnoses that cover over up to 70 years (mean 23 years). We found 70 pairs of diverging trajectories that share some diagnoses at younger ages but develop into markedly different groups of diagnoses at older ages. The disease trajectory framework can help us to identify critical events as specific combinations of risk factors that put patients at high risk for different diagnoses decades later. Our findings enable a data-driven integration of personalized life-course perspectives into clinical decision-making.

Observational and genetic analyses of the bidirectional relationship between depression and hypertension

BACKGROUND

While the association between depression and hypertension has been extensively investigated, the pattern and nature of such association remain inconclusive. We sought to investigate the bidirectional relationship between depression and hypertension and its causal.

METHODS

We first performed observational analyses using longitudinal data from the UK Biobank. We then performed genetic analyses leveraging summary statistics from large-scale genome-wide association studies (GWASs) conducted in European ancestry for depression and hypertension.

RESULTS

Observational analysis suggested a significant bidirectional phenotypic association between depression and hypertension (Depression → Hypertension: HR = 1.27, 95 % CI: 1.19, 1.36; Hypertension → Depression: HR = 1.65, 95 % CI: 1.58, 1.72). Linkage disequilibrium score regression demonstrated a positive genetic correlation between the two conditions (rg=0.15, P = 5.75 × 10-10). Bidirectional two-sample Mendelian randomization (MR) suggested that genetic liability to depression was significantly associated with an increased risk of hypertension (OR = 1.27, 95 % CI: 1.12, 1.43), while the genetic liability to hypertension was not associated with the risk of depression (OR = 1.01, 95 % CI: 0.99, 1.03). Multivariate MR, after adjusting for smoking, drinking, and body mass index, further supported an independent causal effect of genetic liability to depression on hypertension risk (OR = 1.10, 95 % CI: 1.02, 1.18).

LIMITATIONS

(1) interference of confounders, (2) absence of adequate statistical power, and (3) limitation to European populations.

CONCLUSION

Our study indicates depression is a causal risk factor for hypertension, whereas the reverse maybe not. Findings support that prevention of depression might help in decreasing hypertension incidence.

Trajectories network analysis of chronic diseases among middle-aged and older adults: evidence from the China Health and Retirement Longitudinal Study (CHARLS)

BACKGROUND

Given the increased risk of chronic diseases and comorbidity among middle-aged and older adults in China, it is pivotal to identify the disease trajectory of developing chronic multimorbidity and address the temporal correlation among chronic diseases.

METHOD

The data of 15895 participants from the China Health and Retirement Longitudinal Study (CHARLS 2011 - 2018) were analyzed in the current study. Binomial tests and the conditional logistic regression model were conducted to estimate the associations among 14 chronic diseases, and the disease trajectory network analysis was adopted to visualize the relationships.

RESULTS

The analysis showed that hypertension is the most prevalent disease among the 14 chronic conditions, with the highest cumulative incidence among all chronic diseases. In the disease trajectory network, arthritis was found to be the starting point, and digestive diseases, hypertension, heart diseases, and dyslipidemia were at the center, while memory-related disease (MRD), stroke, and diabetes were at the periphery of the network.

CONCLUSIONS

With the chronic disease trajectory network analysis, we found that arthritis was prone to the occurrence and development of various other diseases. In addition, patients of heart diseases/hypertension/digestive disease/dyslipidemia were under higher risk of developing other chronic conditions. For patients with multimorbidity, early prevention can preclude them from developing into poorer conditions, such as stroke, MRD, and diabetes. By identifying the trajectory network of chronic disease, the results provided critical insights for developing early prevention and individualized support services to reduce disease burden and improve patients' quality of life.

Disease clusters subsequent to anxiety and stress-related disorders and their genetic determinants

Anxiety/stress-related disorders have been associated with multiple diseases, whereas a comprehensive assessment of the structure and interplay of subsequent associated diseases and their genetic underpinnings is lacking. Here, we first identify 136, out of 454 tested, medical conditions associated with incident anxiety/stress-related disorders attended in specialized care using a population-based cohort from the nationwide Swedish Patient Register, comprising 70,026 patients with anxiety/stress-related disorders and 1:10 birth year- and sex-matched unaffected individuals. By combining findings from the comorbidity network and disease trajectory analyses, we identify five robust disease clusters to be associated with a prior diagnosis of anxiety/stress-related disorders, featured by predominance of psychiatric disorders, eye diseases, ear diseases, cardiovascular diseases, and skin and genitourinary diseases. These five clusters and their featured diseases are largely validated in the UK Biobank. GWAS analyses based on the UK Biobank identify 3, 33, 40, 4, and 16 significantly independent single nucleotide polymorphisms for the link to the five disease clusters, respectively, which are mapped to several distinct risk genes and biological pathways. These findings motivate further mechanistic explorations and aid early risk assessment for cluster-based disease prevention among patients with newly diagnosed anxiety/stress-related disorders in specialized care.

Stratification of Kidney Transplant Recipients Into Five Subgroups Based on Temporal Disease Trajectories

Background

Kidney transplantation is the treatment of choice for patients with end-stage renal disease. Considerable clinical research has focused on improving graft survival and an increasing number of kidney recipients die with a functioning graft. There is a need to improve patient survival and to better understand the individualized risk of comorbidities and complications. Here, we developed a method to stratify recipients into similar subgroups based on previous comorbidities and subsequently identify complications and for a subpopulation, laboratory test values associated with survival.

Methods

First, we identified significant disease patterns based on all hospital diagnoses from the Danish National Patient Registry for 5752 kidney transplant recipients from 1977 to 2018. Using hierarchical clustering, these longitudinal patterns of diseases segregate into 3 main clusters of glomerulonephritis, hypertension, and diabetes. As some recipients are diagnosed with diseases from >1 cluster, recipients are further stratified into 5 more fine-grained trajectory subgroups for which survival, stratified complication patterns as well as laboratory test values are analyzed.

Results

The study replicated known associations indicating that diabetes and low levels of albumin are associated with worse survival when investigating all recipients. However, stratification of recipients by trajectory subgroup showed additional associations. For recipients with glomerulonephritis, higher levels of basophils are significantly associated with poor survival, and these patients are more often diagnosed with bacterial infections. Additional associations were also found.

Conclusions

This study demonstrates that disease trajectories can confirm known comorbidities and furthermore stratify kidney transplant recipients into clinical subgroups in which we can characterize stratified risk factors. We hope to motivate future studies to stratify recipients into more fine-grained, homogenous subgroups to better discover associations relevant for the individual patient and thereby enable more personalized disease-management and improve long-term outcomes and survival.

Report on the Joint Workshop on the Relations between Health Inequalities, Ageing and Multimorbidity, Iceland, May 3-4, 2023

This paper is a summary of key presentations from a workshop in Iceland on May 3-4, 2023 arranged by Aarhus University and with participation of the below-mentioned scientists. Below you will find the key messages from the presentations made by: Professor Jan Vandenbroucke, Department of Clinical Epidemiology, Aarhus University, Emeritus Professor, Leiden University; Honorary Professor, London School of Hygiene & Tropical Medicine, UKProfessor, Chair Henrik Toft Sørensen, Department of Clinical Epidemiology, Aarhus University and Aarhus University Hospital, DenmarkProfessor David H. Rehkopf, Director, the Stanford Center for Population Health Sciences, Stanford University, CA., USProfessor Jaimie Gradus, Department of Epidemiology, School of Public Health, Boston University, Boston, Massachusetts, USProfessor Johan Mackenbach, Emeritus Professor, Department of Public Health, Erasmus University Rotterdam, HollandProfessor, Chair M Maria Glymour, Department of Epidemiology, Boston University School of Public Health, Boston University, Boston, Massachusetts, USProfessor, Dean Sandro Galea, School of Public Health, Boston University, Boston, Massachusetts, USProfessor Victor W. Henderson, Departments of Epidemiology & Population Health and of Neurology & Neurological Sciences, Stanford University, Stanford, CA, US; Department of Clinical Epidemiology, Aarhus University, Aarhus, DK.

Longitudinal machine learning uncouples healthy aging factors from chronic disease risks

To understand human longevity, inherent aging processes must be distinguished from known etiologies leading to age-related chronic diseases. Such deconvolution is difficult to achieve because it requires tracking patients throughout their entire lives. Here, we used machine learning to infer health trajectories over the entire adulthood age range using extrapolation from electronic medical records with partial longitudinal coverage. Using this approach, our model tracked the state of patients who were healthy and free from known chronic disease risk and distinguished individuals with higher or lower longevity potential using a multivariate score. We showed that the model and the markers it uses performed consistently on data from Israeli, British and US populations. For example, mildly low neutrophil counts and alkaline phosphatase levels serve as early indicators of healthy aging that are independent of risk for major chronic diseases. We characterize the heritability and genetic associations of our longevity score and demonstrate at least 1 year of extended lifespan for parents of high-scoring patients compared to matched controls. Longitudinal modeling of healthy individuals is thereby established as a tool for understanding healthy aging and longevity.

Progression and trajectory network of age-related functional impairments and their combined associations with mortality

Age-related functional impairments (ARFIs) contribute to the loss of independence in older adults, but their progressions, interrelations, and combined relations with mortality are largely unknown. We conducted a prospective study among 17,914 participants in the Health and Retirement Study (2000-2020). The incidence rates of visual impairment, hearing impairment, physical frailty, and cognitive impairment increased exponentially with age, while those of restless sleep and depression increased relatively slowly. These ARFIs were associated with each other in temporal sequence and constituted a hazard network. We observed a dose-response relationship between the number of ARFIs and mortality risk, and the dyads involving physical frailty demonstrated the strongest associations with mortality. Our findings may assist in the identification of individuals at higher mortality risk and highlight the potential for future investigations to explore the impact of multiple ARFIs in aging.

Exploring multimorbidity profiles in middle-aged inpatients: a network-based comparative study of China and the United Kingdom

BACKGROUND

Multimorbidity is better prevented in younger ages than in older ages. This study aims to identify the differences in comorbidity patterns in middle-aged inpatients from China and the United Kingdom (UK).

METHODS

We utilized 184,133 and 180,497 baseline hospitalization records in middle-aged populations (40-59 years) from Shaanxi, China, and UK Biobank. Logistic regression was used to calculate odds ratios and P values for 43,110 unique comorbidity patterns in Chinese inpatients and 21,026 unique comorbidity patterns in UK inpatients. We included the statistically significant (P values adjusted by Bonferroni correction) and common comorbidity patterns (the pattern with prevalence > 1/10,000 in each dataset) and employed network analysis to construct multimorbidity networks and compare feature differences in multimorbidity networks for Chinese and UK inpatients, respectively. We defined hub diseases as diseases having the top 10 highest number of unique comorbidity patterns in the multimorbidity network.

RESULTS

We reported that 57.12% of Chinese inpatients had multimorbidity, substantially higher than 30.39% of UK inpatients. The complete multimorbidity network for Chinese inpatients consisted of 1367 comorbidities of 341 diseases and was 2.93 × more complex than that of 467 comorbidities of 215 diseases in the UK. In males, the complexity of the multimorbidity network in China was 2.69 × more than their UK counterparts, while the ratio was 2.63 × in females. Comorbidities associated with hub diseases represented 68.26% of comorbidity frequencies in the complete multimorbidity network in Chinese inpatients and 55.61% in UK inpatients. Essential hypertension, dyslipidemia, type 2 diabetes mellitus, and gastritis and duodenitis were the hub diseases in both populations. The Chinese inpatients consistently demonstrated a higher frequency of comorbidities related to circulatory and endocrine/nutritional/metabolic diseases. In the UK, aside from these comorbidities, comorbidities related to digestive and genitourinary diseases were also prevalent, particularly the latter among female inpatients.

CONCLUSIONS

Chinese inpatients exhibit higher multimorbidity prevalence and more complex networks compared to their UK counterparts. Multimorbidity with circulatory and endocrine/nutritional/metabolic diseases among both Chinese and UK inpatients necessitates tailored surveillance, prevention, and intervention approaches. Targeted interventions for digestive and genitourinary diseases are warranted for the UK.

Pancreatic cancer symptom trajectories from Danish registry data and free text in electronic health records

Pancreatic cancer is one of the deadliest cancer types with poor treatment options. Better detection of early symptoms and relevant disease correlations could improve pancreatic cancer prognosis. In this retrospective study, we used symptom and disease codes (ICD-10) from the Danish National Patient Registry (NPR) encompassing 6.9 million patients from 1994 to 2018,, of whom 23,592 were diagnosed with pancreatic cancer. The Danish cancer registry included 18,523 of these patients. To complement and compare the registry diagnosis codes with deeper clinical data, we used a text mining approach to extract symptoms from free text clinical notes in electronic health records (3078 pancreatic cancer patients and 30,780 controls). We used both data sources to generate and compare symptom disease trajectories to uncover temporal patterns of symptoms prior to pancreatic cancer diagnosis for the same patients. We show that the text mining of the clinical notes was able to complement the registry-based symptoms by capturing more symptoms prior to pancreatic cancer diagnosis. For example, 'Blood pressure reading without diagnosis', 'Abnormalities of heartbeat', and 'Intestinal obstruction' were not found for the registry-based analysis. Chaining symptoms together in trajectories identified two groups of patients with lower median survival (<90 days) following the trajectories 'Cough→Jaundice→Intestinal obstruction' and 'Pain→Jaundice→Abnormal results of function studies'. These results provide a comprehensive comparison of the two types of pancreatic cancer symptom trajectories, which in combination can leverage the full potential of the health data and ultimately provide a fuller picture for detection of early risk factors for pancreatic cancer.

Age-dependent topic modeling of comorbidities in UK Biobank identifies disease subtypes with differential genetic risk

The analysis of longitudinal data from electronic health records (EHRs) has the potential to improve clinical diagnoses and enable personalized medicine, motivating efforts to identify disease subtypes from patient comorbidity information. Here we introduce an age-dependent topic modeling (ATM) method that provides a low-rank representation of longitudinal records of hundreds of distinct diseases in large EHR datasets. We applied ATM to 282,957 UK Biobank samples, identifying 52 diseases with heterogeneous comorbidity profiles; analyses of 211,908 All of Us samples produced concordant results. We defined subtypes of the 52 heterogeneous diseases based on their comorbidity profiles and compared genetic risk across disease subtypes using polygenic risk scores (PRSs), identifying 18 disease subtypes whose PRS differed significantly from other subtypes of the same disease. We further identified specific genetic variants with subtype-dependent effects on disease risk. In conclusion, ATM identifies disease subtypes with differential genome-wide and locus-specific genetic risk profiles.

Multi-system diseases and death trajectory of metabolic dysfunction-associated fatty liver disease: findings from the UK Biobank

BACKGROUND

Metabolic dysfunction-associated fatty liver disease (MAFLD) is a newly defined condition encompassing hepatic steatosis and metabolic dysfunction. However, the relationship between MAFLD and multi-system diseases remains unclear, and the time-dependent sequence of these diseases requires further clarification.

METHODS

After propensity score matching, 163,303 MAFLD subjects and 163,303 matched subjects were included in the community-based UK Biobank study. The International Classification of Diseases, Tenth Revision (ICD-10), was used to reclassify medical conditions into 490 and 16 specific causes of death. We conducted a disease trajectory analysis to map the key pathways linking MAFLD to various health conditions, providing an overview of their interconnections.

RESULTS

Participants aged 59 (51-64) years, predominantly males (62.5%), were included in the study. During the 12.9-year follow-up period, MAFLD participants were found to have a higher risk of 113 medical conditions and eight causes of death, determined through phenome-wide association analysis using Cox regression models. Temporal disease trajectories of MAFLD were established using disease pairing, revealing intermediary diseases such as asthma, diabetes, hypertension, hypothyroid conditions, tobacco abuse, diverticulosis, chronic ischemic heart disease, obesity, benign tumors, and inflammatory arthritis. These trajectories primarily resulted in acute myocardial infarction, disorders of fluid, electrolyte, and acid-base balance, infectious gastroenteritis and colitis, and functional intestinal disorders. Regarding death trajectories of MAFLD, malignant neoplasms, cardiovascular diseases, and respiratory system deaths were the main causes, and organ failure, infective disease, and internal environment disorder were the primary end-stage conditions. Disease trajectory analysis based on the level of genetic susceptibility to MAFLD yielded consistent results.

CONCLUSIONS

Individuals with MAFLD have a risk of a number of different medical conditions and causes of death. Notably, these diseases and potential causes of death constitute many pathways that may be promising targets for preventing general health decline in patients with MAFLD.

Precision medicine in complex diseases-Molecular subgrouping for improved prediction and treatment stratification

Complex diseases are caused by a combination of genetic, lifestyle, and environmental factors and comprise common noncommunicable diseases, including allergies, cardiovascular disease, and psychiatric and metabolic disorders. More than 25% of Europeans suffer from a complex disease, and together these diseases account for 70% of all deaths. The use of genomic, molecular, or imaging data to develop accurate diagnostic tools for treatment recommendations and preventive strategies, and for disease prognosis and prediction, is an important step toward precision medicine. However, for complex diseases, precision medicine is associated with several challenges. There is a significant heterogeneity between patients of a specific disease-both with regards to symptoms and underlying causal mechanisms-and the number of underlying genetic and nongenetic risk factors is often high. Here, we summarize precision medicine approaches for complex diseases and highlight the current breakthroughs as well as the challenges. We conclude that genomic-based precision medicine has been used mainly for patients with highly penetrant monogenic disease forms, such as cardiomyopathies. However, for most complex diseases-including psychiatric disorders and allergies-available polygenic risk scores are more probabilistic than deterministic and have not yet been validated for clinical utility. However, subclassifying patients of a specific disease into discrete homogenous subtypes based on molecular or phenotypic data is a promising strategy for improving diagnosis, prediction, treatment, prevention, and prognosis. The availability of high-throughput molecular technologies, together with large collections of health data and novel data-driven approaches, offers promise toward improved individual health through precision medicine.

Molecular bases of comorbidities: present and future perspectives

Co-occurrence of diseases decreases patient quality of life, complicates treatment choices, and increases mortality. Analyses of electronic health records present a complex scenario of comorbidity relationships that vary by age, sex, and cohort under study. The study of similarities between diseases using 'omics data, such as genes altered in diseases, gene expression, proteome, and microbiome, are fundamental to uncovering the origin of, and potential treatment for, comorbidities. Recent studies have produced a first generation of genetic interpretations for as much as 46% of the comorbidities described in large cohorts. Integrating different sources of molecular information and using artificial intelligence (AI) methods are promising approaches for the study of comorbidities. They may help to improve the treatment of comorbidities, including the potential repositioning of drugs.

The promise of digital healthcare technologies

Digital health technologies have been in use for many years in a wide spectrum of healthcare scenarios. This narrative review outlines the current use and the future strategies and significance of digital health technologies in modern healthcare applications. It covers the current state of the scientific field (delineating major strengths, limitations, and applications) and envisions the future impact of relevant emerging key technologies. Furthermore, we attempt to provide recommendations for innovative approaches that would accelerate and benefit the research, translation and utilization of digital health technologies.

Detection of Amyotrophic Lateral Sclerosis (ALS) Comorbidity Trajectories Based on Principal Tree Model Analytics

The multifaceted nature and swift progression of Amyotrophic Lateral Sclerosis (ALS) pose considerable challenges to our understanding of its evolution and interplay with comorbid conditions. This study seeks to elucidate the temporal dynamics of ALS progression and its interaction with associated diseases. We employed a principal tree-based model to decipher patterns within clinical data derived from a population-based database in Taiwan. The disease progression was portrayed as branched trajectories, each path representing a series of distinct stages. Each stage embodied the cumulative occurrence of co-existing diseases, depicted as nodes on the tree, with edges symbolizing potential transitions between these linked nodes. Our model identified eight distinct ALS patient trajectories, unveiling unique patterns of disease associations at various stages of progression. These patterns may suggest underlying disease mechanisms or risk factors. This research re-conceptualizes ALS progression as a migration through diverse stages, instead of the perspective of a sequence of isolated events. This new approach illuminates patterns of disease association across different progression phases. The insights obtained from this study hold the potential to inform doctors regarding the development of personalized treatment strategies, ultimately enhancing patient prognosis and quality of life.

Topic modeling identifies novel genetic loci associated with multimorbidities in UK Biobank

Many diseases show patterns of co-occurrence, possibly driven by systemic dysregulation of underlying processes affecting multiple traits. We have developed a method (treeLFA) for identifying such multimorbidities from routine health-care data, which combines topic modeling with an informative prior derived from medical ontology. We apply treeLFA to UK Biobank data and identify a variety of topics representing multimorbidity clusters, including a healthy topic. We find that loci identified using topic weights as traits in a genome-wide association study (GWAS) analysis, which we validated with a range of approaches, only partially overlap with loci from GWASs on constituent single diseases. We also show that treeLFA improves upon existing methods like latent Dirichlet allocation in various ways. Overall, our findings indicate that topic models can characterize multimorbidity patterns and that genetic analysis of these patterns can provide insight into the etiology of complex traits that cannot be determined from the analysis of constituent traits alone.

Gender-specific disease trajectories prior to the onset of COPD allow individualized screening and early intervention

BACKGROUND

Nation-wide hospitalization databases include diagnostic information at the level of an entire population over an extended period of time. Comorbidity network and early disease development can be unveiled. Chronic obstructive pulmonary disease (COPD) is an underdiagnosed condition for which it is crucial to identify early disease indicators. The identification of gender-specific conditions preceding the onset of COPD may reveal disease progression patterns allowing for early diagnosis and intervention. The objective of the study was to investigate the antecedent hospitalization history of patients newly diagnosed with COPD and to retrace a gender-specific trajectory of coded entities prior to the onset of COPD.

MATERIAL AND METHODS

A population-wide hospitalization database including information about all hospitalizations in Switzerland between 2002 and 2018 was used. COPD cases were extracted from the database and comorbidities occurring prior to the onset of COPD identified. Comorbidities significantly over-represented in COPD compared with a 1:1, age- and sex-matched control population were identified and their longitudinal evolution was analyzed.

RESULTS

Between 2002 and 2018, 697,714 hospitalizations with coded COPD were recorded in Switzerland. Sixty-two diagnoses were significantly over-represented before onset of COPD. These preceding comorbidities included both well-established conditions and novel links to COPD. Early pre-conditions included nicotine and alcohol abuse, obesity and cardiovascular diseases. Later comorbidities included atrial fibrillation, diseases of the genitourinary system and pneumonia. Atherosclerotic heart diseases were more prevalent in males, whereas hypothyroidism, varicose and intestinal disorders were more frequent in females. Disease trajectories were validated using an independent data set.

CONCLUSIONS

Gender-specific disease trajectories highlight early indicators and pathogenetic links between COPD and antecedent diseases and could allow for early detection and intervention.

The role of artificial intelligence in hypertensive disorders of pregnancy: towards personalized healthcare

INTRODUCTION

Guidelines advise ongoing follow-up of patients after hypertensive disorders of pregnancy (HDP) to assess cardiovascular risk and manage future patient-specific pregnancy conditions. However, there are limited tools available to monitor patients, with those available tending to be simple risk assessments that lack personalization. A promising approach could be the emerging artificial intelligence (AI)-based techniques, developed from big patient datasets to provide personalized recommendations for preventive advice.

AREAS COVERED

In this narrative review, we discuss the impact of integrating AI and big data analysis for personalized cardiovascular care, focusing on the management of HDP.

EXPERT OPINION

The pathophysiological response of women to pregnancy varies, and deeper insight into each response can be gained through a deeper analysis of the medical history of pregnant women based on clinical records and imaging data. Further research is required to be able to implement AI for clinical cases using multi-modality and multi-organ assessment, and this could expand both knowledge on pregnancy-related disorders and personalized treatment planning.

Adverse health consequences of undiagnosed hearing loss at middle age: A prospective cohort study with the UK Biobank

OBJECTIVES

Hearing impairment is common in the middle-aged population but remains largely undiagnosed and untreated. The knowledge about to what extent and how hearing impairment matters for health is currently lacking. Thus, we aimed to comprehensively examine the adverse health consequences as well as the comorbidity patterns of undiagnosed hearing loss.

STUDY DESIGN

Based on the prospective cohort of the UK Biobank, we included 14,620 individuals (median age 61 years) with audiometry-determined (i.e., speech-in-noise test) objective hearing loss and 38,479 individuals with subjective hearing loss (i.e., tested negative but with self-reported hearing problems; median age 58 years) at recruitment (2006-2010), together with 29,240 and 38,479 matched unexposed individuals respectively.

MAIN OUTCOME MEASURES

Cox regression was used to determine the associations of both hearing-loss exposures with the risk of 499 medical conditions and 14 cause-specific deaths, adjusting for ethnicity, annual household income, smoking and alcohol intake, exposure to working noise, and BMI. Comorbidity patterns following both exposures were visualized by comorbidity modules (i.e., sets of connected diseases) identified in the comorbidity network analyses.

RESULTS

During a median follow-up of 9 years, 28 medical conditions and mortality related to nervous system disease showed significant associations with prior objective hearing loss. Subsequently, the comorbidity network identified four comorbidity modules (i.e., neurodegenerative, respiratory, psychiatric, and cardiometabolic diseases), with the most pronounced association noted for the module related to neurodegenerative diseases (meta-hazard ratio [HR] = 2.00, 95%confidence interval [CI] 1.67-2.39). For subjective hearing loss, we found 57 associated medical conditions, which were partitioned into four modules (i.e., diseases related to the digestive, psychiatric, inflammatory, and cardiometabolic systems), with meta-HRs varying from 1.17 to 1.25.

CONCLUSIONS

Undiagnosed hearing loss captured by screening could identify individuals with at greater risk of multiple adverse health consequences, highlighting the importance of screening for speech-in-noise hearing impairment in the middle-aged population, for potential early diagnosis and intervention.

A deep learning algorithm to predict risk of pancreatic cancer from disease trajectories

Pancreatic cancer is an aggressive disease that typically presents late with poor outcomes, indicating a pronounced need for early detection. In this study, we applied artificial intelligence methods to clinical data from 6 million patients (24,000 pancreatic cancer cases) in Denmark (Danish National Patient Registry (DNPR)) and from 3 million patients (3,900 cases) in the United States (US Veterans Affairs (US-VA)). We trained machine learning models on the sequence of disease codes in clinical histories and tested prediction of cancer occurrence within incremental time windows (CancerRiskNet). For cancer occurrence within 36 months, the performance of the best DNPR model has area under the receiver operating characteristic (AUROC) curve = 0.88 and decreases to AUROC (3m) = 0.83 when disease events within 3 months before cancer diagnosis are excluded from training, with an estimated relative risk of 59 for 1,000 highest-risk patients older than age 50 years. Cross-application of the Danish model to US-VA data had lower performance (AUROC = 0.71), and retraining was needed to improve performance (AUROC = 0.78, AUROC (3m) = 0.76). These results improve the ability to design realistic surveillance programs for patients at elevated risk, potentially benefiting lifespan and quality of life by early detection of this aggressive cancer.

Predicting onset of disease progression using temporal disease occurrence networks

OBJECTIVE

Early recognition and prevention are crucial for reducing the risk of disease progression. This study aimed to develop a novel technique based on a temporal disease occurrence network to analyze and predict disease progression.

METHODS

This study used a total of 3.9 million patient records. Patient health records were transformed into temporal disease occurrence networks, and a supervised depth first search was used to find frequent disease sequences to predict the onset of disease progression. The diseases represented nodes in the network and paths between nodes represented edges that co-occurred in a patient cohort with temporal order. The node and edge level attributes contained meta-information about patients' gender, age group, and identity as labels where the disease occurred. The node and edge level attributes guided the depth first search to identify frequent disease occurrences in specific genders and age groups. The patient history was used to match the most frequent disease occurrences and then the obtained sequences were merged together to generate a ranked list of diseases with their conditional probability and relative risk.

RESULTS

The study found that the proposed method had improved performance compared to other methods. Specifically, when predicting a single disease, the method achieved an area under the receiver operating characteristic curve (AUC) of 0.65 and an F1-score of 0.11. When predicting a set of diseases relative to ground truth, the method achieved an AUC of 0.68 and an F1-score of 0.13.

CONCLUSION

The ranked list generated by the proposed method, which includes the probability of occurrence and relative risk score, can provide physicians with valuable information about the sequential development of diseases in patients. This information can help physicians to take preventive measures in a timely manner, based on the best available information.

Relationship of Family Genetic Risk Score With Diagnostic Trajectory in a Swedish National Sample of Incident Cases of Major Depression, Bipolar Disorder, Other Nonaffective Psychosis, and Schizophrenia

Importance

Since its inception under Kraepelin in the modern era, diagnostic stability and familial/genetic risk have been among the most important psychiatric nosologic validators.

Objective

To assess the interrelationships of family genetic risk score (FGRS) with diagnostic stability or diagnostic change in major depression (MD), bipolar disorder (BD), other nonaffective psychosis (ONAP), and schizophrenia.

Design, Setting, and Participants

This longitudinal population-based cohort (N = 4 171 120) included individuals with incident cases of MD (n = 235 095), BD (n = 11 681), ONAP (n = 16 009), and schizophrenia (n = 6312) who had at least 1 further diagnosis of the 4 disorders during follow-up, as assessed from Swedish national medical registries, observed over a mean (SD) of 13.1 (5.9) years until a mean (SD) age of 48.4 (12.3) years. Data were collected from January 1973 to December 2018, and data were analyzed from August to September 2022.

Exposures

FGRS for MD, BD, ONAP, and schizophrenia, calculated from morbidity risks for disorders in first-degree through fifth-degree relatives, controlling for cohabitation effects.

Main Outcomes and Measures

Final diagnostic outcome of MD, BD, ONAP, or schizophrenia.

Results

Of 269 097 included individuals, 173 061 (64.3%) were female, and the mean (SD) age at first registration was 35.1 (11.9) years. Diagnostic stability was highest for MD (214 794 [91.4%]), followed by schizophrenia (4621 [73.2%]), BD (7428 [63.6%]), and ONAP (6738 [42.1%]). The second most common final diagnosis for each of these MD, schizophrenia, BD, and ONAP were BD (15 506 [6.6%]), ONAP (1110 [17.6%]), MD (2681 [23.0%]), and schizophrenia (4401 [27.5%]), respectively. A high FGRS for the incident diagnosis was consistently associated with diagnostic stability, while a high FGRS for the final diagnosis and a low FGRS for the incident diagnosis was associated with diagnostic change. In multivariate models, those in the upper 5% of genetic risk had an odds ratio (OR) of 1.75 or greater for the following diagnostic transition: for MD FGRS, ONAP to MD (OR, 1.91; 95% CI, 1.59-2.29) and schizophrenia to MD (OR, 2.45; 95% CI, 1.64-3.68); for BD FGRS, MD to BD (OR, 2.60; 95% CI, 2.47-2.73), ONAP to BD (OR, 2.16; 95% CI, 1.85-2.52), and schizophrenia to BD (OR, 2.20; 95% CI, 1.39-3.49); for ONAP FGRS, MD to ONAP (OR, 1.80; 95% CI, 1.62-2.02), MD to schizophrenia (OR, 1.95; 95% CI, 1.58-2.41), and BD to schizophrenia (OR, 1.89; 95% CI, 1.39-2.56); and for schizophrenia FGRS, MD to schizophrenia (OR, 1.80; 95% CI, 1.46-2.23), and BD to schizophrenia (OR, 1.75; 95% CI, 1.25-2.45). FGRS profiles for incident cases confirmed at final diagnosis were more homogenous than genetic profiles for those who changed diagnoses.

Conclusions and Relevance

In a large population-based longitudinal cohort, the genetic risk factors for MD, BD, ONAP, and schizophrenia were meaningfully and systematically associated with the diagnostic trajectories of these 4 disorders. Over time, clinical diagnosis and genetic risk profiles became increasingly consilient, thereby providing genetic validation of these diagnostic constructs. Diagnostically unstable incident cases were more genetically heterogeneous than those who were diagnostically stable over time.

Clinical-GAN: Trajectory forecasting of clinical events using transformer and Generative Adversarial Networks

Predicting the trajectory of a disease at an early stage can aid physicians in offering effective treatment, prompt care to patients, and also avoid misdiagnosis. However, forecasting patient trajectories is challenging due to long-range dependencies, irregular intervals between consecutive admissions, and non-stationarity data. To address these challenges, we propose a novel method called Clinical-GAN, a Transformer-based Generative Adversarial Networks (GAN) to forecast the patients' medical codes for subsequent visits. First, we represent the patients' medical codes as a time-ordered sequence of tokens akin to language models. Then, a Transformer mechanism is used as a Generator to learn from existing patients' medical history and is trained adversarially against a Transformer-based Discriminator. We address the above mentioned challenges based on our data modeling and Transformer-based GAN architecture. Additionally, we enable the local interpretation of the model's prediction using a multi-head attention mechanism. We evaluated our method using a publicly available dataset, Medical Information Mart for Intensive Care IV v1.0 (MIMIC-IV), with more than 500,000 visits completed by around 196,000 adult patients over an 11-year period from 2008-2019. Clinical-GAN significantly outperforms baseline methods and existing works, as demonstrated through various experiments. Source code is at https://github.com/vigi30/Clinical-GAN.

Mapping diagnostic trajectories from the first hospital diagnosis of a psychiatric disorder: a Danish nationwide cohort study using sequence analysis

BACKGROUND

A key clinical problem in psychiatry is predicting the diagnostic future of patients presenting with psychopathology for the first time. The objective of this study was to establish a comprehensive map of subsequent diagnoses after a first psychiatric hospital diagnosis.

METHODS

Through the Danish National Patient Registry, we identified patients aged 18 years or older with an inpatient or outpatient psychiatric hospital contact and who had received one of the 20 most common first-time psychiatric diagnoses (defined at the ICD-10 two-cipher level, F00-F99) between Jan 1, 1995, and Dec 31, 2008. For each first-time diagnosis, the 20 most frequent subsequent psychiatric diagnoses (F00-F99), and death, occurring during 10 years of follow-up were identified as outcomes. To assess diagnostic stability, we used social sequence analyses, assigning a subsequent diagnosis to each state with a length of 6 months following each first-time diagnosis. The subsequent diagnosis was defined as the last diagnosis given within each 6-month period. We calculated the normalised entropy of each sequence to show the uncertainty of predicting the states in a given sequence. Cox proportional hazards models were used to assess the risk of receiving a subsequent diagnosis (at the one-cipher level, F0-F9) after each first-time diagnosis.

FINDINGS

The cohort consisted of 184 949 adult patients (77 129 [41·7%] men and 107 820 [58·3%] women, mean age 42·5 years [SD 18·5; range 18 to >100). Ethnicity data were not recorded. Over 10 years of follow-up, 86 804 (46·9%) patients had at least one subsequent diagnosis that differed from their first-time diagnosis. Measured by mean normalised entropy values, persistent delusional disorders (ICD-10 code F22), mental and behavioural disorders due to multiple drug use and use of other psychoactive substances (F19), and acute and transient psychotic disorders (F23) had the highest diagnostic variability, whereas eating disorders (F50) and non-organic sexual dysfunction (F52) had the lowest. The risk of receiving a subsequent diagnosis with a psychiatric disorder from an ICD-10 group different from that of the first-time diagnosis varied substantially among first-time diagnoses.

INTERPRETATION

These data provide detailed information on possible diagnostic outcomes after a first-time presentation in a psychiatric hospital. This information could help clinicians to plan relevant follow-up and inform patients and families on the degree of diagnostic uncertainty associated with receiving a first psychiatric hospital diagnosis, as well as likely and unlikely trajectories of diagnostic progression.

FUNDING

Mental Health Services, Capital region of Denmark.

TRANSLATION

For the Danish translation of the abstract see Supplementary Materials section.

Identifying and visualising multimorbidity and comorbidity patterns in patients in the English National Health Service: a population-based study

BACKGROUND

Globally, there is a paucity of multimorbidity and comorbidity data, especially for minority ethnic groups and younger people. We estimated the frequency of common disease combinations and identified non-random disease associations for all ages in a multiethnic population.

METHODS

In this population-based study, we examined multimorbidity and comorbidity patterns stratified by ethnicity or race, sex, and age for 308 health conditions using electronic health records from individuals included on the Clinical Practice Research Datalink linked with the Hospital Episode Statistics admitted patient care dataset in England. We included individuals who were older than 1 year and who had been registered for at least 1 year in a participating general practice during the study period (between April 1, 2010, and March 31, 2015). We identified the most common combinations of conditions and comorbidities for index conditions. We defined comorbidity as the accumulation of additional conditions to an index condition over an individual's lifetime. We used network analysis to identify conditions that co-occurred more often than expected by chance. We developed online interactive tools to explore multimorbidity and comorbidity patterns overall and by subgroup based on ethnicity, sex, and age.

FINDINGS

We collected data for 3 872 451 eligible patients, of whom 1 955 700 (50·5%) were women and girls, 1 916 751 (49·5%) were men and boys, 2 666 234 (68·9%) were White, 155 435 (4·0%) were south Asian, and 98 815 (2·6%) were Black. We found that a higher proportion of boys aged 1-9 years (132 506 [47·8%] of 277 158) had two or more diagnosed conditions than did girls in the same age group (106 982 [40·3%] of 265 179), but more women and girls were diagnosed with multimorbidity than were boys aged 10 years and older and men (1 361 232 [80·5%] of 1 690 521 vs 1 161 308 [70·8%] of 1 639 593). White individuals (2 097 536 [78·7%] of 2 666 234) were more likely to be diagnosed with two or more conditions than were Black (59 339 [60·1%] of 98 815) or south Asian individuals (93 617 [60·2%] of 155 435). Depression commonly co-occurred with anxiety, migraine, obesity, atopic conditions, deafness, soft-tissue disorders, and gastrointestinal disorders across all subgroups. Heart failure often co-occurred with hypertension, atrial fibrillation, osteoarthritis, stable angina, myocardial infarction, chronic kidney disease, type 2 diabetes, and chronic obstructive pulmonary disease. Spinal fractures were most strongly non-randomly associated with malignancy in Black individuals, but with osteoporosis in White individuals. Hypertension was most strongly associated with kidney disorders in those aged 20-29 years, but with dyslipidaemia, obesity, and type 2 diabetes in individuals aged 40 years and older. Breast cancer was associated with different comorbidities in individuals from different ethnic groups. Asthma was associated with different comorbidities between males and females. Bipolar disorder was associated with different comorbidities in younger age groups compared with older age groups.

INTERPRETATION

Our findings and interactive online tools are a resource for: patients and their clinicians, to prevent and detect comorbid conditions; research funders and policy makers, to redesign service provision, training priorities, and guideline development; and biomedical researchers and manufacturers of medicines, to provide leads for research into common or sequential pathways of disease and inform the design of clinical trials.

FUNDING

UK Research and Innovation, Medical Research Council, National Institute for Health and Care Research, Department of Health and Social Care, Wellcome Trust, British Heart Foundation, and The Alan Turing Institute.

The temporal association of hyperhidrosis and its comorbidities - a nationwide hospital-based cohort study

BACKGROUND

Research on hyperhidrosis comorbidities has documented the co-occurrence of diseases but has not provided information about temporal disease associations.

OBJECTIVE

To investigate the temporal disease trajectories of individuals with hospital-diagnosed hyperhidrosis.

METHODS

This is a hospital-based nationwide cohort study including all patients with a hospital contact in Denmark between 1994 and 2018. International Classification of Diseases version-10 diagnoses assigned to inpatients, outpatients and emergency department patients were collected from the Danish National Patient Register. The main outcome was the temporal disease associations occurring in individuals with hyperhidrosis, which was assessed by identifying morbidities significantly associated with hyperhidrosis and then examining whether there was a significant order of these diagnoses using binomial tests.

RESULTS

Overall, 7 191 519 patients were included. Of these, 8758 (0.12%) patients had localized hyperhidrosis (5674 female sex [64.8%]; median age at first diagnosis 26.9 [interquartile range 21.3-36.1]) and 1102 (0.015%) generalized hyperhidrosis (606 female sex [59.9%]; median age at first diagnosis 40.9 [interquartile range 26.4-60.7]). The disease trajectories comprised pain complaints, stress, epilepsy, respiratory and psychiatric diseases. The most diagnosed morbidities for localized hyperhidrosis were abdominal pain (relative risk [RR] = 121.75; 95% Confidence Interval [CI] 121.14-122.35; P < 0.001), soft tissue disorders (RR = 151.19; 95% CI 149.58-152.80; P < 0.001) and dorsalgia (RR = 160.15; 95% CI 158.92-161.38; P < 0.001). The most diagnosed morbidities for generalized hyperhidrosis were dorsalgia (RR = 306.59; 95% CI 302.17-311.02; P < 0.001), angina pectoris (RR = 411.69; 95% CI 402.23-421.16; P < 0.001) and depression (RR = 207.92; 95% CI 202.21-213.62; P < 0.001). All these morbidities were diagnosed before hyperhidrosis.

CONCLUSIONS

This paper ascertains which hospital-diagnosed morbidities precede hospital-diagnosed hyperhidrosis. As hyperhidrosis mainly is treated in the primary health care sector, the trajectories suggests that these morbidities may lead to a worse disease course of hyperhidrosis that necessitates treatment in hospitals. Treating these morbidities may improve the disease course of hyperhidrosis.

Network-medicine framework for studying disease trajectories in U.S. veterans

A better understanding of the sequential and temporal aspects in which diseases occur in patient's lives is essential for developing improved intervention strategies that reduce burden and increase the quality of health services. Here we present a network-based framework to study disease relationships using Electronic Health Records from > 9 million patients in the United States Veterans Health Administration (VHA) system. We create the Temporal Disease Network, which maps the sequential aspects of disease co-occurrence among patients and demonstrate that network properties reflect clinical aspects of the respective diseases. We use the Temporal Disease Network to identify disease groups that reflect patterns of disease co-occurrence and the flow of patients among diagnoses. Finally, we define a strategy for the identification of trajectories that lead from one disease to another. The framework presented here has the potential to offer new insights for disease treatment and prevention in large health care systems.

COVID-19 trajectories among 57 million adults in England: a cohort study using electronic health records

BACKGROUND

Updatable estimates of COVID-19 onset, progression, and trajectories underpin pandemic mitigation efforts. To identify and characterise disease trajectories, we aimed to define and validate ten COVID-19 phenotypes from nationwide linked electronic health records (EHR) using an extensible framework.

METHODS

In this cohort study, we used eight linked National Health Service (NHS) datasets for people in England alive on Jan 23, 2020. Data on COVID-19 testing, vaccination, primary and secondary care records, and death registrations were collected until Nov 30, 2021. We defined ten COVID-19 phenotypes reflecting clinically relevant stages of disease severity and encompassing five categories: positive SARS-CoV-2 test, primary care diagnosis, hospital admission, ventilation modality (four phenotypes), and death (three phenotypes). We constructed patient trajectories illustrating transition frequency and duration between phenotypes. Analyses were stratified by pandemic waves and vaccination status.

FINDINGS

Among 57 032 174 individuals included in the cohort, 13 990 423 COVID-19 events were identified in 7 244 925 individuals, equating to an infection rate of 12·7% during the study period. Of 7 244 925 individuals, 460 737 (6·4%) were admitted to hospital and 158 020 (2·2%) died. Of 460 737 individuals who were admitted to hospital, 48 847 (10·6%) were admitted to the intensive care unit (ICU), 69 090 (15·0%) received non-invasive ventilation, and 25 928 (5·6%) received invasive ventilation. Among 384 135 patients who were admitted to hospital but did not require ventilation, mortality was higher in wave 1 (23 485 [30·4%] of 77 202 patients) than wave 2 (44 220 [23·1%] of 191 528 patients), but remained unchanged for patients admitted to the ICU. Mortality was highest among patients who received ventilatory support outside of the ICU in wave 1 (2569 [50·7%] of 5063 patients). 15 486 (9·8%) of 158 020 COVID-19-related deaths occurred within 28 days of the first COVID-19 event without a COVID-19 diagnoses on the death certificate. 10 884 (6·9%) of 158 020 deaths were identified exclusively from mortality data with no previous COVID-19 phenotype recorded. We observed longer patient trajectories in wave 2 than wave 1.

INTERPRETATION

Our analyses illustrate the wide spectrum of disease trajectories as shown by differences in incidence, survival, and clinical pathways. We have provided a modular analytical framework that can be used to monitor the impact of the pandemic and generate evidence of clinical and policy relevance using multiple EHR sources.

FUNDING

British Heart Foundation Data Science Centre, led by Health Data Research UK.

Analysis of mortality metrics associated with a comprehensive range of disorders in Denmark, 2000 to 2018: A population-based cohort study

BACKGROUND

The provision of different types of mortality metrics (e.g., mortality rate ratios [MRRs] and life expectancy) allows the research community to access a more informative set of health metrics. The aim of this study was to provide a panel of mortality metrics associated with a comprehensive range of disorders and to design a web page to visualize all results.

METHODS AND FINDINGS

In a population-based cohort of all 7,378,598 persons living in Denmark at some point between 2000 and 2018, we identified individuals diagnosed at hospitals with 1,803 specific categories of disorders through the International Classification of Diseases-10th Revision (ICD-10) in the National Patient Register. Information on date and cause of death was obtained from the Registry of Causes of Death. For each of the disorders, a panel of epidemiological and mortality metrics was estimated, including incidence rates, age-of-onset distributions, MRRs, and differences in life expectancy (estimated as life years lost [LYLs]). Additionally, we examined models that adjusted for measures of air pollution to explore potential associations with MRRs. We focus on 39 general medical conditions to simplify the presentation of results, which cover 10 broad categories: circulatory, endocrine, pulmonary, gastrointestinal, urogenital, musculoskeletal, hematologic, mental, and neurologic conditions and cancer. A total of 3,676,694 males and 3,701,904 females were followed up for 101.7 million person-years. During the 19-year follow-up period, 1,034,273 persons (14.0%) died. For 37 of the 39 selected medical conditions, mortality rates were larger and life expectancy shorter compared to the Danish general population. For these 37 disorders, MRRs ranged from 1.09 (95% confidence interval [CI]: 1.09 to 1.10) for vision problems to 7.85 (7.77 to 7.93) for chronic liver disease, while LYLs ranged from 0.31 (0.14 to 0.47) years (approximately 16 weeks) for allergy to 17.05 (16.95 to 17.15) years for chronic liver disease. Adjustment for air pollution had very little impact on the estimates; however, a limitation of the study is the possibility that the association between the different disorders and mortality could be explained by other underlying factors associated with both the disorder and mortality.

CONCLUSIONS

In this study, we show estimates of incidence, age of onset, age of death, and mortality metrics (both MRRs and LYLs) for a comprehensive range of disorders. The interactive data visualization site (https://nbepi.com/atlas) allows more fine-grained analysis of the link between a range of disorders and key mortality estimates.

Temporal patterns of multi-morbidity in 570157 ischemic heart disease patients: a nationwide cohort study

Background

Patients diagnosed with ischemic heart disease (IHD) are becoming increasingly multi-morbid, and studies designed to analyze the full spectrum are few.

Methods

Disease trajectories, defined as time-ordered series of diagnoses, were used to study the temporality of multi-morbidity. The main data source was The Danish National Patient Register (NPR) comprising 7,179,538 individuals in the period 1994–2018. Patients with a diagnosis code for IHD were included. Relative risks were used to quantify the strength of the association between diagnostic co-occurrences comprised of two diagnoses that were overrepresented in the same patients. Multiple linear regression models were then fitted to test for temporal associations among the diagnostic co-occurrences, termed length two disease trajectories. Length two disease trajectories were then used as basis for constructing disease trajectories of three diagnoses.

Results

In a cohort of 570,157 IHD disease patients, we identified 1447 length two disease trajectories and 4729 significant length three disease trajectories. These included 459 distinct diagnoses. Disease trajectories were dominated by chronic diseases and not by common, acute diseases such as pneumonia. The temporal association of atrial fibrillation (AF) and IHD differed in different IHD subpopulations. We found an association between osteoarthritis (OA) and heart failure (HF) among patients diagnosed with OA, IHD, and then HF only.

Conclusions

The sequence of diagnoses is important in characterization of multi-morbidity in IHD patients as the disease trajectories. The study provides evidence that the timing of AF in IHD marks distinct IHD subpopulations; and secondly that the association between osteoarthritis and heart failure is dependent on IHD.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12933-022-01527-3.

Temporal trajectories of important diseases in the life course and premature mortality in the UK Biobank

BACKGROUND

Little is known regarding life-course trajectories of important diseases. We aimed to identify diseases that were strongly associated with mortality and test temporal trajectories of these diseases before mortality.

METHODS

Our analysis was based on UK Biobank. Diseases were identified using questionnaires, nurses' interviews, or inpatient data. Mortality register data were used to identify mortality up to January 2021. The association between 60 individual diseases at baseline and in the life course and incident mortality was examined using Cox proportional regression models. Those diseases with great contribution to mortality were identified and disease trajectories in life course were then derived.

RESULTS

During a median follow-up of 11.8 years, 31,373 individuals (median age at death (interquartile range): 70.7 (65.3-74.8) years, 59.4% male) died of all-cause mortality (with complete data on diagnosis date of disease), with 16,237 dying with cancer and 6702 with cardiovascular disease (CVD). We identified 37 diseases including cancers and heart diseases that were associated with an increased risk of mortality independent of other diseases (hazard ratio ranged from 1.09 to 7.77). Among those who died during follow-up, 2.2% did not have a diagnosis of any disease of interest and 90.1% were diagnosed with two or more diseases in their life course. Individuals who were diagnosed with more diseases in their life course were more likely to have longer longevity. Cancer was more likely to be diagnosed following hypertension, hypercholesterolemia, CVD, or digestive disorders and more likely to be diagnosed ahead of CVD, chronic kidney disease (CKD), or digestive disorders. CVD was more likely to be diagnosed following hypertension, hypercholesterolemia, or digestive disorders and more likely to be diagnosed ahead of cancer or CKD. Hypertension was more likely to precede other diseases, and CKD was more likely to be diagnosed as the last disease before more mortality.

CONCLUSIONS

There are significant interplays between cancer and CVD for mortality. Cancer and CVD were frequently clustered with hypertension, CKD, and digestive disorders with CKD highly being diagnosed as the last disease in the life course. Our findings underline the importance of health checks among middle-aged adults for the prevention of premature mortality.

Biomarkers and Disease Trajectories Influencing Women's Health: Results from the UK Biobank Cohort

Women's health is important for society. Despite the known biological and sex-related factors influencing the risk of diseases among women, the network of the full spectrum of diseases in women is underexplored. This study aimed to systematically examine the women-specific temporal pattern (trajectory) of the disease network, including the role of baseline physical examination indexes, and blood and urine biomarkers. In the UK Biobank study, 502,650 participants entered the cohort from 2006 to 2010, and were followed up until 2019 to identify disease incidence via linkage to the patient registers. For those diseases with increased risk among women, conditional logistic regression models were used to estimate odds ratios (ORs), and the binomial test of direction was further used to build disease trajectories. Among 301 diseases, 82 diseases in women had ORs > 1.2 and p < 0.00017 when compared to men, involving mainly diseases in the endocrine, skeletal and digestive systems. Diseases with the highest ORs included breast diseases, osteoporosis, hyperthyroidism, and deformity of the toes. The biomarker and disease trajectories suggested estradiol as a risk predictor for breast cancer, while a high percentage of reticulocyte, body mass index and waist circumference were associated with an increased risk of upper-limb neuropathy. In addition, the risk of cholelithiasis was increased in women diagnosed with dyspepsia and diaphragmatic hernia. In conclusion, women are at an increased risk of endocrine, skeletal and digestive diseases. The biomarker and disease trajectories in women suggested key pathways to a range of adverse outcomes downstream, which may shed light on promising targets for early detection and prevention of these diseases.

Supplementary Information

The online version contains supplementary material available at 10.1007/s43657-022-00054-1.

Trajectories: a framework for detecting temporal clinical event sequences from health data standardized to the Observational Medical Outcomes Partnership (OMOP) Common Data Model

Objective

To develop a framework for identifying temporal clinical event trajectories from Observational Medical Outcomes Partnership-formatted observational healthcare data.

Materials and Methods

A 4-step framework based on significant temporal event pair detection is described and implemented as an open-source R package. It is used on a population-based Estonian dataset to first replicate a large Danish population-based study and second, to conduct a disease trajectory detection study for type 2 diabetes patients in the Estonian and Dutch databases as an example.

Results

As a proof of concept, we apply the methods in the Estonian database and provide a detailed breakdown of our findings. All Estonian population-based event pairs are shown. We compare the event pairs identified from Estonia to Danish and Dutch data and discuss the causes of the differences. The overlap in the results was only 2.4%, which highlights the need for running similar studies in different populations.

Conclusions

For the first time, there is a complete software package for detecting disease trajectories in health data.

Phenome-wide association study (PheWAS) of colorectal cancer risk SNP effects on health outcomes in UK Biobank

BACKGROUND

Associations between colorectal cancer (CRC) and other health outcomes have been reported, but these may be subject to biases, or due to limitations of observational studies.

METHODS

We set out to determine whether genetic predisposition to CRC is also associated with the risk of other phenotypes. Under the phenome-wide association study (PheWAS) and tree-structured phenotypic model (TreeWAS), we studied 334,385 unrelated White British individuals (excluding CRC patients) from the UK Biobank cohort. We generated a polygenic risk score (PRS) from CRC genome-wide association studies as a measure of CRC risk. We performed sensitivity analyses to test the robustness of the results and searched the Danish Disease Trajectory Browser (DTB) to replicate the observed associations.

RESULTS

Eight PheWAS phenotypes and 21 TreeWAS nodes were associated with CRC genetic predisposition by PheWAS and TreeWAS, respectively. The PheWAS detected associations were from neoplasms and digestive system disease group (e.g. benign neoplasm of colon, anal and rectal polyp and diverticular disease). The results from the TreeWAS corroborated the results from the PheWAS. These results were replicated in the observational data within the DTB.

CONCLUSIONS

We show that benign colorectal neoplasms share genetic aetiology with CRC using PheWAS and TreeWAS methods. Additionally, CRC genetic predisposition is associated with diverticular disease.

Cross-tissue transcriptome-wide association studies identify susceptibility genes shared between schizophrenia and inflammatory bowel disease

Genetic correlations and an increased incidence of psychiatric disorders in inflammatory-bowel disease have been reported, but shared molecular mechanisms are unknown. We performed cross-tissue and multiple-gene conditioned transcriptome-wide association studies for 23 tissues of the gut-brain-axis using genome-wide association studies data sets (total 180,592 patients) for Crohn’s disease, ulcerative colitis, primary sclerosing cholangitis, schizophrenia, bipolar disorder, major depressive disorder and attention-deficit/hyperactivity disorder. We identified NR5A2, SATB2, and PPP3CA (encoding a target for calcineurin inhibitors in refractory ulcerative colitis) as shared susceptibility genes with transcriptome-wide significance both for Crohn’s disease, ulcerative colitis and schizophrenia, largely explaining fine-mapped association signals at nearby genome-wide association study susceptibility loci. Analysis of bulk and single-cell RNA-sequencing data showed that PPP3CA expression was strongest in neurons and in enteroendocrine and Paneth-like cells of the ileum, colon, and rectum, indicating a possible link to the gut-brain-axis. PPP3CA together with three further suggestive loci can be linked to calcineurin-related signaling pathways such as NFAT activation or Wnt.

Florian Uellendahl-Werth et al. conduct cross-tissue transcriptome-wide association studies to explore genetic mechanisms shared across immune-related and psychiatric traits. Their results identify several genes (including PPP3CA) that could mediate the interplay between psychiatric and inflammatory disease.

An explainable artificial intelligence approach for predicting cardiovascular outcomes using electronic health records

Understanding the conditionally-dependent clinical variables that drive cardiovascular health outcomes is a major challenge for precision medicine. Here, we deploy a recently developed massively scalable comorbidity discovery method called Poisson Binomial based Comorbidity discovery (PBC), to analyze Electronic Health Records (EHRs) from the University of Utah and Primary Children's Hospital (over 1.6 million patients and 77 million visits) for comorbid diagnoses, procedures, and medications. Using explainable Artificial Intelligence (AI) methodologies, we then tease apart the intertwined, conditionally-dependent impacts of comorbid conditions and demography upon cardiovascular health, focusing on the key areas of heart transplant, sinoatrial node dysfunction and various forms of congenital heart disease. The resulting multimorbidity networks make possible wide-ranging explorations of the comorbid and demographic landscapes surrounding these cardiovascular outcomes, and can be distributed as web-based tools for further community-based outcomes research. The ability to transform enormous collections of EHRs into compact, portable tools devoid of Protected Health Information solves many of the legal, technological, and data-scientific challenges associated with large-scale EHR analyses.