Trajectories of Disease Accumulation Using Electronic Health Records

Multimorbidity is a major problem for patients and health services. However, we still do not know much about the common trajectories of disease accumulation that patients follow. We apply a data-driven method to an electronic health record dataset (CPRD) to analyse and condense the main trajectories to multimorbidity into simple networks. This analysis has never been done specifically for multimorbidity trajectories and using primary care based electronic health records. We start the analysis by evaluating temporal correlations between diseases to determine which pairs of disease appear significantly in sequence. Then, we use patient trajectories together with the temporal correlations to build networks of disease accumulation. These networks are able to represent the main pathways that patients follow to acquire multiple chronic conditions. The first network that we find contains the common diseases that multimorbid patients suffer from and shows how diseases like diabetes, COPD, cancer and osteoporosis are crucial in the disease trajectories. The results we present can help better characterize multimorbid patients and highlight common combinations helping to focus treatment to prevent or delay multimorbidity progression

Multiple morbidities in companion dogs: a novel model for investigating age-related disease

The proportion of men and women surviving over 65 years has been steadily increasing over the last century. In their later years, many of these individuals are afflicted with multiple chronic conditions, placing increasing pressure on healthcare systems. The accumulation of multiple health problems with advanced age is well documented, yet the causes are poorly understood. Animal models have long been employed in attempts to elucidate these complex mechanisms with limited success. Recently, the domestic dog has been proposed as a promising model of human aging for several reasons. Mean lifespan shows twofold variation across dog breeds. In addition, dogs closely share the environments of their owners, and substantial veterinary resources are dedicated to comprehensive diagnosis of conditions in dogs. However, while dogs are therefore useful for studying multimorbidity, little is known about how aging influences the accumulation of multiple concurrent disease conditions across dog breeds. The current study examines how age, body weight, and breed contribute to variation in multimorbidity in over 2,000 companion dogs visiting private veterinary clinics in England. In common with humans, we find that the number of diagnoses increases significantly with age in dogs. However, we find no significant weight or breed effects on morbidity number. This surprising result reveals that while breeds may vary in their average longevity and causes of death, their age-related trajectories of morbidities differ little, suggesting that age of onset of disease may be the source of variation in lifespan across breeds. Future studies with increased sample sizes and longitudinal monitoring may help us discern more breed-specific patterns in morbidity. Overall, the large increase in multimorbidity seen with age in dogs mirrors that seen in humans and lends even more credence to the value of companion dogs as models for human morbidity and mortality

Modelling the trajectories of disease accumulation in multimorbidity

Multimorbidity is defined as the co-occurrence of two or more chronic conditions. The prevalence of multimorbidity is closely related to age, and due to population ageing, multimorbidity has become a major burden for healthcare systems. The biggest challenge when modelling multimorbidity is patient heterogeneity since the patients can suffer from a wide variety of disease combinations. Previous work has shown how age, sex, and socioeconomic status are key determinants of multimorbidity prevalence and multimorbidity disease clusters. However, little is known about the order in which patients acquire multiple chronic conditions. This thesis aims to study the trajectories of disease accumulation that multimorbid patients follow. To address this challenge, we present four models that focus on the different aspects of the problem and apply them to an Electronic Health Record (EHR) dataset. First, we group chronic conditions into concordant clinical clusters and use a Multi-state Markov model to micro-simulate patient cohorts. This approach allows us to estimate how sex, socioeconomic status, and different disease clusters affect the trajectories and Life Expectancy. Second, we adapt a previously proposed method to identify the networks of chronic diseases that condense the most significant trajectories observed in the data. In this model, we avoid grouping or clustering diseases, and the resulting networks describe specific disease accumulation sequences. Third, we use a greedy structure learning algorithm to find the Bayesian Networks that better fit our EHR dataset. The results of this model help better understand the conditional dependencies between chronic conditions. Fourth, we present a Bernoulli mixture model to study multimorbid patient subtypes. The models presented in this thesis characterize the temporal patterns that multimorbid patients follow from multiple perspectives and could be used to inform where to focus treatment to prevent or delay the multimorbidity progression

The lifetime accumulation of multimorbidity and its influence on dementia risk: a UK Biobank study

The number of people living with dementia worldwide is projected to reach 150 million by 2050, making prevention a crucial priority for health services. The co-occurrence of two or more chronic health conditions, termed multimorbidity, occurs in up to 80% of dementia patients, raising the potential of multimorbidity as an important risk factor for dementia. However, precise understanding of which specific conditions, as well as their age of onset, drive the link between multimorbidity and dementia is unclear. We defined the patterns of accumulation of 46 chronic conditions over their lifetime in 282,712 individuals from the UK Biobank. By grouping individuals based on their life-history of chronic illness, we show here that risk of incident dementia can be stratified by both the type and timing of their accumulated chronic conditions. We identified several distinct clusters of multimorbidity, and their associated risks varied in an age-specific manner. Compared to low multimorbidity, cardiometabolic and neurovascular conditions acquired before 55 years were most strongly associated with dementia. Acquisition of mental health and neurovascular conditions between the ages of 55 and 70 was associated with an over two-fold increase in dementia risk compared to low multimorbidity. The age-dependent role of multimorbidity in predicting dementia risk could be used for early stratification of individuals into high and low risk groups and inform targeted prevention strategies based on a person’s prior history of chronic disease

Frailty degree and illness trajectories in older people towards the end-of-life:a prospective observational study

Objectives To assess the degree of frailty in older people with different advanced diseases and its relationship with end-of-life illness trajectories and survival.Methods Prospective, observational study, including all patients admitted to the Acute Geriatric Unit of the University Hospital of Vic (Spain) during 12 consecutive months (2014–2015), followed for up to 2 years. Participants were identified as end-of-life people (EOLp) using the NECPAL (NECesidades PALiativas, palliative care needs) tool and were classified according to their dominant illness trajectory. The Frail-VIG index (Valoración Integral Geriátrica, Comprehensive Geriatric Assessment) was used to quantify frailty degree, to calculate the relationship between frailty and mortality (Receiver Operating Characteristic (ROC) curves), and to assess the combined effect of frailty degree and illness trajectories on survival (Cox proportional hazards model). Survival curves were plotted using the Kaplan-Meier estimator with participants classified into four groups (ie, no frailty, mild frailty, moderate frailty and advanced frailty) and were compared using the log-rank test.Results Of the 590 persons with a mean (SD) age of 86.4 (5.6) years recruited, 260 (44.1%) were identified as EOLp, distributed into cancer (n=31, 11.9%), organ failure (n=79, 30.4%), dementia (n=86, 33.1%) and multimorbidity (n=64, 24.6%) trajectories. All 260 EOLp had some degree of frailty, mostly advanced frailty (n=184, 70.8%), regardless of the illness trajectory, and 220 (84.6%) died within 2 years. The area under the ROC curve (95% CI) after 2 years of follow-up for EOLp was 0.87 (0.84 to 0.92) with different patterns of survival decline in the different end-of-life trajectories (p<0.0001). Cox regression analyses showed that each additional deficit of the Frail-VIG index increased the risk of death by 61.5%, 30.1%, 29.6% and 12.9% in people with dementia, organ failure, multimorbidity and cancer, respectively (p<0.01 for all the coefficients).Conclusions All older people towards the end-of-life in this study were frail, mostly with advanced frailty. The degree of frailty is related to survival across the different illness trajectories despite the differing survival patterns among trajectories. Frailty indexes may be useful to assess end-of-life older people, regardless of their trajectory

The complex network of global cargo ship movements

Transportation networks play a crucial role in human mobility, the exchange of goods, and the spread of invasive species. With 90% of world trade carried by sea, the global network of merchant ships provides one of the most important modes of transportation. Here we use information about the itineraries of 16,363 cargo ships during the year 2007 to construct a network of links between ports. We show that the network has several features which set it apart from other transportation networks. In particular, most ships can be classified in three categories: bulk dry carriers, container ships and oil tankers. These three categories do not only differ in the ships' physical characteristics, but also in their mobility patterns and networks. Container ships follow regularly repeating paths whereas bulk dry carriers and oil tankers move less predictably between ports. The network of all ship movements possesses a heavy-tailed distribution for the connectivity of ports and for the loads transported on the links with systematic differences between ship types. The data analyzed in this paper improve current assumptions based on gravity models of ship movements, an important step towards understanding patterns of global trade and bioinvasion.Comment: 7 figures Accepted for publication by Journal of the Royal Society Interface (2010) For supplementary information, see http://www.icbm.de/~blasius/publications.htm

Sociodemographic characteristics and longitudinal progression of multimorbidity:A multistate modelling analysis of a large primary care records dataset in England

BackgroundMultimorbidity, characterised by the coexistence of multiple chronic conditions in an individual, is a rising public health concern. While much of the existing research has focused on cross-sectional patterns of multimorbidity, there remains a need to better understand the longitudinal accumulation of diseases. This includes examining the associations between important sociodemographic characteristics and the rate of progression of chronic conditions.Methods and findingsWe utilised electronic primary care records from 13.48 million participants in England, drawn from the Clinical Practice Research Datalink (CPRD Aurum), spanning from 2005 to 2020 with a median follow-up of 4.71 years (IQR: 1.78, 11.28). The study focused on 5 important chronic conditions: cardiovascular disease (CVD), type 2 diabetes (T2D), chronic kidney disease (CKD), heart failure (HF), and mental health (MH) conditions. Key sociodemographic characteristics considered include ethnicity, social and material deprivation, gender, and age. We employed a flexible spline-based parametric multistate model to investigate the associations between these sociodemographic characteristics and the rate of different disease transitions throughout multimorbidity development. Our findings reveal distinct association patterns across different disease transition types. Deprivation, gender, and age generally demonstrated stronger associations with disease diagnosis compared to ethnic group differences. Notably, the impact of these factors tended to attenuate with an increase in the number of preexisting conditions, especially for deprivation, gender, and age. For example, the hazard ratio (HR) (95% CI; p-value) for the association of deprivation with T2D diagnosis (comparing the most deprived quintile to the least deprived) is 1.76 ([1.74, 1.78]; p ConclusionsOur results indicate that early phases of multimorbidity development could warrant increased attention. The potential importance of earlier detection and intervention of chronic conditions is underscored, particularly for MH conditions and higher-risk populations. These insights may have important implications for the management of multimorbidity

Contribution of frailty to multimorbidity patterns and trajectories: Longitudinal dynamic cohort study of aging people

Background: Multimorbidity and frailty are characteristics of aging that need individualized evaluation, and there is a 2-way causal relationship between them. Thus, considering frailty in analyses of multimorbidity is important for tailoring social and health care to the specific needs of older people. Objective: This study aimed to assess how the inclusion of frailty contributes to identifying and characterizing multimorbidity patterns in people aged 65 years or older. Methods: Longitudinal data were drawn from electronic health records through the SIDIAP (Sistema d’Informació pel Desenvolupament de la Investigació a l’Atenció Primària) primary care database for the population aged 65 years or older from 2010 to 2019 in Catalonia, Spain. Frailty and multimorbidity were measured annually using validated tools (eFRAGICAP, a cumulative deficit model; and Swedish National Study of Aging and Care in Kungsholmen [SNAC-K], respectively). Two sets of 11 multimorbidity patterns were obtained using fuzzy c-means. Both considered the chronic conditions of the participants. In addition, one set included age, and the other included frailty. Cox models were used to test their associations with death, nursing home admission, and home care need. Trajectories were defined as the evolution of the patterns over the follow-up period. Results: The study included 1,456,052 unique participants (mean follow-up of 7.0 years). Most patterns were similar in both sets in terms of the most prevalent conditions. However, the patterns that considered frailty were better for identifying the population whose main conditions imposed limitations on daily life, with a higher prevalence of frail individuals in patterns like chronic ulcers &peripheral vascular. This set also included a dementia-specific pattern and showed a better fit with the risk of nursing home admission and home care need. On the other hand, the risk of death had a better fit with the set of patterns that did not include frailty. The change in patterns when considering frailty also led to a change in trajectories. On average, participants were in 1.8 patterns during their follow-up, while 45.1% (656,778/1,456,052) remained in the same pattern. Conclusions: Our results suggest that frailty should be considered in addition to chronic diseases when studying multimorbidity patterns in older adults. Multimorbidity patterns and trajectories can help to identify patients with specific needs. The patterns that considered frailty were better for identifying the risk of certain age-related outcomes, such as nursing home admission or home care need, while those considering age were better for identifying the risk of death. Clinical and social intervention guidelines and resource planning can be tailored based on the prevalence of these patterns and trajectories.The project received a research grant from the Carlos III Institute of Health, Ministry of Economy and Competitiveness (Spain), awarded in 2019 under the Health Strategy Action 2013-2016, within the National Research Programme oriented to Societal Challenges, within the Technical, Scientific and Research National Plan 2013-2016 (reference PI19/00535), and the PFIS Grant FI20/00040, co-funded with European Union ERDF (European Regional Development Fund) funds.Peer ReviewedPostprint (published version