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Mining Major Transitions of Chronic Conditions in Patients with Multiple Chronic Conditions.

ABSTRACT: OBJECTIVES:Evolution of multiple chronic conditions (MCC) follows a complex stochastic process, influenced by several factors including the inter-relationship of existing conditions, and patient-level risk factors. Nearly 20% of citizens aged 18 years and older are burdened with two or more (multiple) chronic conditions (MCC). Treatment for people living with MCC currently accounts for an estimated 66% of the Nation's healthcare costs. However, it is still not known precisely how MCC emerge and accumulate among individuals or in the general population. This study investigates major patterns of MCC transitions in a diverse population of patients and identifies the risk factors affecting the transition process. METHODS:A Latent regression Markov clustering (LRMCL) algorithm is proposed to identify major transitions of four MCC that include hypertension (HTN), depression, Post- Traumatic Stress Disorder (PTSD), and back pain. A cohort of 601,805 individuals randomly selected from the population of Iraq and Afghanistan war Veterans (IAVs) who received VA care during three or more years between 2002-2015, is used for training the proposed LRMCL algorithm. RESULTS:Two major clusters of MCC transition patterns with 78% and 22% probability of membership respectively were identified. The primary cluster demonstrated the possibility of improvement when the number of MCC is small and an increase in probability of MCC accumulation as the number of co- morbidities increased. The second cluster showed stability (no change) of MCC overtime as the major pattern. Age was the most significant risk factor associated with the most probable cluster for each IAV. CONCLUSIONS:These findings suggest that our proposed LRMCL algorithm can be used to describe and understand MCC transitions, which may ultimately allow healthcare systems to support optimal clinical decision- making. This method will be used to describe a broader range of MCC transitions in this and non-VA populations, and will add treatment information to see if models including treatments and MCC emergence can be used to support clinical decision-making in patient care.

SUBMITTER: Alaeddini A

PROVIDER: S-EPMC5875179 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Mining Major Transitions of Chronic Conditions in Patients with Multiple Chronic Conditions.

Alaeddini Adel A Jaramillo Carlos A CA Faruqui Syed H A SHA Pugh Mary J MJ

Methods of information in medicine 20170101 5

<h4>Objectives</h4>Evolution of multiple chronic conditions (MCC) follows a complex stochastic process, influenced by several factors including the inter-relationship of existing conditions, and patient-level risk factors. Nearly 20% of citizens aged 18 years and older are burdened with two or more (multiple) chronic conditions (MCC). Treatment for people living with MCC currently accounts for an estimated 66% of the Nation's healthcare costs. However, it is still not known precisely how MCC eme ...[more]

PMID: 29582934

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Project description:Over the past few decades, the rise of multiple chronic conditions has become a major concern for clinicians. However, it is still not known precisely how multiple chronic conditions emerge among patients. We propose an unsupervised multi-level temporal Bayesian network to provide a compact representation of the relationship among emergence of multiple chronic conditions and patient level risk factors over time. To improve the efficiency of the learning process, we use an extension of maximum weight spanning tree algorithm and greedy search algorithm to study the structure of the proposed network in three stages, starting with learning the inter-relationship of comorbidities within each year, followed by learning the intra-relationship of comorbidity emergence between consecutive years, and finally learning the hierarchical relationship of comorbidities and patient level risk factors. We also use a longest path algorithm to identify the most likely sequence of comorbidities emerging from and/or leading to specific chronic conditions. Using a de-identified dataset of more than 250,000 patients receiving care from the U.S. Department of Veterans Affairs for a period of five years, we compare the performance of the proposed unsupervised Bayesian network in comparison with those of Bayesian networks developed based on supervised and semi-supervised learning approaches, as well as multivariate probit regression, multinomial logistic regression, and latent regression Markov mixture clustering focusing on traumatic brain injury (TBI), post-traumatic stress disorder (PTSD), depression (Depr), substance abuse (SuAb), and back pain (BaPa). Our findings show that the unsupervised approach has noticeably accurate predictive performance that is comparable to the best performing semi-supervised and the second-best performing supervised approaches. These findings also revealed that the unsupervised approach has improved performance over multivariate probit regression, multinomial logistic regression, and latent regression Markov mixture clustering.

Project description:OBJECTIVE:To (1) examine the burden of multiple chronic conditions (MCC) in an urban health system, and (2) propose a methodology to identify subpopulations of interest based on diagnosis groups and costs. DESIGN:Retrospective cross-sectional study. SETTING:Mount Sinai Health System, set in all five boroughs of New York City, USA. PARTICIPANTS:192 085 adult (18+) plan members of capitated Medicaid contracts between the Healthfirst managed care organisation and the Mount Sinai Health System in the years 2012 to 2014. METHODS:We classified adults as having 0, 1, 2, 3, 4 or 5+ chronic conditions from a list of 69 chronic conditions. After summarising the demographics, geography and prevalence of MCC within this population, we then described groups of patients (segments) using a novel methodology: we combinatorially defined 18 768 potential segments of patients by a pair of chronic conditions, a sex and an age group, and then ranked segments by (1) frequency, (2) cost and (3) ratios of observed to expected frequencies of co-occurring chronic conditions. We then compiled pairs of conditions that occur more frequently together than otherwise expected. RESULTS:61.5% of the study population suffers from two or more chronic conditions. The most frequent dyad was hypertension and hyperlipidaemia (19%) and the most frequent triad was diabetes, hypertension and hyperlipidaemia (10%). Women aged 50 to 65 with hypertension and hyperlipidaemia were the leading cost segment in the study population. Costs and prevalence of MCC increase with number of conditions and age. The disease dyads associated with the largest observed/expected ratios were pulmonary disease and myocardial infarction. Inter-borough range MCC prevalence was 16%. CONCLUSIONS:In this low-income, urban population, MCC is more prevalent (61%) than nationally (42%), motivating further research and intervention in this population. By identifying potential target populations in an interpretable manner, this segmenting methodology has utility for health services analysts.

Project description:BackgroundIncreases in biological complexity and the origins of life's hierarchical organization are described by the "major transitions" framework. A crucial component of this paradigm is that after the transition in complexity or organization, adaptation occurs primarily at the level of the new, higher-level unit. For collective-level adaptations to occur, though, collective-level traits-properties of the group, such as collective size-must be heritable. Since collective-level trait values are functions of lower-level trait values, collective-level heritability is related to particle-level heritability. However, the nature of this relationship has rarely been explored in the context of major transitions.ResultsWe examine relationships between particle-level heritability and collective-level heritability for several functions that express collective-level trait values in terms of particle-level trait values. For clonal populations, when a collective-level trait value is a linear function of particle-level trait values and the number of particles per collective is fixed, the heritability of a collective-level trait is never less than that of the corresponding particle-level trait and is higher under most conditions. For more complicated functions, collective-level heritability is higher under most conditions, but can be lower when the environment experienced by collectives is heterogeneous. Within-genotype variation in collective size reduces collective-level heritability, but it can still exceed particle-level heritability when phenotypic variance among particles within collectives is large. These results hold for a diverse sample of biologically relevant traits.ConclusionsRather than being an impediment to major transitions, we show that, under a wide range of conditions, the heritability of collective-level traits is actually higher than that of the corresponding particle-level traits. High levels of collective-level trait heritability thus arise "for free," with important implications not only for major transitions but for multilevel selection in general.

Dataset Information

Mining Major Transitions of Chronic Conditions in Patients with Multiple Chronic Conditions.

Publications

Mining Major Transitions of Chronic Conditions in Patients with Multiple Chronic Conditions.

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