Hospital healthcare flows: A longitudinal clustering approach of acute coronary syndrome in women over 45 years

Pinaire, Jessica; Azé, Jérôme; Bringay, Sandra; Poncelet, Pascal; Génolini, Christophe; Landais, Paul

doi:10.1177/14604582211033020

Cited by 6 publications

(3 citation statements)

References 52 publications

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“…Here, the disease pairs have been linked by the Sankey algorithm, thus it may not be the same patient group that traverses an entire trajectory. These types of flow diagrams are getting more focus for visualising longitudinal healthcare data such as prescription trajectories ( Aguayo-Orozco et al, 2021 ), symptom trajectories ( Lademann et al, 2019 ), cancer trajectories ( Hu et al, 2019 ), hospital flow from acute coronary syndrome ( Pinaire et al, 2021 ) etc.…”

Section: The Disease Trajectory Highway and Temporalitymentioning

confidence: 99%

Visualising disease trajectories from population-wide data

Hjaltelin

Currant²,

Jørgensen³

et al. 2023

Front. Bioinform.

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Section: The Disease Trajectory Highway and Temporalitymentioning

confidence: 99%

Visualising disease trajectories from population-wide data

Hjaltelin

Currant²,

Jørgensen³

et al. 2023

Front. Bioinform.

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“…Such methodologies fall short of capturing the dynamic narrative of disease progression and interactions with comorbidities. Furthermore, traditional analysis methods [15][16][17][18][19][20] often interpreted disease trajectories as a chain of disease events, potentially overlooking the simultaneous presence of comorbidities in ALS patients.…”

Section: Introductionmentioning

confidence: 99%

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

Wu,

Taniar,

Adhinugraha

et al. 2023

Biomedicines

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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.

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“…We believe this will help to understand the development of MI and the risks associated with short-term death and to identify potential patients earlier. Compared to the state-of-theart method, which started with identifying disease pairs and directions and then combining overlaps into disease trajectories [12,13], another partitioning-based method, by clustering individuals sharing the same behavior as trajectories [14], we adopted a pattern mining technique for new possibilities. Our proposed approach aims to locate intact pathways founded on the individuals, including crucial temporal information and the intervals between admissions, enabling the potential of analyzing re-hospitalizations and corresponding interval changes between hospital stays.…”

Section: Introductionmentioning

confidence: 99%

Progression to myocardial infarction short-term death based on interval sequential pattern mining

Taniar

Adhinugraha

et al. 2023

BMC Cardiovasc Disord

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Background Myocardial infarction (MI) is one of the significant cardiovascular diseases (CVDs). According to Taiwanese health record analysis, the hazard rate reaches a peak in the initial year after diagnosis of MI, drops to a relatively low value, and maintains stable for the following years. Therefore, identifying suspicious comorbidity patterns of short-term death before the diagnosis may help achieve prolonged survival for MI patients. Methods Interval sequential pattern mining was applied with odds ratio to the hospitalization records from the Taiwan National Health Insurance Research Database to evaluate the disease progression and identify potential subjects at the earliest possible stage. Results Our analysis resulted in five disease pathways, including “diabetes mellitus,” “other disorders of the urethra and urinary tract,” “essential hypertension,” “hypertensive heart disease,” and “other forms of chronic ischemic heart disease” that led to short-term death after MI diagnosis, and these pathways covered half of the cohort. Conclusion We explored the possibility of establishing trajectory patterns to identify the high-risk population of early mortality after MI.

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Hospital healthcare flows: A longitudinal clustering approach of acute coronary syndrome in women over 45 years

Cited by 6 publications

References 52 publications

Visualising disease trajectories from population-wide data

Visualising disease trajectories from population-wide data

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

Progression to myocardial infarction short-term death based on interval sequential pattern mining

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