Temporal disease trajectories condensed from population-wide registry data covering 6.2 million patients

Jensen, Anders B.; Moseley, Pope L.; Oprea, Tudor I.; Ellesøe, Sabrina Gade; Eriksson, Robert; Schmock, Henriette; Jensen, Peter Bjødstrup; Jensen, Lars Juhl; Brunak, Søren

doi:10.1038/ncomms5022

Cited by 315 publications

(403 citation statements)

References 22 publications

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“…Sepsis causes numerous complications, including amputations, neurological and neuromuscular disorders resulting in significant post hospital morbidity and reduced quality of life234. Moreover, sepsis was the most expensive condition in U.S. hospitals in 2011, costing $20.3 billion or 5.2% of the total cost for all hospitalizations5, emphasizing the importance of developing more cost-effective handling and care of sepsis. This includes better stratification of sepsis patients in mortality risk groups.…”

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confidence: 99%

Diagnosis trajectories of prior multi-morbidity predict sepsis mortality

Beck

Jensen

Nielsen

et al. 2016

Sci Rep

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Sepsis affects millions of people every year, many of whom will die. In contrast to current survival prediction models for sepsis patients that primarily are based on data from within-admission clinical measurements (e.g. vital parameters and blood values), we aim for using the full disease history to predict sepsis mortality. We benefit from data in electronic medical records covering all hospital encounters in Denmark from 1996 to 2014. This data set included 6.6 million patients of whom almost 120,000 were diagnosed with the ICD-10 code: A41 ‘Other sepsis’. Interestingly, patients following recurrent trajectories of time-ordered co-morbidities had significantly increased sepsis mortality compared to those who did not follow a trajectory. We identified trajectories which significantly altered sepsis mortality, and found three major starting points in a combined temporal sepsis network: Alcohol abuse, Diabetes and Cardio-vascular diagnoses. Many cancers also increased sepsis mortality. Using the trajectory based stratification model we explain contradictory reports in relation to diabetes that recently have appeared in the literature. Finally, we compared the predictive power using 18.5 years of disease history to scoring based on within-admission clinical measurements emphasizing the value of long term data in novel patient scores that combine the two types of data.

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confidence: 99%

Diagnosis trajectories of prior multi-morbidity predict sepsis mortality

Beck

Jensen

Nielsen

et al. 2016

Sci Rep

Self Cite

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“…cardiac or renal, but also to the various treatments and therapies. In response to Q5, T2 was thus related to research: in disease studies [30][31][32][33][34][35][36][37], to compare care and coding [38][39][40], but also in monitoring of patients over time and the survival rate [31,[41][42][43][44]. Survival rate forms part of a trajectory concept.…”

Section: Discussionmentioning

confidence: 99%

Patient healthcare trajectory. An essential monitoring tool: a systematic review

Pinaire

Azé

Bringay

et al. 2017

Health Inf Sci Syst

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Background: Patient healthcare trajectory is a recent emergent topic in the literature, encompassing broad concepts. However, the rationale for studying patients' trajectories, and how this trajectory concept is defined remains a public health challenge. Our research was focused on patients' trajectories based on disease management and care, while also considering medico-economic aspects of the associated management. We illustrated this concept with an example: a myocardial infarction (MI) occurring in a patient's hospital trajectory of care. The patient follow-up was traced via the prospective payment system. We applied a semi-automatic text mining process to conduct a comprehensive review of patient healthcare trajectory studies. This review investigated how the concept of trajectory is defined, studied and what it achieves.Methods: We performed a PubMed search to identify reports that had been published in peer-reviewed journals between January 1, 2000 and October 31, 2015. Fourteen search questions were formulated to guide our review. A semi-automatic text mining process based on a semantic approach was performed to conduct a comprehensive review of patient healthcare trajectory studies. Text mining techniques were used to explore the corpus in a semantic perspective in order to answer non-a priori questions. Complementary review methods on a selected subset were used to answer a priori questions.Results: Among the 33,514 publications initially selected for analysis, only 70 relevant articles were semi-automatically extracted and thoroughly analysed. Oncology is particularly prevalent due to its already well-established processes of care. For the trajectory thema, 80% of articles were distributed in 11 clusters. These clusters contain distinct semantic information, for example health outcomes (29%), care process (26%) and administrative and financial aspects (16%). Conclusion:This literature review highlights the recent interest in the trajectory concept. The approach is also gradually being used to monitor trajectories of care for chronic diseases such as diabetes, organ failure or coronary artery and MI trajectory of care, to improve care and reduce costs. Patient trajectory is undoubtedly an essential approach to be further explored in order to improve healthcare monitoring.

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“…Temporal patterns of risk have been analyzed with the focus mainly on the progression of specific diseases and less on the general problem of risk estimation covering the full spectrum of diseases and their outcomes [25]. Other large-scale approaches have addressed disease risk and outcomes without considering the time element.…”

Section: Hdc For Policy Empowermentmentioning

confidence: 99%

Big Data for Public Health Policy-Making: Policy Empowerment

Mählmann

Reumann

Evangelatos

et al. 2017

Public Health Genomics

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Digitization is considered to radically transform healthcare. As such, with seemingly unlimited opportunities to collect data, it will play an important role in the public health policy-making process. In this context, health data cooperatives (HDC) are a key component and core element for public health policy-making and for exploiting the potential of all the existing and rapidly emerging data sources. Being able to leverage all the data requires overcoming the computational, algorithmic, and technological challenges that characterize today’s highly heterogeneous data landscape, as well as a host of diverse regulatory, normative, governance, and policy constraints. The full potential of big data can only be realized if data are being made accessible and shared. Treating research data as a public good, creating HDC to empower citizens through citizen-owned health data, and allowing data access for research and the development of new diagnostics, therapies, and public health policies will yield the transformative impact of digital health. The HDC model for data governance is an arrangement, based on moral codes, that encourages citizens to participate in the improvement of their own health. This then enables public health institutions and policymakers to monitor policy changes and evaluate their impact and risk on a population level.

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Temporal disease trajectories condensed from population-wide registry data covering 6.2 million patients

Cited by 315 publications

References 22 publications

Diagnosis trajectories of prior multi-morbidity predict sepsis mortality

Diagnosis trajectories of prior multi-morbidity predict sepsis mortality

Patient healthcare trajectory. An essential monitoring tool: a systematic review

Big Data for Public Health Policy-Making: Policy Empowerment

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