Predicting health futures: Innovative study reveals critical events in disease trajectories

In a recent study published in npj Digital Medicine, researchers identified life-spanning trajectories and critical events that influence hospitalization and death.

Study: Unraveling cradle-to-grave disease trajectories from multilayer comorbidity networks. Image Credit: Thaiview/Shutterstock.comStudy: Unraveling cradle-to-grave disease trajectories from multilayer comorbidity networks. Image Credit: Thaiview/Shutterstock.com

Background

Multimorbidity, or multiple disease presence in an individual, impacts healthcare and long-term care expenses. With millions of Europeans suffering from various chronic illnesses, studying multimorbidity patterns is critical for healthy aging and disease prevention.

Comorbidity networks and disease trajectories examine multimorbidity in electronic health records and clinical registries. However, research on multimorbidity trends throughout a patient's lifespan is limited, underscoring the importance of a lifelong perspective on multiple comorbidities.

About the study

In the present study, researchers developed a unique method for multimorbidity networks using longitudinal population-level health data to detect illness trajectories.

The researchers developed a multiple-layer comorbid conditions network in which nodes represent diagnoses, layers represent ages, intralayer linkages represent co-occurring illnesses, and interlayer linkages encode disease pairing directionality.

The researchers attempted to determine critical events as places along trajectories at which two initially similar trajectories begin to diverge, resulting in differing disease burden outcomes (hospital usage and death).

The researchers examined electronic medical registry data, including over nine million Austrians and >44 million hospital visits between 1997 and 2014. They limited the study to individuals who were healthy at study initiation from 2003 to 2014.

They classified the information into 10-year groups and added a layer to the network for each age group. They categorized converging trajectory pairs as those with no overlapping (no common diagnoses) among younger age groups but non-zero overlapping among groups of older individuals.

The researchers used hospital data to create the multimorbidity network, which included all 3-digit International Classification of Diseases 10 (ICD-10) codes from A00-N99 and a newly added code for undiagnosed individuals.

They also performed a detailed literature review utilizing the PubMed database to evaluate trajectories' interpretations in the context of existing scientific literature.

They assessed the health status of patients with a given disease based on primary and secondary diagnoses, with a wash-out time to guarantee a comparable health state at the start of the study.  

Results

The researchers discovered 1,260 individual illness trajectories (618 for females and 642 for men), with an average of nine diagnoses spanning up to 70 years. They discovered 70 pairs of divergent trajectories that share specific symptoms at earlier ages but grow into significantly diverse groupings of disorders at older ages.

The discovered communities often included more than one age layer. The frequency of hospital stays and nodes increased with age, peaking between 60 and 69 years and then dropping as people age.

The count of linkages and the degree of intralayer and out- or inbound inter-layer linkages for both sexes showed comparable age patterns.

The discovery technique identified 642 and 618 unique illness trajectories in male and female networks, respectively. Repeating the analysis thrice with various random seeds yielded similar findings in every realization.

The trajectories included an average of nine separate diagnoses spanning 20-30 years, with some cases extending to 70 years.

The Jaccard indices varied from zero to one, showing the different extents of similarities between the trajectories. Nested associations were most frequent, demonstrating complete overlapping of short and long trajectories, explaining the Jaccard index peak at one.

There was a robust association between the number, ICD chapter size, and particular ICD codes in both sexes. Furthermore, the magnitude of these trajectories showed a strong relationship with the count of outgoing and incoming linkages.

According to the PubMed analysis, the researchers discovered 2.20% of illness trajectories (n=10) among men and 3.60% (n=16) among women, with certain diseases having no clear connections with other illnesses listed within the same disease trajectory as recorded in PubMed.

Such trajectories included doubtful neoplasms of respiratory-type organs in the sixties and seventies, pulmonary cancers in the seventies among women, and renal cancer in the forties and fifties, as well as questionable neoplasms of urine organs in the forties in men.

The researchers found 35 divergent trajectory pairs among women and 35 among men. Diverging trajectories for females had an average of three age groups, four different diagnostic chapters, and eight diseases, whereas men had three age groups, 11 diseases, and four distinct diagnosis chapters. Some trajectories were permanent (16 trajectory pairs among women and 14 among men).

The study revealed 642 illness trajectories in males and 618 in females, including crucial events like bifurcation points.

For example, in females with arterial hypertension, two distinct trajectories were detected, the first of which led to chronic kidney disease (N18) between the ages of 20 and 29 years.

Obesity is directly associated with arterial hypertension, diabetes mellitus type 2, and dyslipidemia, and its early development is a primary risk factor for subsequent disorders.

The study emphasizes using comorbidity networks and illness trajectories to analyze multimorbidity in electronic health records and clinical registries.

Journal reference:
Pooja Toshniwal Paharia

Written by

Pooja Toshniwal Paharia

Pooja Toshniwal Paharia is an oral and maxillofacial physician and radiologist based in Pune, India. Her academic background is in Oral Medicine and Radiology. She has extensive experience in research and evidence-based clinical-radiological diagnosis and management of oral lesions and conditions and associated maxillofacial disorders.

Citations

Please use one of the following formats to cite this article in your essay, paper or report:

  • APA

    Toshniwal Paharia, Pooja Toshniwal Paharia. (2024, March 11). Predicting health futures: Innovative study reveals critical events in disease trajectories. News-Medical. Retrieved on July 23, 2024 from https://www.news-medical.net/news/20240311/Predicting-health-futures-Innovative-study-reveals-critical-events-in-disease-trajectories.aspx.

  • MLA

    Toshniwal Paharia, Pooja Toshniwal Paharia. "Predicting health futures: Innovative study reveals critical events in disease trajectories". News-Medical. 23 July 2024. <https://www.news-medical.net/news/20240311/Predicting-health-futures-Innovative-study-reveals-critical-events-in-disease-trajectories.aspx>.

  • Chicago

    Toshniwal Paharia, Pooja Toshniwal Paharia. "Predicting health futures: Innovative study reveals critical events in disease trajectories". News-Medical. https://www.news-medical.net/news/20240311/Predicting-health-futures-Innovative-study-reveals-critical-events-in-disease-trajectories.aspx. (accessed July 23, 2024).

  • Harvard

    Toshniwal Paharia, Pooja Toshniwal Paharia. 2024. Predicting health futures: Innovative study reveals critical events in disease trajectories. News-Medical, viewed 23 July 2024, https://www.news-medical.net/news/20240311/Predicting-health-futures-Innovative-study-reveals-critical-events-in-disease-trajectories.aspx.

Comments

The opinions expressed here are the views of the writer and do not necessarily reflect the views and opinions of News Medical.
Post a new comment
Post

While we only use edited and approved content for Azthena answers, it may on occasions provide incorrect responses. Please confirm any data provided with the related suppliers or authors. We do not provide medical advice, if you search for medical information you must always consult a medical professional before acting on any information provided.

Your questions, but not your email details will be shared with OpenAI and retained for 30 days in accordance with their privacy principles.

Please do not ask questions that use sensitive or confidential information.

Read the full Terms & Conditions.

You might also like...
Research highlights importance of dietary education to combat rising childhood obesity rates