Evidence map›Paper›PMID 38649696›Full record

ArticleScientific reports2024

Complex networks approach to study comorbidities in patients with unruptured intracranial aneurysms.

Juri Kivelev, Ilkka Saarenpää, Antti Karlsson, Paride Crisafulli, Federico Musciotto, Jyrki Piilo, Rosario N Mantegna

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Juri KivelevDepartment of Neurosurgery, Turku University Hospital, Turku, Finland. j.v.kivelev@gmail.com.
Ilkka SaarenpääDepartment of Neurosurgery, Turku University Hospital, Turku, Finland.
Antti KarlssonAuria Biobank , University of Turku, Turku, Finland.
Paride CrisafulliDipartimento di Fisica e Chimica, Università degli Studi di Palermo, Palermo, Italy.
Federico MusciottoDipartimento di Fisica e Chimica, Università degli Studi di Palermo, Palermo, Italy.
Jyrki PiiloDepartment of Physics and Astronomy, University of Turku, Turku, Finland.
Rosario N MantegnaDipartimento di Fisica e Chimica, Università degli Studi di Palermo, Palermo, Italy.

Funding

Higher-order complex systems modeling for personalized medicine P2022JAYMH
6 · The paper itself

Abstract

The role of complex network analysis in patients with diagnosis of unruptured intracranial aneurysm is unexplored. The objective of this study is to assess the applicability of this methodology in aneurysm patients. We retrospectively analyze comprehensive unbiased local digital data of a large number of patients treated for any reason between January 2004 and July 2019. We apply an age-cohort approach to a total of 628,831 patients and construct the diagnostic history of each patient-and include the information how old the patient was when diagnosed for the first time with each diagnosis coded according to International Classification of Diseases. For each cohort of age within a 10 year interval and for each gender, we construct a statistically validated comorbidity network and focused on crucial comorbidity links that the aneurysm code has to other disease codes within the whole network. For all cohorts of different age and gender, the analysis shows that 267 diagnose codes have nearest neighbour statistically validated links to unruptured aneurysm ICD code. Among the 267 comorbidities, 204 (76%) were found in patients aged from 40 to 69-years old. Patterns of connectivity with aneurysms were found for smoking, hypertension, chronic obstructive pulmonary disease, dyslipidemia, and mood disorders. A few uncommon connections are also detected in cohorts of female patients. Our study explored the applicability of network analysis and statistical validation in aneurysm observational study.

Indexed as

ComorbidityIntracranial AneurysmAdultAgedAged, 80 and overFemaleHumansMaleMiddle AgedRetrospective Studies

Identifiers

PMID38649696
PMCPMC11035559

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