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Record W7115032495

Comorbidity Clusters in Congenital Heart Disease Patients - Accelerated Disease Trajectories Across the Lifespan

2024· dissertation· en· W7115032495 on OpenAlexaffabout

Bibliographic record

VenueeScholarship@McGill (McGill) · 2024
Typedissertation
Languageen
FieldMedicine
TopicCongenital Heart Disease Studies
Canadian institutionsMcGill University
Fundersnot available
KeywordsComorbidityHeart diseaseDiseaseHeart failureEpidemiology
DOInot available

Abstract

fetched live from OpenAlex

BackgroundResearch has shown that patients with congenital heart disease (CHD) are at risk for the development of cardiac and other systemic complications. No methods have been identified to measure the disease trajectories for cardiovascular and systemic complications across the lifespan. In this study, we tackle the temporal sequence and interrelationships among clusters of comorbidities to explore the trajectories of developing comorbidities in CHD patients across the lifespan. The findings will provide potentially useful information for predicting long-term health outcomes in this patient population.MethodsThis longitudinal cohort study utilized the Quebec CHD database, encompassing 35 years (1983-2017) of follow-up with data in demographics, inpatient visits, hospitalization records, and death registry. Comorbidities were coded according to ICD-9 and ICD-10 codes. A total of 39 comorbidities were included comprising clusters of cardiovascular, respiratory, renal, digestive, endocrine/metabolic, infectious and neurological diseases, as well as cancer and mental disorders. We mapped the temporal sequences of comorbidities by aligning them along age axis using the median age of disease onset. The associations between disease pairs were estimated by Cox proportional hazard model adjusting for competing risk of death and taking into account the time-varying nature of the predictor disease. They were quantified using hazard ratios (HRs). To ensure robustness, a conservative approach was adopted by taking the lower limit of 95% confidence interval rather than the point estimate of HRs and a threshold of 2.0 to indicate the connectedness between disease pairs. Also, only disease pairs with >= 5 years gap in age of onset were considered in association analyses to take into account the variation in disease presentation and diagnoses. Patients with severe and non-severe CHD lesions were studied separately.ResultsThe study cohort comprised 9,764 individuals diagnosed with severe CHD lesion and 127,729 with non-severe CHD lesion. The median follow-up duration was 25.3 years for severe CHD and 21.7 years for non-severe CHD. The cumulative probability of developing at least one vascular disease by early adulthood (age=40 years) were around 70% and 25% in severe and non-severe patients respectively. Analysis of disease onset revealed that severe CHD patients predominantly developed morbid diseases between the ages of 25 and 40 years, whereas non-severe CHD patients exhibited a peak in comorbidity onsets between their 50s and 70s. In severe CHD, the trajectory of sequalae commenced with childhood cardiovascular diseases such as childhood heart failure, progressing to systemic diseases in early childhood such as diabetes, hypertension, chronic liver disease, acute/chronic kidney diseases, and followed by adulthood heart failure and dementia. It is worth to note that mental disorders, such as psychoses, anxiety, depression, manifested during the third to fourth decade of life, showing association with acute/chronic kidney diseases. We also observed an association between obesity and systemic and cardiac diseases for severe CHD patients. For non-severe CHD group, we observed additional connections between diseases, such as mental illnesses exhibiting an elevated association with dementia. ConclusionsOur study revealed measurable trajectories of clusters of multimorbidities among CHD patients demonstrating distinct patterns of disease onset between severe and non-severe CHD patients. Early child and adulthood systemic diseases were found to be pivotal in determining the trajectory of subsequent complications underscoring the importance of timely interventions for comorbidity prevention. The study shed light on understanding comorbidities trajectories across the lifespan in complex cardiovascular disease

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.191
Threshold uncertainty score0.379

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.026
GPT teacher head0.299
Teacher spread0.273 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2024
Admission routes2
Has abstractyes

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