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Record W4400109029 · doi:10.1016/j.lana.2024.100826

Association of hepatitis B virus treatment with all-cause and liver-related mortality among individuals with HBV and cirrhosis: a population-based cohort study

2024· article· en· W4400109029 on OpenAlexafffundabout
Jean Damascène Makuza, Dahn Jeong, Stanley Wong, Mawuena Binka, Prince Adu, Héctor Alexander Velásquez García, Richard L. Morrow, Georgine Cua, Amanda Yu, Maria Alvarez, Sofia Bartlett, Hin Hin Ko, Eric M. Yoshida, Alnoor Ramji, Mel Krajden, Naveed Z. Janjua

Bibliographic record

VenueThe Lancet Regional Health - Americas · 2024
Typearticle
Languageen
FieldMedicine
TopicHepatitis B Virus Studies
Canadian institutionsSt. Paul's HospitalBC Centre for Disease ControlUniversity of British Columbia
FundersBC Cancer AgencyMinistry of Health, British ColumbiaCanadian Institutes of Health ResearchBritish Columbia Centre for Disease ControlPublic Health AgencyPublic Health Agency of Canada
KeywordsCirrhosisMedicineHepatitis B virusCohortHepatitis BPopulationInternal medicineCohort studyVirologyVirusEnvironmental health

Abstract

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Background: We evaluated the association of hepatitis B virus (HBV) treatment with all-cause, and liver-related mortality among individuals with HBV and cirrhosis in British Columbia (BC), Canada. Methods: This analysis included people diagnosed with HBV and had cirrhosis in the BC Hepatitis Testers Cohort, including data on all individuals diagnosed with HBV from 1990 to 2015 in BC and integrated with healthcare administrative data. We followed people with cirrhosis from the first cirrhosis diagnosis date until death or December 31, 2020. We compared all-cause and liver related mortality between those who received treatment and those who did not. HBV treatment was considered a time-varying variable. We performed multivariable Cox proportional hazards model and competing risk regression models to assess the association of HBV treatment with all causes, and liver-related mortality respectively using inverse probability of treatment weighted population. Findings: Among 4962 individuals with HBV and cirrhosis, 48.1% received HBV treatment. Treated individuals had a median follow-up of 2.97 years, compared to 2.87 years for untreated individuals. The treated group was older (median age 57 vs 54 years), had higher proportion of treated of males [1802 (75.50%) vs 1766 (68.8%)], from urban area [2318 (97.2%) vs 2355 (91.8%)], and from East and South Asian ethnicity [1506 (63.1%) vs 709 (27.5%)] compared to untreated group. Untreated people experienced higher all-cause mortality (115.47 vs. 35.72 per 1000 person-years) and liver-related mortality (49.86 vs. 11.39 per 1000 person-years). Multivariable models showed that HBV treatment significantly lowered the risk of all-cause mortality (adjusted hazard ratio (aHR) 0.74; 95% CI: 0.65, 0.84) and liver-related mortality (adjusted subdistribution hazard ratio (asHR) 0.72; 95% CI: 0.58, 0.89) compared to untreated individuals. Among untreated individuals with HBV, those with HCV coinfection had a higher risk of both all-cause and liver-related mortality (aHR 1.57; 95% CI: 1.22, 2.04, and asHR 1.60; 95% CI: 1.25, 2.05, respectively). Interpretation: HBV treatment was associated with a significant reduction in all-cause and liver-related mortality among individuals with cirrhosis. The findings highlight the need for treatment among individuals with HBV related cirrhosis especially those with coinfection with hepatitis C virus. Funding: This work was supported by the BC Centre for Disease Control and the Canadian Institutes of Health Research (CIHR) [Grant # NHC-142832, PJT-156066, and SC1 -178736]. JDM has received doctoral fellowship from the Canadian Network on Hepatitis C (CanHepC). DJ has received Doctoral Research Award (#201910DF1-435705-64343) from the Canadian Institutes of Health Research (CIHR) and doctoral fellowship from the CanHepC. CanHepC is funded by a joint initiative of the Canadian Institutes of Health Research (CIHR) (NHC-142832) and the Public Health Agency of Canada (PHAC).

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.099
Threshold uncertainty score0.961

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.065
GPT teacher head0.361
Teacher spread0.296 · 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 teacher head, 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

Citations8
Published2024
Admission routes3
Has abstractyes

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