ArticleCanadian liver journal2026
Development and evaluation of a hybrid algorithm for identifying hospitalized patients with cirrhosis.
Article in Canadian liver journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Electronic medical record (EMR)-based quality improvement (QI) tools for cirrhosis care require accurate patient identification. Combining administrative and EMR data may enhance cirrhosis identification. This study aims to validate the Alberta code set (a hybrid code set using both administrative and EMR data) for identifying patients with cirrhosis. Methods: Twelve high-performing ICD-10 codes (Alberta code set) were evaluated using a cohort of 719 chart review-confirmed cirrhosis patients. Validation was performed in an independent cohort of 913 consecutively admitted patients at four Albertan hospitals (two tertiary and two non-urban). Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were obtained with a 95% CI. Two other code sets (Shearer and SoLiDaRity-10) were also validated using administrative-only, EMR-only, or hybrid data. Results: Using administrative data alone, the Alberta code set showed a sensitivity of 78.9%, specificity of 97.4%, PPV of 80.4%, and NPV of 97.2%. With hybrid data, sensitivity improved to 87.2% and NPV to 98.2%, while specificity (96.5%) and PPV (77.2%) remained similar. Urban sites showed higher sensitivity (86.2% and 91.7%) than non-urban sites (70% and 82.1%), likely attributed to coding practice variability. The Shearer and SoLiDaRity-10 code sets also demonstrated high sensitivity (86.2% and 83.5%, respectively) and similar specificity (96.6 and 97.1, respectively) when using hybrid data rather than either admin data or EMR data alone. Conclusions: A hybrid administrative and EMR-based approach effectively identifies in-patient cirrhosis cases across health care settings and holds promise to support QI and research for cirrhosis patients.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.