ArticleBMJ health & care informatics2026
Measuring performance trajectories in lung cancer surgery: a longitudinal study using the French national hospital database from 2020 to 2024.
Article in BMJ health & care informatics, 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
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesLung cancer surgery is associated with significant postoperative morbidity (15%-40%) and mortality (2%-5%), with considerable interhospital variability. This study conducts the first national longitudinal analysis of performance trajectories in lung cancer surgery using advanced trajectory modelling approaches to understand temporal dynamics of quality evolution across hospitals.
methodsWe included all adult patients (French national hospital database, n=56 299; 148 hospitals) who underwent lung resection for primary lung cancer in 2020-2024. The primary outcome was severe complications including major postoperative complications and 30-day mortality. Annual logistic regression models incorporating clinical and procedural variables calculated risk-adjusted rates (RAR) for each hospital. Hospitals were classified using funnel plots corrected for overdispersion. Hidden Markov Models estimated transition probabilities between performance categories, while group-based multitrajectory models identified subgroups of hospitals sharing similar evolution profiles, stratified by activity volume (low, <43; medium, 43-100; high, >100 procedures/year).
resultsPredictive models demonstrated excellent stable discrimination (mean area under the ROC curve=0.890). Markov models revealed strong performance inertia (persistence probabilities ≥78%). Trajectory analysis identified a complex non-linear relationship between volume and quality. Medium-volume hospitals showed optimal improvement trajectories (45.4-45.8% RAR reduction). Conversely, some low-volume centres experienced catastrophic deterioration (843% RAR increase), while paradoxically, a subgroup of high-volume hospitals also showed concerning performance degradation (151% increase). DISCUSSION: This study demonstrates heterogeneous performance trajectories in French lung cancer surgery, challenging the assumption that high-volume alone guarantees quality and suggesting organisational complexity challenges.
conclusionThis study shows the value of implementing systematic outcome monitoring using existing administrative data to target quality improvement interventions and reduce inter-hospital performance variations.
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.