Objective:

To conduct a national longitudinal analysis of performance trajectories in lung cancer surgery in France using advanced trajectory modelling approaches.

Approach:

• Study Population: Included all adult patients (n=56,299) who underwent lung resection for primary lung cancer in 2020–2024 across 148 hospitals.
• Outcome Measures: Primary outcome was severe complications including major postoperative complications and 30-day mortality.
• Statistical Analysis: Used annual logistic regression models for risk-adjusted rates, Hidden Markov Models for performance transitions, and group-based multitrajectory models for hospital subgroup analysis.

Key Findings:

• Predictive models showed excellent discrimination (mean ROC curve=0.890).
• Strong performance inertia was observed with persistence probabilities ≥78%.
• Medium-volume hospitals had optimal improvement trajectories with a 45.4–45.8% reduction in risk-adjusted rates.
• Some low-volume hospitals experienced a catastrophic deterioration (843% increase in RAR).
• A subgroup of high-volume hospitals also showed concerning performance degradation (151% increase in RAR).

Interpretation:

The study reveals heterogeneous performance trajectories in lung cancer surgery across French hospitals.

Limitations:

• The study relies on administrative data, which may have inherent limitations.
• Findings may not be generalizable to other healthcare systems without similar data structures.

Conclusion:

The study highlights the value of systematic outcome monitoring using administrative data.