Comparison of PNI and GNRI Nomogram Models for Predicting Postoperative Complications in Elderly Patients with Lung Cancer: A Retrospective Study

Why It Matters

Accurate early prediction enables targeted nutritional optimization and surgical strategies, reducing morbidity in a vulnerable elderly lung‑cancer population.

Key Takeaways

• Smoking, low GNRI/PNI, ΔPNI increase risk, longer surgery
• Nomogram achieved AUC 0.87 in validation, 80% accuracy
• ΔPNI captures peri‑operative nutritional decline, predicts complications
• Visual tool enables individualized risk stratification for elderly lung‑cancer surgery
• Findings support pre‑operative nutrition support and smoking cessation programs

Pulse Analysis

The study underscores a growing recognition that nutritional status is as critical as classic surgical variables in determining outcomes for elderly lung‑cancer patients. While smoking history and operative time have long been linked to postoperative cardiopulmonary events, the inclusion of the Prognostic Nutrition Index (PNI) and the Geriatric Nutrition Risk Index (GNRI) adds a quantifiable measure of immune‑nutritional reserve. By demonstrating that a postoperative drop in PNI (ΔPNI) independently predicts complications, the research provides clinicians with a dynamic marker that can be monitored throughout the peri‑operative window, allowing timely interventions such as albumin supplementation or immunonutrition.

From a health‑system perspective, the nomogram’s strong discrimination (AUC > 0.80) and calibration suggest it could be integrated into electronic health records to flag high‑risk patients automatically. This aligns with value‑based care initiatives that prioritize risk‑adjusted pathways, potentially lowering intensive‑care admissions and associated costs. Moreover, the decision‑curve analysis indicates a net clinical benefit across a broad range of risk thresholds, supporting its use in both high‑volume tertiary centers and community hospitals where resources for intensive monitoring may be limited.

Future research should focus on external validation across diverse geographic cohorts and on prospective trials that test whether pre‑operative nutritional optimization—guided by GNRI and PNI scores—can translate the model’s predictive power into tangible reductions in complication rates. As the population ages, tools that blend simple laboratory data with operative factors will become indispensable for delivering precision surgery to elderly patients with non‑small cell lung cancer.