# A Novel Nomogram Integrating Systemic Immune-Inflammation Index and Serum Prealbumin for Predicting Unplanned Readmission in Male Patients with Coexisting Lung Cancer and Chronic Obstructive Pulmonary Disease

## TL;DR

This study created a tool to predict unplanned hospital readmissions in men with both lung cancer and COPD using inflammation, nutrition, and cancer stage data.

## Contribution

A novel nomogram integrating SII and prealbumin for predicting unplanned readmission in male patients with lung cancer and COPD.

## Key findings

- The nomogram achieved good predictive accuracy with an AUC of 0.809.
- SII and advanced cancer stage were significant independent risk factors for unplanned readmission.
- The tool showed excellent calibration and clinical net benefit via Decision Curve Analysis.

## Abstract

Patients with coexisting lung cancer and chronic obstructive pulmonary disease (COPD) face a disproportionately high risk of unplanned readmission. This study developed and internally validated a user-friendly nomogram based on the “inflammation-nutrition-tumor” framework, using routinely available clinical parameters—specifically age, cancer stage, the Systemic Immune-Inflammation Index (SII), and serum prealbumin. SII is a composite marker derived from standard complete blood counts, reflecting systemic inflammation, while prealbumin serves as a sensitive indicator of nutritional status. The nomogram demonstrated good predictive accuracy (AUC = 0.809) and clinical utility in our male cohort, outperforming simpler models based on age and cancer stage alone. This tool may empower clinicians to identify high-risk patients and implement targeted, cost-effective interventions—such as nutritional optimization, intensified pulmonary rehabilitation, or closer post-discharge follow-up—with the goal of reducing avoidable readmissions and improving outcomes in this vulnerable population. External validation in diverse populations is planned before widespread clinical application.

Background: Patients with coexisting lung cancer and COPD are highly susceptible to unplanned readmissions. This study aimed to develop and internally validate a robust predictive nomogram based on the “inflammation-nutrition-tumor” framework to quantify this risk. Methods: A retrospective cohort of 207 clinical episodes from male patients with lung cancer and COPD was analyzed. Participants were categorized into Planned Readmission (PR, n = 165) and Unplanned Readmission (UR, n = 42) groups. Independent risk factors were identified via univariate and multivariable analyses using Generalized Estimating Equations (GEE). A nomogram was subsequently constructed, and its performance was rigorously evaluated using the Area Under the Curve (AUC), calibration plots, and Decision Curve Analysis (DCA). Results: Multivariable GEE analysis demonstrated that the Systemic Immune-Inflammation Index (SII) was a highly significant independent risk factor (OR for a 500-unit increase = 1.490, 95% CI: 1.234–1.798, p < 0.001). Advanced cancer stage (III–IV) was also a significant predictor (OR = 3.590, 95% CI: 1.301–9.909, p = 0.014), while prealbumin (OR = 0.950, 95% CI: 0.896–1.007, p = 0.087) was identified as a key nutritional predictor. The integrated four-variable nomogram (age, cancer stage, SII, prealbumin) demonstrated good discriminative ability with an AUC of 0.809 (95% CI: 0.733–0.885). The calibration plot indicated excellent agreement, and DCA confirmed a substantial clinical net benefit. Conclusions: This SII-based nomogram provides a reliable and practical tool for individualized risk stratification, facilitating targeted clinical interventions to mitigate unplanned readmission rates in this vulnerable population.