Pre-Operative Measures of Systemic Inflammation Predict Survival After Surgery for Primary Lung Cancer

BACKGROUND

Measures of systemic inflammation (MSIs) have been developed and shown to help predict prognosis in patients with lung cancer. However, studies investigating the impact of MSIs on outcomes solely in cohorts of patients undergoing curative-intent resection of NSCLC are lacking. In the era of individualized therapies, targeting inflammatory pathways could represent a novel addition to the armamentarium of lung cancer treatment.

METHODS

A multicentre retrospective review of patients who underwent primary lung cancer resection between 2012 and 2018 was undertaken. MSIs assessed were neutrophil to lymphocyte ratio (NLR), platelet to lymphocyte ratio (PLR), systemic immune inflammation index (SII), advanced lung cancer inflammation index (ALI), prognostic nutritional index (PNI) and haemoglobin albumin lymphocyte platelet (HALP) score. Cox regression analysis was performed to assess the impact of MSIs on overall survival.

RESULTS

A total of 5029 patients were included in the study. Overall 90-day mortality was 3.7% (n = 185). All MSIs were significantly associated with overall survival on univariable analysis. After multivariable Cox regression analyses, lower ALI (expressed as a continuous variable) (HR 1.000, 95% CI 1.000-1.000, P = .049) and ALI <366.43 (expressed as a dichotomous variable) (HR 1.362, 95% CI 1.137-1.631, P < .001) remained independently associated with reduced overall survival.

CONCLUSIONS

MSIs have emerged in this study as potentially important factors associated with survival following lung resection for NSCLC with curative intent. In particular, ALI has emerged as independently associated with long-term outcomes. The role of MSIs in the clinical management of patients with primary lung cancer requires further investigation.

External validation of six existing multivariable clinical prediction models for short-term mortality in patients undergoing lung resection

 

OBJECTIVES

National guidelines advocate the use of clinical prediction models to estimate perioperative mortality for patients undergoing lung resection. Several models have been developed that may potentially be useful but contemporary external validation studies are lacking. The aim of this study was to validate existing models in a multicentre patient cohort.

METHODS

The Thoracoscore, Modified Thoracoscore, Eurolung, Modified Eurolung, European Society Objective Score and Brunelli models were validated using a database of 6600 patients who underwent lung resection between 2012 and 2018. Models were validated for in-hospital or 30-day mortality (depending on intended outcome of each model) and also for 90-day mortality. Model calibration (calibration intercept, calibration slope, observed to expected ratio and calibration plots) and discrimination (area under receiver operating characteristic curve) were assessed as measures of model performance.

RESULTS

Mean age was 66.8 years (±10.9 years) and 49.7% (n = 3281) of patients were male. In-hospital, 30-day, perioperative (in-hospital or 30-day) and 90-day mortality were 1.5% (n = 99), 1.4% (n = 93), 1.8% (n = 121) and 3.1% (n = 204), respectively. Model area under the receiver operating characteristic curves ranged from 0.67 to 0.73. Calibration was inadequate in five models and mortality was significantly overestimated in five models. No model was able to adequately predict 90-day mortality.

CONCLUSIONS

Five of the validated models were poorly calibrated and had inadequate discriminatory ability. The modified Eurolung model demonstrated adequate statistical performance but lacked clinical validity. Development of accurate models that can be used to estimate the contemporary risk of lung resection is required.