Risk Stratification for Lung Cancer Patients

Area of Science:

• Thoracic Surgery
• Oncology
• Medical Prediction Models

Background:

• Preoperative risk assessment is crucial for lung cancer surgery.
• Existing models for predicting postoperative complications have limitations.

Purpose of the Study:

• To review clinical literature on evidence-based recommendations and prediction models for lung cancer surgery.
• To analyze parameters for predicting postoperative complications.

Main Methods:

• Comprehensive literature review of clinical studies.
• Analysis of pulmonary function tests (PFT), cardiopulmonary exercise testing (CPET), Brunelli models, Thoracoscore, and frailty.
• Evaluation of predictive postoperative forced expiratory volume in one second (FEV1) and diffusion capacity for carbon monoxide (DLCO).

Main Results:

• PFT parameters (FEV1, DLCO) showed conflicting evidence for predicting mortality.
• CPET variables (VO2peak, AT, VE/VCO2) indicated higher complication risk at specific thresholds.
• Thoracic Revised Cardiac Risk Index (ThRCRI) predicted cardiovascular compromise, but Thoracoscore was imprecise.
• Validated frailty models specific to thoracic surgery are lacking.

Conclusions:

• Dilemmas exist regarding the accuracy and external validation of current clinical prediction models for lung cancer surgery.
• A pressing need exists for a consolidated, clinically robust risk stratification model for thoracic resections.