Predicting Postoperative Lung Function Following Lung Cancer Resection: A Systematic Review and Meta-analysis.

Background: Lung resection remains the gold standard treatment for early stage lung cancer; prediction of postoperative lung function is a key selection criterion for surgery with the aim of determining risk of postoperative dyspnoea. We aimed to identify the different prediction techniques used, and compare their accuracy.
Methods: A systematic review and meta-analysis sought to synthesise studies conducted that assess prediction of postoperative lung function up to 18/02/2018 (n = 135). PROBAST was used to assess risk of bias in studies, 17 studies were judged to be at low risk of bias.
Findings: Meta-analysis revealed CT volume and density measurement to be the most accurate (mean difference 71 ml) and precise (standard deviation 207 ml) of the reported techniques used for predicting FEV1; evidence for predicting gas transfer was lacking.
Interpretation: The evidence suggests using CT volume and density is the preferred technique in the prediction of postoperative FEV1. Further studies are required to ensure that the methods and thresholds we propose are linked to patient reported outcomes.
Funding: Salary support for NKO, RM, PN, BN, and AMT was provided by University Hospitals Birmingham NHS Foundation Trust.
Competing Interests: AMT reports grants from Grifols Biotherapeutics, grants from Alpha-1 Foundation, personal fees from CSL Behring, grants and non-financial support from Arrowhead Inc, outside the submitted work. NKO, JH-S, RM, PN, and BN have no conflict of interest to declare.
(© 2019 Published by Elsevier Ltd.)