Outcomes of nodal upstaging comparing video-assisted thoracoscopic surgery versus open thoracotomy for lung cancer

Prognostication Using the Japanese Risk Calculator for Lung Cancer Surgery

INTRODUCTION

Various calculation models to predict surgical risk have been developed globally. These have been reported to be helpful for estimating the long-term prognosis. In Japan, a similar model for lung cancer surgery was developed in 2017; however, there have been no reports investigating its association with the long-term prognosis. The objective of this study was to assess the association of the model's predictions with the long-term prognosis.

PATIENTS AND METHODS

In this retrospective single-institutional study, we analyzed lung cancer patients who underwent radical lobectomy between 2010 and 2016. We calculated the predicted rates of mortality (PRM) and composite outcomes of mortality with major morbidity (PRMM) in eligible patients (N = 1054) using this model and classified them into 2 classes (class A, PRM ≥0.8% and PRMM ≥5.9%; class B, others) based on their models' predictions. We assessed the prognostic impact and clinical utility of the model's predictions.

RESULTS

Class A included patients with significantly poorer postoperative overall survival than class B (log-rank, P < .001; hazard ratio, 3.160; 95% confidence interval, 2.390-4.178). Time-dependent receiver operating characteristic curve analyses revealed that the model's predictions correlated strongly with 1- and 2-year overall survival and decision curve analysis showed that they had high net benefits for prediction of those.

CONCLUSION

The Japanese risk calculator could stratify the long-term prognosis for lung cancer patients after surgery. This model may be a valuable tool not only for multidisciplinary thoracic oncology teams to discuss treatment strategies for high-risk cases but also for them to share the decision-making process with patients.

Regional Analgesia in Video-Assisted Thoracic Surgery: A Bayesian Network Meta-Analysis

Background

A variety of regional analgesia methods are used during video-assisted thoracic surgery (VATS). Our network meta-analysis (NMA) sought to evaluate the advantages of various methods of localized postoperative pain management in VATS patients.

Methods

PubMed, the Cochrane Library, and EMBASE were searched from their date of inception to May 2021 for randomized controlled trials (RCTs) comparing two or more types of locoregional analgesia in adults using any standardized clinical criteria. This was done using Bayesian NMA.

Results

A total of 3,563 studies were initially identified, and 16 RCTs with a total of 1,144 participants were ultimately included. These studies, which spanned the years 2014 to 2021 and included data from eight different countries, presented new information. There were a variety of regional analgesia techniques used, and in terms of analgesic effect, thoracic epidural anesthesia (TEA) [SMD (standard mean difference) = 1.12, CrI (Credible interval): (-0.08 to -2.33)], thoracic paravertebral block (TPVB) (SMD = 0.67, CrI: (-0.25 to 1.60) and erector spinae plane block (ESPB) (SMD = 0.34, CrI: (-0.5 to 1.17) were better than other regional analgesia methods.

Conclusion

Overall, these findings show that TEA, TPVB and ESPB may be effective forms of regional analgesia in VATS. This research could be a valuable resource for future efforts regarding the use of thoracic regional analgesia and enhanced recovery after surgery.

Systematic Review Registration

Identifier [PROSPERO CRD42021253218].

An External-Validated Algorithm to Predict Postoperative Pneumonia Among Elderly Patients With Lung Cancer After Video-Assisted Thoracoscopic Surgery

The aim of the study was to develop an algorithm to predict postoperative pneumonia among elderly patients with lung cancer after video-assisted thoracoscopic surgery. We analyzed 3,009 patients from the Thoracic Perioperative Database for Geriatrics in our hospital and finally enrolled 1,585 elderly patients (age≧65 years) with lung cancer treated with video-assisted thoracoscopic surgery. The included patients were randomly divided into a training group (n = 793) and a validation group (n = 792). Patients in the training group were used to develop the algorithm after screening up to 30 potential risk factors, and patients in the validation group were used to internally validate the algorithm. External validation of the algorithm was achieved in the external validation dataset after enrolling 165 elderly patients with lung cancer treated with video-assisted thoracoscopic surgery from two hospitals in China. Of all included patients, 9.15% (145/1,585) of patients suffered from postoperative pneumonia in the Thoracic Perioperative Database for Geriatrics, and 10.30% (17/165) of patients had postoperative pneumonia in the external validation dataset. The algorithm consisted of seven variables, including sex, smoking, history of chronic obstructive pulmonary disease (COPD), surgery duration, leukocyte count, intraoperative injection of colloid, and intraoperative injection of hormone. The C-index from the receiver operating characteristic curve (AUROC) was 0.70 in the training group, 0.67 in the internal validation group, and 0.71 in the external validation dataset, and the corresponding calibration slopes were 0.88 (95% confident interval [CI]: 0.37–1.39), 0.90 (95% CI: 0.46–1.34), and 1.03 (95% CI: 0.24–1.83), respectively. The actual probabilities of postoperative pneumonia were 5.14% (53/1031) in the low-risk group, 15.07% (71/471) in the medium-risk group, and 25.30% (21/83) in the high-risk group (p < 0.001). The algorithm can be a useful prognostic tool to predict the risk of developing postoperative pneumonia among elderly patients with lung cancer after video-assisted thoracoscopic surgery.