Is Minimally Invasive Resection of Large Thymoma Appropriate?

BACKGROUND: Current practice patterns suggest open rather than minimally invasive (MIS) approaches for thymomas >4 cm. We hypothesized there would be similar perioperative outcomes and overall survival between open and MIS approaches for large (>4 cm) thymoma resection. METHODS: The National Cancer Database was queried for patients who underwent thymectomy from 2010 to 2020. Surgical approach was characterized as either open or MIS. The primary outcome was overall survival and secondary outcomes were margin status, and length of stay (LOS). Differences between approach cohorts were compared after a 1:1 propensity match. RESULTS: Among 4121 thymectomies, 2474 (60%) were open and 1647 (40%) were MIS. Patients undergoing MIS were older, had fewer comorbidities, and had smaller tumors (median; 4.6 vs 6 cm, P < .001). In the unmatched cohort, MIS and open had similar 90-day mortality (1.1% vs 1.8%, P = .158) and rate of positive margin (25.1% vs 27.9%, P = .109). MIS thymectomy was associated with shorter LOS (2 (1-4) vs 4 (3-6) days, P < .001). Propensity matching reduced the bias between the groups. In this cohort, overall survival was similar between the groups by log-rank test (P = .462) and multivariate cox hazard analysis (HR .882, P = .472). Multivariable regression showed shorter LOS with MIS approach (Coef -1.139, P < .001), and similar odds of positive margin (OR 1.130, P = .150). DISCUSSION: MIS has equivalent oncologic benefit to open resection for large thymomas, but is associated with shorter LOS. When clinically appropriate, MIS thymectomy may be considered a safe alternative to open resection for large thymomas.

Ross procedure or mechanical aortic valve, which is the best lifetime option for an 18-year-old? A decision analysis.

Objectives: Identifying the optimal solution for young adults requiring aortic valve replacement (AVR) is challenging, given the variety of options and their lifetime complication risks, impacts on quality of life, and costs. Decision analytic techniques make comparisons incorporating these measures. We evaluated lifetime valve-related outcomes of mechanical aortic valve replacement (mAVR) versus the Ross procedure (Ross) using decision tree microsimulations modeling. Methods: Transition probabilities, utilities, and costs derived from published reports were entered into a Markov model decision tree to explore progression between health states for hypothetical 18-year-old patients. In total, 20,000 Monte Carlo microsimulations were performed to model mortality, quality-adjusted-life-years (QALYs), and health care costs. The incremental cost-effectiveness ratio (ICER) was calculated. Sensitivity analyses was performed to identify transition probabilities at which the preferred strategy switched from baseline. Results: From modeling, average 20-year mortality was 16.3% and 23.2% for Ross and mAVR, respectively. Average 20-year freedom from stroke and major bleeding was 98.6% and 94.6% for Ross, and 90.0% and 82.2% for mAVR, respectively. Average individual lifetime (60 postoperative years) utility (28.3 vs 23.5 QALYs) and cost ($54,233 vs $507,240) favored Ross over mAVR. The average ICER demonstrated that each QALY would cost $95,345 more for mAVR. Sensitivity analysis revealed late annual probabilities of autograft/left ventricular outflow tract disease and homograft/right ventricular outflow tract disease after Ross, and late death after mAVR, to be important ICER determinants. Conclusions: Our modeling suggests that Ross is preferred to mAVR, with superior freedom from valve-related morbidity and mortality, and improved cost-utility for young adults requiring aortic valve surgery.

Computational risk model for predicting 2-year malignancy of pulmonary nodules using demographic and radiographic characteristics.

OBJECTIVES: To determine whether discriminatory performance of a computational risk model in classifying pulmonary lesion malignancy using demographic, radiographic, and clinical characteristics is superior to the opinion of experienced providers. We hypothesized that computational risk models would outperform providers. METHODS: Outcome of malignancy was obtained from selected patients enrolled in the NAVIGATE trial (NCT02410837). Five predictive risk models were developed using an 80:20 train-test split: univariable logistic regression model based solely on provider opinion, multivariable logistic regression model, random forest classifier, extreme gradient boosting model, and artificial neural network. Area under the receiver operating characteristic curve achieved during testing of the predictive models was compared to that of prebiopsy provider opinion baseline using the DeLong test with 10,000 bootstrapped iterations. RESULTS: The cohort included 984 patients, 735 (74.7%) of which were diagnosed with malignancy. Factors associated with malignancy from multivariable logistic regression included age, history of cancer, largest lesion size, lung zone, and positron-emission tomography positivity. Testing area under the receiver operating characteristic curve were 0.830 for provider opinion baseline, 0.770 for provider opinion univariable logistic regression, 0.659 for multivariable logistic regression model, 0.743 for random forest classifier, 0.740 for extreme gradient boosting, and 0.679 for artificial neural network. Provider opinion baseline was determined to be the best predictive classification system. CONCLUSIONS: Computational models predicting malignancy of pulmonary lesions using clinical, demographic, and radiographic characteristics are inferior to provider opinion. This study questions the ability of these models to provide additional insight into patient care. Expert clinician evaluation of pulmonary lesion malignancy is paramount.