Debunking the July Effect in lung transplantation recipients

Objective: The "July Effect" is a theory that the influx of trainees from July to September negatively impacts patient outcomes. We aimed to study this theoretical phenomenon in lung transplant recipients given the highly technical nature of thoracic procedures.

Methods: Adult lung transplant hospitalizations were identified within the National Inpatient Sample (2005-2020). Recipients were categorized as academic Q1 (July to September) or Q2-Q4 (October to June). In-hospital mortality, operator-driven complications (pneumothorax, dehiscence including wound dehiscence, bronchial anastomosis, and others, and vocal cord/diaphragm paralysis, all 3 treated as a composite outcome), length of stay, and inflation-adjusted hospitalization charges were compared between both groups. Multivariable logistic regression was performed to assess the association between academic quarter and in-hospital mortality and operator-driven complications. The models were adjusted for recipient demographics and transplant characteristics. Subgroup analysis was performed between academic and nonacademic hospitals.

Results: Of 30,788 lung transplants, 7838 occurred in Q1 and 22,950 occurred in Q2-Q4. Recipient demographic and clinical characteristics were similar between groups. Dehiscence (n = 922, 4% vs n = 236, 3%), post-transplant cardiac arrest (n = 532, 2% vs n = 113, 1%), and pulmonary embolism (n = 712, 3% vs n = 164, 2%) were more common in Q2-Q4 versus Q1 recipients (all P < .05). Other operator-driven complications, in-hospital mortality, and resource use were similar between groups (P > .05). These inferences remained unchanged in adjusted analyses and on subgroup analyses of academic versus nonacademic hospitals.

Conclusions: The "July Effect" is not evident in US lung transplantation recipient outcomes during the transplant hospitalization. This suggests that current institutional monitoring systems for trainees across multiple specialties, including surgery, anesthesia, critical care, nursing, and others, are robust.