To our knowledge, this study represents the largest and most comprehensive review of contemporary outcomes for lung cancer resection as a function of operative season. In this study, we have demonstrated disparate differences in short-term outcomes among operative season groups. The inclusion of a broad, generalizable surgical population allows us to more confidently comment upon trends that have been previously reported among smaller, more specific, surgical patient groups. Our results indicate that outcomes following surgical resection for lung cancer are independently influenced by time of year. Specifically, we have demonstrated that risk adjusted in-hospital mortality was lowest during the spring season. Moreover, our results demonstrate significant differences in resource use across seasons, as patients undergoing lung cancer operations in spring and summer accrued the shortest hospital length of stay and lowest total costs. These findings bolster those of other smaller series that have been performed in select surgical populations, and it extends the examination of timing of operations for lung cancer resections to include a large, nationwide, diverse, surgical population.
The influence of operative season on patient outcomes has garnered attention within surgical literature. Several studies have demonstrated that patients undergoing operations during the winter months have increased morbidity and mortality. Kocer et al. examined prognostic factors for morbidity and mortality among 269 patients undergoing operations for peptic ulcer perforations [16
]. After adjustment, operative season was determined to be an independent predictor of morbidity within this cohort, and performance of an operation during the winter conferred significantly increased odds of morbidity (OR = 2.239 (1.056–4.751), P
= 0.036). A similar association between season and cardiac surgical outcomes were observed for a cohort of 16 290 patients undergoing coronary artery bypass grafting (CABG) [17
]. For this patient group, performance of CABG during the winter increased the odds of hospital mortality (OR = 1.29 (1.01–1.63), P
= 0.04) and was associated with the longest mean intensive care unit (ICU) length of stay compared with other operative seasons. Further, in a recently presented abstract at the 17th European Conference on General Thoracic Surgery in 2009, Turna et al. from Turkey suggested a significant effect of operative season on survival following surgical resections for non-small-cell lung carcinoma [18
]. Using univariate and multivariate survival analyses, they reported that surgical resections performed in winter conferred reduced survival compared with those performed in the summer in a retrospective, single-institution study of 698 patients (621 men, 57 women). In addition, they suggested a correlate between the presence of a vitamin D receptor polymorphism within surgical patients and improved prognosis. These results are in agreement with the primary outcomes of this study. After adjusting for the potential confounding influence of patient- and hospital-related factors, we found that performance of lung cancer resections in the spring was associated with the lowest odds of in-hospital mortality, while operations performed in winter, summer and fall were associated with a 33%, 25% and 17% increase in the odds of death, respectively. We further demonstrated a seasonal effect on the estimated odds of postoperative complications as well as on overall resource use.
The potential for a ‘July Effect’ has been postulated as one potential explanation for seasonal variations in patient outcomes during the summer following both medical and surgical hospitalizations. The July Effect describes the effect of new trainees on medical and surgical outcomes at the beginning of an academic calendar year at teaching institutions. Several observational studies have examined the influence of a July effect among various patient populations with mixed results [6
]. In a large, multi-institutional cohort study of 60 000 patients undergoing major surgical operations, Englesbe et al. demonstrated a 41% increase in the risk of mortality for operations performed during July and August [19
], and Rich et al. demonstrated the presence of a July effect among patients with internal medicine diagnoses [8
]. However, several studies have failed to demonstrate the effect of a July phenomenon on surgical outcomes [7
], including cardiac surgery operations at teaching hospitals [6
]. In our analyses, we attempted to control for the confounding influence of a July effect through the inclusion of thoracic surgery teaching hospital status as an important covariate in each of our predictive models. Even after these adjustments, the effect of operative season was highly associated with patient mortality and morbidity. Furthermore, within our patient cohort, approximately 85% of all patients underwent lung cancer resections at non-thoracic surgery teaching hospitals. As a result, these data suggest that the seasonal effects we observed on risk-adjusted outcomes occur independently despite the influence of thoracic surgical trainees; and any influence of a July effect within this population during the summer is minimal.
The demonstrated effect of operative season on lung cancer resection outcomes in this study is likely multi-factorial in origin and may reflect the influence of certain chronobiologic influences. Several physiologic (blood pressure, serum cholesterol, glucose tolerance, and infection rates), lifestyle (obesity, exercise, and smoking), and environmental (temperature and ultraviolet radiation) risk factors have been associated with seasonal variations in coronary heart disease, which may indirectly influence overall patient condition and perioperative events related to pulmonary resections [20
]. Neurobiologic phenomena such as activation of the neuroaxial system in response to cold climate has been linked to accelerated inflammatory pathways and enhanced arrhythmia rates in canine models, suggesting a higher propensity for adverse surgical outcomes during the winter months [22
]. A higher incidence of depression, seasonal affective disorder, and physical and emotional stress may also contribute to differences in outcomes for patients operated on at different times of the year. Moreover, seasonal variations in pulmonary-related processes may account for observed trends in lung-cancer-resection patients. Several series have demonstrated the impact of season on asthma exacerbations [3
], pneumonia hospitalizations [2
], respiratory viral infection clustering [23
], and the seasonal onset of bronchiolitis obliterans following lung transplant [25
]. In the current study, several patient, social, and lifestyle-related factors were accounted for during data analyses: patient obesity, depression, alcohol and drug abuse, and cardiac disease. The relative even distribution of these and other co-morbid disease states across operative seasons implies that the seasonal effect we observed is likely unrelated to underlying patient disease, and suggests the concomitant influence of several environmental stressors, pulmonary specific infections or pathology, or other biophysiologic influences related to seasonal change.
This study has important clinical relevance as it provides an extension of an increasingly reported epidemiologic phenomenon to thoracic oncology and surgical outcomes literature. As our data analyses indicate and as others have shown, the independent influence of season on outcomes following lung cancer resections represents a valid, contemporary trend. To our knowledge, this study represents the largest, and first nationwide description of such an influence within a thoracic oncology patient population. While we recognize the moderate effect size represented in our analyses, we have been careful not to conclude or recommend a delay in patient care for those requiring timely oncologic resections but rather to highlight an often-overlooked risk factor for patient morbidity and mortality following lung cancer resections. Furthermore, to completely address the precise clinical impact of seasonal variation on lung cancer resection outcomes as a rationale for delay in surgery would require a more stringent, prospective evaluation. Thus, our results remain hypothesis generating and provide a legitimate clinical context from which future prospective studies should be derived. Nevertheless, the presented data support the adoption of operative season as an important patient risk factor that should be considered during individual patient risk stratification in the preoperative setting.
Despite our significant results, there are several noteworthy limitations to this study. First, as a retrospective study, inherent selection bias must be considered; however, the likelihood of this bias is reduced due to the strict methodology and randomization of the NIS database. Second, the potential for unrecognized miscoding of diagnostic and procedure codes as well as variations in the nature of coded complications must be considered. However, as the NIS data set is validated both internally and externally for each year, we believe it is reasonable to assume that such data are accurately represented in our analyses. In addition, we are only able to comment on in-hospital, short-term outcomes, which may underestimate true perioperative mortality and morbidity rates that may have occurred following the patient’s discharge. With respect to co-morbid disease, we are unable to comment on lung cancer disease stages or severity. In statistical analyses, the possibility of heterogeneity between hospitals was not considered, which could have been taken into account using random-effects logistic regression modeling. We believe, however, that the effect of such an unmeasured influence would result in a small overall impact on the observed results as suggested by the performed sensitivity analysis. Furthermore, due to the constraints of NIS data points, we are unable to include adjustments for other well-established surgical risk factors such as low preoperative albumin levels, poor nutrition, or preoperative performance status measures such as the Zubrod score. However, our statistical models proved resilient to the presence of a potentially unmeasured confounder.