Objective The study objective was to determine contemporary early outcomes associated with pneumonectomy for lung cancer and to identify their predictors using a nationally representative general ...thoracic surgery database (EPITHOR). Methods After discarding inconsistent files, a group of 4498 patients who underwent elective pneumonectomy for primary lung cancer between 2003 and 2013 was selected. Logistic regression analysis was performed on variables for mortality and major adverse events. Then, a propensity score analysis was adjusted for imbalances in baseline characteristics between patients with or without neoadjuvant treatment. Results Operative mortality was 7.8%. Surgical, cardiovascular, pulmonary, and infectious complications rates were 14.9%, 14.1%, 11.5%, and 2.7%, respectively. None of these complications were predicted by the performance of a neoadjuvant therapy. Operative mortality analysis, adjusted for the propensity scores, identified age greater than 65 years (odds ratio OR, 2.1; 95% confidence interval CI, 1.5-2.9; P < .001), underweight body mass index category (OR, 2.2; 95% CI, 1.2-4.0; P = .009), American Society of Anesthesiologists score of 3 or greater (OR, 2.310; 95% CI, 1.615-3.304; P < .001), right laterality of the procedure (OR, 1.8; 95% CI, 1.1-2.4; P = .011), performance of an extended pneumonectomy (OR, 1.5; 95% CI, 1.1-2.1; P = .018), and absence of systematic lymphadenectomy (OR, 2.9; 95% CI, 1.1-7.8; P = .027) as risk predictors. Induction therapy (OR, 0.63; 95% CI, 0.5-0.9; P = .005) and overweight body mass index category (OR, 0.60; 95% CI, 0.4-0.9; P = .033) were protective factors. Conclusions Several risk factors for major adverse early outcomes after pneumonectomy for cancer were identified. Overweight patients and those who received induction therapy had paradoxically lower adjusted risks of mortality.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Objective Our objective was to analyze the time trend variation of 30-day mortality after lung cancer surgery, and to quantify the impact of surgeon and hospital volumes over a 5-year period in ...France. Methods We used Epithor, the French national thoracic database and benchmark tool, which catalogues more than 180,000 procedures of 89 private and public hospitals in France. From January 2005 to December 2010, 19,556 patients who underwent major lung resection (lobectomy, bilobectomy, pneumonectomy) were included in our study. Multilevel logistic models were designed to investigate the relationship between 30-day mortality and surgeon (model 1) or hospital (model 2) volumes. The 3 levels considered were the patient, the surgeon, and the hospital. Results From 2005 to 2007, the 30-day mortality of patients who underwent major lung resection averaged 10%, and then decreased until it reached 3.8% in 2010 ( P < .0001). A significant decrease in 30-day mortality was observed over time ( P = .0046). During the study period, the mean annual number of procedures per surgeon was 46.1 (standard deviation SD = 23.6) and per hospital was 97.9 (SD = 50.8). Model 1 showed that surgeon volume had a significant impact on 30-day mortality ( P = .03), whereas model 2 failed to show that hospital volume influenced 30-day mortality ( P = .75). Conclusions Since 2007, when France's first National Cancer Plan became effective, 30-day mortality of primary lung cancer surgery has decreased and currently measures 3.8%. Low mortality was correlated with higher surgeon volume but was not influenced by hospital volume, which cannot be considered a proxy measure for determining the safety of lung cancer surgery.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Objectives The estimation of risk-adjusted in-hospital mortality is essential to allow each thoracic surgery team to be compared with national benchmarks. The objective of this study is to develop ...and validate a risk model of mortality after pulmonary resection. Methods A total of 18,049 lung resections for non–small cell lung cancer were entered into the French national database Epithor. The primary outcome was in-hospital mortality. Two independent analyses were performed with comorbidity variables. The first analysis included variables as independent predictive binary comorbidities (model 1). The second analysis included the number of comorbidities per patient (model 2). Results In model 1 predictors for mortality were age, sex, American Society of Anesthesiologists score, performance status, forced expiratory volume (as a percentage), body mass index (in kilograms per meter squared), side, type of lung resection,extended resection, stage, chronic bronchitis, cardiac arrhythmia, coronary artery disease, congestive heart failure, alcoholism, history of malignant disease, and prior thoracic surgery. In model 2 predictors were age, sex, American Society of Anesthesiologists score, performance status, forced expiratory volume, body mass index, side, type of lung resection, extended resection, stage, and number of comorbidities per patient. Models 1 and 2 were well calibrated, with a slope correction factor of 0.96 and of 0.972, respectively. The area under the receiver operating characteristic curve was 0.784 (95% confidence interval, 0.76–0.8) in model 1 and 0.78 (95% confidence interval, 0.76–0.797) in model 2. Conclusions Our preference is for the well-calibrated model 2 because it is easier to use in practice to estimate the adjusted postoperative mortality of lung resections for cancer.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Objective This study was undertaken to determine factors associated with in-hospital mortality among patients after general thoracic surgery and to construct a risk model. Methods Data from a ...nationally representative thoracic surgery database were collected prospectively between June 2002 and July 2005. Logistic regression analysis was used to predict the risk of in-hospital death. A risk model was developed with a training set of data (two thirds of patients) and validated on an independent test set (one third of patients). Model fit was assessed by the Hosmer–Lemeshow test; predictive accuracy was assessed by the c-index. Results Of the 15,183 original patients, 338 (2.2%) died during the same hospital admission. Within the data used to develop the model, these factors were found to be significantly associated with the occurrence of in-hospital death in a multivariate analysis: age, sex, dyspnea score, American Society of Anesthesiologists score, performance status classification, priority of surgery, diagnosis group, procedure class, and comorbid disease. The model was reliable (Hosmer–Lemeshow test 3.22; P = .92) and accurate, with a c-index of 0.85 (95% confidence interval 0.83-0.87) for the training set and 0.86 (95% confidence interval 0.83-0.89) for the test set of data. The correlation between the expected and observed number of deaths was 0.99. Conclusions The validated multivariate model Thoracoscore, described in this report for risk of in-hospital death among adult patients after general thoracic surgery was developed with national data, uses only 9 variables, and has good performance characteristics. It appears to be a valid clinical tool for predicting the risk of death.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Abstract Introduction Whenever feasible, sleeve lobectomy is recommended to avoid pneumonectomy for lung cancer, but these guidelines are based on limited retrospective series. The aim of our study ...was to compare outcomes following sleeve lobectomy and pneumonectomy using data from a national database. Methods From 2005 to 2014, 941 sleeve lobectomy and 5318 pneumonectomy patients were recorded in the French database Epithor. Propensity score was generated with 15 pretreatment variables and used to create balanced groups with matching (794 matches) and inverse probability of treatment weighting (standardized difference was 0 for matching, and 0.0025 after weighting). Odds ratio (OR) of postoperative complications and mortality and hazard ratio (HR) for overall survival and disease-free survival were calculated using propensity adjustment techniques and a sensitivity analysis. Results Postoperative mortality after sleeve resection was similar to that after pneumonectomy (matching OR, 1.24; P = .4; weighting OR, 0.77; P = .4) despite significantly lower odds of pulmonary complications with pneumonectomy (matching OR, 0.4; P < .0001; weighting OR, 0.12; P < .001). The adjusted HR for death after pneumonectomy was significantly higher when analyzed using matched analysis but not with weighting (matching HR, 1.63; P = .002; weighting HR, 0.97; P = .92). The same was true for disease-free survival (matching HR, 1.49; P = .01; weighting HR, 1.03; P = .84). Conclusions Despite early differences in perioperative pulmonary outcomes favoring pneumonectomy, early overall and disease-free survival was in favor of sleeve lobectomy in the matched analysis but not the weighted analysis. In our opinion, when it is technically feasible, sleeve lobectomy should be the preferred technique.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Background Bronchopleural fistula (BPF) remains a rare but fatal complication of thoracic surgery. The aim of this study was to develop and validate a predictive model of BPF after pulmonary ...resection and to identify patients at high risk for BPF. Methods From January 2005 to December 2012, 34,000 patients underwent major pulmonary resection (lobectomy, bilobectomy, or pneumonectomy) and were entered into the French National database Epithor. The primary outcome was the occurrence of postoperative BPF at 30 days. The logistic regression model was built using a backward stepwise variable selection. Results Bronchopleural fistula occurred in 318 patients (0.94%); its prevalence was 0.5% for lobectomy (n = 139), 2.2% for bilobectomy (n = 39), and 3% for pneumonectomy (n = 140). The mortality rate was 25.9% for lobectomy (n = 36), 16.7% for bilobectomy (n = 6), and 20% for pneumonectomy (n = 28). In the final model, nine variables were selected: sex, body mass index, dyspnea score, number of comorbidities per patient, bilobectomy, pneumonectomy, emergency surgery, sleeve resection, and the side of the resection. In the development data set, the C-index was 0.8 (95% confidence interval: 0.78 to 0.82). This model was well calibrated because the Hosmer-Lemeshow test was not significant (χ2 = 10.5, p = 0.23). We then calculated the logistic regression coefficient to build the predictive score for BPF. Conclusions This strong model could be easily used by surgeons to identify patient at high risk for BPF. This score needs to be confirmed prospectively in an independent cohort.
Minimally invasive lung resections can be particularly challenging in obese patients. We hypothesized robotic surgery (RTS) is associated with less conversion to thoracotomy than video-assisted ...thoracoscopic surgery (VATS) in obese populations.
The Society of Thoracic Surgeons General Thoracic Surgery Database, Epithor French National Database, and McMaster University Thoracic Surgical Database were queried for obese (body mass index ≥30 kg/m2) patients who underwent VATS or RTS lobectomy or segmentectomy for clinical T1-2, N0-1 non-small cell lung cancer between 2015 and 2019. Propensity score adjusted logistic regression analysis was used to compare the rate of conversion to thoracotomy between the VATS and RTS cohorts.
Overall, 8108 patients (The Society of Thoracic Surgeons General Thoracic Surgery Database: n = 7473; Epithor: n = 572; McMaster: n = 63) met inclusion criteria with a mean (SD) age of 66.6 (9) years and body mass index of 34.7 (4.5) kg/m2. After propensity score adjusted multivariable analysis, patients who underwent VATS were >5-times more likely to experience conversion to thoracotomy than those who underwent RTS (odds ratio, 5.33; 95% CI, 4.14-6.81; P < .001). There was a linear association between the degree of obesity and odds ratio of VATS conversion to thoracotomy compared with RTS. VATS patients had a longer mean length of stay (5.0 vs 4.3 days, P < .001), higher rate of respiratory failure (2.8% 168 of 5975 vs 1.8% 39 of 2133, P = .026), and were less likely to be discharged to their home (92.5% 5525 of 5975 vs 94.3% 2012 of 2133; P = .013) compared with RTS patients.
In obese patients, RTS anatomic lung resection is associated with a lower rate of conversion to thoracotomy than VATS.
Background Video-assisted thoracoscopic surgery (VATS) lobectomy has recently become the recommended approach for stage I non-small cell lung cancer. However, these guidelines are not based on any ...large randomized control trial. Our study used propensity scores and a sensitivity analysis to compare VATS lobectomy with open thoracotomy. Methods From 2005 to 2012, 24,811 patients (95.1%) were operated on by open thoracotomy and 1,278 (4.9%) by VATS. The end points were 30-day postoperative death, postoperative complications, hospital stay, overall survival, and disease-free survival. Two propensity scores analyses were performed: matching and inverse probability of treatment weighting, and one sensitivity analysis to unmask potential hidden bias. A subgroup analysis was performed to compare “high-risk” with “low-risk” patients. Results are reported by odds ratios or hazard ratios and their 95% confidence intervals. Results Postoperative death was not significantly reduced by VATS whatever the analysis. Concerning postoperative complications, VATS significantly decreased the occurrence of atelectasis and pneumopathy with both analysis methods, but there were no differences in the occurrence of other postoperative complications. VATS did not provide a benefit for high-risk patients. The VATS approach decreased the hospital length of stay from 2.4 days (95% confidence interval, –1.7 to –3 days) to –4.68 days (95% confidence interval, –8.5 to 0.9 days). Overall survival and disease-free survival were not influenced by the surgical approach. The sensitivity analysis showed potential biases. Conclusions The results must be interpreted carefully because of the differences observed according to the propensity scores method used. A multicenter randomized controlled trial is necessary to limit the biases.
The national Epithor database was initiated in 2003 in France. Fifteen years on, a quality assessment of the recorded data seemed necessary. This study examines the completeness of the data recorded ...in Epithor through a comparison with the French PMSI database, which is the national medico-administrative reference database. The aim of this study was to demonstrate the influence of data quality with respect to identifying 30-day mortality hospital outliers.
We used each hospital's individual FINESS code to compare the number of pulmonary resections and deaths recorded in Epithor to the figures found in the PMSI. Centers were classified into either the good-quality data (GQD) group or the low-quality data (LQD) group. To demonstrate the influence of case-mix quality on the ranking of centers with low-quality data, we used 2 methods to estimate the standardized mortality rate (SMR). For the first (SMR1), the expected number of deaths per hospital was estimated with risk-adjustment models fitted with low-quality data. For the second (SMR2), the expected number of deaths per hospital was estimated with a linear predictor for the LQD group using the coefficients of a logistic regression model developed from the GQD group.
Of the hospitals that use Epithor, 25 were classified in the GQD group and 75 in the LQD group. The 30-day mortality rate was 2.8% (n = 300) in the GQD group vs. 1.9% (n = 181) in the LQD group (P <0.0001). The between-hospital differences in SMR1 appeared substantial (interquartile range (IQR) 0-1.036), and they were even higher in SMR2 (IQR 0-1.19). SMR1 identified 7 hospitals as high-mortality outliers. SMR2 identified 4 hospitals as high-mortality outliers. Some hospitals went from non-outlier to high mortality and vice-versa. Kappa values were roughly 0.46 and indicated moderate agreement.
We found that most hospitals provided Epithor with high-quality data, but other hospitals needed to improve the quality of the information provided. Quality control is essential for this type of database and necessary for the unbiased adjustment of regression models.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Background The objective of this study was to better characterize prolonged air leak (PAL), defined as an air leak longer than 7 days, and to develop and validate a predictive model of this ...complication after pulmonary resection. Methods All lung resections entered in Epithor, the French national thoracic database (French Society of Thoracic and Cardiovascular Surgery), were analyzed. Data collected between 2004 and 2008 (n = 24,113) were used to build the model using backward stepwise variable selection, and the 2009 data (n = 6,813) were used for external validation. The primary outcome was PAL. Results of the predictive model were used to propose a score: the index of PAL (IPAL). Results Prevalence of PAL after pulmonary resection was 6.9% (n = 1,655) in the development data set. In the final model, 9 variables were selected: gender, body mass index, dyspnea score, presence of pleural adhesions, lobectomy or segmentectomy, bilobectomy, bulla resection, pulmonary volume reduction, and location on upper lobe. In the development data set, the C-index was 0.71 (95% confidence interval CI, 0.70 to 0.72). At external validation, the C-index was 0.69 (95% CI, 0.66 to 0.72) and the calibration slope (ie, the agreement between observed outcomes and predictions) was 0.874 (<1). A score chart based on these analyses has been proposed. The formula to calculate the IPAL is the following: gender (F = 0; M = 4) - (body mass index-24) + 2 × dyspnea score + pleural adhesion (no = 0; yes = 4) + pulmonary resection (wedge = 0; lobectomy or segmentectomy = 7; bilobectomy = 11; bulla resection = 2; volume reduction = 14) + location (lower or middle lobe = 0; upper = 4). Conclusions Surgeons can easily use the well-validated model to determine intraoperative preventive measures of PAL.
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