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OBJECTIVES: To compare the recurrence rate of primary spontaneous pneumothorax (PSP) after uniportal video assisted thoracic surgery (VATS) bullectomy and mechanical pleurodesis in patients managed with a regulating pressure drainage system compared and those managed with a traditional one. METHODS: Retrospective propensity score case-matched analysis of 174 consecutive patients submitted to uniportal VATS bullectomy and mechanical pleural abrasion (2007-13) in two centres. Definition of recurrence: Recurrent PSP requiring new treatment (i.e. aspiration, chest tube reinsertion, reoperation) within 12 months from the operation. All patients were managed with a single 24-Fr chest tube. Group 1 (106 patients): Tube connected to a traditional device (T) maintained on wall suction (-20 cmH2O) for 48 h. Group 2 (68 patients): Tube connected to a regulating pressure device (R) set at -20 cmH2O for 48 h. Chest tube removal criteria: No air leak (no bubbling or air flow <20 ml/min for at least 8 h) and pleural effusion <200 ml/day. Propensity score case-matching analysis was performed using the following variables: Age, gender, height, weight, side of operation, dystrophic score, length of stapled parenchyma. RESULTS: The two groups of 68 pairs were well matched for baseline and surgical characteristics. Patients of Group 2 (R) showed a significantly lower incidence of recurrence rate compared with matched counterparts (T) (3, 4.4 vs 10, 14%, P = 0.041). There were no differences in persistent air leak incidence, chest tube duration or hospital stay between the groups. Group 2 had a higher 48-h output of pleural effusion compared with Group 1 (P < 0.0001). CONCLUSIONS: By stabilizing the pleural pressure at the preset values, novel regulating pressure devices may enhance pleurodesis, leading to a reduced incidence of PSP recurrences after uniportal VATS bullectomy and pleural abrasion. [ABSTRACT FROM AUTHOR]

Background: The use of innovative methodologies, such as Surgical Data Science (SDS), based on artificial intelligence (AI) could prove to be useful for extracting knowledge from clinical data overcoming limitations inherent in medical registries analysis. The aim of the study is to verify if the application of an AI analysis to our database could develop a model able to predict cardiopulmonary complications in patients submitted to lung resection. Methods: We retrospectively analyzed data of patients submitted to lobectomy, bilobectomy, segmentectomy and pneumonectomy (January 2006–December 2018). Fifty preoperative characteristics were used for predicting the occurrence of cardiopulmonary complications. The prediction model was developed by training and testing a machine learning (ML) algorithm (XGBOOST) able to deal with registries characterized by missing data. We calculated the receiver operating characteristic curve, true positive rate (TPR), positive predictive value (PPV) and accuracy of the model. Results: We analyzed 1360 patients (lobectomy: 80.7%, segmentectomy: 11.9%, bilobectomy 3.7%, pneumonectomy: 3.7%) and 23.3% of them experienced cardiopulmonary complications. XGBOOST algorithm generated a model able to predict complications with an area under the curve of 0.75, a TPR of 0.76, a PPV of 0.68. The model's accuracy was 0.70. The algorithm included all the variables in the model regardless of their completeness. Conclusions: Using SDS principles in thoracic surgery for the first time, we developed an ML model able to predict cardiopulmonary complications after lung resection based on 50 patient characteristics. The prediction was also possible even in the case of those patients for whom we had incomplete data. This model could improve the process of counseling and the perioperative management of lung resection candidates. [ABSTRACT FROM AUTHOR]

OBJECTIVE The aim of this study was to assess the immediate influence of chest tube removal on chest pain and forced expiratory volume in 1 s (FEV1) after pulmonary resection. METHODS Prospective longitudinal investigation on 104 consecutive patients (53 wedge/segmentectomies and 51 lobectomies; 69 muscle and nerve-sparing lateral thoracotomy and 35 video-assisted thoracoscopic surgery (VATS)). Post-operative chest pain was controlled in all patients by a standardized combination of oral and intravenous non-opioid analgesics. All patients had one chest tube (24 French). Static and dynamic (after forced expiratory effort) pain and FEV1 were assessed before and 1 h after the chest tube removal by the same operator. No additional analgesics were administered before or after the chest tube removal. The pain level was assessed by the numeric pain scale [range: 0 (no pain)–10 (excruciating pain)]. FEV1 was assessed by a portable spirometer. Bronchodilators were not used in these patients. Pre- and post-removal measurements were compared by the Wilcoxon signed rank test. RESULTS The average pre-removal static and dynamic pain scores were 2.6 and 4.1, respectively. The static and dynamic pain scores decreased by 42 and 41%, respectively, after the tube removal (P < 0.0001). The average FEV1 before the chest tube removal was 1.5 l or 53% of the predicted value and increased by 13% after the tube removal (P = 0.0004). In total, 56 and 78% of patients reported static and dynamic pain scores improvement and 67% showed an FEV1 improvement after the chest tube removal. Similar results were observed in patients operated on through VATS or thoracotomy. Compared with patients whose chest tube was removed later, those who had their chest tube removed before post operative day 3 (POD3), showed a greater reduction in the static pain score (41 vs. 31%, P = 0.05) and greater improvement in FEV1 (18 vs. 0.01%, P = 0.02). CONCLUSIONS The removal of a chest tube reduces pain and improves ventilatory function, independent of surgical access and particularly in the early post-operative phase. A fast track chest tube removal policy may favour patients' recovery. [ABSTRACT FROM AUTHOR]

OBJECTIVES The aim of the present study was to verify whether the implementation of an electronic health record (EHR) in our thoracic surgery unit allows creation of a high-quality clinical database saving time and costs. METHODS Before August 2011, multiple individuals compiled the on-paper documents/records and a single data manager inputted selected data into the database (traditional database, tDB). Since the adoption of an EHR in August 2011, multiple individuals have been responsible for compiling the EHR, which automatically generates a real-time database (EHR-based database, eDB), without the need for a data manager. During the initial period of implementation of the EHR, periodic meetings were held with all physicians involved in the use of the EHR in order to monitor and standardize the data registration process. Data quality of the first 100 anatomical lung resections recorded in the eDB was assessed by measuring the total number of missing values (MVs: existing non-reported value) and inaccurate values (wrong data) occurring in 95 core variables. The average MV of the eDB was compared with the one occurring in the same variables of the last 100 records registered in the tDB. RESULTS The tDB and eDB had similar MVs (0.74 vs 1, P = 0.13). The learning curve showed an initial phase including about 35 records, where MV in the eDB was higher than that in the tDB (1.9 vs 0.74, P = 0.03), and a subsequent phase, where the MV was similar in the two databases (0.7 vs 0.74, P = 0.6). The inaccuracy rate of these two phases in the eDB was stable (0.5 vs 0.3, P = 0.3). Using EHR saved an average of 9 min per patient, totalling 15 h saved for obtaining a dataset of 100 patients with respect to the tDB. CONCLUSION The implementation of EHR allowed streamlining the process of clinical data recording. It saved time and human resource costs, without compromising the quality of data. [ABSTRACT FROM AUTHOR]