Background and Objectives: Spontaneous pneumothorax (SP) and spontaneous pneumomediastinum (SPM) have frequently been cited as complications associated with coronavirus disease 2019 (COVID-19) pneumonia, with especially poor prognosis in mechanically ventilated patients. The current literature is controversial regarding the potential risk factors for developing SP or SPM (SP-SPM) in non-ventilated COVID-19 patients. Our research addressed a twofold objective: (a) to investigate the characteristics of patients with SP-SPM (both with and without COVID-19) and compare them to patients with sole COVID-19; (b) to quantify the risk of in-hospital mortality associated with SP-SPM and COVID-19. Patients and Methods: A retrospective case-control study was conducted in the emergency departments (ED) of two tertiary hospitals in Timisoara, Romania, over one year (1st April 2020-31st March 2021; 64,845 records in total) and 70 cases of SP-SPM were identified (both SARS-CoV-2 positives and negatives). The control group comprised COVID-19 patients with no SP-SPM, included at a 2:1 ratio. Logistic regression was employed to quantify the in-hospital mortality risk associated with age, SP-SPM, and COVID-19. Results: SP-SPM and COVID-19 were connected with prolonged hospitalization, a higher percentage of intensive care admission, and a higher mortality. SP-SPM increased the odds of death by almost four times in patients of the same age, gender, smoking status, and SARS-CoV-2 infection: OR = 3.758, 95% CI (1.443-9.792). Each additional year of age added 9.4% to the mortality risk: OR = 1.094, 95% CI (1.054-1.135). Conclusion:ED physicians should acknowledge these potential risks when attending COVID-19 patients with SP-SPM.

Background and Objectives: Chronic post-thoracotomy pain syndrome (PTPS) is a very common and uncomfortable complication, occurring frequently after thoracic operations, leading to the necessity of further medication and hospitalizations. One important risk factor in developing chronic pain is the chest closure technique, which can lead to chronic intercostal nerve damage. This study proposes an alternative nerve-sparring closure technique to standard peri-costal sutures, aimed toward minimizing the risk of chronic pain in selected patients. Materials and Methods: We performed a prospective randomized study on 311 patients operated for various thoracic pathology over a period of 12 months, evaluating incision types, chest closure technique, and number of drains with drainage duration. The patients were divided into three groups: peri-costal (PC), proposed extra-costal (EC), and simple (SC) suture, respectively. Pain was measured on day 1, 2, 5, 7, and at 6 months post-operatively using the Visual Analogic Scale. Results: No significant differences in pain level were recorded in the first two post-operative days between the PC and EC groups. However, a significant decrease in pain level was observed on day 5 and at 6 months post-operatively, with a mean level of 3.5 ± 1.8, 1.2 ± 1 for the EC group compared to a mean value of 5.3 ± 1.6, 3.2 ± 1.5, respectively. No significant differences were observed regarding other evaluated variables. Conclusions: The lower recorded pain scores in patients with extra-costal chest closure are a strong argument to use this technique. Its ease of use is similar to the classic peri-costal closure, and the time needed to perform it is not significantly increased. The association of this technique with less invasive procedures and short drainage duration limits chronic post-operative pain. This procedure may represent an option for decreasing healthcare costs associated with the management of PTPS.

Stress, anxiety, and post-surgical chest pain are common problems among patients with thoracic surgical pathology. The way in which psychological distress is managed—the coping style—can influence the postsurgical evolution and quality of life of patients. In our study, we monitored the influence of coping style on patients’ anxiety and the intensity of post-operative chest pain. We conducted a cross-sectional study on 90 subjects with thoracic surgical pathology. One month after their surgeries, patients completed the following scales and questionnaires, translated, adapted, and validated for the Romanian population: COPE scale inventory, Generalized Anxiety Disorder-7 Questionnaire, McGill Pain Questionnaire, and Numeric Pain Rating Scale. Anxiety (evaluated using the Generalized Anxiety Disorder-7 Questionnaire) and postoperative thoracic pain intensity (evaluated by means of the Numeric Pain Rating Scale, Number of Words Chosen, and McGill Pain Questionnaire) were significantly higher in patients exhibiting social-focused coping than in patients presenting emotion-focused or problem-focused coping as their main coping style (Kruskal–Wallis, p = 0.028, p = 0.022, p = 0.042, p = 0.007). In our study, there were no differences observed in pain intensity relative to level of anxiety. Coping style is an important concept in the management of anxiety and pain experienced by patients undergoing chest surgery. Therefore, a multidisciplinary approach should be considered in clinical practice.

In this paper, we introduce an AI-based procedure to estimate and assist in choosing the optimal surgery timing, in the case of a thoracic cancer diagnostic, based on an explainable machine learning model trained on a knowledge base. This decision is usually taken by the surgeon after examining a set of clinical parameters and their evolution in time. Therefore, it is sometimes subjective, it depends heavily on the previous experience of the surgeon, and it might not be confirmed by the histopathological exam. Therefore, we propose a pipeline of automatic processing steps with the purpose of inferring the prospective result of the histopathologic exam, generating an explanation of why this inference holds, and finally, evaluating it against the conclusive opinion of an experienced surgeon. To obtain an accurate practical result, the training dataset is labeled manually by the thoracic surgeon, creating a training knowledge base that is not biased towards clinical practice. The resulting intelligent system benefits from both the precision of a classical expert system and the flexibility of deep neural networks, and it is supposed to avoid, at maximum, any possible human misinterpretations and provide a factual estimate for the proper timing for surgical intervention. Overall, the experiments showed a 7% improvement on the test set compared with the medical opinion alone. To enable the reproducibility of the AI system, complete handling of a case study is presented from both the medical and technical aspects.

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