Surgery Plays a Leading Role in Breast Cancer Treatment for Patients Aged ≥90 Years: A Large Retrospective Cohort Study.

BACKGROUND: The population aged ≥90 years is increasing worldwide, yet nearly 50% of elderly breast cancer (BC) patients receive suboptimal treatments, resulting in high rates of BC-related mortality. We analyzed clinical and survival outcomes of nonagenarian BC patients to identify effective treatment strategies. METHODS: This single-institution retrospective cohort study analyzed patients aged ≥90 years diagnosed with stage I-III BC between 2007 and 2018. Patients were categorized into three treatment groups: traditional surgery (TS), performed according to local guidelines; current-standard surgery (CS), defined as breast surgery without axillary surgery (in concordance with 2016 Choosing Wisely guidelines) and/or cavity shaving; and non-surgical treatment (NS). Clinicopathological features were recorded and recurrence rates and survival outcomes were analyzed. RESULTS: We collected data from 113 nonagenarians with a median age of 93 years (range 90-99). Among these patients, 43/113 (38.1%) underwent TS, 34/113 (30.1%) underwent CS, and 36/113 (31.9%) underwent NS. The overall recurrence rate among surgical patients was 10.4%, while the disease progression rate in the NS group was 22.2%. Overall survival was significantly longer in surgical patients compared with NS patients (p = 0.04). BC-related mortality was significantly higher in the NS group than in the TS and CS groups (25.0% vs. 0% vs. 7.1%, respectively; p = 0.01). There were no significant differences in overall survival and disease-free survival between the TS and CS groups (p = 0.6 and p = 0.8, respectively), although the TS group experienced a significantly higher overall postoperative complication rate (p < 0.001). CONCLUSIONS: Individualized treatment planning is essential for nonagenarian BC patients. Surgery, whenever feasible, remains the treatment of choice, with CS emerging as the best option for the majority of patients.

Robotic thymectomy in thymic tumours: a multicentre, nation-wide study.

OBJECTIVES: Robotic thymectomy has been suggested and considered technically feasible for thymic tumours. However, because of small-sample series and the lack of data on long-term results, controversies still exist on surgical and oncological results with this approach. We performed a large national multicentre study sought to evaluate the early and long-term outcomes after robot-assisted thoracoscopic thymectomy in thymic epithelial tumours. METHODS: All patients with thymic epithelial tumours operated through a robotic thoracoscopic approach between 2002 and 2022 from 15 Italian centres were enrolled. Demographic characteristics, clinical, intraoperative, postoperative, pathological and follow-up data were retrospectively collected and reviewed. RESULTS: There were 669 patients (307 men and 362 women), 312 (46.6%) of whom had associated myasthenia gravis. Complete thymectomy was performed in 657 (98%) cases and in 57 (8.5%) patients resection of other structures was necessary, with a R0 resection in all but 9 patients (98.6%). Twenty-three patients (3.4%) needed open conversion, but no perioperative mortality occurred. Fifty-one patients (7.7%) had postoperative complications. The median diameter of tumour resected was 4 cm (interquartile range 3-5.5 cm), and Masaoka stage was stage I in 39.8% of patients, stage II in 56.1%, stage III in 3.5% and stage IV in 0.6%. Thymoma was observed in 90.2% of patients while thymic carcinoma occurred in 2.8% of cases. At the end of the follow-up, only 2 patients died for tumour-related causes. Five- and ten-year recurrence rates were 7.4% and 8.3%, respectively. CONCLUSIONS: Through the largest collection of robotic thymectomy for thymic epithelial tumours we demonstrated that robot-enhanced thoracoscopic thymectomy is a technically sound and safe procedure with a low complication rate and optimal oncological outcomes.

Corrigendum: The new Italian registry of infantile thrombosis (RITI): a reflection on its journey, challenges and pitfalls.

[This corrects the article DOI: 10.3389/fped.2023.1094246.].

Management of Pediatric Foreign Body Injuries during the COVID-19 Pandemic: Results of an International Survey.

The COVID-19 pandemic has had direct and indirect effects on daily life. In hospitals, the impact of the pandemic was observed in the diagnostic and therapeutic workflow. In this work, we explored potential changes in activities related to the treatment of foreign body injuries (FBIs) in children and the behavioral habits of physicians during the first wave of the pandemic. An online survey was conducted among physicians of the Susy Safe network. The survey comprised items related to respondent information, reference center characteristics, the treatment of FBIs during the COVID-19 pandemic, and a modified COVID-19 Anxiety Scale (CAS). The survey was distributed among the Susy Safe project international network surveillance registry for FBIs. A total of 58 physicians responded to the survey, including 18 (32%) from Europe and 16 (28%) from South America. The respondents indicated that the estimated number of aspirated foreign bodies during the pandemic was lower than or the same as that before the pandemic (43, 74%), and the same was observed for ingested foreign bodies (43, 74%). In univariable logistic regression, no single predictor was associated with a delay in routine care for children or an increasing tendency of medical personnel to avoid procedures. The workflow of physicians involved in the management of FBIs in children has not changed drastically during the COVID-19 pandemic, especially in emergency departments.

Intraoperative Ultrasound-Guided Conserving Surgery for Breast Cancer: No More Time for Blind Surgery.

BACKGROUND: Breast-conserving surgery (BCS) still remains a blind surgery despite all available tumor localization methods. Intraoperative ultrasound (IOUS) allows real-time visualization during all resection phases. METHODS: This was a prospective observational cohort study conducted at the Veneto Institute of Oncology between January 2021 and June 2022. Patients with ductal carcinoma in situ, T1-2 invasive cancer, or post-neoadjuvant tumors, suitable for BCS, were recruited. All breast cancer lesion types were included, i.e. solid palpable, solid non-palpable, non-solid non-palpable, and post-neoadjuvant treatment residual lesions. Eligible participants were randomly assigned to either IOUS or traditional surgery (TS) in a 1:1 ratio. The main outcomes were surgical margin involvement, reoperation rate, closest margin width, main specimen and cavity shaving margin volumes, excess healthy tissue removal, and calculated resection ratio (CRR). RESULTS: Overall, 160 patients were enrolled: 80 patients were allocated to the TS group and 80 to the IOUS group. IOUS significantly reduced specimen volumes (16.8 cm3 [10.5-28.9] vs. 24.3 cm3 [15.0-41.3]; p = 0.015), with wider closest resection margin width (2.0 mm [1.0-4.0] vs. 1.0 mm [0.5-2.0] after TS; p < 0.001). Tumor volume to specimen volume ratio was significantly higher after IOUS (4.7% [2.5-9.1] vs. 2.9% [0.8-5.2]; p < 0.001). IOUS yielded significantly better CRR (84.5% [46-120.8] vs. 114% [81.8-193.2] after TS; p < 0.001), lower involved margin rate (2.5 vs. 15%; p = 0.009) and reduced re-excision rate (2.5 vs. 12.5%; p = 0.032). CONCLUSIONS: IOUS allows real-time resection margin visualization and continuous control during BCS. It showed clear superiority over TS in both oncological and surgical outcomes for all breast cancer lesion types. These results disfavor the paradigm of blind breast surgery.

Automatic Forecast of Intensive Care Unit Admissions: The Experience During the COVID-19 Pandemic in Italy.

The experience of the COVID-19 pandemic showed the importance of timely monitoring of admissions to the ICU admissions. The ability to promptly forecast the epidemic impact on the occupancy of beds in the ICU is a key issue for adequate management of the health care system.Despite this, most of the literature on predictive COVID-19 models in Italy has focused on predicting the number of infections, leaving trends in ordinary hospitalizations and ICU occupancies in the background.This work aims to present an ETS approach (Exponential Smoothing Time Series) time series forecasting tool for admissions to the ICU admissions based on ETS models. The results of the forecasting model are presented for the regions most affected by the epidemic, such as Veneto, Lombardy, Emilia-Romagna, and Piedmont.The mean absolute percentage errors (MAPE) between observed and predicted admissions to the ICU admissions remain lower than 11% for all considered geographical areas.In this epidemiological context, the proposed ETS forecasting model could be suitable to monitor, in a timely manner, the impact of COVID-19 disease on the health care system, not only during the early stages of the pandemic but also during the vaccination campaign, to quickly adapt possible preventive interventions.

Pediatric Injury Surveillance From Uncoded Emergency Department Admission Records in Italy: Machine Learning-Based Text-Mining Approach.

BACKGROUND: Unintentional injury is the leading cause of death in young children. Emergency department (ED) diagnoses are a useful source of information for injury epidemiological surveillance purposes. However, ED data collection systems often use free-text fields to report patient diagnoses. Machine learning techniques (MLTs) are powerful tools for automatic text classification. The MLT system is useful to improve injury surveillance by speeding up the manual free-text coding tasks of ED diagnoses. OBJECTIVE: This research aims to develop a tool for automatic free-text classification of ED diagnoses to automatically identify injury cases. The automatic classification system also serves for epidemiological purposes to identify the burden of pediatric injuries in Padua, a large province in the Veneto region in the Northeast Italy. METHODS: The study includes 283,468 pediatric admissions between 2007 and 2018 to the Padova University Hospital ED, a large referral center in Northern Italy. Each record reports a diagnosis by free text. The records are standard tools for reporting patient diagnoses. An expert pediatrician manually classified a randomly extracted sample of approximately 40,000 diagnoses. This study sample served as the gold standard to train an MLT classifier. After preprocessing, a document-term matrix was created. The machine learning classifiers, including decision tree, random forest, gradient boosting method (GBM), and support vector machine (SVM), were tuned by 4-fold cross-validation. The injury diagnoses were classified into 3 hierarchical classification tasks, as follows: injury versus noninjury (task A), intentional versus unintentional injury (task B), and type of unintentional injury (task C), according to the World Health Organization classification of injuries. RESULTS: The SVM classifier achieved the highest performance accuracy (94.14%) in classifying injury versus noninjury cases (task A). The GBM method produced the best results (92% accuracy) for the unintentional and intentional injury classification task (task B). The highest accuracy for the unintentional injury subclassification (task C) was achieved by the SVM classifier. The SVM, random forest, and GBM algorithms performed similarly against the gold standard across different tasks. CONCLUSIONS: This study shows that MLTs are promising techniques for improving epidemiological surveillance, allowing for the automatic classification of pediatric ED free-text diagnoses. The MLTs revealed a suitable classification performance, especially for general injuries and intentional injury classification. This automatic classification could facilitate the epidemiological surveillance of pediatric injuries by also reducing the health professionals' efforts in manually classifying diagnoses for research purposes.