Abstract
Data is a major asset in today's healthcare scenery. Hospitals are one of the primary producers of healthcare-related data and the value this data can provide is enormous. However, to use this to improve healthcare practice and push science forward, it is necessary to safeguard the patient's privacy and the ethical use of the data. The ethical and legal requirements are vast and complex. Synthetic data appears as a tool to overcome these hurdles and provide fast and reliable access to data without compromising utility nor privacy. Even though Generative Adversarial Networks (GANs) are receiving a lot of attention lately, the application of most common models and architectures are not suited to tabular data - the most prevalent healthcare-related data. This study surveys the current GAN implementations tailored to this scenario. The analysis was focused mainly on the models employed, datasets used, and metrics reported regarding the quality of the generated data in terms of utility, privacy and how they compare among themselves. We aim to help institutions and investigators get a grasp of the tools to facilitate access to healthcare data, as well as recommendations for testing data synthesizers with privacy concerns.

Abstract
The emergency department (ED) crowding is a critical healthcare issue worldwide that leads to long waits and poorer healthcare outcomes. Goldratt's theory of constraints (TOC) has been used effectively to improve such problematic environments for more than three decades. While most TOC solutions are simple, with many viewing them as purely common sense, they represent paradigm shifts in how to manage complex, uncertain, and silo environments. Goldratt used a simple dice game with a straight flow (I-shape) to illustrate the impact of dependent resources and statistical fluctuations in managing resources. Additionally, games help to overcome resistance to change and gain ownership by having participants develop their solutions. This new cooperative game illustrates an ED environment where patients may follow different care pathways according to their clinical needs, timeliness of care is measured in minutes, the demand is highly uncertain, and treatment must frequently start almost immediately. A Monte Carlo simulation validated the TOC solution to this ED game, achieving results similar to the real TOC's implementations. Moreover, this article provides a thorough process to Socratically introduce TOC to healthcare professionals and others to recognize that the EDs' (like other healthcare systems') core problem is the traditional approach to managing them.

Abstract
Introduction and Objectives: Obstructive sleep apnea (OSA) is a prevalent sleep condition which is very heterogeneous although not formally characterized as such, resulting in missed or delayed diagnosis. Cluster analysis has been used in different clinical domains, particularly within sleep disorders. We aim to understand OSA heterogeneity and provide a variety of cluster visualizations to communicate the information clearly and efficiently.Materials and Methods: We applied an extension of k-means to be used in categorical variables: k -modes, to identify OSA patients' groups, based on demographic, physical examination, clinical his-tory, and comorbidities characterization variables (n = 40) obtained from a derivation and validation cohorts (211 and 53, respectively) from the northern region of Portugal. Missing values were imputed with k-nearest neighbours (k-NN) and a chi-square test was held for feature selection.Results: Thirteen variables were inserted in phenotypes, resulting in the following three clus-ters: Cluster 1, middle-aged males reporting witnessed apneas and high alcohol consumption before sleep; Cluster 2, middle-aged women with increased neck circumference (NC), non -repairing sleep and morning headaches; and Cluster 3, obese elderly males with increased NC, witnessed apneas and alcohol consumption. Patients from the validation cohort assigned to dif-ferent clusters showed similar proportions when compared with the derivation cohort, for mild (C1: 56 vs 75%, P = 0.230; C2: 61 vs 75%, P = 0.128; C3: 45 vs 48%, P = 0.831), moderate (C1: 24 vs 25%; C2: 20 vs 25%; C3: 25 vs 19%) and severe (C1: 20 vs 0%; C2: 18 vs 0%; C3: 29 vs 33%) levels. Therefore, the allocation supported the validation of the obtained clusters.Conclusions: Our findings suggest different OSA patients' groups, creating the need to rethink these patients' stereotypical baseline characteristics.(c) 2021 Sociedade Portuguesa de Pneumologia. Published by Elsevier Espana, S.L.U. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Abstract
The preoperative localisation of non-palpable lesions guided by breast imaging is an important and required procedure for breast-conserving surgery. We conducted a systematic review and meta-analysis of the literature on the comparative impact of different techniques for guided surgical excision of non-palpable breast lesions from reports of clinical or patient-reported outcomes and costs. A literature search of PubMed, ISI, SCOPUS and Cochrane databases was conducted for relevant publications and their references, along with public documents, national and international guidelines, conference proceedings and presentations. From 5720 retrieved articles screened through title and abstract, 5346 were excluded and 374 assessed for full-text eligibility. For data extraction and quality assessment, 49 studies were included. Results of this review demonstrate that Radioactive Seed Localisation (RSL) and Radioactive Occult Lesion Localisation (ROLL) outperform Wire in terms of involved margins and reoperations. Between RSL and ROLL, there is a tendency to favour RSL. Similarly, Clip-guided localisation seems preferred when compared to ROLL, however further studies are needed. In summary, there seems to exist evidence that RSL and ROLL are better than Wire, representing potential alternatives, with a quick learning curve, better scheduling and management issues. Although, for recent techniques, more research is needed in order to achieve the same level of evidence.

Abstract
Background Stratifying patients with sepsis was the basis of the predisposition, infection, response and organ dysfunction (PIRO) concept, an attempt to resolve the heterogeneity in treatment response. The purpose of this study is to perform an independent validation of the PIRO staging system in an international cohort and explore its utility in the identification of patients in whom time to antibiotic treatment is particularly important. Methods Prospective international cohort study, conducted over a 6-month period in five Portuguese hospitals and one Australian institution. All consecutive adult patients admitted to selected wards or the intensive care, with infections that met the CDC criteria for lower respiratory tract, urinary, intra-abdominal and bloodstream infections were included. Results There were 1638 patients included in the study. Patients who died in hospital presented with a higher PIRO score (10 +/- 3 vs 8 +/- 4, p < 0.001). The observed mortality was 3%, 15%, 24% and 34% in stage I, II, III and IV, respectively, which was within the predicted intervals of the original model, except for stage IV patients that presented a lower mortality. The hospital survival rate was 84%. The application of the PIRO staging system to the validation cohort resulted in a positive predictive value of 97% for stage I, 91% for stage II, 85% for stage III and 66% for stage IV. The area under the receiver operating characteristics curve (AUROC) was 0.75 for the all cohort and 0.70 if only patients with bacteremia were considered. Patients in stage III and IV who did not have antibiotic therapy administered within the desired time frame had higher mortality rate than those who have timely administration of antibiotic. Conclusions To our knowledge, this is the first external validation of this PIRO staging system and it performed well on different patient wards within the hospital and in different types of hospitals. Future studies could apply the PIRO system to decision-making about specific therapeutic interventions and enrollment in clinical trials based on disease stage.

Abstract
Congenital heart disease (CHD) is the most common congenital malformation and has high morbidity and mortality related to late diagnosis. Screening protocols are lacking and only 1% of murmurs are associated with CHD. The decline in auscultation skills highlights the need for better screening. This study aims to create and evaluate models for the detection of CHD using clinical data and sound features. These features were extracted using pure conventional MFCC and selected MFCC through matrix profiling and motif search. Four combinations of data were used to train decision trees (DT) and artificial neural networks (ANN), and the area under the curve (AUC) was compared. Posteriorly, models were also trained for the detection of any cardiac pathology. In both pathologies, the ANN model using clinical data and conventional MFCC showed the highest performance with AUC of 0.761 for CHD and 0.791 for any cardiac pathology. However, this is only a slight improvement when compared with the ANN models using only clinical data (0.747 and 0.789, respectively. Additionally, the inclusion of motif selected MFCC seems to worsen the model performance. Although further research is still needed, this is a potential improvement in CHD screening, particularly for primary care physicians.