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. © 2021 Copyright for this paper by its authors.

Abstract
Objectives High-quality data are crucial for guiding decision-making and practising evidence-based healthcare, especially if previous knowledge is lacking. Nevertheless, data quality frailties have been exposed worldwide during the current COVID-19 pandemic. Focusing on a major Portuguese epidemiological surveillance dataset, our study aims to assess COVID-19 data quality issues and suggest possible solutions. Settings On 27 April 2020, the Portuguese Directorate-General of Health (DGS) made available a dataset (DGSApril) for researchers, upon request. On 4 August, an updated dataset (DGSAugust) was also obtained. Participants All COVID-19-confirmed cases notified through the medical component of National System for Epidemiological Surveillance until end of June. Primary and secondary outcome measures Data completeness and consistency. Results DGSAugust has not followed the data format and variables as DGSApril and a significant number of missing data and inconsistencies were found (eg, 4075 cases from the DGSApril were apparently not included in DGSAugust). Several variables also showed a low degree of completeness and/or changed their values from one dataset to another (eg, the variable 'underlying conditions' had more than half of cases showing different information between datasets). There were also significant inconsistencies between the number of cases and deaths due to COVID-19 shown in DGSAugust and by the DGS reports publicly provided daily. Conclusions Important quality issues of the Portuguese COVID-19 surveillance datasets were described. These issues can limit surveillance data usability to inform good decisions and perform useful research. Major improvements in surveillance datasets are therefore urgently needed-for example, simplification of data entry processes, constant monitoring of data, and increased training and awareness of healthcare providers-as low data quality may lead to a deficient pandemic control.

Abstract
Alzheimer's disease (AD) has become a problem, owing to its high prevalence in an aging society with no treatment available after onset. However, early diagnosis is essential for preventive intervention to delay disease onset due to its slow progression. The current AD diagnostic methods are typically invasive and expensive, limiting their potential for widespread use. Thus, the development of biomarkers in available biofluids, such as blood, urine, and saliva, which enables low or non-invasive, reasonable, and objective evaluation of AD status, is an urgent task. Here, we reviewed studies that examined biomarker candidates for the early detection of AD. Some of the candidates showed potential biomarkers, but further validation studies are needed. We also reviewed studies for non-invasive biomarkers of AD. Given the complexity of the AD continuum, multiple biomarkers with machine-learning-classification methods have been recently used to enhance diagnostic accuracy and characterize individual AD phenotypes. Artificial intelligence and new body fluid-based biomarkers, in combination with other risk factors, will provide a novel solution that may revolutionize the early diagnosis of AD.