Abstract
Background: The American Academy of Sleep Medicine guidelines suggest that clinical prediction algorithms can be used in patients with obstructive sleep apnea (OSA) without replacing polysomnography, which is the gold standard. Objective: This study aims to develop a clinical decision support system for OSA diagnosis according to its standard definition (apnea-hypopnea index plus symptoms), identifying individuals with high pretest probability based on risk and diagnostic factors. Methods: A total of 47 predictive variables were extracted from a cohort of patients who underwent polysomnography. A total of 14 variables that were univariately significant were then used to compute the distance between patients with OSA, defining a hierarchical clustering structure from which patient phenotypes were derived and described. Affinity from individuals at risk of OSA phenotypes was later computed, and cluster membership was used as an additional predictor in a Bayesian network classifier (model B). Results: A total of 318 patients at risk were included, of whom 207 (65.1%) individuals were diagnosed with OSA (111, 53.6% with mild; 50, 24.2% with moderate; and 46, 22.2% with severe). On the basis of predictive variables, 3 phenotypes were defined (74/207, 35.7% low; 104/207, 50.2% medium; and 29/207, 14.1% high), with an increasing prevalence of symptoms and comorbidities, the latter describing older and obese patients, and a substantial increase in some comorbidities, suggesting their beneficial use as combined predictors (median apnea-hypopnea indices of 10, 14, and 31, respectively). Cross-validation results demonstrated that the inclusion of OSA phenotypes as an adjusting predictor in a Bayesian classifier improved screening specificity (26%, 95% CI 24-29, to 38%, 95% CI 35-40) while maintaining a high sensitivity (93%, 95% CI 91-95), with model B doubling the diagnostic model effectiveness (diagnostic odds ratio of 8.14). Conclusions: Defined OSA phenotypes are a sensitive tool that enhances our understanding of the disease and allows the derivation of a predictive algorithm that can clearly outperform symptom-based guideline recommendations as a rule-out approach for screening.

Abstract
Purpose To quantify and understand how to assess the quality of life and health-related QoL of parents with children with congenital abnormalities. Methods We conducted a systematic review with meta-analysis. The search was carried out in 5 bibliographic databases and in ClinicalTrials.gov. No restriction on language or date of publication was applied. This was complemented by references of the studies found and studies of evidence synthesis, manual search of abstracts of relevant congresses/scientific meetings and contact with experts. We included primary studies (observational, quasi-experimental and experimental studies) on parents of children with CA reporting the outcome quality of life (primary outcome) of parents, independently of the intervention/exposure studied. Results We included 75 studies (35 observational non-comparatives, 31 observational comparatives, 4 quasi-experimental and 5 experimental studies). We identified 27 different QoL instruments. The two most frequently used individual QoL instruments were WHOQOL-Bref and SF-36. Relatively to family QoL tools identified, we emphasized PedsQL FIM, IOFS and FQOL. Non-syndromic congenital heart defects were the CA most frequently studied. Through the analysis of comparative studies, we verified that parental and familial QoL were impaired in this population. Conclusions This review highlights the relevance of assessing QoL in parents with children with CA and explores the diverse QoL assessment tools described in the literature. Additionally, results indicate a knowledge gap that can help to draw new paths to future research. It is essential to assess QoL as a routine in healthcare providing and to implement strategies that improve it.

Abstract
Introduction Type 2 diabetes mellitus (T2DM) is a major cause of blindness, kidney failure, myocardial infarction, stroke and lower limb amputation. We are still unable, however, to accurately predict or identify which patients are at a higher risk of deterioration. Most risk stratification tools do not account for novel factors such as sociodemographic determinants, self-management ability or access to healthcare. Additionally, most tools are based in clinical trials, with limited external generalisability. Objective The aim of this work is to design and validate a machine learning-based tool to identify patients with T2DM at high risk of clinical deterioration, based on a comprehensive set of patient-level characteristics retrieved from a population health linked dataset. Sample and design Retrospective cohort study of patients with diagnosis of T2DM on 1 January 2015, with a 5-year follow-up. Anonymised electronic healthcare records from the Whole System Integrated Care (WSIC) database will be used. Preliminary outcomes Outcome variables of clinical deterioration will include retinopathy, chronic renal disease, myocardial infarction, stroke, peripheral arterial disease or death. Predictor variables will include sociodemographic and geographic data, patients' ability to self-manage disease, clinical and metabolic parameters and healthcare service usage. Prognostic models will be defined using multidependence Bayesian networks. The derivation cohort, comprising 80% of the patients, will be used to define the prognostic models. Model parameters will be internally validated by comparing the area under the receiver operating characteristic curve in the derivation cohort with those calculated from a leave-one-out and a 10 times twofold cross-validation. Ethics and dissemination The study has received approvals from the Information Governance Committee at the WSIC. Results will be made available to people with T2DM, their caregivers, the funders, diabetes care societies and other researchers.

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/).