Abstract
PurposeTo identify psychoeducational interventions that target parents of children with congenital abnormalities (CA) and evaluate their impact on quality of life (QoL).MethodsThe search was conducted in six electronic databases, complemented by references of the studies found, studies of evidence synthesis, a manual search of relevant scientific meetings' abstracts and contact with experts. We included primary studies on parents of children with CA that studied psychoeducational interventions versus standard care. We assessed the risk of bias using Cochrane Collaboration's tool.ResultsWe included six studies focusing on congenital heart defects (CHD). They described four different psychoeducational strategies. In four studies, statistically significant differences were found. For clinical practice, we considered three interventions as more feasible: the Educational program for mothers, with a group format of four sessions weekly; CHIP-Family intervention, which includes a parental group workshop followed by an individual follow-up booster session; and WeChat educational health program with an online format.ConclusionsThis review is the first that assesses the impact of psychoeducational interventions targeted at parents of children with CA on their QoL. The best approach to intervention is multiple group sessions. Two essential strategies were to give support material, enabling parents to review, and the possibility of an online program application, increasing accessibility. However, because all included studies focus on CHD, generalizations should be made carefully. These findings are crucial to guide future research to promote and improve comprehensive and structured support for families and integrate them into daily practice.

Abstract
Background: Digital clinical tools are a new technology that can be used in the screening or diagnosis of obstructive sleep apnea (OSA), notwithstanding the crucial role of polysomnography, the gold standard.Objective: This study aimed to identify, gather, and analyze the most accurate digital tools and smartphone-based health platforms used for OSA screening or diagnosis in the adult population. Methods: We performed a comprehensive literature search of PubMed, Scopus, and Web of Science databases for studies evaluating the validity of digital tools in OSA screening or diagnosis until November 2022. The risk of bias was assessed using the Joanna Briggs Institute critical appraisal tool for diagnostic test accuracy studies. The sensitivity, specificity, and area under the curve (AUC) were used as discrimination measures.Results: We retrieved 1714 articles, 41 (2.39%) of which were included in the study. From these 41 articles, we found 7 (17%) smartphone-based tools, 10 (24%) wearables, 11 (27%) bed or mattress sensors, 5 (12%) nasal airflow devices, and 8 (20%) other sensors that did not fit the previous categories. Only 8 (20%) of the 41 studies performed external validation of the developed tool. Of these, the highest reported values for AUC, sensitivity, and specificity were 0.99, 96%, and 92%, respectively, for a clinical cutoff of apnea-hypopnea index (AHI)& GE;30. These values correspond to a noncontact audio recorder that records sleep sounds, which are then analyzed by a deep learning technique that automatically detects sleep apnea events, calculates the AHI, and identifies OSA. Looking at the studies that only internally validated their models, the work that reported the highest accuracy measures showed AUC, sensitivity, and specificity values of 1.00, 100%, and 96%, respectively, for a clinical cutoff AHI & GE;30. It uses the Sonomat-a foam mattress that, aside from recording breath sounds, has pressure sensors that generate voltage when deformed, thus detecting respiratory movements, and uses it to classify OSA events.Conclusions: These clinical tools presented promising results with high discrimination measures (best results reached AUC>0.99). However, there is still a need for quality studies comparing the developed tools with the gold standard and validating them in external populations and other environments before they can be used in clinical settings.

Abstract

Clinical digital tools are an up-and-coming new technology that can be used in the screening or diagnosis of obstructive sleep apnea (OSA) patients, notwithstanding the crucial role of polysomnography (PSG) – the gold standard.

Abstract

Abstract
Background Quantifying and dealing with lack of consistency in administrative databases (namely, under-coding) requires tracking patients longitudinally without compromising anonymity, which is often a challenging task. Objective This study aimed to (i) assess and compare different hierarchical clustering methods on the identification of individual patients in an administrative database that does not easily allow tracking of episodes from the same patient; (ii) quantify the frequency of potential under-coding; and (iii) identify factors associated with such phenomena. Method We analysed the Portuguese National Hospital Morbidity Dataset, an administrative database registering all hospitalisations occurring in Mainland Portugal between 2011-2015. We applied different approaches of hierarchical clustering methods (either isolated or combined with partitional clustering methods), to identify potential individual patients based on demographic variables and comorbidities. Diagnoses codes were grouped into the Charlson an Elixhauser comorbidity defined groups. The algorithm displaying the best performance was used to quantify potential under-coding. A generalised mixed model (GML) of binomial regression was applied to assess factors associated with such potential under-coding. Results We observed that the hierarchical cluster analysis (HCA) + k-means clustering method with comorbidities grouped according to the Charlson defined groups was the algorithm displaying the best performance (with a Rand Index of 0.99997). We identified potential under-coding in all Charlson comorbidity groups, ranging from 3.5% (overall diabetes) to 27.7% (asthma). Overall, being male, having medical admission, dying during hospitalisation or being admitted at more specific and complex hospitals were associated with increased odds of potential under-coding. Discussion We assessed several approaches to identify individual patients in an administrative database and, subsequently, by applying HCA + k-means algorithm, we tracked coding inconsistency and potentially improved data quality. We reported consistent potential under-coding in all defined groups of comorbidities and potential factors associated with such lack of completeness. Conclusion Our proposed methodological framework could both enhance data quality and act as a reference for other studies relying on databases with similar problems.

Abstract
Background: Most previous studies assessing multimorbidity in asthma assessed the frequency of individual comorbid diseases. Objective: We aimed to assess the frequency and clinical and economic impact of co-occurring groups of comorbidities (comorbidity patterns using the Charlson Comorbidity Index) on asthma hospitalizations. Methods: We assessed the dataset containing a registration of all Portuguese hospitalizations between 2011-2015. We applied three different approaches (regression models, association rule mining, and decision trees) to assess both the frequency and impact of comorbidities patterns in the length-of-stay, in-hospital mortality and hospital charges. For each approach, separate analyses were performed for episodes with asthma as main and as secondary diagnosis. Separate analyses were performed by participants' age group. Results: We assessed 198340 hospitalizations in patients >18 years old. Both in hospitalizations with asthma as main or secondary diagnosis, combinations of diseases involving cancer, metastasis, cerebrovascular disease, hemiplegia/paraplegia, and liver disease displayed a relevant clinical and economic burden. In hospitalizations having asthma as a secondary diagnosis, we identified several comorbidity patterns involving asthma and associated with increased length-of-stay (average impact of 1.3 [95%CI=0.6-2.0]-3.2 [95%CI=1.8-4.6] additional days), in-hospital mortality (OR range=1.4 [95%CI=1.0-2.0]-7.9 [95%CI=2.6-23.5]) and hospital charges (average additional charges of 351.0 [95%CI=219.1-482.8] to 1470.8 [95%CI=1004.6-1937.0]) Euro compared with hospitalizations without any registered Charlson comorbidity). Consistent results were observed with association rules mining and decision tree approaches. Conclusions: Our findings highlight the importance not only of a complete assessment of patients with asthma, but also of considering the presence of asthma in patients admitted by other diseases, as it may have a relevant impact on clinical and health services outcomes.