Fast Healthcare Interoperability Resources-Based Support System for Predicting Delivery Type: Model Development and Evaluation Study.

BACKGROUND: The escalating prevalence of cesarean delivery globally poses significant health impacts on mothers and newborns. Despite this trend, the underlying reasons for increased cesarean delivery rates, which have risen to 36.3% in Portugal as of 2020, remain unclear. This study delves into these issues within the Portuguese health care context, where national efforts are underway to reduce cesarean delivery occurrences. OBJECTIVE: This paper aims to introduce a machine learning, algorithm-based support system designed to assist clinical teams in identifying potentially unnecessary cesarean deliveries. Key objectives include developing clinical decision support systems for cesarean deliveries using interoperability standards, identifying predictive factors influencing delivery type, assessing the economic impact of implementing this tool, and comparing system outputs with clinicians' decisions. METHODS: This study used retrospective data collected from 9 public Portuguese hospitals, encompassing maternal and fetal data and delivery methods from 2019 to 2020. We used various machine learning algorithms for model development, with light gradient-boosting machine (LightGBM) selected for deployment due to its efficiency. The model's performance was compared with clinician assessments through questionnaires. Additionally, an economic simulation was conducted to evaluate the financial impact on Portuguese public hospitals. RESULTS: The deployed model, based on LightGBM, achieved an area under the receiver operating characteristic curve of 88%. In the trial deployment phase at a single hospital, 3.8% (123/3231) of cases triggered alarms for potentially unnecessary cesarean deliveries. Financial simulation results indicated potential benefits for 30% (15/48) of Portuguese public hospitals with the implementation of our tool. However, this study acknowledges biases in the model, such as combining different vaginal delivery types and focusing on potentially unwarranted cesarean deliveries. CONCLUSIONS: This study presents a promising system capable of identifying potentially incorrect cesarean delivery decisions, with potentially positive implications for medical practice and health care economics. However, it also highlights the challenges and considerations necessary for real-world application, including further evaluation of clinical decision-making impacts and understanding the diverse reasons behind delivery type choices. This study underscores the need for careful implementation and further robust analysis to realize the full potential and real-world applicability of such clinical support systems.

The Role of Novel Digital Clinical Tools in the Screening or Diagnosis of Obstructive Sleep Apnea: Systematic Review.

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)≥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≥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. TRIAL REGISTRATION: PROSPERO CRD42023387748; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=387748.

Impact of multimorbidity patterns in hospital admissions: the case study of asthma.

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.

Enabling Early Obstructive Sleep Apnea Diagnosis With Machine Learning: Systematic Review.

BACKGROUND: American Academy of Sleep Medicine guidelines suggest that clinical prediction algorithms can be used to screen patients with obstructive sleep apnea (OSA) without replacing polysomnography, the gold standard. OBJECTIVE: We aimed to identify, gather, and analyze existing machine learning approaches that are being used for disease screening in adult patients with suspected OSA. METHODS: We searched the MEDLINE, Scopus, and ISI Web of Knowledge databases to evaluate the validity of different machine learning techniques, with polysomnography as the gold standard outcome measure and used the Prediction Model Risk of Bias Assessment Tool (Kleijnen Systematic Reviews Ltd) to assess risk of bias and applicability of each included study. RESULTS: Our search retrieved 5479 articles, of which 63 (1.15%) articles were included. We found 23 studies performing diagnostic model development alone, 26 with added internal validation, and 14 applying the clinical prediction algorithm to an independent sample (although not all reporting the most common discrimination metrics, sensitivity or specificity). Logistic regression was applied in 35 studies, linear regression in 16, support vector machine in 9, neural networks in 8, decision trees in 6, and Bayesian networks in 4. Random forest, discriminant analysis, classification and regression tree, and nomogram were each performed in 2 studies, whereas Pearson correlation, adaptive neuro-fuzzy inference system, artificial immune recognition system, genetic algorithm, supersparse linear integer models, and k-nearest neighbors algorithm were each performed in 1 study. The best area under the receiver operating curve was 0.98 (0.96-0.99) for age, waist circumference, Epworth Somnolence Scale score, and oxygen saturation as predictors in a logistic regression. CONCLUSIONS: Although high values were obtained, they still lacked external validation results in large cohorts and a standard OSA criteria definition. TRIAL REGISTRATION: PROSPERO CRD42021221339; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=221339.

Quality of life of parents with children with congenital abnormalities: a systematic review with meta-analysis of assessment methods and levels of quality of life.

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.

Process Evaluation of a Mixed Methods Feasibility Study to Identify Hospital Patients with Palliative Care Needs in Portugal.

INTRODUCTION: Evidence shows most patients are not recognised by their attending healthcare professionals as having palliative needs. This feasibility study aimed to aid healthcare professionals identify hospital patients with palliative needs. MATERIAL AND METHODS: Mixed-methods, cross-sectional, observational study. The patient inclusion criteria comprised: age over 18 years old, being mentally capable to give consent judged as such by participating healthcare professionals, and if unable, having a legal substitute to consent, having a diagnosis of an incurable, potentially life-threatening illness. Field notes were taken for reflexive purposes. Outcome measures included: Integrated Palliative Care Outcome scale, surprise question, phase of illness, referral request status, The Eastern Cooperative Oncology Group Performance Status and social needs assessment. An interim data collection period meeting assessed implementation outcomes in each context. A web-based survey was sent to all participating healthcare professionals at the end of data collection period to explore overall experiences of participation and implementation outcomes. RESULTS: Forty-two departments in four hospitals were contacted. The study was presented in nine departments. The field notes were vital to understand the recruitment process and difficulties experienced: time constraints, fear of additional work, department dynamics and organisation, relationships between departments and need of training in palliative care and research. One department agreed to participate. There were six participating healthcare professionals and only 45 patients included. Three participating healthcare professionals responded to the web-based survey. DISCUSSION: The response rate was very low. Legislating palliative care is not enough, and an integrated palliative care plan needs to be implemented at country and institution level. CONCLUSION: There is an urgent need to provide generalist palliative care training to clinicians.

Introdução: A maioria dos pacientes não são reconhecidos pelos seus profissionais de saúde como tendo necessidades paliativas.Este estudo de viabilidade visou ajudar os profissionais de saúde a identificar doentes hospitalares com necessidades paliativas. Material e Métodos: Método misto, transversal e observacional. Os critérios de inclusão dos doentes compreenderam: idade igual ou superior a 18 anos; capacidade mental para dar consentimento informado, avaliado pelos profissionais de saúde participantes ou, caso não tenham essa capacidade, presença de um representante legal para consentir; ser portador de doença incurável, ameaçadora do tempo de vida. As notas de campo serviram fins reflexivos. As medidas de resultados utilizadas foram: escala integrada de cuidados paliativos, pergunta surpresa, fase da doença, estatuto de pedido de encaminhamento, Estado de Desempenho do Grupo de Oncologia Cooperativa Oriental (ECOG) e avaliação das necessidades sociais. A reunião intercalar no período de recolha de dados auxiliou-nos a avaliar os resultados da implementação em cada contexto. No final do período de recolha de dados enviámos um inquérito eletrónico aos profissionais de saúde participantes para explorar experiências globais de participação e resultados de implementação. Resultados: Contactámos 42 serviços em quatro hospitais. Apresentámos o estudo em nove serviços. As notas de campo foram vitais para compreender o processo de recrutamento e as dificuldades vividas: restrições de tempo, medo de trabalho acrescido, dinâmica de serviços e organização, relações entre serviços e necessidade de formação em cuidados paliativos e investigação. Contámos com a participação de um serviço, seis profissionais de saúde e 45 doentes. Três profissionais de saúde participantes responderam ao inquérito eletrónico. Discussão: A taxa de participação foi muito baixa. Não é suficiente legislar sobre os cuidados paliativos. É também necessário implementar um plano integrado de cuidados paliativos a nível nacional e institucional. Conclusão: É urgente a formação em cuidados paliativos generalistas a médicos que trabalham em hospitais.

Impact in the quality of life of parents of children with chronic diseases using psychoeducational interventions - A systematic review with meta-analysis.

OBJECTIVE: This study aimed to identify psychoeducational interventions applied to parents of children with chronic diseases and evaluate their impact on their quality of life (QoL). METHODS: It was conducted in six databases, complemented by references from the included studies and other reviews, manual search, and contact with experts. We included primary studies on parents of children with chronic diseases that studied psychoeducational interventions versus standard care. RESULTS: We screened 6604 titles and abstracts, reviewed the full text of 60 records, and included 37 primary studies. Half of the studies were on Asthma. We found three intervention formats: one-to-one (43%), groups (49%), and combined approach with individual and group settings (8%). More than 60% of the included studies found statistically significant differences between the intervention and the control group (p < 0.05). CONCLUSION: Several interventions have shown efficacy in improving parental QoL. Despite that, there is insufficient evidence of interventions' implementation. PRACTICE IMPLICATIONS: A holistic approach encompassing the patient and the family's biopsychosocial dimensions is fundamental in successfully managing chronic disease in children. It is vital to design and implement interventions accommodating the common issues experienced by children, parents, and families that deal with chronic childhood conditions. Systematic review registration number PROSPERO 2018 CRD42018092135.

COVID-19 surveillance data quality issues: a national consecutive case series.

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.

Using electronic health records to develop and validate a machine-learning tool to predict type 2 diabetes outcomes: a study protocol.

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.

Learner's perception, knowledge and behaviour assessment within a breast imaging E-Learning course for radiographers.

PURPOSE: E-learning has been revealed as an a useful tool among continuing education within health professionals, namely for radiographers or radiologic technologists. Therefore like traditional learning, this teaching approach needs continuous evaluation in order to validate its effectiveness and impact. Kirkpatrick's model has been widely used for this purpose by health information management instructors. Our aim was to assess an E-learning Course on Breast Imaging for radiographers based on the first three levels of Kirkpatrick's framework: reaction, learning and behaviour. METHODS AND MATERIALS: An E-learning course was developed for radiographers in order to provide an easy-to-understand, succinct and current overview in breast imaging, namely mammography technique and image interpretation. The program structure were built based on the guidelines proposed by the European Society of Breast Cancer Specialists (EUSOMA). Learner's satisfaction was assessed through a questionnaire and Knowledge gain was assessed using pre- and post-testing. After 6 months of complying the course, the learners were contacted through a questionnaire in order to give feedback on whether their behaviour changed in workplace. RESULTS: Two editions of the breast imaging course were performed by 64 learners. In general, 97% of the learners stated that the program content was very good and excellent, all learners considered the content was delivered in a very good or excellent way. High percentages of learners stated to be satisfied with the distribution of the content among each module (94%) and 86% of learners stated that your level of dedication was high or very high. Concerning improvement of knowledge, we found an evolution of 4 percentual points between pre and post-tests (p = 0,001). The learners have identified two main changes on their practice, the first one related with patient care, improving communications and positioning skills and the second one related with image interpretation, improving the image processing and analyses. CONCLUSION: These global results show that e-learning can provide statistically relevant knowledge gains in Radiographers. This course is an important contribution to the improvement of mammography education, impacting on the development of students' and radiographers' skills.

Phenotyping Obstructive Sleep Apnea Patients: A First Approach to Cluster Visualization.

The varied phenotypes of obstructive sleep apnea (OSA) poses critical challenges, resulting in missed or delayed diagnosis. In this work, we applied k-modes, aiming to identify groups of OSA patients, based on demographic, physical examination, clinical history, and comorbidities characterization variables (n = 41) collected from 318 patients. Missing values were imputed with k-nearest neighbours (k-NN) and chi-square test was held. Thirteen variables were inserted in cluster analysis, resulting in three clusters. Cluster 1 were middle-aged men, while Cluster 3 were the oldest men and Cluster 2 mainly middle-aged women. Cluster 3 weighted the most, whereas Cluster 1 weighted the least. The same effect was described in increased neck circumference. The percentages of variables driving sleepiness, congestive heart failure, arrhythmias and pulmonary hypertension were very low (<20%) and OSA severity was more common in mild level. Our results suggest that it is possible to phenotype OSA patients in an objective way, as also, different (although not considered innovative) visualizations improve the recognition of this common sleep pathology.

The risk of disabling, surgery and reoperation in Crohn's disease - A decision tree-based approach to prognosis.

INTRODUCTION: Crohn's disease (CD) is a chronic inflammatory bowel disease known to carry a high risk of disabling and many times requiring surgical interventions. This article describes a decision-tree based approach that defines the CD patients' risk or undergoing disabling events, surgical interventions and reoperations, based on clinical and demographic variables. MATERIALS AND METHODS: This multicentric study involved 1547 CD patients retrospectively enrolled and divided into two cohorts: a derivation one (80%) and a validation one (20%). Decision trees were built upon applying the CHAIRT algorithm for the selection of variables. RESULTS: Three-level decision trees were built for the risk of disabling and reoperation, whereas the risk of surgery was described in a two-level one. A receiver operating characteristic (ROC) analysis was performed, and the area under the curves (AUC) Was higher than 70% for all outcomes. The defined risk cut-off values show usefulness for the assessed outcomes: risk levels above 75% for disabling had an odds test positivity of 4.06 [3.50-4.71], whereas risk levels below 34% and 19% excluded surgery and reoperation with an odds test negativity of 0.15 [0.09-0.25] and 0.50 [0.24-1.01], respectively. Overall, patients with B2 or B3 phenotype had a higher proportion of disabling disease and surgery, while patients with later introduction of pharmacological therapeutic (1 months after initial surgery) had a higher proportion of reoperation. CONCLUSIONS: The decision-tree based approach used in this study, with demographic and clinical variables, has shown to be a valid and useful approach to depict such risks of disabling, surgery and reoperation.