The impact of comorbid psychiatric disorders on chronic obstructive pulmonary disease (COPD) hospitalizations: a nationwide retrospective study.

AIMS: To characterize the register of a secondary diagnosis of mental illnesses in all chronic obstructive pulmonary disease (COPD) hospitalizations registered in Portugal from 2008 to 2015 and explore their impact on hospitalization outcomes. METHODS: A retrospective observational study was conducted. Hospitalizations of patients with at least 40 years old, discharged between 2008 and 2015 with a primary diagnosis of COPD (ICD-9-CM codes 491.x, 492.x and 496) were retrieved from a national administrative database. Comorbid psychiatric diagnoses were identified and defined by the HCUP Clinical Classification Software (CCS) category codes 650-670 (excluding 662). Length of hospital stay (LoS), admission type, in-hospital mortality, and estimated hospital charges were analyzed according to psychiatric diagnostic categories using sex and age-adjusted models. RESULTS: Of 66,661 COPD hospitalizations, 25,869 (38.8%) were episodes with a registered psychiatric comorbidity. These were more likely to correspond to younger inpatients (OR = 2.16, 95%CI 2.09-2.23; p < 0.001), to stay longer at the hospital (aOR = 1.08, 95%CI 1.05-1.12; p < 0.001), to incur in higher estimated hospital charges (aOR = 1.37, 95%CI 1.33-1.42; p < 0.001) and to be urgently admitted (aOR = 1.33, 95%CI 1.23-1.44; p < 0.001). After adjustment for age, in-hospital mortality was lower for episodes with psychiatric diagnoses (aOR = 0.90; 95%CI 0.84-0.96; p < 0.001), except for organic and neurodegenerative diseases category and developmental disorders, intellectual disabilities and disorders usually diagnosed in infancy, childhood, or adolescence category. DISCUSSION: These findings corroborate the additional burden placed by psychiatric disorders on COPD hospitalizations, highlighting the importance of individualizing care to address these comorbidities and minimize their impact on treatment outcomes.

Multidisciplinary Development and Initial Validation of a Clinical Knowledge Base on Chronic Respiratory Diseases for mHealth Decision Support Systems.

Most mobile health (mHealth) decision support systems currently available for chronic obstructive respiratory diseases (CORDs) are not supported by clinical evidence or lack clinical validation. The development of the knowledge base that will feed the clinical decision support system is a crucial step that involves the collection and systematization of clinical knowledge from relevant scientific sources and its representation in a human-understandable and computer-interpretable way. This work describes the development and initial validation of a clinical knowledge base that can be integrated into mHealth decision support systems developed for patients with CORDs. A multidisciplinary team of health care professionals with clinical experience in respiratory diseases, together with data science and IT professionals, defined a new framework that can be used in other evidence-based systems. The knowledge base development began with a thorough review of the relevant scientific sources (eg, disease guidelines) to identify the recommendations to be implemented in the decision support system based on a consensus process. Recommendations were selected according to predefined inclusion criteria: (1) applicable to individuals with CORDs or to prevent CORDs, (2) directed toward patient self-management, (3) targeting adults, and (4) within the scope of the knowledge domains and subdomains defined. Then, the selected recommendations were prioritized according to (1) a harmonized level of evidence (reconciled from different sources); (2) the scope of the source document (international was preferred); (3) the entity that issued the source document; (4) the operability of the recommendation; and (5) health care professionals' perceptions of the relevance, potential impact, and reach of the recommendation. A total of 358 recommendations were selected. Next, the variables required to trigger those recommendations were defined (n=116) and operationalized into logical rules using Boolean logical operators (n=405). Finally, the knowledge base was implemented in an intelligent individualized coaching component and pretested with an asthma use case. Initial validation of the knowledge base was conducted internally using data from a population-based observational study of individuals with or without asthma or rhinitis. External validation of the appropriateness of the recommendations with the highest priority level was conducted independently by 4 physicians. In addition, a strategy for knowledge base updates, including an easy-to-use rules editor, was defined. Using this process, based on consensus and iterative improvement, we developed and conducted preliminary validation of a clinical knowledge base for CORDs that translates disease guidelines into personalized patient recommendations. The knowledge base can be used as part of mHealth decision support systems. This process could be replicated in other clinical areas.

Development and Evaluation of a Natural Language Processing System for Curating a Trans-Thoracic Echocardiogram (TTE) Database.

BACKGROUND: Although electronic health records (EHR) provide useful insights into disease patterns and patient treatment optimisation, their reliance on unstructured data presents a difficulty. Echocardiography reports, which provide extensive pathology information for cardiovascular patients, are particularly challenging to extract and analyse, because of their narrative structure. Although natural language processing (NLP) has been utilised successfully in a variety of medical fields, it is not commonly used in echocardiography analysis. OBJECTIVES: To develop an NLP-based approach for extracting and categorising data from echocardiography reports by accurately converting continuous (e.g., LVOT VTI, AV VTI and TR Vmax) and discrete (e.g., regurgitation severity) outcomes in a semi-structured narrative format into a structured and categorised format, allowing for future research or clinical use. METHODS: 135,062 Trans-Thoracic Echocardiogram (TTE) reports were derived from 146967 baseline echocardiogram reports and split into three cohorts: Training and Validation (n = 1075), Test Dataset (n = 98) and Application Dataset (n = 133,889). The NLP system was developed and was iteratively refined using medical expert knowledge. The system was used to curate a moderate-fidelity database from extractions of 133,889 reports. A hold-out validation set of 98 reports was blindly annotated and extracted by two clinicians for comparison with the NLP extraction. Agreement, discrimination, accuracy and calibration of outcome measure extractions were evaluated. RESULTS: Continuous outcomes including LVOT VTI, AV VTI and TR Vmax exhibited perfect inter-rater reliability using intra-class correlation scores (ICC = 1.00, p < 0.05) alongside high R2 values, demonstrating an ideal alignment between the NLP system and clinicians. A good level (ICC = 0.75-0.9, p < 0.05) of inter-rater reliability was observed for outcomes such as LVOT Diam, Lateral MAPSE, Peak E Velocity, Lateral E' Velocity, PV Vmax, Sinuses of Valsalva and Ascending Aorta diameters. Furthermore, the accuracy rate for discrete outcome measures was 91.38% in the confusion matrix analysis, indicating effective performance. CONCLUSIONS: The NLP-based technique yielded good results when it came to extracting and categorising data from echocardiography reports. The system demonstrated a high degree of agreement and concordance with clinician extractions. This study contributes to the effective use of semi-structured data by providing a useful tool for converting semi-structured text to a structured echo report that can be used for data management. Additional validation and implementation in healthcare settings can improve data availability and support research and clinical decision-making.

Epilepsy hospitalizations and mental disorders: A Portuguese population-based observational retrospective study (2008-2015).

BACKGROUND: Psychiatric comorbidities are highly frequent in people with epilepsy and were found to be markers of poorer prognosis. These comorbidities increase the use of healthcare resources, including emergency department visits and inpatient care. Despite this, there is little information on healthcare utilization associated with a wide range of comorbid mental disorders in people with epilepsy (PWE). OBJECTIVE: To characterize registered mental disorders among all hospitalizations with a primary diagnosis of epilepsy and to analyze their association with crucial hospitalization outcomes. METHODS: An observational retrospective study was performed using administrative data from hospitalization episodes with epilepsy as the primary diagnosis discharged between 2008 and 2015. Mental disorder categories 650 to 670 from Clinical Classification Software were selected as secondary diagnoses. Mann-Whitney U, Kruskall-Wallis, and Chi-squared tests were used to establish comparisons. For each episode, data regarding hospitalization outcomes was retrieved, including length of stay (LoS), in-hospital mortality (IHM), 8-year period readmissions, and total estimated charges. RESULTS: Overall, 27,785 hospitalizations were analyzed and 33.9% had registered mental disorders, with alcohol-related disorders being the most prevalent (11.7%). For episodes with a concomitant register of a mental disorder, LoS was significantly longer (5.0 vs. 4.0 days, P <0.001), and IHM was higher (2.8% vs. 2.2%, P <0.001), as were readmissions (25.5% vs. 23.7%, P <0.001), and median episodes' charges (1,578.7 vs. 1,324.4 euros, P <0.001). CONCLUSION: Epilepsy-related hospitalizations with registered mental disorders heightened the utilization of healthcare resources, stressing the importance of diagnosing and treating mental disorders in PWE.

Perceptions of Portuguese medical coders on the transition to ICD-10-CM/PCS: A national survey.

BACKGROUND: In Portugal, trained physicians undertake the clinical coding process, which serves as the basis for hospital reimbursement systems. In 2017, the classification version used for coding of diagnoses and procedures for hospital morbidity changed from the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) to the International Classification of Diseases, Tenth Revision, Clinical Modification/Procedure Coding System (ICD-10-CM/PCS). OBJECTIVE: To assess the perceptions of medical coders on the transition of the clinical coding process from ICD-9-CM to ICD-10-CM/PCS in terms of its impact on data quality, as well as the major differences, advantages, and problems they faced. METHOD: We conducted an observational study using a web-based survey submitted to medical coders in Portugal. Survey questions were based on a literature review and from previous focus group studies. RESULTS: A total of 103 responses were obtained from medical coders with experience in the two versions of the classification system (i.e. ICD-9-CM and ICD-10-CM/PCS). Of these, 82 (79.6%) medical coders preferred the latest version and 76 (73.8%) considered that ICD-10-CM/PCS guaranteed higher quality of the coded data. However, more than half of the respondents (N = 61; 59.2%) believed that more time for the coding process for each episode was needed. CONCLUSION: Quality of clinical coded data is one of the major priorities that must be ensured. According to the medical coders, the use of ICD-10-CM/PCS appeared to achieve higher quality coded data, but also increased the effort. IMPLICATIONS: According to medical coders, the change off classification systems should improve the quality of coded data. Nevertheless, the extra time invested in this process might also pose a problem in the future.

Comparing Decentralized Learning Methods for Health Data Models to Nondecentralized Alternatives: Protocol for a Systematic Review.

BACKGROUND: Considering the soaring health-related costs directed toward a growing, aging, and comorbid population, the health sector needs effective data-driven interventions while managing rising care costs. While health interventions using data mining have become more robust and adopted, they often demand high-quality big data. However, growing privacy concerns have hindered large-scale data sharing. In parallel, recently introduced legal instruments require complex implementations, especially when it comes to biomedical data. New privacy-preserving technologies, such as decentralized learning, make it possible to create health models without mobilizing data sets by using distributed computation principles. Several multinational partnerships, including a recent agreement between the United States and the European Union, are adopting these techniques for next-generation data science. While these approaches are promising, there is no clear and robust evidence synthesis of health care applications. OBJECTIVE: The main aim is to compare the performance among health data models (eg, automated diagnosis and mortality prediction) developed using decentralized learning approaches (eg, federated and blockchain) to those using centralized or local methods. Secondary aims are comparing the privacy compromise and resource use among model architectures. METHODS: We will conduct a systematic review using the first-ever registered research protocol for this topic following a robust search methodology, including several biomedical and computational databases. This work will compare health data models differing in development architecture, grouping them according to their clinical applications. For reporting purposes, a PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 flow diagram will be presented. CHARMS (Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies)-based forms will be used for data extraction and to assess the risk of bias, alongside PROBAST (Prediction Model Risk of Bias Assessment Tool). All effect measures in the original studies will be reported. RESULTS: The queries and data extractions are expected to start on February 28, 2023, and end by July 31, 2023. The research protocol was registered with PROSPERO, under the number 393126, on February 3, 2023. With this protocol, we detail how we will conduct the systematic review. With that study, we aim to summarize the progress and findings from state-of-the-art decentralized learning models in health care in comparison to their local and centralized counterparts. Results are expected to clarify the consensuses and heterogeneities reported and help guide the research and development of new robust and sustainable applications to address the health data privacy problem, with applicability in real-world settings. CONCLUSIONS: We expect to clearly present the status quo of these privacy-preserving technologies in health care. With this robust synthesis of the currently available scientific evidence, the review will inform health technology assessment and evidence-based decisions, from health professionals, data scientists, and policy makers alike. Importantly, it should also guide the development and application of new tools in service of patients' privacy and future research. TRIAL REGISTRATION: PROSPERO 393126; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=393126. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/45823.

Socioeconomic Inequalities and Predictors of Cardiac Rehabilitation Referral: Real-World Evidence.

OBJECTIVE: The aim of the study is to identify factors associated with cardiac rehabilitation referral after an acute coronary syndrome at a university hospital. DESIGN: We analyzed 2814 hospitalizations due to acute coronary syndrome between 2017 and 2019 in Centro Hospitalar São João. The hospital's morbidity database was used to retrieve patient information. Cardiac rehabilitation referral and participation were identified from administrative databases and clinical records. Socioeconomic data were obtained from municipality of residence-level data rather than patient-specific data. RESULTS: Of 2814 hospitalizations, 72% (2028 cases) were eligible for cardiac rehabilitation. Mean age was 65.2 ± 13.4 yrs; 72% men, 33% diabetic, 57.1% hypertensive, and 63.6% dyslipidemic. Cardiac rehabilitation referral rate was low, at 18.8%, with 42 (11.0%) not attending cardiac rehabilitation and 39 (10.2%) dropping out. Females (odds ratio = 0.72 [95% confidence interval = 0.52-1.00]), older patients (odds ratio = 0.57 [0.42-0.77]; 55-64 vs. <55 yrs), and those coming from lower-income municipalities (odds ratio = 0.53 [0.41-0.69], below median vs. above median) with lower education level (odds ratio = 0.70 [0.54-0.92]; ≤4 vs. >4 yrs) were less likely to be referred to cardiac rehabilitation. CONCLUSIONS: There is a need for new strategies to promote cardiac rehabilitation in disadvantaged groups, as sex, age, and socioeconomic inequities in access to cardiac rehabilitation remain unresolved.

A Remote Monitoring Platform for the Management of Lower Limb Vascular Diseases.

This short paper describes a remote monitoring platform proposed in the Inno4health project. The platform aims to guide patients and clinicians during the treatment of lower limb vascular disorders, namely, to correct abnormal foot pressure and temperature to prevent diabetic foot ulcers and to monitor interface pressure, leg position and elevation for venous ulcers patients.

Mobile Application for Improvement of Self-Management of Type 2 Diabetes: Usability Pilot Test.

We intend to evaluate the usability of a mobile app developed for the self-management of T2DM. A pilot usability cross-sectional study was performed with a convenience sample of 6 smartphone users aged 45 years. Participants performed tasks autonomously in a mobile app to assess if users could complete them and filled out a usability and satisfaction questionnaire. About half of the tasks had a successful completion rate. The result of the usability questionnaire was 64/100, below the acceptable value, but the satisfaction value was considered good. This study was fundamental as it allowed us to verify which improvements should be implemented in the next version of the app, contributing to its better acceptance.

Secur-e-Health Project: Towards Federated Learning for Smart Pediatric Care.

The application of machine learning (ML) algorithms to electronic health records (EHR) data allows the achievement of data-driven insights on various clinical problems and the development of clinical decision support (CDS) systems to improve patient care. However, data governance and privacy barriers hinder the use of data from multiple sources, especially in the medical field due to the sensitivity of data. Federated learning (FL) is an attractive data privacy-preserving solution in this context by enabling the training of ML models with data from multiple sources without any data sharing, using distributed remotely hosted datasets. The Secur-e-Health project aims at developing a solution in terms of CDS tools encompassing FL predictive models and recommendation systems. This tool may be especially useful in Pediatrics due to the increasing demands on Pediatric services, and the current scarcity of ML applications in this field compared to adult care. Herein we provide a description of the technical solution proposed in this project for three specific pediatric clinical problems: childhood obesity management, pilonidal cyst post-surgical care and retinography imaging analysis.

Unsupervised algorithms to identify potential under-coding of secondary diagnoses in hospitalisations databases in Portugal.

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.

Social Vulnerability of Brazilian Metropolitan Schools and Teachers' Absence from Work Due to Vocal and Psychological Symptoms: A Multilevel Analysis.

Teachers' voices and psychological symptoms are the main reasons for absence from work. The objectives of this study were: (i) to spatially represent, through a webGIS, the standardized rates of teachers' absences due to voice (outcome 1) and psychological symptoms (outcome 2) in each Brazilian Federative Unit (FU = 26 states plus Federal District) and (ii) to analyze the relationship between each national outcome rate and the Social Vulnerability Index (SVI) of the municipality where urban schools are located, adjusted for sex, age, and working conditions. This cross-sectional study comprised 4979 randomly sampled teachers working in basic education urban schools, of which 83.3% are women. The national absence rates were 17.25% for voice symptoms and 14.93% for psychological symptoms. The rates, SVI, and school locations in the 27 FUs are dynamically visualized in webGIS. The multilevel multivariate logistic regression model showed a positive association between voice outcome and high/very high SVI (OR = 1.05 [1.03; 1.07]), whereas psychological symptoms were negatively associated with high/very high SVI (OR = 0.86 [0.85 0.88]) and positively associated with intermediate SVI (OR = 1.15 [1.13; 1.16]), in contrast with low/very low SVI. Being a woman (voice: OR = 1.36 [1.35; 1.38]; psychological: 1.22 [1.21; 1.24]) and working in schools with various precarious conditions (17 variables) increased the odds of being absent due to voice and psychological symptoms. The results confirm the need for investments to improve working conditions in schools.

Should we adjust health expenditure for age structure on health systems efficiency? A worldwide analysis.

INTRODUCTION: Healthcare expenditure, a common input used in health systems efficiency analyses is affected by population age structure. However, while age structure is usually considered to adjust health system outputs, health expenditure and other inputs are seldom adjusted. We propose methods for adjusting Health Expenditure per Capita (HEpC) for population age structure on health system efficiency analyses and assess the goodness-of-fit, correlation, reliability and disagreement of different approaches. METHODS: We performed a worldwide (188 countries) cross-sectional study of efficiency in 2015, using a stochastic frontier analysis. As single outputs, healthy life expectancy (HALE) at birth and at 65 years-old were considered in different models. We developed five models using as inputs: (1) HEpC (unadjusted); (2) age-adjusted HEpC; (3) HEpC and the proportion of 0-14, 15-64 and 65 + years-old; (4) HEpC and 5-year age-groups; and (5) HEpC ageing index. Akaike and Bayesian information criteria, Spearman's rank correlation, intraclass correlation coefficient and information-based measure of disagreement were computed. RESULTS: Models 1 and 2 showed the highest correlation (0.981 and 0.986 for HALE at birth and HALE at 65 years-old, respectively) and reliability (0.986 and 0.988) and the lowest disagreement (0.011 and 0.014). Model 2, with age-adjusted HEpC, presented the lowest information criteria values. CONCLUSIONS: Despite different models showing good correlation and reliability and low disagreement, there was important variability when age structure is considered that cannot be disregarded. The age-adjusted HE model provided the best goodness-of-fit and was the closest option to the current standard.

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.

Burden of infectious disease studies in Europe and the United Kingdom: a review of methodological design choices.

This systematic literature review aimed to provide an overview of the characteristics and methods used in studies applying the disability-adjusted life years (DALY) concept for infectious diseases within European Union (EU)/European Economic Area (EEA)/European Free Trade Association (EFTA) countries and the United Kingdom. Electronic databases and grey literature were searched for articles reporting the assessment of DALY and its components. We considered studies in which researchers performed DALY calculations using primary epidemiological data input sources. We screened 3053 studies of which 2948 were excluded and 105 studies met our inclusion criteria. Of these studies, 22 were multi-country and 83 were single-country studies, of which 46 were from the Netherlands. Food- and water-borne diseases were the most frequently studied infectious diseases. Between 2015 and 2022, the number of burden of infectious disease studies was 1.6 times higher compared to that published between 2000 and 2014. Almost all studies (97%) estimated DALYs based on the incidence- and pathogen-based approach and without social weighting functions; however, there was less methodological consensus with regards to the disability weights and life tables that were applied. The number of burden of infectious disease studies undertaken across Europe has increased over time. Development and use of guidelines will promote performing burden of infectious disease studies and facilitate comparability of the results.

Validity of the Paediatric Canadian Triage Acuity Scale in a Tertiary Hospital: An Analysis of Severity Markers' Variability.

With the increasing influx of patients and frequent overcrowding, the adoption of a valid triage system, capable of distinguishing patients who need urgent care, from those who can wait safely is paramount. Hence, the aim of this study is to evaluate the validity of the Paediatric Canadian Triage and Acuity Scale (PaedCTAS) in a Portuguese tertiary hospital. Furthermore, we aim to study the performance and appropriateness of the different surrogate severity markers to validate triage. This is a retrospective study considering all visits to the hospital's Paediatric Emergency Department (PED) between 2014 and 2019. This study considers cut-offs on all triage levels for dichotomization in order to calculate validity measures e.g. sensitivity, specificity and likelihood ratios, ROC curves; using hospital admission, admission to intensive care and the use of resources as outcomes/markers of severity. Over the study period there were 0.2% visits triaged as Level 1, 5.7% as Level 2, 39.4% as Level 3, 50.5% as Level 4, 4.2% as Level 5, from a total of 452,815 PED visits. The area under ROC curve was 0.96, 0.71, 0.76, 0.78, 0.59 for the surrogate markers: "Admitted to intensive care"; "Admitted to intermediate care"; "Admitted to hospital"; "Investigations performed in the PED" and "Uses PED resources", respectively. The association found between triage levels and the surrogate markers of severity suggests that the PedCTAS is highly valid. Different surrogate outcome markers convey different degrees of severity, hence different degrees of urgency. Therefore, the cut-offs to calculate validation measures and the thresholds of such measures should be chosen accordingly.

Dementia-related agitation: a 6-year nationwide characterization and analysis of hospitalization outcomes.

OBJECTIVES: To characterize all hospitalizations held in mainland Portugal (2010-2015) with dementia-related agitation based on International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) coding, and to investigate whether there is a relationship between agitation and hospitalization outcomes. METHODS: A retrospective observational study was conducted using an administrative dataset containing data from all mainland Portuguese public hospitals. Only hospitalization episodes for patients aged over 65 years who have received a dementia diagnosis ascertained by an ICD-9-CM code of dementia with behavioral disturbance (294.11 and 294.21) and dementia without behavioral disturbance (294.10 and 294.20) were selected. Episodes were further grouped according to the presence of an agitation code. For each episode, demographic data and hospitalization outcomes, including length of stay (LoS), in-hospital mortality, discharge destination and all-cause hospital readmissions, were sourced from the dataset. Comparative analyses were performed and multivariable logistic methods were used to estimate the adjusted associations between agitation (exposure) and outcomes. RESULTS: Overall, 53,156 episodes were selected, of which 6,586 had an agitation code. These were mostly related to male, younger inpatients (mean 81.19 vs. 83.29 years, p < 0.001), had a higher comorbidity burden, stayed longer at the hospital (median 9.00 vs. 8.00 days, p < 0.001) and frequently ended being transferred to another facility with inpatient care. Agitation was shown to independently increase LoS (aOR = 1.385; 95%CI:1.314-1.461), but not the risk of a fatal outcome (aOR = 0.648; 95%CI:0.600-0.700). CONCLUSION: These results support the importance of detecting and managing agitation early on admission, since its prompt management may prevent lengthy disruptive hospitalizations.

Willingness to institutionalize a relative with dementia: a web-platform assessment with the Portuguese adapted version of the Desire-to-Institutionalize Scale.

Introduction: Older persons with dementia (PwD) are more likely to be institutionalized than their counterparts without dementia. The caregiver's desire to institutionalize has been suggested as the most important predictor of actual institutionalization. This cross-sectional study aimed to culturally adapt the Desire to Institutionalize Scale (DIS) to a country with a high prevalence of dementia (Portugal) and examine its psychometric properties. Methods: The reliability, structural validity, and criterion validity of the DIS-PT were assessed by applying the scale using a remote measurement web platform. A sample of 105 dementia caregivers completed the DIS-PT and several psychosocial measures, including caregiver burden, anxiety, depression, quality of life, PwD functional independence, and neuropsychiatric symptoms. Results: The DIS-PT demonstrated good structural validity, with one factor explaining 75% of the total variance. The internal consistency of the scale was high (α = 0.802). Most caregivers (65.7%) endorsed at least one item on the DIS-PT (Mdn 2). The caregiver's desire to institutionalize was significantly associated with the caregiver, care recipient, and contextual variables previously known to affect institutional placement. These included the caregivers' occupational status, perceived burden, anxiety (but not depression), physical and psychological quality of life, care recipient education, severity of neuropsychiatric symptoms, and cohabitation with the caregiver. Discussion: This study offers preliminary support for the psychometric quality of the DIS-PT. The scale has practical applications in the early identification of caregivers considering nursing home placement, providing room for intervention in modifiable risk factors that may otherwise lead to the institutionalization of PwD. Remote measurement tools may hold value in assessing caregiving dyads non-intrusively and inexpensively.