To evaluate safety and efficacy of balloon pulmonary angioplasty (BPA) in a large cohort of patients with chronic thromboembolic pulmonary hypertension (CTEPH).
From 2014 to 2017, 184 inoperable ...CTEPH patients underwent 1006 BPA sessions. Safety and efficacy during the first 21 months (initial period) were compared with those of the last 21 months (recent period). A total of 154 patients had a full evaluation after a median duration of 6.1 months.
Overall, there was a significant improvement in New York Heart Association functional class, 6-min walk distance (mean change +45 m), and a significant decrease in mean pulmonary artery pressure (PAP) and in pulmonary vascular resistance (PVR) by 26% and 43%, respectively. The percentage decreases of mean PAP and PVR were 22% and 37% in the initial period
30% and 49% in the recent period, respectively (p<0.05). The main complications included lung injury, which occurred in 9.1% of 1006 sessions (13.3% in the initial period
5.9% in the recent period; p<0.001). Per-patient multivariate analysis revealed that baseline mean PAP and the period during which BPA procedure was performed (recent
initial period) were the strongest factors related to the occurrence of lung injury. 3-year survival was 95.1%.
This study confirms that a refined BPA strategy improves short-term symptoms, exercise capacity and haemodynamics in inoperable CTEPH patients with an acceptable risk-benefit ratio. Safety and efficacy improve over time, underscoring the unavoidable learning curve for this procedure.
Evidence-informed strategic planning is a top priority in Mental Health (MH) due to the burden associated with this group of disorders and its societal costs. However, MH systems are highly complex, ...and decision support tools should follow a systems thinking approach that incorporates expert knowledge. The aim of this paper is to introduce a new Decision Support System (DSS) to improve knowledge on the health ecosystem, resource allocation and management in regional MH planning. The Efficient Decision Support-Mental Health (EDeS-MH) is a DSS that integrates an operational model to assess the Relative Technical Efficiency (RTE) of small health areas, a Monte-Carlo simulation engine (that carries out the Monte-Carlo simulation technique), a fuzzy inference engine prototype and basic statistics as well as system stability and entropy indicators. The stability indicator assesses the sensitivity of the model results due to data variations (derived from structural changes). The entropy indicator assesses the inner uncertainty of the results. RTE is multidimensional, that is, it was evaluated by using 15 variable combinations called scenarios. Each scenario, designed by experts in MH planning, has its own meaning based on different types of care. Three management interventions on the MH system in Bizkaia were analysed using key performance indicators of the service availability, placement capacity in day care, health care workforce capacity, and resource utilisation data of hospital and community care. The potential impact of these interventions has been assessed at both local and system levels. The system reacts positively to the proposals by a slight increase in its efficiency and stability (and its corresponding decrease in the entropy). However, depending on the analysed scenario, RTE, stability and entropy statistics can have a positive, neutral or negative behaviour. Using this information, decision makers can design new specific interventions/policies. EDeS-MH has been tested and face-validated in a real management situation in the Bizkaia MH system.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
•Monte Carlo-DEA assessed the Small Health Areas (SHA) efficiency.•Expert knowledge assigned statistical distributions for the variables.•Input/output combinations analysed different perspectives of ...SHA efficiency.•Best combinations were used for benchmarking.•k-means analysis validated a panel of experts’ classification.
This paper uses Monte Carlo Data Envelopment Analysis (Monte Carlo DEA) to evaluate the relative technical efficiency of small health care areas in probabilistic terms with respect to both mental health care as well as the efficiency of the whole system. Taking into account that the number of areas did not permit maximum discrimination to be achieved, all the scenarios of non-correlated inputs and outputs of a specific size were designed using Monte Carlo Pearson to maximize the discrimination of Monte Carlo DEA and the information included in the models. A knowledge base was included in the simulation engine in order to guide the dynamic interpretation of non-standard inputs and outputs. Results show the probability that all DMU and the whole system have of being efficient, as well as the specific inputs and outputs that make the areas or the system efficient or inefficient, along with a classification of the areas into four groups according to their efficiency (k-means cluster analysis). This final classification was compared with an expert-based classification to validate both the knowledge base and the Monte Carlo DEA model. Both classifications showed results that were very similar although not exactly the same, basically due to the difficulty experts experience in recognizing “intermediately-inefficient” DMU. We propose this methodology as an instrument that could help health care managers to assess relative technical efficiency in complex systems under uncertainty.
Decision support systems are appropriate tools for guiding policymaking processes, especially in mental health (MH), where care provision should be delivered in a balanced and integrated way. This ...study aims to develop an analytical process for (i) assessing the performance of an MH ecosystem and (ii) identifying benchmark and target-for-improvement catchment areas. MH provision (inpatient, day and outpatient types of care) was analysed in the Mental Health Network of Gipuzkoa (Osakidetza, Basque Country, Spain) using a decision support system that integrated data envelopment analysis, Monte Carlo simulation and artificial intelligence. The unit of analysis was the 13 catchment areas defined by a reference MH centre. MH ecosystem performance was assessed by the following indicators: relative technical efficiency, stability and entropy to guide organizational interventions. Globally, the MH system of Gipuzkoa showed high efficiency scores in each main type of care (inpatient, day and outpatient), but it can be considered unstable (small changes can have relevant impacts on MH provision and performance). Both benchmark and target-for-improvement areas were identified and described. This article provides a guide for evidence-informed decision-making and policy design to improve the continuity of MH care after inpatient discharges. The findings show that it is crucial to design interventions and strategies (i) considering the characteristics of the area to be improved and (ii) assessing the potential impact on the performance of the global MH care ecosystem. For performance improvement, it is recommended to reduce admissions and readmissions for inpatient care, increase workforce capacity and utilization of day care services and increase the availability of outpatient care services.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Major efforts worldwide have been made to provide balanced Mental Health (MH) care. Any integrated MH ecosystem includes hospital and community-based care, highlighting the role of outpatient care in ...reducing relapses and readmissions. This study aimed (i) to identify potential expert-based causal relationships between inpatient and outpatient care variables, (ii) to assess them by using statistical procedures, and finally (iii) to assess the potential impact of a specific policy enhancing the MH care balance on real ecosystem performance. Causal relationships (Bayesian network) between inpatient and outpatient care variables were defined by expert knowledge and confirmed by using multivariate linear regression (generalized least squares). Based on the Bayesian network and regression results, a decision support system that combines data envelopment analysis, Monte Carlo simulation and fuzzy inference was used to assess the potential impact of the designed policy. As expected, there were strong statistical relationships between outpatient and inpatient care variables, which preliminarily confirmed their potential and a priori causal nature. The global impact of the proposed policy on the ecosystem was positive in terms of efficiency assessment, stability and entropy. To the best of our knowledge, this is the first study that formalized expert-based causal relationships between inpatient and outpatient care variables. These relationships, structured by a Bayesian network, can be used for designing evidence-informed policies trying to balance MH care provision. By integrating causal models and statistical analysis, decision support systems are useful tools to support evidence-informed planning and decision making, as they allow us to predict the potential impact of specific policies on the ecosystem prior to its real application, reducing the risk and considering the population's needs and scientific findings.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Mental health care systems have been dramatically affected by COVID-19. Containment measures have been imposed, with negative consequences on population mental health. Therefore, an increase in both ...symptomatology and mental disorder incidence is expected. This research aims to identify, describe and assess the empirical background on online strategies and recommendations developed by international organizations and governments to cope with the psychological impact of COVID-19 at a very early stage of the pandemic.
The PRISMA guidelines were adapted to review online documents. A new questionnaire was developed to identify the existence of common patterns in the selected documents. Questions were classified into three domains: COVID-19 information, mental health strategies and mental health recommendations. A two-step cluster analysis was carried out to highlight underlying behaviours in the data (patterns). The results are shown as spider graphs (pattern profiles) and conceptual maps (multidimensional links between questions).
Twenty-six documents were included in the review. The questionnaire analysed document complexity and identified their common key mental health characteristics (i.e., does the respondent have the tools for dealing with stress, depression and anxiety?). Cluster analysis highlighted patterns from the questionnaire domains. Strong relationships between questions were identified, such as psychological tips for maintaining good mental health and coping with COVID-19 (question n° 4), describing some psychological skills to help people cope with anxiety and worry about COVID-19 (question n° 6) and promoting social connection at home (question n° 8).
When fast results are needed to develop health strategies and policies, rapid reviews associated with statistical and graphical methods are essential. The results obtained from the proposed analytical procedure can be relevant to a) classify documents according to their complexity in structuring the information provided on how to cope with the psychological impact of COVID-19, b) develop new documents according to specific objectives matching population needs, c) improve document design to face unforeseen events, and d) adapt new documents to local situations. In this framework, the relevance of adapting e-mental health procedures to community mental health care model principles was highlighted, although some problems related to the digital gap must be considered.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The effects of climate change and the rapid growth of societies often lead to water scarcity and inadequate water quality, resulting in a significant number of diseases. The digitalization of ...infrastructure and the use of Digital Twins are presented as alternatives for optimizing resources and the necessary infrastructure in the water cycle. This paper presents a framework for the development of a Digital Twin platform for a wastewater treatment plant, based on a microservices architecture which optimized its design for edge computing implementation. The platform aims to optimize the operation and maintenance processes of the plant's systems, by employing machine learning techniques, process modeling and simulation, as well as leveraging the information contained in BIM models to support decision-making.
Local Indicators of Spatial Aggregation (LISA) can be used as objectives in a multicriteria framework when highly autocorrelated areas (hot-spots) must be identified and geographically located in ...complex areas. To do so, a Multi-Objective Evolutionary Algorithm (MOEA) based on SPEA2 (Strength Pareto Evolutionary Algorithm v.2) has been designed to evaluate three different fitness functions (fine-grained strength, the weighted sum of objectives and fuzzy evaluation of weighted objectives) and three LISA methods. MOEA makes it possible to achieve a compromise between spatial econometric methods as it highlights areas where a specific phenomenon shows significantly high autocorrelation. The spatial distribution of financially compromised olive-tree farms in Andalusia (Spain) was selected for analysis and two fuzzy hot-spots were statistically identified and spatially located. Hot-spots can be considered to be spatial fuzzy sets where the spatial units have a membership degree that can also be calculated.
Rehabilitation services have a key role in ensuring integrated and comprehensive mental health (MH) care in the community for people suffering from long-term and severe mental disorders. MH-supported ...accommodation services aim to promote service users' autonomy and independence. Given the complexity associated with MH-supported accommodation services in England, a comparative evaluation of critical performance indicators, including service provision and quality of care, seems to be necessary in designing evidence-informed policies. This study aims to explore the influence of service quality indicators on the performance of MH-supported accommodation services in England. The analysed sample includes supported accommodation services from 14 nationally representative local authorities in England from the QuEST study grouped by three main types of care: residential care homes (divided into two subgroups: move-on and non-move-on oriented), supported housing and floating outreach. EDeS-MH (efficient decision support-mental health) was used to assess the performance indicators for the selected services by combining a Monte Carlo simulation engine, data envelopment analysis and a fuzzy inference engine for integrating expert knowledge. Depending on the type of care, six/seven quality domains were sequentially included after a baseline scenario (only technical) was analysed. Relative technical efficiency scores for the baseline scenarios revealed high performance in all the selected supported accommodation services, but the statistical variability was high. Quality domains significantly improved performance in every type of care. The inclusion of quality indicators has a positive impact on the global performance of each type of care. Remaining at the corresponding services more than expected for two years has a negative impact on performance. These findings can be considered from a planning perspective to facilitate the design of pathways of care with more realistic expectations about gaining autonomy in two years.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Abstract
Introduction
The global health crisis caused by the COVID-19 pandemic has had a negative impact on mental health (MH). As a response to the pandemic, international agencies and governmental ...institutions provided an initial response to the population’s needs. As the pandemic evolved, the population circumstances changed, and some of these international agencies updated their strategies, recommendations, and guidelines for the populations. However, there is currently a lack of information on the attention given to response strategies by the different countries throughout the beginning of the pandemic.
Objectives
1) To evaluate the evolution of online MH strategies and recommendations of selected countries to cope with the MH impact of COVID-19 from the early stages of the pandemic (15 April 2020) to the vaccination period (9 June 2021) and 2) to review and analyse the current structures of these online MH strategies and recommendations.
Methodology
An adaptation of the PRISMA guidelines to review online documents was developed with a questionnaire for MH strategies and recommendations assessment. The search was conducted on Google, including documents from April 2020 to June 2021. Basic statistics and Student’s t test were used to assess the evolution of the documents, while a two-step cluster analysis was performed to assess the organisation and characteristics of the most recent documents.
Results
Statistically significant differences were found both in the number of symptoms and mental disorders and MH strategies and recommendations included in the initial documents and the updated versions generated after vaccines became available. The most recent versions are more complete in all cases. Regarding the forty-six total documents included in the review, the cluster analysis showed a broad distribution from wide-spectrum documents to documents focusing on a specific topic.
Conclusions
Selected governments and related institutions have worked actively on updating their MH online documents, highlighting actions related to bereavement, telehealth and domestic violence. The study supports the use of the adaptation, including the tailor-made questionnaire, of the PRISMA protocol as a potential standard to conduct longitudinal assessments of online documents used to support MH strategies and recommendations.
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Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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