Achieving sustainability on the ground poses a challenge in decoding globally defined goals, such as sustainable development goals, and aligning them with local perspectives and realities. This decoding necessitates the understanding of the multifaceted dimensions of the sustainability challenges in a given context, including their underlying causes. In case studies from Brazilian drylands, we illustrate how an enhanced multiscale participatory method, combined with systems thinking tools, can shed light on systemic structures that currently entrench unsustainable development trajectories. This method offers insights into co-designing potential pathways toward sustainable futures and unlocking transformative capacities of the local population.

Translating United Nations global sustainable development goals (SDGs) into actions that address local realities and aspirations is an urgent challenge. It requires new thinking and approaches that foster the discussion about the main challenges to implementing the SDGs at multiple levels. This paper presents a novel multiscale participatory approach that combines the popular Three Horizons diagram with the formalism of causal loop diagrams in systems thinking. We present results from six multi-stakeholder dialogues held across drylands in Brazil with a focus on desired futures aligned with SDGs. Focusing on identifying the root causes and systemic structures of unsustainability, participants identified lock-ins, leverage points, and interventions for how these could be changed. The core lock-ins are the discontinuity of public policies, and the historical land and power concentration reinforced by the current expansion of large-scale agricultural, mining, and energy projects. The proposed interventions are structural and – if implemented – would contribute to achieving SDGs in an integrated manner. The unique approach developed in this study can provide leverage as it bridges the inclusivity of participatory visioning with the change potential of systems thinking tools to tackle root causes and unleash societal transformations.

We are not achieving SDGs. Understanding root causes of unsustainability is critical to move toward sustainable and just futures.

Delirium is an acute and fluctuating disorder characterized by a disturbance in attention and cognition that is commonly observed in hospitalized older adults; being present in up to 23% of patients admitted to a general medical service and as many as 85% of patients in the intensive care unit. Delirium causes complications such as increased morbidity, persistent functional decline, mortality, increased frailty and increased length of hospital stay. Nonetheless, it is often underdiagnosed, especially when it occurs in its hypoactive form. The objective of this study is to describe characteristics and factors associated with the presence of delirium in patients ≥65 years treated by the liaison psychiatric units in seven general hospitals.

This is an observational, cross-sectional, multicentre study. We obtained data from a sample of 165 patients (≥65 years) admitted to seven general hospitals in Spain referred from different departments to each liaison psychiatry unit. Data was collected for a month and a half period. Psychiatric evaluations were performed while the patients were on wards.

We obtained a sample of 165 patients (78 women, 88 men) with a mean age of 76,03 years old (42.10% <75 years, 57,83% ≥ 75 years). Most of them were married and they lived accompanied (67,27%). Delirium was diagnosed in 20% of the consultations. A multivariate analysis was developed with the presence of delirium as the independent variable. The nature of the underlying pathology, the presence of a previous mental disorder, functionality using the Barthel and Lawton Brody Indexes and the prescribed pharmacological treatments were used as dependent variables. Cohen’s kappa statistics were used to estimate the agreement between delirium diagnose made by psychiatrists and the diagnoses considered by the referring doctors. Low agreement was found for the presence of delirium (Kappa= 0,2341). We also explored the relationship between the presence of delirium and the mean length of stay, as well as the discharge destination of these patients.

There are still many difficulties in the diagnosis and treatment of patients with delirium. Better knowledge of the factors associated with its appearance would improve the management of these patients.

The International Federation of Medical and Biological Engineering created a multidisciplinary working group to discuss assessments of artificial intelligence and machine learning (AI/ML) applications in health care. Engineers, clinicians, and economists identified evidence generation as a critical topic. Heart failure (HF) was selected to investigate the available evidence on the clinical effectiveness and safety of AI/ML applications. Attention was paid to transparency of AI/ML methods and their data sources.

A scoping review was conducted on AI/ML algorithms developed for the management of HF. A search for systematic reviews, scoping reviews, and meta-analyses published from 1976 to October 2022 was conducted in Embase, MEDLINE, and Scopus.

Of 456 relevant publications, 21 papers were included in the final analysis. Most papers (10 systematic reviews, five meta-analyses, and six non-systematic or scoping reviews) included studies conducted in North America. No study was conducted in Africa. The healthcare setting was not clearly stated in approximately half of the studies. A lack of agreement was noticed regarding the quality assessment tools used among the reviews. The most common data source for AI/ML algorithms was electronic health records, but in some cases data sources were not reported. While deep learning emerged as the most common adopted methodology, covariates were not always included in the algorithm development. The review demonstrated that comparative assessment of algorithms requires further investigation, given the high variability in the comparator used (e.g., clinical gold standard, other AI/ML algorithms, or other statistical methods). The main investigated endpoints were the incidence of HF and the number of hospital admissions.

When assessing innovative health technologies such as AI/ML applications in health care, evidence is among the main challenges. Our scoping review, focusing on algorithms developed to manage HF, showed that the biggest challenges relate to the quality of the studies, the adoption of a comparative approach, and transparency of methods.