The prognostic value of left atrial (LA) strain in patients with heart failure with reduced ejection fraction (HFrEF) has not been fully elucidated. Therefore, this study investigated the prognostic value of LA strain in HFrEF patients in relation to all-cause mortality.

A total of 822 echocardiograms from HFrEF patients admitted to a heart failure clinic were analysed offline. To calculate left atrial reservoir strain (LA RS) and left atrial contractile strain (LA CS), LA two-dimensional speckle tracking was performed in the 4-chamber, 2-chamber and 3-chamber view. The end-point was all-cause mortality. The association between LA strain parameters and outcome was examined using Cox regression.

The median follow-up time was 40 months and follow-up was 100% complete. During follow-up, a total of 137 patients (16.7%) died of all causes. In a final multivariable model adjusted for clinical and echocardiographic parameters including global longitudinal strain, LA RS and LA CS were significantly associated with all-cause death during follow-up (LA RS, HR 0.96, 95% CI 0.92 to 0.99, p=0.014, pr. 1% increase) (LA CS, HR 0.95, 95% CI 0.92 to 0.98, p=0.002, pr. 1% increase).

When added to the final multivariable model, both LA RS and LA CS contributed with incremental prognostic value as determined by C-statistic (LA RS: C-stat difference 0.007, 95% CI 0.000 to 0.020, p=0.050) (LA CS: C-stat difference 0.009, 95% CI 0.000 to 0.023, p=0.030).

In HFrEF patients, LA RS and LA CS were associated with all-cause mortality and contributed incremental prognostic value in addition to established prognostic measures.

To develop and validate the Internalised Stigma Scale for Gestational Diabetes Mellitus (ISS-GDM), a questionnaire measuring self-reported internalised stigma among women with prior gestational diabetes mellitus (GDM). We hypothesised that internalised GDM stigma could be reliably and validly assessed through a short psychometric instrument.

Cross-sectional validation study.

Follow-up data from the Danish, multicentre Face-it trial for women with prior GDM and their families.

In total, 248 women completed the ISS-GDM approximately 1 year after their GDM affected pregnancy.

The primary outcome was psychometric properties of the ISS-GDM, assessed using Cronbach’s alpha, confirmatory factor analysis (CFA) and Rasch analysis (RA). Secondary outcomes included identification of item anomalies (local response dependence, differential item functioning).

A large proportion of respondents endorsed statements reflecting self-disappointment, self-blame and an altered self-perception. Less endorsed statements included feeling inferior to other mothers or guilt towards family members due to GDM. The ISS-GDM demonstrated satisfactory psychometric properties. CFA indicated that item 2 assessing self-perceived capabilities as a mother did not load onto the main factor, while CFA and RA identified local response dependence and differential item functioning by body mass index. After adjustments, a two-factor solution supported calculating a sum score of items 1 and 3–11, with item 2 retained as a stand-alone indicator of perceived parenting capabilities. The 10-item scale demonstrated acceptable reliability (Cronbach’s alpha=0.78).

The ISS-GDM is a reliable and valid tool for assessing internalised stigma among women with prior GDM. Our findings further suggest that a substantial proportion of women with prior GDM experience self-blame and an altered self-perception due to their diagnosis. The ISS-GDM scale enables research into its prevalence, severity and consequences.

Prehospital care, though critical to injury survival, is largely unavailable in many low and middle-income countries, including Cameroon. Lay first responder (LFR) programmes train persons with high injury exposure in first-aid and emergency transport, but stakeholder buy-in from trainees and healthcare workers (HCWs) is essential. To design a context-appropriate prehospital care system, we evaluated barriers and facilitators of implementing a driver-based LFR programme in Cameroon.

In April 2023, we performed a mixed-methods evaluation targeting commercial mototaxi drivers and HCW in Limbe, Cameroon. Drivers were recruited for focus groups through union leaders. Trauma HCW at Limbe Regional Hospital completed Likert surveys and a subgroup completed semistructured interviews. Data collection focused on perceptions, barriers and facilitators of LFR programme implementation. Survey data were summarised using median and IQR. Interviews were recorded, transcribed, translated and analysed with open and axial coding using reflexive thematic analysis.

Overall, 92 mototaxi drivers and 34 HCWs participated in the LFR programme assessment. Among the HCW surveyed, 93% felt mototaxi drivers were capable of training as LFR but only 44% felt that drivers would be able to provide care safely. Interviews identified negative HCW perceptions of drivers, including drivers being uneducated and financially motivated, as key barriers, whereas driver exposure to injury was identified as a facilitator to LFR programme implementation. Driver groups demonstrated a positive perception of LFR training but identified unpaid time spent transporting injured persons as a significant barrier. Both groups described a need for hospital involvement in trainings and bidirectional standardised communication with HCW.

In Cameroon, driver-based LFR may facilitate increased prehospital care but further exploration of possible systems of collaboration that promote long-term success of the programme is required. Specifically, sustainable implementation will need to include clear bidirectional communication guidelines and provide driver incentive commensurate to effort.

Application of artificial intelligence (AI) tools in the healthcare setting gains importance especially in the domain of disease diagnosis. Numerous studies have tried to explore AI in the diagnosis of various diseases, including tropical fevers such as dengue and malaria. However, there is a lack of standard guidelines to develop the AI models, the gap between clinical and engineering expertise and clinical validation of the models, and hence there is a critical need for the development of an integrated diagnostic tool which uses demographical, laboratory variables and epidemiological parameters of patient and provides early prediction.

The present study aimed to develop and evaluate a machine-learning (ML) prediction tool for differential diagnosis of tropical fevers for adult patients (>18 years) using a three-phase approach in a tertiary care centre in South India by January 2026. Phase involves identification of the prevalent tropical fevers and associated clinical parameters to develop the AI model through a retrospective audit and qualitative interview. Phase Ⅱ involves retrospective data collection from hospital medical records for finalised diseases (1000 cases per disease) and clinical parameters, with data being used for model development using the Python language. Support vector machine, logistic regression, K-Nearest Neighbors, Naïve Bayes and ensemble models such as decision tree and Random Forest will be employed along with explainable AI techniques. They are used as they are easy to understand and interpret, well established, most effective for structured data, enhancing the transparency and interpretability of the predictive machine learning models, and their use has been widely supported in previous studies across various contexts. Suitable statistical parameters like specificity, sensitivity and area under receiver operating characteristic (AUROC) will be applied to evaluate model performance. In phase , the developed model will be implemented prospectively to assess the feasibility of model implementation. Model performance such as specificity, sensitivity and AUROC will be calculated, and the finally developed model will be implemented in a single tertiary care hospital to evaluate its overall performance.

Ethical approval for the study has been obtained from the institutional ethics committee of the Kasturba Medical College and Kasturba Hospital, Manipal (IEC number: 6/2024). Informed consent will be taken for obtaining the data of the patient for the evaluation of the model in the third phase of the study, and data will be kept confidential. The study results will be disseminated by publishing them in a peer-reviewed journal.

The protocol has been registered with the Clinical Trial Registry of India (CTRI) (CTRI/2024/04/065866) and approved on 16 April 2024.

Post-COVID syndrome manifests with a diverse array of symptoms for which no standard care plan currently exists. Many questions were raised by patients, which underscored the need for a validated patient-reported outcome measure (PROM). Therefore, a post-COVID module was developed to be included in the Assessment of Burden of Chronic Conditions (ABCC-) tool. The ABCC-tool evaluates and visualises the perceived physical, emotional and social burden of one or multiple chronic disease(s) using a balloon diagram and aims to facilitate person-centred care and structured discussions between patients and healthcare professionals. This study explores the patients’ perspective on the content of the ABCC-tool for post-COVID and the tool’s usability in a home-based setting.

All patients who completed the ABCC-tool for post-COVID were invited for an online semi-structured interview. We selected post-COVID patients who had used the tool in the past three months. Interviews were audio recorded and analysed using a thematic approach with Atlas.ti version 23.

Nineteen post-COVID patients (10 males, mean age 56) were interviewed between May and August 2024. The tool was regarded as user-friendly, and patients indicated they would use the tool again in the future. Patients valued the tool’s broad range of topics, some of which are often overlooked in standard healthcare consultations. The tool was comprehensible and relevant according to all patients. The balloon diagram was easy to understand, but a legend explaining the colours of the balloons was preferred. Other suggestions for improvement included adding open-text fields and periodic reminders to increase usability and adding long-term data.

The ABCC-tool is a promising instrument for post-COVID patients, offering a structured way to monitor and communicate experienced burden in addition to standard healthcare consultations. Refinements addressing usability and comprehensiveness are recommended to facilitate its integration into clinical practices.

To explore whether routine electronic healthcare records can be used to identify triggers for initiating advance care planning (ACP) and the optimal time window to initiate ACP. We aimed to assess the prevalence of triggers for initiating ACP as defined for use in routine data, whether their presence is associated with death, and what their position is relative to a previously identified ‘optimal time window for ACP’.

Nested case-control study within a large dynamic population cohort dataset.

Primary care population-based, anonymised data extracted from GP centres in the South Holland province, The Netherlands.

We selected records of individuals aged ≥65 registered with their general practice from 1 Jan 2014 to 1 Jan 2017. Cases were individuals who died between 1 Jan 2017 and 1 Jan 2020. Controls were individuals who remained alive. Cases were matched by age to controls in a 1:4 ratio.

Outcomes include prevalence of triggers for ACP in the records of deceased and living individuals; association of the triggers’ presence with death; timing of the identified triggers in deceased individuals relative to the ‘optimal time window for ACP’.

We included 17098 records, 4139 from deceased individuals (mean age 81) and 12959 from living individuals (mean age 79). Triggers most strongly associated with death were consultations concerning malignancy (OR 8.35, 95% CI 7.42 to 9.41), hospital admissions (OR 7.32, 95% CI 6.75 to 7.94), emergency department referrals (OR 7.11, 95% CI 6.52 to 7.75), registered home visits (OR 5.97, 95% CI 5.51 to 6.47), consultations concerning heart failure (OR 5.25, 95% CI 4.59 to 5.99), dementia (OR 4.75, 95% CI 3.99 to 6.56), opioid prescriptions (OR 4.58 (4.25–4.93), consultations concerning general decline/feeling old (OR 4.15, 95% CI 3.72 to 4.64) and skin ulcers/pressure sores (OR 4.04, 95% CI 3.55 to 4.61). Those closest to the median of the optimal time window for ACP were consultations regarding dyspnoea, general decline/feeling old, heart failure, skin ulcers/pressure sores and fever, opioid prescriptions, emergency department referrals, registered home visits and hospital admissions.

Clinical triggers for initiating ACP in general practice can be recognised within the routine electronic health records and they align well with the ‘window of opportunity’ to initiate ACP.

To explore perceptions regarding the approved and actual prescribed doses of protein kinase inhibitors (PKIs) in clinical practice in the European Union among medicine regulators and healthcare professionals (HCPs).

A qualitative descriptive study was conducted using semistructured interviews, continuing until thematic saturation was reached. Thematic analysis was undertaken using a combined deductive-inductive approach. Deductive main analytical themes were derived from the theoretical framework of questioning-based policy design, namely problem sensing, problem categorisation and problem decomposition. Subthemes were generated inductively and could coherently be situated within these main analytical themes.

Interviews were held online or in person at a location convenient for the interviewee, depending on the participant’s preference.

Seven medicine regulators involved in the regulation of cancer medicines—including PKIs—and 10 HCPs prescribing PKIs in clinical practice, from various countries within Europe, were included.

Regulators highlighted insufficient attention to optimal dose finding, yielding approved doses often based on outdated maximum tolerated dose concepts, leading to uncertainties in efficacy and safety. HCPs reported using alternative dosing strategies in clinical practice to improve tolerability and quality of life (QoL) but noted a lack of robust evidence to guide such adjustments and faced legal constraints to deviate from the approved dose. Participants emphasised the need for improved pre-approval and post-approval dose optimisation to improve safety, enhance QoL and bridge gaps between trial data and real-world patient diversity.

Collaborative efforts involving multistakeholders including HCPs, regulators, pharmaceutical companies, insurers, governments and patient representatives are essential to advance dose optimisation and improve patient-centric outcomes, with further research needed to understand these stakeholders’ perspectives.

Living with epilepsy, especially drug-resistant epilepsy (DRE), imposes several challenges for people diagnosed with the condition. These challenges include the physical and mental implications of epilepsy on both caregivers and patients with epilepsy. For the more than 120 000 individuals living with this neurological disorder in the Netherlands, along with their families, daily activities become hazardous, limited and costly, significantly affecting their health-related quality of life (HRQoL). As data on the burden of epilepsy in the Netherlands are lacking, studies attempting to capture the impact of epilepsy on individuals, caregivers and society are needed to enhance understanding and help address the burden of epileptic seizures.

The study is part of the AIM@EPILEPSY project. The project aims to develop a planning suite enabling cost-saving, minimally invasive treatment for epilepsy. By surveying 330 people with epilepsy and an anticipated sample of 150–200 informal caregivers across the Netherlands, using standardised questionnaires focusing on associated societal costs and the impact on HRQoL, this bottom-up, prevalence-based prospective study aims to understand the societal burden of DRE in the Netherlands. The data will be collected at 0, 3, 6 and 12 months of follow-up. The study results will describe the economic impact of epilepsy, focusing on cost-of-illness () and HRQoL (utilities) in the Netherlands.

The proposed study was approved by the Maastricht University Medical Ethics Review Committee (Approval reference: FHML-REC/2024/067/Amendment/2024_16). The result of the study is planned to be published in a peer-reviewed journal and presented at international and local scientific conferences.