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Prevalence and Risk Factors of Readmission Among Patients with Cardiovascular Diseases in Africa: A Systematic Review.

BACKGROUND: Cardiovascular diseases (CVDs) are a leading cause of morbidity and mortality globally, with a disproportionately high burden in Africa. Readmissions among patients present an additional challenge. This systematic review aimed to map evidence on the prevalence and risk factors of readmissions among patients with CVDs in Africa. METHOD: A systematic review was conducted following the PRISMA guidelines. Comprehensive searches were carried out in PubMed Central, Scopus, JSTOR, CINAHL and other databases. Studies published in English from January 2000 that reported on the prevalence and risk factors of readmission among CVD patients in Africa were included. The Joanna Briggs Institute critical appraisal tools were used to assess the quality of included studies. Descriptive and thematic analysis were used to synthesise the findings. RESULTS: A total of 37 records were included in the final synthesis. Heart failure was the most extensively studied CVD. The prevalence of readmission among patients with CVDs ranged from 1.53% to 67.7%. Older age (> 65 years) was a key predictor. Clinical factors included hypertension, arrhythmias, advanced disease stages including LVEF < 40%, NYHA Class II-IV, NHISS > 7, anemia, infections and chronic comorbidities. Facility-related factors comprised prolonged hospitalisation and discharge without essential medications (beta-blockers for heart failure, and aspirin, clopidogrel, statins for myocardial infarction). Poor adherence to treatment regimen emerged as self-management determinant. CONCLUSION: The findings suggest that readmissions among patients with CVDs in Africa remain a substantial burden. The review highlights the need for evidence-based interventions focusing on standardised discharge protocols, enhanced patient education, better screening of patients and management of comorbidities. Future research should address geographical gaps and focus on a wider range of CVDs to provide a more comprehensive overview.

RESEARCH EXPLAINED

A Sensorimotor Framework for the Neurorehabilitation of Oculomotor Dysfunction in Parkinson’s Disease

RESEARCH EXPLAINED

Voice-Based Screening of Depression and Anxiety Using Machine Learning and Deep Learning: A Scoping Review of Methods and Clinical Readiness.

Despite the high global prevalence of depressive and anxiety disorders, access to early clinical assessment remains limited for many. Although this field has grown rapidly, existing reviews have focused primarily on technical performance, with limited systematic attention to whether current models meet the prerequisites for clinical implementation. This scoping review mapped methodological approaches across this domain and evaluated clinical readiness using five predefined indicators: sample size adequacy, external validation, prospective data collection, real-world evaluation, and model explainability. Searches of PubMed, Web of Science, and IEEE Xplore (March 2026) identified 2463 records; 34 studies (37 dataset evaluations) were included. The majority of studies (91%) were published from 2022 onwards. Depression was the primary target in 91% of studies, while only one study addressed anxiety. Hand-crafted acoustic features were the most frequent (57%), while classical machine learning was the most common model type (32%). External validation was conducted in only 32% of evaluations and real-world testing in 8%. Clinical readiness was classified as Low in 24%, Moderate in 65%, and High in 11% of evaluations. No evaluation met all five indicators simultaneously. These findings apply primarily to voice-based depression screening; 36 of 37 evaluations targeted depression, and the evidence base for anxiety disorders is limited to a single evaluation, precluding comparable characterisation for that condition. The principal challenges to clinical implementation are insufficient external validation, reliance on laboratory conditions, narrow linguistic coverage, and inconsistent metric reporting. The framework applied in this scoping review provides a replicable structure for assessing the clinical validity of AI-driven psychiatric screening tools.

RESEARCH EXPLAINED

An integrated digital-to-surgical framework for patient-specific chest wall resection and reconstruction using 3D-printed titanium implants: proof-of-concept study.

Patient-specific skeletal reconstruction of the chest wall following extensive resections that result in large, anatomically complex defects remains challenging using conventional reconstructive techniques. We describe an end-to-end digital-to-surgical workflow framework for patient-specific chest wall resection and reconstruction using three-dimensional (3D) printed titanium implants. The four-stage workflow integrates Computed Tomography (CT) imaging data, virtual 3D anatomical modelling, virtual tumour-margin planning, computer-aided implant design (CAD), stereolithographic prototype validation, and additive manufacturing using laser-powder bed fusion (L-PBF). High-resolution CT data in Digital Imaging and Communications in Medicine (DICOM) format were converted into patient-specific 3D-rendered models of the chest wall and tumour to enable virtual delineation of tumour extent and digitally planned resection margins. These digital resection models were then used to design anatomically matched patient-specific implants. Implant designs were validated using stereolithographic prototypes before definitive manufacture in titanium alloy. The workflow underwent clinical feasibility evaluation in six consecutive patients requiring major chest wall resection and reconstruction. Across all cases, digitally planned resections and reconstructions were performed without intraoperative modification. The implants demonstrated accurate anatomical fit, secure fixation, and satisfactory restoration of chest wall anatomy. During the available follow-up period ranging from 5 months to 5 years, no implant failures or implant-related complications were observed. Two patients who underwent reconstruction for palliation subsequently died from progression of their underlying disease. This proof-of-concept single-centre study demonstrates the clinical feasibility of a digital-to-surgical workflow and provides a foundation for future multicentre evaluation.

RESEARCH EXPLAINED