Background: Factors associated with stroke mortality are understudied in sub-Saharan Africa but have implications for designing interventions that improve stroke outcomes. We investigated predictors of in-hospital and 90-day post-discharge stroke mortality in Lusaka, Zambia.
Methods: Data from consecutive adults admitted with stroke at University Teaching Hospital in Lusaka, Zambia between October 2018 and March 2019 were retrospectively reviewed for clinical in-hospital outcomes. Vital status at 90-days post-discharge was determined through phone calls. Factors associated with stroke mortality were included in multivariable logistic regression models utilizing multiple imputation analysis to determine independent predictors of in-hospital and 90-days post-discharge mortality.
Results: In-hospital mortality was 24%, and 90-day post-discharge mortality was 22% among those who survived hospitalization. Hemorrhagic and unknown strokes, ICU care, seizures, and aspiration pneumonia were significantly associated with in-hospital mortality. Among these, hemorrhagic stroke (OR 2.88, 95% CI 1.27-6.53, p = 0.01) and seizures (OR 29.5, 95% CI 2.14-406, p = 0.01) remained independent predictors of in-hospital mortality in multivariable analyses. Ninety-day post-discharge mortality was significantly associated with older age, previous stroke, atrial fibrillation, and aspiration pneumonia, but only older age (OR 1.04, 95% CI 1.01-1.06, p = 0.007) and aspiration pneumonia (OR 3.93, 95% CI 1.30-11.88, p = 0.02) remained independently associated with 90-day mortality in multivariable analyses.
Conclusion: This Zambian stroke cohort had high in-hospital and 90-day post-discharge mortality that were associated with several in-hospital complications. Our data indicate the need for improvement in both acute stroke care and post-stroke systems of care to improve stroke outcomes in Zambia.
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http://dx.doi.org/10.1016/j.jns.2022.120249 | DOI Listing |
Sci Rep
December 2024
Postgraduate Program in Health Sciences, Health Sciences Center, Federal University of Rio Grande do Norte, Natal, RN, Brazil.
Body composition abnormalities are prognostic markers in several types of cancer, including colorectal cancer (CRC). Using our data distribution on body composition assessments and classifications could improve clinical evaluations and support population-specific opportune interventions. This study aimed to evaluate the distribution of body composition from computed tomography and assess the associations with overall survival among patients with CRC.
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December 2024
Department of Pediatrics and Child Health Nursing, College of Medicine and Health Sciences, Injibara University, Injibara, Ethiopia.
Excessive daytime sleepiness is a common finding among type 2 diabetes mellitus patients. However there is scarce data that shows the magnitude of excessive daytime sleepiness, & its association with type 2 diabetes mellitus. Hence, the study aimed to assess the prevalence of excessive daytime sleepiness and its associated factors among type 2 diabetes mellitus patients at Wolkite University Specialized Hospital.
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December 2024
Medical Image Analysis, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
Immune checkpoint inhibitor (ICI) treatment has proven successful for advanced melanoma, but is associated with potentially severe toxicity and high costs. Accurate biomarkers for response are lacking. The present work is the first to investigate the value of deep learning on CT imaging of metastatic lesions for predicting ICI treatment outcomes in advanced melanoma.
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December 2024
Department of Breast Surgery, Second Affiliated Hospital of Dalian Medical University, No. 467 Zhongshan Road, Shahekou District, Dalian, China.
Early prediction of patient responses to neoadjuvant chemotherapy (NACT) is essential for the precision treatment of early breast cancer (EBC). Therefore, this study aims to noninvasively and early predict pathological complete response (pCR). We used dynamic ultrasound (US) imaging changes acquired during NACT, along with clinicopathological features, to create a nomogram and construct a machine learning model.
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December 2024
Department of Medical and Surgical Sciences, Institute of Cardiology, University of Bologna, Policlinico S.Orsola-Malpighi, via Massarenti 9, Bologna, 40138, Italy.
Cardiac implantable electronic devices infections (CIEDI) are associated with poor survival despite the improvement in transvenous lead extraction (TLE). Aetiology and systemic involvement are driving factors of clinical outcomes. The aim of this study was to explore their contribute on overall mortality.
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