Frailty and mortality among older patients in a tertiary hospital in Nigeria

Background: This study determined the frailty status and its association with mortality among older patients.Design: A prospective cohort design.Setting: Study was conducted at the medical wards of University College Hospital, Ibadan, Nigeria. Participants and study tools: Four hundred and fifty older patients (>60 years) were followed up from the day of admission to death or discharge. Information obtained includes socio-demographic characteristics and clinical frailty was assessed using the Canadian Study of Health and Aging (CSHA) scale. Bivariate and multivariate analyses were carried out using SPSS version 21 at a p <0.05.Results: Overall, frailty was identified in 285 (63.3%) respondents. Mortality was significantly higher among frail respondents (25.3%) than non-frail respondents (15.4%) p=0.028. Logistic regression analysis showed factors associated with frailty were: male sex (OR=1.946 [1.005–3.774], p=0.048), non-engagement in occupational activities(OR=2.642 [1.394–5.008], p=0.003), multiple morbidities (OR=4.411 [1.944–10.006], p<0.0001), functional disability (OR=2.114 [1.029–4.343), p=0.042], malnutrition (OR=9.258 [1.029–83.301], p=0.047) and being underweight (OR=7.462 [1.499–37.037], p=0.014).Conclusion: The prevalence of frailty among medical in-hospital older patients is very high and calls for its prompt identification and management to improve their survival.Keywords: Frailty, Mortality, Older patients, in-hospital, NigeriaFunding: The study was self-funded by the author

Physicians’ Knowledge of the Glasgow Coma Scale in a Nigerian University Hospital: Is the Simple GCS Still Too Complex?

Objective: The Glasgow Coma Scale, GCS, is a universal clinical means of quantifying the level of impaired consciousness. Although physicians usually receive undergraduate and postgraduate training in the use of this scale in our university hospital we are aware of studies suggesting that the working knowledge of the GCS among practising physicians might not be adequate. Methods: We carried out a questionnaire-based survey across all specialties and levels of training of physicians in active patient care in a Nigerian university hospital. Results: Of the 100 physicians sampled, 98 correctly spelled out what the three-letter abbreviation, GCS, stands for. Ninety-three percent also conceded it to be an important clinical rating scale. However, only 55–89% of the participants correctly identified the three respective clinical variables, (eye opening, verbal response, and motor response), of the GCS. More particularly, the participants’ ability to itemize and correctly score all the respective components of each of the three clinical variables ranged from 0 to 35% across specialties and levels of training. Performance was best for the four-item eye opening variable and, worst for the six-item motor response variable. Conclusion: In our university hospital, practising physicians’ working knowledge of the GCS is inadequate and is dependent on the degree of the complexity of each of the three clinical variables of the scale

Atrial Fibrillation in Africa-An Underreported and Unrecognized Risk Factor for Stroke:A Systematic Review

Over three-quarters of deaths from cardiovascular disease and diabetes occur in low- and middle-income countries, which include many African countries. Global studies showed that the prevalence of the cardiac arrhythmia atrial fibrillation (AF) appeared to be lower in Africa. A systematic search of PubMed and African Journals Online was conducted to determine the prevalence of AF and associated stroke risk factors in Africa and to quantify the need for screening. The publications search yielded a total of 840 articles of which 41 were included. AF was often not identified as the disease of primary interest with its own risks. Data on prevalence in the general population was scarce. The prevalence of stroke risk factors showed a large variation between studies, as well as within clustered subpopulations. AF in Africa is under-reported in published reports. The study types and populations are highly heterogeneous, making it difficult to draw a definitive conclusion on AF prevalence

Pragmatic solutions to reduce the global burden of stroke : a World Stroke Organization-Lancet Neurology Commission

Peer reviewe

Optimizing Stroke Prediction using Gated Recurrent Unit and Feature Selection in Sub-Saharan Africa

Background Stroke remains a leading cause of death and disability worldwide, with African populations bearing a disproportionately high burden due to limited healthcare infrastructure. Early prediction and intervention are critical to reducing stroke outcomes. This study developed and evaluated a stroke prediction system using Gated Recurrent Units (GRU), a variant of Recurrent Neural Networks (RNN), leveraging the Afrocentric Stroke Investigative Research and Education Network (SIREN) dataset. Method The study utilized secondary data from the SIREN dataset, comprising 4,236 records with 29 phenotypes. Feature selection reduced these to 15 optimal phenotypes based on their significance to stroke occurrence. The GRU model, designed with 128 input neurons and four hidden layers (64, 32, 16, and 8 neurons), was trained and evaluated using 150 epochs, a batch size of 8, and metrics such as accuracy, AUC, and prediction time. Comparisons were made with traditional machine learning algorithms (Logistic Regression, SVM, KNN) and Long Short-Term Memory (LSTM) networks. Results The GRU-based system achieved a performance accuracy of 77.48%, an AUC of 0.84, and a prediction time of 0.43seconds, outperforming all other models. Logistic Regression achieved 73.58%, while LSTM reached 74.88% but with a longer prediction time of 2.23seconds. Feature selection significantly improved the model's performance compared to using all 29 phenotypes. Conclusion The GRU-based system demonstrated superior performance in stroke prediction, offering an efficient and scalable tool for healthcare. Future research should focus on integrating unstructured data, validating the model on diverse populations, and exploring hybrid architectures to enhance predictive accuracy