Introduction Increased mortality has been demonstrated in older adults with COVID-19, but the effect of frailty has been unclear. Methods This multi-centre cohort study involved patients aged 18 years and older hospitalised with COVID-19, using routinely collected data. We used Cox regression analysis to assess the impact of age, frailty, and delirium on the risk of inpatient mortality, adjusting for sex, illness severity, inflammation, and co-morbidities. We used ordinal logistic regression analysis to assess the impact of age, Clinical Frailty Scale (CFS), and delirium on risk of increased care requirements on discharge, adjusting for the same variables. Results Data from 5,711 patients from 55 hospitals in 12 countries were included (median age 74, IQR 54–83; 55.2% male). The risk of death increased independently with increasing age (>80 vs 18–49: HR 3.57, CI 2.54–5.02), frailty (CFS 8 vs 1–3: HR 3.03, CI 2.29–4.00) inflammation, renal disease, cardiovascular disease, and cancer, but not delirium. Age, frailty (CFS 7 vs 1–3: OR 7.00, CI 5.27–9.32), delirium, dementia, and mental health diagnoses were all associated with increased risk of higher care needs on discharge. The likelihood of adverse outcomes increased across all grades of CFS from 4 to 9. Conclusions Age and frailty are independently associated with adverse outcomes in COVID-19. Risk of increased care needs was also increased in survivors of COVID-19 with frailty or older age.
BackgroundDelirium is a common severe neuropsychiatric condition secondary to physical illness, which predominantly affects older adults in hospital. Prior to this study, the UK point prevalence of delirium was unknown. We set out to ascertain the point prevalence of delirium across UK hospitals and how this relates to adverse outcomes.MethodsWe conducted a prospective observational study across 45 UK acute care hospitals. Older adults aged 65 years and older were screened and assessed for evidence of delirium on World Delirium Awareness Day (14th March 2018). We included patients admitted within the previous 48 h, excluding critical care admissions.ResultsThe point prevalence of Diagnostic and Statistical Manual on Mental Disorders, Fifth Edition (DSM-5) delirium diagnosis was 14.7% (222/1507). Delirium presence was associated with higher Clinical Frailty Scale (CFS): CFS 4–6 (frail) (OR 4.80, CI 2.63–8.74), 7–9 (very frail) (OR 9.33, CI 4.79–18.17), compared to 1–3 (fit). However, higher CFS was associated with reduced delirium recognition (7–9 compared to 1–3; OR 0.16, CI 0.04–0.77). In multivariable analyses, delirium was associated with increased length of stay (+ 3.45 days, CI 1.75–5.07) and increased mortality (OR 2.43, CI 1.44–4.09) at 1 month. Screening for delirium was associated with an increased chance of recognition (OR 5.47, CI 2.67–11.21).ConclusionsDelirium is prevalent in older adults in UK hospitals but remains under-recognised. Frailty is strongly associated with the development of delirium, but delirium is less likely to be recognised in frail patients. The presence of delirium is associated with increased mortality and length of stay at one month. A national programme to increase screening has the potential to improve recognition.
Background The first case of infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was diagnosed in Wuhan, China, in 2019. By the first half of 2020, coronavirus disease 2019 (COVID-19) turned into a global pandemic. Objectives The aim of this study is to describe the clinical and demographic characteristics including comorbidities and their outcomes among patients hospitalized with COVID-19 in four tertiary care hospitals across Lahore . This retrospective study was conducted at Fatima Memorial Hospital, Sir Ganga Ram Hospital, Lahore General Hospital, and Jinnah Hospital, all in Lahore, Pakistan, from May 1, 2020, to June 30, 2020. The sample size was 445, which was derived using the convenient sampling method. Clinical outcomes during hospitalization included the requirement of invasive positive pressure ventilation, need for renal replacement therapy (RRT), and death. Data regarding demographics, baseline comorbidities, important vital signs on reporting, and initial workup with results were also collected. Results A total of 445 patients’ data were studied, of whom 291 (65.4%) were male patients and 154 (34.6%) female patients. The median age was 54 years (interquartile range [IQR]: 24). The most common comorbidities were hypertension (HTN) (195; 43.8%) followed by diabetes mellitus (DM) (168; 37.8%) and cardiovascular disease (CVD) (61; 13.7%). The median length of hospital stay was eight days (IQR: 3). Of the total patients, 137 (30.7%) were treated in intensive care unit settings, 40 (9%) received invasive mechanical ventilation, 40 (9%) patients had acute kidney injury, 38 (8.5%) received RRT, and 37 (8.3%) died. It was seen that more patients who were either diabetic or hypertensive received invasive mechanical ventilation as compared to those who did not have these comorbidities. The most common radiological finding on chest X-ray was the classical ground-glass appearance of COVID-19, which was found in 318 (71.4%) patients. Conclusions Patients with one or more underlying comorbidities had poor clinical outcomes compared to those with no comorbidities, with the most vulnerable group being patients with chronic kidney disease, DM, HTN, and CVD in descending order.
In the world, brain tumor (BT) is considered the major cause of death related to cancer, which requires early and accurate detection for patient survival. In the early detection of BT, computer-aided diagnosis (CAD) plays a significant role, the medical experts receive a second opinion through CAD during image examination. Several researchers proposed different methods based on traditional machine learning (TML) and deep learning (DL). The TML requires hand-crafted features engineering, which is a time-consuming process to select an optimal features extractor and requires domain experts to have enough knowledge of optimal features selection. The DL methods outperform the TML due to the end-to-end automatic, high-level, and robust feature extraction mechanism. In BT classification, the deep learning methods have a great potential to capture local features by convolution operation, but the ability of global features extraction to keep Long-range dependencies is relatively weak. A self-attention mechanism in Vision Transformer (ViT) has the ability to model long-range dependencies which is very important for precise BT classification. Therefore, we employ a hybrid transformer-enhanced convolutional neural network (TECNN)-based model for BT classification, where the CNN is used for local feature extraction and the transformer employs an attention mechanism to extract global features. Experiments are performed on two public datasets that are BraTS 2018 and Figshare. The experimental results of our model using BraTS 2018 and Figshare datasets achieves an average accuracy of 96.75% and 99.10%, respectively. In the experiments, the proposed model outperforms several state-of-the-art methods using BraTS 2018 and Figshare datasets by achieving 3.06% and 1.06% accuracy, respectively.
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