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'Ramping' is a commonly used term in contemporary Australian healthcare. It is also a part of the public and political zeitgeist. However, its precise definition varies among sources. In the published literature, there are distinctions between related terms, such as 'entry overload' and 'Patient Off Stretcher Time Delay'. How ramping is defined and how it came to be defined have significance for policies and procedures relating to the described phenomenon. Through examination of the history of the term, insights are obtained into the underlying issues contributing to ramping and, accordingly, associated possible solutions. [ABSTRACT FROM AUTHOR]

Objective Moving into a long-term care facility (LTCF) requires substantial personal, societal and financial investment. Identifying those at high risk of short-term mortality after LTCF entry can help with care planning and risk factor management. This study aimed to: (i) examine individual-, facility-, medication-, system- and healthcare-related predictors for 90-day mortality at entry into an LTCF and (ii) create risk profiles for this outcome. Design Retrospective cohort study using data from the Registry of Senior Australians. Subjects Individuals aged ≥ 65 years old with first-time permanent entry into an LTCF in three Australian states between 01 January 2013 and 31 December 2016. Methods A prediction model for 90-day mortality was developed using Cox regression with the purposeful variable selection approach. Individual-, medication-, system- and healthcare-related factors known at entry into an LTCF were examined as predictors. Harrell's C-index assessed the predictive ability of our risk models. Results 116,192 individuals who entered 1,967 facilities, of which 9.4% (N  = 10,910) died within 90 days, were studied. We identified 51 predictors of mortality, five of which were effect modifiers. The strongest predictors included activities of daily living category (hazard ratio [HR] = 5.41, 95% confidence interval [CI] = 4.99–5.88 for high vs low), high level of complex health conditions (HR = 1.67, 95% CI = 1.58–1.77 for high vs low), several medication classes and male sex (HR = 1.59, 95% CI = 1.53–1.65). The model out-of-sample Harrell's C-index was 0.773. Conclusions Our mortality prediction model, which includes several strongly associated factors, can moderately well identify individuals at high risk of mortality upon LTCF entry. [ABSTRACT FROM AUTHOR]

Introduction: Agitation is a common manifestation of the behavioural and psychological symptoms of dementia (BPSD). Pharmacotherapy is not the firstline management because of its potential harms, particularly in the elderly. Music as a non-pharmacological intervention for agitation has been explored in residential aged-care facilities, but few studies have been situated in hospitals. This pilot aims to evaluate the feasibility of a personalised music listening intervention for reducing agitation in hospitalised patients with dementia in a metropolitan Geriatric Evaluation and Management (GEM) unit. Methods: Two-arm randomised control feasibility trial. Eligible patients were assigned to the music intervention or control group, with the intervention group receiving music daily between 15:00-16:00, and agitation levels measured in both groups hourly based on the Pittsburgh Agitation Score (PAS) over 5 days of hospitalisation. Post-trial semi-structured interviews assessed feasibility of the intervention. Results: Twenty-one patients were recruited over 8 months. Interviews with staff involved indicated that the music intervention was manageable to deliver, assisted engagement with patients which increased efficiency of some clinical tasks, and challenged staff mindset around using psychotropic medication to address agitation. PAS results were inconclusive, because of underpowered numbers in this pilot study. Conclusion: It is feasible for nursing staff to deliver a personalised music listening intervention to patients with dementia in a geriatric unit of a tertiary hospital, without compromising on usual clinical care. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is an insidious disease. Its distinctive pathology forms over a considerable length of time without symptoms. There is a need to detect this disease, before even subtle changes occur in cognition. Hallmark AD biomarkers, tau and amyloid-b, have shown promising results in CSF and blood. However, detecting early changes in these biomarkers and others will involve screening a wide group of healthy, asymptomatic individuals. Saliva is a feasible alternative. Sample collection is economical, non-invasive and saliva is an abundant source of proteins including tau and amyloid-β. This work sought to extend an earlier promising untargeted mass spectrometry study in saliva from individuals with mild cognitive impairment (MCI) or AD with age- and gender-matched cognitively normal from the South Australian Neurodegenerative Disease cohort. Five proteins, with key roles in inflammation, were chosen from this study and measured by ELISA from individuals with AD (n = 16), MCI (n = 15) and cognitively normal (n = 29). The concentrations of Cystatin-C, Interleukin-1 receptor antagonist, Stratifin, Matrix metalloproteinase 9 and Haptoglobin proteins had altered abundance in saliva from AD and MCI, consistent with the earlier study. Receiver operating characteristic analysis showed that combinations of these proteins demonstrated excellent diagnostic accuracy for distinguishing both MCI (area under curve = 0.97) and AD (area under curve = 0.97) from cognitively normal. These results provide evidence for saliva being a valuable source of biomarkers for early detection of cognitive impairment in individuals on the AD continuum and potentially other neurodegenerative diseases. [ABSTRACT FROM AUTHOR]

The metabolomic and proteomic basis of mild cognitive impairment (MCI) and Alzheimer's disease (AD) is poorly understood, and the relationships between systemic abnormalities in metabolism and AD/MCI pathogenesis is unclear. This study compared the metabolomic and proteomic signature of plasma from cognitively normal (CN) and dementia patients diagnosed with MCI or AD, to identify specific cellular pathways and new biomarkers altered with the progression of the disease. We analysed 80 plasma samples from individuals with MCI or AD, as well as age- and gender-matched CN individuals, by utilising mass spectrometry methods and data analyses that included combined pathway analysis and model predictions. Several proteins clearly identified AD from the MCI and CN groups and included plasma actins, mannan-binding lectin serine protease 1, serum amyloid A2, fibronectin and extracellular matrix protein 1 and Keratin 9. The integrated pathway analysis showed various metabolic pathways were affected in AD, such as the arginine, alanine, aspartate, glutamate and pyruvate metabolism pathways. Therefore, our multi-omics approach identified novel plasma biomarkers for the MCI and AD groups, identified changes in metabolic processes, and may form the basis of a biomarker panel for stratifying dementia participants in future clinical trials. [ABSTRACT FROM AUTHOR]

The article focuses on the impact of real-world healthcare system constraints on the effectiveness of artificial intelligence (AI) interventions, considering factors such as space, time, and resources. Topics include the correlation between AI applications and emergency department access block, staffing shortages, and resource limitations, emphasizing the need for strategic deployment and optimization of AI within the context of healthcare system constraints for maximum utility.

Objectives: To examine individual, medication, system, and healthcare related predictors of hospitalization and emergency department (ED) presentation within 90 days of entering the aged care sector, and to create risk‐profiles associated with these outcomes. Design and setting: Retrospective population‐based cohort study using data from the Registry of Senior Australians. Participants: Older people (aged 65 and older) with an aged care eligibility assessment in South Australia between January 1, 2013 and May 31, 2016 (N = 22,130). Measurements Primary outcomes were unplanned hospitalization and ED presentation within 90 days of assessment. Individual, medication, system, and healthcare related predictors of the outcomes at the time of assessment, within 90 days or 1‐year prior. Fine–Gray models were used to calculate subdistribution hazard ratios (sHR) and 95% confidence intervals (CI). Harrell's C‐index assessed predictive ability. Results: Four thousand nine‐hundred and six (22.2%) individuals were hospitalized and 5028 (22.7%) had an ED presentation within 90 days. Predictors of hospitalization included: being a man (hospitalization sHR = 1.33, 95% CI 1.26–1.42), ≥3 urgent after‐hours attendances (hospitalization sHR = 1.21, 95% CI 1.06–1.39), increasing frailty index score (hospitalization sHR = 1.19, 95% CI 1.11–1.28), individuals using glucocorticoids (hospitalization sHR = 1.11, 95% CI 1.02–1.20), sulfonamides (hospitalization sHR = 1.18, 95% CI 1.10–1.27), trimethoprim antibiotics (hospitalization sHR = 1.15, 95% CI 1.03–1.29), unplanned hospitalizations 30 days prior (hospitalization sHR = 1.13, 95% CI 1.04–1.23), and ED presentations 1 year prior (hospitalization sHR = 1.07, 95% CI 1.04–1.10). Similar predictors and hazard estimates were also observed for ED presentations. The hospitalization models out‐of‐sample predictive ability (C‐index = 0.653, 95% CI 0.635–0.670) and ED presentations (C‐index = 0.647, 95% CI 0.630–0.663) were moderate. Conclusions: One in five individuals with aged care eligibility assessments had unplanned hospitalizations and/or ED presentation within 90 days with several predictors identified at the time of aged care eligibility assessment. This is an actionable period for targeting at‐risk individuals to reduce hospitalizations. [ABSTRACT FROM AUTHOR]

Background: The metabolomic and proteomic basis of mild cognitive impairment (MCI) and Alzheimer's disease (AD) is poorly understood and the relationships between systemic abnormalities in metabolism and AD/AMCI pathogenesis are unclear.Objective: The aim of the study was to compare the metabolomic and proteomic signature of saliva from cognitively normal and patients diagnosed with MCI or AD, to identify specific cellular pathways altered with the progression of the disease.Methods: We analyzed 80 saliva samples from individuals with MCI or AD as well as age- and gender-matched healthy controls. Saliva proteomic and metabolomic analyses were conducted utilizing mass spectrometry methods and data combined using pathway analysis.Results: We found significant alterations in multiple cellular pathways, demonstrating that at the omics level, disease progression impacts numerous cellular processes. Multivariate statistics using SIMCA showed that partial least squares-data analysis could be used to provide separation of the three groups.Conclusion: This study found significant changes in metabolites and proteins from multiple cellular pathways in saliva. These changes were associated with AD, demonstrating that this approach might prove useful to identify new biomarkers based upon integration of multi-omics parameters. [ABSTRACT FROM AUTHOR]

To assess a recently described CNN (convolutional neural network) DARC (Detection of Apoptosing Retinal Cells) algorithm in predicting new Subretinal Fluid (SRF) formation in Age-related-Macular-Degeneration (AMD). Anonymized DARC, baseline and serial OCT images (n = 427) from 29 AMD eyes of Phase 2 clinical trial (ISRCTN10751859) were assessed with CNN algorithms, enabling the location of each DARC spot on corresponding OCT slices (n = 20,629). Assessment of DARC in a rabbit model of angiogenesis was performed in parallel. A CNN DARC count >5 at baseline was significantly (p = 0.0156) related to development of new SRF throughout 36 months. Prediction rate of eyes using unique DARC spots overlying new SRF had positive predictive values, sensitivities and specificities >70%, with DARC count significantly (p < 0.005) related to the magnitude of SRF accumulation at all time points. DARC identified earliest stages of angiogenesis in-vivo. DARC was able to predict new wet-AMD activity. Using only an OCT-CNN definition of new SRF, we demonstrate that DARC can identify early endothelial neovascular activity, as confirmed by rabbit studies. Although larger validation studies are required, this shows the potential of DARC as a biomarker of wet AMD, and potentially saving vision-loss. [ABSTRACT FROM AUTHOR]

A key objective in glaucoma is to identify those at risk of rapid progression and blindness. Recently, a novel first-in-man method for visualising apoptotic retinal cells called DARC (Detection-of-Apoptosing-Retinal-Cells) was reported. The aim was to develop an automatic CNN-aided method of DARC spot detection to enable prediction of glaucoma progression. Anonymised DARC images were acquired from healthy control (n=40) and glaucoma (n=20) Phase 2 clinical trial subjects (ISRCTN10751859) from which 5 observers manually counted spots. The CNN-aided algorithm was trained and validated using manual counts from control subjects, and then tested on glaucoma eyes. The algorithm had 97.0% accuracy, 91.1% sensitivity and 97.1% specificity to spot detection when compared to manual grading of 50% controls. It was next tested on glaucoma patient eyes defined as progressing or stable based on a significant (p<0.05) rate of progression using OCT-retinal nerve fibre layer measurements at 18 months. It demonstrated 85.7% sensitivity, 91.7% specificity with AUC of 0.89, and a significantly (p=0.0044) greater DARC count in those patients who later progressed. This CNN-enabled algorithm provides an automated and objective measure of DARC, promoting its use as an AI-aided biomarker for predicting glaucoma progression and testing new drugs. [ABSTRACT FROM AUTHOR]

Objective: To evaluate a holistic multidisciplinary outpatient model of care on hospital readmission, length of stay and mortality in older patients with multimorbidity following discharge from hospital. Design and Participants: A pilot case-control study between March 2006 and June 2009 of patients referred on discharge to a multidisciplinary, integrated outpatient model of care that includes outpatient follow-up, timely GP communication and dial-in service compared with usual care following discharge, within a metropolitan, tertiary referral, public teaching hospital. Controls were matched in a 4:1 ratio with cases for age, gender, index admission diagnosis and length of stay. Main outcome measures: Non-elective readmission rates, total readmission length of stay and overall survival. Results: A total of 252 cases and 1008 control patients were included in the study. Despite the patients referred to the multidisciplinary model of care had slightly more comorbid conditions, significantly higher total length of hospital stay in the previous 12 months and increased prevalence of diabetes and heart failure by comparison to those who received usual care, they had significantly improved survival (adjusted hazard ratio 0.70 95% CI 0.51–0.96, p = 0.029) and no excess in the number of hospitalisations observed. Conclusion: Following discharge from hospital, holistic multidisciplinary outpatient management is associated with improved survival in older patients with multimorbidity. The findings of this study warrant further examination in randomised and cost-effectiveness trials. [ABSTRACT FROM AUTHOR]

Pushing selected information to clinicians, as opposed to the traditional method of clinicians pulling information from an electronic medical record, has the potential to improve care. A digital notification platform was designed by clinicians and implemented in a tertiary hospital to flag dysglycaemia. There were 112 patients included in the study, and the post‐implementation group demonstrated lower rates of dysglycaemia (2.5% vs 1.1%, P = 0.038). These findings raise considerations for information delivery methods for multiple domains in contemporary healthcare. [ABSTRACT FROM AUTHOR]

The article discusses the evolution of network security for digital transformation. Topics mentioned include the first generation stage which used static firewalls to control network connection, the second generation stage where purpose-built security processors emerged to handle network traffic, and the Security Fabric architecture of cybersecurity software maker Fortinet which is considered an effective strategy in the third generation stage.