A longitudinal cohort study evaluating the impact of a geriatrician-led residential care outreach service on acute healthcare utilisation.

BACKGROUND: over the last decade, high demand for acute healthcare services by long-term residents of residential care facilities (RCFs) has stimulated interest in exploring alternative models of care. The Residential Care Intervention Program in the Elderly (RECIPE) service provides expert outreach services to RCFs residents, interventions include comprehensive care planning, management of inter-current illness and rapid access to acute care substitution services. OBJECTIVE: to evaluate whether the RECIPE service decreased acute healthcare utilisation. DESIGN: a retrospective cohort study using interrupted time series analysis to analyse change in acute healthcare utilisation before and after enrolment. SETTING: a 300-bed metropolitan teaching hospital in Australia and 73 RCFs within its catchment. SUBJECTS: there were 1,327 patients enrolled in the service with a median age of 84 years; 61% were female. METHODS: data were collected prospectively on all enrolled patients from 2004 to 2011 and linked to the acute health service administrative data set. Primary outcomes change in admission rates, length of stay and bed days per quarter. RESULTS: in the 2 years prior to enrolment, the mean number of acute care admissions per patient per year was 3.03 (SD 2.9) versus post 2.4 (SD 3.3), the service reducing admissions by 0.13 admissions per patient per quarter (P = 0.046). Prior to enrolment, the mean length of stay was 8.6 (SD 11.0) versus post 3.5 (SD 5.0), a reduction of 1.5 days per patient per quarter (P = 0.003). CONCLUSIONS: this study suggests that an outreach service comprising a geriatrician-led multidisciplinary team can reduce acute hospital utilisation rates.

Using machine learning models to predict falls in hospitalised adults.

BACKGROUND: Identifying patients at high risk of falling is crucial in implementing effective fall prevention programs. While the integration of information systems is becoming more widespread in the healthcare industry, it poses a significant challenge in analysing vast amounts of data to identify factors that could enhance patient safety. OBJECTIVE: To determine fall-associated factors and develop high-performance prediction tools for at-risk patients in acute and sub-acute care services in Australia. METHODS: A retrospective study of 672,400 patients admitted to acute and sub-acute care services within a large metropolitan tertiary health service in Victoria, Australia, between January 1, 2019, and December 31, 2021. Data were obtained from four sources: the Department of Health Victorian Admitted Episodes Dataset, RiskManTM, electronic health records, and the health workforce dataset. Machine learning techniques, including Random Forest and Deep Neural Network models, were used to analyse the data, predict patient falls, and identify the most important risk factors for falls in this population. Model performance was evaluated using accuracy, F1-score, precision, recall, specificity, Matthew's correlation coefficient, and the area under the receiver operating characteristic curve (AUC). RESULTS: The deep neural network and random forest models were highly accurate in predicting hospital patient falls. The deep neural network model achieved an accuracy of 0.988 and a specificity of 0.999, while the RF achieved an accuracy of 0.989 and a specificity of 1.000. The top 20 variables impacting falls were compared across both models, and 12 common factors were identified. These factors can be broadly classified into three categories: patient-related factors, staffing-related factors, and admission-related factors. Although not all factors are modifiable, they must be considered when planning fall prevention interventions. CONCLUSION: The study demonstrated machine learning's potential to predict falls and identify key risk factors. Further validation across diverse populations and settings is essential for broader applicability.

Changes in the BODE index, exacerbation duration and hospitalisation in a cohort of COPD patients.

INTRODUCTION: We investigated the relationship between frequency of exacerbation and duration and change in functional status, as measured by the BODE index in chronic obstructive pulmonary disease (COPD) patients. METHODS: This was a longitudinal cohort study of 56 patients with moderate to severe COPD. Body mass index, spirometry, Modified Medical Research Council (MMRC) dyspnoea score and six-minute walk distance (6MWD) were measured annually when the patients were clinically stable. Data on frequency and duration of COPD exacerbations occurring in the community and requiring hospitalisation were collected prospectively. Early stage exacerbations were identified through the use of individualised patient action plans and further reinforced by fortnightly phone contact. RESULTS: At the two-year follow-up, the BODE index increased in 33 patients, remained stable in 18 and decreased in five patients. Patients with increased BODE index had significantly higher hospital presentation rates and longer total bed-days compared to those with stable BODE index. Among the 33 patients with increased BODE index, 20 had lower 6MWD and higher MMRC scores, indicating deteriorating functional status, and 13 had higher levels of airway obstruction. Between these two subgroups, patients with deteriorating functional status had higher exacerbation frequency, longer exacerbation duration and higher inpatient bed-days. Linear regression showed that total annual duration of exacerbation was predictive of change in 6MWD. CONCLUSION: Change in the BODE index is a sensitive measure of deteriorating functional status in COPD patients. Duration of exacerbation has greater impact on functional status than frequency of exacerbation episodes.

Epigenetic interactions and the structure of phenotypic variation in the cranium.

Understanding the developmental and genetic basis for evolutionarily significant morphological variation in complex phenotypes such as the mammalian skull is a challenge because of the sheer complexity of the factors involved. We hypothesize that even in this complex system, the expression of phenotypic variation is structured by the interaction of a few key developmental processes. To test this hypothesis, we created a highly variable sample of crania using four mouse mutants and their wild-type controls from similar genetic backgrounds with developmental perturbations to particular cranial regions. Using geometric morphometric methods we compared patterns of size, shape, and integration in the sample within and between the basicranium, neurocranium, and face. The results highlight regular and predictable patterns of covariation among regions of the skull that presumably reflect the epigenetic influences of the genetic perturbations in the sample. Covariation between relative widths of adjoining regions is the most dominant factor, but there are other significant axes of covariation such as the relationship between neurocranial size and basicranial flexion. Although there are other sources of variation related to developmental perturbations not analyzed in this study, the patterns of covariation created by the epigenetic interactions evident in this sample may underlie larger scale evolutionary patterns in mammalian craniofacial form.