Predicting fracture outcomes from clinical registry data using artificial intelligence supplemented models for evidence-informed treatment (PRAISE) study protocol

BackgroundDistal radius (wrist) fractures are the second most common fracture admitted to hospital. The anatomical pattern of these types of injuries is diverse, with variation in clinical management, guidelines for management remain inconclusive, and the uptake of findings from clinical trials into routine practice limited. Robust predictive modelling, which considers both the characteristics of the fracture and patient, provides the best opportunity to reduce variation in care and improve patient outcomes. This type of data is housed in unstructured data sources with no particular format or schema. The “Predicting fracture outcomes from clinical Registry data using Artificial Intelligence (AI) Supplemented models for Evidence-informed treatment (PRAISE)” study aims to use AI methods on unstructured data to describe the fracture characteristics and test if using this information improves identification of key fracture characteristics and prediction of patient-reported outcome measures and clinical outcomes following wrist fractures compared to prediction models based on standard registry data.Methods and designAdult (16+ years) patients presenting to the emergency department, treated in a short stay unit, or admitted to hospital for >24h for management of a wrist fracture in four Victorian hospitals will be included in this study. The study will use routine registry data from the Victorian Orthopaedic Trauma Outcomes Registry (VOTOR), and electronic medical record (EMR) information (e.g. X-rays, surgical reports, radiology reports, images). A multimodal deep learning fracture reasoning system (DLFRS) will be developed that reasons on EMR information. Machine learning prediction models will test the performance with/without output from the DLFRS.DiscussionThe PRAISE study will establish the use of AI techniques to provide enhanced information about fracture characteristics in people with wrist fractures. Prediction models using AI derived characteristics are expected to provide better prediction of clinical and patient-reported outcomes following distal radius fracture

Understanding suicidality in Pacific adolescents in New Zealand using network analysis

Introduction: Pacific adolescents in New Zealand (NZ) are three to four times more likely than NZ European adolescents to report suicide attempts and have higher rates of suicidal plans. Suicidal thoughts, plans, and attempts, termed suicidality in this study, result from a complex dynamic interplay of factors, which emerging methodologies like network analysis aim to capture. Methods: This study used cross-sectional network analysis to model the relationships between suicidality, self-harm, and individual depression symptoms, whilst conditioning on a multi-dimensional set of variables relevant to suicidality. A series of network models were fitted to data from a community sample of New Zealand-born Pacific adolescents (n = 550; 51% male; Mean age (SD) = 17 (0.35)). Results: Self-harm and the depression symptom measuring pessimism had the strongest associations with suicidality, followed by symptoms related to having a negative self-image about looks and sadness. Nonsymptom risk factors for self-harm and suicidality differed markedly. Conclusions: Depression symptoms varied widely in terms of their contribution to suicidality, highlighting the valuable information gained from analysing depression at the symptom-item level. Reducing the sources of pessimism and building self-esteem presented as potential targets for alleviating suicidality amongst Pacific adolescents in New Zealand. Suicide prevention strategies need to include risk factors for self-harm

A novel way to quantify schizophrenia symptoms in clinical trials

Background: A major problem in quantifying symptoms of schizophrenia is establishing a reliable distinction between enduring and dynamic aspects of psychopathology. This is critical for accurate diagnosis, monitoring and evaluating treatment effects in both clinical practice and trials. Materials and methods: We applied Generalizability Theory, a robust novel method to distinguish between dynamic and stable aspects of schizophrenia symptoms in the widely used Positive and Negative Symptom Scale (PANSS) using a longitudinal measurement design. The sample included 107 patients with chronic schizophrenia assessed using the PANSS at five time points over a 24‐week period during a multi‐site clinical trial of N‐Acetylcysteine as an add‐on to maintenance medication for the treatment of chronic schizophrenia. Results: The original PANSS and its three subscales demonstrated good reliability and generalizability of scores (G = 0.77‐0.93) across sample population and occasions making them suitable for assessment of psychosis risks and long‐lasting change following a treatment, while subscales of the five‐factor models appeared less reliable. The most enduring symptoms represented by the PANSS were poor attention, delusions, blunted affect and poor rapport. More dynamic symptoms with 40%‐50% of variance explained by patient transient state including grandiosity, preoccupation, somatic concerns, guilt feeling and hallucinatory behaviour. Conclusions: Identified dynamic symptoms are more amendable to change and should be the primary target of interventions aiming at effectively treating schizophrenia. Separating out the dynamic symptoms would increase assay sensitivity in trials, reduce the signal to noise ratio and increase the potential to detect the effects of novel therapies in clinical trials

Risk factors for depression in Pacific adolescents in New Zealand: a network analysis

Background: Network analysis provides opportunities to gain a greater understanding of the complex interplay of risk factors for depression and heterogeneous symptom presentations. This study used network analysis to discover risk factors associated with both depression severity and depression symptoms amongst Pacific adolescents in New Zealand. Methods: Mixed graphical models with regularization were fitted to data from a community sample of New Zealand born, Pacific adolescents, (n=561; 51% male; Mean age (SD) = 17 (0.35)) and associations between a wide range of potentially explanatory variables and depression severity and depression symptoms investigated. The associations identified were then tested for reliability, using resampling techniques and sensitivity analysis. Results: In the networks, the explanatory variables associated with both depression severity and depression symptoms were those related to quality of the relationships with mother or friends, school connectedness, and self-assessed weight, but the symptoms they were associated with varied substantially. In the depression severity networks, impulsivity appeared to be a bridging node connecting depression severity with delinquency and negative peer influence. Limitations: The data were analysed cross-sectionally, so causal inferences about the directions of relationships could not be inferred and most of the data were self-reported. Conclusions: The results illustrate the varied way that adolescent depression can manifest itself in terms of symptoms and suggest specific items on the depression inventory that might be suitable targets for prevention strategies and interventions, based on the risk factor - depression symptom profiles of individuals or groups

Coal-mine fire-related fine particulate matter and medical-service utilization in Australia: a time-series analysis from the Hazelwood Health Study

BackgroundThis study assessed the association between coal-mine-fire-related fine particulate matter (PM2.5) and medical-service utilization, following a 6-week coal-mine fire in Australia, in 2014. Areas in the immediate vicinity of the mine experienced hourly mine-fire-related PM2.5 concentrations of up to 3700 μg/m3.MethodsData on medical-service utilization were collected from the Medicare Benefits Schedule—a national database of payment for medical services. PM2.5 concentrations were modelled using atmospheric chemical transport modelling. Quasi-Poisson interrupted distributed lag time-series analysis examined the association between daily mine-fire-related PM2.5 concentrations and medical-service utilization, including General Practitioner (GP) consultations and respiratory, cardiovascular and mental health services. Confounders included seasonality, long-term trend, day of the week, maximum daily temperature and public holidays. Gender and age stratification were conducted.ResultsA 10-μg/m3 increase in PM2.5 was associated with an increased relative risk of service usage for all long and short GP consultations [11% (95% confidence interval: 7 to 15%)] and respiratory services [22% (4 to 43%)] in both men and women. Sex stratification found an increased relative risk in mental health consultations in men [32% (2 to 72%)] but not women. No associations were found for cardiovascular services in men or women.ConclusionsCoal-mine-fire-related PM2.5 exposure was associated with increased use of medical services for GP consultations and respiratory services in men and women and mental health consultations in men. These findings can inform the development of future public-health-policy responses in the event of major air-pollution episodes

Fine particulate matter exposure and medication dispensing during and after a coal mine fire: A time series analysis from the Hazelwood Health Study

Limited research has examined the impacts of coal mine fire smoke on human health. The aim of this study was to assess the association between prolonged smoke PM2.5 exposure from a brown coal mine fire that burned over a seven week period in 2014 and medications dispensed across five localities in South-eastern Victoria, Australia. Spatially resolved PM2.5 concentrations were retrospectively estimated using a dispersion model coupled with a chemical transport model. Data on medications dispensed were collected from the national Pharmaceutical Benefits Schedule database for 2013-2016. Poisson distributed lag time series analysis was used to examine associations between daily mine fire-related PM2.5 concentrations and daily counts of medications dispensed for respiratory, cardiovascular or psychiatric conditions. Factors controlled for included: seasonality, long-term trend, day of the week, maximum 2.5 and increased risks of medications dispensed for respiratory, cardiovascular and psychiatric conditions, over a lag range of 3-7 days. A 10 μg/m3 increase in coal mine fire-related PM2.5 was associated with a 25% (95%CI 19-32%) increase in respiratory medications, a 10% (95%CI 7-13%) increase in cardiovascular medications and a 12% (95%CI 8-16%) increase in psychiatric medications dispensed. These findings have the potential to better prepare for and develop more appropriate public health responses in the event of future coal mine fires