General Practice - Research Publications

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    Assessing the suitability of general practice electronic health records for clinical prediction model development: a data quality assessment
    Thuraisingam, S ; Chondros, P ; Dowsey, MM ; Spelman, T ; Garies, S ; Choong, PF ; Gunn, J ; Manski-Nankervis, J-A (BMC, 2021-10-30)
    BACKGROUND: The use of general practice electronic health records (EHRs) for research purposes is in its infancy in Australia. Given these data were collected for clinical purposes, questions remain around data quality and whether these data are suitable for use in prediction model development. In this study we assess the quality of data recorded in 201,462 patient EHRs from 483 Australian general practices to determine its usefulness in the development of a clinical prediction model for total knee replacement (TKR) surgery in patients with osteoarthritis (OA). METHODS: Variables to be used in model development were assessed for completeness and plausibility. Accuracy for the outcome and competing risk were assessed through record level linkage with two gold standard national registries, Australian Orthopaedic Association National Joint Replacement Registry (AOANJRR) and National Death Index (NDI). The validity of the EHR data was tested using participant characteristics from the 2014-15 Australian National Health Survey (NHS). RESULTS: There were substantial missing data for body mass index and weight gain between early adulthood and middle age. TKR and death were recorded with good accuracy, however, year of TKR, year of death and side of TKR were poorly recorded. Patient characteristics recorded in the EHR were comparable to participant characteristics from the NHS, except for OA medication and metastatic solid tumour. CONCLUSIONS: In this study, data relating to the outcome, competing risk and two predictors were unfit for prediction model development. This study highlights the need for more accurate and complete recording of patient data within EHRs if these data are to be used to develop clinical prediction models. Data linkage with other gold standard data sets/registries may in the meantime help overcome some of the current data quality challenges in general practice EHRs when developing prediction models.
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    The CORE study-An adapted mental health experience codesign intervention to improve psychosocial recovery for people with severe mental illness: A stepped wedge cluster randomized-controlled trial
    Palmer, VJ ; Chondros, P ; Furler, J ; Herrman, H ; Pierce, D ; Godbee, K ; Densley, K ; Gunn, JM (WILEY, 2021-08-04)
    BACKGROUND: Mental health policies outline the need for codesign of services and quality improvement in partnership with service users and staff (and sometimes carers), and yet, evidence of systematic implementation and the impacts on healthcare outcomes is limited. OBJECTIVE: The aim of this study was to test whether an adapted mental health experience codesign intervention to improve recovery-orientation of services led to greater psychosocial recovery outcomes for service users. DESIGN: A stepped wedge cluster randomized-controlled trial was conducted. SETTING AND PARTICIPANTS: Four Mental Health Community Support Services providers, 287 people living with severe mental illnesses, 61 carers and 120 staff were recruited across Victoria, Australia. MAIN OUTCOME MEASURES: The 24-item Revised Recovery Assessment Scale (RAS-R) measured individual psychosocial recovery. RESULTS: A total of 841 observations were completed with 287 service users. The intention-to-treat analysis found RAS-R scores to be similar between the intervention (mean = 84.7, SD= 15.6) and control (mean = 86.5, SD= 15.3) phases; the adjusted estimated difference in the mean RAS-R score was -1.70 (95% confidence interval: -3.81 to 0.40; p = .11). DISCUSSION: This first trial of an adapted mental health experience codesign intervention for psychosocial recovery outcomes found no difference between the intervention and control arms. CONCLUSIONS: More attention to the conditions that are required for eight essential mechanisms of change to support codesign processes and implementation is needed. PATIENT AND PUBLIC INVOLVEMENT: The State consumer (Victorian Mental Illness Awareness Council) and carer peak bodies (Tandem representing mental health carers) codeveloped the intervention. The adapted intervention was facilitated by coinvestigators with lived-experiences who were coauthors for the trial and process evaluation protocols, the engagement model and explanatory model of change for the trial.
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    Towards optimising chronic kidney disease detection and management in primary care: Underlying theory and protocol for technology development using an Integrated Knowledge Translation approach
    Manski-Nankervis, J-A ; Alexander, K ; Biezen, R ; Jones, J ; Hunter, B ; Emery, J ; Lumsden, N ; Boyle, D ; Gunn, J ; McMorrow, R ; Prictor, M ; Taylor, M ; Hallinan, C ; Chondros, P ; Janus, E ; McIntosh, J ; Nelson, C (SAGE PUBLICATIONS INC, 2021-04-01)
    Worldwide, Chronic Kidney Disease (CKD), directly or indirectly, causes more than 2.4 million deaths annually with symptoms generally presenting late in the disease course. Clinical guidelines support the early identification and treatment of CKD to delay progression and improve clinical outcomes. This paper reports the protocol for the codesign, implementation and evaluation of a technological platform called Future Health Today (FHT), a software program that aims to optimise early detection and management of CKD in general practice. FHT aims to optimise clinical decision making and reduce practice variation by translating evidence into practice in real time and as a part of quality improvement activities. This protocol describes the co-design and plans for implementation and evaluation of FHT in two general practices invited to test the prototype over 12 months. Service design thinking has informed the design phase and mixed methods will evaluate outcomes following implementation of FHT. Through systematic application of co-design with service users, clinicians and digital technologists, FHT attempts to avoid the pitfalls of past studies that have failed to accommodate the complex requirements and dynamics that can arise between researchers and service users and improve chronic disease management through use of health information technology.
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    Matching depression management to severity prognosis in primary care: results of the Target-D randomised controlled trial
    Fletcher, S ; Chondros, P ; Densley, K ; Murray, E ; Dowrick, C ; Coe, A ; Hegarty, K ; Davidson, S ; Wachtler, C ; Mihalopoulos, C ; Lee, YY ; Chatterton, ML ; Palmer, VJ ; Gunn, J (ROYAL COLL GENERAL PRACTITIONERS, 2021-02-01)
    BACKGROUND: Mental health treatment rates are increasing, but the burden of disease has not reduced. Tools to support efficient resource distribution are required. AIM: To investigate whether a person-centred e-health (Target-D) platform matching depression care to symptom severity prognosis can improve depressive symptoms relative to usual care. DESIGN AND SETTING: Stratified individually randomised controlled trial in 14 general practices in Melbourne, Australia, from April 2016 to February 2019. In total, 1868 participants aged 18-65 years who had current depressive symptoms; internet access; no recent change to antidepressant; no current antipsychotic medication; and no current psychological therapy were randomised (1:1) via computer-generated allocation to intervention or usual care. METHOD: The intervention was an e-health platform accessed in the GP waiting room, comprising symptom feedback, priority-setting, and prognosis-matched management options (online self-help, online guided psychological therapy, or nurse-led collaborative care). Management options were flexible, neither participants nor staff were blinded, and there were no substantive protocol deviations. The primary outcome was depressive symptom severity (9-item Patient Health Questionnaire [PHQ-9]) at 3 months. RESULTS: In intention to treat analysis, estimated between- arm difference in mean PHQ-9 scores at 3 months was -0.88 (95% confidence interval [CI] = -1.45 to -0.31) favouring the intervention, and -0.59 at 12 months (95% CI = -1.18 to 0.01); standardised effect sizes of -0.16 (95% CI = -0.26 to -0.05) and -0.10 (95% CI = -0.21 to 0.002), respectively. No serious adverse events were reported. CONCLUSION: Matching management to prognosis using a person-centred e-health platform improves depressive symptoms at 3 months compared to usual care and could feasibly be implemented at scale. Scope exists to enhance the uptake of management options.
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    Clinical efficacy of a Decision Support Tool (Link-me) to guide intensity of mental health care in primary practice: a pragmatic stratified randomised controlled trial
    Fletcher, S ; Spittal, MJ ; Chondros, P ; Palmer, VJ ; Chatterton, ML ; Densley, K ; Potiriadis, M ; Harris, M ; Bassilios, B ; Burgess, P ; Mihalopoulos, C ; Pirkis, J ; Gunn, J (ELSEVIER SCI LTD, 2021-03-01)
    BACKGROUND: The volume and heterogeneity of mental health problems that primary care patients present with is a substantial challenge for health systems, and both undertreatment and overtreatment are common. We developed Link-me, a patient-completed Decision Support Tool, to predict severity of depression or anxiety, identify priorities, and recommend interventions. In this study, we aimed to examine if Link-me reduces psychological distress among individuals predicted to have minimal/mild or severe symptoms of anxiety or depression. METHODS: In this pragmatic stratified randomised controlled trial, adults aged 18-75 years reporting depressive or anxiety symptoms or use of mental health medication were recruited from 23 general practices in Australia. Participants completed the Decision Support Tool and were classified into three prognostic groups (minimal/mild, moderate, severe), and those in the minimal/mild and severe groups were eligible for inclusion. Participants were individually and randomly assigned (1:1) by a computer-generated allocation sequence to receive either prognosis-matched care (intervention group) or usual care plus attention control (control group). Participants were not blinded but intervention providers were only notified of those allocated to the intervention group. Outcome assessment was blinded. The primary outcome was the difference in the change in scores between the intervention and control group, and within prognostic groups, on the 10-item Kessler Psychological Distress Scale at 6 months post randomisation. The trial was registered on the Australian and New Zealand Clinical Trials Registry, ACTRN12617001333303. OUTCOMES: Between Nov 21, 2017, and Oct 31, 2018, 24 616 patients were invited to complete the eligibility screening survey. 1671 of these patients were included and randomly assigned to either the intervention group (n=834) or the control group (n=837). Prognosis-matched care was associated with greater reductions in psychological distress than usual care plus attention control at 6 months (p=0·03), with a standardised mean difference (SMD) of -0·09 (95% CI -0·17 to -0·01). This reduction was also seen in the severe prognostic group (p=0·003), with a SMD of -0·26 (-0·43 to -0·09), but not in the minimal/mild group (p=0·73), with a SMD of 0·04 (-0·17 to 0·24). In the complier average causal effect analysis in the severe prognostic group, differences were larger among those who received some or all aspects of the intervention (SMD range -0·58 to -1·15). No serious adverse effects were recorded. INTERPRETATION: Prognosis-based matching of interventions reduces psychological distress in patients with anxiety or depressive symptoms, particularly in those with severe symptoms, and is associated with better outcomes when patients access the recommended treatment. Optimisation of the Link-me approach and implementation into routine practice could help reduce the burden of disease associated with common mental health conditions such as anxiety and depression. FUNDING: Australian Government Department of Health.
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    The assertive cardiac care trial: A randomised controlled trial of a coproduced assertive cardiac care intervention to reduce absolute cardiovascular disease risk in people with severe mental illness in the primary care setting
    Lewis, M ; Chondros, P ; Mihalopoulos, C ; Lee, YY ; Gunn, JM ; Harvey, C ; Furler, J ; Osborn, D ; Castle, D ; Davidson, S ; Jayaram, M ; Kenny, A ; Nelson, MR ; Morgan, VA ; Harrap, S ; McKenzie, K ; Potiriadis, M ; Densley, K ; Palmer, VJ (ELSEVIER SCIENCE INC, 2020-10-01)
    BACKGROUND: Cardiovascular disease (CVD) accounts for 40% of the excess mortality identified in people with severe mental illness (SMI). Modifiable CVD risk factors are higher and can be exacerbated by the cardiometabolic impact of psychotropic medications. People with SMI frequently attend primary care presenting a valuable opportunity for early identification, prevention and management of cardiovascular health. The ACCT Healthy Hearts Study will test a coproduced, nurse-led intervention delivered with general practitioners to reduce absolute CVD risk (ACVDR) at 12 months compared with an active control group. METHODS/DESIGN: ACCT is a two group (intervention/active control) individually randomised (1:1) controlled trial (RCT). Assessments will be completed baseline (pre-randomisation), 6 months, and 12 months. The primary outcome is 5-year ACVDR measured at 12 months. Secondary outcomes include 6-month ACVDR; and blood pressure, lipids, HbA1c, BMI, quality of life, physical activity, motivation to change health behaviour, medication adherence, alcohol use and hospitalisation at 6 and 12 months. Linear mixed-effects regression will estimate mean difference between groups for primary and secondary continuous outcomes. Economic cost-consequences analysis will be conducted using quality of life and health resource use information and routinely collected government health service use and medication data. A parallel process evaluation will investigate implementation of the intervention, uptake and outcomes. DISCUSSION: ACCT will deliver a coproduced and person-centred, guideline level cardiovascular primary care intervention to a high need population with SMI. If successful, the intervention could lead to the reduction of the mortality gap and increase opportunities for meaningful social and economic participation. Trial registration ANZCTR Trial number: ACTRN12619001112156.
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    Developing prediction models for total knee replacement surgery in patients with osteoarthritis: Statistical analysis plan
    Thuraisingam, S ; Dowsey, M ; Manski-Nankervis, J-A ; Spelman, T ; Choong, P ; Gunn, J ; Chondros, P (Elsevier BV, 2020-12)
    Background Approximately 12–20% of those with osteoarthritis (OA) in Australia who undergo total knee replacement (TKR) surgery do not report any clinical improvement. There is a need to develop prediction tools for use in general practice that allow early identification of patients likely to undergo TKR and those unlikely to benefit from the surgery. First-line treatment strategies can then be implemented and optimised to delay or prevent the need for TKR. The identification of potential non-responders to TKR may provide the opportunity for new treatment strategies to be developed and help ensure surgery is reserved for those most likely to benefit. This statistical analysis plan (SAP) details the statistical methodology used to develop such prediction tools. Objective To describe in detail the statistical methods used to develop and validate prediction models for TKR surgery in Australian patients with OA for use in general practice. Methods This SAP contains a brief justification for the need for prediction models for TKR surgery in general practice. A description of the data sources that will be linked and used to develop the models, and estimated sample sizes is provided. The planned methodologies for candidate predictor selection, model development, measuring model performance and internal model validation are described in detail. Intended table layouts for presentation of model results are provided. Conclusion Consistent with best practice guidelines, the statistical methodologies outlined in this SAP have been pre-specified prior to data pre-processing and model development.
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    When should matching be used in the design of cluster randomized trials?
    Chondros, P ; Ukoumunne, OC ; Gunn, JM ; Carlin, JB (WILEY, 2021-08-14)
    For cluster randomized trials (CRTs) with a small number of clusters, the matched-pair (MP) design, where clusters are paired before randomizing one to each trial arm, is often recommended to minimize imbalance on known prognostic factors, add face-validity to the study, and increase efficiency, provided the analysis recognizes the matching. Little evidence exists to guide decisions on when to use matching. We used simulation to compare the efficiency of the MP design with the stratified and simple designs, based on the mean confidence interval width of the estimated intervention effect. Matched and unmatched analyses were used for the MP design; a stratified analysis was used for the stratified design; and analyses without and with post-stratification adjustment for factors that would otherwise have been used for restricted allocation were used for the simple design. Results showed the MP design was generally the most efficient for CRTs with 10 or more pairs when the correlation between cluster-level outcomes within pairs (matching correlation) was moderate to strong (0.3-0.5). There was little gain in efficiency for the MP or stratified designs compared to simple randomization when the matching correlation was weak (0.05-0.1). For trials with four pairs of clusters, the simple and stratified designs were more efficient than the MP design because greater degrees of freedom were available for the analysis, although an unmatched analysis of the MP design recovered precision for weak matching correlations. Practical guidance on choosing between the MP, stratified, and simple designs is provided.