General Practice and Primary Care - Research Publications

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    Developing and internally validating a prediction model for total knee replacement surgery in patients with osteoarthritis.
    Thuraisingam, S ; Chondros, P ; Manski-Nankervis, J-A ; Spelman, T ; Choong, PF ; Gunn, J ; Dowsey, MM (Elsevier BV, 2022-09)
    OBJECTIVE: The objective of this study was to develop and internally validate a clinical algorithm for use in general practice that predicts the probability of total knee replacement (TKR) surgery within the next five years for patients with osteoarthritis. The purpose of the model is to encourage early uptake of first-line treatment strategies in patients likely to undergo TKR and to provide a cohort for the development and testing of novel interventions that prevent or delay the progression to TKR. METHOD: Electronic health records (EHRs) from 201,462 patients with osteoarthritis aged 45 years and over from 483 general practices across Australia were linked with records from the Australian Orthopaedic Association National Joint Replacement Registry and the National Death Index. A Fine and Gray competing risk prediction model was developed using these data to predict the risk of TKR within the next five years. RESULTS: During a follow-up time of 5 years, 15,979 (7.9%) patients underwent TKR and 13,873 (6.9%) died. Predictors included in the final algorithm were age, previous knee replacement, knee surgery (other than TKR), prescribing of osteoarthritis medication in the 12 months prior, comorbidity count and diagnosis of a mental health condition. Optimism corrected model discrimination was 0.67 (95% CI: 0.66 to 0.67) and model calibration acceptable. CONCLUSION: The model has the potential to reduce some of the economic burden associated with TKR in Australia. External validation and further optimisation of the algorithm will be carried out prior to implementation within Australian general practice EHR systems.
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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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    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)
    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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    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.