General Practice and Primary Care - Research Publications
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ItemMisconceptions and the Acceptance of Evidence-based Nonsurgical Interventions for Knee Osteoarthritis. A Qualitative Study(GEORG THIEME VERLAG KG, 2021-11)BACKGROUND: In contrast to best practice guidelines for knee osteoarthritis (OA), findings from several different healthcare settings have identified that nonsurgical treatments are underused and TKA is overused. Empirical evidence and qualitative observations suggest that patients' willingness to accept nonsurgical interventions for knee OA is low. A qualitative investigation of why patients may feel that such interventions are of little value may be an important step toward increasing their use in the treatment of knee OA QUESTIONS/PURPOSES: This qualitative study was embedded in a larger study investigating patient-related factors (beliefs/attitudes toward knee OA and its treatment) and health-system related factors (access, referral pathways) known to influence patients' decisions to seek medical care. In this paper we focus on the patient-related factors with the aim of exploring why patients may feel that nonsurgical interventions are of little value in the treatment of knee OA. METHODS: A cross-sectional qualitative study was conducted in a single tertiary hospital in Australia. Patients with endstage knee OA on the waiting list for TKA were approached during their preadmission appointment and invited to participate in one-to-one interviews. As prescribed by the qualitative approach, data collection and data analysis were performed in parallel and recruitment continued until the authors agreed that the themes identified would not change through interviews with subsequent participants, at which point, recruitment stopped. Thirty-seven patients were approached and 27 participated. Participants were 48% female; mean age was 67 years. Participants' beliefs about knee OA and its treatment were identified in the interview transcripts. Beliefs were grouped into five belief dimensions: identity beliefs (what knee OA is), causal beliefs (what causes knee OA), consequence beliefs (what the consequences of knee OA are), timeline beliefs (how long knee OA lasts) and treatment beliefs (how knee OA can be controlled). RESULTS: All participants believed that their knee OA was "bone on bone" (identity beliefs) and most (> 14 participants) believed it was caused by "wear and tear" (causal beliefs). Most (> 14 participants) believed that loading the knee could further damage their "vulnerable" joint (consequence beliefs) and all believed that their pain would deteriorate over time (timeline beliefs). Many (>20 participants) believed that physiotherapy and exercise interventions would increase pain and could not replace lost knee cartilage. They preferred experimental and surgical treatments which they believed would replace lost cartilage and cure their knee pain (treatment beliefs). CONCLUSIONS: Common misconceptions about knee OA appear to influence patients' acceptance of nonsurgical, evidence-based treatments such as exercise and weight loss. Once the participants in this study had been "diagnosed" with "bone-on-bone" changes, many disregarded exercise-based interventions which they believed would damage their joint, in favor of alternative and experimental treatments, which they believed would regenerate lost knee cartilage. Future research involving larger, more representative samples are needed to understand how widespread these beliefs are and if/how they influence treatment decisions. In the meantime, clinicians seeking to encourage acceptance of nonsurgical interventions may consider exploring and targeting misconceptions that patients hold about the identity, causes, consequences, timeline, and treatment of knee OA. LEVEL OF EVIDENCE: Level II, prognostic study.
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ItemAssessing the suitability of general practice electronic health records for clinical prediction model development: a data quality assessment(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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ItemTowards optimising chronic kidney disease detection and management in primary care: Underlying theory and protocol for technology development using an Integrated Knowledge Translation approach(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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ItemDeveloping prediction models for total knee replacement surgery in patients with osteoarthritis: Statistical analysis plan(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.