Familial hypercholesterolaemia (FH) is a dominant and highly penetrant monogenic disorder present from birth that markedly elevates plasma low-density lipoprotein (LDL)-cholesterol concentration and, if untreated, leads to premature atherosclerosis and coronary artery disease (CAD). There are approximately 100,000 people with FH in Australia. However, an overwhelming majority of those affected remain undetected and inadequately treated, consistent with FH being a leading challenge for public health genomics. To further address the unmet need, we provide an updated guidance, presented as a series of systematically collated recommendations, on the care of patients and families with FH. These recommendations have been informed by an exponential growth in published works and new evidence over the last 5 years and are compatible with a contemporary global call to action on FH. Recommendations are given on the detection, diagnosis, assessment and management of FH in adults and children. Recommendations are also made on genetic testing and risk notification of biological relatives who should undergo cascade testing for FH. Guidance on management is based on the concepts of risk re-stratification, adherence to heart healthy lifestyles, treatment of non-cholesterol risk factors, and safe and appropriate use of LDL-cholesterol lowering therapies, including statins, ezetimibe, proprotein convertase subtilisin/kexin type 9 inhibitors and lipoprotein apheresis. Broad recommendations are also provided for the organisation and development of health care services. Recommendations on best practice need to be underpinned by good clinical judgment and shared decision making with patients and families. Models of care for FH need to be adapted to local and regional health care needs and available resources. A comprehensive and realistic implementation strategy, informed by further research, including assessments of cost-benefit, will be required to ensure that this new guidance benefits all Australian families with or at risk of FH.

Aim To describe sociodemographic characteristics and comorbidities of a large cohort of Australian general practice‐based patients identified as having chronic kidney disease (CKD), using data from National Prescribing Service (NPS) MedicineWise's MedicineInsight dataset, and compare this dataset to the 2011–2012 Australian Health Survey's (AHS) CKD prevalence estimates. Methods This was a cohort study using deidentified, longitudinal, electronic health record data collected from 329 practices and 1 483 416 patients distributed across Australia, from 1 June 2013 until 1 June 2016. Two methods were used to calculate the CKD prevalence. One used the same method as used by the 2011–2012 AHS, based on one estimate of the estimated glomerular filtration rate (eGFR) or albumin/creatinine ratios (ACR). The other defined CKD more rigorously using eGFR or ACR results at least 90 days apart. Results In 2016, of 1 310 602 active patients, 710 674 (54.2%) did not have an eGFR or ACR test, while 524 961 (40.1%) had an eGFR or ACR test but did not meet AHS criteria for CKD. Age–sex adjusted rates of CKD (compared to AHS) were CKD 1–0.45% (3.9%), CKD 2–0.62% (2.5%), CKD 3a: 3.1% (2.7%), CKD 3b: 1.14% (0.6%), CKD 4–5: 0.41% (0.3%). The CKD cohort defined more rigorously using eGFR and ACR measures >90 days apart, had comorbidities of atrial fibrillation (30.5%), cardiovascular disease (25.0%), diabetes mellitus (17.1%) and hypertension (14.8%). Conclusion The MedicineInsight dataset contains valuable and timely information about Australian patients with CKD, and provides prevalence estimates similar to those from AHS data.

Background: Chronic kidney disease (CKD) affects drug elimination and patients with CKD require appropriate adjustment of renally cleared medications to ensure safe and effective pharmacotherapy. The main objective of this study was to determine the extent of potentially inappropriate prescribing (PIP; defined as the use of a contraindicated medication or inappropriately high dose according to the kidney function) of renally-cleared medications commonly prescribed in Australian primary care, based on two measures of kidney function. A secondary aim was to assess agreement between the two measures. Methods: Retrospective analysis of routinely collected de-identified Australian general practice patient data (NPS MedicineWise MedicineInsight from January 1, 2013, to June 1, 2016; collected from 329 general practices). All adults (aged ≥18 years) with CKD presenting to general practices across Australia were included in the analysis. Patients were considered to have CKD if they had two or more estimated glomerular filtration rate (eGFR) recorded values < 60 mL/min/1.73m 2 , and/or two urinary albumin/creatinine ratios ≥3.5 mg/mmol in females (≥2.5 mg/mmol in males) at least 90 days apart. PIP was assessed for 49 commonly prescribed medications using the Cockcroft-Gault (CG) equation/eGFR as per the instructions in the Australian Medicines Handbook. Results: A total of 48,731 patients met the Kidney Health Australia (KHA) definition for CKD and had prescriptions recorded within 90 days of measuring serum creatinine (SCr)/estimated glomerular filtration rate (eGFR). Overall, 28, 729 patients were prescribed one or more of the 49 medications of interest. Approximately 35% (n = 9926) of these patients had at least one PIP based on either the Cockcroft-Gault (CG) equation or eGFR (CKD-EPI; CKD-Epidemiology Collaboration Equation). There was good agreement between CG and eGFR while determining the appropriateness of medications, with approximately 97% of the medications classified as appropriate by eGFR also being considered appropriate by the CG equation. Conclusion: This study highlights that PIP commonly occurs in primary care patients with CKD and the need for further research to understand why and how this can be minimised. The findings also show that the eGFR provides clinicians a potential alternative to the CG formula when estimating kidney function to guide drug appropriateness and dosing.

BackgroundThe past decade has witnessed the increasing adoption of Web 2.0 technologies in medical education. Recently, the notion of digital habitats, Web 2.0 supported learning environments, has also come onto the scene. While there has been initial research on the use of digital habitats for educational purposes, very limited research has examined the adoption of digital habitats by medical students and educators on mobile devices.This paper reports the Stage 1 findings of a two-staged study. The whole study aimed to develop and implement a personal digital habitat, namely digiMe, for medical students and educators at an Australian university. The first stage, however, examined the types of Web 2.0 tools and mobile devices that are being used by potential digiMe users, and reasons for their adoption.MethodsIn this first stage of research, data were collected through a questionnaire and semi-structured interviews. Questionnaire data collected from 104 participants were analysed using the Predictive Analytics SoftWare (PASW). Frequencies, median and mean values were pursued. Kruskal Wallis tests were then performed to examine variations between views of different participant groups. Notes from the 6 interviews, together with responses to the open-ended section of the questionnaire, were analysed using the constructivist grounded theory approach, to generate key themes relevant to the adoption of Web 2.0 tools and mobile devices.ResultsThe findings reflected the wide use of mobile devices, including both smart phones and computing tablets, by medical students and educators for learning, teaching and professional development purposes. Among the 22 types of Web 2.0 tools investigated, less than half of these tools were frequently used by the participants, this reflects the mismatch between users’ desires and their actual practice. Age and occupation appeared to be the influential factors for their adoption. Easy access to information and improved communication are main purposes.ConclusionsThis paper highlights the desire of medical students and educators for a more effective use of Web 2.0 technologies and mobile devices, and the observed mismatch between the desire and their actual practice. It also recognises the critical role of medical education institutions in facilitating this practice to respond to the mismatch.Electronic supplementary materialThe online version of this article (doi:10.1186/s12911-016-0283-6) contains supplementary material, which is available to authorized users.