Suchergebnisse

IntroductionBeta-blockers, renin-angiotensin system inhibitors and statins are evidence-based pharmacotherapies for preventing recurrent acute coronary syndrome (ACS), however their use in older people may depend on clinical factors such as comorbidity and frailty. Deriving comorbidity information from linked administrative data is well established. The recent development of the Hospital Frailty Risk Score (HFRS) now allows ascertainment of frailty from linked administrative data.
Objectives and ApproachWe determined the ability of the HFRS to discriminate between groups of older patients initiated on these three cardio-protective medicines following hospitalisation for an incident ACS. We used a 15-year look back to identify incident ACS cases between 2005 and 2008 from Western Australian hospital data and linked them to national pharmaceutical dispensing records. The study was limited to patients aged ≥65 years who had not received these medicines in the last two years and were discharged alive from their index ACS admission. Separate competing risk regression models assessed the association between HFRS and initiation of each medicine, controlling for comorbidities and other variables available from the linked datasets.
ResultsOverall, increasing levels of HFRS were associated with lower subdistribution hazards of initiation of each medicine examined, however these varied by age group and sex. For example, the subdistribution hazard ratios (SHR) for beta-blocker initiation among men in the oldest age group (≥85 years) were 0.65 (95% CI: 0.44-0.95) and 0.51 (95% CI: 0.29-0.90) in the intermediate and high HFRS groups respectively compared to the low frailty group. The corresponding SHR for women were 1.01 (95% CI: 0.76-1.35) and 0.89 (95% CI: 0.63-1.27), respectively.
Conclusion / ImplicationsThe HFRS applied to linked administrative data discriminates between groups of older people who received cardio-protective medicines following an incident ACS. There is potential for it to be used in other chronic disease conditions.

IntroductionHospital administrative data is a valuable source to measure myocardial infarction (MI) rates. However, admission counts are susceptible to over-inflation if the patient is transferred multiple times during a single episode of care, and variables denoting transfers may not be reliable. To obtain an accurate number of events, hospital transfers need to be correctly identified.
Objectives and ApproachWe assessed multivariable logistic regression and various machine-learning models to predict transfers in hospital administrative data. Using Western Australian linked hospital data, we identified records from 2000-2016 with a principal discharge diagnosis of MI. Our standard method to compare against was a 24-hour look-back to identify a transfer using just admission and separation dates from the current and previous records for the same patient. Multivariable logistic regression and decision trees with various boosting algorithms were used to predict if a single record was a transfer, using variables recorded in the admission (e.g. age, sex, type of hospital, admitted from, emergency/elective admission). The performance of each model was calculated using metrics including area under the curve (AUC).
ResultsRecords in the training, validation and testing samples had similar characteristics: mean age=68.9 years, 66% were male and 58% admitted to tertiary hospitals. Gradient Boosting Decision Tree (AUC=0.887, 95%CI: 0.886-0.887) outperformed multivariable logistic regression (AUC=0.875; 95% CI: 0.869-0.881) and random forest models (AUC=0.859; 95% CI: 0.853-0.865).
Conclusion / ImplicationsMultivariable logistic regression and machine-learning models are able to identify transfers in a single record from existing variables. They can be used in unlinked hospital administrative data where records belonging to the same patient cannot be identified.

ABSTRACTObjectivesNational statistics for hospital admissions for acute CHD based on unlinked administrative data are inflated because of inter/intra-hospital transfers or related readmissions for further investigations or procedures. Our objective was to estimate the inflation of CHD inpatient counts using multiple approaches based initially on Western Australian data that can be applied to future National studies.
ApproachWe used a linked hospital morbidity dataset from the Western Australian Data Linkage System to determine hospitalisations for each CHD subcategory from 1990-2010. Transfers were defined as contiguous admissions separated by ≤1 day. Episodes-of-care (EOC) were defined as admissions (with/without transfers) that were within 28 days of the initial CHD admission. As the principal diagnosis may vary between hospitals involved in transfers or admissions within an EOC, we explored four approaches for allocating a diagnosis:i. Hierarchical diagnosis: selection of diagnosis based on clinical severity (ST-elevation myocardial infarction (STEMI)>non-STEMI>unstable angina>stable angina>other CHD>chest pain); ii. Hospital hierarchy: diagnosis based on highest hospital level (tertiary>private>other metropolitan non-tertiary>rural);iii/iv. Temporal order of diagnosis: diagnosis based on first or last record in transfer/EOC.
ResultsThe proportion of cases that were transferred varied according to disease severity and time: 13% (1990) to 27% (2010) for STEMI; 5% to 7% for stable angina and unchanged at 4% for chest pain. Compared to transfer-level data using the first approach, unlinked data overestimated STEMI counts by 3% (1990) to 11% (2010), stable angina by 3% to 5% and chest pain by 6% to 6%. Similarly for EOC-level data, the overestimates were 5% (1990) to 12% (2010) for STEMI, 13% to 19% for stable angina and 20% to 14% for chest pain. The four approaches for allocating a diagnosis produced differing counts with the difference being larger for more clinically severe diagnoses than for less clinically severe diagnoses. For example, using transfer-level data, the differences between approaches i and iv in 2010 were 12%, 2% and 1% for STEMI, stable angina and chest pain respectively.
ConclusionThere is a potential to overestimate counts of CHD in inpatient data if transfers and readmissions are not taken into account, and this inaccuracy can differ across disease subcategories and approach used. This has important implications where higher disease severity, such as myocardial infarction, is an indicator of population health. Transfer- or EOC-level data are more likely to reflect true CHD hospitalisation counts than unlinked-level data, and are more appropriate for epidemiological studies of CHD rates.

Background: Accurate coded diagnostic data are important for epidemiological research of stroke. Objective: To develop, implement and evaluate an online education program for improving clinical coding of stroke. Method: The Australia and New Zealand Stroke Coding Working Group co-developed an education program comprising eight modules: rationale for coding of stroke; understanding stroke; management of stroke; national coding standards; coding trees; good clinical documentation; coding practices; and scenarios. Clinical coders and health information managers participated in the 90-minute education program. Pre- and post-education surveys were administered to assess knowledge of stroke and coding, and to obtain feedback. Descriptive analyses were used for quantitative data, inductive thematic analysis for open-text responses, with all results triangulated. Results: Of 615 participants, 404 (66%) completed both pre- and post-education assessments. Respondents had improved knowledge for 9/12 questions ( p < 0.05), including knowledge of applicable coding standards, coding of intracerebral haemorrhage and the actions to take when coding stroke (all p < 0.001). Majority of respondents agreed that information was pitched at an appropriate level; education materials were well organised; presenters had adequate knowledge; and that they would recommend the session to colleagues. In qualitative evaluations, the education program was beneficial for newly trained clinical coders, or as a knowledge refresher, and respondents valued clinical information from a stroke neurologist. Conclusion: Our education program was associated with increased knowledge for clinical coding of stroke. To continue to address the quality of coded stroke data through improved stroke documentation, the next stage will be to adapt the educational program for clinicians.