Readmission destination following cardiac surgery and its association with mortality outcomes: a population-based retrospective study.

Background: It is unclear how pre-surgery transfer relates to readmission destination among patients undergoing cardiac surgery and whether readmission to a hospital other than the operating hospital is associated with increased mortality. Methods: We analysed linked hospital and death records for residents of New South Wales, Australia, aged ≥18 years who had an emergency readmission within 30 days following coronary artery bypass graft (CABG) or surgical aortic valve replacement (SAVR) in 2003-2022. Mixed-effect multi-level modelling was used to evaluate associations of readmission destination with 30-day mortality, overall and stratified by pre-surgery transfer. Findings: Of 102,540 patients undergoing cardiac surgery (isolated CABG = 63,000, SAVR = 27,482, combined = 12,058), 28.7% (n = 29,398) had pre-surgery transfer, while the 30-day readmission rate was 14.7% (n = 14,708). During readmission, 35.7% (3499/9795) of those without pre-surgery transfer and 12.0% (590/4913) of those with pre-surgery transfer returned to the operating hospital. Among readmitted patients, 30-day mortality did not differ significantly for those who were readmitted to a non-index hospital, both overall (adjusted odds ratio [aOR] = 1.03 95% CI 0.75-1.41), and in analyses stratified by pre-surgery transfer (no transfer: aOR = 1.07, 95% CI 0.75-1.52; transfer: aOR = 0.88, 95% CI 0.45-1.72). Among patients who had pre-surgery transfer, 30-day mortality was similar among patients who were readmitted to the index operating hospital (reference), the initial admitting hospital (aOR = 1.00, 95% CI 0.50-2.00) or a third, different, hospital (aOR = 0.70, 95% CI 0.33-1.48). Interpretation: Although many Australian patients who are readmitted following cardiac surgery are readmitted to hospitals different to the operating or initial admitting hospital, such readmissions are not associated with increased mortality. Funding: This study was funded by a National Health and Medical Research Foundation of Australia (NHMRC) Project Grant (#1162833).

The Medicines Intelligence Data Platform: A Population-Based Data Resource From New South Wales, Australia.

BACKGROUND: The Medicines Intelligence (MedIntel) Data Platform is an anonymised linked data resource designed to generate real-world evidence on prescribed medicine use, effectiveness, safety, costs and cost-effectiveness in Australia. RESULTS: The platform comprises Medicare-eligible people who are ≥18 years and residing in New South Wales (NSW), Australia, any time during 2005-2020, with linked administrative data on dispensed prescription medicines (Pharmaceutical Benefits Scheme), health service use (Medicare Benefits Schedule), emergency department visits (NSW Emergency Department Data Collection), hospitalisations (NSW Admitted Patient Data Collection) plus death (National Death Index) and cancer registrations (NSW Cancer Registry). Data are currently available to 2022, with approval to update the cohort and data collections annually. The platform includes 7.4 million unique people across all years, covering 36.9% of the Australian adult population; the overall population increased from 4.8 M in 2005 to 6.0 M in 2020. As of 1 January 2019 (the last pre-pandemic year), the cohort had a mean age of 48.7 years (51.1% female), with most people (4.4 M, 74.7%) residing in a major city. In 2019, 4.4 M people (73.3%) were dispensed a medicine, 1.2 M (20.5%) were hospitalised, 5.3 M (89.4%) had a GP or specialist appointment, and 54 003 people died. Anti-infectives were the most prevalent medicines dispensed to the cohort in 2019 (43.1%), followed by nervous system (32.2%) and cardiovascular system medicines (30.2%). CONCLUSION: The MedIntel Data Platform creates opportunities for national and international research collaborations and enables us to address contemporary clinically- and policy-relevant research questions about quality use of medicines and health outcomes in Australia and globally.

Non-index hospital re-admissions after hospitalisation with acute myocardial infarction and geographic remoteness, New South Wales, 2005-2020: a retrospective cohort study.

OBJECTIVES: To examine the frequency of re-admissions to non-index hospitals (hospitals other than the initial discharging hospital) within 30 days of admission with acute myocardial infarction in New South Wales; to examine the relationship between non-index hospital re-admissions and 30-day mortality. STUDY DESIGN: Retrospective cohort study; analysis of hospital admissions (Admitted Patient Data Collection) and mortality data (Registry of Births, Deaths and Marriages). SETTING, PARTICIPANTS: Adults admitted to NSW hospitals with acute myocardial infarction re-admitted to any hospital within 30 days of discharge from the initial hospitalisation, 1 January 2005 - 31 December 2020. MAIN OUTCOME MEASURES: Proportion of re-admissions within 30 days of discharge to non-index hospitals, and associations of non-index hospital re-admissions with demographic and initial hospitalisation characteristics and with 30-day and 12-month mortality, each by residential remoteness category. RESULTS: Of 168 097 people with acute myocardial infarction discharged alive, 28 309 (16.8%) were re-admitted to hospital within 30 days of discharge, including 11 986 to non-index hospitals (42.3%); the proportion was larger for people from regional or remote areas (50.1%) than for people from major cities (38.3%). The odds of non-index hospital re-admission were higher for people with ST-elevation myocardial infarction, for people whose index admissions were to private hospitals, who were transferred between hospitals or had undergone revascularisation during the initial admission, were under 65 years of age, or had private health insurance; the influence of these factors was generally larger for people from regional or remote areas than for those from large cities. After adjustment for potential confounders, non-index hospital re-admission did not influence mortality among people from major cities (30-day: adjusted odds ratio [aOR], 1.09; 95% confidence interval [CI], 0.99-1.20; 12-month: aOR, 0.98, 95% CI, 0.93-1.03), but was associated with reduced mortality for people from regional or remote areas (30-day: aOR, 0.81; 95% CI, 0.70-0.95; 12-month: aOR, 0.88; 95% CI, 0.81-0.96). CONCLUSIONS: The geographically dispersed Australian population and the mixed public and private provision of specialist services means that re-admission to a non-index hospital can be unavoidable for people with acute myocardial infarction who are initially transferred to specialised facilities. Non-index hospital re-admission is associated with better mortality outcomes for people from regional or remote areas.

Factors associated with electroconvulsive therapy treatment for adults with serious psychiatric conditions in Australia.

OBJECTIVE: To identify factors associated with receiving electroconvulsive therapy (ECT) for serious psychiatric conditions. METHODS: Retrospective observational study using hospital administrative data linked with death registrations and outpatient mental health data in New South Wales (NSW), Australia. The cohort included patients admitted with a primary psychiatric diagnosis between 2013 and 2022. The outcome measure was receipt of ECT. RESULTS: Of 94,950 patients, 3465 (3.6%) received ECT. The likelihood of receiving ECT was higher in older (hazard ratio [HR] = 1.03), female (HR = 1.24) patients. Compared to depression, patients with schizophrenia/schizoaffective disorder (HR = 0.79), schizophrenia-related disorders (HR = 0.37), mania (HR = 0.64) and other mood disorders (HR = 0.45) had lower odds of receiving ECT. Patients with depression and one other serious psychiatric condition had higher odds of receiving ECT than depression alone. Bipolar disorder likelihood of ECT did not differ from depression. A higher number of mental health outpatient visits in the prior year and an involuntary index admission with depression were also associated with receiving ECT. Likelihood of receiving ECT increased with year of admission (HR = 1.32), private patient status (HR = 2.06), higher socioeconomic status (HR = 1.09) and being married (HR = 1.25). CONCLUSIONS: ECT use for depression and bipolar disorder in NSW aligns with clinical national guidelines. Patients with schizophrenia/schizoaffective, schizophrenia-related disorders, mania and other mood disorders had lower likelihood of ECT than depression, despite ECT being recommended by clinical guidelines for these diagnoses. Variations in ECT were strongly associated with healthcare access, with private patients twice as likely to receive ECT than their public counterparts, suggesting a need to explore ECT accessibility.

Factors associated with cardiac implantable electronic device-related infections, New South Wales, 2016-21: a retrospective cohort study.

OBJECTIVES: To quantify the rate of cardiac implantable electronic device (CIED)-related infections and to identify risk factors for such infections. DESIGN: Retrospective cohort study; analysis of linked hospital admissions and mortality data. SETTING, PARTICIPANTS: All adults who underwent CIED procedures in New South Wales between 1 January 2016 and 30 June 2021 (public hospitals) or 30 June 2020 (private hospitals). MAIN OUTCOME MEASURES: Proportions of patients hospitalised with CIED-related infections (identified by hospital record diagnosis codes); risk of CIED-related infection by patient, device, and procedural factors. RESULTS: Of 37 675 CIED procedures (23 194 men, 63.5%), 500 were followed by CIED-related infections (median follow-up, 24.9 months; interquartile range, 11.2-40.8 months), including 397 people (1.1%) within twelve months of their procedures, and 186 of 10 540 people (2.5%) at high risk of such infections (replacement or upgrade procedures; new cardiac resynchronisation therapy with defibrillator, CRT-D). The overall infection rate was 0.50 (95% confidence interval [CI], 0.45-0.54) per 1000 person-months; it was highest during the first month after the procedure (5.60 [95% CI, 4.89-6.42] per 1000 person-months). The risk of CIED-related infection was greater for people under 65 years of age than for those aged 65-74 years (adjusted hazard ratio [aHR], 1.71; 95% CI, 1.32-2.23), for people with CRT-D devices than for those with permanent pacemakers (aHR, 1.46; 95% CI, 1.02-2.08), for people who had previously undergone CIED procedures (two or more v none: aHR, 1.51; 95% CI, 1.02-2.25) or had CIED-related infections (aHR, 11.4; 95% CI, 8.34-15.7), or had undergone concomitant cardiac surgery (aHR, 1.62; 95% CI, 1.10-2.39), and for people with atrial fibrillation (aHR, 1.33; 95% CI, 1.11-1.60), chronic kidney disease (aHR, 1.54; 95% CI, 1.27-1.87), chronic obstructive pulmonary disease (aHR, 1.37; 95% CI, 1.10-1.69), or cardiomyopathy (aHR 1.60; 95% CI, 1.25-2.05). CONCLUSIONS: Knowledge of risk factors for CIED-related infections can help clinicians discuss them with their patients, identify people at particular risk, and inform decisions about device type, upgrades and replacements, and prophylactic interventions.

Incidence and Predictors of Readmissions to Non-Index Hospitals After Transcatheter Aortic Valve Implantation in the Contemporary Era in New South Wales, Australia.

BACKGROUND: In Australia, transcatheter aortic valve implantation (TAVI) is only performed in a limited number of specialised metropolitan centres, many of which are private hospitals, making it likely that TAVI patients who require readmission will present to another (non-index) hospital. It is important to understand the impact of non-index readmission on patient outcomes and healthcare resource utilisation. METHOD: We analysed linked hospital and death records for residents of New South Wales, Australia, aged ≥18 years, who had an emergency readmission within 90 days following a TAVI procedure in 2013-2022. Mixed-effect, multi-level logistic regression models were used to evaluate predictors of non-index readmission, and associations between non-index readmission and readmission length of stay, 90-day mortality, and 1-year mortality. RESULTS: Of 4,198 patients (mean age, 82.7 years; 40.6% female) discharged alive following TAVI, 933 (22.2%) were readmitted within 90 days of discharge. Over three-quarters (76.0%) of those readmitted returned to a non-index hospital, with no significant difference in readmission principal diagnosis between index hospital and non-index hospital readmissions. Among readmitted patients, independent predictors of non-index readmission included: residence in regional or remote areas, lower socio-economic status, having a pre-procedure transfer, and a private index hospital. Readmission length of stay (median, 4 days), 90-day mortality (adjusted odds ratio [OR] 1.04, 95% confidence interval [CI] 0.56-1.96) and 1-year mortality (adjusted OR 1.01, 95% CI 0.64-1.58) were similar between index and non-index readmissions. CONCLUSIONS: Non-index readmission following TAVI was highly prevalent but not associated with increased mortality or healthcare utilisation. Our results are reassuring for TAVI patients in regional and remote areas with limited access to return to index TAVI hospitals.

Identifying prehospital trauma patients from ambulance patient care records; comparing two methods using linked data in New South Wales, Australia.

BACKGROUND: Linked datasets for trauma system monitoring should ideally follow patients from the prehospital scene to hospital admission and post-discharge. Having a well-defined cohort when using administrative datasets is essential because they must capture the representative population. Unlike hospital electronic health records (EHR), ambulance patient-care records lack access to sources beyond immediate clinical notes. Relying on a limited set of variables to define a study population might result in missed patient inclusion. We aimed to compare two methods of identifying prehospital trauma patients: one using only those documented under a trauma protocol and another incorporating additional data elements from ambulance patient care records. METHODS: We analyzed data from six routinely collected administrative datasets from 2015 to 2018, including ambulance patient-care records, aeromedical data, emergency department visits, hospitalizations, rehabilitation outcomes, and death records. Three prehospital trauma cohorts were created: an Extended-T-protocol cohort (patients transported under a trauma protocol and/or patients with prespecified criteria from structured data fields), T-protocol cohort (only patients documented as transported under a trauma protocol) and non-T-protocol (extended-T-protocol population not in the T-protocol cohort). Patient-encounter characteristics, mortality, clinical and post-hospital discharge outcomes were compared. A conservative p-value of 0.01 was considered significant RESULTS: Of 1 038 263 patient-encounters included in the extended-T-population 814 729 (78.5 %) were transported, with 438 893 (53.9 %) documented as a T-protocol patient. Half (49.6 %) of the non-T-protocol sub-cohort had an International Classification of Disease 10th edition injury or external cause code, indicating 79644 missed patients when a T-protocol-only definition was used. The non-T-protocol sub-cohort also identified additional patients with intubation, prehospital blood transfusion and positive eFAST. A higher proportion of non-T protocol patients than T-protocol patients were admitted to the ICU (4.6% vs 3.6 %), ventilated (1.8% vs 1.3 %), received in-hospital transfusion (7.9 vs 6.8 %) or died (1.8% vs 1.3 %). Urgent trauma surgery was similar between groups (1.3% vs 1.4 %). CONCLUSION: The extended-T-population definition identified 50 % more admitted patients with an ICD-10-AM code consistent with an injury, including patients with severe trauma. Developing an EHR phenotype incorporating multiple data fields of ambulance-transported trauma patients for use with linked data may avoid missing these patients.

Predictive analytics for cardiovascular patient readmission and mortality: An explainable approach.

BACKGROUND: Cardiovascular patients experience high rates of adverse outcomes following discharge from hospital, which may be preventable through early identification and targeted action. This study aimed to investigate the effectiveness and explainability of machine learning algorithms in predicting unplanned readmission and death in cardiovascular patients at 30 days and 180 days from discharge. METHODS: Gradient boosting machines were trained and evaluated using data from hospital electronic medical records linked to hospital administrative and mortality data for 39,255 patients admitted to four hospitals in New South Wales, Australia between 2017 and 2021. Sociodemographic variables, admission history, and clinical information were used as potential predictors. The performance was compared to LASSO regression, as well as the HOSPITAL and LACE risk score indices. Important risk factors identified by the gradient-boosting machine model were explored using Shapley values. RESULTS: The models performed well, especially for the mortality outcomes. Area under the receiver operating characteristic curve values were 0.70 for readmission and 0.87-0.90 for mortality using the full gradient boosting machine algorithms. Among the top predictors for 30-day and 180-day readmission were increased red cell distribution width, old age (especially above 80 years), high measured troponin and urea levels, not being married or in a relationship, and low albumin levels. For mortality, these included increased red cell distribution width, old age (especially older than 70 years), high measured troponin and urea levels, high neutrophil and monocyte counts, and low eosinophil and lymphocyte counts. The Shapley values gave clear insight into the dynamics of decision-tree-based models. CONCLUSIONS: We demonstrated an explainable predictive algorithm to identify cardiovascular patients who are at high risk of readmission or death at discharge from the hospital and identified key risk factors.

Estimating the impact of bias in causal epidemiological studies: the case of health outcomes following assisted reproduction.

Researchers interested in causal questions must deal with two sources of error: random error (random deviation from the true mean value of a distribution), and bias (systematic deviance from the true mean value due to extraneous factors). For some causal questions, randomization is not feasible, and observational studies are necessary. Bias poses a substantial threat to the validity of observational research and can have important consequences for health policy developed from the findings. The current piece describes bias and its sources, outlines proposed methods to estimate its impacts in an observational study, and demonstrates how these methods may be used to inform debate on the causal relationship between medically assisted reproduction (MAR) and health outcomes, using cancer as an example. In doing so, we aim to enlighten researchers who work with observational data, especially regarding the health effects of MAR and infertility, on the pitfalls of bias, and how to address them. We hope that, in combination with the provided example, we can convince readers that estimating the impact of bias in causal epidemiologic research is not only important but necessary to inform the development of robust health policy and clinical practice recommendations.

The Australian Health Care Homes trial: quality of care and patient outcomes. A propensity score-matched cohort study.

OBJECTIVE: To assess the impact of the Health Care Homes (HCH) primary health care initiative on quality of care and patient outcomes. DESIGN, SETTING: Quasi-experimental, matched cohort study; analysis of general practice data extracts and linked administrative data from ten Australian primary health networks, 1 October 2017 - 30 June 2021. PARTICIPANTS: People with chronic health conditions (practice data extracts: 9811; linked administrative data: 10 682) enrolled in the HCH 1 October 2017 - 30 June 2019; comparison groups of patients receiving usual care (1:1 propensity score-matched). INTERVENTION: Participants were involved in shared care planning, provided enhanced access to team care, and encouraged to seek chronic condition care at the HCH practice where they were enrolled. Participating practices received bundled payments based on clinical risk tier. MAIN OUTCOME MEASURES: Access to care, processes of care, diabetes-related outcomes, hospital service use, risk of death. RESULTS: During the first twelve months after enrolment, the mean numbers of general practitioner encounters (rate ratio, 1.14; 95% confidence interval [CI], 1.11-1.17) and Medicare Benefits Schedule claims for allied health services (rate ratio, 1.28; 95% CI, 1.24-1.33) were higher for the HCH than the usual care group. Annual influenza vaccinations (relative risk, 1.20; 95% CI, 1.17-1.22) and measurements of blood pressure (relative risk, 1.09; 95% CI, 1.08-1.11), blood lipids (relative risk, 1.19; 95% CI, 1.16-1.21), glycated haemoglobin (relative risk, 1.06; 95% CI, 1.03-1.08), and kidney function (relative risk, 1.13; 95% CI, 1.11-1.15) were more likely in the HCH than the usual care group during the twelve months after enrolment. Similar rate ratios and relative risks applied in the second year. The numbers of emergency department presentations (rate ratio, 1.09; 95% CI, 1.02-1.18) and emergency admissions (rate ratio, 1.13; 95% CI, 1.04-1.22) were higher for the HCH group during the first year; other differences in hospital use were not statistically significant. Differences in glycaemic and blood pressure control in people with diabetes in the second year were not statistically significant. By 30 June 2021, 689 people in the HCH group (6.5%) and 646 in the usual care group (6.1%) had died (hazard ratio, 1.07; 95% CI, 0.96-1.20). CONCLUSIONS: The HCH program was associated with greater access to care and improved processes of care for people with chronic diseases, but not changes in diabetes-related outcomes, most measures of hospital use, or risk of death.