Factors affecting 12-month unplanned readmissions for chronic obstructive pulmonary disease patients: the effect of mental disorders in an Australian cohort.

BACKGROUND: Many individuals with chronic obstructive pulmonary disease (COPD) experience frequent hospitalization and readmissions, which is burdensome on the health system. This study aims to investigate factors associated with unplanned readmissions and mortality following a COPD-related hospitalization over a 12-month period in Australia, focusing on mental disorders and accounting for the acute phase of the COVID-19 pandemic. METHODS: A retrospective cohort study using linked hospitalization and mortality records identified individuals aged ≥40 years who had at least one hospital admission with a principal diagnosis of COPD between 2014 and 2020 in New South Wales, Australia. A semi-competing risk analysis was conducted to examine factors associated with unplanned readmission and mortality. RESULTS: Adults with a mental disorder diagnosis, specifically anxiety, had a higher risk of 12-month unplanned readmission. Individuals with anxiety and dementia also had a higher risk of mortality pre- and post-unplanned readmission. Individuals who were admitted during the acute phase of the COVID-19 pandemic period had lower risk of unplanned readmission, but higher risk of mortality without unplanned readmission. CONCLUSION: Interventions aimed at reducing admissions should consider adults living with mental disorders such as anxiety or dementia to improve healthcare delivery and health outcomes for individuals living with COPD.

Impact of mental disorders on unplanned readmissions for congestive heart failure patients: a population-level study.

AIMS: Reducing preventable hospitalization for congestive heart failure (CHF) patients is a challenge for health systems worldwide. CHF patients who also have a recent or ongoing mental disorder may have worse health outcomes compared with CHF patients with no mental disorders. This study examined the impact of mental disorders on 28 day unplanned readmissions of CHF patients. METHODS AND RESULTS: This retrospective cohort study used population-level linked public and private hospitalization and death data of adults aged ≥18 years who had a CHF admission in New South Wales, Australia, between 1 January 2014 and 31 December 2020. Individuals' mental disorder diagnosis and Charlson comorbidity and hospital frailty index scores were derived from admission records. Competing risk and cause-specific risk analyses were conducted to examine the impact of having a mental disorder diagnosis on all-cause hospital readmission. Of the 65 861 adults with index CHF admission discharged alive (mean age: 78.6 ± 12.1; 48% female), 19.2% (12 675) had at least one unplanned readmission within 28 days following discharge. Adults with CHF with a mental disorder diagnosis within 12 months had a higher risk of 28 day all-cause unplanned readmission [hazard ratio (HR): 1.21, 95% confidence interval (CI): 1.15-1.27, P-value < 0.001], particularly those with anxiety disorder (HR: 1.49, 95% CI: 1.35-1.65, P-value < 0.001). CHF patients aged ≥85 years (HR: 1.19, 95% CI: 1.11-1.28), having ≥3 other comorbidities (HR: 1.35, 95% CI: 1.25-1.46), and having an intermediate (HR: 1.34, 95% CI: 1.28-1.40) or high (HR: 1.37, 95% CI: 1.27-1.47) frailty score on admission had a higher risk of unplanned readmission. CHF patients with a mental disorder who have ≥3 other comorbidities and an intermediate frailty score had the highest probability of unplanned readmission (29.84%, 95% CI: 24.68-35.73%) after considering other patient-level factors and competing events. CONCLUSIONS: CHF patients who had a mental disorder diagnosis in the past 12 months are more likely to be readmitted compared with those without a mental disorder diagnosis. CHF patients with frailty and a mental disorder have the highest probability of readmission. Addressing mental health care services in CHF patient's discharge plan could potentially assist reduce unplanned readmissions.

The role of frailty risk for fracture-related hospital readmission and mortality after a hip fracture.

BACKGROUND: Frailty risk estimated using hospital administrative data may provide a useful clinical tool to identify older hip fracture patients at-risk of fracture-related readmissions and mortality. This study examined hip fracture hospitalisation temporal trends and explore the role of frailty risk in fracture-related readmission and mortality. METHODS: This retrospective cohort study was conducted using linked hospital admission and mortality data in New South Wales, Australia. Patients aged ≥65 years were admitted after a hip fracture between 2014 and 2021 for temporal trends and those admitted and discharged after a hip fracture in 2014-2018 for fracture-related readmission. The Hospital Frailty Risk Score was estimated, and patients were followed for at least 36 months after discharge. A semi-competing risk analysis was used to examine the associations of frailty with fracture-related readmission and/or mortality. RESULTS: Hip fracture hospitalisation rate was 472 per 100,000 and declined by 2.9 % (95 % confidence intervals (CI): -3.7 to -2.1) annually. Amongst 28,567 patients, 9.8 % were identified with low frailty risk, 39.4 %, intermediate frailty risk, and 50.6 % with high frailty risk. Patients with intermediate or high frailty risk had a higher chance of fracture-related readmission (Hazard ratios (HR): 1.33, 95 %CI: 1.21-1.47, HR: 1.65, 95 %CI: 1.49-1.83), death (HR: 1.50, 95 %CI: 1.38-1.63, HR: 1.80, 95 %CI: 1.65-1.96) and death post fracture-related readmission (HR: 1.32, 95 %CI: 1.12-1.56, HR: 1.56, 95 %CI: 1.32-1.84) than those with low frailty risk. CONCLUSIONS: It appears that frailty risk estimated using hospital administrative data can contribute to identify patients who could benefit from targeted interventions to prevent further fractures.

Extract, transform, load framework for the conversion of health databases to OMOP.

Common data models standardize the structures and semantics of health datasets, enabling reproducibility and large-scale studies that leverage the data from multiple locations and settings. The Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) is one of the leading common data models. While there is a strong incentive to convert datasets to OMOP, the conversion is time and resource-intensive, leaving the research community in need of tools for mapping data to OMOP. We propose an extract, transform, load (ETL) framework that is metadata-driven and generic across source datasets. The ETL framework uses a new data manipulation language (DML) that organizes SQL snippets in YAML. Our framework includes a compiler that converts YAML files with mapping logic into an ETL script. Access to the ETL framework is available via a web application, allowing users to upload and edit YAML files via web editor and obtain an ETL SQL script for use in development environments. The structure of the DML maximizes readability, refactoring, and maintainability, while minimizing technical debt and standardizing the writing of ETL operations for mapping to OMOP. Our framework also supports transparency of the mapping process and reuse by different institutions.