Publications by authors named "Aimee Linkens"

Background: Inappropriate prescribing is associated with negative patient outcomes. In hospitalized patients, the use of Clinical Decision Support Systems (CDSSs) may reduce inappropriate prescribing and thereby improve patient-related outcomes. However, recently published large clinical trials (OPERAM and SENATOR) have shown negative results on the use of CDSSs and patient outcomes and strikingly low acceptance of recommendations.

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Aims: An overlooked aspect of diabetes is an increased risk of hip fractures, with associated mortality. We investigated whether fracture type and/or burden of comorbidities explains the increased risk of mortality in diabetes after hip fracture.

Methods: For this cohort study, we used a de-identified data set of hip fracture patients registered in a quality-of-care registry (2017/2018) included in Maastricht University Medical Centre.

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Background: Due to ageing of the population the incidence of multimorbidity and polypharmacy is rising. Polypharmacy is a risk factor for medication-related (re)admission and therefore places a significant burden on the healthcare system. The reported incidence of medication-related (re)admissions varies widely due to the lack of a clear definition.

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Objectives: To develop (part I) and validate (part II) an electronic fall risk clinical rule (CR) to identify nursing home residents (NH-residents) at risk for a fall incident.

Design: Observational, retrospective case-control study.

Setting: Nursing homes.

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Background: Hip fractures are a major cause of mortality and disability in frail older adults. Therefore, orthogeriatrics has been embraced to improve patient outcomes. With the optimal template of orthogeriatric care still unknown, and to curtail rising healthcare expenditure we implemented a nurse practitioner-led orthogeriatric care program (NPOCP).

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Introduction: Coronavirus disease 2019 (COVID-19) has a high burden on the healthcare system. Prediction models may assist in triaging patients. We aimed to assess the value of several prediction models in COVID-19 patients in the emergency department (ED).

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Background/objectives: Nursing home (NH) residents are a vulnerable population, susceptible to respiratory disease outbreaks such as coronavirus disease 2019 (COVID-19). Poor outcome in COVID-19 is at least partly attributed to hypercoagulability, resulting in a high incidence of thromboembolic complications. It is unknown whether commonly used antithrombotic therapies may protect the vulnerable NH population with COVID-19 against mortality.

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