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Introduction: Personalized disease management informed by quantitative risk prediction has the potential to improve patient care and outcomes. The integration of risk prediction into clinical workflow should be informed by the experiences and preferences of stakeholders, and the impact of such integration should be evaluated in prospective comparative studies. The objectives of the IMplementing Predictive Analytics towards efficient chronic obstructive pulmonary disease (COPD) treatments (IMPACT) study are to integrate an exacerbation risk prediction tool into routine care and to determine its impact on prescription appropriateness (primary outcome), medication adherence, quality of life, exacerbation rates, and sex and gender disparities in COPD care (secondary outcomes).
Methods: IMPACT will be conducted in two phases. Phase 1 will include the systematic and user-centered development of two decision support tools: (1) a decision tool for pulmonologists called the ACCEPT decision intervention (ADI), which combines risk prediction from the previously developed Acute COPD Exacerbation Prediction Tool with treatment algorithms recommended by the Canadian Thoracic Society's COPD pharmacotherapy guidelines, and (2) an information pamphlet for COPD patients (patient tool), tailored to their prescribed medication, clinical needs, and lung function. In phase 2, we will conduct a stepped-wedge cluster randomized controlled trial in two outpatient respiratory clinics to evaluate the impact of the decision support tools on quality of care and patient outcomes. Clusters will be practicing pulmonologists (n ≥ 24), who will progressively switch to the intervention over 18 months. At the end of the study, a qualitative process evaluation will be carried out to determine the barriers and enablers of uptake of the tools.
Discussion: The IMPACT study coincides with a planned harmonization of electronic health record systems across tertiary care centers in British Columbia, Canada. The harmonization of these systems combined with IMPACT's implementation-oriented design and partnership with stakeholders will facilitate integration of the tools into routine care, if the results of the proposed study reveal positive association with improvement in the process and outcomes of clinical care. The process evaluation at the end of the trial will inform subsequent design iterations before largescale implementation.
Trial Registration: NCT05309356.
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http://dx.doi.org/10.1186/s41512-023-00140-6 | DOI Listing |
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Translational Neuropathology Research Laboratory, Department of Pathology and Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia.
Importance: Exposure to fine particulate matter air pollution (PM2.5) may increase risk for dementia. It is unknown whether this association is mediated by dementia-related neuropathologic change found at autopsy.
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Department of Radiology, University of Washington, Seattle.
Importance: Recent longitudinal studies in patients with unruptured intracranial aneurysms (UIAs) suggested that aneurysm wall enhancement (AWE) on magnetic resonance imaging (MRI) predicts growth and rupture. However, because these studies were limited by small sample size and short follow-up duration, it remains unclear whether this radiological biomarker has predictive value for UIA instability.
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J Gerontol A Biol Sci Med Sci
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Institute of Epidemiology and Medical Biometry, Ulm University, Ulm, Germany.
Background: Ambulatory older residents in long-term care(LTC) have the highest risk of falling. However, the relationship between ambulatory activity (steps per day) and fall risk in LTC is unclear. This study examined whether baseline daily step count, functional capacity and cognitive function predicted falls in LTC residents, and whether functional capacity modified the relationship between step count and fall risk.
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M-DT1, Roquefort-les Pins, France.
To date, the closed-loop system represents the best commercialized management of type 1 diabetes. However, mealtimes still require carbohydrate estimation and are often associated with postprandial hyperglycemia which may contribute to poor metabolic control and long -term complications. A multicentre, prospective, non-interventional clinical trial was designed to determine the effectiveness of a novel algorithm to predict changes in blood glucose levels two hours after a usual meal.
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Medical Intensive Care Unit, 108 Military Central Hospital, Hanoi, Vietnam.
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