(1) Background: Cardiac amyloidosis or “stiff heart syndrome” is a rare condition that occurs when amyloid deposits occupy the heart muscle. Many patients suffer from it and fail to receive a timely diagnosis mainly because the disease is a rare form of restrictive cardiomyopathy that is difficult to diagnose, often associated with a poor prognosis. This research analyses the characteristics of this pathology and proposes a statistical learning algorithm that helps to detect the disease. (2) Methods: The hospitalization clinical (medical and nursing ones) records used for this study are the basis of the learning and training techniques of the algorithm. The approach consisted of using the information generated by the patients in each admission and discharge episode and treating it as data vectors to facilitate their aggregation. The large volume of clinical histories implied a high dimensionality of the data, and the lack of diagnosis led to a severe class imbalance caused by the low prevalence of the disease. (3) Results: Although there are few patients with amyloidosis in this study, the proposed approach demonstrates that it is possible to learn from clinical records despite the lack of data. In the validation phase, the algorithm first acted on data from the general study population. It then was applied to a sample of patients diagnosed with heart failure. The results revealed that the algorithm detects disease when data vectors profile each disease episode. (4) Conclusions: The prediction levels showed that this technique could be useful in screening processes on a specific population to detect the disease.
Aim
The gap between research and clinical practice leads to inconsistent decision‐making and clinical audits are an effective way of improving the implementation of best practice. Our aim is to assess the effectiveness of a model that implements evidence‐based recommendations for patient outcomes and healthcare quality.
Design
National quasi‐experimental, multicentre, before and after study.
Methods
This study focuses on patients attending primary care and hospital care units and associated socio‐healthcare services. It uses the Joanna Brigg's Institute Getting Research into Practice model, which improves processes by referring to prior baseline clinical audits. The variables are process and outcome criteria for pain, urinary incontinence, and fall prevention, with data collection at baseline and key points over 12 months drawn from clinical histories and records. Project funding was received from the Spanish Strategic Health Action in November 2014.
Discussion
The project results will provide knowledge on the effectiveness of the Getting Research into Practice model, to apply evidence‐based recommendations for the detection and management of pain, urinary incontinence, and fall prevention. It will also establish whether using research results, based on clinical audits and situation analysis, is effective for implementing evidence‐based recommendations and improving patients’ health.
Impact
This nationwide Spanish project aims to detect and prevent high‐prevalence healthcare problems, namely pain in patients at any age and falls and urinary incontinence in people aged 65 and over. Tailoring clinical practice to evidence‐based recommendations will reduce unjustified clinical variations in providing healthcare services.
Clinical Trial ID: NCT03725774.
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