Basque Foundation for Health Innovation and Research

BackgroundPrimary care electronic medical records contain clinical-administrative information on a high percentage of the population. Before this information can be used for epidemiological purposes, its quality must be verified.This study aims to validate diagnoses of atrial fibrillation (AF) recorded in primary care electronic medical records and to estimate the prevalence of AF in the population attending primary care consultations.MethodsWe performed a cross-sectional validation study of all diagnoses of AF recorded in primary care electronic medical records in Madrid (Spain).We also performed simple random sampling of diagnoses of AF (ICPC-2 code K78) registered by 55 physicians and random age- and sex-matched sampling of the records that included a diagnosis of AF. Electrocardiograms, echocardiograms, and hospital discharge or cardiology clinic reports were matched.Sensitivity, specificity, positive and negative predictive values (PPV and NPV), and overall agreement were calculated using the kappa statistic (κ). The prevalence of AF in the community of Madrid was estimated considering the sensitivity and specificity obtained in the validation. All calculations were performed overall and by sex and age groups.ResultsThe degree of agreement was very high (κ = 0.952), with a sensitivity of 97.84%, specificity of 97.39%, PPV of 97.37%, and NPV of 97.85%.The prevalence of AF in the population aged over 18 years was 2.41% (95%CI 2.39–2.42% [2.25% in women and 2.58% in men]). This increased progressively with age, reaching 16.95% in those over 80 years of age (15.5% in women and 19.44% in men).ConclusionsThe validation results obtained enable diagnosis of AF recorded in primary care to be used as a tool for epidemiological studies.A high prevalence of AF was found, especially in older patients.

Abstract Issue Health care systems actors are in need to take decisions every day. The basis for a good decision is to consider all the factors, the value of the action, the values that support a “go” or “not go” decision and the intended and unintended consequences that those decisions could entail. Furthermore, understanding decisions implies knowing which the context and the circumstances that surround decisions are. Description of the issue HTA should cover all those aforementioned aspects and make it along the life cycle of health technologies. Thus, it should be the pivotal element that health systems should look at when making decisions. Nevertheless, this is not the case in all the countries/regions or health systems. Empirical research (facts) and normative inquiry (values) are not consistently considered and holistic approaches to the determination of value are lacking. Results Policymakers, health care systems and HTA bodies are differentially approaching to value determination, and, in most cases, this is not explicitly shared with the stakeholders concerned. The consideration of the implication of life cycle concept is not well addressed and decisions are mainly focused on introduction and implementation, meanwhile decisions on deletion or modification of use are lacking, although relevant for health systems sustainability. Mostly systems failed by individual determination of technologies’ value without considering that they are used on systems in combination to other technologies, by different professionals, applied to different patients and within different contexts. Lessons There is a need for a holistic approach of value determination. Likewise, the life cycle concept offers a unique opportunity to promote proactive actions in improving added value generation for technologies under development. Similarly, the assessment of value once solutions are introduced is substantially a way to improve systems’ outcomes and their efficiency.

The arrival of precision oncology is challenging the evidence standards under which technologies are evaluated for regulatory approval as well as for health technology assessment (HTA) purposes. Several key concepts are discussed to highlight the source of the challenges in evaluating these products, particularly those impacting the HTA of histology-independent therapies. These include the basket trial design, high uncertainty in (potentially substantial) benefits for histology-independent therapies, and the inability to identify and quantify benefits of standard of care in daily practice when the biomarker is not currently used in practice. There is little precedent for a technology with the unique mixture of challenges for HTA of histology-independent therapies and they will be evaluated using standard HTA, as there currently is no evidence suggesting the standard HTA framework is not appropriate. A number of questions proposed to help guide HTA bodies when assessing the appropriateness of local processes to optimally evaluate histology-independent therapies. Pragmatic solutions are further proposed to decrease uncertainty in the benefits of histology independent therapies as well as fill gaps in comparative evidence. The proposed solutions ensure a consistent and streamlined approach to evaluation across histology-independent products, although with varying strengths and limitations. Alongside these solutions, sponsors should engage early with HTA bodies/payers and regulatory agencies through parallel/joint scientific advice to facilitate the integration of both regulatory and HTA perspectives into one clinical development programme, potentially reconciling evidence requirements.

Colorectal cancer is one of the world leading death causes. Fortunately, an early diagnosis allows for effective treatment, increasing the survival rate. Deep learning techniques have shown their utility for increasing the adenoma detection rate at colonoscopy, but a dataset is usually required so the model can automatically learn features that characterize the polyps. In this work, we present the PICCOLO dataset, that comprises 3433 manually annotated images (2131 white-light images 1302 narrow-band images), originated from 76 lesions from 40 patients, which are distributed into training (2203), validation (897) and test (333) sets assuring patient independence between sets. Furthermore, clinical metadata are also provided for each lesion. Four different models, obtained by combining two backbones and two encoder–decoder architectures, are trained with the PICCOLO dataset and other two publicly available datasets for comparison. Results are provided for the test set of each dataset. Models trained with the PICCOLO dataset have a better generalization capacity, as they perform more uniformly along test sets of all datasets, rather than obtaining the best results for its own test set. This dataset is available at the website of the Basque Biobank, so it is expected that it will contribute to the further development of deep learning methods for polyp detection, localisation and classification, which would eventually result in a better and earlier diagnosis of colorectal cancer, hence improving patient outcomes.

The purpose of the present work is to underline the importance of obtaining a standardized procedure to ensure and evaluate both clinical and research usability of human tissue samples. The study, which was carried out by the Biospecimen Science Working Group of the Spanish Biobank Network, is based on a general overview of the current situation about quality assurance in human tissue biospecimens. It was conducted an exhaustive review of the analytical techniques used to evaluate the quality of human tissue samples over the past 30 years, as well as their reference values if they were published, and classified them according to the biomolecules evaluated: (i) DNA, (ii) RNA, and (iii) soluble or/and fixed proteins for immunochemistry. More than 130 publications released between 1989 and 2019 were analysed, most of them reporting results focused on the analysis of tumour and biopsy samples. A quality assessment proposal with an algorithm has been developed for both frozen tissue samples and formalin-fixed paraffin-embedded (FFPE) samples, according to the expected quality of sample based on the available pre-analytical information and the experience of the participants in the Working Group. The high heterogeneity of human tissue samples and the wide number of pre-analytic factors associated to quality of samples makes it very difficult to harmonize the quality criteria. However, the proposed method to assess human tissue sample integrity and antigenicity will not only help to evaluate whether stored human tissue samples fit for the purpose of biomarker development, but will also allow to perform further studies, such as assessing the impact of different pre-analytical factors on very well characterized samples or evaluating the readjustment of tissue sample collection, processing and storing procedures. By ensuring the quality of the samples used on research, the reproducibility of scientific results will be guaranteed.

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