BackgroundComprehensive biomarker testing is essential in selecting optimal treatment for patients with metastatic colorectal cancer (mCRC); however, incomplete genotyping is widespread, with most patients not receiving testing for all guideline-recommended biomarkers, in part due to reliance on burdensome sequential tissue-based single-biomarker tests with long waiting times or availability of only archival tissue samples. We aimed to demonstrate that liquid biopsy, associated with rapid turnaround time (TAT) and lower patient burden, effectively identifies guideline-recommended biomarkers in mCRC relative to standard of care (SOC) tissue testing.Patients and methodsProspectively enrolled patients with previously untreated mCRC undergoing physician discretion SOC tissue genotyping submitted pretreatment blood samples for comprehensive circulating tumor DNA (ctDNA) analysis with Guardant360 and targeted RAS and BRAF analysis with OncoBEAM.ResultsAmong 155 patients, physician discretion SOC tissue genotyping identified a guideline-recommended biomarker in 82 patients, versus 88 identified with comprehensive ctDNA (52.9% versus 56.8%, noninferiority demonstrated down to α = 0.005) and 69 identified with targeted PCR ctDNA analysis (52.9% versus 44.5%, noninferiority rejected at α = 0.05). Utilizing ctDNA in addition to tissue increased patient identification for a guideline-recommended biomarker by 19.5% by rescuing those without tissue results either due to tissue insufficiency, test failure, or false negatives. ctDNA median TAT was significantly faster than tissue testing when the complete process from sample acquisition to results was considered (median 10 versus 27 days, P < 0.0001), resulting in accelerated biomarker discovery, with 52.0% biomarker-positive patients identified by ctDNA versus 10.2% by SOC tissue 10 days after sample collection (P < 0.0001).ConclusionsComprehensive ctDNA genotyping accurately identifies guideline-recommended biomarkers in patients with mCRC at a rate at least as high as SOC tissue genotyping, in a much shorter time. Based on these findings, the addition of ctDNA genotyping to clinical practice has significant potential to improve the care of patients with mCRC.

The benefits of structured reporting (SR) in radiology are well-known and have been widely described. However, there are limitations that must be overcome. Radiologists may be reluctant to change the conventional way of reporting. Error rates could potentially increase if SR is used improperly. Interruption of the visual search pattern by keeping the eyes focused on the report rather than the images may increase reporting time. Templates that include unnecessary or irrelevant information may undermine the consistency of the report. Last, the lack of support for multiple languages may hamper the adaptation of the report to the target audience. This work aims to mitigate these limitations with a web-based structured reporting system based on templates. By including field validators and logical rules, the system avoids reporting mistakes and allows to automatically calculate values and radiological qualitative scores. The system can manage quantitative information from imaging biomarkers, combining this with qualitative radiological information usually present in the structured report. It manages SR templates as plugins (IHE MRRT compliant and compatible with RSNA's Radreport templates), ensures a seamless integration with PACS/RIS systems, and adapts the report to the target audience by means of natural language extracts generated in multiple languages. We describe a use case of SR template for prostate cancer including PI-RADS 2.1 scoring system and imaging biomarkers. For the time being, the system comprises 24 SR templates and provides service in 37 hospitals and healthcare institutions, endorsing the success of this contribution to mitigate some of the limitations of the SR.

Early palliative care (EPC) in patients with advanced cancer is associated with better quality of life and fewer cancer-related symptoms during the dying process and improved overall survival (OS). Patients with small cell lung cancer (SCLC) have a biologically aggressive disease, so early inclusion may be relevant to modify clinical practice guidelines.

The immune system plays a central role in the pathogenesis of cancer, inhibiting as well as promoting effects on tumor development. Tumor-infiltrating lymphocytes (TIL) and PD-L1 expression have been described as factors with prognostic influence in the early stages of breast cancer. However, its relationship to other classical prognostic factors of estimated importance is unknown.

Background: SNOMED CT Expression Constraint Language (ECL) is a declarative language developed by SNOMED International for the definition of SNOMED CT Expression Constraints (ECs). ECs are executable expressions that define intensional subsets of clinical meanings by stating constraints over the logic definition of concepts. The execution of an EC on some SNOMED CT substrate yields the intended subset, and it requires an execution engine able to receive an EC as input, execute it, and return the matching concepts. An important issue regarding subsets of clinical concepts is their use in terminology binding between clinical information models and terminologies for defining the set of valid values of codified data. Objective: To define and implement methods for the simplification, semantic validation and execution of ECs over a graph-oriented SNOMED CT database, and to provide a method for the visual representation of subsets in order to explore, understand and validate its content, as well as to develop an EC execution platform, called SNQuery, which makes use of these methods. Methods: Since SNOMED CT is a directed and acyclic graph, we have used a graph-oriented database to represent the content of SNOMED CT, where the schema and instances are represented as graphs and the data manipulation is expressed by graph-oriented operations. For the execution of ECs over the graph database, it is performed a translation process in which ECs are translated into a set of Cypher Query Language queries. We have defined some EC simplification methods that leverage the logic structure underlying SNOMED CT. The purpose of these methods is to reduce the complexity of ECs and, in turn, its execution time, as well as to validate them from a SNOMED CT Concept Model and logical definition points of view. We also have developed a graphic representation based on the circle packing geometrical concept, which allows validating subsets, as well as predefined refsets and the terminology itself. Results: We have developed SNQuery, a platform for the definition of intensional subsets of SNOMED CT concepts by means of the execution of ECs over a graph-oriented SNOMED CT database. Additionally, we have incorporated methods for the simplification and semantic validation of ECs, as well as for the visualization of subsets as a mechanism to understand and validate them. SNQuery has been evaluated in terms of EC execution times. Conclusion: In this paper, we provide methods to simplify, semantically validate and execute ECs over a graphoriented database. We also offer a method to visualize the intensional subsets obtained by executing ECs to explore, understand and validate them, as well as refsets and the terminology itself. The definition of intensional subsets is useful to bind content between clinical information models and clinical terminologies, which is a necessary step to achieve semantic interoperability between EHR systems.

This work presents methods to combine data from the Semantic Web into existing EHRs, leading to an augmented EHR. An existing EHR extract is augmented by combining it with additional information from external sources, typically linked data sources. The starting point is a standardized EHR extract described by an archetype. The method consists of combining specific data from the original EHR with contents from the external information source by building a semantic representation, which is used to query the external source. The results are converted into a standardized EHR extract according to an archetype. This work sets the foundations to transform Semantic Web contents into normalized EHR extracts. Finally, to exemplify the approach, the work includes a practical use case in which the summarized EHR is augmented with drug–drug interactions and disease-related treatment information.

Clinical Information Models (CIMs) expressed as archetypes play an essential role in the design and development of current Electronic Health Record (EHR) information structures. Although there exist many experiences about using archetypes in the literature, a comprehensive and formal methodology for archetype modeling does not exist. Having a modeling methodology is essential to develop quality archetypes, in order to guide the development of EHR systems and to allow the semantic interoperability of health data. In this work, an archetype modeling methodology is proposed. This paper describes its phases, the inputs and outputs of each phase, and the involved participants and tools. It also includes the description of the possible strategies to organize the modeling process. The proposed methodology is inspired by existing best practices of CIMs, software and ontology development. The methodology has been applied and evaluated in regional and national EHR projects. The application of the methodology provided useful feedback and improvements, and confirmed its advantages. The conclusion of this work is that having a formal methodology for archetype development facilitates the definition and adoption of interoperable archetypes, improves their quality, and facilitates their reuse among different information systems and EHR projects. Moreover, the proposed methodology can be also a reference for CIMs development using any other formalism.

We present the results of a pilot project of the Spanish Ministry of Health, Social Services and Equality, envisaged to the development of a national integrated data repository of maternal-child care information. Based on health information standards and data quality assessment procedures, the developed repository is aimed to a reliable data reuse for (1) population research and (2) the monitoring of healthcare best practices. Data standardization was provided by means of two main ISO 13606 archetypes (composed of 43 sub-archetypes), the first dedicated to the delivery and birth information and the second about the infant feeding information from delivery up to two years. Data quality was assessed by means of a dedicated procedure on seven dimensions including completeness, consistency, uniqueness, multi-source variability, temporal variability, correctness and predictive value. A set of 127 best practice indicators was defined according to international recommendations and mapped to the archetypes, allowing their calculus using XQuery programs. As a result, a standardized and data quality assessed integrated data respository was generated, including 7857 records from two Spanish hospitals: Hospital Virgen del Castillo, Yecla, and Hospital 12 de Octubre, Madrid. This pilot project establishes the basis for a reliable maternal-child care data reuse and standardized monitoring of best practices based on the developed information and data quality standards.

The need to achieve high levels of semantic interoperability in the health domain is regarded as a crucial issue. Nowadays, one of the weaknesses when working in this direction is the lack of a coordinated use of information and terminological models to define the meaning and content of clinical data. IHTSDO is aware of this problem and has recently developed the SNOMED CT Expression Constraint Language to specify subsets of concepts. In this paper, we describe an implementation of an execution engine of this language. Our final objective is to allow advanced terminological binding between archetypes and SNOMED CT as a fundamental pillar to get semantically interoperable systems. The execution engine is available at http://snquery.veratech.es.

Clinical decision-support systems (CDSSs) should be able to interact with the electronic health record (EHR) to obtain the patient data they require. A recent solution for the interoperability of CDSSs and EHR systems consists in the use of a mediated schema which provides a unified view of their two schemas. The use of such a mediated schema requires the definition of a mapping between this schema and the EHR one. In this paper we investigate the use of the SNOMED CT Expression Constraint Language to characterize these mappings.

The heterogeneity of clinical data is a key problem in the sharing and reuse of Electronic Health Record (EHR) data. We approach this problem through the combined use of EHR standards and semantic web technologies, concretely by means of clinical data transformation applications that convert EHR data in proprietary format, first into clinical information models based on archetypes, and then into RDF/OWL extracts which can be used for automated reasoning. In this paper we describe a proof-of-concept platform to facilitate the (re)configuration of such clinical data transformation applications. The platform is built upon a number of web services dealing with transformations at different levels (such as normalization or abstraction), and relies on a collection of reusable mappings designed to solve specific transformation steps in a particular clinical domain. The platform has been used in the development of two different data transformation applications in the area of colorectal cancer.

Objective To assess the variability in data distributions among data sources and over time through a case study of a large multisite repository as a systematic approach to data quality (DQ).Materials and Methods Novel probabilistic DQ control methods based on information theory and geometry are applied to the Public Health Mortality Registry of the Region of Valencia, Spain, with 512 143 entries from 2000 to 2012, disaggregated into 24 health departments. The methods provide DQ metrics and exploratory visualizations for (1) assessing the variability among multiple sources and (2) monitoring and exploring changes with time. The methods are suited to big data and multitype, multivariate, and multimodal data.Results The repository was partitioned into 2 probabilistically separated temporal subgroups following a change in the Spanish National Death Certificate in 2009. Punctual temporal anomalies were noticed due to a punctual increment in the missing data, along with outlying and clustered health departments due to differences in populations or in practices.Discussion Changes in protocols, differences in populations, biased practices, or other systematic DQ problems affected data variability. Even if semantic and integration aspects are addressed in data sharing infrastructures, probabilistic variability may still be present. Solutions include fixing or excluding data and analyzing different sites or time periods separately. A systematic approach to assessing temporal and multisite variability is proposed.Conclusion Multisite and temporal variability in data distributions affects DQ, hindering data reuse, and an assessment of such variability should be a part of systematic DQ procedures.

In this paper, we propose an automatic method to segment the five main brain sub-regions (i.e. left/right hemispheres, left/right cerebellum and brainstem) from magnetic resonance images. The proposed method uses a library of pre-labeled brain images in a stereotactic space in combination with a non-local label fusion scheme for segmentation. The main novelty of the proposed method is the use of a multi-label block-wise label fusion strategy specifically designed to deal with the classification of main brain sub-volumes that process only specific parts of the brain images significantly reducing the computational burden. The proposed method has been quantitatively evaluated against manual segmentations. The evaluation showed that the proposed method was faster while producing more accurate segmentations than a current state-of-the-art method. We also present evidences suggesting that the proposed method was more robust against brain pathologies than the compared method. Finally, we demonstrate the clinical value of our method compared to the state-of-the-art approach in terms of the asymmetry quantification in Alzheimer's disease.

Objective This systematic review aims to identify and compare the existing processes and methodologies that have been published in the literature for defining clinical information models (CIMs) that support the semantic interoperability of electronic health record (EHR) systems. Material and Methods Following the preferred reporting items for systematic reviews and meta-analyses systematic review methodology, the authors reviewed published papers between 2000 and 2013 that covered that semantic interoperability of EHRs, found by searching the PubMed, IEEE Xplore, and ScienceDirect databases. Additionally, after selection of a final group of articles, an inductive content analysis was done to summarize the steps and methodologies followed in order to build CIMs described in those articles. Results Three hundred and seventy-eight articles were screened and thirty six were selected for full review. The articles selected for full review were analyzed to extract relevant information for the analysis and characterized according to the steps the authors had followed for clinical information modeling. Discussion Most of the reviewed papers lack a detailed description of the modeling methodologies used to create CIMs. A representative example is the lack of description related to the definition of terminology bindings and the publication of the generated models. However, this systematic review confirms that most clinical information modeling activities follow very similar steps for the definition of CIMs. Having a robust and shared methodology could improve their correctness, reliability, and quality. Conclusion Independently of implementation technologies and standards, it is possible to find common patterns in methods for developing CIMs, suggesting the viability of defining a unified good practice methodology to be used by any clinical information modeler.

O aumento na prevalência da obesidade na população geral se estende para mulheres na idade reprodutiva. O objetivo deste estudo é relatar o controle perioperatório de uma gestante obesa mórbida com índice de massa corporal > 50 Kg/m2, submetida a parto cesariano sob anestesia geral.Gestante de 35 anos com índice de massa corporal de 59,8 Kg/m2. Estava em trabalho de parto. Foi indicado parto cesariano devido a macrossomia fetal presumida. A paciente recusou raquianestesia. Ela foi posicionada em rampa com coxins no dorso até a cabeça para facilitar a intubação traqueal. Outro coxim foi colocado na parte superior do glúteo direito para criar uma angulação próxima de 15° com a mesa cirúrgica. Imediatamente antes da indução anestésica, procedeu‐se a assepsia e colocação de campos cirúrgicos estéreis. Foi feita indução em sequência rápida com manobra de Sellick, com remifentanil, propofol e succinilcolina. A intubação foi feita com auxílio de gum elastic bougie. A anestesia foi mantida com remifentanil e sevoflurano. O intervalo entre a incisão na pele e a extração fetal foi de 21 minutos e foi usada uma das colheres do fórceps de Simpson para auxílio na extração. Paciente concebeu recém‐nascido com peso de 4.850 g, apresentou índice de Apgar de 2 no primeiro minuto (recebeu ventilação com pressão positiva sob máscara por aproximadamente dois minutos) e 8 no quinto minuto. A paciente foi extubada, sem intercorrências. Foi feita analgesia multimodal e profilaxia de náuseas e vômitos. Mãe e récem‐nascido receberam alta no quarto dia de pós‐operatório.The increased prevalence of obesity in the general population extends to women of reproductive age. The aim of this study is to report the perioperative management of a morbidly obese pregnant woman, body mass index > 50 kg/m2, who underwent cesarean section under general anesthesia.Pregnant woman in labor, 35 years of age, body mass index 59.8 kg/m2. Caesarean section was indicated due to the presumed fetal macrosomia. The patient refused spinal anesthesia. She was placed in the ramp position with cushions from back to head to facilitate tracheal intubation. Another cushion was placed on top of the right gluteus to create an angle of approximately 15° to the operating table. Immediately before induction of anesthesia, asepsis was carried out and sterile surgical fields were placed. Anesthesia was induced in rapid sequence, with Sellick maneuver and administration of remifentanil, propofol, and succinilcolina. Intubation was performed using a gum elastic bougie, and anesthesia was maintained with sevoflurane and remifentanil. The interval between skin incision and fetal extraction was 21 minutes, with the use of a Simpson's forceps scoop to assist in the extraction. The patient gave birth to a newborn weighing 4850 g, with Apgar scores of 2 in the 1st minute (received positive pressure ventilation by mask for about 2 minutes) and 8 in the 5th minute. The patient was extubated uneventfully. Multimodal analgesia and prophylaxis of nausea and vomiting was performed. Mother and newborn were discharged on the 4th postoperative day.

In the last decades, and following the new trends in medicine, statistical learning techniques have been used for developing automatic diagnostic models for aiding the clinical experts throughout the use of Clinical Decision Support Systems. The development of these models requires a large, representative amount of data, which is commonly obtained from one hospital or a group of hospitals after an expensive and time-consuming gathering, preprocess, and validation of cases. After the model development, it has to overcome an external validation that is often carried out in a different hospital or health center. The experience is that the models show underperformed expectations. Furthermore, patient data needs ethical approval and patient consent to send and store data. For these reasons, we introduce an incremental learning algorithm base on the Bayesian inference approach that may allow us to build an initial model with a smaller number of cases and update it incrementally when new data are collected or even perform a new calibration of a model from a different center by using a reduced number of cases. The performance of our algorithm is demonstrated by employing different benchmark datasets and a real brain tumor dataset; and we compare its performance to a previous incremental algorithm and a non-incremental Bayesian model, showing that the algorithm is independent of the data model, iterative, and has a good convergence.

Knowledge discovery on biomedical data can be based on on-line, data-stream analyses, or using retrospective, timestamped, off-line datasets. In both cases, changes in the processes that generate data or in their quality features through time may hinder either the knowledge discovery process or the generalization of past knowledge. These problems can be seen as a lack of data temporal stability. This work establishes the temporal stability as a data quality dimension and proposes new methods for its assessment based on a probabilistic framework. Concretely, methods are proposed for (1) monitoring changes, and (2) characterizing changes, trends and detecting temporal subgroups. First, a probabilistic change detection algorithm is proposed based on the Statistical Process Control of the posterior Beta distribution of the Jensen–Shannon distance, with a memoryless forgetting mechanism. This algorithm (PDF-SPC) classifies the degree of current change in three states: In-Control, Warning, and Out-of-Control. Second, a novel method is proposed to visualize and characterize the temporal changes of data based on the projection of a non-parametric information-geometric statistical manifold of time windows. This projection facilitates the exploration of temporal trends using the proposed IGT-plot and, by means of unsupervised learning methods, discovering conceptually-related temporal subgroups. Methods are evaluated using real and simulated data based on the National Hospital Discharge Survey (NHDS) dataset.

Diabetes Mellitus (DM) affects hundreds of millions of people worldwide and it imposes a large economic burden on healthcare systems. We present a web patient empowering system (PHSP4) that ensures continuous monitoring and assessment of the health state of patients with DM (type I and II). PHSP4 is a Knowledge-Based Personal Health System (PHS) which follows the trend of P4 Medicine (Personalized, Predictive, Preventive, and Participative). It provides messages to outpatients and clinicians about the achievement of objectives, follow-up, and treatments adjusted to the patient condition. Additionally, it calculates a four-component risk vector of the associated pathologies with DM: Nephropathy, Diabetic retinopathy, Diabetic foot, and Cardiovascular event. The core of the system is a Rule-Based System which Knowledge Base is composed by a set of rules implementing the recommendations of the American Diabetes Association (ADA) (American Diabetes Association: http://www.diabetes.org/ ) clinical guideline. The PHSP4 is designed to be standardized and to facilitate its interoperability by means of terminologies (SNOMED-CT [The International Health Terminology Standards Development Organization: http://www.ihtsdo.org/snomed-ct/ ] and UCUM [The Unified Code for Units of Measure: http://unitsofmeasure.org/ ]), standardized clinical documents (HL7 CDA R2 [Health Level Seven International: http://www.hl7.org/index.cfm ]) for managing Electronic Health Record (EHR). We have evaluated the functionality of the system and its users' acceptance of the system using simulated and real data, and a questionnaire based in the Technology Acceptance Model methodology (TAM). Finally results show the reliability of the system and the high acceptance of clinicians.