Ontology-based categorization of clinical studies by their conditions

OBJECTIVE

The free-text Condition data field in the ClinicalTrials.gov is not amenable to computational processes for retrieving, aggregating and visualizing clinical studies by condition categories. This paper contributes a method for automated ontology-based categorization of clinical studies by their conditions.

MATERIALS AND METHODS

Our method first maps text entries in ClinicalTrials.gov's Condition field to standard condition concepts in the OMOP Common Data Model by using SNOMED CT as a reference ontology and using Usagi for concept normalization, followed by hierarchical traversal of the SNOMED ontology for concept expansion, ontology-driven condition categorization, and visualization. We compared the accuracy of this method to that of the MeSH-based method.

RESULTS

We reviewed the 4,506 studies on Vivli.org categorized by our method. Condition terms of 4,501 (99.89%) studies were successfully mapped to SNOMED CT concepts, and with a minimum concept mapping score threshold, 4,428 (98.27%) studies were categorized into 31 predefined categories. When validating with manual categorization results on a random sample of 300 studies, our method achieved an estimated categorization accuracy of 95.7%, while the MeSH-based method had an accuracy of 85.0%.

CONCLUSION

We showed that categorizing clinical studies using their Condition terms with referencing to SNOMED CT achieved a better accuracy and coverage than using MeSH terms. The proposed ontology-driven condition categorization was useful to create accurate clinical study categorization that enables clinical researchers to aggregate evidence from a large number of clinical studies.

Standardized Integration of Person-Generated Data Into Routine Clinical Care

Person-generated data (PGD) are a valuable source of information on a person’s health state in daily life and in between clinic visits. To fully extract value from PGD, health care organizations must be able to smoothly integrate data from PGD devices into routine clinical workflows. Ideally, to enhance efficiency and flexibility, such integrations should follow reusable processes that can easily be replicated for multiple devices and data types. Instead, current PGD integrations tend to be one-off efforts entailing high costs to build and maintain custom connections with each device and their proprietary data formats. This viewpoint paper formulates the integration of PGD into clinical systems and workflow as a PGD integration pipeline and reviews the functional components of such a pipeline. A PGD integration pipeline includes PGD acquisition, aggregation, and consumption. Acquisition is the person-facing component that includes both technical (eg, sensors, smartphone apps) and policy components (eg, informed consent). Aggregation pools, standardizes, and structures data into formats that can be used in health care settings such as within electronic health record–based workflows. PGD consumption is wide-ranging, by different solutions in different care settings (inpatient, outpatient, consumer health) for different types of users (clinicians, patients). The adoption of data and metadata standards, such as those from IEEE and Open mHealth, would facilitate aggregation and enable broader consumption. We illustrate the benefits of a standards-based integration pipeline for the illustrative use case of home blood pressure monitoring. A standards-based PGD integration pipeline can flexibly streamline the clinical use of PGD while accommodating the complexity, scale, and rapid evolution of today’s health care systems.

Sensor Data Integration: A New Cross-Industry Collaboration to Articulate Value, Define Needs, and Advance a Framework for Best Practices

Data integration, the processes by which data are aggregated, combined, and made available for use, has been key to the development and growth of many technological solutions. In health care, we are experiencing a revolution in the use of sensors to collect data on patient behaviors and experiences. Yet, the potential of this data to transform health outcomes is being held back. Deficits in standards, lexicons, data rights, permissioning, and security have been well documented, less so the cultural adoption of sensor data integration as a priority for large-scale deployment and impact on patient lives. The use and reuse of trustworthy data to make better and faster decisions across drug development and care delivery will require an understanding of all stakeholder needs and best practices to ensure these needs are met. The Digital Medicine Society is launching a new multistakeholder Sensor Data Integration Tour of Duty to address these challenges and more, providing a clear direction on how sensor data can fulfill its potential to enhance patient lives.

Recent Academic Research on Clinically Relevant Digital Measures: Systematic Review

Background

Digital clinical measures collected via various digital sensing technologies such as smartphones, smartwatches, wearables, ingestibles, and implantables are increasingly used by individuals and clinicians to capture health outcomes or behavioral and physiological characteristics of individuals. Although academia is taking an active role in evaluating digital sensing products, academic contributions to advancing the safe, effective, ethical, and equitable use of digital clinical measures are poorly characterized.

Objective

We performed a systematic review to characterize the nature of academic research on digital clinical measures and to compare and contrast the types of sensors used and the sources of funding support for specific subareas of this research.

Methods

We conducted a PubMed search using a range of search terms to retrieve peer-reviewed articles reporting US-led academic research on digital clinical measures between January 2019 and February 2021. We screened each publication against specific inclusion and exclusion criteria. We then identified and categorized research studies based on the types of academic research, sensors used, and funding sources. Finally, we compared and contrasted the funding support for these specific subareas of research and sensor types.

Results

The search retrieved 4240 articles of interest. Following the screening, 295 articles remained for data extraction and categorization. The top five research subareas included operations research (research analysis; n=225, 76%), analytical validation (n=173, 59%), usability and utility (data visualization; n=123, 42%), verification (n=93, 32%), and clinical validation (n=83, 28%). The three most underrepresented areas of research into digital clinical measures were ethics (n=0, 0%), security (n=1, 0.5%), and data rights and governance (n=1, 0.5%). Movement and activity trackers were the most commonly studied sensor type, and physiological (mechanical) sensors were the least frequently studied. We found that government agencies are providing the most funding for research on digital clinical measures (n=192, 65%), followed by independent foundations (n=109, 37%) and industries (n=56, 19%), with the remaining 12% (n=36) of these studies completely unfunded.

Conclusions

Specific subareas of academic research related to digital clinical measures are not keeping pace with the rapid expansion and adoption of digital sensing products. An integrated and coordinated effort is required across academia, academic partners, and academic funders to establish the field of digital clinical measures as an evidence-based field worthy of our trust.

Chia, a large annotated corpus of clinical trial eligibility criteria

We present Chia, a novel, large annotated corpus of patient eligibility criteria extracted from 1,000 interventional, Phase IV clinical trials registered in ClinicalTrials.gov. This dataset includes 12,409 annotated eligibility criteria, represented by 41,487 distinctive entities of 15 entity types and 25,017 relationships of 12 relationship types. Each criterion is represented as a directed acyclic graph, which can be easily transformed into Boolean logic to form a database query. Chia can serve as a shared benchmark to develop and test future machine learning, rule-based, or hybrid methods for information extraction from free-text clinical trial eligibility criteria.

Measurement(s)	Clinical Trial Eligibility Criteria • Analytical Procedure Accuracy
Technology Type(s)	digital curation • computational modeling technique
Sample Characteristic - Organism	Homo sapiens

Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.12765602

Evaluating Data Abstraction Assistant, a novel software application for data abstraction during systematic reviews: protocol for a randomized controlled trial

BACKGROUND

Data abstraction, a critical systematic review step, is time-consuming and prone to errors. Current standards for approaches to data abstraction rest on a weak evidence base. We developed the Data Abstraction Assistant (DAA), a novel software application designed to facilitate the abstraction process by allowing users to (1) view study article PDFs juxtaposed to electronic data abstraction forms linked to a data abstraction system, (2) highlight (or "pin") the location of the text in the PDF, and (3) copy relevant text from the PDF into the form. We describe the design of a randomized controlled trial (RCT) that compares the relative effectiveness of (A) DAA-facilitated single abstraction plus verification by a second person, (B) traditional (non-DAA-facilitated) single abstraction plus verification by a second person, and (C) traditional independent dual abstraction plus adjudication to ascertain the accuracy and efficiency of abstraction.

METHODS

This is an online, randomized, three-arm, crossover trial. We will enroll 24 pairs of abstractors (i.e., sample size is 48 participants), each pair comprising one less and one more experienced abstractor. Pairs will be randomized to abstract data from six articles, two under each of the three approaches. Abstractors will complete pre-tested data abstraction forms using the Systematic Review Data Repository (SRDR), an online data abstraction system. The primary outcomes are (1) proportion of data items abstracted that constitute an error (compared with an answer key) and (2) total time taken to complete abstraction (by two abstractors in the pair, including verification and/or adjudication).

DISCUSSION

The DAA trial uses a practical design to test a novel software application as a tool to help improve the accuracy and efficiency of the data abstraction process during systematic reviews. Findings from the DAA trial will provide much-needed evidence to strengthen current recommendations for data abstraction approaches.

TRIAL REGISTRATION

The trial is registered at National Information Center on Health Services Research and Health Care Technology (NICHSR) under Registration # HSRP20152269: https://wwwcf.nlm.nih.gov/hsr_project/view_hsrproj_record.cfm?NLMUNIQUE_ID=20152269&SEARCH_FOR=Tianjing%20Li . All items from the World Health Organization Trial Registration Data Set are covered at various locations in this protocol. Protocol version and date: This is version 2.0 of the protocol, dated September 6, 2016. As needed, we will communicate any protocol amendments to the Institutional Review Boards (IRBs) of Johns Hopkins Bloomberg School of Public Health (JHBSPH) and Brown University. We also will make appropriate as-needed modifications to the NICHSR website in a timely fashion.

Multivariate analysis of the population representativeness of related clinical studies

OBJECTIVE

To develop a multivariate method for quantifying the population representativeness across related clinical studies and a computational method for identifying and characterizing underrepresented subgroups in clinical studies.

METHODS

We extended a published metric named Generalizability Index for Study Traits (GIST) to include multiple study traits for quantifying the population representativeness of a set of related studies by assuming the independence and equal importance among all study traits. On this basis, we compared the effectiveness of GIST and multivariate GIST (mGIST) qualitatively. We further developed an algorithm called "Multivariate Underrepresented Subgroup Identification" (MAGIC) for constructing optimal combinations of distinct value intervals of multiple traits to define underrepresented subgroups in a set of related studies. Using Type 2 diabetes mellitus (T2DM) as an example, we identified and extracted frequently used quantitative eligibility criteria variables in a set of clinical studies. We profiled the T2DM target population using the National Health and Nutrition Examination Survey (NHANES) data.

RESULTS

According to the mGIST scores for four example variables, i.e., age, HbA1c, BMI, and gender, the included observational T2DM studies had superior population representativeness than the interventional T2DM studies. For the interventional T2DM studies, Phase I trials had better population representativeness than Phase III trials. People at least 65years old with HbA1c value between 5.7% and 7.2% were particularly underrepresented in the included T2DM trials. These results confirmed well-known knowledge and demonstrated the effectiveness of our methods in population representativeness assessment.

CONCLUSIONS

mGIST is effective at quantifying population representativeness of related clinical studies using multiple numeric study traits. MAGIC identifies underrepresented subgroups in clinical studies. Both data-driven methods can be used to improve the transparency of design bias in participation selection at the research community level.

A method for analyzing commonalities in clinical trial target populations

ClinicalTrials.gov presents great opportunities for analyzing commonalities in clinical trial target populations to facilitate knowledge reuse when designing eligibility criteria of future trials or to reveal potential systematic biases in selecting population subgroups for clinical research. Towards this goal, this paper presents a novel data resource for enabling such analyses. Our method includes two parts: (1) parsing and indexing eligibility criteria text; and (2) mining common eligibility features and attributes of common numeric features (e.g., A1c). We designed and built a database called "Commonalities in Target Populations of Clinical Trials" (COMPACT), which stores structured eligibility criteria and trial metadata in a readily computable format. We illustrate its use in an example analytic module called CONECT using COMPACT as the backend. Type 2 diabetes is used as an example to analyze commonalities in the target populations of 4,493 clinical trials on this disease.

The Ontology of Clinical Research (OCRe): an informatics foundation for the science of clinical research

To date, the scientific process for generating, interpreting, and applying knowledge has received less informatics attention than operational processes for conducting clinical studies. The activities of these scientific processes - the science of clinical research - are centered on the study protocol, which is the abstract representation of the scientific design of a clinical study. The Ontology of Clinical Research (OCRe) is an OWL 2 model of the entities and relationships of study design protocols for the purpose of computationally supporting the design and analysis of human studies. OCRe's modeling is independent of any specific study design or clinical domain. It includes a study design typology and a specialized module called ERGO Annotation for capturing the meaning of eligibility criteria. In this paper, we describe the key informatics use cases of each phase of a study's scientific lifecycle, present OCRe and the principles behind its modeling, and describe applications of OCRe and associated technologies to a range of clinical research use cases. OCRe captures the central semantics that underlies the scientific processes of clinical research and can serve as an informatics foundation for supporting the entire range of knowledge activities that constitute the science of clinical research.

Deriving an abstraction network to support quality assurance in OCRe

An abstraction network is an auxiliary network of nodes and links that provides a compact, high-level view of an ontology. Such a view lends support to ontology orientation, comprehension, and quality-assurance efforts. A methodology is presented for deriving a kind of abstraction network, called a partial-area taxonomy, for the Ontology of Clinical Research (OCRe). OCRe was selected as a representative of ontologies implemented using the Web Ontology Language (OWL) based on shared domains. The derivation of the partial-area taxonomy for the Entity hierarchy of OCRe is described. Utilizing the visualization of the content and structure of the hierarchy provided by the taxonomy, the Entity hierarchy is audited, and several errors and inconsistencies in OCRe's modeling of its domain are exposed. After appropriate corrections are made to OCRe, a new partial-area taxonomy is derived. The generalizability of the paradigm of the derivation methodology to various families of biomedical ontologies is discussed.

Ontology-based federated data access to human studies information

Human studies are one of the most valuable sources of knowledge in biomedical research, but data about their design and results are currently widely dispersed in siloed systems. Federation of these data is needed to facilitate large-scale data analysis to realize the goals of evidence-based medicine. The Human Studies Database project has developed an informatics infrastructure for federated query of human studies databases, using a generalizable approach to ontology-based data access. Our approach has three main components. First, the Ontology of Clinical Research (OCRe) provides the reference semantics. Second, a data model, automatically derived from OCRe into XSD, maintains semantic synchrony of the underlying representations while facilitating data acquisition using common XML technologies. Finally, the Query Integrator issues queries distributed over the data, OCRe, and other ontologies such as SNOMED in BioPortal. We report on a demonstration of this infrastructure on data acquired from institutional systems and from ClinicalTrials.gov.

EliXR-TIME: A Temporal Knowledge Representation for Clinical Research Eligibility Criteria

Effective clinical text processing requires accurate extraction and representation of temporal expressions. Multiple temporal information extraction models were developed but a similar need for extracting temporal expressions in eligibility criteria (e.g., for eligibility determination) remains. We identified the temporal knowledge representation requirements of eligibility criteria by reviewing 100 temporal criteria. We developed EliXR-TIME, a frame-based representation designed to support semantic annotation for temporal expressions in eligibility criteria by reusing applicable classes from well-known clinical temporal knowledge representations. We used EliXR-TIME to analyze a training set of 50 new temporal eligibility criteria. We evaluated EliXR-TIME using an additional random sample of 20 eligibility criteria with temporal expressions that have no overlap with the training data, yielding 92.7% (76 / 82) inter-coder agreement on sentence chunking and 72% (72 / 100) agreement on semantic annotation. We conclude that this knowledge representation can facilitate semantic annotation of the temporal expressions in eligibility criteria.

Network analysis of clinical trials on depression: implications for comparative effectiveness research

A comprehensive understanding of evidence related to treatments for a disease is critical for planning effective clinical care, and for designing future trials. However, it is often difficult to comprehend the available evidence because of the complex combination of interventions across trials, in addition to the limited search and retrieval tools available in databases such as ClinicalTrials.gov. Here we demonstrate the use of networks to visualize and quantitatively analyze the co-occurrence of drug interventions across trials on depression in ClinicalTrials.gov. The analysis identified general co-occurrence patterns of interventions across all depression trials, and specific co-occurrence patterns related to antidepressants and natural supplements. These results led to insights about the current state of depression trials, and to a graph-theoretic measure to categorize interventions for a disease. We conclude by discussing the opportunities and challenges of generalizing our approach to analyze comparative interventional studies for any disease.

ExaCT: automatic extraction of clinical trial characteristics from journal publications

BACKGROUND

Clinical trials are one of the most important sources of evidence for guiding evidence-based practice and the design of new trials. However, most of this information is available only in free text - e.g., in journal publications - which is labour intensive to process for systematic reviews, meta-analyses, and other evidence synthesis studies. This paper presents an automatic information extraction system, called ExaCT, that assists users with locating and extracting key trial characteristics (e.g., eligibility criteria, sample size, drug dosage, primary outcomes) from full-text journal articles reporting on randomized controlled trials (RCTs).

METHODS

ExaCT consists of two parts: an information extraction (IE) engine that searches the article for text fragments that best describe the trial characteristics, and a web browser-based user interface that allows human reviewers to assess and modify the suggested selections. The IE engine uses a statistical text classifier to locate those sentences that have the highest probability of describing a trial characteristic. Then, the IE engine's second stage applies simple rules to these sentences to extract text fragments containing the target answer. The same approach is used for all 21 trial characteristics selected for this study.

RESULTS

We evaluated ExaCT using 50 previously unseen articles describing RCTs. The text classifier (first stage) was able to recover 88% of relevant sentences among its top five candidates (top5 recall) with the topmost candidate being relevant in 80% of cases (top1 precision). Precision and recall of the extraction rules (second stage) were 93% and 91%, respectively. Together, the two stages of the extraction engine were able to provide (partially) correct solutions in 992 out of 1050 test tasks (94%), with a majority of these (696) representing fully correct and complete answers.

CONCLUSIONS

Our experiments confirmed the applicability and efficacy of ExaCT. Furthermore, they demonstrated that combining a statistical method with 'weak' extraction rules can identify a variety of study characteristics. The system is flexible and can be extended to handle other characteristics and document types (e.g., study protocols).

A practical method for transforming free-text eligibility criteria into computable criteria

Formalizing eligibility criteria in a computer-interpretable language would facilitate eligibility determination for study subjects and the identification of studies on similar patient populations. Because such formalization is extremely labor intensive, we transform the problem from one of fully capturing the semantics of criteria directly in a formal expression language to one of annotating free-text criteria in a format called ERGO annotation. The annotation can be done manually, or it can be partially automated using natural-language processing techniques. We evaluated our approach in three ways. First, we assessed the extent to which ERGO annotations capture the semantics of 1000 eligibility criteria randomly drawn from ClinicalTrials.gov. Second, we demonstrated the practicality of the annotation process in a feasibility study. Finally, we demonstrate the computability of ERGO annotation by using it to (1) structure a library of eligibility criteria, (2) search for studies enrolling specified study populations, and (3) screen patients for potential eligibility for a study. We therefore demonstrate a new and practical method for incrementally capturing the semantics of free-text eligibility criteria into computable form.

Analysis of eligibility criteria complexity in clinical trials

Formal, computer-interpretable representations of eligibility criteria would allow computers to better support key clinical research and care use cases such as eligibility determination. To inform the development of such formal representations for eligibility criteria, we conducted this study to characterize and quantify the complexity present in 1000 eligibility criteria randomly selected from studies in ClinicalTrials.gov. We classified the criteria by their complexity, semantic patterns, clinical content, and data sources. Our analyses revealed significant semantic and clinical content variability. We found that 93% of criteria were comprehensible, with 85% of these criteria having significant semantic complexity, including 40% relying on temporal data. We also identified several domains of clinical content. Using the findings of the study as requirements for computer-interpretable representations of eligibility, we discuss the challenges for creating such representations for use in clinical research and practice.

Ontology mapping and data discovery for the translational investigator

An integrated data repository (IDR) containing aggregations of clinical, biomedical, economic, administrative, and public health data is a key component of an overall translational research infrastructure. But most available data repositories are designed using standard data warehouse architecture that employs arbitrary data encoding standards, making queries across disparate repositories difficult. In response to these shortcomings we have designed a Health Ontology Mapper (HOM) that translates terminologies into formal data encoding standards without altering the underlying source data. We believe the HOM system promotes inter-institutional data sharing and research collaboration, and will ultimately lower the barrier to developing and using an IDR.

Development and evaluation of a study design typology for human research

A systematic classification of study designs would be useful for researchers, systematic reviewers, readers, and research administrators, among others. As part of the Human Studies Database Project, we developed the Study Design Typology to standardize the classification of study designs in human research. We then performed a multiple observer masked evaluation of active research protocols in four institutions according to a standardized protocol. Thirty-five protocols were classified by three reviewers each into one of nine high-level study designs for interventional and observational research (e.g., N-of-1, Parallel Group, Case Crossover). Rater classification agreement was moderately high for the 35 protocols (Fleiss' kappa = 0.442) and higher still for the 23 quantitative studies (Fleiss' kappa = 0.463). We conclude that our typology shows initial promise for reliably distinguishing study design types for quantitative human research.

Automated information extraction of key trial design elements from clinical trial publications

Clinical trials are one of the most valuable sources of scientific evidence for improving the practice of medicine. The Trial Bank project aims to improve structured access to trial findings by including formalized trial information into a knowledge base. Manually extracting trial information from published articles is costly, but automated information extraction techniques can assist. The current study highlights a single architecture to extract a wide array of information elements from full-text publications of randomized clinical trials (RCTs). This architecture combines a text classifier with a weak regular expression matcher. We tested this two-stage architecture on 88 RCT reports from 5 leading medical journals, extracting 23 elements of key trial information such as eligibility rules, sample size, intervention, and outcome names. Results prove this to be a promising avenue to help critical appraisers, systematic reviewers, and curators quickly identify key information elements in published RCT articles.

An interactive tool for visualizing design heterogeneity in clinical trials

Clinical questions are often studied by randomized clinical trials (RCTs) of heterogeneous design. Systematic reviewers and trial designers need to compare the design and results across these trials. If trial information is available in computer processable form, computer-based visualization techniques can provide cognitive support for such comparisons. CTeXplorer offers systematic reviewers and trial designers a tool to better and more quickly understand design heterogeneity in RCTs. CTeXplorer supports dynamic queries on eligibility criteria, interventions, and outcomes in three linked views. We tested CTeXplorer for displaying 12 RCTs on prevention of mother-to-child transmission of HIV. Three target users found the representation and organization of information intuitive and easy to learn. They were able to use CTeXplorer to achieve a quick cognitive overview of a heterogeneous group of RCTs. This work shows the benefit of capturing trial information in computable form. Future work includes leveraging ontologies to enhance CTeXplorer visualizations.

An ontology of randomized controlled trials for evidence-based practice: content specification and evaluation using the competency decomposition method

Randomized controlled trials (RCTs) are one of the least biased sources of clinical research evidence, and are therefore a critical resource for the practice of evidence-based medicine. With over 10,000 new RCTs indexed in Medline each year, knowledge systems are needed to help clinicians translate evidence into practice. Common ontologies for RCTs and other domains would facilitate the development of these knowledge systems. However, no standard method exists for developing domain ontologies. In this paper, we describe a new systematic approach to specifying and evaluating the conceptual content of ontologies. In this method, called competency decomposition, the target task for an ontology is hierarchically decomposed into subtasks and methods, and the ontology content is specified by identifying the domain information required to complete each of the subtasks. We illustrate the use of this competency decomposition approach for the content specification and evaluation of an RCT ontology for evidence-based practice.

Trial bank publishing: phase I results

BACKGROUND

Randomized clinical trials (RCTs) are an important source of evidence for clinical practice, but finding and applying RCT reports to care is time consuming. Publishing RCTs directly into machine-understandable "trial banks" may allow computers to deliver RCT evidence more selectively and effectively to clinicians.

METHODS

Authors of eligible RCTs published in JAMA or the Annals of Internal Medicine between January 2002 and July 2003 were invited to co-publish their trial in RCT Bank, an electronic knowledge base containing details of trial design, execution, and summary results. Trial bank staff used Bank-a-Trial, a web-based trial-bank entry tool, to enter information from the manuscript into RCT Bank, obtaining additional information as necessary from the authors.

RESULTS

The author participation rate rose from 38% to 76% after the first co-published trial was available as an example. Seven diverse RCTs are now co-published, with 14 in progress.

CONCLUSIONS

We have demonstrated proof of concept for co-publishing RCTs with leading journals into a structured knowledge base. Phase II of trial bank publishing will introduce direct author submission to RCT Bank.

SysBank: a knowledge base for systematic reviews of randomized clinical trials

The Systematic Review Bank (SysBank) is a structured knowledge base that captures information about the design, execution, and results of systematic reviews of randomized controlled trials (RCTs). The SysBank data model has been adapted from RCT Bank, a knowledge base of randomized trials, and refined using three published systematic reviews. SysBank links directly to the RCT Bank entries of studies included in the systematic review. SysBank builds upon RCT Bank to support computer-assisted evidence-based medicine.

The Trial Bank system: capturing randomized trials for evidence-based medicine

Randomized controlled trials (RCTs) are one of the best sources of evidence for the scientific practice of medicine. However, RCT findings are published only as text articles that are of limited machine understandability. The Trial Bank system captures information about the design, execution, and summary results of RCTs into a structured electronic knowledge base called RCT Bank.

Cystic malformations of the posterior cranial fossa originating from a defect of the posterior membranous area. Mega cisterna magna and persisting Blake's pouch: two separate entities

Cystic malformations of the posterior cranial fossa are all but arachnoid cysts contained within the general context of the Dandy-Walker complex and may be further classified in two groups on the basis of their embryological origin: anomalies of the anterior membranous area (AMA) and anomalies of the posterior membranous area (PMA). Whether the latter group of malformations can be regarded as separate entities is still quite controversial. The present authors give a detailed account of the various embryological stages in the formation of the posterior cranial fossa and its contents and propose the identification of two anomalies derived from a defect of the PMA: the mega cisterna magna (MCM) and the persisting Blake's pouch, a new entity with different MRI features from MCM. Criteria for their recognition are discussed, stressing the capital importance of a differential diagnosis in view of the radically different therapeutic approach.

[Results of early external ventricular diversion in posthemorrhagic ventricular dilatation in the newborn]

OBJECTIVE AND DESIGN

The authors report the outcome of early treatment with long-term external ventricular drainage (EVD) of progressive post-hemorrhagic ventricular dilatation (PPHVD), following peri-intraventricular hemorrhage (PIVH) in a population of preterm newborns.

SETTING

Neonatal Intensive Care Unit (NICU) of a Children's Hospital.

PATIENTS

Twenty-one preterms of 29.6 +/- 2.4 weeks of gestational age, weighing at birth 1443 +/- 445 g, mechanically ventilated, submitted to early EVD because of PPHVD following PIVH of III (n 11) e IV (n 10) grade.

METHODS

PPHVD was diagnosed on the basis of US and TC findings. An external liquoral drainage suitable, for its technical characteristics, to be maintained for a long period of time and peculiar anesthesiologic, intra and postoperative treatments were utilized.

RESULTS

EVD was placed at 21 +/- 5.8 days of life and maintained for 40 +/- 16 days. In all cases reduction of ventricular size was observed. One case (5%) developed liquoral infection and recovered with antibiotic therapy. No obstruction or dislocation of the ventricular catheter occurred. During EVD 3 patients (14%) died because of respiratory complications. After the normalization of cerebrospinal fluid (CSF), a "permeability test" was performed to assess the canalization of the liquoral system. Seven patients (33.5%) underwent ventriculo-peritoneal shunt (VPS) and 11 (52.5%) became shunt-free.

CONCLUSIONS

Our results indicate that long-term use of EVD has a low risk of complications, avoids the need for transcutaneous tips and allows monitoring of CSF characteristics. Furthermore EVD protects the brain from liquoral hypertension, while waiting for a possible recurrence of natural CSF circulation, and is associated with a low number of definitive VPS.

Halo ring supporting the Brown-Roberts-Wells stereotactic frame for fractionated radiotherapy

The authors describe a new instrumentation for repositioning of the Brown-Roberts-Wells (BRW) stereotaxic system, useful for precise fractionated radiotherapy. A lucite ring is fixed to the patient's skull with four screws. Another ring, partially open, is then firmly connected co-axially to the lower part of the first one with four spacer-bars. The fixture permits an exact repositioning of the B.R.W. stereotaxic system, placing the target point in the linear accelerator isocenter. The preliminary technical results obtained in five children are reported and the fixture performance, advantages, and perspectives are discussed.

A new model for non-invasive, reproducible fixation of a stereotaxic frame using an orthodontic resin plate. Technical note

The authors describe a new method for reproducible, non-invasive fixation of a stereotaxic localizing frame. A localizing system similar to that of Brown-Roberts-Wells for MR can be fixed at the base of the facial skeleton to the upper dental arch by an orthodontic resin plate. Results of trials with CT scan, advantages and disadvantages are discussed. The new fixture could be employed in open surgery and in fractionated radiotherapy.

Transclival transcervical approach to the upper cervical spine and clivus

The transclival-transcervical approach to lesions of the craniocervical junction is described. It gives reasonable access to the lower part of the clivus and to C1 and C2 for removal to tumours and stabilization of fractures and otherwise caused dislocations of this region. Because an opening of the pharynx can be avoided, reconstruction work can be done using bone graft or reinforced methyl-methacrylate without risk of infectious contamination. The results obtained in 6 cases are presented.

Bilateral posterolateral approach to the thoracolumbar spine through transversoarthropediculectomy with corporectomy

Surgery of the dorsolumbar spine is currently benefiting from diverse approaches and the advent of computed tomography. This report describes a bilateral posterolateral approach with transversoarthropediculectomy and corporectomy. The advantages and disadvantages of this approach and its indications are discussed.

[Meningoencephalitis in children and computerized axial tomography. Clinico-prognostic evaluation]

A large casistic of severe CNS infectious diseases in pediatric age was reviewed to underline mean clinic, EEG features and correlated them with CT images. We conclude that CT scanning is useful in CNS infectious diseases, above all in the first life years, because it can provide diagnostic and prognostic information and sometimes therapeutic indication.

Cervical neurological complications of rheumatoid arthritis. Surgical treatment techniques and indications

A critical study of 11 cases with neurological complications resulting from damage to the cervical spine during rheumatoid arthritis prompts the authors to reconsider their therapeutic strategy. After recalling the various neurological complications and their mechanisms, they propose an extension of the range of indications for surgical treatment. The limits of this extension are defined according to neurological and radiological criteria and the risks inherent in each method.

Anterointernal approach to the lumbar spine: technical note

Lumbar surgery is currently benefiting from the development of anterior approaches to the spine and the possibilities of anterior arthrodesis. The transperitoneal approach is being replaced more often by an extraperitoneal approach that enables anterior access to the spine. The authors report an anterointernal approach to the lumbar spine.

Use of Harrington rods with segmental wiring for spinal pathology. Technical note

The Authors report their experience using Harrington rods with segmental wiring for spinal pathology. They describe the advantages of this technical point in neoplastic and traumatological dorsal compression.