Manual Screening Process Research Articles

Developing a manual clinical trials screening process in a diverse southern gynecologic oncology practice in the Deep South.

e23209 Background: At this time, there is no standard clinical trial screening process or definition that has been adopted within Gynecologic Oncology. In our low resource, highly diverse Gynecologic Oncology patient population, we sought to create an equitable, adaptable, manual screening process that better reflected our patient population. Our objective is to describe our clinical trial screening process and our success in improving trial enrollment and a matched trial portfolio. Methods: An IRB-approved, QI project was implemented in July 2022 to evaluate trial access for Gyn Onc patients. First, screenable events were defined as: the start of one’s first scheduled visit, bi-monthly tumor boards, weekly pre-op meetings, and by keeping track of on a shared Epic list. Potential patients were those with a screenable event: new patients or diagnoses, regimen changes, progressions, and recurrences. Events were categorized into screen positive or screened no trial available. Screen positives were further categorized as screen fail, screen positive, enrollment failure, or enrolled. Data about patients was collected via weekly research team meetings. Monthly meetings occurred to review progress. This data was compared to trials available, number of patients with trail available, and those that enrolled. Reasons for enrollment fails were tracked to improve access and anticipate barriers. Results: Overall demographic data of patients was collected. The average age of participants was 60.67 years (SD = 13.45), and the average distance to a treatment center was 59.27 miles (SD = 137.43). Other demographics are presented in Table 1. For each month, the percentages of “screen no trial available (SNTA)” events and patients who screened positive and did enroll on trial were recorded. Over time, SNTA rates stayed stable, but enrollment increased. Patient preference accounted for 32.8% of enrollment fails (n = 42), pre-existing symptoms/conditions accounted for 23.4% (n = 30), and distance/location accounted for 21.1% (n = 27). During a time of high employee turnover in the summer months of 2023, there was a rise in enrollment fails due to staffing (n = 6, 4.7%). Conclusions: Through a collaborative quality initiative, we describe an effective process of clearly defining and tracking our patient population and ‘screenable events’ for which all patients are screened and offered trial participation if eligible. We show that we improved understanding of the patient population, built a clinical trial portfolio better matched to population served, exceeded national averages for enrolling patients on trials, and are moving toward improved number eligible over time. [Table: see text]

PENTAGON (Predicting Clinical Trials in Gynecologic Oncology): A retrospective study assessing study design factors that affect enrollment in gynecologic cancer trials.

e13647 Background: Obstacles to attaining a diverse clinical trial population contribute to poor representation in medicine, increasing health disparities by limiting generalizability and applicability of findings. Our objective is to evaluate trial enrollment trends according to patient and trial demographics for those who have sought care at our multi-center, mixed-setting Gynecologic Oncology practice. Additionally, we aim to determine specific trial criteria that promote or impede enrollment for patients and physicians. Methods: An IRB-approved, retrospective cohort study evaluating all patients who screened positive for a clinical trial through the Gyn Onc practice’s manual screening process was performed. Screening events were new neoplasms, recurrences or progressions, and changes in treatment. Data was collected from July 2022 to December 2023. Demographic information, trials for which each patient was considered, trials patients enrolled on, and trial characteristics were recorded. Results: After adjustment, there were no significant differences between patient factors (age, marital status, race, ethnicity, and number of screening events) and enrollment status. Of trials available, 9% require biopsy and 94.7% are in person. Most studies are interventional (78.9%) and pharmaceutical trials (73.7%). There was a drastic unadjusted estimated increase in enrollment for Medicaid patients compared to other insurances (55.2% vs 32%, p=.031). There was also an increase in enrollment for cervical cancer patients (18.4%, p=.106) compared to other cancer sites (56.5% vs 32.8%, p=.036). There was a positive relationship between distance traveled to the treatment site and clinical trial enrollment. Patients who lived within 20 miles of the clinical trial site had an enrollment percentage of 30.1% (n=93), compared to 46.55% enrollment for those who lived further (n=58). There was a significant increase in enrollment likelihood without an exclusion criterion of previous history of other cancer (50% vs 33.3%, p=.046). Trials that required no history of chemotherapy had an enrollment of 27.3% (compared to 44.1%). Conclusions: Unlike prior reports, we did not demonstrate differences in trial enrollment by race, socioeconomic or insurance status. This suggests that our objective manual screening process enhances equitable clinical trial offerings. It is possible that this objective manual screening process attenuates provider bias and offers clinical trials to historically marginalized and underrepresented groups.

Central bank digital currencies: a comprehensive systematic literature review on worldwide research emergence and methods used

PurposeThe purpose of this paper is to review the literature on Central Bank Digital Currencies (CBDCs) in light of the increasing demand for digital payments globally. It aims to assess the global research landscape, methodologies and data utilized in CBDC studies as their popularity grows.Design/methodology/approachThe paper employs a systematic literature review (SLR) framework, utilizing the Scopus database to identify 323 studies related to Central Bank Digital Currency or CBDC. Through a thorough manual screening process, 169 studies were selected for inclusion. The research employs R, Biblioshiny and Excel for data evaluation, classifying the studies into three methodological categories: quantitative, qualitative and mixed approaches. This classification allows for a detailed assessment of the research techniques and data used in the literature on CBDCs.FindingsThe findings include a diverse range of research methodologies and data employed in CBDCs literature, highlighting the growing interest and depth of academic investigation into this area. By categorizing papers according to research technique, the study provides a comprehensive evaluation of the academic landscape regarding CBDC research. It offers valuable insights for researchers, policymakers and stakeholders, contributing to an enhanced understanding of the complexities and opportunities presented by the development and implementation of CBDCs.Originality/valueThis study’s originality lies in its rigorous and transparent methodology for data from CBDC studies, offering a solid framework for future research. By evaluating global research growth within an SLR framework and categorizing studies by research technique, it contributes uniquely to the academic discourse on digital currencies. The paper provides a critical resource for researchers, lawmakers and stakeholders, enriching the knowledge base on CBDCs and supporting informed decision-making in the context of digital financial innovation.

Improving cervical cancer classification in PAP smear images with enhanced segmentation and deep progressive learning-based techniques.

Cervical cancer, a prevalent and deadly disease among women, comes second only to breast cancer, with over 700 daily deaths. The Pap smear test is a widely utilized screening method for detecting cervical cancer in its early stages. However, this manual screening process is prone to a high rate of false-positive outcomes because of human errors. Researchers are using machine learning and deep learning in computer-aided diagnostic tools to address this issue. These tools automatically analyze and sort cervical cytology and colposcopy images, improving the precision of identifying various stages of cervical cancer. This article uses state-of-the-art deep learning methods, such as ResNet-50 for categorizing cervical cancer cells to assist medical professionals. The method includes three key steps: preprocessing, segmentation using k-means clustering, and classifying cancer cells. The model is assessed based on performance metrics viz; precision, accuracy, kappa score, precision, sensitivity, and specificity. In the end, the high success rate shows that the ResNet50 model is a valuable tool for timely detection of cervical cancer. In conclusion, the infected cervical region is pinpointed using spatial K-means clustering and preprocessing operations. This sequence of actions is followed by a progressive learning technique. The Progressive Learning technique then proceeded through several stages: Stage 1 with 64 × 64 images, Stage 2 with 224 × 224 images, Stage 3 with 512 × 512 images, and the final Stage 4 with 1024 × 1024 images. The outcomes show that the suggested model is effective for analyzing Pap smear tests, achieving 97.4% accuracy and approx. 98% kappa score.

Intelligent Detection and Classification of Surface Defects on Cold-Rolled Galvanized Steel Strips Using a Data-Driven Faulty Model With Attention Mechanism

Abstract In the production of cold-rolled galvanized steel strips used for stamping car body parts, the in-situ and real-time defect detection is crucial for quality control, in which various types of defects inevitably occur. It is challenging to improve the accuracy of defect detection and classification by appropriate means to assist the manual screening process better. Defects under actual production conditions are often not prominent enough in defect characteristics, and there may be a significant similarity between different defect categories. To eliminate this weakness, we propose a data-driven deep learning approach named steel surface faulty detection attention net (SSFDANet) that uses images of the galvanized steel surfaces as input to identify whether the product is qualified and automatic classification of defect types instantaneously. This method can shorten product inspection time and improve the production line automation efficiency. In addition, the attention mechanism is utilized to enhance the performance of SSFDANet. Compared with the baseline ResNet, SSFDANet achieves a noticeable improvement in classification accuracy on test data. The well-trained model can successfully show an improved performance than the baseline models on the multiple types of faulty. Enhanced by SSFDANet with high classification accuracy, the defect rate of products is significantly reduced, and the production speed of the production line is significantly improved. Future prospective studies that are inspired by this article are also discussed.

Screening for Social Determinants of Health: Active and Passive Information Retrieval Methods.

Screening for social determinants of health (SDOH) is recommended, but numerous barriers exist to implementing SDOH screening in clinical spaces. In this study, the authors identified how both active and passive information retrieval methods may be used in clinical spaces to screen for SDOH and meet patient needs. The authors conducted a retrospective sequential cohort analysis comparing the active identification of SDOH through a patient-led digital manual screening process completed in primary care offices from September 2019 to January 2020 and passive identification of SDOH through natural language processing (NLP) from September 2016 to August 2018, among 1735 patients at a large midwestern tertiary referral hospital system and its associated outlying primary care and outpatient facilities. The percent of patients identified by both the passive and active identification methods as experiencing SDOH varied from 0.3% to 4.7%. The active identification method identified social integration, domestic safety, financial resources, food insecurity, transportation, housing, and stress in proportions ranging from 5% to 36%. The passive method contributed to the identification of financial resource issues and stress, identifying 9.6% and 3% of patients to be experiencing these issues, respectively. SDOH documentation varied by provider type. The combination of passive and active SDOH screening methods can provide a more comprehensive picture by leveraging historic patient interactions, while also eliciting current patient needs. Using passive, NLP-based methods to screen for SDOH will also help providers overcome barriers that have historically prevented screening.

Towards Automated Screening of Literature on Artificial Intelligence in Nursing.

We evaluate the performance of multiple text classification methods used to automate the screening of article abstracts in terms of their relevance to a topic of interest. The aim is to develop a system that can be first trained on a set of manually screened article abstracts before using it to identify additional articles on the same topic. Here the focus is on articles related to the topic "artificial intelligence in nursing". Eight text classification methods are tested, as well as two simple ensemble systems. The results indicate that it is feasible to use text classification technology to support the manual screening process of article abstracts when conducting a literature review. The best results are achieved by an ensemble system, which achieves a F1-score of 0.41, with a sensitivity of 0.54 and a specificity of 0.96. Future work directions are discussed.

Abstract screening using the automated tool Rayyan: results of effectiveness in three diagnostic test accuracy systematic reviews

ObjectiveTo evaluate the performance of the automated abstract screening tool Rayyan.MethodsThe records obtained from the search for three systematic reviews were manually screened in four stages. At the end of each stage, Rayyan was used to predict the eligibility score for the remaining records. At two different thresholds (≤2.5 and < 2.5 for exclusion of a record) Rayyan-generated ratings were compared with the decisions made by human reviewers in the manual screening process and the tool’s accuracy metrics were calculated.ResultsTwo thousand fifty-four records were screened manually, of which 379 were judged to be eligible for full-text assessment, and 112 were eventually included in the final review. For finding records eligible for full-text assessment, at the threshold of < 2.5 for exclusion, Rayyan managed to achieve sensitivity values of 97-99% with specificity values of 19-58%, while at the threshold of ≤2.5 for exclusion it had a specificity of 100% with sensitivity values of 1-29%. For the task of finding eligible reports for inclusion in the final review, almost similar results were obtained.DiscussionAt the threshold of < 2.5 for exclusion, Rayyan managed to be a reliable tool for excluding ineligible records, but it was not much reliable for finding eligible records. We emphasize that this study was conducted on diagnostic test accuracy reviews, which are more difficult to screen due to inconsistent terminology.

The Use of Validated Visual Scales in Plastic Surgery: Where Are We Now?

The assessment of the human body, whether for aesthetic or reconstructive purposes, is an inherently visual endeavor. Ideally, reproducible, prompt, and cost-effective systems of visual evaluation would exist that can provide validated assessments of the aesthetic endpoints of treatment. One method to accomplish a standardization of the appreciation of visual endpoints is the use of visual scales. The goal of this systematic review is to summarize and evaluate the use of validated visual scales within aesthetic medicine, dermatology, and plastic and reconstructive surgery. A literature search was performed with a defined search strategy and extensive manual screening process. The Medical Outcomes Trust guidelines for visual scales in medicine were used, with special attention paid to each study's validation metrics. The review process identified 44 publications with validation data of sufficient quality from an initial survey of 27,745 articles. All rating scales based on imaging other than standardized clinical photographs were excluded. The review demonstrates that validated visual assessment in plastic surgery is incomplete. Within specific subfields of aesthetic medicine and dermatology, many of the ( n = 20) facial aging scales were well-validated and demonstrated high reliability. Publications ( n = 8) focused on the evaluation of facial clefts demonstrated heterogeneity in the methods of validation and in overall reliability. Within the areas of breast surgery ( n = 9), body contouring ( n = 2), and scarring ( n = 5), the scales were variable in the methods used and the validation procedures were diverse. Scales using a visual guide tended to have better interrater (kappa = 0.75) and intrarater reliability (kappa = 0.78), regardless of the specific area of interest. The fields of aesthetic medicine, and aesthetic and reconstructive plastic surgery require assessment of visual states over time and between many observers. For these reasons, the development of validated and reliable methods of visual assessment are critical. Until recently, the use of these tools has been limited by their time-consuming nature and cost.

Text Mining for Employee Candidates Automatic Profiling Based on Application Documents

Opening job vacancies using the Internet will receive many applications quickly. Manually filtering resumes takes a lot of time and incurs huge costs. In addition, this manual screening process tends to be inaccurate due to fatigue conditions and fails in obtaining the right candidate for the job. This paper proposed a solution to automatically generate the most suitable candidate from the application document. In this study, 126 application documents from a private company were used for the experiment. The documents consist of 41 documents for Human Resource and Development (HRD) staff, 42 documents for IT (Data Developer), and 43 documents for the Marketing position. Text Processing is implemented to extract relevant information such as skills, education, experiences from the unstructured resumes and summarize each application. A specific dictionary for each vacancy is generated based on terms used in each profession. Two methods are implemented and compared to match and score the application document, namely Document Vector and N-gram analysis. The highest the score obtained by one document, the highest the possibility of application to be accepted. The two methods’ results are then validated by the real selection process by the company. The highest accuracy was achieved by the N-Gram method in IT vacancy with 87,5%, while the Document Vector showed 75% accuracy. For Marketing staff vacancy, both methods achieved the same accuracy as 78%. In HRD staff vacancy, the N-Gram method showed 68%, while Document Vector showed 74%. In conclusion, overall the N-gram method showed slightly better accuracy compared to the Document Vector method.

Artificial Intelligence Based Prostate Cancer Classification Model Using Biomedical Images

Medical image processing becomes a hot research topic in healthcare sector for effective decision making and diagnoses of diseases. Magnetic resonance imaging (MRI) is a widely utilized tool for the classification and detection of prostate cancer. Since the manual screening process of prostate cancer is difficult, automated diagnostic methods become essential. This study develops a novel Deep Learning based Prostate Cancer Classification (DTL-PSCC) model using MRI images. The presented DTL-PSCC technique encompasses EfficientNet based feature extractor for the generation of a set of feature vectors. In addition, the fuzzy k-nearest neighbour (FKNN) model is utilized for classification process where the class labels are allotted to the input MRI images. Moreover, the membership value of the FKNN model can be optimally tuned by the use of krill herd algorithm (KHA) which results in improved classification performance. In order to demonstrate the good classification outcome of the DTL-PSCC technique, a wide range of simulations take place on benchmark MRI datasets. The extensive comparative results ensured the betterment of the DTL-PSCC technique over the recent methods with the maximum accuracy of 85.09%.

Systematic online academic resource (SOAR) review: Endocrine, metabolic, and nutritional disorders.

Free open-access medical education (FOAM) has become an integral resource for medical school and residency education. However, questions of quality and inconsistent coverage of core topics remain. In this second entry of the SAEM Systematic Online Academic Resource (SOAR) series, we describe the application of a systematic methodology to identify, curate, and describe FOAM topics specific to endocrine, metabolic, and nutritional disorders as defined by the 2016Model of the Clinical Practice of Emergency Medicine (MCPEM). We developed an automated algorithm to search 264keywords derived from ninesubtopics within the MCPEM category in the FOAM Search (a customized FOAM search tool) and the Social Media index. The top 100 results were extracted for each keyword. Resources underwent a manual iterative screening process, and those relevant to endocrine, metabolic, or nutritional disorders and EM were evaluated with the revised Medical Education Translational Resources: Impact and Quality (rMETRIQ) tool. The search yielded 36,346 resources, of which 756met the criteria for quality assessment. After rMETRIQ tool training, four raters demonstrated an average measured intraclass correlation coefficient of 0.94 (95% confidence interval= 0.88 to 0.97, p<0.001). A total of 121 posts (16% of posts) covering 25subtopics were identified as high quality (rMETRIQ ≥16). The most covered subtopic was potassium disorders, representing 15% of all posts. Subtopics that did not have a high-quality resource identified include metabolic alkalosis, respiratory alkalosis, fluid overload, phosphorus metabolism, hyperglycemia, malabsorption, malnutrition, and thyroiditis. From most to least common, the overall target audience was junior resident (91%), PGY-1 resident (88%), senior resident (81%), clerk (64%), attending (50%), and preclerkship (9%). We systematically identified, described, and curated FOAM resources for EM learners on the topic of endocrine, metabolic, and nutritional disorders. A final list of high-quality resources can guide trainees, educator recommendations, and FOAM authors.

Identifying candidate hosts for quantum defects via data mining

Atom-like defects in solid-state hosts are promising candidates for the development of quantum information systems, but despite their importance, the host substrate/defect combinations currently under study have almost exclusively been found serendipitously. Here we systematically evaluate the suitability of host materials by applying a combined four-stage data mining and manual screening process to all entries in the Materials Project database, with literature-based experimental confirmation of band gap values. We identify a total of 541 viable hosts (16 unary and 74 binary) for quantum defect introduction and potential use in quantum information systems. This represents a significant (99.57%) reduction from the total number of known inorganic phases, and the application of additional selection criteria for specific applications will reduce their number even further. The screening principles outlined may easily be applied to previously unrealized phases and other technologically important materials systems.

A Real-Time Automated Patient Screening System for Clinical Trials Eligibility in an Emergency Department: Design and Evaluation.

BackgroundOne critical hurdle for clinical trial recruitment is the lack of an efficient method for identifying subjects who meet the eligibility criteria. Given the large volume of data documented in electronic health records (EHRs), it is labor-intensive for the staff to screen relevant information, particularly within the time frame needed. To facilitate subject identification, we developed a natural language processing (NLP) and machine learning–based system, Automated Clinical Trial Eligibility Screener (ACTES), which analyzes structured data and unstructured narratives automatically to determine patients’ suitability for clinical trial enrollment. In this study, we integrated the ACTES into clinical practice to support real-time patient screening.ObjectiveThis study aimed to evaluate ACTES’s impact on the institutional workflow, prospectively and comprehensively. We hypothesized that compared with the manual screening process, using EHR-based automated screening would improve efficiency of patient identification, streamline patient recruitment workflow, and increase enrollment in clinical trials.MethodsThe ACTES was fully integrated into the clinical research coordinators’ (CRC) workflow in the pediatric emergency department (ED) at Cincinnati Children’s Hospital Medical Center. The system continuously analyzed EHR information for current ED patients and recommended potential candidates for clinical trials. Relevant patient eligibility information was presented in real time on a dashboard available to CRCs to facilitate their recruitment. To assess the system’s effectiveness, we performed a multidimensional, prospective evaluation for a 12-month period, including a time-and-motion study, quantitative assessments of enrollment, and postevaluation usability surveys collected from the CRCs.ResultsCompared with manual screening, the use of ACTES reduced the patient screening time by 34% (P<.001). The saved time was redirected to other activities such as study-related administrative tasks (P=.03) and work-related conversations (P=.006) that streamlined teamwork among the CRCs. The quantitative assessments showed that automated screening improved the numbers of subjects screened, approached, and enrolled by 14.7%, 11.1%, and 11.1%, respectively, suggesting the potential of ACTES in streamlining recruitment workflow. Finally, the ACTES achieved a system usability scale of 80.0 in the postevaluation surveys, suggesting that it was a good computerized solution.ConclusionsBy leveraging NLP and machine learning technologies, the ACTES demonstrated good capacity for improving efficiency of patient identification. The quantitative assessments demonstrated the potential of ACTES in streamlining recruitment workflow and improving patient enrollment. The postevaluation surveys suggested that the system was a good computerized solution with satisfactory usability.

Surgical Treatment of Nonsyndromic Unicoronal Craniosynostosis

Unicoronal synostosis (UCS) remains one of the most difficult craniofacial conditions to treat. This review attempts to consolidate all existing literature from the past 25 years that has investigated surgical treatment of nonsyndromic UCS. Additional attention is paid to specific areas of controversy regarding surgical management and evaluation of UCS: outcomes of fronto-orbital advancement (FOA) versus endoscopic strip craniectomy (ESC), emergence of spring-associated cranioplasty for craniosynostosis, and morphologic assessment metrics. A literature search from 1992 to 2017 was performed with a defined search strategy and manual screening process. About 24 studies were included in the final review. There is inconsistency among studies in measuring outcomes. Despite wide utilization, FOA produces mixed results, especially when looking at long-term outcomes. Preliminary data suggest advantages to using ESC over FOA with regard to facial symmetry, postoperative strabismus, and operating time. Distraction osteogenesis has long been utilized in Asia and is now being actively studied in the United States. Data on spring-assisted cranioplasty are lacking. Long-term results at skeletal maturity for many of these techniques are lacking. Despite some promising results, methods of measurement are inconsistent and long-term data are lacking. All future research in this area would benefit from consistent and standardized reporting of data, including perioperative statistics and reproducible 3-dimensional computed tomography craniofacial measurements. Additionally, no definitive recommendations can be made until data at skeletal maturity are studied, and all studies would benefit from long-term follow-up data.

Discovering discoveries: Identifying biomedical discoveries using citation contexts

Abstract A procedure for identifying discoveries in the biomedical sciences is described that makes use of citation context information, or more precisely citing sentences, drawn from the PubMed Central database. The procedure focuses on use of specific terms in the citing sentences and the joint appearance of cited references. After a manual screening process to remove non-discoveries, a list of over 100 discoveries and their associated articles is compiled and characterized by subject matter and by type of discovery. The phenomenon of multiple discovery is shown to play an important role. The onset and timing of recognition of the articles are studied by comparing the number of citing sentences with and without discovery terms, and show both early onset and delays in recognition. A comparative analysis of the vocabularies of the discovery and non-discovery sentences reveals the types of words and concepts that scientists associate with discoveries. A machine learning application is used to efficiently extend the list. Implications of the findings for understanding the nature and justification of scientific discoveries are discussed.

Warning letters to sponsor-investigators at academic health centres - the regulatory "canaries in a coal mine".

This study highlights Warning Letter (WL) findings issued to sponsor-investigators (S-Is) by the Food and Drug Administration (FDA). The online index of WLs issued from October 1, 2007 through September 30, 2012 was reviewed [1]. Through a manual screening process, letters were evaluated if specifically issued to 'clinical investigators', 'sponsors' or 'sponsor-investigators'. A particular focus was given to S-Is at Academic Health Centres (AHCs). Each letter was scored for the presence of violations in 40 general regulatory categories. A review of FDA WLs issued over a five-year period (FDA Fiscal Years 2008-2012) revealed that WLs to S-Is represent half of the WLs issued to all sponsors (16 of 32 letters). A review of these letters indicates that S-Is are not aware of, or simply do not meet, their regulatory responsibilities as either investigators or sponsors. In comparing total sponsor letters to those of S-Is, the most cited violation was the same: a lack of monitoring. A review of publicly available inspection data indicates that these 16 letters merely represent the tip of the iceberg. This review of the WL database reveals the potential for serious regulatory violations among S-Is at AHCs. Recent translational funding initiatives may serve to increase the number of S-Is, especially among Academic Health Centres (AHCs) [2]; thus, AHCs must become aware of this S-I role and work to support investigators who assume both roles in the course of their research.

Evaluation of data completeness in the electronic health record for the purpose of patient recruitment into clinical trials: a retrospective analysis of element presence.

BackgroundComputerized clinical trial recruitment support is one promising field for the application of routine care data for clinical research. The primary task here is to compare the eligibility criteria defined in trial protocols with patient data contained in the electronic health record (EHR). To avoid the implementation of different patient definitions in multi-site trials, all participating research sites should use similar patient data from the EHR. Knowledge of the EHR data elements which are commonly available from most EHRs is required to be able to define a common set of criteria. The objective of this research is to determine for five tertiary care providers the extent of available data compared with the eligibility criteria of randomly selected clinical trials.MethodsEach participating study site selected three clinical trials at random. All eligibility criteria sentences were broken up into independent patient characteristics, which were then assigned to one of the 27 semantic categories for eligibility criteria developed by Luo et al. We report on the fraction of patient characteristics with corresponding structured data elements in the EHR and on the fraction of patients with available data for these elements. The completeness of EHR data for the purpose of patient recruitment is calculated for each semantic group.Results351 eligibility criteria from 15 clinical trials contained 706 patient characteristics. In average, 55% of these characteristics could be documented in the EHR. Clinical data was available for 64% of all patients, if corresponding data elements were available. The total completeness of EHR data for recruitment purposes is 35%. The best performing semantic groups were ‘age’ (89%), ‘gender’ (89%), ‘addictive behaviour’ (74%), ‘disease, symptom and sign’ (64%) and ‘organ or tissue status’ (61%). No data was available for 6 semantic groups.ConclusionsThere exists a significant gap in structure and content between data documented during patient care and data required for patient eligibility assessment. Nevertheless, EHR data on age and gender of the patient, as well as selected information on his disease can be complete enough to allow for an effective support of the manual screening process with an intelligent preselection of patients and patient data.

Performance of an automated electronic acute lung injury screening system in intensive care unit patients*

Lung protective ventilation reduces mortality in patients with acute lung injury, but underrecognition of acute lung injury has limited its use. We recently validated an automated electronic acute lung injury surveillance system in patients with major trauma in a single intensive care unit. In this study, we assessed the system's performance as a prospective acute lung injury screening tool in a diverse population of intensive care unit patients. Patients were screened prospectively for acute lung injury over 21 wks by the automated system and by an experienced research coordinator who manually screened subjects for enrollment in Acute Respiratory Distress Syndrome Clinical Trials Network (ARDSNet) trials. Performance of the automated system was assessed by comparing its results with the manual screening process. Discordant results were adjudicated blindly by two physician reviewers. In addition, a sensitivity analysis using a range of assumptions was conducted to better estimate the system's performance. The Hospital of the University of Pennsylvania, an academic medical center and ARDSNet center (1994-2006). Intubated patients in medical and surgical intensive care units. None. Of 1270 patients screened, 84 were identified with acute lung injury (incidence of 6.6%). The automated screening system had a sensitivity of 97.6% (95% confidence interval, 96.8-98.4%) and a specificity of 97.6% (95% confidence interval, 96.8-98.4%). The manual screening algorithm had a sensitivity of 57.1% (95% confidence interval, 54.5-59.8%) and a specificity of 99.7% (95% confidence interval, 99.4-100%). Sensitivity analysis demonstrated a range for sensitivity of 75.0-97.6% of the automated system under varying assumptions. Under all assumptions, the automated system demonstrated higher sensitivity than and comparable specificity to the manual screening method. An automated electronic system identified patients with acute lung injury with high sensitivity and specificity in diverse intensive care units of a large academic medical center. Further studies are needed to evaluate the effect of automated prompts that such a system can initiate on the use of lung protective ventilation in patients with acute lung injury.

Development of the Adductome Approach to Detect DNA Damage in Humans

The development of new strategies designed to detect DNA damage caused by oxidative stress and other means may advance our understanding of the roles of such types of damage in the etiology of cancers, in aging processes, and as biomarkers of exposure. A DNA adduct detection method that uses liquid chromatography coupled with electrospray ionization tandem mass spectrometry (LC/ESI-MS/MS) to detect multiple DNA adducts in human lung tissue is reported herein. This adductome analysis strategy is designed to detect the neutral loss of 2 -deoxyribose from positively ionized 2 -deoxynucleoside adducts in multiple reaction ion monitoring mode (MRM) transmitting the [M + H](+) > [M + H - 116](+) transition over a total of 374 transitions in the mass range from m/z 228.8 to m/z 602.8. Data analysis is optimized and coupled with a comprehensive manual screening process designed to minimize the number of artifactual adducts appearing in the final analysis. In the final analysis, putative adducts were organized into an adductome map and unambiguous confirmation of selected oxidative adducts were made by stable isotope dilution and comparison to authentic standards. The future applications of this method are discussed.