Evidence-based Medical Decision Research Articles

Exome sequencing reveals genetic heterogeneity and clinically actionable findings in children with cerebral palsy.

Cerebral palsy (CP) is the most common motor disability in children. To ascertain the role of major genetic variants in the etiology of CP, we conducted exome sequencing on a large-scale cohort with clinical manifestations of CP. The study cohort comprised 505 girls and 1,073 boys. Utilizing the current gold standard in genetic diagnostics, 387 of these 1,578 children (24.5%) received genetic diagnoses. We identified 412 pathogenic and likely pathogenic (P/LP) variants across 219 genes associated with neurodevelopmental disorders, and 59 P/LP copy number variants. The genetic diagnostic rate of children with CP labeled at birth with perinatal asphyxia was higher than the rate in children without asphyxia (P = 0.0033). Also, 33 children with CP manifestations (8.5%, 33 of 387) had findings that were clinically actionable. These results highlight the need for early genetic testing in children with CP, especially those with risk factors like perinatal asphyxia, to enable evidence-based medical decision-making.

Artificial Intelligence in Evidence-based Medicine: Challenges and Opportunities

Evidence-based Medicine (EBM) is facing a new challenge in applying Artificial Intelligence (AI) for better services. Here the fundamental question is: can AI be used as a human intelligence amplifier to make better evidence-based medical decisions? This argument can only be answered if AI-supported decisions are compared and contrasted with those of human experts, and if the implementation of AI-enabled Evidence-Based Medicine (AiEBM) can be explained or justified using the available evidence and experience. In this paper, we propose AiEBM as a human-centered approach to be integrated into processes of healthcare systems, thereby increasing the credibility of the clinical decision support systems. We provide insights into the applicability of AI and its challenges and opportunities in EBM. The AaaS (AI as a Service) and high-order reasoning facilitated by machine learning and AI for medical decisions will be discussed.

Priority ranking of Chinese patent medicine prescriptions for type 2 diabetes mellitus (syndrome of deficiency of both Qi and Yin): based on analytic hierarchy process (AHP)

Clinical expertise, patient preference, and the best evidence are the three elements of evidence-based medicine. Based on high-level and high-quality evidence, qualitative and quantitative analysis of the prescribing decisions of physicians is beneficial to improving clinical efficacy. A mature methodological system is available for the retrieval, analysis, summary, evaluation, and recommendation of the evidence, but there are still few studies on physicians' prescribing decisions. How to analyze the trend of physicians' prescribing decisions based on the priority ranking in addition and subtraction of prescriptions? Analytic hierarchy process(AHP) is a method for decision making, which arranges the elements of the decision problem into overall goal, criteria, and operational sub-criteria, and uses the matrix eigenvector method to solve the problem. This study aims to analyze the priority of physicians' prescribing decisions for diabetes mellitus with deficiency of both Qi and Yin based on AHP. To be specific, a database of diabetes mellitus cases with deficiency of both Qi and Yin was established and AHP was used to yield the priority ranking of Chinese patent medicine prescriptions in specific clinical scenarios. In the selected cases of diabetes mellitus with deficiency of both Qi and Yin, Xiaoke Pills was the best prescription for the treatment of type 2 diabetes mellitus(deficiency of both Qi and Yin)(normalized=0.388), followed by Liuwei Dihuang Pills(normalized=0.269), Qishen Capsules(normalized=0.230), and Shengmai Injection(normalized=0.113). According to the analysis the available data, for type 2 diabetes mellitus(deficiency of both Qi and Yin), Xiaoke Pills was the most effective prescription in specific scenarios. When the physicians' prescribing decisions are consistent with the evidence, quantitative analysis of physicians' cognition will boost the evidence-based medical decision-making. However, the research results are also affected by the quality of literature, evidence level and priority, which are thus have some limitations. It is recommended that further small data research based on individual cases be carried out to lay a evidence-based basis for the clinical decision-making of type 2 diabetes mellitus.

Willingness of Patients from an Urban Safety-Net Dermatology Clinic to Receive a COVID-19 Vaccine

Background: While recent vaccine development has initiated a return to pre-COVID "normalcy" both in the dermatology clinic and worldwide, significant challenges remain regarding the public’s willingness to receive a COVID-19 vaccine. Dermatologists often discuss vaccinations with their patients and aid them in making evidence-based medical decisions. Previous studies have looked at the U.S. population’s willingness to receive a COVID-19 vaccine, but no studies have examined the dermatology patient population from an urban, safety-net hospital. Studies have shown that understanding the target audience is the first step towards increasing vaccine acceptance. Methods: A cross-sectional, telephone-based survey study was administered to 326 patients of an urban, safety-net hospital from July 2020 to August 2020 in order to assess willingness to obtain a COVID-19 vaccine. Results: Our survey study showed that 57.7% of patients with a recent dermatology appointment are willing to receive a COVID-19 vaccine and that safety concerns represent the main reason for patient hesitancy. Patients who do not regularly receive a flu vaccine, non-Caucasian patients, and those who know someone who tested positive for COVID-19 are less willing to receive a COVID-19 vaccine. Patients with a recent dermatology appointment are more willing to receive a COVID-19 vaccine than those who did not have a recent dermatology appointment. Conclusions: Our results provide dermatologists, especially those working in urban safety-net clinics, with key information about the attitude of patients toward the COVID-19 vaccine.

Development and validation of a risk index to predict kidney graft survival: the kidney transplant risk index

BackgroundKidney graft failure risk prediction models assist evidence-based medical decision-making in clinical practice. Our objective was to develop and validate statistical and machine learning predictive models to predict death-censored graft failure following deceased donor kidney transplant, using time-to-event (survival) data in a large national dataset from Australia.MethodsData included donor and recipient characteristics (n = 98) of 7,365 deceased donor transplants from January 1st, 2007 to December 31st, 2017 conducted in Australia. Seven variable selection methods were used to identify the most important independent variables included in the model. Predictive models were developed using: survival tree, random survival forest, survival support vector machine and Cox proportional regression. The models were trained using 70% of the data and validated using the rest of the data (30%). The model with best discriminatory power, assessed using concordance index (C-index) was chosen as the best model.ResultsTwo models, developed using cox regression and random survival forest, had the highest C-index (0.67) in discriminating death-censored graft failure. The best fitting Cox model used seven independent variables and showed moderate level of prediction accuracy (calibration).ConclusionThis index displays sufficient robustness to be used in pre-transplant decision making and may perform better than currently available tools.

IEEE Access Special Section Editorial: Smart Health Sensing and Computational Intelligence: From Big Data to Big Impacts

Smart health big data is paving a promising way for ubiquitous health management, leveraging exciting advances in biomedical engineering technologies, such as convenient bio-sensing, health monitoring, in-home monitoring, biomedical signal processing, data mining, health trend tracking, and evidence-based medical decision support. To build and utilize the smart health big data, advanced data sensing and data mining technologies are closely coupled key enabling factors. In smart health big data innovations, challenges arise in how to informatively and robustly build the big data with advanced sensing technologies, and how to automatically and effectively decode patterns from the big data with intelligent computational methods. More specifically, advanced sensing techniques should be able to capture more modalities that can reflect rich physiological and behavioral states of humans, and enhance the signal robustness in daily wearable applications. In addition, intelligent computational techniques are required to unveil patterns deeply hidden in the data and nonlinearly convert the patterns to high-level medical insights.

A visual analytics system for multi-model comparison on clinical data predictions

There is a growing trend of applying machine learning methods to medical datasets in order to predict patients’ future status. Although some of these methods achieve high performance, challenges still exist in comparing and evaluating different models through their interpretable information. Such analytics can help clinicians improve evidence-based medical decision making. In this work, we develop a visual analytics system that compares multiple models’ prediction criteria and evaluates their consistency. With our system, users can generate knowledge on different models’ inner criteria and how confidently we can rely on each model’s prediction for a certain patient. Through a case study of a publicly available clinical dataset, we demonstrate the effectiveness of our visual analytics system to assist clinicians and researchers in comparing and quantitatively evaluating different machine learning methods.

Should Tocilizumab Be Used Routinely in New Patients With a Diagnosis of Giant Cell Arteritis?

Should Tocilizumab Be Used Routinely in New Patients With a Diagnosis of Giant Cell Arteritis?

Factors associated with evidence-based decision-making among patients and providers.

Aim: For comparative effectiveness research to achieve its purpose, providers and patients must use research evidence to make medical decisions. Therefore, this study examined factors associated with evidence-based decision-making by patients and providers. Methods: Data were collected via cross-sectional online surveys of patients (n=603) and providers (n=628) between November 2011 and January 2012. Results: For both patients and providers, evidence-based medical decision-making is associated with perceptions, that is, some combination of self efficacy, attitudes and opinions. However, whereas knowledge is the most consistent factor associated with decision-making for providers, it is not associated at all for patients. Conclusion: Efforts to promote evidence-based medical decision-making among patients and providers should focus on skills training to improve self efficacy, and messages that highlight the benefits of patient engagement in medical decisions.

Point-Of-Care Testing Curriculum and Accreditation for Public Health-Enabling Preparedness, Response, and Higher Standards of Care at Points of Need.

Objectives: To develop awareness of benefits of point-of-care testing (POCT) education in schools of public health, to identify learning objectives for teaching POCT, to enable public health professionals and emergency responders to perform evidence-based diagnosis and triage effectively and efficiently at points of need, and to better improve future standards of care for public health practice, including in limited-resource settings and crisis situations.Methods: We surveyed all U.S. schools of public health, colleges of public health, and public health schools accredited by the Council on Education in Public Health (CEPH). We included accredited public health programs, so that all states offering public health education were represented. We analyzed survey data, public health books, and board certification guidelines. We used PubMed to identify public health curriculum papers, and assessed 2019 CEPH accreditation requirements. We merged POCT knowledge bases to design a new curriculum for teaching public health students and practitioners the principles and practice of POCT.Results: Public health curricula, certification requirements, and textbooks generally do not include POCT instruction. Only one book, Global Point of Care: Strategies for Disasters, Emergencies, and Public Health Resilience, and one online course on public health preparedness address POCT and public health intervention issues. The topic, POC HIV/HCV ED testing, appeared in one course and POC diagnostics in local clinics, in another. Papers on public health curriculum have not incorporated POCT. No curriculum addresses POCT in isolation units during quarantine, despite evidence that recent Ebola virus disease cases in the U.S. and elsewhere proved unequivocally the need for POCT. The modular learning objectives identified in this paper were customized for public health students. Public health graduates can use boot camps, online credentialing, and self-study to acquire POCT skills.Conclusions: Enhancing accreditation requirements, academic training, board certification, and field experience will generate public health healthcare professionals who will rely upon evidence-based medical decision making at points of care, including during crises when time is of the essence. A POCT-enabled public health workforce can help prevent and stop outbreaks. Public health-based medical professionals urgently need the skills necessary to perform POCT and prepare America and other nations for threats portending significant adverse medical, economic, social, and cultural impact.

Development and Evaluation of a Crowdsourcing Platform for Education and Evidence-Based Medical Decision-Making

ABSTRACT The medical education for clinical decision-making has undergone changes in recent years. Previously supported by printed material, problem solving in clinical practice has recently been aided by digital tools known as summaries platforms. Doctors and medical students have been using such tools from questions found in practice scenarios. These platforms have the advantage of high-quality, evidence-based and always up-to-date content. Its popularization was mainly due to the rise of the internet use and, more recently, of mobile devices such as tablets and smartphones, facilitating their use in clinical practice. Despite this platform is widely available, the most of them actually present several access barriers as costs, foreign language and not be able to Brazilian epidemiology. A free national platform of evidence-based medical summaries was proposed, using the crowdsourcing concept to resolve those barriers. Furthermore, concepts of gamification and content evaluation were implemented. Also, there is the possibility of evaluation by the users, who assigns note for each content created. The platform was built with modern technological tools and made available for web and mobile application. After development, an evaluation process was conducted by researchers to attest to the valid of content, usability, and user satisfying. Consolidated questionnaires and evaluation tools by the literature were applied. The process of developing the digital platform fostered interdisciplinarity, from the involvement of medical and information technology professionals. The work also allowed the reflection on the innovative educational processes, in which the learning from real life problems and the construction of knowledge in a collaborative way are integrated. The assessment results suggest that platform can be real alternative form the evidence-based medical decision-making.

Development and Evaluation of a Crowdsourcing Platform for Education and Evidence-Based Medical Decision-Making

ABSTRACT The medical education for clinical decision-making has undergone changes in recent years. Previously supported by printed material, problem solving in clinical practice has recently been aided by digital tools known as summaries platforms. Doctors and medical students have been using such tools from questions found in practice scenarios. These platforms have the advantage of high-quality, evidence-based and always up-to-date content. Its popularization was mainly due to the rise of the internet use and, more recently, of mobile devices such as tablets and smartphones, facilitating their use in clinical practice. Despite this platform is widely available, the most of them actually present several access barriers as costs, foreign language and not be able to Brazilian epidemiology. A free national platform of evidence-based medical summaries was proposed, using the crowdsourcing concept to resolve those barriers. Furthermore, concepts of gamification and content evaluation were implemented. Also, there is the possibility of evaluation by the users, who assigns note for each content created. The platform was built with modern technological tools and made available for web and mobile application. After development, an evaluation process was conducted by researchers to attest to the valid of content, usability, and user satisfying. Consolidated questionnaires and evaluation tools by the literature were applied. The process of developing the digital platform fostered interdisciplinarity, from the involvement of medical and information technology professionals. The work also allowed the reflection on the innovative educational processes, in which the learning from real life problems and the construction of knowledge in a collaborative way are integrated. The assessment results suggest that platform can be real alternative form the evidence-based medical decision-making.

Non-Invasive Diagnosis Model for Pancreatic Cystic Tumors Based on Machine Learning-Radiomics Using Contrast-Enhanced CT

Background: To establish and validate a radiomics diagnosis model for classification of three subtypes of the pancreatic lesion, including intraductal papillary mucinous neoplasm (IPMN), solid-pseudopapillary neoplasm (SPN) and pancreatic cystadenoma (PCN). Methods: The contrast-enhanced (CECT) images and clinical characteristics of 134 pathological proven pancreatic cystic tumor patients were retrospectively collected in this study, including IPMN (n=40), SPN (n=47) and PCN (n=47). All patients were randomly split into the training cohort (n=90) and independent validation cohort (n=44). A total of 468 radiomics features were extracted from each tumor region. The highly inter-correlated radiomics features were excluded, and Boruta algorithm was used for further feature selection. Supporting vector machine (SVM) and random forest (RF) methods were used to construct the diagnosis model based on selected features. The diagnosis overall accuracy, precision, recall, F1 score were calculated to evaluate the performance of both diagnosis models. Results: Eighty-two radiomics features with an inter-correlation coefficient of less than 0.75 were selected. Sixteen radiomics features, and eight clinical parameters, including age, gender, tumor location, tumor number, alanine aminotransferase, aspartate aminotransferase, carcinoembryonic antigen and fasting blood glucose, showed a significant difference between IPMN, SPN and PCN. Utilizing the RFE algorithm, six radiomics features and six clinical parameters were demonstrated essential for model construction. An SVM model with a linear kernel was constructed showing overall diagnosis accuracy of 82.2% in the training dataset and 81.8% in the validation dataset. The built RF model showed diagnosis overall accuracy of 100% in the training dataset and 81.8% in the validation dataset. Both SVM and RF model can identify IPMN, SPN and PCN with high diagnostic accuracy. Conclusion: Our study suggests CECT-based radiomics model can serve as a diagnostic tool for preoperative diagnosis for IPMN, SPN and PCN patients in a non-invasive, convenient and accurate way, thus facilitating evidence-based medical decision making. Funding Statement: This work was supported by the National Science and Technology Major Project of China [Grant number: 2017ZX10203205]; National Science and Technology Project of China [Grant number: 2017YFC0108704]; Natural Science Foundation of China [Grant No. 81871351]; Zhejiang Provincial Natural Science Foundation of China [LR16F010001, Y16H180003]; Zhejiang University Education Foundation ZJU-Stanford Collaboration Fund; Opening Fund of Engineering Research Center of Cognitive Healthcare of Zhejiang Province Declaration of Interests: [The authors] declare no personal conflicts of interest. Ethics Approval Statement: This retrospective study was approved by the Institutional Review Board of the First Affiliated Hospital, Zhejiang University School of Medicine. The patient consent was waived by the patients for the use of the pre-existing medical images.

Procalcitonin-guided treatment of lower respiratory tract infections.

The FDA recently approved a procalcitonin assay for use in hospitals and EDs to help prescribers determine if an antibiotic should be prescribed and for what duration. This article reviews the use of procalcitonin testing and evidence-based medical decision-making for patients with sepsis or lower respiratory tract infections.

Assessment of risk of bias in controlled studies

Practicing physicians are faced with many medical decisions daily. These are mainly influenced by personal experience but should also consider patient preferences and the scientific evidence reflected by a constantly increasing number of medical publications and guidelines. With the objective of optimal medical treatment, the concept of evidence-based medicine is founded on these three aspects. It should be considered that there is a high risk of misinterpreting evidence, leading to medical errors and adverse effects without knowledge of the methodological background. This article explains the concept of systematic error (bias) and its importance. Causes and effects as well as methods to minimize bias are discussed. This information should impart a deeper understanding, leading to a better assessment of studies and implementation of its recommendations in daily medical practice. Developed by the Cochrane Collaboration, the risk of bias (RoB) tool is an assessment instrument for the potential of bias in controlled trials. Good handling, short processing time, high transparency of judgements and a graphical presentation of findings that is easily comprehensible are among its strengths. Attached to this article the German translation of the RoB tool is published. This should facilitate the applicability for non-experts and moreover, support evidence-based medical decision-making.

Abstract A68: Awareness of ongoing clinical trial information: physicians' and clinical trial administrators' perspectives

Abstract Introduction: Evidence-based medical decision-making is the current standard for clinical practice. Evidence is obtained from clinical trials yet few patients participate in clinical trials due to numerous barriers encountered by patients. However, barriers faced by physicians have not been comprehensively investigated. This pilot study investigated information seeking behaviors of physicians and information dissemination strategies employed by clinical trial administrators (CTAs). Methods: All attending physicians and CTAs at a public university in the southeast were eligible to participate in face-to-face, semi-structured interviews. Thematic content analysis using the constant comparative method yielded a priori themes regarding physicians' knowledge, attitude, behavior and barriers to information seeking about ongoing clinical trials and their perception regarding reliability of information obtained. Data were validated through peer debriefing. Results: Sixteen physicians from nine specialties and three CTAs were interviewed (Family Medicine/Neurology 44%; >20 years in practice 50%; seeing >100 patients per month 56%). Almost half of the physicians did not actively search for information on ongoing trials in general and a majority of physicians do not actively search for information on trials conducted at their home institution, relying mostly on direct contact from CTAs through mass email and word-of-mouth. Of those who searched, a third admitted to only searching rarely. Most of the physicians who actively search rely on Google search and clinicaltrials.gov, but few used the institution's research website. Half physicians interviewed felt that current trial advertising strategies are ineffective and disorganized, and cited time constraints as a reason why they do not search actively. Most physicians would prefer to have trial staff present trial information directly to them at staff meetings. Most physicians rely on the reputation of the Principal Investigator and Institutional Review Board approval status to determine the reliability and sufficiency of the information they gather. Few physicians read the protocol of the study or verify the information given with outside sources. Physicians who see more patients per month (>100) tended to search less than those who see less patients per month. More experienced physicians (> 20 years of practice) tended to search about the same as less experienced physicians. Of the CTAs interviewed, most used mass emails, the institution's research website and physical newsletter advertisements to advertise trials. Most assessed the effectiveness of these strategies by reviewing patient enrollment. Most do not receive or seek feedback for their strategies from physicians. Conclusion: Most physicians do not actively search for information on trials. There is a disconnect between the strategies desired by physicians and those employed by administrators. There is no assessment of the effectiveness of the advertisement strategies except for enrollment numbers. Most physicians do not use an independent search to verify the information that they have received. These findings have implications, not only for overall accrual to clinical trials but also for disparities in access to trials for medically underserved and racial ethnic minority patients who may not routinely seek out clinical trials on their own. Citation Format: Dariam Cardentey Oliva, Jose M. Soto, Hesborn Wao, Clement K. Gwede, Rahul Mhaskar. Awareness of ongoing clinical trial information: physicians' and clinical trial administrators' perspectives. [abstract]. In: Proceedings of the Sixth AACR Conference: The Science of Cancer Health Disparities; Dec 6–9, 2013; Atlanta, GA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2014;23(11 Suppl):Abstract nr A68. doi:10.1158/1538-7755.DISP13-A68

Cost-of-Illness Trend of Thyroid Gland Disease in Korea.

Cost-of-Illness Trend of Thyroid Gland Disease in Korea.

Deficiencies in the transfer and availability of clinical trials evidence: a review of existing systems and standards

BackgroundDecisions concerning drug safety and efficacy are generally based on pivotal evidence provided by clinical trials. Unfortunately, finding the relevant clinical trials is difficult and their results are only available in text-based reports. Systematic reviews aim to provide a comprehensive overview of the evidence in a specific area, but may not provide the data required for decision making.MethodsWe review and analyze the existing information systems and standards for aggregate level clinical trials information from the perspective of systematic review and evidence-based decision making.ResultsThe technology currently used has major shortcomings, which cause deficiencies in the transfer, traceability and availability of clinical trials information. Specifically, data available to decision makers is insufficiently structured, and consequently the decisions cannot be properly traced back to the underlying evidence. Regulatory submission, trial publication, trial registration, and systematic review produce unstructured datasets that are insufficient for supporting evidence-based decision making.ConclusionsThe current situation is a hindrance to policy decision makers as it prevents fully transparent decision making and the development of more advanced decision support systems. Addressing the identified deficiencies would enable more efficient, informed, and transparent evidence-based medical decision making.

A turning point for Alzheimer's disease?

Despite an archive of over 73,000 research papers published in the last two decades on the subject of Alzheimer's disease (AD), little clinical progress has been made relative to how people get sporadic AD and what can be done to help them avoid it. This review spotlights strategic steps that could be a turning point in the dramatic lowering of Alzheimer prevalence. The main strategy includes application of four pillars of prevention: 1) early identification of AD vascular risk factors; 2) early detection of AD vascular risk factors; 3) early intervention of AD vascular risk factors based on evidence-based medical decisions; 4) patient follow-up to assess and modify interventions as needed. Tandem to these four pillars of prevention, a proactive lifestyle consisting of a healthy diet coupled to physical and mental activity should be applied as part of any therapeutic intervention. We are persuaded by mounting and compelling evidence that AD is a multifactorial disorder kindled by vascular risk factors that generate chronic brain hypoperfusion (CBH) during advanced aging. A pathobiological cascade of biochemical events in the presence of CBH that leads to oxidative stress and neurodegeneration appears to involve multiple biofactors including micronutrients, trace metals, lipids, and pro-oxidants, as reviewed in this special issue of BioFactors. Modulation of these biofactors may help prevent or control incipient AD. © 2012 International Union of Biochemistry and Molecular Biology, Inc.

Understanding the Building Blocks of Interpreting Complex Medical Graphs for Enabling Evidence-Based Medical Decision Making

Abstract Understanding health statistics in graphical or numerical format is essential for evidence-based medical decision making. It has been reported repeatedly that physicians, medical students, and patients perform poorly when interpreting those kinds of information. With the current research we started to decompose skills and competencies needed for interpreting complex medical graphs. We invited students at Weill Cornell Medical College in Qatar to fill in a questionnaire on www.SurveyMonkey.com that contained three visual medical tasks and one well-investigated complex diagrammatic reasoning task from economics. We expected that students’ performance would improve with years at the medical college for medical tasks, but not necessarily for an unrelated diagrammatic reasoning task. 85 students (between 8 and 19 students per class) participated in this research. For the diagrammatic reasoning task, students performed as poorly as reported in the literature and there was no statistically significant difference between pre-medical and medical students. In contrast, medical students outperformed pre-medical students for a gastrointestinal anatomy test and for two general surgery tasks on steps and structures associated with cholecystectomy. These results replicate findings of low performance for interpreting complex graphs. At the same time, medical education seems to foster students’ understanding of simple graphs in their domain and might prepare them for understanding more complex graphs in that domain. Currently, we are working on extending our research to decompose students’, physicians’, and patients’ understanding of survival curves as complex graphs in the medical domain needed for evidence-based medical decision making.