Clinical trials are experiments or observations done in clinical research. Such prospective biomedical or behavioral research studies on human participants are designed to answer specific questions about biomedical or behavioral interventions, including new treatments such as novel vaccines , drugs , dietary choices , dietary supplements , and medical devices and known interventions that warrant further study and comparison. Clinical trials generate data on safety and efficacy. Depending on product type and development stage, investigators initially enroll volunteers or patients into small pilot studies , and subsequently conduct progressively larger scale comparative studies. Clinical trials can vary in size and cost, and they can involve a single research center or multiple centers , in one country or in multiple countries. Clinical study design aims to ensure the scientific validity and reproducibility of the results.
Therefore, in this study we evaluate the proposed algorithm on both trial-centered and patient-centered scenarios. Approval of ethics for this study was given by the CCHMC institutional review board study ID: - and a waiver of consent was authorized.
We composed a comprehensive list of the 70 oncology trials, which enrolled patients at CCHMC during the study period. To be more conservative in the evaluation of the ES algorithm, we excluded all repository trials, which customarily enrolled all patients, and the institutional trials for which we did not find the trial announcements on ClinicalTrials.
This process resulted in a set of 55 trials for the current study. To obtain the narrative eligibility criteria of the trials, we searched their NCT identifiers on ClinicalTrials.
The list of the trials including NCT identifiers, number of enrolled patients during the study period, opening and closing dates, and special circumstances in enrollment are presented in Additional file 1 : Table S1.
In addition, two demographic attributes, age and gender, were retrieved from the eligibility criteria via NLP techniques. During the study period CCHMC patients participated in cancer treatment and all of them were included in our study. Compared with the ED patients investigated in our earlier study [ 17 ], the pediatric oncology patients had more diagnoses and clinical notes. Frequencies of the collected EHR fields a and descriptive statistics of the unstructured clinical notes b.
The information collected until that point represented the information that was available to the physician at the time of making the enrollment decision.
One hundred and twenty seven patients were enrolled in one or more of the 55 trials, providing us with patient-trial matches as a reference standard. Unlike for adult clinical trials, the enrollment of pediatric oncology patients is almost universal.
Almost all eligible patients accept trial invitations.
Often a clinical trial is used to learn if a new treatment is more effective and/or has less harmful side effects than the standard treatment. Other clinical trials test ways to find a disease early, sometimes before there are symptoms. Still others test ways to prevent a health problem. A clinical trial may also look at how to make life better. Clinical Trials Past and Present. The history of clinical trials is said to date back to , when British physician James Lind conducted a systematic trial among British sailors with scurvy. Clinical Lactation Studies - Study Design, Data Analysis, and Recommendations for Labeling / Data Monitoring Committees for Clinical Trial Sponsors, The Establishment and Operation of.
The enrollment rate is lower in adolescents but it is still a magnitude higher than in adults. The special circumstances of pediatric oncology trial screening have two important consequences. First, although the historical enrollment decisions do not build a traditional gold standard because they were not made as part of a controlled double chart review process e. Second, because of the high enrollment rate specific for the study population, to determine the generalizability of conclusions drawn from testing the algorithm on retrospective pediatric oncology trial enrollment decisions will require additional research.
The diagnoses and clinical notes of pre-filtered patients were then processed, from which the medical terms were extracted and stored in the patient pattern vectors Step 2. The same process was applied to the trial criteria to construct the trial pattern vector Step 3. Finally, the IE function computed the degree of match between the patient vectors and the trial pattern vector Step 4 and output the ranked list of patients based on the matching scores Step 5.
Vice versa, the ES algorithm also output a ranked list of trials for a patient in patient-centered trial recommendation Step 6. We also applied the trial enrollment window to facilitate the pre-filtering process.
If a patient did not have clinical notes within the enrollment window of a trial e.
Details of the NLP process can be found in our earlier publications [ 1732 - 35 ]. To summarize, the algorithm first extracted text-driven, term-level medical information e. The same process was applied to identify text and medical terms from the diagnosis strings.
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To identify negations, we implemented a negation detector based on the NegEx algorithm [ 41 ]. The text and medical terms were converted if necessary in the assertion detection component. Finally, all identified text and medical terms were stored as bag-of-words in a patient vector.
For the trial eligibility description, the same text and medical term processing was applied to the inclusion and exclusion criteria to extract term-level patterns. All terms extracted from the exclusion criteria were converted into negated format. The same process was executed to build the pattern vector for a clinical trial.
Clinical trial management systems are often used by research sponsors or CROs to help plan and manage the operational cts of a clinical trial, particularly with respect to investigational sites. Advanced analytics for identifying researchers and research sites with expertise in a given area utilize public and private information about. still applies to Phase 2 and Phase 3 clinical trial materials. 7. Meetings Pre-IND Meetings (Expiration Dating Period) Purity Chemical purity (process impurities, degradation. Search Tips and Examples. You can enter a word or a phrase, such as the name of a medical condition or an intervention. Use AND (in uppercase) to search for multiple terms. For more information, see How to Search.; Click on the links below to practice some sample searches.
The IE function then matched the trial and the patient vectors and computed the matching score for each trial-patient pair [ 42 ]. Finally, a ranked list of patient candidates was returned for a trial in trial-centered patient cohort identification and a ranked list of trials for a patient in patient-centered trial recommendation.
We used two methods to evaluate the performance of the ES algorithm. We refer to this evaluation as retrospective workload evaluation. Second, an oncologist performed a manual review of the algorithm's randomly selected 76 trial-patient assignments. We refer to this evaluation as physician chart review. For comparison, we used the output of the demographics-based filter, which has been implemented in many EHR products, as the baseline. In trial-centered patient cohort identification, the baseline excluded ineligible patients by demographics and randomly shuffled the rest of the candidates for a trial.
Similarly, it excluded ineligible trials and randomly shuffled the pre-filtered trials for a patient in patient-centered trial recommendation. The baseline simulated the screening process without automated ES, replicating current practice. That is, we used the two data sets individually and in combination in the ES algorithm and assessed the performance respectively.
To assess the screening efficiency of the algorithms, we calculated the average workload of the recruitment process [ 16 ]. In trial-centered patient cohort identification, the workload is defined as the number of patients an oncologist would be required to review, from the population of patients, to identify all patients historically enrolled in a particular trial. For this scenario, the algorithm was evaluated on the patients who had historical enrollments.
We refer to this result as sub-population case. In practice some patients e.
To assure the integrity of the evaluation, we also evaluated the algorithms on all patients, which we refer to as the full-population case. In addition to the average workload, precision denoted by P and specificity denoted by Sp were applied to measure screening performance. Since the goal of the retrospective workload evaluation was to identify all historical enrollments i.
Consequently, the retrospective workload evaluation would report a lower algorithm precision than the true measure would be based on the scope of our study.
Adding the manual retrospective chart review accounted for the external factors e.
We reported the results of the physician chart review on trial-centered patient cohort identification. Finally, the precision of the ES algorithm was re-calculated based on the results of the chart review.
The physician chart review also contributed information to our error analysis and identified limitations of the automated ES algorithm. For trial-centered patient cohort identification, an oncologist would need to review patients per trial using the baseline.
In automated patient-centered trial recommendation, we observed consistent improvement in workload when more EHR data was used. From the list of algorithm generated patient candidates the oncologist found that 34 patients were truly eligible for the ten randomly selected trials.
On the other hand, the retrospective data showed only 29 enrolled patients. Consequently, the adjusted precision of the ES algorithm increased to 0. The precision of the ES algorithm against the historical enrollments and the list of eligible patients found by the oncologist. The observations validate the value of unstructured clinical notes in automated ES and confirm the effectiveness of the NLP and IE techniques as previously demonstrated by us and other groups [ 17242529 ].
Projecting the results of the physician chart review to the entire data set, the performance of the automated ES algorithm would be improved by Further refinements of the algorithm are required to increase precision. The error analysis suggested several areas for improvement. First, The reason is that our algorithm used individual words as patterns, limiting its ability in finding semantic relations between consecutive words.
In the future we will integrate advanced NLP techniques to analyze semantic relations to see if they improve the accuracy of medical concept identification.
The observation validated the need for including temporal reasoning in automated ES. Finally, the logic-based filter was restricted to screening the demographics only, which limited its ability in capturing certain exclusion criteria e. The algorithm will be more powerful if more information from the structured data fields e.
The steps to extract this information from narrative eligibility criteria to design a complex logic-based filter will also be investigated in future works. The false positive errors made by the ES algorithm with the causes described by the oncologist.
One limitation of our study is that the evaluation was restricted to retrospective data. Project planning is in progress to evaluate the practicality of the automated ES in a randomized controlled prospective test environment.
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To verify the transferability of the findings, we also plan to test the ES algorithm on a more diversified oncology patient population e. Finally, the text of the eligibility criteria in oncology trials is more descriptive than in other subspecialties, providing a potentially more suitable foundation for NLP and IE.
By leveraging NLP and IE technologies on both the trial criteria and the EHR content of the patients, an automated eligibility pre-screening algorithm could dramatically increase the trial screening efficiency of oncologists.
Consequently, we hypothesize that the algorithm, when rolled out for production, will have the potential to substantially reduce the time and effort necessary to execute clinical research, particularly as important new initiatives of the cancer care community e.
However, the results showed the need for manual chart review to determine the true level of algorithm precision when such evaluation is conducted. Additional file 1: Table S1.
IPPCR 2015: Overview of Clinical Study Design
Competing interests. YN extracted the patient EHR data, designed and ran all the experiments, analyzed the results, created the tables and figures, and wrote the manuscript.
JW and JP performed the physician chart review of trial-patient matches, provided suggestions in developing the ES algorithm, analyzed the errors and contributed to the manuscript. IS coordinated the work, supervised the experiments, data cleaning, analysis of the results, and contributed to the manuscript.
IK coordinated the work and contributed to the manuscript.
All authors read and approved the final manuscript. Yizhao Ni, Email: gro. Jordan Wright, Email: gro. John Perentesis, Email: gro. Todd Lingren, Email: gro. Louise Deleger, Email: rf. Megan Kaiser, Email: gro. Isaac Kohane, Email: ude. Imre Solti, Email: moc. National Center for Biotechnology InformationU.
Published online Apr Author information Article notes Copyright and License information Disclaimer. Corresponding author. Received Aug 13; Accepted Mar This article has been cited by other articles in PMC.
Clinical trial dating
Abstract Background Manual eligibility screening ES for a clinical trial typically requires a labor-intensive review of patient records that utilizes many resources. Methods We collected narrative eligibility criteria from ClinicalTrials. Results Without automation, an oncologist would need to review patients per trial on average to replicate the historical patient enrollment for each trial.
Clinical trials are research studies performed in people that are aimed at evaluating a medical, surgical, or behavioral intervention. They are the primary way that researchers find out if a new treatment, like a new drug or diet or medical device for example, a pacemaker is safe and effective in people. Other clinical trials test ways to find a disease early, sometimes before there are symptoms.
Still others test ways to prevent a health problem. A clinical trial may also look at how to make life better for people living with a life-threatening disease or a chronic health problem. Clinical trials sometimes study the role of caregivers or support groups. Before the U. If these studies show favorable results, the FDA gives approval for the intervention to be tested in humans. Clinical trials advance through four phases to test a treatment, find the appropriate dosage, and look for side effects.
If, after the first three phases, researchers find a drug or other intervention to be safe and effective, the FDA approves it for clinical use and continues to monitor its effects. Clinical trials of drugs are usually described based on their phase. Like Mr. Jackson, you might have heard of clinical trials but may not be sure what they are or if you want to join one.
Apr 14, The clinical trial phase is the most expensive component of drug development; therefore, any improvement in the efficiency of the recruitment process should be highly consequential. The factor that most clinical practices are not staffed for manual patient screening is also a challenge for clinical trial recruitment. For these reasons. Match isn't a niche dating site - it's true. But it has the most black, Asian, and biracial members of almost every other dating site. Founded in , Match was the first dating site, so it really knows what it's doing. The proof is in the fact that it has matched more people than all the other sites in the industry. middleburyfloralvt.com is a registry and results database of publicly and privately supported clinical studies of human participants conducted around the world. Explore , research studies in all 50 states and in countries. See listed clinical studies related to the coronavirus disease (COVID).
Here is some information that can help you decide if participating in a clinical trial is right for you. There are many reasons why people choose to join a clinical trial.
Some join a trial because the treatments they have tried for their health problem did not work. Others participate because there is no treatment for their health problem. By being part of a clinical trial, participants may find out about new treatments before they are widely available. Some studies are designed for, or include, people who are healthy but want to help find ways to prevent a disease, such as one that may be common in their family.
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Many people say participating in a clinical trial is a way to play a more active role in their own health care. Other people say they want to help researchers learn more about certain health problems. Whatever the motivation, when you choose to participate in a clinical trial, you become a partner in scientific discovery.
And, your contribution can help future generations lead healthier lives. Major medical breakthroughs could not happen without the generosity of clinical trial participants-young and old. There are many ways you can get help to find a clinical trial. You can talk to your doctor or other healthcare provider. Or, you can search ClinicalTrials. You can sign up for a registry or matching service to connect you with trials in your area.
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Support groups and websites that focus on a particular condition sometimes have lists of clinical studies. Also, you may see ads for trials in your area in the newspaper or on TV. Once you find a study that you might want to join, contact the clinical trial or study coordinator. You can usually find this contact information in the description of the study.
The first step is a screening appointment to see if you qualify to participate. This appointment also gives you a chance to ask your questions about the study. Let your doctor know that you are thinking about joining a clinical trial.