How I do it: a practical database management system to assist clinical research teams with data collection, organization, and reporting

Data management system for diabetes clinical trials: a pre-post evaluation study

BACKGROUND

Data management system for diabetes clinical trials is used to support clinical data management processes. The purpose of this study was to evaluate the quality and usability of this system from the users' perspectives.

METHODS

This study was conducted in 2020, and the pre-post evaluation method was used to examine the quality and usability of the designed system. Initially, a questionnaire was designed and distributed among the researchers who were involved in the diabetes clinical trials (n = 30) to investigate their expectations. Then, the researchers were asked to use the system and explain their perspectives about it by completing two questionnaires.

RESULTS

There was no statistically significant differences between the users' perspectives about the information quality, service quality, achievements, and communication before and after using the system. However, in terms of the system quality (P = 0.042) and users' autonomy (P = 0.026), the users' expectations were greater than the system performance. The system usability was at a good level based on the users' opinions.

CONCLUSION

It seems that the designed system largely met the users' expectations in most areas. However, the system quality and users' autonomy need further attentions. In addition, the system should be used in multicenter trials and re-evaluated by a larger group of users.

Analysis of Tumor Burden as a Biomarker for Patient Survival with Neuroendocrine Tumor Liver Metastases Undergoing Intra-Arterial Therapies: A Single-Center Retrospective Analysis

PURPOSE

To assess the value of quantitative analysis of tumor burden on baseline MRI for prediction of survival in patients with neuroendocrine tumor liver metastases (NELM) undergoing intra-arterial therapies.

MATERIALS AND METHODS

This retrospective single-center analysis included 122 patients with NELM who received conventional (n = 74) or drug-eluting beads, (n = 20) chemoembolization and radioembolization (n = 28) from 2000 to 2014. Overall tumor diameter (1D) and area (2D) of up to 3 largest liver lesions were measured on baseline arterially contrast enhanced MR images. Three-dimensional quantitative analysis was performed using the qEASL tool (IntelliSpace Portal Version 8, Philips) to calculate enhancing tumor burden (the ratio between enhancing tumor volume and total liver volume). Based on Q-statistics, patients were stratified into low tumor burden (TB) or high TB.

RESULTS

The survival curves were significantly separated between low TB and high TB groups for 1D (p < 0.001), 2D (p < 0.001) and enhancing TB (p = 0.008) measurements, with, respectively, 2.7, 2.6 and 2.2 times longer median overall survival (MOS) in the low TB group (p < 0.001, p < 0.001 and p = 0.008). Multivariate analysis showed that 1D, 2D, and enhancing TB were independent prognostic factors for MOS, with respective hazard ratios of 0.4 (95%CI: 0.2-0.6, p < 0.001), 0.4 (95%CI: 0.3-0.7, p < 0.001) and 0.5 (95%CI: 0.3-0.8, p = 0.003).

CONCLUSION

The overall tumor diameter, overall tumor area, and enhancing tumor burden are strong prognostic factors of overall survival in patients with neuroendocrine tumor liver metastases undergoing intra-arterial therapies.

The need for data management standards in public health nursing: A narrative review and case study

BACKGROUND

Data management is the key to the success of all projects and research. The ability to safely store, manipulate, and decipher data in real time is invaluable. Currently data management standards in public health are non-existent. Since the invention of computers real-time data retrieval and analysis has been possible but underutilized by researchers in the field. Historically, most small research studies and field-based projects have utilized spreadsheets for data management, which often proves problematic as the project grows. However, a viable and superior alternative exists in relational databases, such as REDCap. Relational databases allow for easier concatenation of multiple legacy datasets, facilitate data entry with surveys that incorporate branching logic, and allow for real time data entry in the field without the need for WIFI.

METHODS

One example of a public health project being transitioned from spreadsheet data management to a relational database is the Farmworker Family Health Program based out of the Lillian Carter Center for Global Health & Social Responsibility at Emory University's Nell Hodgson Woodruff School of Nursing. The data management transition from spreadsheets to REDCap has provided the team with unique insight into the data that has been collected in the 30 years the program has been running.

CONCLUSION

Through this case study, we identify the need for and recommend that those in public health nursing utilize relational databases when collecting data during research studies or as electronic medical records for field clinics.

Data management in diabetes clinical trials: a qualitative study

Background

Clinical trials play an important role in expanding the knowledge of diabetes prevention, diagnosis, and treatment, and data management is one of the main issues in clinical trials. Lack of appropriate planning for data management in clinical trials may negatively influence achieving the desired results. The aim of this study was to explore data management processes in diabetes clinical trials in three research institutes in Iran.

Method

This was a qualitative study conducted in 2019. In this study, data were collected through in-depth semi-structured interviews with 16 researchers in three endocrinology and metabolism research institutes. To analyze data, the method of thematic analysis was used.

Results

The five themes that emerged from data analysis included (1) clinical trial data collection, (2) technologies used in data management, (3) data security and confidentiality management, (4) data quality management, and (5) data management standards. In general, the findings indicated that no clear and standard process was used for data management in diabetes clinical trials, and each research center executed its own methods and processes.

Conclusion

According to the results, the common methods of data management in diabetes clinical trials included a set of paper-based processes. It seems that using information technology can help facilitate data management processes in a variety of clinical trials, including diabetes clinical trials.

A Clinical Data Management System for Diabetes Clinical Trials

Background

The use of novel medications and methods to prevent, diagnose, treat, and manage diabetes requires confirmation of safety and efficacy in a well-designed study prior to widespread adoption. Diabetes clinical trials are the studies that examine these issues. The aim of the present study was to develop a web-based system for data management in diabetes clinical trials.

Methods

The present research was a mixed-methods study conducted in 2019. To identify the required data elements and functions to develop the system, 60 researchers completed a questionnaire. The designed system was evaluated using two methods. The usability of the system was initially evaluated by a group of researchers (n = 6) using the think-aloud method, and after system improvement, the system functions were evaluated by other researchers (n = 30) using a questionnaire.

Results

The main data elements which were required to develop a case report form included “study data,” “participant's personal data,” and “clinical data.” The functional requirements of the system were “managing the study,” “creating case report forms,” “data management,” “data quality control,” and “data security and confidentiality.” After using the system, researchers rated the system functions at a “good” level (6.3 ± 0.73) on a seven-point Likert scale.

Conclusion

Given the complexity of the data management processes in diabetes clinical trials and the widespread use of information technologies in research, the use of clinical data management systems in diabetes clinical trials seems inevitable. The system developed in the current study can facilitate and improve the process of creating and managing case report forms as well as collecting data in diabetes clinical trials.

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering

Science relies on increasingly complex data sets for progress, but common data management methods such as spreadsheet programs are inadequate for the growing scale and complexity of this information. While database management systems have the potential to rectify these issues, they are not commonly utilized outside of business and informatics fields. Yet, many research labs already generate "medium sized", low velocity, multi-dimensional data that could greatly benefit from implementing similar systems. In this article, we provide a conceptual overview explaining how databases function and the advantages they provide in tissue engineering applications. Structural fibroblast data from individuals with a lamin A/C mutation was used to illustrate examples within a specific experimental context. Examples include visualizing multidimensional data, linking tables in a relational database structure, mapping a semi-automated data pipeline to convert raw data into structured formats, and explaining the underlying syntax of a query. Outcomes from analyzing the data were used to create plots of various arrangements and significance was demonstrated in cell organization in aligned environments between the positive control of Hutchinson-Gilford progeria, a well-known laminopathy, and all other experimental groups. In comparison to spreadsheets, database methods were enormously time efficient, simple to use once set up, allowed for immediate access of original file locations, and increased data rigor. In response to the National Institutes of Health (NIH) emphasis on experimental rigor, it is likely that many scientific fields will eventually adopt databases as common practice due to their strong capability to effectively organize complex data.

Clinical Trial Data Management Software: A Review of the Technical Features

BACKGROUND

Data management is an important, complex and multidimensional process in clinical trials. The execution of this process is very difficult and expensive without the use of information technology. A clinical data management system is software that is vastly used for managing the data generated in clinical trials. The objective of this study was to review the technical features of clinical trial data management systems.

METHODS

Related articles were identified by searching databases, such as Web of Science, Scopus, Science Direct, ProQuest, Ovid and PubMed. All of the research papers related to clinical data management systems which were published between 2007 and 2017 (n=19) were included in the study.

RESULTS

Most of the clinical data management systems were web-based systems developed based on the needs of a specific clinical trial in the shortest possible time. The SQL Server and MySQL databases were used in the development of the systems. These systems did not fully support the process of clinical data management. In addition, most of the systems lacked flexibility and extensibility for system development.

CONCLUSION

It seems that most of the systems used in the research centers were weak in terms of supporting the process of data management and managing clinical trial's workflow. Therefore, more attention should be paid to design a more complete, usable, and high quality data management system for clinical trials. More studies are suggested to identify the features of the successful systems used in clinical trials.

A Review of Clinical Data Management Systems Used in Clinical Trials

BACKGROUND

A clinical data management system is a software supporting the data management process in clinical trials. In this system, the effective support of clinical data management dimensions leads to the increased accuracy of results and prevention of diversion in clinical trials. The aim of this review article was to investigate the dimensions of data management in clinical data management systems.

METHODS

This study was conducted in 2017. The used databases included Web of Science, Scopus, Science Direct, ProQuest, Ovid Medline and PubMed. The search was conducted over a period of 10 years from 2007 to 2017. The initial number of studies was 101 reaching 19 in the final stage. The final studies were described and compared in terms of the year, country and dimensions of the clinical data management process in clinical trials.

RESULTS

The research findings indicated that none of the systems completely supported the data management dimensions in clinical trials. Although these systems were developed for supporting the clinical data management process, they were similar to electronic data capture systems in many cases. The most significant dimensions of data management in such systems were data collection or entry, report, validation, and security maintenance.

CONCLUSION

Seemingly, not sufficient attention has been paid to automate all dimensions of the clinical data management process in clinical trials. However, these systems could take positive steps towards changing the manual processes of clinical data management to electronic processes.

Clinical Research Informatics: Supporting the Research Study Lifecycle

Objectives: The primary goal of this review is to summarize significant developments in the field of Clinical Research Informatics (CRI) over the years 2015-2016. The secondary goal is to contribute to a deeper understanding of CRI as a field, through the development of a strategy for searching and classifying CRI publications. Methods: A search strategy was developed to query the PubMed database, using medical subject headings to both select and exclude articles, and filtering publications by date and other characteristics. A manual review classified publications using stages in the "research study lifecycle", with key stages that include study definition, participant enrollment, data management, data analysis, and results dissemination. Results: The search strategy generated 510 publications. The manual classification identified 125 publications as relevant to CRI, which were classified into seven different stages of the research lifecycle, and one additional class that pertained to multiple stages, referring to general infrastructure or standards. Important cross-cutting themes included new applications of electronic media (Internet, social media, mobile devices), standardization of data and procedures, and increased automation through the use of data mining and big data methods. Conclusions: The review revealed increased interest and support for CRI in large-scale projects across institutions, regionally, nationally, and internationally. A search strategy based on medical subject headings can find many relevant papers, but a large number of non-relevant papers need to be detected using text words which pertain to closely related fields such as computational statistics and clinical informatics. The research lifecycle was useful as a classification scheme by highlighting the relevance to the users of clinical research informatics solutions.

The impact of antiangiogenic therapy combined with Transarterial Chemoembolization on enhancement based quantitative tumor response assessment in patients with hepatocellular carcinoma

PURPOSE

To investigate whether bevacizumab compromises early response assessment after Transarterial Chemoembolization (TACE) in patients with hepatocellular carcinoma by 3D quantitative European Association for the Study of the Liver (qEASL) criteria in comparison to other imaging-based criteria.

MATERIALS AND METHODS

Each of 14 patients receiving TACE and bevacizumab was matched with two patients receiving TACE alone. Baseline and Follow-up MRI was retrospectively analyzed regarding qEASL and other imaging-based criteria.

RESULTS

Percentage-based qEASL achieved significant separation in both therapy arms (p=0.046 and p=0.015). Response and Overall Survival showed similar association among treatment groups (p=0.749).

CONCLUSIONS

Anti-angiogenic therapy with bevacizumab does not impede early response assessment by qEASL.

Validation of the Hong Kong Liver Cancer Staging System in Determining Prognosis of the North American Patients Following Intra-arterial Therapy

BACKGROUND & AIMS

There is debate over the best way to stage hepatocellular carcinoma (HCC). We attempted to validate the prognostic and clinical utility of the recently developed Hong Kong Liver Cancer (HKLC) staging system, a hepatitis B-based model, and compared data with that from the Barcelona Clinic Liver Cancer (BCLC) staging system in a North American population that underwent intra-arterial therapy (IAT).

METHODS

We performed a retrospective analysis of data from 1009 patients with HCC who underwent IAT from 2000 through 2014. Most patients had hepatitis C or unresectable tumors; all patients underwent IAT, with or without resection, transplantation, and/or systemic chemotherapy. We calculated HCC stage for each patient using 5-stage HKLC (HKLC-5) and 9-stage HKLC (HKLC-9) system classifications, and the BCLC system. Survival information was collected up until the end of 2014 at which point living or unconfirmed patients were censored. We compared performance of the BCLC, HKLC-5, and HKLC-9 systems in predicting patient outcomes using Kaplan-Meier estimates, calibration plots, C statistic, Akaike information criterion, and the likelihood ratio test.

RESULTS

Median overall survival time, calculated from first IAT until date of death or censorship, for the entire cohort (all stages) was 9.8 months. The BCLC and HKLC staging systems predicted patient survival times with significance (P < .001). HKLC-5 and HKLC-9 each demonstrated good calibration. The HKLC-5 system outperformed the BCLC system in predicting patient survival times (HKLC C = 0.71, Akaike information criterion = 6242; BCLC C = 0.64, Akaike information criterion = 6320), reducing error in predicting survival time (HKLC reduced error by 14%, BCLC reduced error by 12%), and homogeneity (HKLC chi-square = 201, P < .001; BCLC chi-square = 119, P < .001) and monotonicity (HKLC linear trend chi-square = 193, P < .001; BCLC linear trend chi-square = 111, P < .001). Small proportions of patients with HCC of stages IV or V, according to the HKLC system, survived for 6 months and 4 months, respectively.

CONCLUSIONS

In a retrospective analysis of patients who underwent IAT for unresectable HCC, we found the HKLC-5 staging system to have the best combination of performances in survival separation, calibration, and discrimination; it consistently outperformed the BCLC system in predicting survival times of patients. The HKLC system identified patients with HCC of stages IV and V who are unlikely to benefit from IAT.

Imaging Biomarkers of Tumor Response in Neuroendocrine Liver Metastases Treated with Transarterial Chemoembolization: Can Enhancing Tumor Burden of the Whole Liver Help Predict Patient Survival?

Purpose To investigate whether whole-liver enhancing tumor burden [ETB] can serve as an imaging biomarker and help predict survival better than World Health Organization (WHO), Response Evaluation Criteria in Solid Tumors (RECIST), modified RECIST (mRECIST), and European Association for the Study of the Liver (EASL) methods in patients with multifocal, bilobar neuroendocrine liver metastases (NELM) after the first transarterial chemoembolization (TACE) procedure. Materials and Methods This HIPAA-compliant, institutional review board-approved retrospective study included 51 patients (mean age, 57.8 years ± 13.2; range, 13.5-85.8 years) with multifocal, bilobar NELM treated with TACE. The largest area (WHO), longest diameter (RECIST), longest enhancing diameter (mRECIST), largest enhancing area (EASL), and largest enhancing volume (ETB) were measured at baseline and after the first TACE on contrast material-enhanced magnetic resonance images. With three-dimensional software, ETB was measured as more than 2 standard deviations the signal intensity of a region of interest in normal liver. Response was assessed with WHO, RECIST, mRECIST, and EASL methods according to their respective criteria. For ETB response, a decrease in enhancement of at least 30%, 50%, and 65% was analyzed by using the Akaike information criterion. Survival analysis included Kaplan-Meier curves and Cox regressions. Results Treatment response occurred in 5.9% (WHO criteria), 2.0% (RECIST), 25.5% (mRECIST), and 23.5% (EASL criteria) of patients. With 30%, 50%, and 65% cutoffs, ETB response was seen in 60.8%, 39.2%, and 21.6% of patients, respectively, and was the only biomarker associated with a survival difference between responders and nonresponders (45.0 months vs 10.0 months, 84.3 months vs 16.7 months, and 85.2 months vs 21.2 months, respectively; P < .01 for all). The 50% cutoff provided the best survival model (hazard ratio [HR]: 0.2; 95% confidence interval [CI]: 0.1, 0.4). At multivariate analysis, ETB response was an independent predictor of survival (HR: 0.2; 95% CI: 0.1, 0.6). Conclusion Volumetric ETB is an early treatment response biomarker and surrogate for survival in patients with multifocal, bilobar NELM after the first TACE procedure. ^© RSNA, 2016.

3D Quantitative tumour burden analysis in patients with hepatocellular carcinoma before TACE: comparing single-lesion vs. multi-lesion imaging biomarkers as predictors of patient survival

OBJECTIVES

To compare the ability of single- vs. multi-lesion assessment on baseline MRI using 1D- and 3D-based measurements to predict overall survival (OS) in patients with hepatocellular carcinoma (HCC) before transarterial chemoembolization (TACE).

METHODS

This retrospective analysis included 122 patients. A quantitative 3D analysis was performed on baseline MRI to calculate enhancing tumour volume (ETV [cm(3)]) and enhancing tumour burden (ETB [%]) (ratio between ETV [cm(3)] and liver volume). Furthermore, enhancing and overall tumour diameters were measured. Patients were stratified into two groups using thresholds derived from the BCLC staging system. Statistical analysis included Kaplan-Meier plots, uni- and multivariate cox proportional hazard ratios (HR) and concordances.

RESULTS

All methods achieved good separation of the survival curves (p < 0.05). Multivariate analysis showed an HR of 5.2 (95 % CI 3.1-8.8, p < 0.001) for ETV [cm(3)] and HR 6.6 (95 % CI 3.7-11.5, p < 0.001) for ETB [%] vs. HR 2.6 (95 % CI 1.2-5.6, p = 0.012) for overall diameter and HR 3.0 (95 % CI 1.5-6.3, p = 0.003) for enhancing diameter. Concordances were highest for ETB [%], with no added predictive power for multi-lesion assessment (difference between concordances not significant).

CONCLUSION

3D quantitative assessment is a stronger predictor of survival as compared to diameter-based measurements. Assessing multiple lesions provides no substantial improvement in predicting OS than evaluating the dominant lesion alone.

KEY POINTS

• 3D quantitative tumour assessment on baseline MRI predicts survival in HCC patients. • 3D quantitative tumour assessment predicts survival better than any current radiological method. • Multiple lesion assessment provides no improvement than evaluating the dominant lesion alone. • Measuring enhancing tumour volume in proportion to liver volume reflects tumour burden.

Medical Registry Data Collection Efficiency: A Crossover Study Comparing Web-Based Electronic Data Capture and a Standard Spreadsheet

Background

Electronic medical records and electronic data capture (EDC) have changed data collection in clinical and translational research. However, spreadsheet programs, such as Microsoft Excel, are still used as data repository to record and organize patient data for research.

Objective

The objective of this study is to assess the efficiency of EDC as against a standard spreadsheet in regards to time to collect data and data accuracy, measured in number of errors after adjudication.

Methods

This was a crossover study comparing the time to collect data in minutes between EDC and a spreadsheet. The EDC tool used was Research Electronic Data Capture (REDCap), whereas the spreadsheet was Microsoft Excel. The data collected was part of a registry of patients who underwent coronary computed tomography angiography in the emergency setting. Two data collectors with the same experience went over the same patients and collected relevant data on a case report form identical to the one used in our Emergency Department (ED) registry. Data collection tool was switched after the patient that represented half the cohort. For this, the patient cohort was exactly 30 days of our ED coronary Computed Tomography Angiography registry and the point of crossover was determined beforehand to be 15 days. We measured the number of patients admitted, and time to collect data. Accuracy was defined as absence of blank fields and errors, and was assessed by comparing data between data collectors and counting every time the data differed. Statistical analysis was made using paired t -test.

Results

The study included 61 patients (122 observations) and 55 variables. The crossover occurred after the 30th patient. Mean time to collect data using EDC in minutes was 6.2±2.3, whereas using Excel was 8.0±2.0 (P <.001), a difference of 1.8 minutes between both means (22%). The cohort was evenly distributed with 3 admissions in the first half of the crossover and 4 in the second half. We saw 2 (<0.1%) continuous variable typos in the spreadsheet that a single data collector made. There were no blank fields. The data collection tools showed no differences in accuracy of data on comparison.

Conclusions

Data collection for our registry with an EDC tool was faster than using a spreadsheet, which in turn allowed more efficient follow-up of cases.

Radiologic-pathologic analysis of quantitative 3D tumour enhancement on contrast-enhanced MR imaging: a study of ROI placement

OBJECTIVES

To investigate the influence of region-of-interest (ROI) placement on 3D tumour enhancement [Quantitative European Association for the Study of the Liver (qEASL)] in hepatocellular carcinoma (HCC) patients treated with transcatheter arterial chemoembolization (TACE).

METHODS

Phase 1: 40 HCC patients had nine ROIs placed by one reader using systematic techniques (3 ipsilateral to the lesion, 3 contralateral to the lesion, and 3 dispersed throughout the liver) and qEASL variance was measured. Intra-class correlations were computed. Phase 2: 15 HCC patients with histosegmentation were selected. Six ROIs were systematically placed by AC (3 ROIs ipsilateral and 3 ROIs contralateral to the lesion). Three ROIs were placed by 2 radiologists. qEASL values were compared to histopathology by Pearson's correlation, linear regression, and median difference.

RESULTS

Phase 1: The dispersed method (abandoned in phase 2) had low consistency and high variance. Phase 2: qEASL correlated strongly with pathology in systematic methods [Pearson's correlation coefficient = 0.886 (ipsilateral) and 0.727 (contralateral)] and in clinical methods (0.625 and 0.879). However, ipsilateral placement matched best with pathology (median difference: 5.4 %; correlation: 0.89; regression CI: [0.904, 0.1409]).

CONCLUSIONS

qEASL is a robust method with comparable values among tested placements. Ipsilateral placement showed high consistency and better pathological correlation.

KEY POINTS

Ipsilateral and contralateral ROI placement produces high consistency and low variance. Both ROI placement methods produce qEASL values that correlate well with histopathology. Ipsilateral ROI placement produces best correlation to pathology along with high consistency.