Report of the National Institutes of Health task force on research standards for chronic low back pain

OBJECTIVES

Despite rapidly increasing intervention, functional disability due to chronic low back pain (cLBP) has increased in recent decades. We often cannot identify mechanisms to explain the major negative impact cLBP has on patients' lives. Such cLBP is often termed nonspecific and may be due to multiple biologic and behavioral etiologies. Researchers use varied inclusion criteria, definitions, baseline assessments, and outcome measures, which impede comparisons and consensus. The purpose of this article is to disseminate the report of the National Institutes of Health (NIH) task force on research standards for cLBP.

METHODS

The NIH Pain Consortium charged a research task force (RTF) to draft standards for research on cLBP. The resulting multidisciplinary panel developed a 3-stage process, each with a 2-day meeting.

RESULTS

The panel recommended using 2 questions to define cLBP; classifying cLBP by its impact (defined by pain intensity, pain interference, and physical function); use of a minimal data set to describe research subjects (drawing heavily on the Patient Reported Outcomes Measurement Information System methodology); reporting "responder analyses" in addition to mean outcome scores; and suggestions for future research and dissemination. The Pain Consortium has approved these recommendations, which investigators should incorporate into NIH grant proposals.

CONCLUSIONS

The RTF believes that these recommendations will advance the field, help to resolve controversies, and facilitate future research addressing the genomic, neurologic, and other mechanistic substrates of cLBP. Greater consistency in reporting should facilitate comparisons among studies and the development of phenotypes. We expect the RTF recommendations will become a dynamic document and undergo continual improvement.

Characteristics desired in clinical data warehouse for biomedical research

Objectives

Due to the unique characteristics of clinical data, clinical data warehouses (CDWs) have not been successful so far. Specifically, the use of CDWs for biomedical research has been relatively unsuccessful thus far. The characteristics necessary for the successful implementation and operation of a CDW for biomedical research have not clearly defined yet.

Methods

Three examples of CDWs were reviewed: a multipurpose CDW in a hospital, a CDW for independent multi-institutional research, and a CDW for research use in an institution. After reviewing the three CDW examples, we propose some key characteristics needed in a CDW for biomedical research.

Results

A CDW for research should include an honest broker system and an Institutional Review Board approval interface to comply with governmental regulations. It should also include a simple query interface, an anonymized data review tool, and a data extraction tool. Also, it should be a biomedical research platform for data repository use as well as data analysis.

Conclusions

The proposed characteristics desired in a CDW may have limited transfer value to organizations in other countries. However, these analysis results are still valid in Korea, and we have developed clinical research data warehouse based on these desiderata.

Evaluating a complex system-wide intervention using the difference in differences method: the Delivering Choice Programme

Objectives

We report the use of difference in differences (DiD) methodology to evaluate a complex, system-wide healthcare intervention. We use the worked example of evaluating the Marie Curie Delivering Choice Programme (DCP) for advanced illness in a large urban healthcare economy.

Methods

DiD was selected because a randomised controlled trial was not feasible. The method allows for before and after comparison of changes that occur in an intervention site with a matched control site. This enables analysts to control for the effect of the intervention in the absence of a local control. Any policy, seasonal or other confounding effects over the test period are assumed to have occurred in a balanced way at both sites. Data were obtained from primary care trusts. Outcomes were place of death, inpatient admissions, length of stay and costs.

Results

Small changes were identified between pre- and post-DCP outputs in the intervention site. The proportion of home deaths and median cost increased slightly, while the number of admissions per patient and the average length of stay per admission decreased slightly. None of these changes was statistically significant.

Conclusions

Effects estimates were limited by small numbers accessing new services and selection bias in sample population and comparator site. In evaluating the effect of a complex healthcare intervention, the choice of analysis method and output measures is crucial. Alternatives to randomised controlled trials may be required for evaluating large scale complex interventions and the DiD approach is suitable, subject to careful selection of measured outputs and control population.

Lessons Learned from Development of De-identification System for Biomedical Research in a Korean Tertiary Hospital

Objectives

The Korean government has enacted two laws, namely, the Personal Information Protection Act and the Bioethics and Safety Act to prevent the unauthorized use of medical information. To protect patients' privacy by complying with governmental regulations and improve the convenience of research, Asan Medical Center has been developing a de-identification system for biomedical research.

Methods

We reviewed Korean regulations to define the scope of the de-identification methods and well-known previous biomedical research platforms to extract the functionalities of the systems. Based on these review results, we implemented necessary programs based on the Asan Medical Center Information System framework which was built using the Microsoft. NET Framework and C#.

Results

The developed de-identification system comprises three main components: a de-identification tool, a search tool, and a chart review tool. The de-identification tool can substitute a randomly assigned research ID for a hospital patient ID, remove the identifiers in the structured format, and mask them in the unstructured format, i.e., texts. This tool achieved 98.14% precision and 97.39% recall for 6,520 clinical notes. The search tool can find the number of patients which satisfies given search criteria. The chart review tool can provide de-identified patient's clinical data for review purposes.

Conclusions

We found that a clinical data warehouse was essential for successful implementation of the de-identification system, and this system should be tightly linked to an electronic Institutional Review Board system for easy operation of honest brokers. Additionally, we found that a secure cloud environment could be adopted to protect patients' privacy more thoroughly.

A comprehensive model for trauma research design

Concomitant research and education are invaluable for patient care and medical practice in trauma. Elucidation of a foundation for the integration of training and service that can be combined with research in trauma is crucial, and every trauma case should be studied for this purpose. In this study, we investigated the unique features of trauma research to formulate a generic comprehensive model that can be used at any point at which one may desire to develop a research plan. The framework of this model is designed to enable proper trauma research plain in combination with the best routine trauma care. Selection of the appropriate method of study, the corresponding basic questions raised, aims, and the relevant epidemiologic context are factors that are included in this review. Furthermore, suitable sources, proper time for data collection, reliable and valid measures, and criteria for the scaling and quantification of the findings are indicated. In addition, the levels, orders, operational stages, and steps to be taken in planning research projects are logically set based on the principles of cognitive task analysis, and correspond to the entire spectrum of trauma care situations. Lastly, a measure of utility value is assigned in terms of the expected extent of efficiency and presumed level of effectiveness.

Acceptability of cancer chemoprevention trials: impact of the design

BACKGROUND

Chemoprevention could significantly reduce cancer burden. Assessment of efficacy and risk/benefit balance is at best achieved through randomized clinical trials.

METHODS

At a periodic health examination center 1463 adults were asked to complete a questionnaire about their willingness to be involved in different kinds of preventive clinical trials.

RESULTS

Among the 851 respondents (58.2%), 228 (26.8%) agreed to participate in a hypothetical chemoprevention trial aimed at reducing the incidence of lung cancer and 116 (29.3%) of 396 women agreed to a breast cancer chemoprevention trial. Randomization would not restrain participation (acceptability rate: 87.7% for lung cancer and 93.0% for breast cancer). In these volunteers, short-term trials (1 year) reached a high level of acceptability: 71.5% and 73.7% for lung and breast cancer prevention respectively. In contrast long-term trials (5 years or more) were far less acceptable: 9.2% for lung cancer (OR=7.7 CI(95%) 4.4-14.0) and 10.5 % for breast cancer (OR=6.9 CI(95%) 3.2-15.8). For lung cancer prevention, the route of administration impacts on acceptability with higher rate 53.1% for a pill vs. 7.9% for a spray (OR=6.7 CI(95%) 3.6-12.9).

CONCLUSION

Overall healthy individuals are not keen to be involved in chemo-preventive trials, the design of which could however increase the acceptability rate.