Journal:Laboratory information system requirements to manage the COVID-19 pandemic: A report from the Belgian national reference testing center
|Full article title||Laboratory information system requirements to manage the COVID-19 pandemic: A report from the Belgian national reference testing center|
|Journal||Journal of the American Medical Informatics Association|
Weemaes, Matthias; Martens, Steven; Cuypers, Lize; Van Elslande, Jan; Hoet, Katrien; Welkenhuysen, Joris; Goossens, Ria; Wouters, Stijn;|
Houben, Els; Jeuris, Kirsten; Laenen, Lies; Bruyninckx, Katrien; Beuselinck, Kurt; André, Emmanuel; Depypere, Melissa; Desmet, Stefanie;
Lagrou, Katrien; Van Ranst, Marc; Verdonck, Ann K.L.C.; Goveia, Jermaine
|Author affiliation(s)||University Hospitals Leuven, Katholieke Universiteit Leuven|
|Primary contact||Email: jermaine dot goveia at uzleuven dot be|
|Volume and issue||Ahead of print|
|Distribution license||Creative Commons Attribution Non-Commercial 4.0 International|
Objective: The study sought to describe the development, implementation, and requirements of laboratory information system (LIS) functionality to manage test ordering, registration, specimen flow, and result reporting during the coronavirus disease 2019 (COVID-19) pandemic.
Materials and methods: Our large (more than 12,000,000 tests/year) academic hospital laboratory is the Belgian National Reference Center for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) testing. We have performed a moving total of more than 25,000 SARS-CoV-2 polymerase chain reaction tests in parallel to standard routine testing since the start of the outbreak. A LIS implementation team dedicated to developing tools to remove workflow bottlenecks—primarily situated in the pre- and post-analytical phases—was established early in the crisis.
Results: We outline the design, implementation, and requirements of LIS functionality related to managing increased test demand during the COVID-19 crisis, including tools for test ordering, standardized order sets integrated into a computerized physician order entry module, notifications on shipping requirements, automated triaging based on digital metadata forms, and the establishment of databases with contact details of other laboratories and primary care physicians to enable automated reporting. We also describe our approach to data mining and reporting of actionable daily summary statistics to governing bodies and other policymakers.
Conclusions: Rapidly developed, agile extendable LIS functionality and its meaningful use alleviates the administrative burden on laboratory personnel and improves turnaround time of SARS-CoV-2 testing. It will be important to maintain an environment that is conducive for the rapid adoption of meaningful LIS tools after the COVID-19 crisis.
Keywords: COVID-19, laboratory information system, health information technology implementation, computerized provider order entry, change management
Background and significance
The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus has caused a pandemic with unprecedented medical and socioeconomic adversity. Second- and third-wave outbreaks are becoming a reality in several countries, at least partially, due to low herd immunity. Laboratory testing to rapidly detect SARS-CoV-2 using polymerase chain reaction (PCR) is essential to guide proper patient management, while serological assays will soon be required to assess population immunity (e.g., antibody tests) and guide national coronavirus disease 2019 (COVID-19) pandemic policies.
The increased demand for round-the-clock laboratory testing during the COVID-19 pandemic has surpassed surge capacity in many clinical laboratories, resulting in significant strain on laboratory personnel and infrastructure. These developments are especially undesirable when laboratories already have to process a large numbers of potentially biohazardous specimens every day. Additional delays in testing directly translate into delayed clinical decision making and consequently congest emergency departments and isolation units. Therefore, leveraging the capabilities of laboratory information systems (LIS) to streamline all phases of laboratory testing workflow (preanalytical, analytical, and postanalytical) has proven essential.
To our knowledge, only a single report has been published that discusses the implementation of health informatics to support clinical management of the COVID-19 pandemic through novel electronic health record (EHR) functionality. COVID-19 is a laboratory diagnosis with immediate consequences in the health sector (hospitalization, patient isolation, postponing surgery, etc.). Therefore, clinical laboratories face specific challenges that require dedicated LIS functionality to ensure safe, reliable testing and acceptable turnaround times. However, no literature exists on tools that leverage the LIS in order to alleviate the burden on laboratory personnel, streamline laboratory testing, improve test reporting, facilitate epidemiological and translational research, and enable data-driven policy making.
Here, we describe the challenges faced by the Belgian National Reference Center for COVID-19 testing when demand passed allocated surge capacity during the initial phases of the COVID-19 pandemic. We used Kotter’s principles as a framework to rapidly develop and implement additional LIS functionality (Table 1). We implemented tools to manage specimen and data streams, and detail functionality to improve (1) the prelaboratory phase (test ordering, specimen packaging, and shipping), (2) the preanalytical phase (specimen registration, tracking, and test prioritization [triaging]), and (3) the postanalytical phase (automated reporting and facilitating data-driven policymaking). We also briefly discuss unexpected opportunities in which the COVID-19 crisis accelerated the adoption of practices that promote the meaningful use of LIS systems.
Materials and methods
The University Hospitals Leuven is a 2000-bed hospital providing nationwide services via approximately 700,000 consultations, 55,000 admissions, 60,000 emergencies, and 55,000 surgeries annually. The hospital uses a fully in-house–developed EHR that is also commercially available and used in approximately 50% of hospitals in the Flemish Region of Belgium. The clinical laboratory department annually performs around 12,000,000 tests and has national reference functions for several infectious diseases, including the respiratory disease COVID-19. The laboratory performed a total of >25,000 SARS-CoV-2 PCR tests on respiratory specimens between February 1 and April 20, 2020. Specimens were sent to the reference laboratory from across Belgium, and once analyzed, results were reported to referring clinical laboratories as well as to hospitals and primary care physicians. The LIS is developed in-house and maintained by a dedicated team of computer science engineers and implementation staff. Our LIS includes a computerized physician order entry (CPOE) module for in-house test ordering, which is fully integrated into the EHR.
The LIS automatically sends all validated results to a national EHR database that is directly connected to patient-accessible web-based and mobile applications. All external orders, including those for reference testing, are paper-based and require that request forms accompany the specimen. LIS development is directed by a clinical pathologist, allowing for rapid signaling of practical bottlenecks and guidance on the development of health information technology (IT) tools.
COVID-19–specific challenges to laboratory management
During the early stages of the COVID-19 outbreak, our laboratory was the only SARS-CoV-2 testing center in Belgium. The first case of COVID-19 in Belgium was confirmed by our laboratory on February 3, 2020. The following month, SARS-CoV-2 PCR testing grew exponentially to >750 tests per day, at which point both traditional analytical infrastructure and reagents became limiting factors (Figure 1). To manage the acute crisis, we established a crisis management team (CMT) consisting of clinical pathology staff specialized in microbiology and in LIS development. The CMT readily implemented a test prioritization (triage) system to reduce stress on reagents and addressed key bottlenecks in the preanalytical and postanalytical phase. Maximum upscaling in terms of full-time equivalents (FTEs) was estimated to be an additional 20 FTEs for the preanalytical phase, five for the analytical phase, and 13 for the postanalytical phase (Figure 2). These FTEs were divided among three different shifts. After stabilization of the specimen workflow, a fraction of laboratory staff was reallocated to perform test validation, leaving some scientific staff to be reallocated to epidemiological studies.
Notably, the large majority of our expanded workforce (30 of the 38 additional FTEs) was assigned to help with administrative tasks (specimen reception, triaging, patient registration, result validation and reporting, and epidemiological studies), and was not directly involved in expanding analytical capacity (i.e., PCR analysis) (Figure 2). By April 2020, reagent supply and machine capacity were more adequate (Figure 1), but pre- and postanalytical bottlenecks could only be resolved by the implementation of dedicated perianalytical IT solutions that reduced administrative burden on laboratory staff by streamlining data flows (Table 2).
COVID-19 test panel–based CPOE tool
During the early stages of the COVID-19 pandemic, laboratory tests (SARS-CoV-2 PCR) were requested via paper forms and entered into the LIS by dedicated administrative personnel. To capture any accompanying clinical information and contact details of the requesting physicians, which were handwritten on the forms, each document was digitized using an image scanner and uploaded to the LIS in PDF format.
Limitations of paper-based order system
The on-paper request system proved a major bottleneck to the COVID-19 specimen workflow. First, critical clinical information and contact details were often not provided, significantly delaying test order triaging and reporting, respectively. Second, because clinical information and triage categorizations were not registered into our LIS database, epidemiological and research studies were only possible after additional administrative personnel retrospectively entered those data using an ad hoc structured data entry module. Third, too often, preanalytical and shipping biosafety procedures (triple packaging) for suspected SARS-CoV-2–infected specimens were not strictly followed. Fourth, the paper-based order system did not allow to rapidly and immediately change test ordering behavior in function of evolving criteria and scientific insight.
Evidence-based COVID-19 test panel
To address problems stemming from paper-based ordering systems, we designed a CPOE-based COVID-19–specific order set in joint collaboration with the CMT, the directors of infectious disease, and emergency departments. This laboratory set could be ordered for COVID-19–positive patients (Supplementary Table 1, Supplementary data). The COVID-19 set contained classical pneumonia-associated parameters (C-reactive protein, white blood cell count), parameters associated with COVID-19 pathophysiology (prekallikrein, complement), and exploratory study parameters for follow-up and correlation with clinical progression (human chorionic gonadotropin, itraconazole). These tests were integrated into a predefined set that could be ordered directly from the EHR via an easy-to-use point-and-click interface. The COVID-19–specific CPOE module was linked to both the LIS and EHR, allowing to automatically retrieve demographic information such as age, birth date, sex, residence, nationality, etc., which dramatically improved metadata completeness.
Metadata recording and automated triaging
After selecting the COVID-19 test panel, physicians were prompted a discrete data entry field (DDEF) consisting of check boxes that corresponded with up-to-date triage criteria. The DDEF not only was easy to use for physicians, but it also effectively functioned as a structured data entry system, thereby automating triaging and allowing clinical metadata to be stored in our LIS database for subsequent secondary analysis and epidemiological research.
Preanalytical decision support
The COVID-19 ordering tool not only improved data quality, but it also allowed the laboratory to proactively guide specimen preparation and shipping procedures through automated notifications. For example, the system algorithmically calculated the minimum number of blood tubes required to perform all analyses (taking into consideration not only the total volume of blood, but also intralaboratory specimen handling and aliquoting). The tool then notified the phlebotomist on the optimal sampling strategy. The tool also prompted the phlebotomist with packaging instructions, explaining triple packaging requirements. Finally, the COVID-19 ordering tool was effectively used by the CMT to implement updated triage criteria with immediate effect (Figure 1). This was an especially useful functionality, as both laboratory and governmental test criteria were adapted frequently in function of the developing COVID-19 pandemic, while clinical staff rotated with high frequency.
Postanalytical decision support
In our hospital, patients were categorized as cases not suspected for COVID-19, cases suspected but not confirmed for COVID-19 (test results were pending), and confirmed COVID-19–positive cases. Patients were hospitalized in different units according to this categorization in order to minimize intrahospital disease transmission. Consequently, rapid and effective communication of SARS-CoV-2 testing was required to ensure adequate patient allocation. Therefore, we displayed a “COVID-19 status” button on the main page of the EHR of each patient. This button indicated, in real time, the results of SARS-CoV-2 laboratory testing and allowed physicians to quickly triage and (re)allocate patients, based on PCR and computed tomography scan results.
Automated reporting of referred SARS-CoV-2 reference testing
Our laboratory has national reference functions for respiratory pathogens and several other specialized in vitro tests but does not perform referred testing for routine parameters. Under normal circumstances, approximately 2.5% of our testing is performed for external laboratories or healthcare professionals outside of the hospital, and consequently our laboratory did not invest in, nor does it have access to, a web-based CPOE that could have been be utilized to streamline test ordering during the COVID-19 pandemic. Therefore, we implemented an alternative system to manage data streams for referred testing.
We developed an LIS-specific DDEF tool that allowed administrative personnel to register clinical information that was written on request forms. We also compiled a database with contact details and preferred reporting methods (fax, email, electronic mailbox system, etc.) of every laboratory in Belgium, to enable automated test reporting. Using this ad hoc system, we were able to automatically report the large majority (>98%) of test results. Although technically simple, this system dramatically reduced the number of patients and physicians that called our laboratory to inquire about test results, in turn significantly reducing administrative burden on our call center and staff.
Automated epidemiological reporting of COVID-19 statistics
Epidemiological reporting of clinically relevant pathogens is a key function of clinical laboratories. Reporting of epidemiological and clinically relevant pathogens as SARS-CoV-2 is mandatory in Belgium. Under normal circumstances, epidemiological reporting is centralized and represents only a limited administrative burden. However, during the COVID-19 pandemic, all laboratories were required to report daily summary statistics not only to government bodies, but also to a variety of policymakers and medical and hospital directors. These laboratory-provided statistics were required to effectively monitor and manage the SARS-CoV-2 outbreak and indeed directly affected national lockdown policies.
Our LIS was designed to enable structured data entry into an underlying Structured Query Language (SQL) database, and to efficiently visualize those data through a graphical user interface. However, the system had neither data mining nor data visualization functionality. To meet epidemiological reporting requirements, we had to develop custom-tailored SQL queries, retrieve the data in flat file format, subsequently analyze the data in a spreadsheet program, and then generate several individualized reports that were emailed to specific stakeholders. Consequently, epidemiological reporting and generating infographics quickly became a significant burden requiring full-time staff (Figure 2).
As an extension of our LIS, we developed a tool for automated report generation and emailing. Briefly, the tool accepted output from SQL queries, interfaced with Google Sheets to access reported statistics from other laboratories, automatically compiled and processed all data, and then sent customized emails or text messages to predefined recipients, including national governing bodies and policymakers. Automating the full epidemiological reporting process allowed us to send updates with higher frequency and significantly alleviated administrative burden on scientific staff.
Facilitating scientific research
Projections of the transmission dynamics of SARS-CoV-2 through the postpandemic period predict recurrent wintertime outbreaks after the current first wave. Absent of any other interventions, prolonged intermittent social distancing may be required for the next 24 months, with active SARS-CoV-2 surveillance for at least 48 months. Such measures will inevitably result in significant economic adversity, with fears of a recession growing. Correlating data from clinical laboratories (PCR, serological testing, inflammatory parameters, etc.) with clinical data, diagnostic imaging, and molecular (omics) profiling data have the potential to significantly accelerate the development of predictive models and in vitro diagnostics, while providing insight into the mechanism of SARS-CoV-2 spread and infection.
As a first step to facilitate scientific research, we developed a separate database that included results for all parameters in the COVID-19 test panel for all COVID-19–suspected patients (defined as all patients tested by PCR) evaluated in our hospital. As an extension of our previous work in making large biomedical datasets accessible to non-bioinformaticians, we used the R/Shiny Web framework to develop an easy-to-use standalone data mining application. This application allowed select staff and researchers to quickly interrogate and visualize the data in order to identify previously unknown correlations, without additional informatics support. This tool not only accelerated research, but it also alleviated the burden on IT staff, who could then focus on the development and implementation of increasingly tailored COVID-19–related LIS functionality.
We have identified key bottlenecks in the laboratory management of the COVID-19 pandemic. The administrative burden on all staff increased exponentially with peaks in sample volume. IT solutions could effectively reduce administrative burden and streamline sample flow, resulting in higher staff engagement and reduced turnaround times.
Opportunities to promote the meaningful use of health IT
Our COVID-19–specific tools not only addressed immediate needs, but they also highlighted opportunities to leverage the meaningful use of LIS systems to guide policymaking and advance biomedical science. So far, the meaningful use of health IT is lagging, despite widespread implementation and adoption of electronic systems. Consistently, in our hospital, the COVID-19 crisis contributed to the accelerated requirement of meaningful use and the full advantages of the CPOE-associated tools.
One of the key hurdles in promoting the meaningful use of health IT is the resistance of healthcare professionals to change their daily procedures. The implementation of electronic systems temporarily increases stress (emotionally and organizationally) for medical and nonmedical staff, adding to resistance to rapidly adopting technological advances. Therefore, a key aspect of successful health IT implementation is effective change management. Practical strategies to proactively plan organizational change in the context of health IT are largely derived from the Kotter framework of change management. Kotter proposed a three-step change model. The first step, establishing a sense of urgency, is the most difficult but is absolutely required to create an environment that stimulates the meaningful use of health IT.
The COVID-19 crisis has put tremendous administrative burden on our staff due to initial shortcomings of our LIS. The realization that digitalization, automation, and meaningful use of our LIS could alleviate some of the most important administrative bottlenecks provided a sense of emergency, which accelerated the development, implementation, and adoption of new functionality (Tables 1 and 2). It would be beneficial to maintain such dynamics after the COVID-19 crisis to advance LIS functionality that may ultimately increase efficiency and quality, and improve employee engagement by removing repetitive and administrative tasks. We anticipate that applying the Kotter principles for consolidating a dynamic environment, or similar change management strategies, can significantly accelerate health IT implementation and adoption.
The development of robust IT solutions typically requires several months to implement, which is not sufficiently fast for the acute phases of crisis management. However, it is important to immediately develop a near-term (days to weeks) plan for the implementation of IT tools that can alleviate the administrative burden on personnel and facilitate epidemiological and scientific research and reporting. Therefore, we recommend that any CMT consists of not only staff focused on increasing analytical capacity, but also dedicated staff responsible for IT. We further recommend using a change management framework (e.g., Kotter’s principles) to effectively implement these new IT tools (Table 1). We found that in our scenario the most effective solutions were to streamline sample ordering through a CPOE system, and streamline reporting by developing a database with contact details of all laboratories in Belgium. The implementation of the R/Shiny-based statistical tools facilitated epidemiological reporting and enabled explorative data mining.
The COVID-19 pandemic is especially challenging for clinical laboratories that are tasked with rapid and reliable testing of a significantly increased number of samples. Demand above surge capacity readily clogs standard infrastructure. Alleviating personnel from repetitive and administrative tasks via digitalization and automation using ad hoc LIS functionality and reporting tools significantly streamlines sample processing and reduces turnaround time, features that are also beneficial after the initial phases of the COVID-19 crisis. Indeed, lockdown exit strategies will critically depend on laboratory results, including emerging antibody tests. We hope that sharing our experiences in developing these tools will help other LIS implementation teams facing similar challenges.
- Supplementary Table 1: Example of a COVID-19 lab order set (.docx file)
MW, SM, and JG conceptualized the study; MW assessed key administrative bottlenecks and aided in the implementation of the computerized provider order entry module; SM performed comparisons with Kotter’s framework of change management and developed the database for automated reporting; LC, SW, EH, KJ, LL, KBr, and KBe organized sample flows, managed human resources and were responsible for statistical reporting. JW and RG guided the implementation of information technology solutions. JVE and KH also assisted in development of the database. EA, MD, SD, KL, MVR, and AKLCV formed the crisis management team. MW, SM, and JG wrote the manuscript; AKLCV supervised the implementation of the computerized provider order entry module and all COVID-19–related tools, provided advice, and helped improve the manuscript. All authors commented and contributed to the manuscript.
This work was partially supported by Sciensano, which coordinates the financing of the national reference centers in Belgium.
Conflict of interest
- ↑ 1.0 1.1 Hellewell, J.; Abbott, S.; Gimma, A. et al. (2020). "Feasibility of controlling COVID-19 outbreaks by isolation of cases and contacts". Lancet Global Health 8 (4): e488-e496. doi:10.1016/S2214-109X(20)30074-7. PMC PMC7097845. PMID 32119825. http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=PMC7097845.
- ↑ Guan, W.-J.; Ni, Z.-Y.; Hu, Y. et al. (2020). "Clinical Characteristics of Coronavirus Disease 2019 in China". New England Journal of Medicine 382 (18): 1708–20. doi:10.1056/NEJMoa2002032. PMC PMC7092819. PMID 32109013. http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=PMC7092819.
- ↑ 3.0 3.1 3.2 Lippi, G.; Plebani, M. (2020). "The critical role of laboratory medicine during coronavirus disease 2019 (COVID-19) and other viral outbreaks". Clinical Chemistry and Laboratory Medicine 58 (7): 1063–69. doi:10.1515/cclm-2020-0240. PMID 32191623.
- ↑ Posteraro, B.; Marchetti, S.; Roman, L. et al. (2020). "Clinical microbiology laboratory adaptation to COVID-19 emergency: Experience at a large teaching hospital in Rome, Italy". Clinical Microbiology and Infection 26 (8): 1109-1111. doi:10.1016/j.cmi.2020.04.016. PMC 32330569. PMID 32330569. http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=32330569.
- ↑ Reeves, J.J.; Hollandsworth, H.M.; Torriani, F.J. et al. (2020). "Rapid response to COVID-19: Health informatics support for outbreak management in an academic health system". Journal of the American Medical Informatics Association 27 (6): 853-859. doi:10.1093/jamia/ocaa037. PMC PMC7184393. PMID 32208481. http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=PMC7184393.
- ↑ 6.0 6.1 6.2 Kotter, J.P. (May–June 1995). "Leading Change: Why Transformation Efforts Fail". Harvard Business Review. https://hbr.org/1995/05/leading-change-why-transformation-efforts-fail-2. Retrieved 22 April 2020.
- ↑ "NexuzHealth". Nexuzhealth. https://www.nexuzhealth.be/en. Retrieved 22 April 2020.
- ↑ Bush, R.A.; Kuelbs, C.; Ryu, J. et al. (2017). "Structured Data Entry in the Electronic Medical Record: Perspectives of Pediatric Specialty Physicians and Surgeons". Journal of Medical Systems 41 (5): 75. doi:10.1007/s10916-017-0716-5. PMC PMC5510605. PMID 28324321. http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=PMC5510605.
- ↑ 9.0 9.1 Kissler, S.M.; Tedijanto, C.; Goldstein, E. et al. (2020). "Projecting the transmission dynamics of SARS-CoV-2 through the postpandemic period". Science 368 (6493): 860-868. doi:10.1126/science.abb5793. PMC PMC7164482. PMID 32291278. http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=PMC7164482.
- ↑ OECD (15 April 2020). "OECD updates G20 summit on outlook for global economy". http://www.oecd.org/newsroom/oecd-updates-g20-summit-on-outlook-for-global-economy.htm. Retrieved 22 April 2020.
- ↑ Rohlenova, K.; Goveia, J.; García-Caballero, M. et al. (2020). "Single-Cell RNA Sequencing Maps Endothelial Metabolic Plasticity in Pathological Angiogenesis". Cell Metabolism 31 (4): 862-877. doi:10.1016/j.cmet.2020.03.009. PMID 32268117.
- ↑ Goveia, J.; Rohlenova, K.; Taverna, F. et al. (2020). "An Integrated Gene Expression Landscape Profiling Approach to Identify Lung Tumor Endothelial Cell Heterogeneity and Angiogenic Candidates". Cancer Cell 37 (1): 21–36.E13. doi:10.1016/j.ccell.2019.12.001. PMID 31935371.
- ↑ Kalucka, J.; de Rooij, L.P.M.H.; Goveia, J. et al. (2020). "Single-Cell Transcriptome Atlas of Murine Endothelial Cells". Cell 180 (4): 764-779.e20. doi:10.1016/j.cell.2020.01.015. PMID 32059779.
- ↑ Khan, S.; Taverna, F.; Rohlenova, K. et al. (2019). "EndoDB: A database of endothelial cell transcriptomics data". Nucleic Acids Research 47 (D1): D736–D744. doi:10.1093/nar/gky997. PMC PMC6324065. PMID 30357379. http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=PMC6324065.
- ↑ Blumenthal, D.; Tavenner, M. (2010). "The "meaningful use" regulation for electronic health records". New England Journal of Medicine 363 (6): 501-4. doi:10.1056/NEJMp1006114. PMID 20647183.
- ↑ Sánchez-Mendiola, M.; Martínez-Franco, A.I.; Rosales-Vega, A. et al. (2013). "Development and implementation of a biomedical informatics course for medical students: Challenges of a large-scale blended-learning program". JAMIA 20 (2): 381-7. doi:10.1136/amiajnl-2011-000796. PMC PMC3638174. PMID 22700870. http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=PMC3638174.
This presentation is faithful to the original, with only a few minor changes to presentation. In some cases important information was missing from the references, and that information was added.