Literature-Related Discovery (LRD): Introduction and background

Discovery in science is the generation of novel, interesting, plausible, and intelligible knowledge about the objects of study. Literature-related discovery (LRD) is the linking of two or more literature concepts that have heretofore not been linked (i.e., disjoint), in order to produce novel, interesting, plausible, and intelligible knowledge (i.e., potential discovery). LRD has two main components that differ in their methodological approach to discovery:

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Literature-based discovery (LBD) produces potential discovery through analysis of the technical literature alone.

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Literature-assisted discovery (LAD) produces potential discovery through both analysis of the technical literature and use of selected authors of that literature. These authors generate potential discovery as proposers, workshop/panel participants, or in other active roles.

LRD offers the promise of large amounts of potential discovery, for the following reasons:

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the burgeoning technical literature contains a very large pool of technical concepts in myriad technical areas;

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researchers spend full time trying to cover the literature in their own research fields and are relatively unfamiliar with research in other especially disparate fields of research;

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the large number of technical concepts (and disparate technical concepts) means that many combinations of especially disparate technical concepts exist

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by the laws of probability, some of these combinations will produce novel, interesting, plausible, and intelligible knowledge about the objects of study.

This Special Issue presents the LRD methodology and voluminous discovery results from five problem areas: four medical (treatments for Parkinson's Disease (PD), Multiple Sclerosis (MS), Raynaud's Phenomenon (RP), and Cataracts) and one non-medical (Water Purification (WP)). In particular, the open discovery systems (ODS) aspect of LRD (start with problem, generate potential solution(s), or vice versa) is addressed, rather than the closed discovery systems (CDS) aspect (start with problem and potential solution(s), generate linking mechanism(s)). In the presentation of potential discovery, a ‘vetting’ process is used that insures both requirements for ODS LBD are met: concepts are linked that have not been linked previously, and novel, interesting, plausible, and intelligible knowledge is produced.The potential discoveries for the PD, MS, Cataracts, and WP problems are the first we have seen reported by this ODS LBD approach, and the numbers of potential discoveries for the ODS LBD benchmark RP problem are almost two orders of magnitude greater than those reported in the open literature by any other ODS LBD researcher who has addressed this benchmark RP problem. The WP problem is the first non-medical technical topic to have been addressed successfully by ODS LBD.In all cases, but especially the medical, we have barely scratched the surface of quantity and quality of potential discovery that could be generated with adequately resourced studies. Based on the many potential discoveries we have obtained, and the promise of far more potential discoveries with adequately resourced studies, we have generated a new paradigm relative to discovery: while the key challenge in traditional discovery is finding a needle-in-a-haystack, the key challenge in ODS LRD (used appropriately) is handling the overwhelming amount of potential discovery available.Additionally, it is our thesis, as the specific ODS LBD studies will demonstrate, that synergistic combinations of our mainly individual potential discoveries are themselves potential discoveries. We demonstrate throughout this Special Issue the synergistic effects of combining a very few potential discoveries or interesting core literature concepts, and believe that these synergistic benefits are operable at larger scales of combination. In the final lessons-learned paper of this Special Issue, we also show that providing evidence for the synergistic benefits of large numbers of potential discoveries or interesting core concepts is very difficult due to the large numbers of potential combinations involved.One variant of the LAD operational mode (identifying disparate discipline recipients for Broad Agency Announcement (BAA) notifications in order to stimulate proposals of new ideas from these disparate disciplines) is presented for WP. Other possible applications of LAD include:

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Recipients of solicitation announcements (other solicitations similar to BAA, journal Special Issue calls for papers, etc),

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Participants in Workshops, Advisory Panels, Review Panels, Roadmaps, and War Games,

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Points of Contact for Field Science Advisors, Foreign Field Offices, Program Officer site visits, and potential transitions.

     

Literature-related discovery (LRD): Methodology

Literature-related discovery (LRD) is linking two or more literature concepts that have heretofore not been linked (i.e., disjoint), in order to produce novel, interesting, plausible, and intelligible knowledge. LRD has two components: Literature-based discovery (LBD) generates potential discovery through literature analysis alone, whereas literature-assisted discovery (LAD) generates potential discovery through a combination of literature analysis and interactions among selected literature authors. In turn, there are two types of LBD and LAD: open discovery systems (ODS), where one starts with a problem and arrives at a solution, and closed discovery systems (CDS), where one starts with a problem and a solution, then determines the mechanism(s) that links them.The generic methodology for identifying potential discovery candidates through ODS LRD, focusing mainly on its ODS LBD component, is described in this paper. A comprehensive flow chart showing the details of our systematic potential discovery generation process, including the evolution of the flow chart steps through each of the studies performed, is presented. Also shown is a vetting procedure that insures potential discoveries claimed are potential discoveries realized. The semantic filters that replace the numerical filters of other ODS LBD approaches are overviewed. The rationale for addressing the five topics studied (Raynaud's Phenomenon (RP), Cataracts, Parkinson's Disease (PD), Multiple Sclerosis (MS), and Water Purification (WP)) is summarized.     

Literature-related discovery (LRD): Potential treatments for Raynaud's Phenomenon

Literature-related discovery (LRD) is the linking of two or more literature concepts that have heretofore not been linked (i.e., disjoint), in order to produce novel, interesting, plausible, and intelligible knowledge (i.e., potential discovery). The open discovery systems (ODS) component of LRD starts with a problem to be solved, and generates solutions to that problem through potential discovery. We have been using ODS LRD to identify potential treatments or preventative actions for challenging medical problems, among myriad other applications.Raynaud's Phenomenon (RP) is a condition in which small arteries, most commonly in fingers and toes, contract and cause the skin to turn pale or a patchy red to blue. We selected the subject of RP for analysis by LRD because of RP's global prevalence, and its apparent intractability to all treatments except for palliative remediation mainly through drugs or surgery. Our main goal was to identify non-drug non-surgical treatments that would 1) prevent the occurrence, or 2) reduce the progression rate, or 3) stop the progression, or 4) maybe even reverse the progression, of RP. Our secondary goal was to compare our ODS LRD approach to the RP problem with other investigators who have addressed the RP problem since Swanson's pioneering 1986 ODS LRD paper on potential RP treatments [D.R. Swanson, Fish oil, Raynauds syndrome, and undiscovered public knowledge, Perspectives in Biology and Medicine 30 (1) (1986) 7-18].We used Medline from 1965-1985 to identify potential discovery for RP. We differ from all the other authors who have addressed this problem in two major respects: we make no numerically-based filtering assumptions, and we generate substantial potential discovery (∼ 130 potential discoveries). Further, we believe our reported results are the tip of the iceberg. Much more potential discovery is possible with an adequately resourced study using the lessons learned from this demonstration study and the other demonstration studies that follow in this Special Issue.     

Literature-related discovery (LRD): Potential treatments for cataracts

Literature-related discovery (LRD) is the linking of two or more literature concepts that have heretofore not been linked (i.e., disjoint), in order to produce novel, interesting, plausible, and intelligible knowledge (i.e., potential discovery). The open discovery systems (ODS) component of LRD starts with a problem to be solved, and generates solutions to that problem through potential discovery. We have been using ODS LRD to identify potential treatments or preventative actions for challenging medical problems, among myriad other applications.This paper describes the second medical problem we addressed (cataract) using ODS LRD; the first problem addressed was Raynaud's Phenomenon (RP), and was described in the third paper of this Special Issue. Cataract was selected because it is ubiquitous globally, appears intractable to all forms of treatment other than surgical removal of cataracts, and is a major cause of blindness in many developing countries.The ODS LRD study had three objectives: a) identify non-drug non-surgical treatments that would 1) help prevent cataracts, or 2) reduce the progression rate of cataracts, or 3) stop the progression of cataracts, or 4) maybe even reverse the progression of cataracts; b) demonstrate that we could solve an ODS LRD problem with no prior knowledge of any results or prior work (unlike the case with the RP problem); c) determine whether large time savings in the discovery process were possible relative to the time required for conducting the RP study. To that end, we used the MeSH taxonomy of MEDLINE to restrict potential discoveries to selected semantic classes, as a substitute for the manually-intensive process used in the RP study to restrict potential discoveries to selected semantic classes. We also used additional semantic filtering to identify potential discovery within the selected semantic classes.All these goals were achieved. As will be shown, we generated large amounts of potential discovery in more than an order of magnitude less time than required for the RP study. We identified many non-drug non-surgical treatments that may be able to reduce or even stop the progression rate of cataracts. Time, and much testing, will determine whether this is possible. Finally, the methodology has been developed to the point where ODS LRD problems can be solved with no results or knowledge of any prior work.     

Literature-related discovery (LRD): Potential treatments for Multiple Sclerosis

Literature-related discovery (LRD) is the linking of two or more literature concepts that have heretofore not been linked (i.e., disjoint), in order to produce novel, interesting, plausible, and intelligible knowledge (i.e., potential discovery). The open discovery systems (ODS) component of LRD starts with a problem to be solved, and generates solutions to that problem through potential discovery. We have been using ODS LRD to identify potential treatments or preventative actions for challenging medical problems, among myriad other applications. The previous three papers in this Special Issue describe the application of ODS LRD to Raynaud's Phenomenon (RP), cataracts, and Parkinson's Disease (PD).Multiple Sclerosis (MS) is a progressive neurodegenerative disorder (typically preceded by periods of remission and relapse), affecting mainly people in their early-mid life. MS is characterized by changes in sensation (hypoesthesia), muscle weakness, abnormal muscle spasms, or difficulty to move; difficulties with coordination and balance (ataxia); problems in speech (Dysarthria) or swallowing (Dysphagia), visual problems (Nystagmus, optic neuritis, or diplopia), fatigue and acute or chronic pain syndromes, bladder and bowel difficulties, cognitive impairment, or emotional symptomatology (mainly depression).We selected the subject of MS because of its global prevalence, and its apparent intractability to all treatments except for palliative remediation mainly through drugs or surgery. Our first goal was to identify non-drug non-surgical treatments that would 1) prevent the occurrence, or 2) reduce the progression rate, or 3) stop the progression, or 4) maybe even reverse the progression, of MS. Our second goal was to demonstrate that we could again solve an ODS problem (using LRD) with no prior knowledge of any results or prior work (unlike the case of the RP problem). As in the ‘cataract’ and PD examples, we used the MeSH taxonomy of MEDLINE to restrict potential discoveries to selected semantic classes, and to identify potential discoveries efficiently. Our third goal was to generate large amounts of potential discovery in more than an order of magnitude less time than required for the RP study. The discovery generation methodology has been developed to the point where ODS LRD problems can be solved with no results or knowledge of any prior work.     

Literature-related discovery (LRD): Lessons learned, and future research directions

Literature-related discovery (LRD) is the linking of two or more literature concepts that have heretofore not been linked (i.e., disjoint), in order to produce novel, interesting, plausible, and intelligible knowledge (i.e., potential discovery). The open discovery systems (ODS) component of LRD starts with a problem to be solved, and generates solutions to that problem through potential discovery. We have been using ODS LRD to identify potential treatments or preventative actions for challenging medical problems, among myriad other applications. The five immediately preceding papers in this Special Issue describe the application of ODS LRD to Raynaud's Phenomenon (RP), cataracts, Parkinson's Disease (PD), Multiple Sclerosis (MS), and Water Purification (WP). We describe the lessons learned from each application, and how the techniques can be improved further.Generation of much potential discovery using ODS LRD is possible when the conceptual roadblocks to discovery are removed. Some of these roadblocks include use of numerical filters that are unrelated to generating discovery, and excessive reliance on literatures directly related to the problem literature of interest. The issue of how to handle large amounts of potential discovery has not been addressed in the literature, since most ODS LRD researchers have tried to find a relatively few potential discovery items. We present a development strategy that capitalizes on the large amounts of potential discovery we have identified.     

Literature-related discovery (LRD): Water purification

Literature-related discovery (LRD) is the linking of two or more literature concepts that have heretofore not been linked (i.e., disjoint), in order to produce novel, interesting, plausible, and intelligible knowledge (i.e., potential discovery). LRD has two main components that differ in their methodological approach to discovery: Literature-based discovery (LBD) produces potential discovery through analysis of the technical literature alone; Literature-assisted discovery (LAD) produces potential discovery through both analysis of the technical literature and use of selected authors of that literature. These authors generate potential discovery as proposers, workshop/panel participants, or in other active roles.The open discovery systems (ODS) component of LRD starts with a problem to be solved, and generates solutions to that problem through potential discovery. We have been using ODS LRD to identify potential treatments or preventative actions for challenging medical problems, among myriad other applications. The previous four papers in this Special Issue describe the application of ODS LRD (specifically, the ODS LBD variant) to Raynaud's Phenomenon (RP), cataracts, Parkinson's Disease (PD), and Multiple Sclerosis (MS).One goal of the present study was to determine whether LRD could be successfully applied (for the first time) to a challenging non-medical technical problem to generate potential discovery. The second goal was to explore the use of both LRD variants (LBD and LAD) to a non-medical technical problem. We selected the problem of water purification (WP) because of universal applicability and sponsor interest.We used LRD to identify purification concepts, technology components and systems that could lead to improved water purification techniques. We accessed many disparate disciplines to identify purification concepts from literatures not normally associated with water purification. We used two LBD approaches, Cluster Filtering and Latent Semantic Indexing (LSI), to search for potential discovery. We generated voluminous amounts of potential discovery, and believe we have only scratched the surface of what is possible. We also ran a short experiment using LAD to identify experts associated with potential discovery concepts, and use their expertise to generate potential discovery for water purification.     

Literature-related discovery: Potential treatments and preventatives for SARS

Literature-related discovery (LRD) is the linking of two or more previously disjoint concepts in order to produce novel, interesting, plausible, and intelligible connections (i.e., potential discovery). LRD has been used to identify potential treatments or preventative actions for challenging medical problems, among myriad other applications.Severe acute respiratory syndrome (SARS) was the first pandemic of the 21st century. SARS was eventually controlled through increased hygienic measures (e.g., face mask protection, frequent hand washing, living quarter disinfection), travel restrictions, and quarantine. According to recent reviews of SARS, none of the drugs that were used during the pandemic worked.For the present paper, SARS was selected as the first application of LRD to an infectious disease. The main goal of this research was to identify non-drug non-surgical treatments that would 1) prevent the occurrence, or 2) reduce the progression rate, or 3) stop/reverse the progression of SARS. The MeSH taxonomy of Medline was used to restrict potential discoveries to selected semantic classes, and to identify potential discoveries efficiently. To enhance the volume of potential discovery, databases were used in addition to Medline. These included the Science Citation Index (SCI) and, in contrast to previous work, a full text database. Because of the richness of the full text, ‘surgical’ queries were developed that targeted the exact types of potential discovery of interest while eliminating clutter more efficiently.     

Cost-effectiveness analysis of disease modifiying drugs (interferons and glatiramer acetate) as first line treatments in remitting-relapsing multiple sclerosis patients

Abstract

Objective:

The aim of this study was to assess cost-effectiveness of the different Disease Modifying Drugs (DMD) used as first-line treatments (interferons IM IFNβ-1a, SC IFNβ-1a, SC IFNβ-1b, and glatiramer acetate, GA) in Remitting-Relapsing Multiple Sclerosis (RRMS) in Spain.

Methods:

A Markov model was developed to simulate the progression of a cohort of patients with RRMS, during a period of 10 years. Seven health states, defined by the Expanded Disability Status Scale (EDSS), were considered in the model. Patients with an EDSS score less than 6.0 were assumed to be treated with one of the DMD. In addition, all patients were assumed to receive symptomatic treatment. The monthly transition probabilities of the model were obtained from the literature. The analysis was performed from the societal perspective, in which both direct and indirect (losses in productivity) healthcare costs (€, 2010) were included. A discount rate of 3% was applied to both costs and efficacy results.

Results:

GA was the less costly strategy (€322,510), followed by IM IFNβ-1a (€329,595), SC IFNβ-1b (€ 333,925), and SC IFNβ-1a (€348,208). IM IFNβ-1a has shown the best efficacy results, with 4.176 quality-adjusted life years (QALY), followed by SC IFNβ-1a (4.158 QALY), SC IFNβ-1b (4.157 QALY), and GA (4.117 QALY). Incremental costs per QALY gained with IM IFNβ-1a were €?1,005,194/QALY, €?223,397/QALY, and €117,914/QALY in comparison to SC IFNβ-1a, SC IFNβ-1b, and GA, respectively.

Conclusions:

First-line treatment with GA is the less costly strategy for the treatment of patients with RRMS. Treatment with IM IFNβ-1a is a dominant strategy (lower cost and higher QALY) compared with SC IFNβ-1a and SC IFNβ-1b. However, IM IFNβ-1a is not a cost-effective strategy vs GA, because incremental cost per QALY gained with IM IFNβ-1a exceeds the €30,000 per QALY threshold commonly used in Spain.

Limitations:

The highly-restrictive inclusion criteria of clinical trials limits generalization of the results on efficacy to all patients with multiple sclerosis. Availability of data for head-to-head comparisons is associated with the use of information from clinical trials.     

Buying Behavior Under Coronavirus Disease(COVID-19)Pandemic Situation:A Online Perspective Case in Bangladeshi Shoppers

Coronavirus disease(COVID-19)is an highly infectious respiratory disease caused by a newly discovered coronavirus.Most people infected by the COVID-19 virus wil...     

Accounting for single and aggregated forest incomes: Application to public cork oak forests in Jerez (Spain) and Iteimia (Tunisia)

This study presents an applied improvement in the agroforestry accounting system (AAS) approach to two public cork oak forests in the Mediterranean region: Jerez (Spain) and Iteimia (Tunisia). Both forests have similar environments but differ in land property rights, labour markets and countries (developed and developing economy, respectively).The income analysis considers the differences between forest ownership, and household and landowner economic rationalities. In the case of Jerez, the public landowner has a right to exclude others from using the forest resources; community employment and natural resource conservation criteria determine Jerez's management. In the Iteimia case, the public landowner has regulated free-use rights for livestock grazing, firewood and crops so that local households can meet their needs and improve their income. Households operate by maximizing their income from the full employment of their own family workforce.The results show that Jerez's management generates negative commercial capital income for the public landowner, despite receiving significant public subsidies, while it maintains high internal forestry investment that generates additional local employment. Conversely, Iteimia produces positive commercial capital income for the public landowner and high household self-employed labour income per hectare.     

A new method for assessing the sustainability of land-use systems (II): Evaluating impact indicators

In the past decade, numerous indicators and indicator sets for sustainable agriculture and sustainable land management have been proposed. In addition to their interest in comparing different management systems on an indicator by indicator basis, land managers are often interested in comparing individual indicators against a threshold, or, in order to study trade-offs, against each other. To this end it is necessary to (1) transform the original indicators into a comparable format, and (2) score these transformed indicators against a sustainability function.This paper introduces an evaluation method for land-use-related impact indicators, which was designed to accomplish these tasks. It is the second of a series of two papers, and as such it links into a larger framework for sustainability assessment of land use systems.The evaluation scheme introduced here comprises (1) a standardisation procedure, which aims at making different indicators comparable. In this procedure indicators are first normalised, by referencing them to the total impact they contribute towards, and then they are corrected by a factor describing the severity of this total impact in terms of exceeding a threshold. The procedure borrows conceptually from Life Cycle Assessment (LCA) Impact Analysis methodology; (2) a valuation procedure, which judges the individual standardised indicators with regard to sustainability.This methodology is then tested on an indicator set for the environmental impact of a spinach production system in Northwest Germany. The method highlights mineral resource consumption, greenhouse gas emission, eutrophication and impacts on soil quality as the most important environmental effects of the studied system.We then explore the effect of introducing weighting factors, reflecting the differing societal perception of diverse environmental issues. Two different sets of weighting factors are used. The influence of weighting is, however, small compared to that of the standardisation procedure introduced earlier.Finally, we explore the propagation of uncertainty (defined as a variable's 95% confidence limits) throughout the standardisation procedure using a stochastic simulation approach. The uncertainty of the analysed standardised indicator was higher than that of the non-standardised indicators by a factor of 2.0 to 2.5.     

Green not (only) for profit: An empirical examination of the effect of environmental-related standards on employees’ recruitment

Thanks to an original database on French firms we investigate the impact of environmental-related standards on employees’ recruitment. Using a bivariate probit model, we show that voluntary environmental-related standards (i.e., ISO 14001 standard, organic labeling, fair trade, etc.) improve the recruitment of professional and non-professional employees. This finding suggests that, beyond environmental considerations, human resources management can drive registration.     

A methodology to estimate impacts of domestic policies on deforestation: Compensated Successful Efforts for “avoided deforestation” (REDD)

Climate change mitigation would benefit from Reduced Emissions from Deforestation and Degradation (REDD) in developing countries. The REDD mechanism, still in discussion, would be in charge of distilling the right incentives and promoting the right policies for fostering forest conservation. The estimation of reduced emissions induced by the mechanism has been raised as an issue, either for issuing the proper amount of carbon credits or for providing appropriate compensations of foregone revenues and other costs to host countries. This estimation would be based on the gap between observed deforestation and a counterfactual value. Although any prediction of deforestation rates (i.e. business-as-usual scenarios) is challenging, and any negotiated target is subject to obvious political influence, these two ways have been prioritirized so far to determine the counterfactual value. In other words proposals focused on a results-based approach, the relevance of which is questionable because estimations of avoided deforestation are hardly reliable. With this approach, issuance of carbon credits and distribution of financial compensations could threaten respectively environmental integrity of the scheme and equity outcomes. Rather than considering overall deforestation (predicted and observed), we argue that a REDD mechanism would gain from linking distribution of carbon finance to real efforts (opposed to “results”) that developing countries implement for slowing deforestation rates. This would provide strong incentives to design and enforce suitable policies and measures. The methodology we present to measure these efforts (labeled Compensated Successful Efforts) is based on the rationale that overall deforestation is partly due to structural factors, and to domestic policies and measures. This typology differs from others presented in the literature such as proximate/underlying causes, or economic/institutional factors. Using an econometric model, our approach estimates efforts that are (i) independent of structural factors (economic development, population, initial forest area, agricultural export prices), (ii) estimated ex post at the end of the crediting period, and (iii) relative to other countries. In order to illustrate the methodology we apply the model to a panel of 48 countries (Asia, Latin America, Africa) and four periods between 1970 and 2005. We conclude on the feasibility to estimate avoided deforestation using the Compensated Successful Efforts approach. In addition to being conservative from an environmental perspective, this approach tends to guarantee fairness by accounting for dramatic changes during the commitment period. Last, such estimations of avoided deforestation could provide guidance for decisions on further financing of national programs to curb deforestation, as it would help to distinguish between successful and unsuccessful policies.     

Foresight analysis of tilapia supply chains (Sistema Producto) in four states in Mexico: Scenarios and strategies for 2018

In order to increase competitiveness among individual producers and to encourage their integration, the federal government of Mexico has pursued the establishment, at both the state and national levels, of supply chains, or Sistema Producto (SP). For fisheries and aquaculture, 33 SPs exist at the state level and 7 at the national level, 1 of them for tilapia. The objective of this study is to carry out a foresight analysis (FA) of 4 tilapia SPs in Mexico—for the States of Colima, Sonora, Tabasco and Yucatán—analyzing their 2018 vision for the development of the tilapia chain. FA provides an integral vision of both the internal and external environments, identifying the key factors for each SP and helping to develop the best strategies in order to compete successfully. Sonora, Tabasco and Yucatán identify marketing aspects as key elements for their 10-year future: adding value to the product by means of new processing technologies, developing their own brand, and identifying and differentiating their product based on sanitary issues. Colima focuses its industry future success on efficiency improvements. Finally, the main technological disruptions identified by the 4 states are the incremental use of information technology, product and process certification, new hatcheries with genetically-improved fry, and new processing methodologies developed and/or applied.     

The economic importance of non-timber forest products (NTFPs) for livelihood maintenance of rural west African communities: A case study from northern Benin

Non-timber forest products (NTFPs) contribute significantly to a rural household's livelihood in the African semi-arid tropics. This study examines the income from NTFPs and the dependency on these of different socio-economic groups in Northern Benin. Using survey data from 230 households of two villages, we firstly compared incomes of five different ethnic groups being differentiated by their traditional source of livelihood and regional provenance. Secondly, we investigated disparities between three income groups. On average, income from NTFPs accounted for 39% of total household income and had a strong equalizing effect on it. However, the economic relevance of NTFPs differs between households: Poorer households are relatively more dependent on NTFPs in order to fulfill basic needs than wealthier households. However, the latter extract more NTFPs in quantitative terms and have significantly higher cash returns than poorer ones. This is mainly due to a significant greater land holding. Moreover, our study revealed that net income from NTFPs reflects traditional sources of livelihoods of different ethnic groups. In conclusion, both conservation and development strategies should take into consideration the socio-economic context of different beneficiaries of savanna woodland resources in order to apply appropriate measures to poverty reduction.     

Prediction of acute and chronic complications by a new computer simulation model for type 1 and type 2 diabetes: the Diabetes Mel l it us Model (DMM)

Summary

An epidemiological simulation model for patients with type 1 and type 2 diabetes (the Diabetes Mellitus Model (DMM)) was developed based on published clinical and observational data and expert estimations, for prediction of short- and long-term outcomes in defined patient cohorts. A computer program was developed with an interface for definition of patient cohorts and for results display. Patient cohorts can be user-defined by gender, age, duration and type of diabetes, glycosylated haemoglobin, blood pressure, albumin excretion and therapy. Based on risk

equations and current risk variable levels, the DMM simulates complications over 10 years (hypoglycaemia; retinopathy; blindness; microalbuminuria and macroalbuminuria; end-stage renal disease; neuropathy; amputation; diabetic foot syndrome; myocardial infarction; stroke; angina pectoris; heart failure; and death). The DMM is suitable for simulation of complications and for estimation of clinical implications of various diabetes care strategies, and may be particularly valuable in lieu of long-term clinical trial data.