Dataset Information

Pain Medicine in Crisis-A Possible Model toward a Solution: Empowering Community Medicine to Treat Chronic Pain.

ABSTRACT: Pain medicine in Israel and in the world has reached a crisis. The lack of available pain medicine services is resulting in the unsatisfactory treatment for chronic pain sufferers. The main causes of this crisis are: 1) the high prevalence of chronic pain, reaching levels of 17% in the adult population;2) the lack of appropriate training of primary care physicians in the field of chronic pain management; and 3) the paucity of consultation services in the field of chronic pain. In this journal article, we propose a possible model for the solution of the problem, based upon levels of treatment according to the severity of the disease and upon training of primary and secondary care physicians in the treatment of pain. According to the model, the vast majority of treatment and management will take place in the community after appropriate training of primary care physicians. More complex cases will be referred to secondary care community-based pain clinics manned by physicians with further in-depth training. Only the most complex of patients, or those needing specialized treatment such as invasive analgesic therapy, will be referred to tertiary pain centers manned by specialists in pain medicine. Implementation of this model will necessitate training of primary care physicians and the establishment of secondary care facilities and can, in our opinion, pose a pragmatic solution for the hundreds of thousands of patients suffering from chronic pain.

SUBMITTER: Minerbi A

PROVIDER: S-EPMC3820300 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Pain Medicine in Crisis-A Possible Model toward a Solution: Empowering Community Medicine to Treat Chronic Pain.

Minerbi Amir A Vulfsons Simon S

Rambam Maimonides medical journal 20131029 4

Pain medicine in Israel and in the world has reached a crisis. The lack of available pain medicine services is resulting in the unsatisfactory treatment for chronic pain sufferers. The main causes of this crisis are: 1) the high prevalence of chronic pain, reaching levels of 17% in the adult population;2) the lack of appropriate training of primary care physicians in the field of chronic pain management; and 3) the paucity of consultation services in the field of chronic pain. In this journal ar ...[more]

PMID: 24228170

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Project description:BackgroundOne component of precision medicine is to construct prediction models with their predicitve ability as high as possible, e.g. to enable individual risk prediction. In genetic epidemiology, complex diseases like coronary artery disease, rheumatoid arthritis, and type 2 diabetes, have a polygenic basis and a common assumption is that biological and genetic features affect the outcome under consideration via interactions. In the case of omics data, the use of standard approaches such as generalized linear models may be suboptimal and machine learning methods are appealing to make individual predictions. However, most of these algorithms focus mostly on main or marginal effects of the single features in a dataset. On the other hand, the detection of interacting features is an active area of research in the realm of genetic epidemiology. One big class of algorithms to detect interacting features is based on the multifactor dimensionality reduction (MDR). Here, we further develop the model-based MDR (MB-MDR), a powerful extension of the original MDR algorithm, to enable interaction empowered individual prediction.ResultsUsing a comprehensive simulation study we show that our new algorithm (median AUC: 0.66) can use information hidden in interactions and outperforms two other state-of-the-art algorithms, namely the Random Forest (median AUC: 0.54) and Elastic Net (median AUC: 0.50), if interactions are present in a scenario of two pairs of two features having small effects. The performance of these algorithms is comparable if no interactions are present. Further, we show that our new algorithm is applicable to real data by comparing the performance of the three algorithms on a dataset of rheumatoid arthritis cases and healthy controls. As our new algorithm is not only applicable to biological/genetic data but to all datasets with discrete features, it may have practical implications in other research fields where interactions between features have to be considered as well, and we made our method available as an R package ( https://github.com/imbs-hl/MBMDRClassifieR ).ConclusionsThe explicit use of interactions between features can improve the prediction performance and thus should be included in further attempts to move precision medicine forward.

Project description:ContextParticipants in a randomized trial of traditional Chinese medicine (TCM) for temporomandibular joint dysfunction (TMD) had a linear decline in pain over 16 TCM visits.ObjectiveTo investigate whether reductions in pain among participants receiving TCM can be explained by increased use of pain medications, or whether use of pain medications also declined in this group.DesignOne hundred sixty-eight participants with TMD were treated with TCM or enhanced self-care according to a stepped-care design. Those for whom self-care failed were sequentially randomized to further self-care or TCM. This report includes 111 participants during their first 16 TCM visits. The initial 8 visits occurred more than once a week; participants and practitioners determined the frequency of subsequent visits.Outcome measuresAverage pain (visual analog scale, range 0-10) and morphine and aspirin dose equivalents.ResultsThe sample was 87% women and the average age was 44 ± 13 years. Average pain of narcotics users (n = 21) improved by 2.73 units over 16 visits (p < 0.001). Overall narcotics use trended downward until visit 11 (-3.27 doses/week, p = 0.156), and then trended upward until week 16 (+4.29 doses/week, p = 0.264). Among those using narcotics, use of nonsteroidal anti-inflammatory drugs (NSAIDs) declined linearly over visits 1-16 (-1.94 doses/week, p = 0.002).Among the top quartile of NSAID-only users (n = 22), average pain decreased linearly over 16 visits (-1.52 units, p = 0.036). Overall NSAID doses/week declined between visits 1 and 7 (-9.95 doses/week, p < 0.001) and then remained stable through 16 visits. NSAID use also declined among the third quartile (n = 23) and remained low and stable among the lower half (sorted by total intake) of NSAID users.ConclusionsAmong the heaviest NSAID users, we observed a short-term reduction in NSAID use that was sustained as TCM visits became less frequent. There was no indication that pain reduction during TCM treatment was influenced by drug use.

Dataset Information

Pain Medicine in Crisis-A Possible Model toward a Solution: Empowering Community Medicine to Treat Chronic Pain.

Publications

Pain Medicine in Crisis-A Possible Model toward a Solution: Empowering Community Medicine to Treat Chronic Pain.

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