SCH: INT: Collaborative Research: Development and analysis of new mathematical and statistical models for chronic pain

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Program Director/Principal Investigator (Last, First, Middle): Abrams, Daniel, M Project Description 1. Intellectual merit (see Sec. 2, pages 13-14, for "Broader impacts") 1.1 Introduction and background 1.1.1 General introduction During recent decades there has been an extraordinary growth in the availability of data relating to a wide range of microbiological systems. That data has enabled new quantitative approaches to biology, including the development of new mathematical and statistical models that given fun- damental insight into the workings of biological systems. Another source is now growing explosively: biomedical data. This data has significant potential for use in treatment of human disease, but thus far comparatively fewer mathematical models for medical phenomena have been developed. The hope is that quantitative models will allow for "personalized" or "precision" medicine, where treatment protocols are customized based on an understanding of how individual patient characteristics impact the effectiveness of the treatment. Deep mathematical understanding of biomedical systems also promises to allow for optimization of medical interventions: the physical and/or financial costs of intervention could be minimized for a given desired level of benefit. The broad goal of the proposed research is to develop new integrative mathematical models for the dynamics of subjective pain in patients suffering from chronic pain. These models will combine existing qualitative knowledge with insight gained from newly available patient data, with the goal of incorporating data streams corning on line in the near future. We plan to develop multiple models in parallel using a variety of approaches and then to select the best rnodel(s) based on agreement with objective data. 1.1.2 Background on biological application: Sickle cell disease Sickle cell disease (SCD) is a chronic illness associated with frequent medical complications and hospitalizations. Approximately 90% of acute care visits are for pain events, and 30-day reuti- lization rates are alarmingly high [27]. While factors influenci.ng these high re-utilization rates are poorly understood, close follow-up and continued use of pain medication has been shown to de- crease re-hospitalization rates. Mobile technology has become an integral part of health care management and Pl Shah's recently developed mobile application (SMART app - see Figure 1) for SCD assists with documentation of pain and interventions. 1.1.3 Background on hybrid approach Perhaps because of the often distinct educational backgrounds of practitioners or distinct typical applications, statistical and mechanistic approaches are not frequently combined in addressing a single problem. The majority of attempts in the scientific literature have appeared in the context of neural networks [37, 38, 29] and chemical engineering [38, 33, 11], where they largely play a computational rather than analytical role. Some attempts have also been made with medical applications: Rosenberg et al. [30] and Adams et al. [4] developed a model by combining a dy- namical systems approach with a statistical model to predict a patient's CD4 cell counts and HIV viral load over time in an HIV study. Timms et al. [39] proposed a dynamical systems approach 1 0MB No. 0925-0001/0002 (Rev. 01/18 Approved Through 03/31/2020) Page_ Continuation Format Page