Hisham M.F. Sherif M.D.

BME PhD Candidate Hisham M.F. Sherif M.D. will be defending his dissertation on Tuesday, July 3rd, 2018  at 4:00 pm in  304 Pearson Lab (Studio C)


A Novel Hybrid Model for Clinical Decision-Making Corbin Strauss Model and Resilience Engineering

When:  4:00 pm Tuesday, July 3rd, 2018
Where:  304 Pearson Lab
Committee Chair: Dr. Nii Attoh-Okine
Committee:  Dr. Ryan Zurakowski, Dr. Mark Mirotznik, Dr.  Hagit Shatkay

ABSTRACT:  This work has been motivated by real-life experience regarding the challenging decision-making of high-impact interventions in complex, high-risk clinical situations, when recommendations from existing practice guidelines are either ambiguous or based on low-level scientific evidence.

The central research question is as follows: Can we develop a new model that integrates the expected, time-related course of the disease to estimate the benefit from intervention in improving survival and/or quality of life against the cost of this intervention in terms of risk to the patient, financial cost and healthcare system resources?
A literature review was conducted, with review of the existing methodologies for data analysis, the basis for optimal approach to data analysis, the Corbin-Strauss disease trajectory model and principles of resilience engineering.

The objective of this research is to introduce a novel methodology for data analysis that can help clinicians make time-related, patient-specific and disease- specific recommendations for diagnostic and therapeutic interventions.

To achieve this objective, this work introduces a novel, hybrid model for clinical decision-making through the utilization of the Corbin-Strauss disease trajectory, which describes the expected/historical pattern of change in functional status and/or survival over time in chronic conditions and integrates resilience engineering tools to quantify the change in system function.

Based on this, our research strategy included the development of a new resilience model, integrating the Corbin-Strauss trajectory.
To test this model, data from a local cardiac surgery database was utilized in an index case of aortic valve replacement for aortic stenosis. Several equations were tested to quantify the survival benefit in this model. Our findings support a resilience model incorporating the Corbin-Strauss disease trajectory as a tool to help quantify the benefit of interventions in terms of improved survival and/or functional status.

In conclusion, we introduce a novel, hybrid model based on the Corbin-Strauss trajectory and rooted in resilience engineering as a tool to assist in clinical decision- making, especially in complex, high-risk situations.

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