Browsing by Subject "Volume"

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    Design and Rationale for the Use of Magnetic Resonance Imaging Biomarkers to Predict Diabetes After Acute Pancreatitis in the Diabetes RElated to Acute Pancreatitis and Its Mechanisms Study: From the Type 1 Diabetes in Acute Pancreatitis Consortium
    (Wolters Kluwer, 2022) Tirkes, Temel; Chinchilli, Vernon M.; Bagci, Ulas; Parker, Jason G.; Zhao, Xuandong; Dasyam, Anil K.; Feranec, Nicholas; Grajo, Joseph R.; Shah, Zarine K.; Poullos, Peter D.; Spilseth, Benjamin; Zaheer, Atif; Xie, Karen L.; Wachsman, Ashley M.; Campbell-Thompson, Martha; Conwell, Darwin L.; Fogel, Evan L.; Forsmark, Christopher E.; Hart, Phil A.; Pandol, Stephen J.; Park, Walter G.; Pratley, Richard E.; Yazici, Cemal; Laughlin, Maren R.; Andersen, Dana K.; Serrano, Jose; Bellin, Melena D.; Yadav, Dhiraj; Type 1 Diabetes in Acute Pancreatitis Consortium (T1DAPC); Radiology and Imaging Sciences, School of Medicine
    This core component of the Diabetes RElated to Acute pancreatitis and its Mechanisms (DREAM) study will examine the hypothesis that advanced magnetic resonance imaging (MRI) techniques can reflect underlying pathophysiologic changes and provide imaging biomarkers that predict diabetes mellitus (DM) following acute pancreatitis (AP). A subset of participants in the DREAM study will enroll and undergo serial MRI examinations using a specific research protocol. We aim to differentiate at-risk individuals from those who remain euglycemic by identifying parenchymal features following AP. Performing longitudinal MRI will enable us to observe and understand the natural history of post-AP DM. We will compare MRI parameters obtained by interrogating tissue properties in euglycemic, prediabetic and incident diabetes subjects and correlate them with metabolic, genetic, and immunological phenotypes. Differentiating imaging parameters will be combined to develop a quantitative composite risk score. This composite risk score will potentially have the ability to monitor the risk of DM in clinical practice or trials. We will use artificial intelligence, specifically deep learning, algorithms to optimize the predictive ability of MRI. In addition to the research MRI, the DREAM study will also correlate clinical computerized tomography and MRI scans with DM development.
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    Diagnosis of chronic pancreatitis using semi-quantitative MRI features of the pancreatic parenchyma: results from the multi-institutional MINIMAP study Temel Tirkes1,18, Dhiraj Yadav2
    (Springer, 2023) Tirkes, Temel; Yadav, Dhiraj; Conwell, Darwin L.; Territo, Paul R.; Zhao, Xuandong; Persohn, Scott A.; Dasyam, Anil K.; Shah, Zarine K.; Venkatesh, Sudhakar K.; Takahashi, Naoki; Wachsman, Ashley; Li, Liang; Li, Yan; Pandol, Stephen J.; Park, Walter G.; Swaroop Vege, Santhi; Hart, Phil A.; Topazian, Mark; Andersen, Dana K.; Fogel, Evan L.; Consortium for the Study of Chronic Pancreatitis, Diabetes, Pancreatic Cancer (CPDPC); Radiology and Imaging Sciences, School of Medicine
    Purpose: To determine the diagnostic performance of parenchymal MRI features differentiating CP from controls. Methods: This prospective study performed abdominal MRI scans at seven institutions, using 1.5 T Siemens and GE scanners, in 50 control and 51 definite CP participants, from February 2019 to May 2021. MRI parameters included the T1-weighted signal intensity ratio of the pancreas (T1 score), arterial-to-venous enhancement ratio (AVR) during venous and delayed phases, pancreas volume, and diameter. We evaluated the diagnostic performance of these parameters individually and two semi-quantitative MRI scores derived using logistic regression: SQ-MRI Model A (T1 score, AVR venous, and tail diameter) and Model B (T1 score, AVR venous, and volume). Results: When compared to controls, CP participants showed a significantly lower mean T1 score (1.11 vs. 1.29), AVR venous (0.86 vs. 1.45), AVR delayed (1.07 vs. 1.57), volume (54.97 vs. 80.00 ml), and diameter of the head (2.05 vs. 2.39 cm), body (2.25 vs. 2.58 cm), and tail (1.98 vs. 2.51 cm) (p < 0.05 for all). AUCs for these individual MR parameters ranged from 0.66 to 0.79, while AUCs for the SQ-MRI scores were 0.82 and 0.81 for Model A (T1 score, AVR venous, and tail diameter) and Model B (T1 score, AVR venous, and volume), respectively. After propensity-matching adjustments for covariates, AUCs for Models A and B of the SQ-MRI scores increased to 0.92 and 0.93, respectively. Conclusion: Semi-quantitative parameters of the pancreatic parenchyma, including T1 score, enhancement ratio, pancreas volume, diameter and multi-parametric models combining these parameters are helpful in diagnosis of CP. Longitudinal analyses including more extensive population are warranted to develop new diagnostic criteria for CP.
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    Physician Compensation Models and Quality of Healthcare Services in the United Arab Emirates
    (2023) Elrefaey, Mahmoud; Tierney, William Michael; Babich, Suzanne M.; Czabanowska, Katarzyna
    Physicians working in different healthcare systems receive financial compensation by means of several structures (e.g., the salaried model, the fee-for-service model, and the revenue-share model) depending on how and where they practice. Most research on the relationships, if any, between physicians' compensation models and the outcomes of healthcare services has been conducted in North America and Europe, but no equivalent research has been conducted in the United Arab Emirates (UAE). The purpose of my exploratory qualitative research study was to address two open-ended research questions: 1) what are the perceptions of hospital stakeholders about the idiographic effects of different physician payment models on quality of healthcare services in the hospital? 2) What changes might be implemented to physician payment models to improve healthcare services in the hospital? I audio-recorded semi-structured interviews with a purposive sample of N = 17 stakeholders at one private sector hospital in UAE. The heterogenous or maximum variation sample included five hospital leaders, two financial or insurance managers, five physicians, two nurses, and three patients. I conducted a qualitative analysis and identified ten primary semantic themes by deductive reasoning to address the first research question. I based four semantic themes on a template extracted from the literature, specifically: 1) Physician Payment Models Implemented at the Hospital; 2) Environmental Context for Payment Models; 3) Stakeholders Affected by Payment Models; 4) Misuse of Payment models. I underpinned six semantic themes by the dimensions of healthcare quality proposed by the Institute of Medicine, specifically: 5) Payment Models and Safe Care; 6) Payment Models and Effectiveness of Care; 7) Payment Models and Patient-Centered Care; 8) Payment Models and Timely Care; 9) Payment Models and Efficiency of Care; 10) and Payment Models and Equity of Care. Subsequently, I synthesized the semantic themes and identified two latent themes by inductive reasoning, specifically: 1) Relationships between Physicians' Compensation Models and Healthcare Services; and 2) Proposed Changes to Physician Compensation Models. I propose innovative changes underpinned by Kotter's Management Change Theory and Roger's Theory of Diffusion of Innovations. I recommend future confirmatory research using a quantitative correlational design to validate these themes.