Consider further differential diagnoses.
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ABSTRACT:
SUBMITTER: Schneider H
PROVIDER: S-EPMC4003449 | biostudies-literature | 2014 Apr
REPOSITORIES: biostudies-literature
Publications
Consider further differential diagnoses.
Deutsches Arzteblatt international 20140401 14
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Project description:Health care providers are likely to encounter patients with recurrent unexplained abdominal pain. Because hereditary angioedema (HAE) is a rare disease, it may not be part of the differential diagnosis, especially for patients who do not have concurrent skin swelling in addition to abdominal symptoms. Abdominal pain is very common in patients with HAE, occurring in up to 93% of patients, with recurrent abdominal pain reported in up to 80% of patients. In 49% of HAE attacks with abdominal symptoms, isolated abdominal pain was the only symptom. Other abdominal symptoms that commonly present in patients with HAE include distension, cramping, nausea, vomiting, and diarrhea. The average time from onset of symptoms to diagnosis is 6 to 23 years. Under-recognition of HAE in patients presenting with predominant gastrointestinal symptoms is a key factor contributing to the delay in diagnosis, increasing the likelihood of unnecessary or exploratory surgeries or procedures and the potential risk of related complications. HAE should be considered in the differential diagnosis for patients with unexplained abdominal pain, nausea, vomiting, and/or diarrhea who have complete resolution of symptoms between episodes. As highly effective targeted therapies for HAE exist, recognition and diagnosis of HAE in patients presenting with isolated abdominal pain may significantly improve morbidity and mortality for these individuals.
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Project description:The improved capacity to acquire quantitative data in a clinical setting has generally failed to improve outcomes in acutely ill patients, suggesting a need for advances in computer-supported data interpretation and decision making. In particular, the application of mathematical models of experimentally elucidated physiological mechanisms could augment the interpretation of quantitative, patient-specific information and help to better target therapy. Yet, such models are typically complex and nonlinear, a reality that often precludes the identification of unique parameters and states of the model that best represent available data. Hypothesizing that this non-uniqueness can convey useful information, we implemented a simplified simulation of a common differential diagnostic process (hypotension in an acute care setting), using a combination of a mathematical model of the cardiovascular system, a stochastic measurement model, and Bayesian inference techniques to quantify parameter and state uncertainty. The output of this procedure is a probability density function on the space of model parameters and initial conditions for a particular patient, based on prior population information together with patient-specific clinical observations. We show that multimodal posterior probability density functions arise naturally, even when unimodal and uninformative priors are used. The peaks of these densities correspond to clinically relevant differential diagnoses and can, in the simplified simulation setting, be constrained to a single diagnosis by assimilating additional observations from dynamical interventions (e.g., fluid challenge). We conclude that the ill-posedness of the inverse problem in quantitative physiology is not merely a technical obstacle, but rather reflects clinical reality and, when addressed adequately in the solution process, provides a novel link between mathematically described physiological knowledge and the clinical concept of differential diagnoses. We outline possible steps toward translating this computational approach to the bedside, to supplement today's evidence-based medicine with a quantitatively founded model-based medicine that integrates mechanistic knowledge with patient-specific information.
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