Abdominal pain with a twist: a rare presentation of acute gastric volvulus.

Acute gastric volvulus is a life threatening condition requiring early diagnosis and aggressive management. Diagnosis of gastric volvulus remains a challenge for clinicians due to variable, non-specific clinical presentation, which requires a high level of suspicion. It should be considered in patients presenting with chest pain and/or epigastric pain, especially in the elderly population. Endoscopic de-rotation could be initially attempted as a therapeutic modality especially in patients who cannot undergo surgery. However, surgery remains the main stay of treatment. Delay in diagnosis can lead to complications like mucosal ischemia, necrosis or perforation, shock, which substantially increase the morbidity and mortality.

Latent physiological factors of complex human diseases revealed by independent component analysis of clinarrays.

BACKGROUND: Diagnosis and treatment of patients in the clinical setting is often driven by known symptomatic factors that distinguish one particular condition from another. Treatment based on noticeable symptoms, however, is limited to the types of clinical biomarkers collected, and is prone to overlooking dysfunctions in physiological factors not easily evident to medical practitioners. We used a vector-based representation of patient clinical biomarkers, or clinarrays, to search for latent physiological factors that underlie human diseases directly from clinical laboratory data. Knowledge of these factors could be used to improve assessment of disease severity and help to refine strategies for diagnosis and monitoring disease progression. RESULTS: Applying Independent Component Analysis on clinarrays built from patient laboratory measurements revealed both known and novel concomitant physiological factors for asthma, types 1 and 2 diabetes, cystic fibrosis, and Duchenne muscular dystrophy. Serum sodium was found to be the most significant factor for both type 1 and type 2 diabetes, and was also significant in asthma. TSH3, a measure of thyroid function, and blood urea nitrogen, indicative of kidney function, were factors unique to type 1 diabetes respective to type 2 diabetes. Platelet count was significant across all the diseases analyzed. CONCLUSIONS: The results demonstrate that large-scale analyses of clinical biomarkers using unsupervised methods can offer novel insights into the pathophysiological basis of human disease, and suggest novel clinical utility of established laboratory measurements.

Special article: an optimistic prognosis for the clinical utility of laboratory test data.

It is hoped that anesthesiologists and other clinicians will be able to increasingly rely upon laboratory test data to improve the perioperative care of patients. However, it has been suggested that in order for a laboratory test to have clinically useful diagnostic performance characteristics (sensitivity and specificity), its performance must be considerably better than those that have been evaluated in most etiologic or epidemiologic studies. This pessimism about the clinical utility of laboratory tests is based upon the untested assumption that laboratory data are normally distributed within case and control populations. We evaluated the data distribution for 700 commonly ordered laboratory tests, and found that the vast majority (99%) do not have a normal distribution. The deviation from normal was most pronounced at extreme values, which had a large quantitative effect on laboratory test performance. At the sensitivity and specificity values required for diagnostic utility, the minimum required odds ratios for laboratory tests with a nonnormal data distribution were significantly smaller (by orders of magnitude) than for tests with a normal distribution. By evaluating the effect that the data distribution has on laboratory test performance, we have arrived at the more optimistic outlook that it is feasible to produce laboratory tests with diagnostically useful performance characteristics. We also show that moderate errors in the classification of outcome variables (e.g., death vs. survival at a specified end point) have a small impact on test performance, which is of importance for outcomes research that uses anesthesia information management systems. Because these analyses typically seek to identify factors associated with an undesirable outcome, the data distributions of the independent variables need to be considered when interpreting the odds ratios obtained from such investigations.

Validating pathophysiological models of aging using clinical electronic medical records.

Bioinformatics methods that leverage the vast amounts of clinical data promises to provide insights into underlying molecular mechanisms that help explain human physiological processes. One of these processes is adolescent development. The utility of predictive aging models generated from cross-sectional cohorts and their applicability to separate populations, including the clinical population, has yet to be completely explored. In order to address this, we built regression models predictive of adolescent chronological age from 2001 to 2002 National Health and Nutrition Examination Survey (NHANES) data and validated them against independent 2003-2004 NHANES data and clinical data from an academic tertiary-care pediatric hospital. The results indicate distinct differences between male and female models with both alkaline phosphatase and creatinine as predictive biomarkers for both genders, hematocrit and mean cell volume for males, and total serum globulin for females. We also suggest that the models are generalizable, are clinically relevant, and imply underlying molecular and clinical differences between males and females that may affect prediction accuracy. The integration of both epidemiological and clinical data promises to create more robust models that shed new light on physiological processes.

ChMP: A collaborative medical history portal.

Family medical histories play an invaluable role in disease prevention, diagnosis and treatment. Self reported medical histories frequently contain incorrect or incomplete information, severely diminishing the quality of care and clinical outcome of the patient. While tools for obtaining and analyzing medical histories are available to medical professionals, no system exists to allow families to actively participate in the collection and utilization of medical history data. We have developed a free web-based service (http://www.inherithealth.com) that allows a family to collaboratively capture and store medical history information relevant to breast cancer. The service is built on a custom framework that enables the integration of existing breast cancer risk assessment models with web-based software to communicate evidence-based risk assessment to consumers. Preliminary user evaluations indicate that consumers find the tool usable, and are interested in learning about their breast cancer risk.

Novel integration of hospital electronic medical records and gene expression measurements to identify genetic markers of maturation.

Traditionally, the elucidation of genes involved in maturation and aging has been studied in a temporal fashion by examining gene expression at different time points in an organism's life as well as by knocking out, knocking in, and mutating genes thought to be involved. Here, we propose an in silico method to combine clinical electronic medical record (EMR) data and gene expression measurements in the context of disease to identify genes that may be involved in the process of human maturation and aging. First we show that absolute lymphocyte count may serve as a biomarker for maturation by using statistical methods to compare trends among different clinical laboratory tests in response to an increase in age. We then propose using the rate of decay for absolute lymphocyte count across 12 diseases as a proxy for differences in aging. We correlate the differing rates with gene expression across the same diseases to find maturation/aging related genes. Among the 53 genes with strongest correlations between expression profile and change in rate of decay, we found genes previously implicated in the process of aging, including MGMT (DNA repair), TERF2 (telomere stability), POLD1 (DNA replication and repair), and POLG (mtDNA replication).

Clinical arrays of laboratory measures, or "clinarrays", built from an electronic health record enable disease subtyping by severity.

The severity of diseases has often been assigned by direct observation of a patient and by pathological examination after symptoms have appeared. As we move into the genomic era, the ability to predict disease severity prior to manifestation has improved dramatically due to genomic sequencing and analysis of gene expression microarrays. However, as the severity of diseases can be exacerbated by non genetic factors, the ability to predict disease severity by examining gene expression alone may be inadequate. We propose the creation of a "clinarray" to examine phenotypic expression in the form of clinical laboratory measurements. We demonstrate that the clinarray can be used to distinguish between the severities of patients with cystic fibrosis and those with Crohn's disease by applying unsupervised clustering methods that have been previously applied to microarrays.