An estimated 1.2 million people in the US have an acute myocardial infarction (AMI) each year. An estimated 7% of AMI hospitalizations result in death. Most patients experiencing acute coronary symptoms, such as unstable angina, visit an emergency department (ED). Some patients hospitalized with AMI after a treat-and-release ED visit likely represent missed opportunities for correct diagnosis and treatment. The purpose of the present study is to estimate the frequency of missed AMI or its precursors in the ED by examining use of EDs prior to hospitalization for AMI. We estimated the rate of probable missed diagnoses in EDs in the week before hospitalization for AMI and examined associated factors. We used Healthcare Cost and Utilization Project State Inpatient Databases and State Emergency Department Databases for 2007 to evaluate missed diagnoses in 111,973 admitted patients aged 18 years and older. We identified missed diagnoses in the ED for 993 of 112,000 patients (0.9% of all AMI admissions). These patients had visited an ED with chest pain or cardiac conditions, were released, and were subsequently admitted for AMI within 7 days. Higher odds of having missed diagnoses were associated with being younger and of Black race. Hospital teaching status, availability of cardiac catheterization, high ED admission rates, high inpatient occupancy rates, and urban location were associated with lower odds of missed diagnoses. Administrative data provide robust information that may help EDs identify populations at risk of experiencing a missed diagnosis, address disparities, and reduce diagnostic errors.

Neurological emergencies often pose diagnostic challenges for emergency physicians because these patients often present with atypical symptoms and standard imaging tests are imperfect. Misdiagnosis occurs due to a variety of errors. These can be classified as knowledge gaps, cognitive errors, and systems-based errors. The goal of this study was to describe these errors through review of quality assurance (QA) records. This was a retrospective pilot study of patients with neurological emergency diagnoses that were missed or delayed at one urban, tertiary academic emergency department. Cases meeting inclusion criteria were identified through review of QA records. Three emergency physicians independently reviewed each case and determined the type of error that led to the misdiagnosis. Proportions, confidence intervals, and a reliability coefficient were calculated. During the study period, 1168 cases were reviewed. Forty-two cases were found to include a neurological misdiagnosis and twenty-nine were determined to be the result of an error. The distribution of error types was as follows: knowledge gap 45.2% (95% CI 29.2, 62.2), cognitive error 29.0% (95% CI 15.9, 46.8), and systems-based error 25.8% (95% CI 13.5, 43.5). Cerebellar strokes were the most common type of stroke misdiagnosed, accounting for 27.3% of missed strokes. All three error types contributed to the misdiagnosis of neurological emergencies. Misdiagnosis of cerebellar lesions and erroneous radiology resident interpretations of neuroimaging were the most common mistakes. Understanding the types of errors may enable emergency physicians to develop possible solutions and avoid them in the future.

Nanotechnology has provided a plethora of valuable tools that can be applied for the detection of biomolecules and analytes relevant for diagnosis purposes - nanodiagnostics. This surging new field of molecular diagnostics has been revolutionizing laboratory procedures and providing new ways to assess disease biomarkers with increased sensitivity. While most of the reported nanodiagnostics systems are proof-of-concepts that demonstrate their efficacy in the lab, several nanodiagnostics platforms have already matured to a level that open the way for effective translation to the clinics. Nanodiagnostics platforms (e.g., gold nanoparticles containing systems) have been remarkably useful for the development of molecular diagnosis strategies for DNA/RNA detection and characterization, including systems suitable for point-of-care. How near are nanodiagnostics to go from the bench to the bedside?

Diagnostic errors comprise a critical subset of medical errors and often stem from errors in individual cognition. While traditional patient safety methods for dissecting medical errors focus on faulty systems, such methods are often less useful in cases of diagnostic error, and a broader cognitive framework is needed to ensure a comprehensive analysis of these complex events. The fishbone diagram is a widely utilized patient safety tool that helps to facilitate root cause analysis discussions. This tool was expanded by the authors to reflect the contributions of both systems and individual cognitive errors to diagnostic errors. We describe how two medical centers have applied this modified fishbone diagram to approach diagnostic errors in a way that better meets the patient safety and educational needs of their respective institutions.

Clinical laboratories play a vital role in patient care, but many diagnostic errors are associated with laboratory testing. The past decades have seen sustained improvements in analytical performances but the error rates, particularly in pre- and post-analytical phases is still high. Although the seminal concept of the brain-to-brain laboratory loop has been described more than four decades ago, the awareness about the importance of extra-analytical aspects in laboratory quality is a recent achievement. According to this concept, all phases and activities of the testing cycle should be assessed, monitored and improved in order to decrease the total error rates and thereby improve patient safety. In the interests of patients, any direct or indirect negative consequence related to a laboratory test must be considered, irrespective of which step is involved and whether the error depends on a laboratory professional (e.g., calibration or testing error) or a non-laboratory operator (e.g., inappropriate test request, error in patient identification and/or blood collection). Data collected in various clinical settings demonstrate that many diagnostic errors are associated with laboratory testing. In particular, errors are due to inappropriate test request and/or result interpretation and utilization. Collaborations between laboratory professionals and other care providers, namely clinicians and nurses, are needed to achieve the goal of improved patient safety.

Introduction: As a result of the increasing use and improvements in imaging technologies such as computed tomography and magnetic resonance imaging, the frequency at which pancreatic cysts are detected has been growing. Diagnosing the biological nature of such pancreatic cystic lesions remains a difficult challenge to clinicians, as diagnosis by cytology is insensitive and there are currently no reliable methods to measure the degree malignant potential of cysts. Therefore, new markers capable of differentiating benign from malignant IPMNs would assist in establishing an optimal therapeutic strategy for treatment by identifying patients who would benefit from surgery or surveillance. We applied liquid chromatography-tandem mass spectrometry (LC-MS/MS) shotgun proteomics and multiple reaction monitoring (LC-MRM-MS) analyses on a collection of IPMN cyst fluid samples to identify and verify potential biomarker which may serve as diagnostic indicators of malignant potential and assist in risk stratification. Methods: A total of 15 IPMN pancreatic cyst fluid samples (5 adenomas, 5 carcinomas in situ and 5 invasive carcinomas) were obtained from freshly resected cysts and were analyzed by shotgun proteomics. Aliquots of each sample were reduced, alkylated and digested with trypsin and the resulting peptides were fractionated by isoelectric focusing (pI 3.5-4.7) into 15 fractions. Reversed-phase liquid chromatography-tandem mass spectrometry (LC-MS/MS) was performed using data-dependent scanning on a Thermo Orbitrap Velos instrument. Following LC-MS/MS analysis, tandem mass spectra were searched against the human IPI database (v.3.56) using the Myrimatch algorithm and the search results filtered using IDPicker at a 5% FDR. A spectral counting approach was applied to detect statistically significant differences in protein abundances with the Quasitel algorithm. MRM assays were configured for differential proteins and further verified in a new collection of unfractionated IPMN cyst fluids with varying degrees of dysplasia (mild, moderate, severe and invasive carcinoma). LC-MRM-MS was performed on peptide samples and were analyzed in triplicate (4 peptides per protein and 5 transitions per peptides) on a triple quadrupole mass spectrometer (TSQ Vantage, Thermo Fisher Scientific, Waltham MA) equipped with an Eksigent nanoLC Ultra 1D Plus pump and AS-2 autosampler (Eksigent Technologies, Dublin CA). A single isotopically labeled peptide was spiked into the samples which served as the normalization reference for all of the peptides in the analysis. All data analysis was performed using Skyline. Preliminary Results: A total of 373,673 spectra were confidently identified, which corresponded to 5,259 protein groups across all biological and technical replicates. An average of 3,534, 3,393, and 4,597 protein groups were identified in the adenomas, carcinomas in situ, and invasive carcinomas, respectively. Spectral count data indicated approximately 50 proteins differentially expressed between benign adenomas and carcinoma in situ and 176 proteins different between adenomas and invasive cancers. From these data, a list of 30 potential candidates was generated, which included proteins upregulated in carcinoma in situ and invasive cancer, as well as several down-regulated proteins, which included common digestive enzymes, such as amylase 2B and elastases. In addition, several MUC isoforms and CEACAM were also targeted, as these have been suggested to distinguish nonmucinous from mucinous cystic lesions. The preliminary MRM data are consistent with the trends observed in the spectral count data from shotgun analyses. The analyses of pancreatic cyst fluids using proteomics platforms may lead to biomarkers that can assist clinicians in differentiating benign from malignant cysts, thus leading to improved diagnosis and treatment. Citation Format: Lisa J. Zimmerman, Cristina R. Ferrone, Nipun Merchant, Robbert J. C. Slebos, Daniel C. Liebler. Proteomic analysis of cyst fluids from intraductal papillary mucinous neoplasms [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer: Progress and Challenges; Jun 18-21, 2012; Lake Tahoe, NV. Philadelphia (PA): AACR; Cancer Res 2012;72(12 Suppl):Abstract nr B8.

Human pancreatic ductal adenocarcinoma (PDAC) displays high heterogeneity in terms of morphology, onset, and therapy response. Genetically engineered mouse models (GEMMs) reflect differences in human PDACs and are valuable, predictive tools for preclinical research. In this study, a comprehensive noninvasive imaging approach using various GEMMs was performed with the aim to identify markers that allow prediction of tumor type and response to therapy in humans. GEMMs with different PDAC characteristics were analyzed for tumor growth kinetics, tissue composition, perfusion and response to chemotherapies using multiparametric MRI and [18F]-FDG-PET imaging. Following genetically engineered mouse models of PDAC were used: Ptf1a wt/Cre ( C ) Kras wt/LSL-G12D ( K ) Ela-Tgfa ( T ), CKp53 ( P ) wt/fl , CKP fl/fl , CKTP wt/R172H and CKTP wt/fl . Beginning at tumor onset animals were subjected to serial T2-weighted (T2w) diffusion weighted (DWI) and dynamic contrast-enhanced (DCE)-MRI employing a 1.5 T Achieva, Philips with a dedicated surface coil. Additionally, metabolic status of tumors was assessed by [18F]-FDG-PET. In a subset of CKP fl/fl mice therapy response to clinical standard of care chemotherapy gemcitabine was monitored. Imaging data was validated by histopathology. Murine PDACs developing from either PanIN or IPMN showed differences in tumor onset, growth kinetics and appearance, mimicking the wide spectrum of human disease. T2w imaging differentiated hypointense mIPMNs and acinus cell carcinomas from hyperintense solid ductal PDACs. Calculated apparent diffusion coefficients (ADC) showed good correlation with tissue cellularity (r2 = 0.73) and collagen content (r2 = 0.65). DWI allowed differentiation of preneoplastic lesions with inflammatory tissue (≥ 1.2 mm2/s), mIPMNs (0.99 ± 0.02 mm2/s), well differentiated mPDAC (1.031 ± 0.02 mm2/s), less differentiated mPDACs (0.80 ± 0.01 mm2/s) and acinus cell carcinomas or anaplastic mPDACs (0.67 ± 0.03 mm2/s). DCE-MRI data was highly heterogeneous and revealed major intraindividual differences between tumor regions. [18F]-FDG tracer uptake tumor to muscle ratios (T/M) enabled differentitation of preneoplastic lesions (T/M 3), independent of the underlying genotype. PDAC in CKP fl/fl mice showed similar resistance to gemcitabine treatment as human PDAC. Only single animals were identified as responders based on an increase in survival and calculated ADC value and a decrease in tumor growth and [18F]-FDG uptake. Here we provide evidence that morphological heterogeneity of PDAC is well modeled with GEMMs and identifiable with multimodal multiparametric imaging. The tissue structure related parameter ADC is identified as a sensitive marker of tumor heterogeneity. The combined evaluation of tumor growth, [18F]-FDG tracer uptake and ADC value identified individual therapy responders. Citation Format: Irina Heid, Marija Trajkovic-Arsic, Irene Esposito, Manuela R. Gretzinger, Andreas Steingoetter, Bernhard Haller, Markus Schwaiger, Ernst J. Rummeny, Jens T. Siveke, Rickmer F. Braren. Imaging heterogeneity of endogenous mouse models of pancreatic cancer. [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer: Progress and Challenges; Jun 18-21, 2012; Lake Tahoe, NV. Philadelphia (PA): AACR; Cancer Res 2012;72(12 Suppl):Abstract nr A5.

Most patients with pancreatic adenocarcinoma (PA) present after the disease has advanced to an incurable stage. Screening for PA would likely improve outcomes through earlier detection, but could result in unacceptable levels of false-positive diagnoses using current biomarkers. Recent efforts to identify individual biomarkers or biomarker panels have been disappointing. Furthermore, it is unlikely that an individual biomarker will provide sufficient accuracy for detection of PA given the high amount of molecular heterogeneity in the disease. We propose a novel, intuitive panel design that allows for diverse biomarker selection for accurate diagnosis. Using characteristics of nine PA biomarkers measured in human sera to model the behavior of biomarker panels, we delineate the number of biomarkers required for accurate detection of PA in our panel design. Levels of AXL, CA 19-9, haptoglobin, hyaluronic acid, MMP-7, MMP-11, osteopontin, serum amyloid A, and TIMP1 were measured in sera from 117 healthy control subjects and 58 chronic pancreatitis patients, and 159 PA patients prior to treatment. Threshold indicators were constructed for individual biomarkers at the 95th percentile of the control value. We modeled the behavior of a biomarker panel consisting of a sum of indicator variables, then chose a cutoff for the sum to force specificity to be high, and calculated the resulting sensitivity. To generate correlated biomarkers, we simulated correlated continuous biomarker data, made a 95th percentile cutoff for each biomarker, and then assessed performance as above. Between 17% and 75% of the PA cases had values above the 95th percentile of control values with an average sensitivity for all biomarkers of 32%. The correlation between the indicator variables was near zero in controls and slightly positive in PA cases. None of the biomarkers were highly correlated. The model shows that a panel consisting of 40 biomarkers characterized individually by 32% sensitivity at 95% specificity would require any 7 biomarkers to be above the threshold and would result in a panel specificity and sensitivity of 99% each. The addition of correlation assumptions reduced sensitivity for the 40 biomarker panel to 94% at an average correlation of 0.05 and 84% at an average correlation of 0.15. Our modeling shows that a highly accurate, blood-based PA diagnostic panel can be developed from a reasonable number of individual serum biomarkers that are relatively weak classifiers when used singly. The model provides a framework for maximizing biomarker sensitivities and minimizing biomarker correlation. A panel constructed as described is advantageous in that a high level of specificity can be forced and allows for heterogeneity among patients and their tumor characteristics. Citation Format: Matthew A. Firpo, Kenneth M. Boucher, Sean J. Mulvihill. Prospects for developing a biomarker panel for accurate detection of pancreatic adenocarcinoma. [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer: Progress and Challenges; Jun 18-21, 2012; Lake Tahoe, NV. Philadelphia (PA): AACR; Cancer Res 2012;72(12 Suppl):Abstract nr A4.