Is KRAS mutation associated with interval colorectal cancers?

BACKGROUND: Colorectal cancers diagnosed in the interval after a complete colonoscopy may occur due to rapid tumor growth. Interval colorectal cancers are associated with microsatellite instability (MSI). AIMS: Our aim was to study the association of KRAS mutation with interval colorectal cancers and MSI. METHODS: We searched our institution's cancer registry for interval colorectal cancers, defined as colorectal cancers that developed within 5 years of a complete colonoscopy. These were frequency matched to patients with non-interval colorectal cancers. Archived cancer specimens were evaluated for KRAS mutations in codons 12 and 13 using sequencing, and MSI by sequencing microsatellite loci. Multivariable logistic regression was used to analyze the association between KRAS mutation status, MSI status and interval colorectal cancers. RESULTS: There were 63 interval and 131 non-interval colorectal cancers. KRAS mutation was present in 12.9% of interval cancers compared to 28.9% of non-interval cancers (P = 0.03). In multivariable logistic regression model, KRAS was inversely associated with interval cancers (OR 0.36; 95% CI 0.15-0.90). In Cox proportional hazards model, adjusting for age, tumor grade, TNM Stage and MSI status, we found no association between KRAS mutation and 5-year survival compared to cancers without KRAS mutation (HR 0.84; 95% CI 0.4-1.46; P = 0.5). CONCLUSIONS: KRAS mutation is inversely associated with interval cancers and with MSI, suggesting that it is a marker of the chromosomal instability pathway associated with slow tumor growth, and distinct from MSI rapidly growing cancers. Molecular characterization of colorectal cancers is helpful in determining underlying pathway and may determine therapy.

Evaluating the impact of a computerized surveillance algorithm and decision support system on sepsis mortality.

OBJECTIVE: We created a system using a triad of change management, electronic surveillance, and algorithms to detect sepsis and deliver highly sensitive and specific decision support to the point of care using a mobile application. The investigators hypothesized that this system would result in a reduction in sepsis mortality. METHODS: A before-and-after model was used to study the impact of the interventions on sepsis-related mortality. All patients admitted to the study units were screened per the Institute for Healthcare Improvement Surviving Sepsis Guidelines using real-time electronic surveillance. Sepsis surveillance algorithms that adjusted clinical parameters based on comorbid medical conditions were deployed for improved sensitivity and specificity. Nurses received mobile alerts for all positive sepsis screenings as well as severe sepsis and shock alerts over a period of 10 months. Advice was given for early goal-directed therapy. Sepsis mortality during a control period from January 1, 2011 to September 30, 2013 was used as baseline for comparison. RESULTS: The primary outcome, sepsis mortality, decreased by 53% (P = 0.03; 95% CI, 1.06-5.25). The 30-day readmission rate reduced from 19.08% during the control period to 13.21% during the study period (P = 0.05; 95% CI, 0.97-2.52). No significant change in length of hospital stay was noted. The system had observed sensitivity of 95% and specificity of 82% for detecting sepsis compared to gold-standard physician chart review. CONCLUSION: A program consisting of change management and electronic surveillance with highly sensitive and specific decision support delivered to the point of care resulted in significant reduction in deaths from sepsis.

Validation of pre-operative patient self-assessment of cardiac risk for non-cardiac surgery: foundations for decision support.

OBJECTIVES: To better characterize patient understanding of their risk of cardiac complications from non-cardiac surgery and to develop a patient driven clinical decision support system for preoperative patient risk management. METHODS: A patient-driven preoperative self-assessment decision support tool for perioperative assessment was created. Patient' self-perception of cardiac risk and self-report data for risk factors were compared with gold standard preoperative physician assessment to evaluate agreement. RESULTS: The patient generated cardiac risk profile was used for risk score generation and had excellent agreement with the expert physician assessment. However, patient subjective self-perception risk of cardiovascular complications had poor agreement with expert assessment. CONCLUSION: A patient driven cardiac risk assessment tool provides a high degree of agreement with expert provider assessment demonstrating clinical feasibility. The limited agreement between provider risk assessment and patient self-perception underscores a need for further work including focused preoperative patient education on cardiac risk.

Social and behavioral history information in public health datasets.

Social and behavioral history is increasingly recognized as integral for understanding important determinants of disease and critical for patient care, research, clinical guidelines, and public health policies. Social and behavioral history information in the public health domain, specifically large public health surveys, has not been well described. In this study, a content analysis was performed and information model constructed and contrasted with clinically-based models for each of three widely used public health surveys: BRFSS (Behavioral Risk Factor Surveillance System), NHANES (National Health and Nutrition Examination Survey), and NHIS (National Health Interview Survey). Survey items were predominantly related to alcohol use, drug use, occupation, and tobacco use. Although the clinical social history information model was similar, public health social history demonstrated additional complexity in coding temporality, degree of exposure, and certainty. Our results give insight into ongoing efforts to integrate clinical and public health information resources for improving and measuring health.

A multi-site content analysis of social history information in clinical notes.

Within Electronic Health Records (EHRs), the social history section contains information relevant to social, behavioral, and environmental determinants of health. While social history is playing an increasingly important role in patient care, biomedical research, and public health, little analysis has been done to describe content in the EHR or the adequacy of existing standards for representing this information. In this study, social history sections from 260 clinical notes containing 989 sentences and 1,439 statements were analyzed from three sources. In total, 35 statement types were identified along with categories of information within statements for each type. For the 8 most common types, HL7 CDA and openEHR were found to provide different representations capable of capturing the breadth and granularity of information to some extent. The results of this study provide valuable insights for guiding efforts in the enhanced collection, standardization, and use of social history information in the EHR.