Optimized approach to decision fusion of heterogeneous data for breast cancer diagnosis.

As more diagnostic testing options become available to physicians, it becomes more difficult to combine various types of medical information together in order to optimize the overall diagnosis. To improve diagnostic performance, here we introduce an approach to optimize a decision-fusion technique to combine heterogeneous information, such as from different modalities, feature categories, or institutions. For classifier comparison we used two performance metrics: The receiving operator characteristic (ROC) area under the curve [area under the ROC curve (AUC)] and the normalized partial area under the curve (pAUC). This study used four classifiers: Linear discriminant analysis (LDA), artificial neural network (ANN), and two variants of our decision-fusion technique, AUC-optimized (DF-A) and pAUC-optimized (DF-P) decision fusion. We applied each of these classifiers with 100-fold cross-validation to two heterogeneous breast cancer data sets: One of mass lesion features and a much more challenging one of microcalcification lesion features. For the calcification data set, DF-A outperformed the other classifiers in terms of AUC (p < 0.02) and achieved AUC=0.85 +/- 0.01. The DF-P surpassed the other classifiers in terms of pAUC (p < 0.01) and reached pAUC=0.38 +/- 0.02. For the mass data set, DF-A outperformed both the ANN and the LDA (p < 0.04) and achieved AUC=0.94 +/- 0.01. Although for this data set there were no statistically significant differences among the classifiers' pAUC values (pAUC=0.57 +/- 0.07 to 0.67 +/- 0.05, p > 0.10), the DF-P did significantly improve specificity versus the LDA at both 98% and 100% sensitivity (p < 0.04). In conclusion, decision fusion directly optimized clinically significant performance measures, such as AUC and pAUC, and sometimes outperformed two well-known machine-learning techniques when applied to two different breast cancer data sets.

Hand hygiene noncompliance and the cost of hospital-acquired methicillin-resistant Staphylococcus aureus infection.

BACKGROUND: Hand hygiene noncompliance is a major cause of nosocomial infection. Nosocomial infection cost data exist, but the effect of hand hygiene noncompliance is unknown. OBJECTIVE: To estimate methicillin-resistant Staphylococcus aureus (MRSA)-related cost of an incident of hand hygiene noncompliance by a healthcare worker during patient care. DESIGN: Two models were created to simulate sequential patient contacts by a hand hygiene-noncompliant healthcare worker. Model 1 involved encounters with patients of unknown MRSA status. Model 2 involved an encounter with an MRSA-colonized patient followed by an encounter with a patient of unknown MRSA status. The probability of new MRSA infection for the second patient was calculated using published data. A simulation of 1 million noncompliant events was performed. Total costs of resulting infections were aggregated and amortized over all events. SETTING: Duke University Medical Center, a 750-bed tertiary medical center in Durham, North Carolina. RESULTS: Model 1 was associated with 42 MRSA infections (infection rate, 0.0042%). Mean infection cost was $47,092 (95% confidence interval [CI], $26,040-$68,146); mean cost per noncompliant event was $1.98 (95% CI, $0.91-$3.04). Model 2 was associated with 980 MRSA infections (0.098%). Mean infection cost was $53,598 (95% CI, $50,098-$57,097); mean cost per noncompliant event was $52.53 (95% CI, $47.73-$57.32). A 200-bed hospital incurs $1,779,283 in annual MRSA infection-related expenses attributable to hand hygiene noncompliance. A 1.0% increase in hand hygiene compliance resulted in annual savings of $39,650 to a 200-bed hospital. CONCLUSIONS: Hand hygiene noncompliance is associated with significant attributable hospital costs. Minimal improvements in compliance lead to substantial savings.

Host determinants of HIV-1 control in African Americans.

We performed a whole-genome association study of human immunodeficiency virus type 1 (HIV-1) set point among a cohort of African Americans (n = 515), and an intronic single-nucleotide polymorphism (SNP) in the HLA-B gene showed one of the strongest associations. We use a subset of patients to demonstrate that this SNP reflects the effect of the HLA-B*5703 allele, which shows a genome-wide statistically significant association with viral load set point (P = 5.6 x 10(-10)). These analyses therefore confirm a member of the HLA-B*57 group of alleles as the most important common variant that influences viral load variation in African Americans, which is consistent with what has been observed for individuals of European ancestry, among whom the most important common variant is HLA-B*5701.

An integrated alcohol abuse and medical treatment model for patients with hepatitis C.

BACKGROUND: Patients with chronic hepatitis C virus (HCV) infection have high rates of alcohol consumption, which is associated with progression of fibrosis and lower response rates to HCV treatment. AIMS: This prospective cohort study examined the feasibility of a 24-week integrated alcohol and medical treatment to HCV-infected patients. METHODS: Patients were recruited from a hepatology clinic if they had an Alcohol Use Disorders Identification Test score >4 for women and >8 for men, suggesting hazardous alcohol consumption. The integrated model included patients receiving medical care and alcohol treatment within the same clinic. Alcohol treatment consisted of 6 months of group and individual therapy from an addictions specialist and consultation from a study team psychiatrist as needed. RESULTS: Sixty patients were initially enrolled, and 53 patients participated in treatment. The primary endpoint was the Addiction Severity Index (ASI) alcohol composite scores, which significantly decreased by 0.105 (41.7% reduction) between 0 and 3 months (P < 0.01) and by 0.128 (50.6% reduction) between 0 and 6 months (P < 0.01) after adjusting for covariates. Alcohol abstinence was reported by 40% of patients at 3 months and 44% at 6 months. Patients who did not become alcohol abstinent had reductions in their ASI alcohol composite scores from 0.298 at baseline to 0.219 (26.8% reduction) at 6 months (P = 0.08). CONCLUSION: This study demonstrated that an integrated model of alcohol treatment and medical care could be successfully implemented in a hepatology clinic with significant favorable impact on alcohol use and abstinence among patients with chronic HCV.

Dynamic neural networks supporting memory retrieval.

How do separate neural networks interact to support complex cognitive processes such as remembrance of the personal past? Autobiographical memory (AM) retrieval recruits a consistent pattern of activation that potentially comprises multiple neural networks. However, it is unclear how such large-scale neural networks interact and are modulated by properties of the memory retrieval process. In the present functional MRI (fMRI) study, we combined independent component analysis (ICA) and dynamic causal modeling (DCM) to understand the neural networks supporting AM retrieval. ICA revealed four task-related components consistent with the previous literature: 1) medial prefrontal cortex (PFC) network, associated with self-referential processes, 2) medial temporal lobe (MTL) network, associated with memory, 3) frontoparietal network, associated with strategic search, and 4) cingulooperculum network, associated with goal maintenance. DCM analysis revealed that the medial PFC network drove activation within the system, consistent with the importance of this network to AM retrieval. Additionally, memory accessibility and recollection uniquely altered connectivity between these neural networks. Recollection modulated the influence of the medial PFC on the MTL network during elaboration, suggesting that greater connectivity among subsystems of the default network supports greater re-experience. In contrast, memory accessibility modulated the influence of frontoparietal and MTL networks on the medial PFC network, suggesting that ease of retrieval involves greater fluency among the multiple networks contributing to AM. These results show the integration between neural networks supporting AM retrieval and the modulation of network connectivity by behavior.

Early clopidogrel versus prasugrel use among contemporary STEMI and NSTEMI patients in the US: insights from the National Cardiovascular Data Registry.

BACKGROUND: P2Y12 antagonist therapy improves outcomes in acute myocardial infarction (MI) patients. Novel agents in this class are now available in the US. We studied the introduction of prasugrel into contemporary MI practice to understand the appropriateness of its use and assess for changes in antiplatelet management practices. METHODS AND RESULTS: Using ACTION Registry-GWTG (Get-with-the-Guidelines), we evaluated patterns of P2Y12 antagonist use within 24 hours of admission in 100 228 ST elevation myocardial infarction (STEMI) and 158 492 Non-ST elevation myocardial infarction (NSTEMI) patients at 548 hospitals between October 2009 and September 2012. Rates of early P2Y12 antagonist use were approximately 90% among STEMI and 57% among NSTEMI patients. From 2009 to 2012, prasugrel use increased significantly from 3% to 18% (5% to 30% in STEMI; 2% to 10% in NSTEMI; P for trend <0.001 for all). During the same period, we observed a decrease in use of early but not discharge P2Y12 antagonist among NSTEMI patients. Although contraindicated, 3.0% of patients with prior stroke received prasugrel. Prasugrel was used in 1.9% of patients ≥75 years and 4.5% of patients with weight <60 kg. In both STEMI and NSTEMI, prasugrel was most frequently used in patients at the lowest predicted risk for bleeding and mortality. Despite lack of supporting evidence, prasugrel was initiated before cardiac catheterization in 18% of NSTEMI patients. CONCLUSIONS: With prasugrel as an antiplatelet treatment option, contemporary practice shows low uptake of prasugrel and delays in P2Y12 antagonist initiation among NSTEMI patients. We also note concerning evidence of inappropriate use of prasugrel, and inadequate targeting of this more potent therapy to maximize the benefit/risk ratio.

Big or fast: two strategies in the developmental control of body size.

Adult body size is controlled by the mechanisms that stop growth when a species-characteristic size has been reached. The mechanisms by which size is sensed and by which this information is transduced to the growth regulating system are beginning to be understood in a few species of insects. Two rather different strategies for control have been discovered; one favors large body size and the other favors rapid development.