The association of stressful life events on weight loss efforts among African American breast cancer survivors.

OBJECTIVE: This study examined whether stressful life events were associated with weight loss, central adiposity, and health behavior changes of African American breast cancer survivors (AABCS) participating in a weight loss intervention. METHODS: We conducted a secondary-data analyses of Moving Forward, a weight loss efficacy trial for AABCS conducted in 2011-2014. Two-hundred forty-six eligible women were randomized to a 6-month interventionist-guided (IG) or self-guided (SG) weight loss intervention. Data was collected on height, weight, self-reported diet, and self-reported physical activity. Stress (e.g., financial, legal, employment, relationships, safety, prejudice) was measured using an abbreviated version of the Crisis in Family Systems (CRISYS) urban life stress measure. Generalized linear models stratified by group examined the degree to which stress was associated with weight loss or changes in central adiposity, physical activity, and diet during the intervention (Months 1-6) or maintenance (Months 7 to 12) phases. RESULTS: Participants reported a median of 3.0 life stressors (range 0 to 22) mostly relating to relationships, safety concerns, and financial problems. In the IG group during the intervention phase, exposure to life stressors was not associated with weight loss (p = 0.15) or change in central adiposity (p = 0.69), physical activity (p = 0.15), or diet (p = 0.26). We found similar associations for the maintenance phase and in the SG group. CONCLUSION/IMPLICATIONS: Despite facing stress across a myriad of domains (e.g., relationships, safety, finances), AABCS were successful at initiating and maintaining behaviors to achieve weight loss, reductions in central adiposity, and behavioral changes. Future randomized controlled trials are warranted that include more strategies to address the challenges that AABCS face, to determine whether AABCS in particular might benefit from interventions that address barriers (e.g., stress management) to weight loss. Such strategies are critical for improving quality of life and lowering the risk of cancer recurrence.

The "resident's dilemma"? Values and strategies of medical residents for education interactions: a cellular automata simulation.

OBJECTIVES: Medical residents engage in formal and informal education interactions with fellow residents during the working day, and can choose whether to spend time and effort on such interactions. Time and effort spent on such interactions can bring learning and personal satisfaction to residents, but may also delay completion of clinical work. METHODS: Using hypothetical cases, we assessed the values and strategies of internal medicine residents at one hospital for both cooperative and non-cooperative education interactions with fellow residents. We then used these data and cellular automata models of two-person games to simulate repeated interactions between residents, and to determine which strategies resulted in greatest accrued value. We conducted sensitivity analyses on several model parameters, to test the robustness of dominant strategies to model assumptions. RESULTS: Twenty-nine of the 57 residents (50.9%) valued cooperation more than non-cooperation no matter what the other resident did during the current interaction. Similarly, thirty-six residents (63.2%) endorsed an unconditional always-cooperate strategy no matter what the other resident had done during their previous interaction. In simulations, an always-cooperate strategy accrued more value (776.42 value units) than an aggregate of strategies containing non-cooperation components (675.0 value units, p = 0.052). Only when the probability of strategy errors reached 50%, or when values were re-ordered to match those of a Prisoner's Dilemma, did non-cooperation-based strategies accrue the most value. CONCLUSIONS: Cooperation-based values and strategies were most frequent among our residents, and dominated in simulations of repeated education interactions between them.

Selection of predictor variables for pneumonia using neural networks and genetic algorithms.

BACKGROUND: Artificial neural networks (ANN) can be used to select sets of predictor variable that incorporate nonlinear interactions between variables. We used a genetic algorithm, with selection based on maximizing network accuracy and minimizing network input-layer cardinality, to evolve parsimonious sets of variables for predicting community-acquired pneumonia among patients with respiratory complaints. METHODS: ANN were trained on data from 1044 patients in a training cohort, and were applied to 116 patients in a testing cohort. Chromosomes with binary genes representing input-layer variables were operated on by crossover recombination, mutation, and probabilistic selection based on a fitness function incorporating both network accuracy and input-layer cardinality. RESULTS: The genetic algorithm evolved best 10-variable sets that discriminated pneumonia in the training cohort (ROC areas, 0.838 for selection based on average cross entropy (ENT); 0.954 for selection based on ROC area (ROC)), and in the testing cohort (ROC areas, 0.847 for ENT selection; 0.963 for ROC selection), with no significant differences between cohorts. Best variable sets based on the genetic algorithm using ROC selection discriminated pneumonia more accurately than variable sets based on stepwise neural networks (ROC areas, 0.954 versus 0.879, p = 0.030), or stepwise logistic regression (ROC areas, 0.954 versus 0.830, p = 0.000). Variable sets of lower cardinalities were also evolved, which also accurately discriminated pneumonia. CONCLUSION: Variable sets derived using a genetic algorithm for neural networks accurately discriminated pneumonia from other respiratory conditions, and did so with greater accuracy than variables derived using stepwise neural networks or logistic regression in some cases.

Entering the black box of neural networks.

OBJECTIVES: Artificial neural networks have proved to be accurate predictive instruments in several medical domains, but have been criticized for failing to specify the information upon which their predictions are based. We used methods of relevance analysis and sensitivity analysis to determine the most important predictor variables for a validated neural network for community-acquired pneumonia. METHODS: We studied a feed-forward, back-propagation neural network trained to predict pneumonia among patients presenting to an emergency department with fever or respiratory complaints. We used the methods of full retraining, weight elimination, constant substitution, linear substitution, and data permutation to identify a consensus set of important demographic, symptom, sign, and comorbidity predictors that influenced network output for pneumonia. We compared predictors identified by these methods to those identified by a weight propagation analysis based on the matrices of the network, and by logistic regression. RESULTS: Predictors identified by these methods were clinically plausible, and were concordant with those identified by weight analysis, and by logistic regression using the same data. The methods were highly correlated in network error, and led to variable sets with errors below bootstrap 95% confidence intervals for networks with similar numbers of inputs. Scores for variable relevance tended to be higher with methods that precluded network retraining (weight elimination) or that permuted variable values (data permutation), compared with methods that permitted retraining (full retraining) or that approximated its effects (constant and linear substitution). CONCLUSION: Methods of relevance analysis and sensitivity analysis are useful for identifying important predictor variables used by artificial neural networks.

The patient physician relationship in the Internet age: future prospects and the research agenda.

In the "Internet Age," physicians and patients have unique technological resources available to improve the patient physician relationship. How they both utilize online medical information will influence the course of their relationship and possibly influence health outcomes. The decision-making process may improve if efforts are made to share the burden of responsibility for knowledge. Further benefits may arise from physicians who assist patients in the information-gathering process. However, further research is necessary to understand these differences in the patient physician relationship along with their corresponding effects on patient and physician satisfaction as well as clinical outcomes.

Airway reopening pressure in isolated rat lungs.

In a previous modeling study, we predicted that the yield pressure for airway reopening (Pyield) should depend on airway fluid surface tension (gamma) and airway radius (R), according to the relationship Pyield = 8.3 gamma/R. To test this prediction, we studied tantalum bronchograms of isolated perfused rat lungs from three rats by using microfocal X-ray imaging. Thirty-two airways with diameters ranging from 300 to 2,400 microns were recorded as the airways were collapsed and reinflated. Airway pressure was reduced transiently to -40 cmH2O to produce airway closure. Airway pressure was then slowly increased from 0 to 25 cmH2O. In each airway, the observed diameter remained constant until a Pyield was reached; at this pressure, airways were seen to "pop" open, allowing clear identification of airway reopening pressure. When Pyield was plotted against diameter at maximum inflation, the experimental data were in approximate agreement with predictions of Pyield made assuming a gamma of 35 dyn/cm. The close correspondence of the measured values with these predictions suggests that surfactant is present in these airways and facilitates airway reopening.

Non-invasive assessment of cardiovascular mechanics using a new, user-friendly software application.

Currently, in most non-invasive imaging laboratories, echocardiographic tracings are interpreted by "eyeballing" M-mode and 2-dimensional echocardiographic recordings. This subjective method of data analysis severely hampers the possibility of serial assessment of physiological interventions on cardiovascular disease states. Moreover, acquisition of important cardiovascular physiological data usually requires recordings of instantaneous aortic pressure and flow data that, until recently, could only be acquired invasively in the cardiac catheterization laboratory. Recently, our laboratory has developed and validated new "non-invasive" methods for the acquisition of aortic pressure and flow using calibrated subclavian pulse tracings and continuous wave aortic Doppler, respectively. With these limitations and new developments in mind, we developed new software that enables simultaneous non-invasive acquisition of left ventricular (LV) chamber geometry and aortic pressure and flow data. This new, user-friendly software in conjunction with other non-invasive tools allows non-invasive quantification of multiple cardiovascular physiological parameters. More importantly, the new software enables objective and serial assessment of multiple pharmacological interventions on various patients' disease states.