Analysis of the U.S. patient referral network.

In this paper, we analyze the US Patient Referral Network (also called the Shared Patient Network) and various subnetworks for the years 2009 to 2015. In these networks, two physicians are linked if a patient encounters both of them within a specified time interval, according to the data made available by the Centers for Medicare and Medicaid Services. We find power law distributions on most state-level data as well as a core-periphery structure. On a national and state level, we discover a so-called small-world structure as well as a "gravity law" of the type found in some large-scale economic networks. Some physicians play the role of hubs for interstate referral. Strong correlations between certain network statistics with health care system statistics at both the state and national levels are discovered. The patterns in the referral network evinced using several statistical analyses involving key metrics derived from the network illustrate the potential for using network analysis to provide new insights into the health care system and opportunities or mechanisms for catalyzing improvements.

Optimal Physician Shared-Patient Networks and the Diffusion of Medical Technologies.

Social network analysis has created a productive framework for the analysis of the histories of patient-physician interactions and physician collaboration. Notable is the construction of networks based on the data of "referral paths" - sequences of patient-specific temporally linked physician visits - in this case, culled from a large set of Medicare claims data in the United States. Network constructions depend on a range of choices regarding the underlying data. In this paper we introduce the use of a five-factor experiment that produces 80 distinct projections of the bipartite patient-physician mixing matrix to a unipartite physician network derived from the referral path data, which is further analyzed at the level of the 2,219 hospitals in the final analytic sample. We summarize the networks of physicians within a given hospital using a range of directed and undirected network features (quantities that summarize structural properties of the network such as its size, density, and reciprocity). The different projections and their underlying factors are evaluated in terms of the heterogeneity of the network features across the hospitals. We also evaluate the projections relative to their ability to improve the predictive accuracy of a model estimating a hospital's adoption of implantable cardiac defibrillators, a novel cardiac intervention. Because it optimizes the knowledge learned about the overall and interactive effects of the factors, we anticipate that the factorial design setting for network analysis may be useful more generally as a methodological advance in network analysis.

Effects of rosuvastatin and atorvastatin on renal function: meta-analysis.

BACKGROUND: Several clinical trials have reported inconsistent findings for the effects of rosuvastatin (RSV) and atorvastatin (ATV) on renal function. The aim of this meta-analysis was to investigate the effects of these 2 statins on glomerular filtration rate (GFR) and proteinuria respectively, and determine which is better. METHODS AND RESULTS: PubMed, CENTRAL, Web of Knowledge, and ClinicalTrials.gov website were searched for randomized controlled trials. Eligible studies reported GFR and/or proteinuria during treatment with RSV or ATV compared with control (placebo, no statins, or usual care), or RSV compared with ATV head to head. Trials that enrolled dialysis participants and teenagers were excluded. Statistical heterogeneity was assessed using the I(2) statistic, and pooled results using the random-effects model. The standardized mean differences (SMD) and ratio of means (ROM) were measured, respectively, to analyze GFR and proteinuria. Sixteen trials with a total number of 24,278 participants were identified. Compared with control, changes in the SMD of GFR were 0.04 (95% confidence interval [CI]: 0.01-0.07) and 0.59 (95%CI: 0.12-1.06) for RSV and ATV, respectively. The ROMs of proteinuria were 0.59 (95%CI: 0.46-0.74) for RSV vs. the control group, and 1.23 (95%CI: 1.05-1.43) in the head-to-head comparison. CONCLUSIONS: Both RSV and ATV improve GFR, and ATV seems to be more effective in reducing proteinuria. The validity and clinical significance require high-quality intensive studies with composite clinic endpoints of kidney and death.

Serum amyloid A directly accelerates the progression of atherosclerosis in apolipoprotein E-deficient mice.

Although serum amyloid A (SAA) is an excellent marker for coronary artery disease, its direct effect on atherogenesis in vivo is obscure. In this study we investigated the direct effect of SAA on promoting the formation of atherosclerosis in apolipoprotein E-deficient (ApoE⁻/⁻) mice. Murine SAA lentivirus was constructed and injected into ApoE⁻/⁻ mice intravenously. Then, experimental mice were fed a chow diet (5% fat and no added cholesterol) for 14 wks. The aortic atherosclerotic lesion area was larger with than without SAA treatment. With increased SAA levels, the plasma levels of interleukin-6 and tumor necrosis factor-α were significantly increased. Macrophage infiltration in atherosclerotic regions was enhanced with SAA treatment. A migration assay revealed prominent dose-dependent chemotaxis of SAA to macrophages. Furthermore, the expression of monocyte chemotactic protein-1 and vascular cell adhesion molecule-1 (VCAM-1) was upregulated significantly with SAA treatment. SAA-induced VCAM-1 production was detected in human aortic endothelial cells in vitro. Thus, an increase in plasma SAA directly accelerates the progression of atherosclerosis in ApoE⁻/⁻ mice. SAA is not only a risk marker for atherosclerosis but also an active participant in atherogenesis.

Towards intelligent complex networks: the space and prediction of information walks.

In this paper we study the problem of walk-specific information spread in directed complex social networks. Classical models usually analyze the "explosive" spread of information on social networks (e.g., Twitter) - a broadcast or epidemiological model focusing on the dynamics of a given source node "infecting" multiple targets. Less studied, but of equal importance is the case of single-track information flow, wherein the focus is on the node-by-node (and not necessarily a newly visited node) trajectory of information transfer. An important and motivating example is the sequence of physicians visited by a given patient over a presumed course of treatment or health event. This is the so-called a referral sequence which manifests as a path in a network of physicians. In this case the patient (and her health record) is a source of "information" from one physician to the next. With this motivation in mind we build a Bayesian Personalized Ranking (BPR) model to predict the next node on a walk of a given network navigator using network science features. The problem is related to but different from the well-investigated link prediction problem. We present experiments on a dataset of several million nodes derived from several years of U.S. patient referral records, showing that the application of network science measures in the BPR framework boosts hit-rate and mean percentile rank for the task of next-node prediction. We then move beyond the simple information walk to consider the derived network space of all information walks within a period, in which a node represents an information walk and two information walks are connected if have nodes in common from the original (social) network. To evaluate the utility of such a network of information walks, we simulate outliers of information walks and distinguish them with the other normal information walks, using five distance metrics for the derived feature vectors between two information walks. The experimental results of such a proof-of-concept application shows the utility of the derived information walk network for the outlier monitoring of information flow on an intelligent network.

Referral paths in the U.S. physician network.

In this paper, we analyze the millions of referral paths of patients' interactions with the healthcare system for each year in the 2006-2011 time period and relate them to U.S. cardiovascular treatment records. For a patient, a "referral path" records the chronological sequence of physicians encountered by a patient (subject to certain constraints on the times between encounters). It provides a basic unit of analysis in a broader referral network that encodes the flow of patients and information between physicians in a healthcare system. We consider referral networks defined over a range of interactions as well as the characteristics of referral paths, producing a characterization of the various networks as well as the physicians they comprise. We further relate these metrics and findings to outcomes in the specific area of cardiovascular care. In particular, we match a referral path to occurrences of Acute Myocardial Infarction (AMI) and use the summary measures of the referral path to predict the treatment a patient receives and medical outcomes following treatment. Some referral path features are more significant with respect to their ability to boost a tree-based predictive model, and have stronger correlations with numerical treatment outcome variables. The patterns of referral paths and the derived informative features illustrate the potential for using network science to optimize patient referrals in healthcare systems for improved treatment outcomes and more efficient utilization of medical resources.