Hospital-acquired infections (HAIs), including emerging multi-drug resistant organisms, threaten healthcare systems worldwide. Efficient containment measures of HAIs must mobilize the entire healthcare network. Thus, to best understand how to reduce the potential scale of HAI epidemic spread, we explore patient transfer patterns in the French healthcare system. Using an exhaustive database of all hospital discharge summaries in France in 2014, we construct and analyze three patient networks based on the following: transfers of patients with HAI (HAI-specific network); patients with suspected HAI (suspected-HAI network); and all patients . All three networks have heterogeneous patient flow and demonstrate small-world and scale-free characteristics. Patient populations that comprise these networks are also heterogeneous in their movement patterns. Ranking of hospitals by centrality measures and comparing community clustering using community detection algorithms shows that despite the differences in patient population, the HAI-specific and suspected-HAI networks rely on the same underlying structure as that of the general network. As a result, the general network may be more reliable in studying potential spread of HAIs. Finally, we identify transfer patterns at both the French regional and departmental (county) levels that are important in the identification of key hospital centers, patient flow trajectories, and regional clusters that may serve as a basis for novel wide-scale infection control strategies. Hospital-acquired infections (HAIs), including emerging multi-drug resistant organisms, threaten healthcare systems worldwide. Efficient containment measures of HAIs must mobilize the entire healthcare network. Thus, to best understand how to reduce the scale of potential HAI epidemic spread, we explore patient transfer patterns in the French healthcare system. We construct and compare the characteristics of three different patient transfer networks based on data on transfers of patients with diagnosed HAIs, suspected HAIs, or of all patients. Our analyses show that these healthcare networks, the patient populations that comprise them and the patient movement patterns are heterogeneous and centralized. Despite the differences in patient populations, the HAI-specific and suspected-HAI healthcare networks have the same underlying structure as that of the general healthcare network. We identify key hospital centers, patient flow trajectories, at both the regional and department (county) level that may serve as a basis for proposing novel wide-scale infection control strategies. Staphylococcus aureus, vancomycin-resistant enterococci, and multi-resistant gram-negative bacteria such as carbapenemase-producing Enterobacteriaceae (CPE). Spread of CPE is now a global public health problem associated with patient transfers between healthcare facilities within the same country as well as across national borders, as shown in many recent studies.[The emergence and spread of multi-drug resistant organisms threatens healthcare systems worldwide. This is  studies.–7In recent years, patient transfer or referral data has been used to construct “healthcare networks” to propose innovative approaches for hospital infection prevention and control. Healthcare networks are cooperative healthcare systems where hospitals and other healthcare centers are linked by shared patients through secondary transfers or referral., 9 RatheAs early as 2007, studies applied more complex social network analysis approaches to reconstructed healthcare networks in order to demonstrate that infection control measures that take into account network properties can decrease the risk for outbreaks., 10 Lee Although national healthcare networks are informative regarding novel HAI control strategies, the impact of reconstructing these networks based on a general patient population rather than a HAI-diagnosed patient population has rarely been addressed. In this study, we assess and compare French healthcare networks based on either patients diagnosed with HAIs or the general patient population, in order to better understand the potential implications in terms of HAI spread predictions. To that aim, we perform social network analyses to describe the different patient flow patterns, network topology characteristics, and community clustering structure.We analyzed and compared three different networks built using transfer data from an exhaustive database of all hospital discharge summaries in France in 2014: (1) a network based on transfers of patients with diagnosed HAI (HAI-specific network); (2) a network based on transfers of patients with suspected HAI (suspected-HAI network); and (3) the network of all patient transfers .More than 10 million hospital transfers were recorded in France in 2014, for a total of 2.3 million transferred patients, creating a hospital network of 2063 hospitals (nodes) and 50026 patient trajectories (edges) linking them . Patientectively .Overall, the three networks displayed “scale-free” and “small-world” characteristics that indicated the presence of a small number of very highly connected hospitals with high degrees, referred to as “hubs.” Analyses of the degree, strength, and shortest path length distributions in addition to the small-world characteristics of the healthcare networks are discussed in The hospitals involved in the patient transfers recorded in the three networks were of various types, including private rehabilitation and postoperative care facilities, acute-care hospitals or clinics, and hospital centers . HoweverTo better understand the role of hub hospitals across the networks, the shared hospitals between the networks were ranked based on their degree, closeness, and betweenness . Overallth ranked hospital to the 1159th ranking hospital. For the suspected-HAI network, statistically significant degree ranking differences were observed between the 405th ranked hospital to the 1078th ranked hospital.Suspecting that the differences between rankings might exist between subsets of hospitals, we tested the differences between rankings on an increasing subset of shared hospitals, starting with the highest rank, adding the next ranked hospital, and testing for significant differences. As a result, we determined the range of hospital rankings across the networks where the rankings significantly differed. We defined significant differences as Wilcoxon rank sum test p-values under the 5% alpha risk which we represent as a grey area in rd highest ranking to the 1183rd ranking in the HAI-specific network and the 71st highest ranking to the 1757th ranking in the suspected-HAI network . Closeness rankings differences were observed for almost all rankings after the first 3 rankings in the HAI-specific network and after the first 6 in the suspected-HAI network. The lack of statistically significant differences for the highest rankings may have been only due to insufficient power and for lowest hospital rankings due to a series of repeating small closeness values. With this method, we highlight that differences do exist for subsets of hospitals, but we also observe that the most highly connected hub hospitals were consistently highly connected across the networks, irrespective of the different patient population that connected them.For hospital rankings based on betweenness and closeness centrality measures, the hospitals ranked with highest and lowest centralities in the general network were also the hospitals ranked with highest and lowest centralities ranking in the HAI-specific and suspected-HAI networks. Even though hospital rankings of all hospitals did not differ, the majority did differ for betweenness ranks between the 33To further assess patient movement patterns in the networks, we investigated how our healthcare networks displayed “community” or hospital clustering structure. We compared hospital communities detected with two different community clustering algorithms: 1) the Greedy algorithm  that selFor each network, we calculated the modularity, the number of communities, community size, and average community clustering distance using the Greedy and Map Equation community detection algorithms . For eacThe regional community clustering using the Greedy algorithm in the three networks are represented in To further understand the community structure, we constructed intercommunity networks by combining patient flows between hospitals of the same community and across communities. The Greedy-based intercommunity network was composed of 18 nodes representing the sum of all patient transfers that occurred between hospitals of each community with 306 regional transfer trajectories . Out of Building the intercommunity network where community affiliation was determined by the Map Equation algorithm allowed us to consider communities based on the directionality of patient flow, which was overlooked by the Greedy algorithm. The intercommunity network was composed of 113 community nodes with 3215 trajectories with an average degree of 57 and an average of 2597 patients moving between these connections . Map EquPatient sharing patterns and community clustering in the networks were also analyzed based on patient age groups in which new communities were identified –S12 FigsHaving assessed the role of hospitals, hospital communities, and patient trajectories in each network, we considered if the differences in the patient-specific networks and the general networks are due to the number of patient transfers that could lead to structural differences between the specific patient population flows. We first compared the general patient network to two sets of 1000 networks built from a subset of randomly chosen patients: in the first set we selected the same number of patients as the HAI-specific network (21276 patients) at random and in the second set the same number as in the suspected-HAI network (394859 patients) at random 1000 times and reconstructed each network. Overall, both sets of random patient networks (RP) were smaller in size compared the general network in terms of the number of nodes, edges, edge weight, and as a result average degree . In addiWe then compared the characteristics of the HAI-specific and suspected-HAI networks to the RP networks with the same number of patients to assess if HAI patients modified network structure differently than other patients. Overall, the RP networks were larger than their HAI-specific and suspected-HAI healthcare networks analogues meaning that HAI patients were transferred to fewer hospitals than other patients . HoweverIn this study we show that the French healthcare networks have heterogeneous patient flows, demonstrate characteristics of small-world and scale-free networks, and are characterized with highly centralized movement of patients towards hub hospital centers. Hub hospitals are characterized as university hospitals and private hospitals in the large metropoles that dominate patient flow. The healthcare networks displayed two-level community clustering: regional community clustering reflecting the French administrative structure, and department or county-level clustering. Certain patient transfer trajectories play a more important role in transferring patients between the regional and departmental communities. Despite differences in the patient population and size, both the HAI-specific and suspected-HAI specific healthcare networks seem to rely on the same underlying structure as that of the general healthcare network.Due to weak sensitivity and specificity of the PMSI database to detect nosocomial infections with the sole ICD-10 Y95 diagnostic, the HAI-specific network is not reliable in demonstrating the real patient movement patterns for those infected with an HAI.–28 TherePublic university hospital centers and private hospitals in the main metropoles of France dominate patient flow. A study conducted in the Bourgogne region of France has shown that patient flow was centered towards the university hospital that admitted patients from the entire region and based on the regional proximity of the patients’ residence and patients also sought care in two of the closest main healthcare hubs for specialized care (Paris or Lyon). Highly cRegional community clustering patterns as a form of network connectedness are also important in the development of strategies for coordinated HAI control., 13 Our Studies have proposed reducing hospital connectedness in order to reduce the risk of epidemic spread of HAI in networks., 35 DeceWe considered temporal dynamics, masked in a network constructed with data for the entire year of 2014, in which observed that monthly healthcare networks were smaller and displayed less centralized patient flow; hence, infection control strategies–for short-term control–should rely more on the local department-level dynamics to minimize hospital-level outbreaks and transmission to neighboring hospitals. In the long term, regional community dynamics may give us clues regarding the gradual propagation of specific HAI pathogens over time assuming HAI carriage patterns follow that of patient flow patterns in the healthcare networks. Further studies are required to assess the temporal dynamics of HAI spread in networks to identify any potential seasonality patterns of flow and how to prevent emerging multi-drug resistant bacteria from becoming endemic.Our study was subject to certain limitations which should be considered. Many of the university hospitals represent more than one public hospital or healthcare facility due to sharing the same identification number. For example, the largest outlier hub in Paris (AP-HP) represented 39 hospitals, 12 hospitals and 2 specialized health facilities constituted Hospices Civils de Lyon, 9 hospitals make up the university hospital of Toulouse, and 4 hospitals make up the APHM of Marseille. Consequently, university hospital centers accommodated a larger patient population than hospital centers or local hospitals, influencing the network characteristics, which may have led us to overestimate the specific patient movement patterns to and from these centers. However, the high concentration of other hospitals especially hub private hospital centers, armed forces hospitals, cancer centers, psychiatric hospitals, and private post-operative and rehabilitation centers within proximity of these public hospital hubs demonstrates that despite this, major cities such as Paris play the most important role in connecting patients in the national network and that the French healthcare network is a highly centralized system.The healthcare networks did not include patient flow from private nursing homes that have been shown to play an important role in HAI spread.–42 WithoBy considering all HAIs as a whole, our networks and recommendations reflect action for a broad spectrum of HAIs; however, one should consider that specific HAIs can vary in terms of carriage and transmission patterns. In addition, recommendations based on our networks would overlook the potential exposure to community-acquired infections, although these may later spread in hospital settings, leading to healthcare-associated outbreaks. Future studies should consider all potential components of patient exposure to both community-associated and healthcare-associated infections and account for individual exposure histories to these infections.Despite these limitations, our study provides a first description and analysis of the healthcare networks in France. The identified characteristics and community structures may greatly improve future inter-hospital HAI control strategies. The general patient network responds best to informing regional control strategies targeting key patient trajectories and hub hospital centers. We show that the scale-free structure, the number of communities, and their distribution over the country remain qualitatively similar across all networks and that patient-specific networks rely on the underlying structure of the general patient network. Future studies should take into consideration network topology in the prediction of HAI spread and should consider the potential impact of different community definitions for multi-level infection control strategies.The Programme de Médicalisation des Systèmes d’information (PMSI) database, a comprehensive French medico-administrative database of hospital activity and patient discharge information, is used to construct the hospital networks., 25 The Hence, Gerbier et al.  use patiUsing patient transfer data from 2014, three healthcare networks are reconstructed based on the following criteria:■All patient transfers (non-specific diagnoses)■Patient transfers with the ICD-10 code of Y95  as their principal, related, or associated diagnosis in the medical, surgery, obstetric hospitals (MCO) and postoperative and rehabilitation centers (SSR)■Patient transfers identified with all possible and suspected cases of HAIs in the surgical, intensive care, and obstetric wards in 2014, by referencing the diagnoses with known specificities and sensitivities listed in Gerbier et al. publication with supOnly direct transfers of patients who are discharged from a hospital and sent to another in another jurisdiction (“transfer”) or those who are discharged from one medical unit and move to another in the same hospital jurisdiction (“mutation”) are included. The hospital discharge summaries reflected the overall hospital stay of patients and a single diagnosis made them eligible without specification if it occurred during admission or at discharge. Patients who are discharged to their residence or deceased in a hospital are excluded. Patients hospitalized in non-continental European departments are also excluded.in silico using patient transfer data to model the potential movement of patients with HAIs from one hospital to another. In the PMSI database, each patient discharge summary contains information on the hospital facility of stay. Each hospital facility is identified by its unique FINESS number .[First, the networks of hospitals and healthcare centers are re-built Sociaux). For thisN × N adjacency matrix A defined as:i and target j hospitals for each patient stay by assessing if for each discharge the patient entered the hospital i as a transfer or mutation and left hospital i as a transfer or mutation. The same is computed for each j hospital. Using the iGraph package for R statistical software, we create the adjacency matrix of all i and j hospitals, including i and j if i did not transfer out any patients but received them and vice versa for j.[The adjacency matrix , a graphsa for j.i and j, as wij. The sum of the edge weights of the adjacent edges, the weight strength, is given by:i) is the set of neighbor hospitals of i.[We also compute the number of patients moving between hospitals als of i. Edge weik, is the number of hospitals one hospital is connected to through its patient trajectories [ij is a directed graph in which the directionality of patient transfers from one hospital to another is taken into account. Consequently, we can calculate the indegree (deg-) and outdegree (deg+) of any given node in which the degree sum formula is given by:g(i) is equal to the sum of the σst the number of shortest paths going from s to t through hospital i measuring the importance of hospital i to the organization of flow in the network.[g(e) is equal to the sum of the σst the number of shortest paths going from s to t through edge e measuring the importance of edge e to the organization of flow in the network.[To identify the most important hospitals of a network, a series of centrality measures are calculated. The degree of a hospital, ectories  defined a network is given network. The same network.Two community detection algorithms are used to assess community clustering for each network, which both take into account weighted graphs. A commonhttps://www.data.gouv.fr/). Using an online batch geocoding server (http://www.findlatitudeandlongitude.com/), the hospitals’ addresses were converted to latitude and longitude coordinates. A distance matrix was calculated using the haversine formula to measure great-circle distances between all hospitals.[Based on the community partitioning for each network, the mean geographic distance between hospitals of the same community is measured. To geo-localize hospitals, we used public government data on French hospital facilities and postal code addresses  Greedy algorithm-based communities 2) Map Equation-based communities. Based on the algorithm, each hospital node is assigned a community number. A matrix summing the individual hospitals transfers for hospitals that share the same community is constructed and converted into a directed graph. In addition, the mean latitude and longitude are calculated for each community from individual geocodes of the member hospitals. For the Map Equation intercommunity network, the Greedy algorithm is applied to identify the number of communities present when modularity is maximized.Hospitals were ranked by their degree, betweenness, and closeness centrality measures for each network. When the centrality measures were equal, we replaced the rankings by the mean rankings. We tested the differences between rankings on an increasing subset of shared hospitals with the Wilcoxon rank sum test. The test was conducted as follows: starting with the highest ranked hospital in the general network, adding the next ranked general network hospital, and testing for significant differences between the general network rank and either the HAI-specific or suspected-HAI network rank of the same hospital until we compared all shared hospitals. As a result, we determined the thresholds where hospital rankings across the networks start to significantly differ which was defined as Wilcoxon rank sum test p-values under the 5% alpha risk.To compare the networks between each other, we built 1000 random patients networks from the general network. We selected the same number of patients as either the HAI (21276 patients) or suspected HAI networks (394859 patients) from the general patient network at random and reconstructed these networks using their hospital discharge summaries. We calculated various network measures and the proportion of random patient networks that had values greater than, equal to, or less than the general patient network and the respective patient-specific networks.S1 Annex(PDF)Click here for additional data file.S1 Text(PDF)Click here for additional data file.S2 Text(PDF)Click here for additional data file.S3 Text(PDF)Click here for additional data file.S4 Text(PDF)Click here for additional data file.S5 Text(PDF)Click here for additional data file.S6 Text(PDF)Click here for additional data file.S7 Text(PDF)Click here for additional data file.S1 FigThe degree k represents the number of hospital connections of each hospital in the general network and the average strength s(k) stands for the number of patient transfers as a function of degree. The number of patient transfers and number of hospital connections were highly positively correlated (r = 0.91). The best-fitting power law model was s(k) = k1.51 (dashed line). The curves for s(k) = k (dotted line) and s(k) = 10*k (dash-dot line) are shown for comparison.(PDF)Click here for additional data file.S2 FigDistribution of hospital connections k of each hospital in the suspected-HAI network and the average strength s(k) or number of patient transfers as a function of degree. The number of patient transfers and number of hospital connections were highly positively correlated (r = 0.95). The best-fitting power law model was s(k) = k1.36 (dashed line). The curves for s(k) = k (dotted line) and s(k) = 10*k (dash-dot line) are shown for comparison.(PDF)Click here for additional data file.S3 FigDistribution of hospital connections k of each hospital in the HAI-specific network and the average strength s(k) or number of patient transfers as a function of degree. The number of patient transfers and number of hospital connections were highly positively correlated (r = 0.99). The best-fitting power law model was s(k) = k1.26 (dashed line). The curves for s(k) = k (dotted line) and s(k) = 10*k (dash-dot line) are shown for comparison.(PDF)Click here for additional data file.S4 FigCumulative distribution functions of degree k (top left) and strength s (bottom left) for the general network, suspected-HAI network , and HAI-specific network . Fitted power-law (red), log-normal (green), and Poisson (blue) distributions are shown when: x-min for degree = 77 and strength = 1191 in the general network; x-min for degree = 20 and strength = 119 in the suspected-HAI network; and x-min for degree = 7 and strength = 32 in the HAI-specific network.(PDF)Click here for additional data file.S5 FigThe cumulative distribution functions of k- indegree for the general network (top left) and s- instrength (bottom left), suspected-HAI networks , and HAI-specific network . Fitted power-law (red), log-normal (green), and Poisson (blue) distributions are shown when: x-min for indegree = 36 and instrength = 698 in the general network; x-min for indegree = 13 and instrength = 131 in the suspected-HAI network; and x-min for indegree = 5 and instrength = 18 in the HAI-specific network. Power-law and log-normal had good fit for indegree and instrength in the three networks  with the exception of log-normal distribution of indegree in the general and suspected-HAI network . Poisson distribution was not a good fit for indegree and instrength in all networks .(PDF)Click here for additional data file.S6 FigThe cumulative distribution functions of k+ outdegree for the general network (top left) and s+ outstrength (bottom left), suspected-HAI networks , and HAI-specific network . Fitted power-law (red), log-normal (green), and Poisson (blue) distributions are shown when: x-min for outdegree = 101 and outstrength = 1102 in the general network; x-min for outdegree = 27 and outstrength = 70 in the suspected-HAI network; and x-min for outdegree = 7 and outstrength = 3 in the HAI-specific network. Only power-law distribution had a good fit for both outdegree and outstrength  while log-normal distribution was only a good fit for the HAI-specific network .(PDF)Click here for additional data file.S7 FigThe length of the shortest paths or steps between any two nodes in the networks are calculated and plotted by their frequency.(PDF)Click here for additional data file.S8 Fig(PDF)Click here for additional data file.S9 Fig(PDF)Click here for additional data file.S10 FigHealthcare networks for all transferred patients (a) aged 18 and younger (b) aged 18 to 60 (c) older than 60 years old. Greedy communities were colored by the corresponding general network community regions with additional community color if not present for (a) 23 communities in which 3 were black  (b) 17 communities in which 1 was black  (c) 29 communities in which 14 were black .(PDF)Click here for additional data file.S11 FigHealthcare networks for all transferred patients (a) aged 18 and younger (b) aged 18 to 60 (c) older than 60 years old. We detected a total number of Greedy-based communities for each age network (a) 22 total and 19 with over 2 hospitals from a network of 1894 hospitals and 11234 edges (b) 30 total with 17 with over 2 hospitals from a network of 1559 hospitals and 5423 edges (c) 29 total with 14 with over 2 hospitals per community from a network of 218 hospitals and 318 trajectories.(PDF)Click here for additional data file.S12 FigHealthcare networks for all transferred patients (a) aged 18 and younger (b) aged 18 to 60 (c) older than 60 years old. Network communities are detected using the Greedy algorithm and colored according to community membership for (a) 1143 hospitals and 2260 edges with 50 total communities with only 28 composed of more than 5 hospitals (b) 603 hospitals and 593 edges with 41 total communities and 19 with over 5 hospitals (c) 44 hospitals and 33 edges, 18 total communities, 2 communities with more than 5 hospitals, and 9 communities with more than 2 hospitals.(PDF)Click here for additional data file.S1 TableComparison of the healthcare network topology measures with the average measures of 100 simulated Erdos-Renyi (ER) networks that are parameterized with same number of nodes, edges, and Poisson-distributed average edge weight. For each measure, a t-test is conducted to compare the difference between the health network value and the average values of the ER networks with given 95% confidence intervals and p-values.(PDF)Click here for additional data file.


Malignant tumors display remarkable heterogeneity to the extent that even at the same tissue site different types of cells with varying genetic background may be found. In contrast, a relatively consistent marker the scavenger receptor type B1 (SR-B1) has been found to be consistently overexpressed by most tumor cells. Scavenger Receptor Class B Type I (SR-BI) is a high density lipoprotein (HDL) receptor that facilitates the uptake of cholesterol esters from circulating lipoproteins. Additional findings suggest a critical role for SR-BI in cholesterol metabolism, signaling, motility, and proliferation of cancer cells and thus a potential major impact in carcinogenesis and metastasis. Recent findings indicate that the level of SR-BI expression correlate with aggressiveness and poor survival in breast and prostate cancer. Moreover, genomic data show that depending on the type of cancer, high or low SR-BI expression may promote poor survival. This review discusses the importance of SR-BI as a diagnostic as well as prognostic indicator of cancer to help elucidate the contributions of this protein to cancer development, progression, and survival. In addition, the SR-B1 receptor has been shown to serve as a potential gateway for the delivery of therapeutic agents when reconstituted high density lipoprotein nanoparticles are used for their transport to cancer cells and tumors. Opportunities for the development of new technologies, particularly in the areas of cancer therapy and tumor imaging are discussed. Figure 1A). Decreased plasma cholesterol levels  have been found in several groups of cancer patients representing a broad range of malignancies (High density lipoproteins (HDLs) have been designated as “the good cholesterol” carriers due to their athero-protective effects. The beneficial impact of elevated HDL levels have been attributed to several factors, including participation in reverse cholesterol transport, anti-oxidant effects and anti-inflammatory functions ,b, 2001.gnancies . RegardiThe precursor to circulating HDL is produced in the liver and subsequently released into the circulation. These are cholesterol-poor, discoidal structures, composed primarily of apolipoprotein A-I (apo A-I) and phospholipid . Next thFigure 1B). The subsequent steps involve the release of the now cholesterol-depleted HDL via a process called retroendocytosis .Unlike the delivery of Low density lipoprotein (LDL) cholesterol that involves endocytic uptake of the entire lipoprotein particle, most of the cholesterol from HDL undergoes a process termed selective lipid uptake, facilitated by the SR-B1 receptor, involving only the removal of cholesteryl esters from the HDL complex . The cholesteryl esters were subsequently hydrolyzed by hormone sensitive lipase  selectively through SR-B1. The cells internalized CE in a dose-dependent manner in response to the HDLe lipase . A studye lipase . In patie lipase . BecauseIn a study involving high grade metastatic prostate cancer increased accumulation of CE into lipid droplets was observed. The CE accumulation was found to be in response to the loss of the tumor suppressor PTEN, activation of the mTOR pathway and downstream activation of SREBP, and the up-regulation of LDL receptor . This stHigh density lipoprotein, beyond binding to SR-B1 and completing reverse cholesterol transport has also been found to subsequently activate the PI3K/Akt and MAPK pathways . SR-B1 hHypercholesterolemia and excessive consumption of a cholesterol or fat rich diet have been recognized as risk factors for prostate cancer. Additional studies have also shown that the aggressive form of the disease is likely to be associated with the intracellular accumulation of esterified cholesterol . While cThe Akt signaling pathway is activated in 40% of breast cancers . StudiesCancer cells are considered to be dependent on cholesterol for their proliferation and rapid progression . ComparaHigh density lipoprotein, a carrier of plasma cholesterol has also been found to be a signaling molecule inducing the migration of endothelial cells by activating the Akt and MAPK pathways . SimilarFigure 2). The result is activation of transcription of VEGF and induction of angiogenesis, which could be inhibited by transduction with a lentivirus containing a shRNA against SR-B1  is a HDL receptor that facilitates the uptake of cholesterol esters from circulating lipoproteins (primarily from HDL). Recent findings indicate that the level of SR-B1 expression is correlated with aggressiveness and poor survival in breast and prostate cancer . Moreovein vitro. This study also identified a link between HDL-SR-B1 and Akt where breast cancer cells stimulated with cholesteryl ester from HDL exhibited continued proliferation, migration/invasion and subsequent tumor growth  levels and cell viability in prostate cancer cells . Using m1 along with publicly accessible microarray data confirmed the correlation of high SR-B1 expression and poor prognosis in neuroblastoma, Ewing sarcoma, bladder, breast, and myeloma cancers  with the SR-B1 receptor  tissuesnt cells . Sabnis receptor .Traditional chemotherapy has always been aﬄicted with long term and short term unpleasant side effects. In some cases the side effects are worse than the disease itself. It is well documented by different research groups now that malignant cells consistently overexpress SR-B1 receptors as compared to normal cells. Lipoprotein-based technologies could address the above concerns via their ability to selectively deliver their payload to cancer cells and tumors. Safety of apoA-1 containing HDL-type formulations for human systemic administration has been confirmed by numerous clinical studies . AdditioIf determination of SR-B1 expression levels is included as a pretest before starting the chemotherapy regimen, the HDL based drug delivery could be tailored to respective cancer patients with high SR-B1 expressing tumors. Alternatively for low SR-B1 expressing patients, specific functionalized rHDL nanoparticles could be designed that will target different overexpressing tumor antigens. Thus the oncogenic drugs could be rerouted from the natural lipoproteins receptors thereby personalizing HDL-based therapeutics.Although the concept of using lipoproteins as targeted drug-delivery agents was introduced back in 1982, and the suitability of their functional characteristics for pharmaceutical formulations was well known, there are currently no lipoprotein-based drug delivery formulations in clinical trials or in clinical use. However, considering their versatility and selective-targeting capability, biofunctional HDL-type nanoparticles are likely to bring a shift in the paradigm of next generation cancer theranostics. The SR-B1 receptor-mediated gateway of lipophilic anticancer drugs or imaging agents via HDL and HDL-mimetic nanoparticles has strengthened the potential for these preparations as theranostic agents.All authors listed, have made substantial, direct and intellectual contribution to the work, and approved it for publication.AL is a co-inventor on two issued patents that involve SR-B1 mediated drug delivery. NS is a co-inventor on one issued patent that involves SR-B1 related drug delivery. All the other authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Schizophrenia is a chronic disease with negative impact on patients’ employment status and quality of life. This post-hoc analysis uses data from the QUALIFY study to elucidate the relationship between work readiness and health-related quality of life and functioning. QUALIFY was a 28-week, randomized study (NCT01795547) comparing the treatment effectiveness of aripiprazole once-monthly 400 mg and paliperidone palmitate once-monthly using the Heinrichs-Carpenter Quality-of-Life Scale as the primary endpoint. Also, patients’ capacity to work and work readiness (Yes/No) was assessed with the Work Readiness Questionnaire. We categorized patients, irrespective of treatment, by work readiness at baseline and week 28: No to Yes (n = 41), Yes to Yes (n = 49), or No at week 28 (n = 118). Quality-of-Life Scale total, domains, and item scores were assessed with a mixed model of repeated measures. Patients who shifted from No to Yes in work readiness showed robust improvements on Quality-of-Life Scale total scores, significantly greater than patients not ready to work at week 28 . Scores on Quality-of-Life Scale instrumental role domain and items therein–occupational role, work functioning, work levels, work satisfaction–significantly improved in patients shifting from No to Yes in work readiness (vs patients No at Week 28). Quality-of-Life Scale total scores also significantly predicted work readiness at week 28. Overall, these results highlight a strong association between improvements in health-related quality of life and work readiness, and suggest that increasing patients’ capacity to work is an achievable and meaningful goal in the treatment of impaired functioning in schizophrenia. Schizophrenia is a chronic and disabling disease in which repeated relapses have a negative impact on patients’ functioning and are disruptive for patients’ education and employment . Unemplo®) study is one of the few randomized studies directly comparing two different long-acting injectable antipsychotics (LAIs), and was the first to compare the effects of two atypical LAIs on a measure of health-related quality of life and functioning as the primary outcome. The Heinrichs-Carpenter Quality-of-Life scale (QLS) is a detailed assessment , p<0.0001 . In contAcross QLS items, patients rated ready to work at week 28 generally showed significant improvement vs patients not ready to work at week 28 . On the The logistic regression describes how QLS total and CGI-S scores predicted readiness to work at week 28 . SignifiThis post-hoc analysis of the QUALIFY study showed that after switching to atypical LAI therapy strong associations between improvement in functioning and work readiness were revealed, as measured with QLS and WoRQ, respectively. Robust and clinically relevant 14-point improvements from baseline to week 28 on QLS total score were observed in patients who concurrently shifted from No to Yes in work readiness. The 14-point improvement in QLS total score corresponds to more than twice the minimal clinically important difference [The QUALIFY study was the first to assess improvements in health-related qualify of life with concurrent systematic assessments of functional capacity to work and the data from this study provide a unique opportunity to investigate the association between treatment-related improvements in two distinct measures of functioning in schizophrenia. The patients who shifted from No to Yes in work readiness showed the largest changes on the QLS domain related to work—Instrumental role items on work role, level, performance, and satisfaction—and it is not surprising that the association between improvement in QLS and work readiness was strongest for QLS items related to work function. These results are also in line with a published outpatient study showing that the highest correlations between hours worked and QLS were also in the Instrumental role domain . Our resThe large and consistent improvements in QLS total scores in patients shifting from No to Yes in work readiness suggesting a strong association between health-related qualify of life (QLS) and the functional capacity to work (WoRQ) is also supported by the QLS total scores at every visit significantly predicting work readiness at week 28 in the logistic regression. Although CGI-S scores also significantly predicted work readiness, the estimates of prediction were generally lower as compared to those obtained with QLS total scores. Thus, these associations suggest that general status in functioning (QLS) strongly predicts the possibility of attaining an important functional milestone (work readiness) in the treatment of schizophrenia. With regard to the CGI-S analysis, however, it should be noted that the study inclusion criteria restricted CGI-S scores to between 3 and 5. The limited range allowed for baseline CGI-S may have affected the parameter estimate of predictability and resulted in the absence of a significant association between CGI-S scores at baseline and work readiness at week 28. Nevertheless, the association between baseline CGI-S and work readiness at week 28 strengthened during the study, as illustrated by increases in parameter estimates for CGI-S scores at each subsequent post-baseline visit. Despite the time-dependent increases in CGI-S predictability, larger parameter estimates at all visits for QLS still suggest stronger predictive value of QLS total scores than for CGI-S scores on improvements in work readiness.The QUALIFY study used CGI-S as a surrogate measurement of clinical status, instead of symptom severity as measured by the Positive and Negative Syndrome Scale or the Brief Psychiatric Rating Scale. Previous studies have shown a close correlation between Positive and Negative Syndrome Scale, Brief Psychiatric Rating Scale and CGI-S . These pIt should be noted that the present post-hoc analyses are exploratory in nature. Furthermore, the analyses were done with both treatment groups combined, and thus do not serve to compare AOM 400 and PP in the treatment of schizophrenia. However, we have previously reported pre-specified analyses showing superior improvements with AOM 400 (n = 136) vs PP (n = 132) on QLS total score , as wellIn summary, the large and consistent improvement in mean QLS scores in patients shifting from No to Yes in work readiness suggests a strong association between health-related qualify of life (QLS) and functional capacity to work (WoRQ). These findings highlight that quality of life improvements can translate to tangible and meaningful outcomes for the patient, and importantly, suggest that increasing patients’ capacity to work may be a more realistic goal in the treatment of schizophrenia than previously realized. Evaluating capacity to work may further optimize functioning in schizophrenia by directing rehabilitation services to the subgroup with evaluated capacity to work. The WoRQ scale may be useful as a practical, brief, and systematic measure of work readiness that reflects broader functioning in patients with schizophrenia.S1 Text(DOCX)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.


Objective: To date, motion trajectory prediction (MTP) of a limb from non-invasive electroencephalography (EEG) has relied, primarily, on band-pass filtered samples of EEG potentials i.e., the potential time-series model. Most MTP studies involve decoding 2D and 3D arm movements i.e., executed arm movements. Decoding of observed or imagined 3D movements has been demonstrated with limited success and only reported in a few studies. MTP studies normally use EEG potentials filtered in the low delta (~1 Hz) band for reconstructing the trajectory of an executed or an imagined/observed movement. In contrast to MTP, multiclass classification based sensorimotor rhythm brain-computer interfaces aim to classify movements using the power spectral density of mu (8–12 Hz) and beta (12–28 Hz) bands.Approach: We investigated if replacing the standard potentials time-series input with a power spectral density based bandpower time-series improves trajectory decoding accuracy of kinesthetically imagined 3D hand movement tasks  and whether imagined 3D hand movements kinematics are encoded also in mu and beta bands. Twelve naïve subjects were asked to generate or imagine generating pointing movements with their right dominant arm to four targets distributed in 3D space in synchrony with an auditory cue (beep).Main results: Using the bandpower time-series based model, the highest decoding accuracy for motor execution was observed in mu and beta bands whilst for imagined movements the low gamma (28–40 Hz) band was also observed to improve decoding accuracy for some subjects. Moreover, for both (executed and imagined) movements, the bandpower time-series model with mu, beta, and low gamma bands produced significantly higher reconstruction accuracy than the commonly used potential time-series model and delta oscillations.Significance: Contrary to many studies that investigated only executed hand movements and recommend using delta oscillations for decoding directional information of a single limb joint, our findings suggest that motor kinematics for imagined movements are reflected mostly in power spectral density of mu, beta and low gamma bands, and that these bands may be most informative for decoding 3D trajectories of imagined limb movements. Brain-computer interface (BCI) research and development aims to achieve movement-free communication between a user  and an electronic device using information encoded in neural signals. A prominent application of BCIs is controlling objects in real  based brain-computer interfaces (BCIs) use voluntary modulation of the sensorimotor activity during an imagined movement (motor imagery) for communication or control. Electrodes positioned centrally over sensorimotor areas contralaterally and ipsilaterally to the imagined movement are commonly used. Multiclass classification (MC) based sensorimotor rhythm BCIs enable multi-dimensional control in real or virtual spaces using a classifier trained to distinguish between the imagined movement of different limbs, commonly the left hand, right hand, feet, and/or tongue  BCIs aim to estimate the track of the spatial coordinates or velocity vectors of moving limb joints  based kinematic data estimation module with a time-series of band-pass filtered EEG potentials as input features  of mu (8–12 Hz) and beta (12–28 Hz) bands —slow direct-current shifts in the EEG, originating mainly from gradual changes in the postsynaptic potential on the pyramidal cells apical dendrites in the upper cortical layer, were also found to encode movement-related information. For example, the Bereitschaftspotential (BP)—a bilateral negative direct current shift that is detectable prior to the onset of a voluntary movement  gave informed and written consent to participate in the study, which was approved by the Wolfson Medical Center Helsinki committee. All subjects were healthy without any medical or psychological illness and/or medication and had normal or corrected to normal vision . Data acquisition took place at the Hybrid BCI lab at Holon Institute of Technology (HIT), Israel.Prior to the experiment, the subjects were informed about the experimental protocol. Subjects were seated in an armchair positioned 1.5 m in front of a 3D Microsoft Kinect camera Kinect, . The subThe experiment involves eight runs, each run comprised four blocks, each block comprising twenty executed or imagined periodic arm movements between the home position and one of the four targets. For runs involving imagined arm movements, the subjects were asked to refrain from moving the hand and to kinesthetically imagine moving the arm toward the corresponding target, synchronously with an auditory cue. Each run that involved executed movements was followed by a run involving imagined movements. Inter-run resting periods lasting one minute provided an opportunity for the subject to relax, however, the subject was asked not to move or talk during the inter-run resting periods. The experimental paradigm is presented in Figure Eight seconds before commencing each run, a voice message played automatically to inform the subject about the incoming run. Twelve seconds before commencing of each block, a vocal message announced the identification number of the upcoming target  that can be detected with the Kinect sensor Kinect, ]. SuppleThe impedance of the EEG electrodes was measured with the g.HIamp80 EEG software package  was mostly over frontal cortical areas, including AF3, AF4, F5, F4, FC5, and FC6 electrodes. Here we aim to compare motion trajectory prediction (MTP) accuracy using two different approaches , the remaining preprocessing steps differed when applying each of the two approaches.For the band-pass filtered potential time-series based PTS model, six non-overlapped, 8th order zero-phase band-pass filters were applied separately to the ICA filtered EEG in the lower delta (0.5–2 Hz), theta (4–8 Hz), mu (8–12 Hz), lower beta (12–18 Hz), upper beta (18–28 Hz), and gamma (28–40 Hz) bands (the gap between 2 and 4 Hz is covered by the cutting edges of lower delta and theta band-pass filters). Each of the six band-pass filtered EEG datasets was re-sampled to 100 Hz.For the PSD (bandpower) time-series based BTS model, the time-varying bandpower was calculated based on the ICA filtered EEG signals using the six non-overlapped EEG bands described above, whilst the time-varying bandpower was calculated from a 500 ms width sliding window with a 10 ms time lag between adjacent windows. This time lag was chosen to match the 100 Hz re-sampling frequency rate. The 500 ms window width is supported by the results of a pilot analysis that compared decoding accuracies obtained using four different window sizes . As the analysis , within a 500 ms width time window at time t. M is the number of samples within a time window and S(m)fn[t] is the mth band-pass filtered sample within the time window using the above described f, n, and t parameters.where Using a manual inspection, a high-frequency noise (>10 Hz) was detected in the kinematic data in the form of transient peaks  which did not match the curve of the joint movement but were generated by the 3D Microsoft Kinect camera system Kinect, . The movThe core module of movement trajectory prediction is the kinematic data estimator block, which is dedicated to reconstructing the kinematic trajectory using an optimal time-series of the preprocessed EEG features. In the training stage, the key parameters of the estimation block are optimized. The mLR-based PTS model using time-resolved band-pass filtered potentials (the PTS model approach) proposed by Bradberry et al.  isaif and bifnk are regression parameters representing the relationship between the input Sfn[t − k] and output xi[t] data. xi[t] are three orthogonal components of the velocity vector where i represents the three spatial dimensions in the 3D coordinate system. Sfn[t − k] is a standardized temporal difference of those EEG potentials on which band-pass filter f is applied at sensor n at time lag k. N is the number of EEG sensors, L is the number of time lags, and ε[t] is the residual error. The embedding dimension  is equal to the number of time lags plus one (L+1), i.e., the number of time-lagged samples that are selected from each channel for estimating kinematic data at time point t. The standardized difference for the PTS model is given bywhere Pfn[t] is the value of the band-pass filtered potential based input time-series at time t , μPfn is the mean value, and σPfn is the standard deviation of Pfn .where Sfn[t − k] is calculated from PSD of the specified EEG band , rather than from band-pass filtered EEG potentials. As the range of the bandpower values is limited to positive values, the standardized difference is calculated differently for the BTS model compared to the PTS model (Equation 3) where the range of the input was roughly symmetric. The standardized difference for the BTS model is given byThe time-resolved PSD based BTS model use the same equation for mLR as described for the PTS model in Equation (2) but the standardized temporal difference Bfn[t] is the value of the PSD based input time-series at time t  and σBfn is the standard deviation of Bfn.where The input-output data structures for the PTS and BTS models were prepared based on the same principles. The optimal time lag and the optimal number of lagged time points  was selected for both models separately, as described in the following section.The optimal parameter selection and final result calculation were processed in the framework of the inner-outer (nested) cross-validation (CV) technique Figure  based onAs the homogeneous distribution of movement dependent data intervals in the outer folds is essential for each type of movement to be weighted equivalently, the analyzed EEG-kinematic dataset was re-distributed into the six outer-folds based on a data separation method that guaranteed homogeneity Figure . FurtherThe optimal time lag, embedding dimension, and most prominent frequency bands were selected in the inner level CV Figure  using a (x), v(y), and v(z)), separately. In order to obtain the optimal EEG montage, the MTP was trained and tested using a single channel input. The test accuracy values were calculated separately for each of the 41 Laplace filtered and further preprocessed EEG channels in each of the five inner folds. Each channel was assigned an average score based on the Pearson correlation value and was ranked. The eight EEG channels that provided the highest accuracy rates were included in the EEG montage. The final results were calculated by averaging MTP accuracy across the outer folds for each subject, separately.As the accuracy of trajectory reconstruction in MTP BCIs is assessed by comparing the trajectory of the performed movement and the reconstructed limb movement .In order to assess the validity of the obtained trajectory reconstruction accuracy, a shuffling test was performed. For the shuffling tests, the trajectory was reconstructed using the original  EEG test dataset but the order of the blocks was shuffled in the kinematic test dataset. Thus, the reconstructed trajectory in the shuffled test was compared with an incorrect target trajectory. The correlation of the reconstructed and shuffled target trajectories is expected to be low. The correlation values between shuffled and non-shuffled tests from six outer folds were compared using the Student's two-tailed t-test was run . As each channel was assessed, the p value was corrected for multiple comparison . Finally, significant R values were normalized and plotted, separately for each MTP model, movement type, subject, and frequency band, in the form of a subject specific topographical map. Next, in order to identify those cortical areas which provide a significant contribution to trajectory reconstruction across 12 subjects, the contribution of each Laplacian channel was estimated using 6 × 5 inner folds. Again, Laplace filtered channels that contributed significantly higher than expected given the null hypothesis  were determined . Finally, the R values were normalized and plotted (for channels which successfully completed the t-test), separately for each MTP model, movement type, and frequency band, in the form of a cross-subject topographical map.In order to study the contribution of each of the Laplace filtered EEG channels to trajectory reconstruction, the average accuracy rate was computed for the 12 subjects in 6 × 5 inner folds. For subject specific topographical maps, all Laplacian channels were assessed by checking the contribution to trajectory reconstruction that is significantly higher than expected given the null hypothesis . To that end, for each subject and fold, the R value  of each Laplacian channel was normalized by the highest R value and the absolute value taken (looking for a high correlation - both positive and negative). Next, for each channel, the R values from all folds of the actual subject were pooled together and a The cross-subject averages of reconstructed trials for imagined and executed movements were calculated, separately, using the PTS and BTS models. Each average trial was calculated using twelve subjects, six outer folds, and four runs based on low delta information for the PTS model and based on mu, lower beta, upper beta, and gamma information for the BTS model. The cross-subject average of reconstructed trials and an example of single velocity trial reconstruction for imagined and executed arm movements using the PTS and BTS models are presented in the results section.A general overview of the signal processing steps and evaluation blocks is illustrated in Figure p < 0.05, Student two-tailed t-test).Figure  Figures . In conts Figure . For imas Figure  for imagMTP accuracy values using different single channel setups were calculated  and the accuracy values were calculated for each model (PTS and BTS), subject and frequency band, separately. The topographical distribution of the electrodes' contribution to trajectory reconstruction revealed that different sets of electrodes conveyed most of the information regarding the generation of arm movements and imagined arm movements. With the PTS model, electrodes positioned over the sensorimotor cortex conveyed most of the trajectory-related information, as evident in Figures An example of a single velocity trial reconstruction using the PTS and BTS models for movement and imagined movements is presented in Figure Decoding the trajectory of imagined movements from EEG has been reported in only a limited number of studies. The aim of this study was to investigate if the trajectory of 3D hand movements could be decoded from EEG and determine which neural oscillations and detection methods provide maximal decoding accuracy. We evaluated the possibility of decoding imagined 3D arm movements by decoding the imagined 3D trajectory of the right (dominant) hand and its relationship with the average trajectory of 3D movements using time-resolved band-pass filtered potentials and time-resolved PSD values, in six non-overlapped EEG bands covering the 0.5–40Hz frequency range. The results of this study, which focused on direct and implicit decoding of the trajectory of the hand during kinesthetically imagined 3D arm movements , provided a clear evidence that mu, beta, and low gamma oscillations are more likely to provide better performance for MTP of imagined 3D arm movements using a power spectral density estimation approach compared with low delta oscillations using a band-pass filtered EEG potential approach.To the best of the author's knowledge, all other 3D motion trajectory studies to date involve arm movements and not imagined arm movements and most arm motion trajectory prediction (MTP) BCIs use time-resolved band-pass filtered EEG potentials (referred to here as the PTS model) for reconstructing the 3D trajectory of the movement  was analyzed across four runs as follows: First, averaged trials were calculated based on the same target for the same subject and same vector component but based on different runs. Next, the cross-correlation of averaged trials corresponding to the same target, subject, and vector component was calculated for each paired-combination of the four runs, separately. The cross-correlation values obtained using twelve subjects, four targets, three vector components, and all combination of four runs were very high . This result indicates that a target-related averaged trial computed for a specific run can represent an imagined movement between the home position and the corresponding target position.p > 0.05, Student two-tailed t-test) were found  was used as the training target for all movement trials i.e., each movement trial target was identical. Test results were calculated for both approaches and no significant differences in decoding accuracy  models using periodic movements may lead to an overestimated accuracy in the low delta band if the metric is the correlation between target and reconstructed trajectories  based decoding accuracy of arm movement trajectories is found to be maximal using information in the low delta band. The most commonly studied EEG-based brain-computer interface (BCI) decodes directional information of an executed limb movement from a band-pass filtered potential time-series. In the present study, we replaced the time-resolved band-pass filtered EEG potential based potential time-series input with a time-resolved power spectral density based bandpower time-series. The accuracy rates of three dimensional (3D) trajectory reconstruction of the right (dominant) hand for executed and imagined arm movements were compared for both approaches in six non-overlapped bands selected within the 0.5–40 Hz frequency range. Our results not only show that the 3D trajectory of an imagined arm movement can be decoded from power spectral density of the mu, beta, and low gamma bands but the bandpower time-series based model provides higher accuracy rates in these bands compared to the potential time-series based model produces with the low delta band. The results present evidence that the power spectral density of mu, beta, and low gamma bands encode movement-related information during imagined 3D arm movement, thus, mu, beta, and low gamma bands are better candidates for imagined 3D arm movement decoding than the low delta band. The evidence presented here also corroborates the evidence from the extensive literature on classical motor imagery BCI paradigms that predominantly use mu and beta oscillations and rarely use low delta oscillations. The results support the development of BCIs which may enable physical movement independent 3D control of artificial or virtual limbs. Our current focus is on evaluating the proposed methods in a closed-loop BCI where users imagine 3D arm movements to control virtual arms in real-time. BCIs that decode imagined movements are necessary for applications that are targeted at enabling physically impaired individuals perform movement-independent communication and control in real and virtual spaces.AK: Carried out the research, computation and data analysis, and prepared the figures and draft of the manuscript; RS: Did the data collection, contributed to the topographical analysis, and revised the manuscript; NS: Reviewed and revised the manuscript and contributed to the optimal parameter selection section; DC: Supervised the research, contributed to experimental design, data and results verification, literature search and discussion of the results, and revised the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Hospitals and death registries in Australia are operated under individual state government jurisdictions. Some state borders are located in heavily populated areas or are located near to major capital cities. Mortality indicators for hospital located near state borders may not be estimated accurately if patients are lost as they cross state borders. The aim of this study was to evaluate how cross-jurisdictional linkage of state hospital and death records across state borders may improve estimation of the hospital standardized mortality ratio (HSMR), a tool used in Australia as a hospital performance indicator.Retrospective cohort study of 7.7 million hospital patients from July 2004 to June 2009. Inhospital deaths and deaths within 30 days of hospital discharge from four state jurisdictions were used to estimate the standardized mortality ratio of hospital groups defined by geography and type of hospital (grouped HSMR) under three record linkage scenarios, as follows: (1) cross-jurisdictional person-level linkage, (2) within-jurisdictional (state-based) person-level linkage, and (3) unlinked records. All public and private hospitals in New South Wales, Queensland, Western Australia, and public hospitals in South Australia were included in this study. Death registrations from all four states were obtained from state-based registries of births, deaths, and marriages.Cross-jurisdictional linkage identified 11,116 cross-border hospital transfers of which 170 resulted in a cross-border inhospital death. An additional 496 cross-border deaths occurred within 30 days of hospital discharge. The inclusion of cross-jurisdictional person-level links to unlinked hospital records reduced the coefficient of variation among the grouped HSMRs from 0.19 to 0.15; the inclusion of 30-day deaths reduced the coefficient of variation further to 0.11. There were minor changes in grouped HSMRs between cross-jurisdictional and within-jurisdictional linkages, although the impact of cross-jurisdictional linkage increased when restricted to regions with high cross-border hospital use.Cross-jurisdictional linkage modified estimates of grouped HSMRs in hospital groups likely to receive a high proportion of cross-border users. Hospital identifiers will be required to confirm whether individual hospital performance indicators change. Advances in information technology are changing the research environment in public health with increasing access to affordable, large, and complex administrative and surveillance health datasets. The potential of such data to improve population health outcomes is undisputed as whole populations can be followed more precisely in time and space. It has been proposed that precision public health could have particular benefit in preventative health with earlier detection and more precise risk estimates . HoweverFollowing a $20 million government investment strategy, the Population Health Research Network (PHRN) was established to develop an accurate, reliable, and load-bearing national capability for data linkage in Australia. In 2009, the Centre for Data Linkage (CDL) was established within Curtin University and it provides the secure data linkage infrastructure necessary for cross-jurisdictional linkage of health-related data in Australia  number of hospital-related deaths relative to the overall mortality experience. HSMR is calculated by dividing the observed number of deaths by the expected number of deaths in that hospital. The expected number of deaths is estimated as the average of all deaths in all hospitals after accounting for case-mix variation by a range of possible risk-adjustment methodologies.Hospital standardized mortality ratios as a measure of hospital quality of care have been the subject of considerable debate as to their value and how they should be used. It has been argued that HSMRs are a poor indicator of quality of care for several reasons. First, risk adjustment usually relies on variables collected from administrative data and not all may have been identified and reported accurately ; second,Proponents of the HSMR argue that they should be used as a screening tool that alerts institutions to a possible problem rather than being a definitive measure of quality of care . MoreoveIn Australia, the Australian Commission on Safety and Quality in Health Care (ACSQHC) developed a toolkit that contains a set of risk-adjusted coefficients constructed from national inhospital mortality data . This enIn the absence of a unique person identifier in Australia, some of these limitations can be overcome by using person-level linkage methods. Until recently, person-level linkage of administrative hospital and death records has been limited to only two standalone state-based data linkage centers; the Western Australia (WA) Data Linkage System and the Centre for Health Record Linkage in New South Wales (NSW). A constraint of state based or within-jurisdictional person-linkage is that it cannot follow patients if they cross state borders to attend hospital, a problematic issue when major urban areas such as Brisbane (QLD) are located close to a heavily populated region across a state border (NSW). Cross-jurisdictional linkage can overcome this limitation.Cross-jurisdictional linkages of hospital and death records from NSW, WA, SA, and Queensland (QLD) were generated by the CDL, the first study in Australia to combine hospital and death data from multiple jurisdictions at the person level . This alA retrospective cohort of all persons who were discharged (separated) from a NSW, WA, SA, or QLD participating hospital during the period 1st July 2004 to 30th June 2009 was identified. An additional 5 years of prior hospital separation records back to 1st July 1999 (where available) were used to identify past history of inpatient hospital use and preexisting comorbid medical conditions.The main outcome measure was hospital-related deaths: both inhospital deaths and deaths that occurred within 30 days of separating from the last hospital stay. These deaths were used to estimate SMRs under three different record linkage scenarios, as follows: (1) cross-jurisdictional linkage, (2) jurisdictional (state-based) linkage, and (3) unlinked records. Ethical approval for this study was obtained from Human Research Ethics Committees in WA Health, QLD Health, SA Health Departments, the Cancer Institute NSW, and Curtin University (WA).A detailed description of the hospital and death records used in this study have been published elsewhere . BrieflyHospital records from the four jurisdictions underwent extensive cleaning and standardization to maximize analytical comparability. A standard set of exclusions included hospital boarders, organ procurements, aged care residents, funding hospital (duplicate) cases, canceled procedure admissions, unqualified newborns, and healthy qualified newborns. Records with missing age, sex, principal diagnosis or mode of separation were also excluded. Consensus categorical variables were constructed based on the variables from the jurisdictions that provided the least number of categories compared to other jurisdictions.A number of jurisdictional coding differences were observed. For example, admissions for chemotherapy (ICD-10-AM code Z51.1) in public hospitals in NSW are mostly coded as outpatient events and were not included in the data, whereas they were coded as inpatient events and included in the data from the other three jurisdictions. Jurisdictional variations were identified by systematic cross-checking and with reference to the published metadata and local expertise.Eligible hospital stays had (i) acute care or, for multiple episodes of care, the first episode of care was acute care, (ii) a final discharge date that fell from 1st July 2004 to 30th June 2009, (iii) a total length of stay less than 1 year, and (iv) an Australian postcode of residence.For this study, a hospital transfer was defined as a compilation of hospital records that indicated either a subsequent transfer to another acute hospital or a statistical discharge within the same hospital had occurred. A maximum of 48 h was allowed for a patient to transfer from one acute hospital to another.The principal reasons for admission to hospital  were aggregated into broader diagnostic groups by recoding the ICD-10-AM code into one of 256 Clinical Classification System (CCS) groups . These 2The Quan ICD-10 coding algorithm for the Deyo/Charlson index was used to create a Charlson comorbidity score  with a 5Estimation of GHSMRs was restricted to (A) principal referral and specialist women’s and children’s hospitals, (B) large hospitals, (C) medium hospitals, and (D) small acute hospitals peer groups as defined by the Australian Institute of Health and Welfare . HospitaThe method for risk adjustment was based on that reported for the SHMI  with modThe expected number of hospital-related deaths was calculated by summing the probability of a hospital-related death for each hospital stay over each of the 43 different hospital groups. The GHSMRs were calculated as the ratio of actual observed number of hospital-related deaths in a hospital grouping to the expected number of deaths in that hospital grouping × 100. The 95% confidence intervals for the GHSMR estimates were calculated using Byar’s approximation to the exact results based on the Poisson distribution . To incrThere were 19.7 million hospital records from July 2004 to June 2009 that met the inclusion criteria. After applying jurisdictional person-level linkages that allowed multiple records pertaining to the same individual and admission to be bought together into a single hospital stay, the total number of records reduced 4% to 18.9 million hospital stays, which represented 7.8 million unique individuals  into a single hospital stay and allowed patients who had hospital stays in more than one jurisdiction to be merged into a single patient. Cross-jurisdictional linkage reduced the number of unique hospital stays by 0.6% and reduced the total number of individual patients by a further 1.4% compared with jurisdictional linkages.The further addition of cross-jurisdictional linkages brought together both episodes of care that involved hospital transfers across a state border .The logistic regression models used to estimate the probability of hospital-related death in each of the 48 most frequent diagnostic groups had areas under the ROC curve (c-statistics) that ranged from 0.95 for the cardiac arrest and ventricular fibrillation to 0.70 for non-hypertensive congestive heart failure; a consistent finding for both the cross-jurisdictional and single-jurisdictional linked data. The ability of the logistic regression models to correctly classify inhospital deaths in the unlinked separation-level data varied from the person-linked hospital data with a maximum c-statistic of 0.95 for biliary tract disease and a lower 0.82 for cardiac arrest and ventricular fibrillation. The average c-statistic for the unlinked separation-level data for inhospital deaths was 0.84, slightly less than the average for person-linked data models at 0.85.Grouped hospital SMRs estimated using inhospital deaths only were compared for cross-jurisdictional and unlinked hospital records Figure A. The adFor example, Hospital Group 1 with a SMR of 118 (95% CI: 116–119) using unlinked data dropped to 109 (95% CI: 108–110) with person-level cross-jurisdictional linked records. For some hospital groups with relatively low numbers of observed deaths, the observed changes in GHSMR were not always statistically significant. For example, Hospital Group 7 with around 50 observed deaths had an unlinked GHSMR of 118 (95% CI: 83–162) that increased to 136 (95% CI: 101–180) with person-level cross-jurisdictional linkage.The inclusion of deaths within 30 days of hospital discharge into the GHSMR estimates for the cross-jurisdictional linkage scenario resulted in GHSMR changes more consistently toward the group average Figure B. In somThere were only minor changes to the GHSMR estimates when cross-jurisdictional linkages were compared to jurisdictional linkages Figure A. HospitWe have demonstrated that using cross-jurisdictional linked hospital and death records can modify estimates of SMRs based on broad hospital groupings compared with both unlinked and jurisdictional linked records. For this study, the largest changes in GHSMRs for inhospital deaths were between unlinked records and person-level linked data. Person-level data allowed multiple episodes of care to be bought together into a single hospital stay. This allowed more accurate estimation of the number of patients, and their care pathways, and improved the identification of hospital-related deaths during non-acute care that were linked to an acute care admission. Additionally, the more complete ascertainment of patient comorbidity and hospital stay history improved the GHSMR estimation.Including all 30-day deaths into the GHSMR estimation reduced the overall spread of GHSMRs and tended to bring outlying hospital groups toward the group average. This is consistent with previous work for NSW hospital data that showed that including 30-day deaths reduced the variation in HSMRs . It is lEstimation of GHSMRs for hospital-related deaths using cross-jurisdictional links compared with jurisdictional links included additional deaths associated with the 11,116 cross-jurisdictional hospital transfers and the 496 cross-border hospital deaths. These additional deaths made only minor changes to the GHSMRs in this study because of the reduced sensitivity of using hospital groups rather than individual hospitals. In this study, individual hospital identifiers were not available and SMR estimation was restricted to broad geographical and peer group categories. It is expected that significant changes in mortality rates could result for hospitals located close to jurisdictional borders when cross-jurisdictional linkages are included at an individual hospital level. This hypothesis is supported by the larger effect observed for cross-jurisdictional linked GHSMRs when restricted to patients living in high cross-border hospital use regions.The risk-adjustment method used in this report were designed to make full use of the linked data available and thus differs from the method presented in the toolkit developed by the ACSQHC for hospitals to estimate their HSMR core hospital-based outcome indicators . While tA condition of data release for this study prevented identification of individual hospitals, which was a major limitation. This restriction was primarily the result of privacy concerns and prevented the comparison of individual hospitals with similar characteristics. As a result, the GHSMRs reported here cannot, nor are meant to be, interpreted in any clinically meaningful way. This limitation highlights that there are still ethical, legal, and social barriers to overcome before cross-jurisdictional linkage is implemented regularly in Australia. Ensuring public confidence in the technology of data linkage to maintain individual confidentiality, advocating for changes to out-dated legislation and providing a strong ethical base to research training undertaken by organization such as the PHRN and the Centre for Big Data Research in Health will contribute to positive change. Other innovations such as secure remote-access computer environments and the development and use of privacy-preserving record linkage techniques will continue to play a role in the future of data linkage.We have shown that linking individuals and their hospital stays across jurisdictional borders can modify estimates of standardized mortality ratios. Hospital identifiers will be required to confirm these findings. Improving the precision of the HSMR as a hospital performance indicator is particularly relevant for hospitals that are located close to borders or that have relatively high numbers of interstate travelers.KS carried out the data manipulation, data analysis, and drafted the manuscript. DR conceived the design of the study, negotiated data acquisition, and contributed to manuscript preparation. JA conceived the design of the study, negotiated data acquisition, and contributed to manuscript preparation. AF contributed to data acquisition, data analysis, and manuscript preparation. JB contributed to data acquisition, data analysis, and manuscript preparation. JS contributed to overall study concept and critically reviewed the manuscript.KS, AF, and JB were funded by the PHRN to conduct the study reported here. The other authors declare no conflict of interest.


Osteoid osteoma in periarticular lesions tends to have an unusual presentation that likely leads to a delayed or missed diagnosis compared with a typical osteoid osteoma in the metaphysis or diaphysis of the long bone. In cases that are unresponsive to conservative treatment, surgical interventions including en bloc resection, computed tomography-guided percutaneous treatment, and arthroscopic resection have been performed; however, these methods frequently result in inadequate tumor resection and recurrence. Here we present a case of a 16-year-old girl with osteoid osteoma in the talar neck presenting as anterior impingement syndrome due to marked synovitis in the ankle joint which was successfully treated without complications by arthroscopic synovectomy and tumor resection followed by intraoperative 3D C-arm-based imaging confirming complete tumor lesion removal. Her pain was relieved immediately after the surgery, and there was no recurrence at 12 months of follow-up. This is the first case report of the surgical treatment of the osteoid osteoma in the talar neck with the combination methods of arthroscopy and 3D C-arm-based imaging. Osteoid osteoma (OO) is a common benign osteoblastic bone tumor that affects mainly children and young adults . AlthougA 16-year-old Japanese girl with no past medical history or previous injuries presented to a nearby clinic complaining of chronic anterior left ankle pain for the past 2 years. She was diagnosed with anterior ankle impingement syndrome by ultrasonographic findings of synovial hyperplasia in the anterior aspect of the ankle joint and referred to our hospital for further treatment. A physical examination revealed tenderness and swelling across the anterior aspect of the left ankle but no local heat or redness of the overlying skin. She played badminton three times a week and complained of increasing pain with sports activities. Careful interviewing revealed that she also had pain at rest which increased with motion in the morning which was strong especially immediately after awakening.The range of motion of the left ankle was normal, and marked pain was observed with forced dorsiflexion. There were no signs of ankle instability. A blood test showed that her white blood cell count, C-reactive protein level, and matrix metalloproteinase-3 level were within normal limits. Rheumatoid factor and anticyclic citrullinated peptide antibody tests were negative. Plain radiographs showed a small exostotic bony bulge on the talar neck which resembled a traction spur and a recess on the talar neck . ComputeWe initially considered two pathologies as differential diagnoses: one was anterior ankle impingement syndrome considering the bone lesion as a recess, flake, and spur caused by impaction of the distal tibia against the talar neck; and the other was OO in the talar neck considering the bone lesion in CT and MRI as a nidus with secondary synovitis in the ankle joint. Activity modification and daily oral aspirin therapy slightly reduced but did not eliminate her symptoms. An intra-articular steroid injection provided some pain relief; however, the effect lasted only 1 week. Persistent synovitis unresponsive to conservative treatments for several months prompted us to narrow down the differential diagnosis to OO. The patient opted for conservative treatment consisting of oral aspirin or nonsteroidal anti-inflammatory drugs (NSAIDs); however, her symptoms persisted and she finally decided to undergo surgical resection 14 months after the first visit.We performed arthroscopic surgery using anteromedial and anterolateral portals. The patient was placed in the supine position on a radiolucent carbon fiber table with the ankle manually distracted. Arthroscopy revealed capillary hyperemia and synovial hyperplasia in the anterior aspect of the ankle joint . We perfPostoperatively, the patient reported immediate relief of her ankle pain. She was followed up for 12 months without lesion recurrence. Symptoms of anterior ankle impingement disappeared with no functional disability at the latest follow-up.OO in the foot typically involves a subperiosteal lesion of the talar neck . The diaProstaglandins produced in OO via the strong expression of cyclooxygenase-1 (COX-1) and COX-2 are considered to cause characteristic nocturnal pain that is responsive to nonsteroidal anti-inflammatory drugs (NSAIDs) in typical OO and synovitis in OO located within periarticular lesions . The natOO is treated by several invasive methods including open curettage, en bloc resection, CT-guided percutaneous local ablation, and arthroscopic resection . Open reThe open surgical excision of OO was recently replaced by less invasive methods. CT-guided percutaneous therapies including core drill excision , laser aArthroscopic excision is another minimally invasive treatment option for intra-articular OO which has shown good clinical results , 22–26. Several studies have reported that minimally invasive surgery of OO with 3D C-arm-based imaging is a highly effective and safe procedure –31. ThisIntraoperative CT imaging  is another option for intraoperative imaging. O-arm delivers higher image quality and quicker 3D reconstruction time than 3D C-arm; however, O-arm has some disadvantages . First, To our knowledge, this is the first report to describe a combination method of arthroscopy and 3D C-arm-based imaging. Based on the previous studies and our experience in the present case, we believe that this combination method is a useful option for the treatment of intra-articular OO. The advantages of this method against arthroscopy alone are capabilities to access the nidus easily and less invasively and to confirm the complete resection. The disadvantages are its cost and radiation exposure to patients, though the amount is of acceptable level.We described a case of talar neck OO showing an unusual presentation of anterior ankle impingement syndrome. Using arthroscopy and 3D C-arm-based imaging, we performed a synovectomy, clearly exposed the nidus, and intraoperatively confirmed complete lesion removal without complications.