Background: Tracking population trends in childhood obesity and identifying target areas for prevention requires accurate prevalence data. This study quantified the magnitude of non-participation bias for mean Body Mass Index-z scores and overweight/obesity prevalence associated with low (opt-in) compared to high (opt-out) participation consent methodologies. Methods: Data arose from all Local Government Areas (LGAs) participating in the Healthy Together Victoria Childhood Obesity Study, Australia. Primary schools were randomly selected in 2013 and 2014 and all Grades 4 and 6 students (aged approx. 9–12 years) were invited to participate via opt-in consent (2013) and opt-out consent (2014). For the opt-in wave N = 38 schools (recruitment rate (RR) 24.3%) and N = 856 students participated (RR 36.3%). For the opt-out wave N = 47 schools (RR 32%) and N = 2557 students participated (RR 86.4%). Outcomes: differences between opt-in and opt-out sample estimates (bias) for mean BMI-z, prevalence of overweight/obesity and obesity . Standardized bias (Std bias) estimates defined as bias/standard error are reported for BMI-z. Results: The results demonstrate strong evidence of non-participation bias for mean BMI-z overall  and for girls , but not for boys . The opt-in strategy underestimated the overall population prevalence of overweight/obesity and obesity by −5.4 and −4.5 percentage points respectively (p < 0.001 for both). Significant underestimation was seen in girls, but not for boys. Conclusions: Opt-in consent underestimated prevalence of childhood obesity, particularly in girls. Prevalence, monitoring and community intervention studies on childhood obesity should move to opt-out consent processes for better scientific outcomes. Stemming the obesity epidemic is one of the greatest public health challenge for 21st century . These cDifficulties in obtaining accurate and timely estimates of prevalence, trends and intervention effectiveness  present This heterogeneity in PRs raises questions about non-participation bias; a bias that occurs when those who consent to participate are a sub-population which is not representative of the broader study population. Examination of the magnitude of non-participation bias is hampered by the limited ability to obtain information on those who declined participation. Previous attempts to assess non-participation bias in school-based obesity studies include comparing respondents and non-respondents’ demographic characteristics  and collecting teachers’ estimates of non-participants’ heights and weights . The twoAccurate assessment of non-participation bias is difficult because seldom are the same sampling methods repeated within short time frames with the only variation being in recruitment method. Here we report one such example where the only methodological change between two subsequent waves of the study was a shift from opt-in consent in 2013 to opt-out consent in 2014. Herein we examined the magnitude of non-participation bias on estimates of overweight and obesity prevalence and mean Body Mass Index z-score (BMI-z). We hypothesized that, compared to opt-out consent, opt-in consent would produce lower participation rates and result in underestimates of overweight and obesity prevalence and mean BMI-z.This repeat cross-sectional study examined changes in BMI-z and associated risk factors of primary school children in Grade 4 and Grade 6. Schools were selected from 26 Local Government Areas (LGA) that formed the embedded cluster randomized control trial within the Healthy Together Victoria initiative. Detailed descriptions of the study design, sampling approach, sample size, evaluation measures and protocol are reported elsewhere . Herein,Of the 79 LGAs in Victoria, Australia, 26 LGAs were randomly selected as part of a cluster randomized controlled trial. Within each LGA, three schools were selected at random and invited to participate from a list of all primary schools. Where all three schools declined participation, the next three primary schools were invited, until at-least one school per LGA consented. Principals were sent written invitations to participate with follow-up phone calls, emails and/or visits to the school. In consenting schools, all students in Grades 4 (approx. 9–10 years old) and 6 (approx. 11–12 years old) were invited to participate via a plain language statement and consent form. In 2013 an opt-in recruitment procedure was used, with students who returned a consent form signed by their parent/guardian enrolled in the study. In 2014, an opt-out consent procedure was used whereby students were enrolled unless they returned a signed opt-out form from parents/guardians or the student did not agree to participate either previously or on the day of data collection. Other than the change from opt-in to opt-out consent, an identical methodology was used in 2013 and 2014 to recruit participants and collect data. In 2013, data collection occurred during Term 4 (Monday 7 October–Friday 20 December 2013) and in 2014, during Term 3 (Monday 14 July–Friday 19 September 2014). The change in school term was due to feedback from school principals that Term 4 was overburdened with other commitments.Participants had their height, weight and waist measured by trained researchers and completed a behavioural questionnaire on physical activity, sedentary behaviour, diet quality and wellbeing . There wDemographic characteristics: Date of birth, gender, residential postcode, language spoken at home, country of birth and ancestry were collected through a self-reported questionnaire. For this study, school postcode was used to categorise individuals within quintiles of Relative Socio-Economic Advantage and Disadvantage which was derived from the Australian Bureau of Statistics (ABS) Socio-Economic Indexes for Areas (SEIFA) index, 2011 Australian Census [https://www.myschool.edu.au) [n Census . In addi.edu.au) . The ICS.edu.au) .Anthropometry: Height was measured to the nearest 0.1 cm using a portable stadiometer  and weight to the nearest 0.1 kg using an electronic weight scale  without shoes and whilst wearing light clothing. Age and sex-specific body mass index (BMI) z-scores were calculated using the World Health Organizations’ (WHO) growth reference [eference . Definiteference . The aveThe study was approved by Deakin University’s Human Research Ethics committee (2013-095), the Victoria Department of Education and Training (2013_002013), and the Catholic Education Offices of Melbourne, Sale, Sandhurst and Ballarat .p < 0.05.The target populations for this study consisted of children attending Grade 4 and Grade 6 in schools located in a selected set of LGAs of Victoria, Australia, in 2013 and 2014. Analyses were based on two assumptions: (1) that the distribution of the outcomes of interest (BMI-z and weight status) were identical in the 2013- and 2014-population; and (2) that the sample of children/families who were invited to participate in 2013 can be thought as similar/equivalent to the sample of children/families invited to participate in 2014. The first assumption is highly plausible because population-level BMI-z is unlikely to have changed significantly over the 9-months between study waves . The secUnder the former assumptions the children invited to participate under the opt-out and opt-in strategies are random samples from the same underlying population. Each sample provides an estimate of the mean  of the outcome of interest in this population, for example, BMI score (denoted as resampling). A small Presampling value provides evidence of non-participation bias, indicating that the sample obtained under opt-in consent is not “representative” of the larger sample obtained under opt-out consent. For simplicity, we will refer to Presampling as P.We denote Bias estimates are presented for mean BMI-z-score, prevalence of obesity/overweight and prevalence of obesity. The estimates were calculated for the whole data set and for the subpopulations defined by gender, gender and Grade (4 and 6), and gender and location .p < 0.001) , while student participation rate was 36.3% under opt-in and 84.4% under opt-out consent (< 0.001) . Charact< 0.001) .The sampling distribution of mean BMI-z, prevalence of overweight/obesity and prevalence of obesity overall and for boys and girls based on 10,000 random samples of size p < 0.0001) and for girls overall  and girls in Grade 4  (p < 0.0001) and obesity prevalence by 6.4 percentage points (p < 0.0001). Negative bias attributable to opt-in consent was evident by region and gender with the exception of boys in major cities. In boys in major cities, there was no evidence of bias due to opt-in consent.There was strong evidence of non-participation bias for BMI-z overall  . Opt-in Compared to opt-out consent, opt-in consent methods resulted in significant underestimation of overweight/obesity (−5.4 percentage points) and obesity (−4.5 percentage points) for boys and girls. Additionally, the magnitude of underestimation was significant for girls but not boys; although patterns of underestimation were evident for inner and outer regional boys and younger girls. These findings indicate that lower participation rates associated with opt-in consent could mask the prevalence of combined overweight/obesity and particularly obesity by as much as 8.5 percentage points.These results demonstrate that participants who opt-in to school-based childhood obesity measurement programs are significantly different  than those recruited using opt-out consent. The magnitude of this non-participation bias was greater among girls compared to boys, which may reflect differential social pressures around body image, girls’ greater pre-occupation with weight, concerns about dieting, and drive for thinness compared to boys’ during childhood . StudiesCaution should be taken in assuming that improving overall response rates will automatically improve sample representativeness. In our study, the largest bias was not necessarily related to the lowest participation rates. As an example, obesity prevalence bias was larger among boys from inner regional and remote areas, as compared to city-dwelling peers, even though participation rates were almost double among the first subgroup . Improving the overall response rate would not necessarily improve the sample representativeness with regards to weight status as a study with a reasonably high response rate can still be subject to participation bias if non-participants are systematically different from those who participated in the study.This study extends lessons from the NCMP in England  to proviThe maintenance of identical protocol, with the exception of a change from opt-in to opt-out consent, in subsequent studies 9 months apart provide a unique data set to examine the influence of non-participation bias on estimates of BMI-z and overweight and obesity prevalence. Comparability of schools was further supported by their similar demographic characteristics supporting the contention that observed differences were not attributable to school-level differences between study waves.Our resampling approach underestimates the standard error of the bias in two ways. First, it does not account for the variability in the opt-out estimates, the opt-out sample played the role of a “fixed” population in this approach. The rationale was that the opt-out sample, with >80% participation rate, is a large and well representative sample of the children population that we can expect to reach in a school-based survey in Victoria, Australia. Second, when resampling, we selected samples without accounting for the clustering induced by schools because the schools enrolled in the two waves were different and sampling inside schools was not possible. Of note, the intraclass correlation coefficient was generally small . We chose the resampling approach instead of a standard two sample comparison under a multilevel model because it graphically highlights the “non-representativeness” of the opt-in sample especially for some sub-populations. One of the limitations of our study is that our estimates might be influenced by the fact that data collection occurred in different school terms for 2013 and 2014. In England, month of measurement was associated with BMI-z among very young children (aged approx. 5–6 years) and a decline in BMI z-score of −0.00788 every month compared to September (Spring) as the reference month . HoweverThis large sample of primary school children has demonstrated significant non-participation bias between opt-in and opt-out consent methods. The differences in estimates should not be assumed as standard for comparing studies employing these consent methods in other populations but our study does suggest that opt-out consent methods will provide higher participation rates and more reliable estimates than more traditional methods. These results would indicate that estimates of population prevalence and trends over time require large scale data with high participation which in Australia can only be realistically achieved with opt-out consent.These results indicate that comparison of samples with differing participation rates under an opt-in model will have inherent bias on estimates of BMI-z and overweight/obesity due to low participation rates and should be interpreted with caution. The study also suggests that studies conducted under alternate opt-in and opt-out consent procedures over time will have fundamentally different samples, limiting the comparability of the estimates.These findings also have implications for understanding intervention effectiveness. That opt-in participants are more likely to be healthier than a general population sample leads to the caution that any observed effects are drawn from an already healthier sample. Therefore trial outcomes based on opt-in consent cannot be generalized to the whole population as they may overestimate or underestimate the intervention effects depending on whether the intervention had a greater or lesser impact on those children who were already overweight or obese and would not participate in measurements with opt-in consent processes.The findings also strengthen the imperative for high quality anthropometric surveillance under an opt-out model. While contentious in some countries, surveillance programs are used in Arkansas, USA , and by Based on this evidence, assessments of school-based anthropometry studies by ethics committees and Internal Review Boards should factor in the higher chances of better science outcomes without adverse effects using the opt-out consent process. The standard practice of opt-in consent produces biased population estimates of childhood overweight and obesity prevalence. Opt-out consent is essential to provide accurate estimates of current obesity prevalence and trends and to understand effectiveness of community childhood obesity prevention interventions.


Flaws in the design of randomized trials may bias intervention effect estimates and increase between-trial heterogeneity. Empirical evidence suggests that these problems are greatest for subjectively assessed outcomes. For the Risk of Bias in Evidence Synthesis (ROBES) Study, we extracted risk-of-bias judgements  from a large collection of meta-analyses published in the Cochrane Library . We categorized outcome measures as mortality, other objective outcome, or subjective outcome, and we estimated associations of bias judgements with intervention effect estimates using Bayesian hierarchical models. Among 2,443 randomized trials in 228 meta-analyses, intervention effect estimates were, on average, exaggerated in trials with high or unclear (versus low) risk-of-bias judgements for sequence generation (ratio of odds ratios (ROR) = 0.91, 95% credible interval (CrI): 0.86, 0.98), allocation concealment , and blinding . In contrast to previous work, we did not observe consistently different bias for subjective outcomes compared with mortality. However, we found an increase in between-trial heterogeneity associated with lack of blinding in meta-analyses with subjective outcomes. Inconsistency in criteria for risk-of-bias judgements applied by individual reviewers is a likely limitation of routinely collected bias assessments. Inadequate randomization and lack of blinding may lead to exaggeration of intervention effect estimates in randomized trials. Meta-analyses of randomized trials are often more influential than single trials, and they increasingly inform health-care decisions made by clinicians and health authorities. For their results to be valid, randomized trials should employ rigorous methods that can achieve and preserve comparability of the intervention and control groups . For exaMeta-epidemiologic studies analyze collections of meta-analyses to provide empirical evidence about the influence of trial design characteristics on trial results . Such stSince January 2008, authors of Cochrane reviews have used a “risk-of-bias” tool for assessing included trials . The assIn this paper, we describe and report the main results from a new, large empirical study investigating the associations of risk-of-bias judgements for sequence generation, allocation concealment, blinding, and incomplete outcome data with treatment effect estimates— the Risk of Bias in Evidence Synthesis (ROBES) Study. Our aims were to examine whether routinely collected risk-of-bias assessments relating to methodological characteristics are associated with effect estimates, to compare these associations with findings from our previous study , and to The April 2011 issue of the Cochrane Database of Systematic Reviews (issue 4) included 4,371 intervention reviews (excluding protocols), of which 1,399 had at least 2 completed domains in the Risk of Bias tables. The complete 1,399 reviews were supplied by the Cochrane Informatics and Knowledge Management Department in electronic format, as Review Manager (version 5.0) files . We convhttps://academic.oup.com/aje). Meta-analyses can inform estimation of the bias associated with a particular domain only if they contain at least 1 trial at “low risk” of bias and 1 at “high or unclear” risk of bias. We refer to these as informative meta-analyses for that bias domain.We selected meta-analyses that fulfilled the following criteria: 1) address a binary outcome; 2) include at least 5 randomized trials, each with at least 1 event across the 2 trial arms; 3) accompanied by risk-of-bias assessments, with all 5 core domains of the tool having been assessed ; 4) compare an active intervention with a control or “older” intervention; and 5) include no trials that overlap with another meta-analysis in the data set. Details of the process for selecting eligible meta-analyses are provided in International Classification of Diseases, Tenth Revision) , direction of outcome (adverse or favorable) , 17, typevision) , and wheevision) , 17: We To explore correlations between bias domains, we computed odds ratios for the association between risk-of-bias judgements for pairs of domains using logistic regression in Stata 14 . For the main analyses, we modeled intervention effects as log odds ratios with outcomes coded so that odds ratios less than 1 corresponded to beneficial intervention effects in all meta-analyses. In the main analysis, “high risk” and “unclear risk” bias judgements were grouped together. The underlying idea of the analysis is described in 2 term representing the average increase in between-trial heterogeneity in trials at “high or unclear” risk of bias (vs. “low” risk of bias) for each bias domain. Variation in mean bias across meta-analyses was quantified by a measure of between–meta-analysis variance, φ2. Posterior mean values for average bias were exponentiated and are reported as the ratio of odds ratios; posterior median values for κ and φ are reported on the log odds ratio scale. All are presented with 95% credible intervals. Meta-analyses containing fewer than 2 studies at “low risk” of bias and at “high or unclear” risk of bias are uninformative for κ and thus were prevented from influencing the estimation of this parameter. Additional statistical analysis information and analysis code is provided in We fitted Bayesian hierarchical bias models, assuming a binomial likelihood . This aWe conducted univariable analyses for each of 4 risk-of-bias domains  using all informative meta-analyses for that domain  and mental health , followed by circulatory system conditions  and respiratory system conditions . Subjectively assessed outcomes were reported most frequently, in 127 (55.7%) meta-analyses, followed by all-cause mortality  , followed by sequence generation , blinding , and allocation concealment . The proportion of trials with unclear risk of bias was highest for allocation concealment  and sequence generation  and was markedly lower for blinding  and incomplete outcome data . The proportion of trials rated as being at high risk of bias was highest for blinding , followed by incomplete outcome data , with low proportions rated as high risk for allocation concealment  and sequence generation   informative meta-analyses, of which 1,006 (46.6%) were judged as having low risk of bias, 1,081 (50.1%) as having unclear risk of bias, and 71 (3.3%) as having high risk of bias. For allocation concealment, 2,121 trials were included in 188 (82.5%) informative meta-analyses, of which 933 (44.0%) were judged as having low, 1,068 (50.3%) as having unclear, and 120 (5.7%) as having high risk of bias. Only 144 (63.2%) meta-analyses  were informative for blinding: 854 (50.9%) trials were judged as low, 437 (26.0%) as unclear, and 387 (23.1%) as high risk of bias. For incomplete outcome data, 1,956 trials were included in 167 (73.2%) informative meta-analyses: 1,156 (59.1%) were judged as low, 475 (24.3%) as unclear, and 325 (16.6%) as high risk of bias.There was a strong association between judgements of low risk of bias for sequence generation and allocation concealment  Table . Odds raTable Because there was a strong association between sequence generation and allocation concealment, the estimates of average bias for these 2 domains may be expected to be similar. Intervention effect estimates were exaggerated by an average of 8%  in trials judged to be at high or unclear risk of bias for allocation concealment, but there was very little evidence of an increase in between-trial heterogeneity ). The variability in average bias across meta-analyses was small ). There was little evidence that the average bias varied according to type of outcome. Estimates of both between-trial and between–meta-analysis heterogeneity in bias were low for all outcome types. As for sequence generation, the analysis including adjustment for the other 3 domains (model B) produced an attenuated estimate of average bias , and the estimates were very similar across all outcome types Table .Intervention effect estimates were exaggerated by an average of 13%  in trials judged to be at high or unclear risk of bias for blinding. Between-trial heterogeneity was modestly increased for such studies ), and average bias varied between meta-analyses ). There was little evidence that intervention effects differed according to type of outcome. Increases in between-trial heterogeneity ) and between–meta-analysis heterogeneity in average bias ) appeared greater in meta-analyses assessing subjective outcomes than for all-cause mortality or other objective outcomes. In adjusted analysis (model B), the estimated effect of high or unclear risk of bias due to blinding was similar to the unadjusted estimate .There was little evidence that intervention effects were exaggerated in trials judged to be at high or unclear risk of bias for incomplete outcome data . The corresponding estimated increase in between-trial heterogeneity was small ). There was little evidence that average bias or increases in between-trial heterogeneity varied according to type of outcome. The adjusted estimates were very similar to the unadjusted estimates Table .The results of the sensitivity analysis (model A) in which trials with an unclear risk-of-bias judgement were combined with those at low risk of bias are shown in The separate estimates for subgroups of meta-analyses with “other objective” and “semiobjective” outcomes  were similar to each other for allocation concealment and blinding. They differed somewhat for sequence generation  for other objective outcomes and ROR = 1.08  for semiobjective outcomes) and incomplete outcome data  for other objective outcomes and ROR = 1.11  for semiobjective outcomes), but the credible intervals were wide and overlapping .In multivariable models with interaction terms (model C), an interaction was observed between allocation concealment and blinding  and between sequence generation and blinding  . This meUsing a collection of 2,443 randomized trials included in 228 meta-analyses, our estimates of the association between average intervention effect estimates and routinely collected risk-of-bias judgements for sequence generation, allocation concealment, blinding, and incomplete outcome data confirm that problems with randomization and a lack of blinding are, on average, associated with a modest (around 10%) exaggeration of treatment effect estimates. Lack of blinding appears to have the largest influence on treatment effect estimates, and this remains after adjustment for other domains. There was little evidence that these biases varied according to the type of outcome measure assessed. Although there were some differences in the ratios of odds ratios for different outcome types in univariable analyses, the 95% credible intervals overlapped, and the differences were attenuated or disappeared in adjusted analyses. We found little evidence that trials assessed as being at high or unclear risk of bias for incomplete outcome data produced systematically different estimates compared with trials at low risk of bias for this domain, for all types of outcome measures. Variability of treatment effects was higher in trials that lacked blinding and had subjective outcomes, suggesting that for such trials the direction and magnitude of bias is unpredictable. Such variability in bias was observed both between trials within a meta-analysis and across meta-analyses. There was little evidence of such variation in bias for other bias domains or for objectively determined outcomes. Multivariable analyses suggested that effects of individual risk-of-bias domain judgements were less than additive, in that estimated effects of 2 bias domain judgements together were less than the combined individual effects.To our knowledge, this study represents the most comprehensive attempt to date to quantify the influence of 4 bias domains on intervention effect estimates from randomized controlled trials using routinely collected risk-of-bias assessments from published Cochrane reviews. Our findings indicate that assessments are associated with effect sizes, on average, for 3 of the 4 domains, providing some degree of validation of the risk-of-bias tool. However, to interpret our findings as evidence of bias due to the methods implemented in the trials, it is important to consider the accuracy and reliability of these risk-of-bias assessments. The assessments were made by a large number of Cochrane review authors with varying degrees of experience and training, and we did not replicate assessments to determine how appropriate they were. Although detailed guidance on how to assess risk of bias in trials included in Cochrane reviews is available in chapter 8 of the Cochrane Handbook , review In our main analyses, risk-of-bias judgements were dichotomized so that “high” risk and “unclear” risk were considered together. This allows for like-for-like comparisons with results from most of the previous empirical studies, including our previous study . FurtherOur adjusted results for sequence generation and allocation concealment were largely consistent with meta-analyses of all previous meta-epidemiologic studies reported in a recent systematic review . BlindinIn summary, our results confirm that some aspects of the conduct of randomized trials, particularly blinding, are associated with a modest exaggeration of treatment effects on average, but there is little evidence that the average bias differs according to whether the outcome was subjectively or objectively assessed. However, lack of blinding in trials with subjective outcomes leads to increased heterogeneity and hence unpredictable bias in effect estimates. As far as possible, clinical and policy decisions should be cautious when they are based on trials in which blinding was not reported or not feasible and outcome measures were subjectively assessed. Future development of tools for assessing risk of bias in randomized trials , 28 shouWeb MaterialClick here for additional data file.


Systemic inflammation and white matter (WM) alterations have been noted as effects of Parkinson’s disease (PD). This study sought to evaluate WM integrity in PD patients using diffusion tensor imaging (DTI) and to assess its relationship with systemic inflammation.Sixty-six patients with PD (23 men and 43 women) and 67 healthy volunteers (29 men and 38 women) underwent blood sampling to quantify inflammatory markers and DTI scans to determine fiber integrity. The inflammatory markers included leukocyte apoptosis, as well as cellular and serum adhesion molecules, in each peripheral blood sample. DTI-related indices  were derived from DTI scans. The resulting FA maps were compared using voxel-based statistics to determine differences between the PD and control groups. The differences in the DTI indices, clinical severity, and inflammatory markers were correlated.Exploratory group-wise comparison between the two groups revealed that the PD patients exhibited extensive DTI index differences. Low FA accompanied by high RD and MD, without significant differences in AD, suggesting a demyelination process, were found in the parietal, occipital, cerebellar, and insular WM of the PD patients. The declined DTI indices were significantly correlated with increased clinical disease severity, adhesion molecules, and leukocyte apoptosis.Patients with PD experience WM integrity damage in vulnerable regions, and these impairments are associated with increased disease severity and systemic inflammation. The possible interactions among them may represent variant neuronal injuries and their consequent processes in PD.The online version of this article (doi:10.1186/s12868-017-0367-y) contains supplementary material, which is available to authorized users. Parkinson’s disease (PD) is a neurodegenerative disease characterized by dopamine (DA) neuron loss in the substantia nigra (SN). It is believed that neuroinflammatory processes contribute to the development of PD . The altMicroglia-associated neuroinflammation in the SN contributes to the development of PD  and may Previous clinical studies have used diffusion tensor imaging (DTI) to describe WM integrity alterations in PD . Among vIn technology, the voxel-based statistics and the tract-based spatial statistics (TBSS) are both FA data analysis tools to localize brain changes related to development, degeneration and disease. The former is compromised by voxel to voxel in a region of interest or whole brain, and the latter is compromised by the use of standard registration algorithm, the alignment-invariant tract estimated by FA data (the “mean FA skeleton”) , 18. UsiIn the present study, we hypothesized that the increased systemic inflammation observed in patients with PD correlates with the WM damage. The purposes of this study were (1) to evaluate WM integrity in PD patients using DTI; (2) to investigate the differences in systemic inflammation and WM integrity between patients with PD and healthy controls; and (3) to evaluate the relationships between WM alterations and systemic inflammation.This prospective study enrolled 66 patients  with idiopathic PD diagnosed by an experienced neurology specialist in the Neurology Department of Chang Gung Memorial Hospital according to the United Kingdom Brain Bank criteria , and witThe disease severity and functional status of each patient were evaluated using the Unified Parkinson’s Disease Rating Scale (UPDRS) , the modSystemic inflammation was measured in terms of the percentage of apoptotic peripheral leukocytes and the plasma levels of adhesion molecules. Blood was drawn by forearm venipuncture on the same day as the MRI study. The evaluators of clinical and immunological data were blinded to the other data and MRI data.Detailed description of the assessment of leukocyte apoptosis, cellular adhesion molecules, and serum adhesion molecules were presented in our previous studies , 25. TheThe quantities of cellular adhesion molecules were expressed as the mean fluorescence intensity (MFI) of antibody-positive leukocytes. The samples were simultaneously incubated with saturating concentrations of PE-conjugated antibodies against LFA-1 or Mac-1 (Becton–Dickinson Biosciences) and PE-Cy5-labeled antibodies against CD45 (clone VI-PL2). Leukocytes were incubated with PE-coupled unspecific mouse IgG1 (Becton–Dickinson Biosciences) as control experiments. After immuno-labeling, commercial lysing buffer (BD Biosciences Pharmingen) was added to lyse red blood cells, and then paraformaldehyde was added for fixation. Anti-LFA-1 or anti-Mac-1 antibodies were then determined by analyzing 10,000 leukocytes for PE-positive fluorescence. The results were expressed as the MFI of antibody-positive leukocytes.Serum P-selectin, E-selectin, and L-selectin levels were assessed using commercially available enzyme-linked immunosorbent assays  as previously described . In thes3, 55 slices without gaping). The diffusion images gradient encoding schemes included 13 noncollinear directions with a b-value of 1000 s/mm2 and a nondiffusion weighted image volume b-value of 0 s/mm2.For each subject, an MRI scan was performed using a 3.0 Tesla whole-body GE Signa MRI system  equipped with an eight-channel head coil. DTI was conducted along the anterior-posterior commissure line in the axial plane using a single shot spin-echo echoplanar imaging sequence  software package . Age data for the study groups was compared using the independent t test. Sex data for the study groups was compared using the Pearson Chi square test. Analysis of covariance (ANCOVA) with age and sex as potential confounding variables was used to compare the group differences in terms of years of education. ANCOVA with age and sex as potential confounding variables was used to compare the group differences in laboratory data in terms of leukocyte apoptosis and adhesion molecules. Statistical significance was set at P < 0.05.http://www.fil.ion.ucl.ac.uk/spm/; University College London, London, UK) software package, which itself used Matlab R2010a  for voxel-wise group comparisons. Smoothed, normalized FA images were analyzed using SPM8 within the framework of a general linear model, whereas ANCOVA was performed with age, sex, and years of education as covariates to investigate the FA differences between the PD and control groups. The FA threshold of the mean WM was set at 0.2 to successfully exclude voxels, which consisted of gray matter (GM) or cerebrospinal fluid in most subjects. The resultant statistical inferences were considered significant under the criteria of cluster level family-wise error (FWE) corrected P value <0.05, with a cluster size of at least 610 voxels, based on the results of the Monte Carlo simulation . The most probable fiber tracts and anatomic locations of each significant cluster were determined using the FSL atlas tool (http://www.fmrib.ox.ac.uk/fsl/fslwiki/Atlases).Statistical analyses were conducted using the SPM8  was used to extract the ROI masks. The mean DTI-related indices  of these areas were compared between groups by multivariate analysis of covariance, with age, sex, and years of education as covariates. Significance was set at a Bonferroni corrected P < 0.05, accounting for multiple ROI comparisons.Region of interest (ROI) analyses were conducted to determine the mean FA value of each significantly different area between the two groups based on whole-brain voxel-wise comparisons. The Marsbar toolbox  increased MD and increased RD values in the Left ILF, right SLF, left cerebellum, and left IFOF; (2) Increased RD values in the right ILF. No region exhibited significant group differences in terms of AD values.Correlation analyses were conducted to evaluate the relationships among the disease severity and systemic inflammation and DTI indices (P < 0.05 for multiple comparisons) (marked as *). The detailed values resulting from those analyses are listed in a Additional file There was no significant correlation between systemic inflammation and disease severity.In the comparison between the patients with PD and the healthy controls, the patients with PD presented with significantly increased systemic inflammation markers, including increased apoptosis in total leukocytes and its subsets, Mac-1, LFA-1, and serum P-selectin levels. DTI revealed impaired fiber integrity in the patients with PD, indicating increased WM damage. Furthermore, the WM damage was associated with higher disease severity and systemic inflammation. The granulocyte apoptosis and granulocyte LFA-1 showed the strongest correlation with DTI integrity. We may thus say that these two inflammatory markers made greater contributions to the WM damage than the other inflammation variables. The increased systemic inflammation and DTI differences, as well as the associations between them, found in this study help to explain the pathogenesis of altered WM integrity in PD.Parkinson’s disease is characterized by accumulation of α-synuclein, a kind of misfolded proteins. Recently, more reports point out that inflammation may be the focus for defining disease mechanism . The sprHigher levels of peripheral leukocyte apoptosis in PD were observed in this study. A review of related studies indicates that leukocyte apoptosis may be either the pathogenesis or the outcome of neuroinflammation in PD. Microglial apoptosis is a sedation process in microglial activation. Through interferon-γ (IFN-γ) signaling, not only is the activation of microglia stimulated , but theMac-1 plays a critical role in reactive microgliosis in PD . ActivatLFA-1 contributes to T cell mediated host defense mechanisms. The detailed pathogenesis in PD is not well-known, but the infiltration of increased LFA-1-positive leukocytes in the SN was previously observed in the autopsied brains of 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-induced monkeys . A similPatients with PD show significantly reduced brain FA values, and these changes were found to be localized in various brain regions in our results, which correspond to those of previous studies , 40. TheThe most common pattern of alterations seen in this study was significantly decreased FA with significantly increased RD and MD, along with a non-significant degree of increased AD, in the bilateral parietal lobes, left insula, and left cerebellum, which may reflect myelin and axon loss  and may Another pattern of DTI index changes consists of significantly decreased FA and significantly increased RD, along with non-significantly increased MD and non-significantly decreased AD, in the right occipital lobe. Such FA reduction along with increased RD has previously been reported to be related to the loss of myelin . MeanwhiAll of the affected tracts identified in the current study were consistent with those identified in previous reports , includiThe association between WM damages and systemic inflammation was demonstrated in current and previous studies , 51. TheAlthough the current study has yielded useful findings, its design is not without flaws. First, a control group comprising other neurodegenerative disorders was lacking, and it is possible that the identified inflammatory markers are also associated with other neurodegenerative disorders. Second, some investigators believe that voxel-based statistics are less sensitive than tract-based spatial statistics . HoweverThe patients with PD in this study experienced WM integrity damage in vulnerable regions, and these impairments were associated with increased disease severity and systemic inflammation. The possible interactions among them may represent variant neuronal injuries and their consequent processes in PD.


Informing health systems and monitoring hospital performances using administrative data sets, mainly hospital discharge data coded according to International-Classification-Diseases-9edition-Clinical-Modifiers (ICD9-CM), is now commonplace in several countries, but the reliability of diagnostic coding of acute ischemic stroke in the routine practice is uncertain. This study aimed at estimating accuracy of ICD9-CM codes for the identification of acute ischemic stroke and the use of thrombolysis treatment comparing hospital discharge data with medical record review in all the six hospitals of the Florence Area, Italy, through 2015.We reviewed the medical records of all the 3915 potential acute stroke events during 2015 across the six hospitals of the Florence Area, Italy. We then estimated sensitivity and Positive Predictive Value of ICD9-CM code-groups 433*1, 434*1 and thrombolysis code 99.10 against medical record review with clinical adjudication. For each false-positive case we obtained the actual diagnosis. For each false-negative case we obtained the primary and secondary ICD9-CM diagnoses.The medical record review identified 1273 acute ischemic stroke events. The hospital discharge records identified 898 among those (true-positive cases),but missed 375 events , and identified 104 events that were not eventually confirmed as acute ischemic events . Code-group specific Positive Predictive Value was 85.7%  for 433*1 and 89.9%  for 434*1 codes. Thrombolysis treatment, as identified by ICD9-CM code 99.10, was only documented in 6.0% of acute ischemic stroke events, but was 13.6% in medical record review.Hospital discharge data were found to be fairly specific but insensitive in the reporting of acute ischemic stroke and thrombolysis, providing misleading indications about both quantity and quality of acute ischemic stroke hospital care. Efforts to improve coding accuracy should precede the use of hospital discharge data to measure hospital performances in acute ischemic stroke care. These dDiseases , and remDiseases . PreviouDiseases ,6. AccorDiseases , the senTuscany region  is currently implementing a coordinated system of stroke care  that cou2 with a total population of 838,000 inhabitants [The FA spans 3500 KmWe estimated accuracy of ICD9-CM AIS codes in terms of sensitivity and PPV against medical record review with clinical adjudication.We identified all potential acute stroke (any type of stroke) events by summing up all events triaged as stroke or TIA, recorded either in the Emergency Department (ED) or in hospital discharge data with an ICD9-CM diagnostic code 430–435 (any type of stroke and TIA) in primary position, from January 1 through December 31, 2015 .Selection was limited to the primary position codes to increase the likelihood of identifying acute incident rather than prevalent stroke events. For each potential stroke event an exhaustive medical record was built up (see below) and reviewed by a stroke neurologist (MB). All FA hospitals have CT scanners available in ED and could therefore provide neuroimaging to all the potential stroke events included in our study, except patients who died before reaching the hospital or the CT scanner. Repeated hospital admissions referring to the same event were dropped from selected discharges; they were defined as hospital admissions within 28 days from the previous admission or represented patients transferred from one hospital to another. We also did not include spinal and retinal arterial infarctions .In Italy, all hospitals, both public and private, are required for every episode of in-hospital care to submit a discharge diagnoses and interventions abstract, to a central administrative database. This single repository allows for accurate denominators, i.e., all hospitalizations in Italy, and is the only national source of data for stroke.Six diagnoses and six interventions are coded by the caring physicians using . The priWe tested sensitivity and positive predictive value (PPV) of ICD9-CM codes 433*1 and 434*1 when recorded in the primary diagnosis position, as well as 99.10 code in any intervention position on the HDR, against the medical record review and adjudication .For each potential acute stroke event (see above) an exhaustive medical record was built up by merging data from 6 sources: Emergency Medical Services (EMS) database, ED database, HDR, ED and hospital records, neuroimaging database, and hospital-pharmacy records. Medical records were reviewed by a single stroke neurologist (MB) and each event was assigned to one of the following according to the WHO-MONICA criteria : acute iAdvances in neuroimaging have resulted in many events that would have been labelled as TIA now being considered as minor strokes, but this is an area of ongoing controversy . We tookData linkage contributed in creating the medical record database for review, clinical adjudication and analyses, that is the reference method (see above).A secure third party (the FA Epidemiology Center) carried out data linkage from the 6 databases: EMS database, ED database, HDR, ED and hospital records, neuroimaging database, and hospital-pharmacy records. These 6 databases were automatically linked using the fiscal code, a personal number that works as unique identifier all over Italy.Descriptive statistics were used to estimate the frequency of baseline characteristics. AIS events identified by both test and reference method were true-positive cases; AIS identified only by the reference method were false-negative cases; events identified as AIS on test method but not confirmed by reference method were false-positive cases.ICD9-CM coding accuracy was estimated in terms of sensitivity and PPV. Sensitivity was defined as the proportion of true-positive cases identified by reference method also identified by test method. PPV was defined as the proportion of true-positive cases among those identified as AIS by the test method. PPV was estimated for each code group separately and altogether. Sensitivity and PPV were presented as percentages with their 95% confidence intervals (CI).For each false-positive case we obtained the actual diagnosis. For each false-negative case we obtained the primary and secondary ICD9-CM diagnoses on the HDR.Factors influencing the probability of being a true-positive (correctly coded as AIS on the HDR) versus the probability of being a false-negative were assessed by means of a multivariate stepwise logistic regression model . Age, gender, EMS transportation, and hospital were entered as covariates; odds ratios (OR) and 95% Confidence Intervals (CI) were calculated. The goodness of fit of the model was checked through the Hosmer-Lemeshow test. A P value <0.05 was considered significant.Statistical analyses were performed using the Stata/IC 14.1 software .The Regional Committee for Medical and Research Ethics gave the approval to conduct this study without patient consent .The inclusion process  yielded Among the 1273 AIS events, 898 (70.5%) were correctly identified by HDR, but 375 (29.5%) were missed. There were 104 events identified as AIS on the HDR, but discarded after medical record review . Sensitivity of the test method was, therefore, 70.5%  and the PPV was 89.9% . Codes 434*1were used much more often (93.7%) than 433*1 codes. Code group-specific PPV was 85.7  for 433*1 and 89.9%  for 434*1 .Within the medical record review, we found 97 t-PA treatments that were not coded on HDR . Out of None of these events were given ICD9-CM codes for other conditions that might have warranted thrombolysis . The 4 t-PA treatments for stroke events coded as intracerebral hemorrhage (431 in primary position) did have a symptomatic intracranial hemorrhage, nevertheless AIS was not recorded in any position. The remaining 26 false-negative treated events were mainly miscoded as TIA, acute but ill-defined cerebrovascular disease (ICD9-CM code 436), occlusion of precerebral/cerebral arteries without mention of cerebral infarction (ICD9-CM codes 433*0–434*0), and hemiplegia, or coded as AIS in secondary positions. We took a careful look into the 375 false-negative events : 107 eveIn the FA, the main limit to the current use of hospital discharge data for capturing AIS events was a high proportion of false-negative cases, that yielded a suboptimal level of sensitivity. PPV was satisfying, over 85% for each ICD-9CM code group used to record AIS.Moreover, the use of procedure code 99.10 to identify t-PA treatments proved even more incomplete. This treatment is mainly administered in the ED departments, and only seldom coded in HDR.The reasons for the high rate of missed cases are reported, and most of them can benefit from ad-hoc guidelines, training, as well as proper databasing. According to our findings, the main reasons for the undercount of AIS is the lack or the unavailability of HDR, suggesting that a more comprehensive databasing is needed to identify all AIS cases. Misclassification to non-acute precerebral and cerebral arteries occlusion (ICD9-CM codes 433*0 and 434*0), to TIA (435), and to acute but ill-defined cerebrovascular disease (436), as well as the misuse of the right codes in secondary positions, largely contribute to false-negative cases but might benefit from coding guidelines and training.As far as the misuse of code 436 is concerned, we believe that, when CT is negative for recent brain lesions, as it usually is in the first 12 hours from onset , conservThe availability of either neurological ward or neurological consulting, as it actually occurred both in the Hub hospital and in one of the Spoke hospitals, are the major predictors of correct AIS coding.We found that medical record review could detect 94 (55.3%) more events treated with thrombolysis than by using 99.10 code on HDR. This is much higher than previously reported  and madeMoreover, in our survey 30 (17.6%) of treatments were also false-negative AIS events on HDR. Events coded with hemorrhagic stroke code do represent hemorrhagic transformation after receiving t-PA, but coding physicians should well understand the distinction between these two entities and code accordingly. Estimations of t-PA treatments as well as of hemorrhagic transformations based on HDR proved extremely inaccurate.Most studies validating ICD9-CM stroke codes select patients who have records with the diagnostic codes of interest ,4,14. FuOur study is likely to have identified the vast majority of AIS events in the FA, because we did not sample either hospitals or patients, we reviewed medical records of all events with cerebrovascular codes, not only ischemic stroke codes, both from ED, triage, and discharge diagnosis as well as HDR. In fact we identified a large number of non stroke-events (2246) due to our inclusive method of databasing, that allowed the identification of false- negative AIS events and therefore sensitivity estimates. We even reviewed 107 potential stroke events without HDR, because of transfer to hospitals outside the FA or long-term facilities, or dying in ED.Case identification is based on extensive medical record review and not on ICD-9CM. We could identify a large number of AIS events, only one study could enroll a larger sample (2308 AIS) and estimate sensitivity , but acrMoreover, our study was not confined to one hospital, but could explore stroke coding across any ward of the six hospitals with more than 60 coding physicians, that are likely to be representative of different coding practices. This enhances the external validity of our findings.Data linkage between the 6 databases proved successful, because the fiscal code allowed for data from multiple databases to be linked reliably, for comprehensive databasing.Our experience suggests a realistic and rapidly implementable contribution to retrieve and reviewed stroke patients’ medical records.The misclassification of AIS events might be influenced by stroke severity  that we We had medical records reviewed and adjudicated by only one stroke neurologist, even though we partially amended this limitation providing intra-rater reliability.Changes in clinical practice may influence documentation and coding: stroke and TIA, using discharge hospital data have been shown to be decreasing . This isWe did not aim to evaluate HDR accuracy to estimate incidence, given the inability to distinguish first-ever from recurrent stroke events. Moreover, our inclusion criteria likely missed most in-hospital AIS cases and could not capture residents that have been hospitalized outside the FA, as well as any stroke event that did not reach hospital.These findings have relevant implications for stroke surveillance. Tracking AIS by hospital discharge data proved fairly specific but insensitive, because of inaccurate coding. Up to now, these data may provide misleading indications both about quantity and quality of AIS hospital care. On the other hand, measuring the impact of stroke by the means of population-based and hospital-based registers is time-and cost-consuming, limited in sample size, duration of follow-up and geographical coverage, while hospital discharge data are continuously collected, cheaper and ideally covering the 100% of acute stroke care events. Therefore, improving the accuracy of coding should be further appraised, by the means of standard guidelines, ad-hoc training and auditing focusing on the relevance of precise diagnosis coding.Efforts to improve accuracy in AIS coding should precede the use of hospital discharge data to measure hospital performances in AIS care.


The epidemiology and outcomes of acute kidney injury (AKI) in prevalent non-renal solid organ transplant recipients is unknown.We assessed the epidemiology of trends in acute kidney injury (AKI) in orthotopic cardiac and liver transplant recipients in the United States. We used the Nationwide Inpatient Sample to evaluate the yearly incidence trends (2002 to 2013) of the primary outcome, defined as AKI requiring dialysis (AKI-D) in hospitalizations after cardiac and liver transplantation. We also evaluated the trend and impact of AKI-D on hospital mortality and adverse discharge using adjusted odds ratios (aOR).trend<0.01) and AKI-D  increased from 2002-2013. This increase in AKI-D was explained by changes in race and increase in age and comorbidity burden of transplant hospitalizations. AKI-D was associated with increased odds of in hospital mortality  and adverse discharge [discharge other than home] .The proportion of hospitalizations with AKI  contains supplementary material, which is available to authorized users. Acute kidney injury (AKI) is an common condition in hospitalized patients , and is The incidence of non-renal solid organ transplantation (NRSOT) including cardiac and liver transplantation has been increasing in the United States . NRSOT rOverall, allograft and patient survival have been improving over time, and this may be reflective of our ability to provide better supportive care –14. WithThere are several (mostly single center) studies outlining frequency, risk factors or outcomes of AKI in immediate post-transplant period , 15. HowWe extracted our study cohort from the Nationwide Inpatient Sample (NIS) and National Inpatient Sample of Healthcare Cost and Utilization Project (HCUP), Agency for Healthcare Research and Quality . We sele>18 years of age) with prevalent cardiac and liver transplant status by the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes V42.1 and V42.7 respectively. We excluded other solid organ transplants  due to lack of adequate sample size for modeling and excluded pancreas transplant due to high concurrence with kidney transplants. We also excluded hospitalizations in the immediate postoperative period and hospitalizations for complications of transplant using ICD-9-CM codes of 996.8 in any diagnosis field and hospitalizations with transplant related procedures using Clinical Classification Software (CCS) for ICD-9-CM categories 13, 64, 105 and 176. To avoid misclassification of hospitalizations for chronic hemodialysis initiation, we excluded those with procedure codes for arteriovenous access creation or revision [This study was a retrospective database analysis utilizing the Nationwide Inpatient Sample database from 2002–2013. We included adult hospitalizations . Hospital-level characteristics included hospital location307 , hospital bed size  and teaching status. We defined the severity of co-morbid conditions using Deyo’s modification of the Charlson co-morbidity index (CCI), which contains 17 co-morbid conditions with differential weights.We classified AKI during hospitalization in two different ways; AKI with dialysis (AKI-D) and AKI without dialysis, defined based on the descriptions provided above. Discharge disposition was grouped by  home or Although we reported temporal trends for all AKI, we present detailed results for only AKI-D. We chose to do this because the validity of administrative codes for non-dialysis requiring AKI are poor and have changed over time, limiting their accuracy . We compWe identified 130,143 hospitalizations with cardiac transplant from 2002-13 of which 30744 (23.7%) had AKI and 2776 (2.13%) had AKI-D. Similarly, we identified 266,987 hospitalizations with liver transplant of which 56324 (21.1%) had AKI and 5689 (2.14%) had AKI-D . Analysis of annual rates demonstrated a forty five percent increase in AKI-D in cardiac transplant admissions over the study period, from 1.63% in 2002 to 2.33% hospitalizations in 2013; ptrend<0.01. Similarly, we demonstrated a two-fold increase in AKI-D in liver transplant admissions over the study period, from 1.32% in 2002 to 2.65% hospitalizations in 2013; ptrend<0.01.  tripled from 27.4% to 68.8%.  than those without. They also had worse co-morbidity scores (p<0.01) Finally, they exhibited significantly higher proportions of hypertension, liver disease, acute myocardial infarction, sepsis, cardiac catheterizations and mechanical ventilation. Interestingly, we identified a lower proportion of diabetes mellitus in AKI-D patients vs. those without. AKI-D patients were more likely to have a lower income status and more likely to be Medicare/Medicaid beneficiaries. With respect to hospital level characteristics, hospitalizations in large, urban-teaching hospitals were more likely to have AKI-D documented.The demographics and comorbidity burden of hospitalization with heart transplant changed significantly over the study period. The mean age increased from 56.8 to 59.1 years; hospitalizations with black race increased from 5.4 to 16.1% and hospitalizations with high comorbidity burden CCI> tripled p=0.39). The significant comorbidities associated with increased temporal trend in AKI-D is shown in Table A series of sequential models to attempt to explain temporal increase in AKI-D trends is shown in Table AKI-D occurring during hospitalizations in cardiac transplant recipients was associated with three-fold odds of mortality  and two-fold odds of adverse discharge  after adjusting for patient and hospital level confounders Table .Table 4A>2) doubled from 24.2 to 53%. . The significant comorbidities associated with increased temporal trend in AKI-D is shown in Table Similar to cardiac transplant, a univariable model including only calendar year demonstrated that the odds of AKI-D increased annually by 6%  between 2002 and 2013. Adjustment for patient demographics attenuated this to a 4% annual increase . After adjustment for both demographics and concurrent comorbidities and procedures, the impact of calendar year was completely attenuated  and two-fold odds of adverse discharge  after adjusting for patient and hospital level confounders Table .In our analysis of nationally representative data, we demonstrate that the incidence of AKI-D in subsequent hospitalizations after receiving orthotopic cardiac and liver transplant has significantly increased from 2002–2013. We also show that temporal changes in demographics, comorbidities and procedures in part explain the increased yearly incidence. Finally AKI-D is significantly associated with an increased risk of mortality and adverse discharge among cardiac and liver transplant recipients.Previous estimates of AKI and AKI-D in the immediate post transplantation period for orthotopic cardiac transplant range from 5–10% and for liver transplant range from 8–17% , 20, 21,We demonstrate that the incidence of AKI-D in subsequent hospitalizations following cardiac and liver transplantations increased over time and is tenfold higher than in the general population without NRSOT . In addiWe also demonstrate that risks for AKI-D differ in cardiac and liver transplant hospitalizations. For example, CKD was associated with AKI-D in liver but not cardiac transplant hospitalizations. It is plausible that under-recognition of renal dysfunction due to inability of serum creatinine to accurately estimate renal function in in liver transplant settings may lead to “CKD” diagnosis to represent more severe kidney disease than other settings . HoweverOur analysis has limitations. NIS registries do not provide patient level data, including laboratory data or medication data. Thus we could not ascertain baseline CKD stage or the contribution of medications to the development of AKI-D. Also since the data are completely de-identified, we cannot delineate the risk of recurrent episodes of AKI-D or recurrent hospitalizations in the same patients over time. Also, we chose to focus on AKI-D as our main outcome measure. This was largely due to prior publications emanating from administrative databases indicating that the specificity and sensitivity of transplant status and AKI-D codes are excellent . AlthougIn summary, this nationwide sample provides secular, temporal trends of AKI-D in recipients of orthotopic heart and liver transplant hospitalizations. With an established connection between post-transplant AKI and CKD, it is important to study the growing burden of those who develop AKI and are at risk of CKD after transplant. Also, the analysis indicates that the rising trends in AKI-D requirement is in part explained by demographics and co-morbid disease burden of transplant recipients over time. Said in another way, as we get better at caring for older and sicker patients to survive with transplantation, we need to recognize the medical and healthcare economic burden associated with complex complications such as AKI-D. As was shown, AKI-D in transplant recipients is associated with higher mortality and adverse discharge. Future studies need to delineate risk factors and outcomes of milder forms of AKI by utilizing patient-level laboratory information and medication information. Additionally, we need to develop strategies to reduce the risk of AKI in solid organ transplant recipients to improve health outcomes.Additional file 1: Figure S1.Sequential derivation of the study population from NIS 2002-2013 database. This figure demonstrates the derivation of the study population of interest from the larger Nationwide Inpatient Sample database. (PPTX 57 kb)Additional file 2: Table S1.Changes in Demographics and Comorbidities for Heart and Liver Transplant Hospitalizations from 2002-2013. This table shows the changes in demographics and Charlson comorbidity index for Heart and Liver Transplant Hospitalizations from 2002-2013. (DOCX 145 kb)


Background. The Berlin Initiative Study (BIS) eGFR equations were developed specifically for aged populations, but their predictive validity compared to standard formulae is unknown in older women. Methods. In a prospective study of 1289 community-dwelling older women (mean age 79.5 years), we compared the performance of the BIS1 SCr-based equation to the CKD-EPIcr and the BIS2 SCr- and Scysc-based equation to the CKD-EPIcr,cysc to predict cardiovascular and all-cause mortality. Results. Prevalence of specific eGFR category  according to eGFR equation was 12.3%, 38.4%, 37.3%, and 12.0% for BIS1; 48.3%, 27.8%, 16.2%, and 7.8% for CKD-EPIcr; 14.1%, 38.6%, 37.6%, and 9.6% for BIS2; and 33.5%, 33.4%, 22.0%, and 11.1% for CKD-EPIcr,cysc, respectively. Over 9 ± 4 years, 667 (51.8%) women died. For each equation, women with eGFR <45 were at increased risk of mortality compared to eGFR ≥75 . Net reclassification analyses found no material difference in discriminant ability between the BIS and CKD-EPI equations. Results were similar for cardiovascular death. Conclusions. Compared to CKD-EPI, BIS equations identified a greater proportion of older women as having CKD but performed similarly to predict mortality risk. Thus, the BIS equations should not replace CKD-EPI equations to predict risk of death in older women. Reduced renal function defined by varying cut-points of estimated glomerular filtration rate (eGFR) in older adults has been associated with greater risk of cardiovascular (CVD) and all-cause mortality –3. Howevcr) 2009 equation to estimate GFR in adults ) and then converted to standardized values traceable to a certified reference material. SCr was measured with a Modular P Chemistry Analyzer  using an enzymatic method equation calibrated with materials traceable to an isotope-dilution mass spectrometry (IDMS) reference measurement procedure. Interassay CV is 4.0%.cr, BIS2, and CKD-EPIcr,cysc, and equations are shown in Appendix Table 1 in Supplementary Material available online at https://doi.org/10.1155/2017/8216878.We estimated GFR using the BIS1, CKD-EPIWomen were contacted every 4 months after the Year 10 examination until May 2012 to ascertain vital status (mean follow-up 9 ± 4 years); 95% of these contacts were completed in surviving participants. Cause of death was confirmed by death certificate and, when available, hospital discharge summaries. We used ICD-9 codes to classify causes of death as CVD .2) as a measure of obesity, smoking status, and self-reported health status were obtained at the Year 10 examination. Comorbid conditions as defined by self-report alone at the Year 10 examination included stroke, coronary heart disease (CHD), and congestive heart failure (CHF). Hypertension (HTN) and diabetes mellitus (DM) were defined as self-report or use of antihypertensive or antihyperglycemic medications, respectively.Demographic data such as race and education level were taken from baseline examination for white women and Year 10 examination for Black women. Body-mass index  between each eGFR equation. We compared baseline characteristics of participants included in this analysis across 4 categories of eGFR using ANOVA and χ2 test for continuous and categorical variables, respectively.We used the Chi-square test to compare the prevalence of CKD in 4 categories of eGFR  during the follow-up time by category of baseline eGFR for each estimating equation. We calculated unadjusted hazard ratios for death by eGFR category using Cox proportional hazards regression. We further adjusted for factors determined a priori to be clinically relevant potential confounders of the association between renal function and death  and other factors that were associated with both predictor and outcomes. Thus, we present the following models: (1) an unadjusted model (site only); (2) a base model, further adjusted for age, race, and BMI; (3) a multivariate model, further adjusted for HTN and DM; and (4) a final model, further adjusted for CHD and self-reported health status.cr,cysc equation (see Appendix Table 2 for NRI formulae) [cr,cysc). We also present a category-free NRI which defines reclassification as 1 unit change in eGFR that allows us to provide estimates that may be more comparable across studies that may not choose the eGFR categories we have used in our analyses [Finally, we calculated a metric comparable to the net reclassification improvement (NRI) for each formula compared to the CKD-EPIormulae) . The poianalyses .In secondary analyses, we repeated all of the above analyses expressing eGFR in quartiles as the outcome (see Appendix Tables 3 and 4). All significance levels reported were two-sided and all analyses were conducted using SAS 9.4 .2. Mean follow-up time was 9 ± 4 years. The BIS2 equation identified 47% and 10% as having eGFR <60 and eGFR <45, respectively, compared to 33% and 11% by the CKD-EPIcr,cysc equation. The BIS1 equation identified 49% and 12% as having eGFR <60 and eGFR <45, respectively, compared to 24% and 8% for the CKD-EPIcr equation . Women with eGFR <45 compared to those with eGFR ≥75 had a 3.2-fold higher risk of death when eGFR was calculated using CKD-EPIcr,cysc formula and a 3.7-fold higher risk of death when eGFR was estimated using BIS2 equation (p-trend across category of eGFR <0.001 for both equations). Further adjustment for age, race, BMI, comorbid conditions, and health status somewhat attenuated the point estimates of the associations, though point estimates of the association were similar between each BIS equation and its respective CKD-EPI comparator formula . When we defined renal function by quartiles of eGFR, the point estimates were less robust, but similar significant trends across quartiles of eGFR were observed (Appendix Table 3).Over the follow-up period, 667 (51.8%) women died overall and 239 (18.3%) of women experienced a CVD death. In unadjusted analyses, lower eGFR category (worse function) calculated using any one of the four equations was associated with a graded, increased risk of all-cause mortality that was similar in magnitude across eGFR formulae . Women wWhen CVD mortality was substituted for all-cause mortality outcome in the analyses, lower eGFR category calculated using each one of the four equations was similarly associated with higher risk of CVD death across formulae . Point ecr equation more appropriately categorized women who died to a lower eGFR category. However, to a greater degree, the BIS1 inappropriately categorized women who did not die to a lower eGFR category. Overall, the net reclassification favored less appropriate risk classification for BIS1 compared to that for the CKD-EPIcr, though this difference was not statistically significant. We observed similar results for the BIS2 compared to the CKD-EPIcr,cysc. Findings were not altered when analyses were repeated using the category-free classification of eGFR. When CVD mortality was the outcome, we noted similar risk-reclassification patterns in both category-based and category-free NRI analyses, though the point estimate of NRI for nonevents was more strongly in the direction of inappropriate recategorization to a lower eGFR for those who did not experience a CVD death. None of the NRI values were statistically different from those of the referent CKD-EPI equation.Finally, in category-based NRI analyses , the BISIn this cohort of community-dwelling older women, lower eGFR irrespective of estimating equation used was associated with a higher risk of all-cause mortality and CVD death. The BIS eGFR equations, which were developed specifically to estimate kidney function in older adults, did not outperform the KDIGO-recommended CKD-EPI equations to predict death including CVD mortality in this population. Thus, our findings lend no support to the replacement of the CKD-EPI equations with the BIS equations to predict death in older women.cr and CKD-EPIcr,cysc equations. These results are consistent with those of other studies examining the prevalence of CKD by the BIS and CKD-EPI formulae in both older women and men [We found that nearly half of our participants had an eGFR <60 by either BIS equation compared to 27% and 33% for the CKD-EPI and men , 14–16.  and men , 16. In  and men , 17. Forcr equation to predict mortality. Another population-based cohort study of 1017 German men and women  aged 71–75 years observed that the BIS2 equation more inappropriately classified participants who did not die to a lower eGFR category when compared to the CKD-EPIcr,cysc [cr,cysc formula more inappropriately reclassified men to lower eGFR who did not die. While none of these studies were performed solely in women, their findings corroborate our observation that the BIS equations do not outperform the CKD-EPI equations with respect to mortality risk prediction.Furthermore, findings from other studies of older men and women have been consistent with our observation that the BIS equations do not outperform the CKD-EPI equations in predicting all-cause and CVD mortality , 18, 19.The clinical implication of our study is of importance to both clinicians and researchers. The 2012 KDIGO guideline recommends a paradigm shift from the clinical standard MDRD eGFR equation to the CKD-EPI equations to estimate GFR . For popOur study has several key strengths that set it apart from other studies. First, to our knowledge, we are the first study to test the performance of the BIS equations against the CKD-EPI equations to predict death in a cohort entirely composed of older women. Our cohort had long-term follow-up and accurate ascertainment of outcomes and covariate data. Furthermore, we present equations based not only on SCr but also on SCr and Scysc, both of which are traceable to the international reference standard. This allowed us to compare the predictive validity of each BIS equation against a comparable CKD-EPI equation. In addition, the incorporation of Scysc in older adults may be important given studies that have shown that Scysc-based estimates are more sensitive predictors of death in older adults .We also note that our study has limitations to consider. We lack generalizability to male populations given our cohort is entirely female. However, we have previously published similar findings in a cohort consisting only of older men, thus corroborating that our findings are consistent in women and men. In addition, our cohort is largely Caucasian and though 10% are Black, there is limited racial or ethnic diversity otherwise. The BIS equations were developed in entirely Caucasian population which makes SOF a fitting cohort in which to test the BIS equations. However, because of the limited diversity in the BIS Study, there is no race adjustment for those equations which may limit their reliability among Black SOF participants. Also, we lack information regarding a complementary marker of renal function and predictor of CKD-related death, albuminuria. However, because the purpose of this analysis was purely to compare the performance of 4 eGFR formulae to predict death, the addition of albuminuria across equations would not likely have changed our observations. In addition, we lack information on the cause of CKD, if present, or episodes of acute kidney injury during the follow-up period. These factors may be related to risk of CKD-related outcomes such as mortality. However, we have adjusted for common comorbid conditions that cause CKD such as DM and HTN. Finally, we cannot be certain that our estimates of renal function represent the true baseline eGFR of the participants because we do not have prior estimates of renal function in these population-based cohorts in which we measured SCr from stored serum from a single time point.To sum, the BIS formulae identify a higher proportion of older community-dwelling women as having CKD. Our results suggest that the BIS equations do not outperform guideline-recommended CKD-EPI equations. Thus, with respect to predicting risk of death from estimated renal function in older women, our findings do not lend support for incorporation of the BIS equations into clinical practice or research.The supplementary materials provided include four tables. The first shows the 4 equations used to estimate GFR in this manuscript. The second table outlines the net reclassification improvement formulae used. Finally, the last two tables display the results of Cox proportional hazards regression analysis using quartiles of eGFR instead of categories of eGFR as the predictor of all-cause or cardiovascular death.


Japanese encephalitis virus (JEV) is the most commonly identified cause of acute encephalitis syndrome (AES) in Asia. The WHO recommended test is anti-JEV IgM-antibody-capture-enzyme-linked-immunosorbent-assay (JEV MAC-ELISA). However, data suggest this has low positive predictive value, with false positives related to other Flavivirus infections and vaccination. JEV RT-PCR in cerebrospinal fluid (CSF) and/or serum is highly specific, but is rarely positive; 0–25% of patients that fulfil the WHO definition of JE  and JEV MAC-ELISA positive). Testing other body fluids by JEV RT-qPCR may improve the diagnosis. As a pilot study thirty patients admitted to Mahosot Hospital 2014–2017, recruited to the South-East-Asia-Encephalitis study, were tested by JEV MAC-ELISA and two JEV real-time RT-PCR (RT-qPCR) assays (NS2A and NS3). Eleven (36.7%) were JEV MAC-ELISA positive. Available CSF and serum samples of these patients were JEV RT-qPCR negative but 2 (7%) had JEV RNA detected in their throat swabs. JEV RNA was confirmed by re-testing, and sequencing of RT-qPCR products. As the first apparent report of JEV RNA detection in human throat samples, the provides new perspectives on human JEV infection, potentially informing improving JEV detection. We suggest that testing patients’ throat swabs for JEV RNA is performed, in combination with molecular and serological CSF and serum investigations, on a larger scale to investigate the epidemiology of the presence of JEV in human throats. Throat swabs are an easy and non-invasive tool that could be rolled out to a wider population to improve knowledge of JEV molecular epidemiology. Japanese encephalitis virus (JEV) as a major cause of encephalitis in Asia, with recent evidence of possible autochthonous transmission in Africa3. Among the key factors in its persistent role as a public health problem are the limitations of existing diagnostic tests, our understanding of its epidemiology and inadequate implementation of vaccination programmes.Evidence continues to implicate 4. However, there are concerns about the accuracy of JEV MAC-ELISA for diagnosing JEV. Low positive predictive value of JEV MAC-ELISA has been reported5, and there are recognised difficulties with false positives related to vaccination and in areas where other Flavivirus infections are endemic7.The conventional mainstay of JEV encephalitis diagnosis is serology, with serum and cerebrospinal fluid (CSF) anti-JEV IgM antibody capture enzyme-linked immunosorbent assays (JEV MAC-ELISA) recommended by the World Health Organization (WHO)8. However, JEV RT-PCR testing of CSF and serum samples has low sensitivity, rarely positive in patients at presentation  and JEV MAC-ELISA positivity)9. Other body fluids may provide additional source of JEV RNA but there has been little investigation of this possibility. Other Flaviviruses, such as West Nile virus, Zika virus and Dengue virus, have been detected in urine, and JEV RNA has recently been detected in the urine of two patients13.Diagnosis of JEV by Reverse-Transcription (RT)-PCR is highly specific, and enables improved understanding of the molecular epidemiology of JEV14.Human throat swab samples have apparently not been used to test for JEV RNA, although there is recent evidence of high JEV viral loads in the oronasal secretions in the pigs, the amplifying host, and that the tonsils may be a site of viral replication and contribute to transmissionWe therefore hypothesised that JEV may be present in the throats of human patients. If this is the case, the use of non-invasive throat swab clinical samples could potentially improve the diagnosis of JEV and our understanding of JEV molecular epidemiology. We performed a pilot study to evaluate if JEV RNA could be detected by RT-qPCR in throat swabs of patients admitted to Mahosot Hospital, Vientiane, Laos, with AES clinical presentation.From November 2014 to June 2017, 129 patients admitted to Mahosot Hospital who had lumbar puncture presented with clinical AES presentation. Of these, 61 patients presented within 7 days of disease onset and 40 of these were anti-JEV IgM positive on CSF and/or serum or undiagnosed following routine testing. Throat swab samples from 30 patients were available and were included in this study. Eleven (36.7%) patients were anti-JEV IgM MAC-ELISA positive in CSF and admission serum . This throat swab was re-extracted and retested, and results demonstrated a NS2A RT-qPCR Ct of 34 and a negative NS3 RT-qPCR. For patient 10, only NS2A RT-qPCR was performed from the throat swab and was found positive with a Ct of 36. Unfortunately, sample volume was insufficiency to permit repetition or NS3 RT-qPCR.Sequences of NS2A RT-qPCR products for patients 5 and 10 are displayed in Fig. We provide strong evidence of JEV RNA detection in throat swab from two patients with clinical presentation of AES. Sequencing of JEV RT-qPCR products confirmed the detection of JEV sequences with nucleotide differences as compared to all JEV strains handled in the laboratory, reducing the likelihood of contamination having occurred. JEV RNA was detected from patient 5 using two different RT-qPCR systems targeting two different genes (NS2A and NS3) and in two sample aliquots from same patient extracted at different time.Consistent with the literature that JEV RNA is rarely detected in CSF or serum samples, neither of the throat swab JEV positive patients had JEV RNA detected in CSF or serum. Therefore, the use of throat swabs in addition to serum and CSF increased JEV molecular detection. In contrast to JEV MAC-ELISA, the detection of JEV RNA is highly specific for confirming JEV infection. Throat swabs are non-invasive samples, which may be useful to improve diagnosis, especially in patients in whom a lumbar puncture (LP) is not possible or where LP facilitates are not available. Throat swab collection does not require specific facilities, and therefore could facilitate sampling from a wider population to improve our understanding of JEV molecular epidemiology.15. More recently, JEV RNA has been detected in the urine of two patients13. It is likely that JEV RNA may be excreted in urine intermittently and at low levels. We are not aware of any published studies testing for JEV RNA in human throat swabs. However, our findings are consistent with the study by Ricklin et al., in pigs (14). However, the current study was performed on only a small number patients’ samples, and larger studies are needed to better understand the role of throat samples in the detection of JEV RNA by RT-PCR, especially on the frequency and duration of throat swab positivity in relation to clinical presentation, anti-JEV IgM and CSF positivity and patient age.There are minimal data on the detection of JEV RNA in other body fluids of patients. One retrospective study did not detect JEV RNA in urine of 52 patients in ChinaOur findings also raise the question of possible throat excretion of live JEV virus in infected patients. Throat swab samples from patients 5 and 10 were inoculated on Vero and C6/36 cells but unfortunately the virus could not be isolated, probably due to the low virus titre, but we cannot exclude that JEV RNA fragments alone were present in the throat. More extensive studies are required to investigate the epidemiology of live JEV in human throats.16 conducted at Mahosot Hospital, Vientiane, Laos, between November 2014 to June 2017. CSF, serum and throat swab  samples at patient inclusion, and follow-up serum at hospital discharge, were collected and stored at −80 °C for subsequent testing. Patients included in our study met all the following criteria: (i) were admitted to hospital with sings/symptoms fulfilling the WHO clinical case definition for AES4, (ii) had no contraindication for lumbar puncture, (iii) presented within 7 days of onset of symptoms, (iv) gave written informed consent, (v) were found anti-JEV IgM positive or undiagnosed following routine diagnosis (vi) had throat swabs available for testing.We performed a retrospective study of patients recruited in the South-East Asia Encephalitis studyEthical clearance was granted by the Ethical Review Committee of the Faculty of Medical Sciences, National University of Lao, and the Oxford University Tropical Ethics Research Committee, Oxford, UK. The study was performed in accordance with relevant guidelines and regulations.17. As hospital routine diagnosis procedure, CSF and serum samples from patients with suspicion of CNS infections were tested using this kit following manufacturer’s instruction. A 1/10 dilution was used for CSF samples as recommended by WHO. For each tested sample, ISR (Immune Status Ratio) was calculated and interpreted for the qualitative detection of anti-JEV IgM following manufacturer’s instructions: positive if >6.0, equivocal if 4.0–6.0, or negative if <4.0.A WHO recommended commercial JE MAC-ELISA assay is the InBios JE Detect kit After defrosting, 200 µL of each sample was extracted and eluted in 60 µL using EZ1 Virus Mini Kit v2.0  following manufacturer’s instruction.et al., in prep., Supplementary Data Fig. Two in-house JEV RT-qPCR assays were performed, targeting different parts of the JEV genome, NS2A and NS3. Primers and probes were designed to identify the best in-silico matching from a multiple genome alignment of all JEV sequences (303) above 9,000 base pairs available on GenBank. Optimisation and validation assays  was added to each patient sample prior to extraction. MS2 RT-qPCR was performed to control the extraction process and to exclude inhibition as previously describedWhen available, positive RT-qPCR results were further investigated by sending the RT-qPCR product for next-generation sequencing using Ion S5 system , to Unité des Virus Emergents, Faculty of Medicine, Marseille, France.19, with the sequence of the positive control used for the JEV RT-qPCR and the sequences of any other JEV RNA handled in the laboratory.Sequences were aligned, using ClustalX 2.1Every effort has been made to present the data generated and analysed during the study in entirety. The corresponding author will be able to provide further details on reasonable request.Supplementary Information