The spectrum of neutralizing and non-neutralizing anti-FVIII antibodies in a nationwide cohort of 788 persons with hemophilia A

ObjectivesAnti-factor VIII (FVIII) antibodies have been reported to exhibit both neutralizing and non-neutralizing characteristics. This is the first study investigating the full spectrum of FVIII-specific antibodies, including non-neutralizing antibodies, very-low titer inhibitors, and inhibitors, in a large nationwide population of persons with hemophilia A of all severities.MethodsAll persons with hemophilia A (mild (FVIII > 5–40 IU/dL)/moderate [FVIII 1–5 IU/dL)/severe (FVIII < 1 IU/dL)] with an available plasma sample who participated in the sixth Hemophilia in the Netherlands study between 2018 and 2019 were included. The presence of anti-FVIII antibodies of the immunoglobulin A, M, and G isotypes and IgG subclasses, along with antibody titer levels, were assessed using direct-binding ELISAs. FVIII specificity was assessed using a competition-based ELISA approach. The inhibitor status was determined using the Nijmegen ultra-sensitive Bethesda assay (NusBA) and the Nijmegen Bethesda assay (NBA).ResultsIn total, 788 persons with hemophilia A (336 (42.6%) mild, 123 (15.6%) moderate, 329 (41.8%) severe hemophilia) were included. The median age was 45 years (IQR 24–60), and the majority (50.9%) had over 150 exposure days to FVIII concentrates. Within our population, 144 (18.3%) individuals had non-neutralizing FVIII-specific antibodies, 10 (1.3%) had very low-titer inhibitors (NusBA positive; NBA negative), and 13 (1.6%) had inhibitors (both NusBA and NBA positive). IgG1 was the most abundant FVIII-specific antibody subclass, and the highest titer levels were found for IgG4. In individuals without a reported history of inhibitor development, no clear differences were observed in antibody patterns between those who were minimally or highly exposed to FVIII concentrates. IgG4 subclass antibodies were only observed in persons with a reported history of FVIII inhibitor or in those with a currently detected (very low-titer) inhibitor.ConclusionIn this cross-sectional study, we identified non-neutralizing antibodies in a relatively large proportion of persons with hemophilia A. In contrast, in our population, consisting of persons highly exposed to FVIII concentrates, (very low-titer) inhibitors were detected only in a small proportion of persons, reflecting a well-tolerized population. Hence, our findings suggest that only a small subpopulation of non-neutralizing FVIII-specific antibodies is associated with clinically relevant inhibitors

Plasma lipid profiles discriminate bacterial from viral infection in febrile children

Fever is the most common reason that children present to Emergency Departments. Clinical signs and symptoms suggestive of bacterial infection are often non-specific, and there is no definitive test for the accurate diagnosis of infection. The 'omics' approaches to identifying biomarkers from the host-response to bacterial infection are promising. In this study, lipidomic analysis was carried out with plasma samples obtained from febrile children with confirmed bacterial infection (n = 20) and confirmed viral infection (n = 20). We show for the first time that bacterial and viral infection produces distinct profile in the host lipidome. Some species of glycerophosphoinositol, sphingomyelin, lysophosphatidylcholine and cholesterol sulfate were higher in the confirmed virus infected group, while some species of fatty acids, glycerophosphocholine, glycerophosphoserine, lactosylceramide and bilirubin were lower in the confirmed virus infected group when compared with confirmed bacterial infected group. A combination of three lipids achieved an area under the receiver operating characteristic (ROC) curve of 0.911 (95% CI 0.81 to 0.98). This pilot study demonstrates the potential of metabolic biomarkers to assist clinicians in distinguishing bacterial from viral infection in febrile children, to facilitate effective clinical management and to the limit inappropriate use of antibiotics

Serious child and adolescent behaviour disorders; a valuation study by professionals, youth and parents

Abstract Background In child and youth care, quantitative estimates of the impact of serious behaviour problems have not yet been made. Such input is needed to support decision making on investments in treatment. The aim of this paper was to elicit valuations of social and conduct disorders in children and adolescents from three different perspectives: professionals, youth, and parents. Methods We obtained valuations from 25 youth care professionals, 50 children (age 9–10) without serious behaviour problems and 36 adolescents (age 16–17) with and without serious behaviour disorders, and 46 parents with children in the aforementioned age categories. Valuations were estimated from 18 descriptions of behaviour disorders in youth aged 9 and 15 years. Descriptions included Oppositional Defiant Disorder (ODD), Conduct Disorder (CD), and Disruptive Behaviour Disorder (DBD). Comorbid conditions were Attention Deficit Hyperactivity Disorder and substance abuse. Valuations were obtained with the EuroQol questionnaire (EQ-5D-3 L) and a visual analogue scale (VAS). Results Valuations were generally severe; problems were by and large reported to worsen quality of life by 50% compared to being fully healthy. Professionals regarded DBD with substance abuse as most severe (VAS values 0.41 for children, and 0.43 for adolescents, i.e. less than half of normal). They rated ODD as least severe (VAS values 0.58 for children, 0.59 for adolescents). Children, adolescents and parents gave lower valuations than professionals, and had a wider range of scores, particularly at the lower end of the scale. Conclusions Behaviour disorders pose a formidable burden from the perspectives of professionals as well as children, adolescents and parents. These results may support medical decision making to set priorities with regard to prevention and treatment based on perceived severity

Puzzling findings in studying the outcome of "Real World" adolescent mental health services: The TRAILS Study

Background: The increased use and costs of specialist child and adolescent mental health services (MHS) urge us to assess the effectiveness of these services. The aim of this paper is to compare the course of emotional and behavioural problems in adolescents with and without MHS use in a naturalistic setting. Method and Findings: Participants are 2230 (pre)adolescents that enrolled in a prospective cohort study, the TRacking Adolescents' Individual Lives Survey (TRAILS). Response rate was 76%, mean age at baseline 11.09 (SD 0.56), 50.8% girls. We used data from the first three assessment waves, covering a six year period. Multiple linear regression analysis, propensity score matching, and data validation were used to compare the course of emotional and behavioural problems of adolescents with and without MHS use. The association between MHS and follow-up problem score (beta 0.20, SE 0.03, p-value<0.001) was not confounded by baseline severity, markers of adolescent vulnerability or resilience nor stressful life events. The propensity score matching strategy revealed that follow-up problem scores of non-MHS-users decreased while the problem scores of MHS users remained high. When taking into account future MHS (non) use, it appeared that problem scores decreased with limited MHS use, albeit not as much as without any MHS use, and that problem scores with continuous MHS use remained high. Data validation showed that using a different outcome measure, multiple assessment waves and multiple imputation of missing values did not alter the results. A limitation of the study is that, although we know what type of MHS participants used, and during which period, we lack information on the duration of the treatment. Conclusions: The benefits of MHS are questionable. Replication studies should reveal whether a critical examination of everyday care is necessary or an artefact is responsible for these results

Univariate regression analyses with CBCL scores at T2 as dependent variable (standardised regression coefficients with standard errors).

<p>CBCL, Child Behaviour Checklist; MHS, Mental Health Services; IQ, Intelligence Quotient; EXT, Externalising disorder; INT, internalising disorder; (t) teacher and (p) parent rating.</p>*<p>p-value<0.05;</p>**<p>p-value<0.01;</p>***<p>p-value<0.001.</p

MHS use and CBCL scores across the three measurement waves.

<p>In this figure, mean CBCL total problem scores are displayed of propensity matched TRAILS participants that did or did not use MHS during a certain time period. Legend: Blue line denotes TRAILS participants with no MHS at any time (N = 146). Red line denotes TRAILS participants with MHS between T1 and T2 (N = 114). Green line denotes TRAILS participants with MHS between T2 and T3 (N = 21). Purple line denotes TRAILS participants with continuous MHS use (N = 53). CBCL: Child Behaviour Checklist, total problem score. MHS: Mental health services.</p

A and B. Uncorrected and propensity adjusted mean CBCL-scores of TRAILS participants with and without MHS use.

<p>In <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0044704#pone-0044704-g001" target="_blank">figure 1A</a>, mean total problem scores (CBCL) are displayed of TRAILS participants with and without MHS use at baseline (T1) and follow up (T2). In <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0044704#pone-0044704-g001" target="_blank">figure 1B</a>, mean total problem scores (CBCL) are displayed of propensity matched TRAILS participants with and without MHS use. The participants who did not use MHS had, at baseline, the same propensity (i.e. likelihood) to receive MHS as the participants who actually used MHS. Legend A: Red square denotes TRAILS participants with MHS use (N = 188). Blue square denotes TRAILS participants without MHS use (N = 1692). CBCL: Child Behaviour Checklist, total problem score. MHS: Mental health services. Legend B: Red square denotes TRAILS participants with MHS use (N = 167). Blue square denotes propensity score matched TRAILS participants without MHS use (N = 167). CBCL: Child Behaviour Checklist, total problem score. MHS: Mental health services.</p

CBCL scores at T2 predicted by MHS use between T1 and T2, adjusted for baseline severity of symptoms (model 1); baseline severity and markers of adolescents vulnerability and resilience (model 2); and baseline severity, markers of adolescent vulnerability and resilience, and stressful life events (model 3).

<p>Standardised regression coefficients (ß) and standard errors (SE) are presented.</p><p>CBCL, Child Behaviour Checklist; MHS, Mental Health Services; IQ, Intelligence Quotient; (t) teacher and (p) parent rating. Adjusted R² model 1: 0.51; model 2: 0.52; model 3: 0.52.</p>*<p>p-value<0.05;</p>**<p>p-value<0.01;</p>***<p>p-value<0.001.</p