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BackgroundDifferent diagnostic and classification criteria are available for hereditary recurrent fevers (HRF)—familial Mediterranean fever (FMF), tumour necrosis factor receptor-associated periodic fever syndrome (TRAPS), mevalonate kinase deficiency (MKD) and cryopyrin-associated periodic syndromes (CAPS)—and for the non-hereditary, periodic fever, aphthosis, pharyngitis and adenitis (PFAPA). We aimed to develop and validate new evidence-based classification criteria for HRF/PFAPA.MethodsStep 1: selection of clinical, laboratory and genetic candidate variables; step 2: classification of 360 random patients from the Eurofever Registry by a panel of 25 clinicians and 8 geneticists blinded to patients’ diagnosis (consensus ≥80%); step 3: statistical analysis for the selection of the best candidate classification criteria; step 4: nominal group technique consensus conference with 33 panellists for the discussion and selection of the final classification criteria; step 5: cross-sectional validation of the novel criteria.ResultsThe panellists achieved consensus to classify 281 of 360 (78%) patients (32 CAPS, 36 FMF, 56 MKD, 37 PFAPA, 39 TRAPS, 81 undefined recurrent fever). Consensus was reached for two sets of criteria for each HRF, one including genetic and clinical variables, the other with clinical variables only, plus new criteria for PFAPA. The four HRF criteria demonstrated sensitivity of 0.94–1 and specificity of 0.95–1; for PFAPA, criteria sensitivity and specificity were 0.97 and 0.93, respectively. Validation of these criteria in an independent data set of 1018 patients shows a high accuracy (from 0.81 to 0.98).ConclusionEurofever proposes a novel set of validated classification criteria for HRF and PFAPA with high sensitivity and specificity.

Background There is an increasing number of papers reporting the real world use of Nusinersen in different cohorts of SMA patients. Main body The aim of this paper was to critically review the literature reporting real world data on motor function in type 2 and 3 patients treated with Nusinersen, subdividing the results according to SMA type, age and type of assessment and performing a meta-analysis of the available results. We also report the available data collected in untreated patients using the same measures. Of the 400 papers identified searching for Nusinersen and spinal muscular atrophy, 19 reported motor function in types 2 and 3: 13 in adults, 4 in children and 2 included both. Twelve papers reported untreated patients’ data. All studies reported positive changes on at least one of the functional measures and at every time point while all-untreated cohorts showed negative changes. Conclusion Our review suggests that Nusinersen provides a favorable benefit in motor function across a wide range of SMA type 2 and 3 patients over a 10–14 month observation period. Although a direct comparison with studies reporting data from untreated patients cannot be made, the longitudinal changes in the treated cohorts (consistently positive) are divergent from those observed in the untreated cohorts (consistently negative). The difference could be observed both in the global cohorts and in smaller groups subdivided according to age, type or functional status.

Disability progression in multiple sclerosis remains resistant to treatment. The absence of a suitable biomarker to allow for phase 2 clinical trials presents a high barrier for drug development. We propose to enable short proof-of-concept trials by increasing statistical power using a deep-learning predictive enrichment strategy. Specifically, a multi-headed multilayer perceptron is used to estimate the conditional average treatment effect (CATE) using baseline clinical and imaging features, and patients predicted to be most responsive are preferentially randomized into a trial. Leveraging data from six randomized clinical trials ( n  = 3,830), we first pre-trained the model on the subset of relapsing-remitting MS patients ( n  = 2,520), then fine-tuned it on a subset of primary progressive MS (PPMS) patients ( n  = 695). In a separate held-out test set of PPMS patients randomized to anti-CD20 antibodies or placebo ( n  = 297), the average treatment effect was larger for the 50% (HR, 0.492; 95% CI, 0.266-0.912; p  = 0.0218) and 30% (HR, 0.361; 95% CI, 0.165-0.79; p  = 0.008) predicted to be most responsive, compared to 0.743 (95% CI, 0.482-1.15; p  = 0.179) for the entire group. The same model could also identify responders to laquinimod in another held-out test set of PPMS patients ( n  = 318). Finally, we show that using this model for predictive enrichment results in important increases in power. There are limited predictive biomarkers for drug treatment responses in individuals with multiple sclerosis. Here using existing clinical trials data, the authors propose a deep-learning predictive enrichment strategy to identify which participants are most likely to respond to a treatment.

Background The availability of methotrexate and the introduction of multiple biological agents have revolutionized the treatment of juvenile idiopathic arthritis (JIA). Several international and national drug registries have been implemented to accurately monitor the long-term safety/efficacy of these agents. This report aims to present the combined data coming from Pharmachild/PRINTO registry and the national registries from Germany (BiKeR) and Sweden. Methods Descriptive statistics was used for demographic, clinical data, drug exposure, adverse events (AEs) and events of special interest (ESIs). For the Swedish register, AE data were not available. Results Data from a total of 15,284 patients were reported: 8274 (54%) from the Pharmachild registry and 3990 (26%) and 3020 (20%) from the German and the Swedish registries, respectively. Pharmachild children showed a younger age (median of 5.4 versus 7.6 years) at JIA onset and shorter disease duration at last available visit (5.3 versus 6.1–6.8) when compared with the other registries. The most frequent JIA category was the rheumatoid factor–negative polyarthritis (range of 24.6–29.9%). Methotrexate (61–84%) and etanercept (24%–61.8%) were the most frequently used synthetic and biologic disease-modifying anti-rheumatic drugs (DMARDs), respectively. There was a wide variability in glucocorticoid use (16.7–42.1%). Serious AEs were present in 572 (6.9%) patients in Pharmachild versus 297 (7.4%) in BiKeR. Infection and infestations were the most frequent AEs (29.4–30.1%) followed by gastrointestinal disorders (11.5–19.6%). The most frequent ESIs were infections (75.3–89%). Conclusions This article is the first attempt to present a very large sample of data on JIA patients from different national and international registries and represents the first proposal for data merging as the most powerful tool for future analysis of safety and effectiveness of immunosuppressive therapies in JIA. Registry registration The Pharmachild registry is registered at ClinicalTrials.gov ( NCT01399281 ) and at the European Network of Centres for Pharmacoepidemiology and Pharmacovigilance (ENCePP) ( http://www.encepp.eu/encepp/viewResource.htm?id=19362 ). The BiKeR registry is registered at ENCePP ( http://www.encepp.eu/encepp/viewResource.htm?id=20591 ).

Background Human papillomavirus (HPV) adversely affects human reproduction. We aimed to evaluate the prevalence of HPV infection in men and its correlation with semen parameters and reproductive outcomes. Methods In this prospective observational cohort study, 384 semen samples were collected from 237 male partners of infertile couples. The presence of HPV DNA and genotyping were analyzed in semen by quantitative PCR. A total of 186 intrauterine inseminations (IUI) in 101 couples and 186 assisted reproduction techniques (ART) cycles in 155 couples were performed. Associations between HPV positivity and semen parameters and fertility outcomes were evaluated using a generalized linear mixed model. Results The prevalence of HPV was 22.7%. Twenty-three HPV types were detected and 69.5% of positive samples presented at least one high risk (HR)-HPV genotype. HPV-18 (14%), HPV-53 (10%), and HPV-56 (10%) were the most prevalent HR-HPV genotypes followed by HPV-16, HPV-31, and HPV-51 (8%). HPV-42 was the most prevalent low risk (LR)-HPV genotype (25%). More than one HPV type was detected in 41% of HPV + samples. After capacitation, 30% of HPV + samples remained positive. We found no relationship between HPV infection and sperm volume, sperm concentration, and progressive motility both before and after semen capacitation. We observed a not significant different clinical pregnancy per cycle in the HPV − (6.8%) and HPV + (5.0%) IUI. We did not find any significant difference in fertilization, cleavage, quality of developed embryos, blastocyst formation nor in embryo utilization of ART cycles. Slightly lower cumulative pregnancy (33% vs 39%) and live-birth (25% vs 30%) rates and higher miscarriage rate (53% and 29%) were observed in HPV + with respect to HPV − cycles. Fifty-five neonatal outcomes from HPV − ( n  = 45) and HPV + ( n  = 10) cycles were available. No stillbirths as well as no malformations were recorded. Conclusions This study confirmed previous findings that HPV DNA is present in semen of one quarter of infertile couples. No significant association of seminal HPV presence with semen parameters was found. We observed a trend of worst clinical outcomes in the HPV + group that is worth further investigation in a large population to draw definitive conclusions.

Microglia (MG) is suggested to play an immunopathological role of in Multiple Sclerosis (MS). Since hyper-reflective foci (HRF) might mark MG activation, analysis by Optic Coherence Tomography (OCT) in MS patients under disease modifying therapies may help to clarify MS immunopathology as well as drug's mechanism of intrathecal action. To analyze HRF in patients treated with Natalizumab (NTZ), a high efficacy therapy for MS. The effect of NTZ on the retina of 36 Relapsing-Remitting MS patients was investigated in a prospective, single-center study. OCT was performed immediately before the first infusion and then between infusion 3 and 4, infusion 6 and 7, infusion 11 and 13. Peripapillary and macular scans were acquired, evaluating peripapillary RNFL thickness, macular volumes (vertical scans), and HRF count (horizontal scan) in Ganglion Cell Layer (GCL), Inner Plexiform Layer (IPL) and Inner Nuclear Layer (INL). Clinical examination was performed every six months. HRF count significantly increased under NTZ therapy (p<0.001) in both GCL (18.85 ± 6.93 at baseline, 28.24 ± 9.55 after 12 months) and IPL (25.73 ± 7.03 at baseline, 33.21 ± 8.50 after 12 months) but remained stable in INL (33.65 ± 7.76 at baseline, 36.06 ± 6.86 after 12 months, p=0.87), while no relevant modification of pRNFL and macular volumes were observed during the study. EDSS remained stable and no clinical relapse was observed between month 6 and 12. In RRMS NTZ affects HRF count in GCL and IPL, but not in INL, suggesting that NTZ does not impact on some aspects of MS immunopathology.

Aims Investigate if different clinical and psychophysical bedside tools can differentiate between district migraine phenotypes in ictal/perictal (cohort 1) and interictal (cohort 2) phases. Method This observational study included two independent samples in which patients were subgrouped into distinct clusters using standardized bedside assessment tools (headache frequency, disability, cervical active range of motion, pressure pain threshold in different areas): (A) cohort 1—ictal/perictal migraine patients were subgrouped, based on previous studies, into two clusters, i.e., Cluster-1.1 No Psychophysical Impairments (NPI) and Cluster-1.2 Increased Pain Sensitivity and Cervical Musculoskeletal Dysfunction (IPS-CMD); (B) cohort 2—interictal migraine patients were subgrouped into three clusters, i.e., Cluster-2.1 NPI, Cluster-2.2 IPS, and Cluster-2.3 IPS-CMD. Clinical characteristics (multiple questionnaires), somatosensory function (comprehensive quantitative sensory testing (QST)), and cervical musculoskeletal impairments (cervical musculoskeletal assessment) were assessed and compared across headache clusters and a group of 56 healthy controls matched for sex and age. Results Cohort 1: A total of 156 subjects were included. Cluster-1.2 (IPS-CMD) had higher headache intensity ( p  = 0.048), worse headache-related ( p  = 0.003) and neck-related disability ( p  = 0.005), worse quality of life ( p  = 0.003), and higher symptoms related to sensitization ( p  = 0.001) and psychological burden ( p  = 0.005) vs. Cluster-1.1(NPI). Furthermore, Cluster-1.2 (IPS-CMD) had (1) reduced cervical active and passive range of motion ( p  < 0.023), reduced functionality of deep cervical flexors ( p  < 0.001), and reduced values in all QST( p  < 0.001) vs. controls, and (2) reduced active mobility in flexion, left/right lateral flexion ( p  < 0.045), and reduced values in QST ( p  < 0.001) vs. Cluster-1.1 (NPI). Cohort 2: A total of 154 subjects were included. Cluster-2.3 (IPS-CMD) had (1) longer disease duration ( p  = 0.006), higher headache frequency ( p  = 0.006), disability ( p  < 0.001), and psychological burden ( p  = 0.027) vs. Cluster-2.2 (IPS) and (2) higher headache-related disability ( p  = 0.010), neck-related disability ( p  = 0.009), and higher symptoms of sensitization ( p  = 0.018) vs. Cluster-2.1 (NPI). Cluster-2.3(IPS-CMD) had reduced cervical active and passive range of motion ( p  < 0.034), and reduced functionality of deep cervical flexors ( p  < 0.001), vs. controls, Custer-2.1 (NPI), and Cluster-2.2 (IPS). Cluster-2.2 (IPS) and 2.3 (IPS-CMD) had reduced QST values vs. controls ( p  < 0.001) and Cluster-2.1 ( p  < 0.039). Conclusion A battery of patient-related outcome measures (PROMs) and quantitative bedside tools can separate migraine clusters with different clinical characteristics, somatosensory functions, and cervical musculoskeletal impairments. This confirms the existence of distinct migraine phenotypes and emphasizes the importance of migraine phases of which the characteristics are assessed. This may have implications for responders and non-responders to anti-migraine medications.

Background Evidence from clinical trials providing average effects in populations is often used to forecast individualized patient outcomes similar to the trial patients. Multiple sclerosis (MS), known for notable heterogeneity in outcomes, makes the evaluation of potential heterogeneity of treatment effect (HTE) significant. Identifying factors that predict individual treatment response is crucial for optimizing patient care, and this study aimed to demonstrate the feasibility (proof of concept) of applying a statistical method to predict individual treatment response in MS trials. Methods We developed an individualized response score (RS) to predict treatment response in patients with active secondary progressive MS (SPMS). The RS was a continuous combination of baseline clinical characteristics, including age, sex, previous relapses, EDSS, and disease duration. We used data from the EXPAND trial to train and validate the RS. A training dataset (70% of the data) was used to identify optimal response thresholds for four key outcomes: Expanded Disability Status Scale (EDSS), Timed 25 Foot Walk (T25FW), 9‐Hole Peg Test (9HP), and the Symbol Digit Modalities Test (SDMT). The remaining 30% of the data served as a validation set to assess the RS's predictive performance. The continuous RS was binarized (into responder and non‐responder) based on the threshold representing the top 25% versus the bottom 75% of the continuous score distribution. Results Using baseline profiles, SPMS patients exhibiting varying benefits from Siponimod across different outcomes were successfully categorized as responders or non‐responders. The overall effect of Siponimod on the EDSS was HR = 0.79 (95% CI: 0.65‐0.95), while responders’ demonstrated a HR = 0.64 (95% CI: 0.49‐0.84) versus a HR = 0.97 (95% CI: 0.74‐1.27) for non‐responders’, interaction p = 0.027. Siponimod's overall effect on SDMT progression was HR = 0.75 (95% CI: 0.63‐0.88). Responders' demonstrated a HR = 0.59 (95% CI: 0.43‐0.80) vs a HR = 1.00 (95% CI: 0.69‐1.44) for non‐responders, interaction p = 0.031. On the entire dataset, Siponimod exhibited a non‐significant effect on 9HPT (HR = 0.86, 95% CI: 0.66‐1.10) and on T25FW (HR = 0.95, 95% CI: 0.81‐1.12), whereas responders’ demonstrated a HR = 0.68 (95% CI: 0.47‐0.97) on 9HPT and a HR = 0.77 (95% CI: 0.60‐0.98) for T25FW. Conclusions This analysis demonstrated the ability to define responders to a therapy based on their baseline profile and evaluate the treatment effect on multiple endpoints, showing that the benefit on different outcomes can vary across patients. Multiple sclerosis (MS) is a highly heterogeneous disease, making it challenging to predict individual treatment responses. This study explored the feasibility of a statistical approach to identify patients most likely to benefit from treatment in clinical trials. Using data from the EXPAND trial, we developed an individualized response score based on baseline characteristics to predict Siponimod's effect in secondary progressive MS. Patients were categorized as responders or non‐responders, revealing significant variations in treatment outcomes across key measures. These findings highlight the potential for personalized treatment strategies, allowing for more tailored and effective therapy selection in MS.

Background To derive a list of opportunistic infections (OI) through the analysis of the juvenile idiopathic arthritis (JIA) patients in the Pharmachild registry by an independent Safety Adjudication Committee (SAC). Methods The SAC (3 pediatric rheumatologists and 2 pediatric infectious disease specialists) elaborated and approved by consensus a provisional list of OI for use in JIA. Through a 5 step-procedure, all the severe and serious infections, classified as per MedDRA dictionary and retrieved in the Pharmachild registry, were evaluated by the SAC by answering six questions and adjudicated with the agreement of 3/5 specialists. A final evidence-based list of OI resulted by matching the adjudicated infections with the provisional list of OI. Results A total of 772 infectious events in 572 eligible patients, of which 335 serious/severe/very severe non-OI and 437 OI (any intensity/severity), according to the provisional list, were retrieved. Six hundred eighty-two of 772 (88.3%) were adjudicated as infections, of them 603/682 (88.4%) as common and 119/682 (17.4%) as OI by the SAC. Matching these 119 opportunistic events with the provisional list, 106 were confirmed by the SAC as OI, and among them infections by herpes viruses were the most frequent (68%), followed by tuberculosis (27.4%). The remaining events were divided in the groups of non-OI and possible/patient and/or pathogen-related OI. Conclusions We found a significant number of OI in JIA patients on immunosuppressive therapy. The proposed list of OI, created by consensus and validated in the Pharmachild cohort, could facilitate comparison among future pharmacovigilance studies. Trial registration Clinicaltrials.gov NCT 01399281; ENCePP seal: awarded on 25 November 2011.

Apart from IgG oligoclonal bands, no other biomarker has, to date, been validated for diagnostic and/or prognostic purposes in multiple sclerosis (MS). To investigate a wide panel of cytokines and chemokines in the cerebrospinal fluid (CSF) of relapsing-remitting MS (RRMS) patients and evaluate their association with clinical and magnetic resonance imaging (MRI) parameters, as well as their predictive clinical value. Fifty-one RRMS at clinical onset and 17 other not inflammatory neurological disorders (ONINDs) underwent brain MRI (including 3D-T1, 3D-FLAIR, and 3-DIR sequences) and CSF examination. Eighty-seven cytokines and chemokines were analyzed in CSF by Multiplex technology. Compared to ONIND, CXCL-10, CXCL-11, CXCL-13, CCL-1, CCL-2, CCL-3, CCL-22, IL-16, and BAFF were significantly ( < 0.05) increased in RRMS CSF. However, only CCL-3 was associated with both MS diagnosis and IgGOB detection. Based on a 95%CI in ONIND (cut-off value: 0.798 pg/ml) and ROC analysis (cut-off value: 0.495 pg/ml), RRMS patients were stratified in CCL-3 (>0.736 pg/mL), CCL-3 , and CCL-3 (<0.495 pg/ml). Survival analysis disclosed a strong association between high CCL-3 values and disease reactivation (OR = 4.9, 95%CI: 1.8-13.3, < 0.005) in the following 2 years. CCL-3 deserves further investigation as a candidate prognostic biomarker for RRMS.