Niemann-Pick type C Suspicion Index tool: analyses by age and association of manifestations

The Suspicion Index (SI) screening tool was developed to identify patients suspected of having Niemann-Pick disease type C (NP-C). The SI provides a risk prediction score (RPS) based on NP-C manifestations within and across domains (visceral, neurological, and psychiatric). The aim of these subanalyses was to further examine the discriminatory power of the SI by age and manifestation-associations by NP-C suspicion-level and leading manifestations. The original retrospectively collected data were split into three patient age groups, where NP-C-positive cases were >16 years (n = 30), 4-16 years (n = 18), and 16 years) and 0.981 (4-16 years) but weaker 0.562 for infants ( 4 years, prominent leading manifestation-associations were ataxia with dystonia, dysarthria/dysphagia, and cognitive decline. Psychosis was associated with dysarthria/dysphagia but also with cognitive decline and treatment-resistant psychiatric symptoms. The SI tool maintains strong discriminatory power in patients >4 years but is not as useful for infants <4 years. The SI is also informative regarding the association and co-occurrence of manifestations in patients with NP-

Identifying early pulmonary arterial hypertension biomarkers in systemic sclerosis: Machine learning on proteomics from the DETECT cohort

Pulmonary arterial hypertension (PAH) is a devastating complication of Systemic Sclerosis (SSc). Screening for PAH in SSc has increased detection, allowed early treatment for PAH, and improved patient outcomes. Blood-based biomarkers that reliably identify SSc patients at risk of PAH, or with early disease, would significantly improve screening, potentially leading to improved survival, and provide novel mechanistic insights into early disease. The main objective of this study was to identify a proteomic biomarker signature that could discriminate SSc patients with, and without PAH using a Machine Learning approach, and to validate the findings in an external cohort.Serum samples from patients with SSc and PAH (n=77) and SSc without PH (non-PH, n=80) were randomly selected from the clinical DETECT study and underwent proteomic screening using the MYRIAD RBM discovery platform consisting of 313 proteins. Samples from an independent validation cohort (SSc-PAH, n=22 and non-PH, n=22) were obtained from University of Sheffield, UK. Random Forest (RF) analysis identified a novel panel of eight proteins, comprising Collagen IV, Endostatin, IGFBP-2, IGFBP-7, MMP-2, Neuropilin-1, NT-proBNP and RAGE, that discriminated PAH from non-PH in SSc patients in the DETECT discovery cohort (average area under the ROC values (ROC-AUC) of 0.741, 65.1% sensitivity / 69.0% specificity) was reproduced in the Sheffield cohort (81.1% accuracy, 77.3% sensitivity / 86.5% specificity). This novel 8-protein biomarker panel has the potential to improve early detection of PAH in SSc patients and may provide novel insights into the pathogenesis of PAH in the context of SSc

Nailfold Videocapillaroscopic and Other Clinical Risk Factors for Digital Ulcers in Systemic Sclerosis: A Multicenter, Prospective Cohort Study.

OBJECTIVE: To identify nailfold videocapillaroscopic and other clinical risk factors for new digital ulcers (DUs) in a 6-month period in patients with systemic sclerosis (SSc), the videoCAPillaroscopy (CAP) study. METHODS: Overall 623 patients with SSc from 59 centers (14 countries) were stratified into two groups: "DU History" and "No-DU History". At enrollment, patients underwent detailed nailfold videocapillaroscopic evaluation and an assessment of demographics, DU status, and clinical and SSc characteristics. Risk factors for developing new DUs were assessed using univariable and multivariable logistic regression analyses. RESULTS: Of the "DU History" group (n = 468), 79.5% were female, the mean age was 54.0\u2009\ub1\u200913.7 years, 59.8% had limited cutaneous SSc, and 22% developed a new DU during follow-up. The strongest risk factors for new DUs identified by multivariable logistic regression (MLR) in the "DU History" group included: mean number of capillaries/mm in the middle finger of the dominant hand, number of DUs (0, 1, 2, 653), and presence of critical digital ischemia. The receiver operating characteristic area under the curve (ROC-AUC) (95% confidence interval [CI]) of the final MLR model was 0.738 (0.681-0.795). Internal validation through bootstrap generated a ROC-AUC (95% CI) of 0.633 (0.510-0.756). CONCLUSION: This international, prospective study including detailed nailfold videocapillaroscopic evaluation and extensive clinical characterization of patients with SSc identified the mean number of capillaries/mm in the middle finger of the dominant hand, number of DUs and presence of critical digital ischemia at enrollment as risk factors for the development of new DUs. This article is protected by copyright. All rights reserved