Disease Severity Metrics Research Articles

The effect of maxillary sinus antrostomy size on the sinus microbiome.

The optimal maxillary antrostomy size to surgically treat sinusitis is not well known. In this study, we examined clinical metrics of disease severity and symptom scores, measured secreted inflammatory markers, and characterized the sinus microbiome to determine if there were significant differences in outcome between different maxillary ostial sizes. Prospective randomized, single-blinded clinical trial enrolling 12 individuals diagnosed with recurrent acute or chronic rhinosinusitis. Each patient was blinded and randomized to receive minimal maxillary ostial dilation via balloon sinuplasty on 1 side vs a mega-antrostomy on the contralateral side. Data collected included symptom scores (20-item Sino-Nasal Outcome Test [SNOT-20]), endoscopy, and radiologic Lund-Mackay scores. During surgery and at their postoperative visit swabs were obtained from each maxillary sinus, and 16S DNA and inflammatory cytokine levels analyzed. The use of each patient as their own control allowed us to minimize confounding variables. There was statistically significant improvement in SNOT-20 symptom scores postoperatively in all patients. There were no significant differences between maxillary ostial size in postoperative endoscopy scores, cytokine profile, or bacterial burden. There were statistically significant differences in relative postoperative abundance of Staphylococcus, Lactococcus, and Cyanobacteria between the mega-antrostomy and mini-antrostomy. The method used in surgical maxillary antrostomies had no effect on endoscopy scores or cytokine profiles. Microbiome analysis determined significant differences between the different antrostomy sizes in postoperative Staphylococcus, Lactococcus, and Cyanobacteria abundance. The clinical significance of these changes in the sinus microbiome are not known but may be a result of increased access to postoperative sinonasal irrigations.

A provider global assessment quality measure for clinical practice for inflammatory skin disorders

In our evolving health care system, dermatologists are increasingly being asked to prove the value of care they provide to patients with severe skin diseases. Current quality measures for inflammatory dermatoses have limited validity and feasibility. Through collaboration and a modified Delphi process, International Dermatology Outcome Measures and the American Academy of Dermatology sought to reach consensus on a valid and feasible provider-assessed global disease severity metric to be incorporated into a quality measure for inflammatory dermatoses. To inform the modified Delphi process, a review of the literature was performed, and data were collected on current provider-assessed global disease severity metrics. After literature review, 36 members of International Dermatology Outcome Measures and the American Academy of Dermatology participated in the modified Delphi process to reach consensus on features of the metric. Psoriasis, atopic dermatitis, and acne achieved overwhelming consensus for inflammatory dermatoses that could be measured in a global disease severity metric. Consensus was also reached on the use of a 5-point ordinal scale with descriptors provided through referenced electronic platforms. Expert development of quality measures incorporating this metric and its inclusion in data collection platforms are critical to enabling dermatologists to prove the value of care provided to patients with severe inflammatory dermatoses.

Plasma surfactant proteins and lipid mediators in the airway associate with different stages of viral infection and resolution in a sub-lethal mouse Influenza A model

Abstract Viral quantification serves as a valid biomarker for detecting Influenza A infection however viral burden is not closely associated with disease severity or clinical outcomes. Biomarkers reflecting the status of the host immune response are needed to monitor and/or predict disease outcomes in a hospitalized setting. Here, we utilized a sub-lethal mouse model of Influenza A infection to explore plasma and airway cytokines, plasma surfactant proteins (SP-D, SP-A and CC16), and airway bioactive lipid mediators as potential biomarkers of lung leakage and host response, respectively. This study aimed to assess the pharmacodynamic response of these biomarkers after treatment with an efficacious dose of an anti-HA stalk-binding antibody and to assess association of these biomarkers with disease severity metrics. Plasma SP-D and IP-10 correlated with lung viral titers while SP-A and CC16 tracked more closely with changes in bronchial albumin levels and body weight. Bioactive lipid precursors Arachidonic Acid (AA), Docosahexaenoicacid (DHA) and Eicosapentaenoic acid (EPA), reported to have both pro- and anti-inflammatory properties, increase in the lung with infection. 17-HDHA, a DHA metabolite reported to be pro-resolving, was induced later during disease resolution. With anti-HA antibody treatment, all plasma surfactant proteins and precursor lipids in the airway were reduced and 17-HDHA production was accelerated. In summary, plasma SP-D may serve as an early biomarker of host response and/or potentially indicate a decline in lung epithelium integrity. Plasma SP-A, CC16, and pro-inflammatory lipid mediators such as AA may reflect the ongoing host response and may therefore be more closely associated with disease outcomes.

Alkaptonuria Severity Score Index Revisited: Analysing theAKUSSI and Its Subcomponent Features.

Alkaptonuria (AKU) is a rare disorder with no licensed treatment; nitisinone may reduce symptoms and progression. The All Alkaptonuria Severity Score Index (AKUSSI) measures disease severity in clinical, joint and spine domains, with 57 subcomponent feature scores. Our primary aim was to assess tools for validating scores such as the AKUSSI by detecting relationships between features both before and during nitisinone treatment. AKUSSI measurements from nitisinone-treated patients visiting the National AKU Centre between 01-Jun-2012 and 31-May-2016 were analysed pre-treatment, at first treatment and annually to Year 3 post-treatment. Principal component analysis (PCA) and redundancy analysis assessed whether any AKUSSI features contributed little information to the overall score. 65 AKU patients were included: 17 with a pre-treatment AKUSSI measurement (10 later received nitisinone) and 48 with a first measurement at their first treatment visit. In PCA, the first four principal components (PC1-PC4) explained ≥50% of AKUSSI variance at all visits (54.1-87.3%). Some features regularly dominated their domain's PC1: ears, aortic sclerosis, and nasal/temporal eye scores (clinical), pain-related scores (joint) and cervical, lumbar and thoracic spine scores (spine). Only the right-hand/wrist score was consistently redundant. Right eye (nasal) and left ear scores were redundant pre-treatment, potentially correlating with other dominant clinical PC1 features. PCA and redundancy analysis supported the AKUSSI as a robust AKU disease severity measure, although some AKUSSI features could be removed for simplicity. For small patient populations and rare diseases, PCA and redundancy analysis together can aid validation of disease severity metrics.

Translating Pharmacometrics to a Pharmacoeconomic Model of COPD.

A model-based meta-analysis (MBMA) is a type of meta-regression that uses nonlinear mixed-effects models estimated on trial-level data to relate patient and trial characteristics, dosing, biomarkers, and outcomes of treatment. To use a pharmacometric MBMA within a pharmacoeconomic model of chronic obstructive pulmonary disease (COPD). A Markov microsimulation model was developed to estimate monthly changes in the key disease severity metrics of COPD (forced expiratory volume in 1 second [FEV1] and exacerbations) to compare a hypothetical drug that increases FEV1 to usual care. The MBMA was used to predict a baseline exacerbation rate in a group of actual trial patients, given their known baseline FEV1. The hypothetical drug increased FEV1, thereby decreasing individuals' predicted exacerbation rates. Individual patient simulations allowed stochastic changes in monthly FEV1 decline. In a sample of 1097 trial patients with a mean FEV1 of 50%, the MBMA predicted 0.93 exacerbations per year on average. The exacerbation rate ranged from 0.52 to 1.3 per year across moderate and severe patient subgroups. A hypothetical anti-inflammatory drug that increased FEV1 by 50 ml decreased exacerbations by 26%. Given a simplified estimation of costs and quality-adjusted life-years (QALYs) associated with COPD, a drug with a 50-ml increase priced at €35/mo had an incremental cost-effectiveness ratio ranging from €13,000/QALY to approximately €207,000/QALY across patient severity subgroups. The synergistic aspects of MBMA and pharmacoeconomic modeling are highlighted in this hypothetical example. Markov microsimulation modeling allows the finer predictions of MBMA to inform parameters. Such an approach has utility in both early-phase cost-effectiveness estimations and trial design.

Evaluation of Antihemagglutinin and Antineuraminidase Antibodies as Correlates of Protection in an Influenza A/H1N1 Virus Healthy Human Challenge Model

ABSTRACTDespite long-term investment, influenza continues to be a significant worldwide problem. The cornerstone of protection remains vaccination, and approved vaccines seek to elicit a hemagglutination inhibition (HAI) titer of ≥1:40 as the primary correlate of protection. However, recent poor vaccine performance raises questions regarding the protection afforded and whether other correlates of protection should be targeted. A healthy volunteer challenge study was performed with a wild-type 2009 A(H1N1)pdm influenza A challenge virus at the NIH Clinical Center to evaluate two groups of participants with HAI titers of ≥1:40 and <1:40. The primary objective was to determine whether participants with HAI titers of ≥1:40 were less likely to develop mild to moderate influenza disease (MMID) after intranasal inoculation. HAI titers of ≥1:40 were protective against MMID but did not reduce the incidence of symptoms alone. Although the baseline HAI titer correlated with some reduction in disease severity measures, overall, the baseline NAI titer correlated more significantly with all disease severity metrics and had a stronger independent effect on outcome. This study demonstrates the importance of examining other immunological correlates of protection rather than solely HAI titers. This challenge study confirms the importance of NAI titer as a correlate and for the first time establishes that it can be an independent predictor of reduction of all aspects of influenza disease. This suggests that NAI titer may play a more significant role than previously thought and that neuraminidase immunity should be considered when studying susceptibility after vaccination and as a critical target in future influenza vaccine platforms.

F10 Quantifying putative biomarkers of Huntington disease in blood and urine by selected reaction monitoring mass spectrometry

Background Although the gene expansion associated with Huntington Disease (HD) is well known, the downstream effects are complex and poorly understood. Delineating molecular pathways that become disrupted in the presence of an expanded repeat protein will provide insight into the relationship between the expression of proteins and underlying pathophysiology, and begin to point the way to tailored therapeutics. Aims Protein biomarkers can be sourced from bodily fluids, including blood plasma and urine, and may identify presence of disease, either before or after the appearance of signs or symptoms. The aim of the present study was to systematically profile protein expression in HD-expanded individuals, and relate these findings to metrics of disease severity. Methods We applied selected reaction monitoring (SRM) mass spectrometry (MS) proteomic profiling techniques to analyse samples from HD gene-expansion individuals that participated in PREDICT-HD. Plasma and urine samples, along with corresponding UHDRS Motor subscale scores, were supplied from 10 male HD motor manifest gene-expansion carriers (CAG repeat length >36; UHDRS Diagnostic Confidence Level >3) and 10 age-matched male controls (non-gene-expanded family members). Samples were collected as part of the PREDICT-HD protocol. Results Candidate proteins were identified to be differentially expressed in HD gene-expansion carriers relative to non-expanded familial controls. Moreover, within gene-expansion carriers, a sub-set of proteins were identified that were correlated with severity of motor manifestations, and may represent potential biomarkers of disease severity. Preliminary investigations using pooled specimens from these same patients revealed that some proteins showing differential expression in plasma appear to show similar differences in urine. Conclusions Using proteomic profiling techniques we have identified proteins showing differential expression in relation to HD, many of which to our knowledge have never before been measured in plasma. The identification of biomarkers through the use of SRM-MS holds great promise to increasing our understanding of HD.