Similar Disease States Research Articles

Differentiation between descending thoracic aortic diseases using machine learning and plasma proteomic signatures

BackgroundDescending thoracic aortic aneurysms and dissections can go undetected until severe and catastrophic, and few clinical indices exist to screen for aneurysms or predict risk of dissection.MethodsThis study generated a plasma proteomic dataset from 75 patients with descending type B dissection (Type B) and 62 patients with descending thoracic aortic aneurysm (DTAA). Standard statistical approaches were compared to supervised machine learning (ML) algorithms to distinguish Type B from DTAA cases. Quantitatively similar proteins were clustered based on linkage distance from hierarchical clustering and ML models were trained with uncorrelated protein lists across various linkage distances with hyperparameter optimization using fivefold cross validation. Permutation importance (PI) was used for ranking the most important predictor proteins of ML classification between disease states and the proteins among the top 10 PI protein groups were submitted for pathway analysis.ResultsOf the 1,549 peptides and 198 proteins used in this study, no peptides and only one protein, hemopexin (HPX), were significantly different at an adjusted p < 0.01 between Type B and DTAA cases. The highest performing model on the training set (Support Vector Classifier) and its corresponding linkage distance (0.5) were used for evaluation of the test set, yielding a precision-recall area under the curve of 0.7 to classify between Type B from DTAA cases. The five proteins with the highest PI scores were immunoglobulin heavy variable 6–1 (IGHV6-1), lecithin-cholesterol acyltransferase (LCAT), coagulation factor 12 (F12), HPX, and immunoglobulin heavy variable 4–4 (IGHV4-4). All proteins from the top 10 most important groups generated the following significantly enriched pathways in the plasma of Type B versus DTAA patients: complement activation, humoral immune response, and blood coagulation.ConclusionsWe conclude that ML may be useful in differentiating the plasma proteome of highly similar disease states that would otherwise not be distinguishable using statistics, and, in such cases, ML may enable prioritizing important proteins for model prediction.

The rates of septicemia in older adults with prostate cancer treated with abiraterone or enzalutamide: A population-based study

IntroductionProstate cancer (PCa) is the most common non-cutaneous tumor among American men. Androgen receptor signaling inhibitors such as abiraterone and enzalutamide have been approved for similar disease states among patients with advanced PCa. Existing data suggest using steroids is associated with an increased risk of infection. Because abiraterone is usually prescribed with prednisone, we sought to compare the risk of septicemia in patients using abiraterone vs. enzalutamide. Materials and MethodsWe utilized the SEER-Medicare-linked data and used negative binomial regression models to compare the changes in the rates of septicemia-related hospitalizations six months pre- and post-abiraterone and enzalutamide initiation. ResultsWe found that the incidence of septicemia-related hospitalizations increased 2.77 fold within six months of initiating abiraterone (incidence rate ratio [IRR]: 2.77, 95% confidence interval [CI]: 2.17–3.53) 1.97 fold within six months of starting enzalutamide (IRR: 1.97, 95% CI: 1.43–2.72). However, the difference in the changes did not reach statistical significance (interaction IRR: 0.71, 95% CI: 0.48–1.06). DiscussionThe findings suggest that both abiraterone and enzalutamide are associated with an increased risk of septicemia-related hospitalizations. However, the difference in the increase of septicemia risk following the two treatments did not reach statistical significance. Further studies are warranted to understand the mechanisms at play.

The impact of spectral basis set composition on estimated levels of cingulate glutamate and its associations with different personality traits

Background1H-MRS is increasingly used in basic and clinical research to explain brain function and alterations respectively. In psychosis research it is now one of the main tools to investigate imbalances in the glutamatergic system. Interestingly, however, the findings are extremely variable even within patients of similar disease states. One reason may be the variability in analysis strategies, despite suggestions for standardization. Therefore, our study aimed to investigate the extent to which the basis set configuration– which metabolites are included in the basis set used for analysis– would affect the spectral fit and estimated glutamate (Glu) concentrations in the anterior cingulate cortex (ACC), and whether any changes in levels of glutamate would be associated with psychotic-like experiences and autistic traits.MethodsTo ensure comparability, we utilized five different exemplar basis sets, used in research, and two different analysis tools, r-based spant applying the ABfit method and Osprey using the LCModel.ResultsOur findings revealed that the types of metabolites included in the basis set significantly affected the glutamate concentration. We observed that three basis sets led to more consistent results across different concentration types (i.e., absolute Glu in mol/kg, Glx (glutamate + glutamine), Glu/tCr), spectral fit and quality measurements. Interestingly, all three basis sets included phosphocreatine. Importantly, our findings also revealed that glutamate levels were differently associated with both schizotypal and autistic traits depending on basis set configuration and analysis tool, with the same three basis sets showing more consistent results.ConclusionsOur study highlights that scientific results may be significantly altered depending on the choices of metabolites included in the basis set, and with that emphasizes the importance of carefully selecting the configuration of the basis set to ensure accurate and consistent results, when using MR spectroscopy. Overall, our study points out the need for standardized analysis pipelines and reporting.

MMINP: A computational framework of microbe-metabolite interactions-based metabolic profiles predictor based on the O2-PLS algorithm

ABSTRACT The gut metabolome acts as an intermediary between the gut microbiota and host, and has tremendous diagnostic and therapeutic potential. Several studies have utilized bioinformatic tools to predict metabolites based on the different aspects of the gut microbiome. Although these tools have contributed to a better understanding of the relationship between the gut microbiota and various diseases, most of them have focused on the impact of microbial genes on the metabolites and the relationship between microbial genes. In contrast, relatively little is known regarding the effect of metabolites on the microbial genes or the relationship between these metabolites. In this study, we constructed a computational framework of Microbe-Metabolite INteractions-based metabolic profiles Predictor (MMINP), based on the Two-Way Orthogonal Partial Least Squares (O2-PLS) algorithm to predict the metabolic profiles associated with gut microbiota. We demonstrated the predictive value of MMINP relative to that of similar methods. Additionally, we identified the features that would profoundly impact the prediction performance of data-driven methods (O2-PLS, MMINP, MelonnPan, and ENVIM), including the training sample size, host disease state, and the upstream data processing methods of the different technical platforms. We suggest that when using data-driven methods, similar host disease states and preprocessing methods, and a sufficient number of training samples are necessary to achieve accurate prediction.

Cystic fibrosis transmembrane conductance regulator potentiators attenuate platelet activation and aggregation in blood of healthy donors and COVID-19 patients

Cystic fibrosis transmembrane conductance regulator (CFTR) is a Cl - channel and ABC transporter; its mutations cause the clinical picture of cystic fibrosis (CF). Of late, CFTR has emerged as an important regulator of platelet function, as CFTR dysfunction causes agonist-induced platelet hyperactivation. These findings are reminiscent of platelets from SARS-CoV-2 infected patients since thromboembolic complications represent hallmarks of severe COVID-19 that may critically contribute to morbidity and mortality. CFTR modulators have recently been introduced as a treatment for patients with various CFTR mutations, but have also been reported by us and others to enhance channel function of wild type CFTR. We therefore postulated that CFTR modulators may exert anti-coagulant effects on platelets of healthy donors (HD) and COVID-19 patients.We recruited 36 COVID-19 patients with moderate, and 34 COVID-19 patients with severe disease course (all w/o anti-platelet drugs), and 38 HDs. In line with our hypothesis, we observed significant reductions in platelet agonists adenosine diphosphate (ADP)- or thrombin receptor activating protein-6 (TRAP6)-induced CD62p/CD63 expression, Ca 2+ -mobilization, aggregation, and adhesion of platelets from HDs by pre-treatment with ivacaftor. In blood from COVID-19 patients, platelet activation correlates with disease severity, as demonstrated by a 5-fold and 8-fold increase in the proportion of CD62p + platelets from patients with moderate and severe disease, respectively, relative to HDs. Similarly, the proportion of CD63 + platelets in patients with severe COVID-19 was 2-fold higher than in HDs. Retrospective analysis of clinical data from a total of 4,050 CF patients with COVID-19 receiving single or combination therapy of ivacaftor, lumacaftor, tezacaftor, or elexacaftor in comparison to an untreated cohort revealed that CF therapy reduced the relative risk to suffer thromboembolism-associated cardiovascular events such as heart attack or deep vein thrombosis by 50.0% or 61.1%, respectively, suggesting an anti-thrombotic effect of CFTR modulators in CF COVID-19 patients. In line with this observation, ex vivo pre-treatment of platelets from acute COVID-19 patients with ivacaftor reduced Ca 2+ mobilization, adhesion, and aggregation of platelets .Our results demonstrate an anticoagulant effect of CFTR potentiators on platelets from HDs and severe COVID-19 patients and thus, suggest CFTR potentiators as a promising strategy to reduce the risk of thrombotic events in the clinical management of COVID-19 and similar pro-thrombotic disease states. F. Behrens received funding from the Berlin Institute of Health (BIH). L. Michalick reports grants from the BIH and the German Centre for Cardiovascular Research (DZHK). A. Haghikia is participant in the BIH-Charité Advanced Clinician Scientist Pilotprogram funded by the Charité - Universitätsmedizin Berlin and the BIH and reports a research grant within the BIH &amp; MDC Focus Area Translational Vascular Biomedicine. R. Preissner reports partial funding of this work by the German Research Foundation (KFO339, TRR295). M. Witzenrath reports grants from the German Research Foundation (SFB-TR84 C06 and C09, SFB 1449 B02), and from the German Ministry of Education and Research (BMBF) in the framework of CAPSyS (01ZX1604B, 01ZX1304B), SYMPATH (01ZX1906A), PROVID (01KI20160A), Phage4Cure (16GW0141), MAPVAP (16GW0247) and NUM-NAPKON. W. M. Kuebler reports grants from the German Research Foundation (SFB-TR84 A2 and C9, SFB 1449 B1, SFB 1470 A4, KU1218/9-1, KU1218/11-1, and KU1218/12-1), the BMBF in the framework of SYMPATH (01ZX1906A) and PROVID (01KI20160A), and the DZHK. S. Simmons reports grants from the DZHK and the German Foundation for Heart Research (F-09-19). This is the full abstract presented at the American Physiology Summit 2023 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Dual Attention and Patient Similarity Network for drug recommendation.

Artificially making clinical decisions for patients with multi-morbidity has long been considered a thorny problem due to the complexity of the disease. Drug recommendations can assist doctors in automatically providing effective and safe drug combinations conducive to treatment and reducing adverse reactions. However, the existing drug recommendation works ignored two critical information. (i) Different types of medical information and their interrelationships in the patient's visit history can be used to construct a comprehensive patient representation. (ii) Patients with similar disease characteristics and their corresponding medication information can be used as a reference for predicting drug combinations. To address these limitations, we propose DAPSNet, which encodes multi-type medical codes into patient representations through code- and visit-level attention mechanisms, while integrating drug information corresponding to similar patient states to improve the performance of drug recommendation. Specifically, our DAPSNet is enlightened by the decision-making process of human doctors. Given a patient, DAPSNet first learns the importance of patient history records between diagnosis, procedure and drug in different visits, then retrieves the drug information corresponding to similar patient disease states for assisting drug combination prediction. Moreover, in the training stage, we introduce a novel information constraint loss function based on the information bottleneck principle to constrain the learned representation and enhance the robustness of DAPSNet. We evaluate the proposed DAPSNet on the public MIMIC-III dataset, our model achieves relative improvements of 1.33%, 1.20% and 2.03% in Jaccard, F1 and PR-AUC scores, respectively, compared to state-of-the-art methods. The source code is available at the github repository: https://github.com/andylun96/DAPSNet.

Insights into CD24 and Exosome Physiology and Potential Role in View of Recent Advances in COVID-19 Therapeutics: A Narrative Review.

Cluster of differentiation (CD) 24, a long-known protein with multifaceted functions, has gained attention as a possible treatment for Coronavirus Disease 19 (COVID-19) due to its known anti-inflammatory action. Extracellular vesicles (EVs), such as exosomes and microvesicles, may serve as candidate drug delivery platforms for novel therapeutic approaches in COVID-19 and various other diseases due to their unique characteristics. In the current review, we describe the physiology of CD24 and EVs and try to elucidate their role, both independently and as a combination, in COVID-19 therapeutics. CD24 may act as an important immune regulator in diseases with complex physiologies characterized by excessive inflammation. Very recent data outline a possible therapeutic role not only in COVID-19 but also in other similar disease states, e.g., acute respiratory distress syndrome (ARDS) and sepsis where immune dysregulation plays a key pathophysiologic role. On the other hand, CD24, as well as other therapeutic molecules, can be administered with the use of exosomes, exploiting their unique characteristics to create a novel drug delivery platform as outlined in recent clinical efforts. The implications for human therapeutics in general are huge with regard to pharmacodynamics, pharmacokinetics, safety, and efficacy that will be further elucidated in future randomized controlled trials (RCTs).

Abstract P1-18-21: Abemaciclib plus fulvestrant or nonsteroidal aromatase inhibitor in participants with HR+, HER2- breast cancer - A pooled analysis of the endocrine therapy-naïve participants with measurable disease in MONARCH 2 and MONARCH 3

Abstract Background: Abemaciclib is an oral selective cyclin dependent kinases 4 &amp; 6 inhibitor (CDK4 &amp; 6i), administered on a continuous schedule. Abemaciclib demonstrated significant overall survival (OS) and progression-free survival (PFS) benefit in women with hormone receptor positive (HR+), human epidermal growth factor receptor 2-negative (HER2-) advanced breast cancer (ABC) in combination with fulvestrant in MONARCH 2 (M2). Similarly, abemaciclib demonstrated a PFS benefit in women with HR+, HER2- ABC in combination with nonsteroidal aromatase inhibitors (NSAI) in MONARCH 3 (M3). Here, we present the objective response rate (ORR) from the pooled cohort of endocrine-naïve (EN) participants with measurable disease enrolled in M2 and M3 that received abemaciclib. Methods: M2 (NCT02107703) and M3 (NCT02246621) were double-blind, Phase 3 studies in women with HR+, HER2- ABC. In M2, all patients received fulvestrant (500 mg, per label) and were randomized to receive either abemaciclib (150 mg or 200 mg BID) or placebo. 20 EN participants with measurable disease at baseline were enrolled to the abemaciclib arm. A M2 single arm addendum enrolled 90 additional EN participants with measurable disease to abemaciclib. EN M2 participants had received no previous endocrine therapy (ET) in any setting nor prior chemotherapy in the metastatic setting. In M3, all participants received NSAI (anastrozole 1 mg or letrozole 2.5 mg daily) and were randomized to receive abemaciclib (150 mg BID) or placebo based on stratification factors including prior neoadjuvant or adjuvant ET (NSAI, no ET or other) with 142 M3 EN participants with measurable disease randomized to abemaciclib. The population for analysis consisted of a pooled EN cohort from M2 and M3 with measurable disease that received abemaciclib (N=252). The primary endpoint was investigator-assessed ORR (percentage of participants with best response of complete [CR] or partial response [PR]). The secondary endpoints included PFS, clinical benefit rate (CBR = CR + PR + stable disease persistent for ≥6 months), disease control rate (DCR = CR + PR + SD), duration of response (DoR), and safety. Results: 252 EN participants with measurable disease (43.7% M2, 56.3% M3) from 21 countries were included in the analysis population. Median participant age was 59.0 years. Most patients (n=167 [66.3%]) had ≥3 metastatic organ sites involved. In the pooled EN cohort, confirmed ORR was 57.5% (95% CI 51.4-63.6). CBR was 78.6% (95% CI 73.5-83.6) and DCR was 92.9% (95% CI 89.7-96.0). PFS and DoR data for the M2 EN addendum are not yet mature. No new safety signals were observed. The safety profile was consistent with the previously reported M2 and M3 populations. Conclusion: Primary analysis of confirmed ORR in M2 and M3 EN participants with measurable disease compares favorably with previously reported ORR for fulvestrant monotherapy (FALCON study: 46% unconfirmed; FIRST study: 36% unconfirmed) or NSAI (PALOMA-2 study: 44.8% confirmed; MONALEESA-2: 34% unconfirmed) in participants with a similar disease state. The safety profile is similar to that reported in the primary M2 and M3 main studies. Citation Format: Matthew P. Goetz, Jose Luis Gonzalez Trujillo, Masakazu Toi, Jens Huober, Antonio Llombart-Cussac, Wei Zhang, Holly Knoderer, Nadine Haddad, Gertjan Van Hal, George W. Sledge, Jr. Abemaciclib plus fulvestrant or nonsteroidal aromatase inhibitor in participants with HR+, HER2- breast cancer - A pooled analysis of the endocrine therapy-naïve participants with measurable disease in MONARCH 2 and MONARCH 3 [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P1-18-21.

Psychological complications of COVID-19 following hospitalization and ICU discharge: Recommendations for treatment.

Patients who have contracted COVID-19 experience alarming rates of severe illness, medical complications, and mortality compared with that observed in other respiratory diseases such as influenza. In addition, those who have recovered from COVID-19 are at higher risk of lasting medical complications postdischarge. Importantly, many of these physical complications are likely to cause, and in turn be exacerbated by, psychological complications experienced as a function of illness, extended hospitalization, ventilation, and rigorous quarantine measures. At this time, little is known about the nature and prevalence of these complications, or treatment approaches psychologists may use in providing intervention. Nevertheless, it is possible to identify some of the most likely psychological concerns based on the literature of similar disease states and on psychological sequelae that typically occur following extended intensive care unit (ICU) stays in general. In this article, we characterize psychological presentations likely to be experienced by patients recently discharged from COVID treatment in the ICU based on the review of relevant literature. Specifically, we focus on the domains of: (a) medical trauma, acute stress reaction and posttraumatic stress, (b) cognitive deficits, (c) fatigue and depression, (d) social stress and readjustment, and (e) illness anxiety. Within each characterized domain, we provide empirically supported treatment recommendations for psychologists should they encounter postdischarge COVID patients seeking psychological treatment in outpatient or telehealth settings. Public Significance Statement—Little is known about the mental health challenges faced by patients after being critically ill and hospitalized due to COVID-19. In this article, we provide descriptions of the psychological problems that could result from COVID-related illness which cause difficulty functioning during recovery, along with recommendations for the delivery of therapy and support. (PsycInfo Database Record (c) 2021 APA, all rights reserved) © 2021 American Psychological Association

Methods for Optimizing Student Pharmacist Learning of Clinical Note Writing

Objective. To investigate the effectiveness of using problem-solving and worked examples in teaching clinical note writing to Doctor of Pharmacy students.Methods. First year student pharmacists who were recruited to participate in the study first studied a worked example on generating a clinical note from a written patient case. Participants were then randomized either to study another worked example or to practice writing a clinical note from a written patient case. Embedded in each condition was problem variability (ie, participants encountered either a similar disease state as that in the initial worked example or a different disease state). The primary outcome was the combined performance on writing two clinical notes. Secondary outcomes included quiz performance on knowledge of the components of a clinical note and ability to transfer writing skills to a novel disease state.Results. Seventy-nine students completed the study. Participants who studied a worked example followed by problem-solving (WE-PS) practice performed better than participants who studied two worked examples (WE-WE) on clinical note writing. However, there was no difference in their respective knowledge as determined by quiz performance.Conclusion. Both worked examples and problem-solving facilitated students' learning of the basic knowledge of clinical note writing. However, only problem-solving improved student pharmacists' ability to apply that knowledge. While there were significant improvements in student pharmacists' knowledge of the basics of clinical note writing, it is unclear how worked examples or problem-solving influence the clinical decision-making skills needed to write a clinical note.

Abstract PO-086: Local quantification of protein expression on frozen tissue sections to evaluate tumor heterogeneity

Abstract The discernment of intra-tumor heterogeneity is critical to understand therapy failure and differences of prognosis between patients with similar disease states. Part of the heterogeneity can be appreciated at the protein level, where quantitation can aid the elucidation of complex biological processes. We propose a workflow to quantify protein expression on a “local” level on frozen breast tumor tissue sections, providing a measure and easy visualization of the phenotypical heterogeneity present in the tissue. In this workflow, we extract 8-10 regions of interest (ROIs) of ~100 µm in diameter from frozen tissue sections of 5 patients using a microfluidic probe (MFP). The MFP is a microfluidic scanning probe device that confines a biochemical on a selected region of a tissue, with the capacity of working in liquid environments, and which allows the extraction of individual regions (footprints) in less than 3 min. The rapid extraction together with the liquid environment in which the tissue is present are key for minimizing protein degradation. The extracted proteins are analyzed through an antibody microarray with a panel of 13 proteins that aids a proteomic-based classification of molecular subtypes of breast cancer. Protein expression is quantified based on the greyscale intensity of the corresponding spot in the microarray and normalized by the area of the extracted region. Further analysis determines the presence and extension of phenotypical clusters. Using local protein expression, we generated an expression map of the 13 analyzed proteins. We observe large variations of protein concentrations in the case of beta-actin, estrogen receptor (ER) and EGFR. Cytokeratins (CK)5, CK8, CK17, progesterone receptor and GAPDH presented lower variations in concentration between the analyzed regions in this set of patients. We then evaluated whether the observed differences were statistically significant using Cohen size D effect, showing higher heterogeneity among some proteins (beta-actin, ER, or androgen receptor) and some patients (Patient 1). Finally, we performed a hierarchical clustering analysis across the patients to understand the presence and distribution of phenotypical variants. Patients 3 and 5 presented one single phenotypical variant, while the other patients had up to four. Quantitative methods to describe heterogeneity on the proteomic level are essential to improve our comprehension of tumor biology and thus develop more precise cancer diagnostic approaches. Techniques such as the one presented here can advance our understanding of the spatial location of phenotypical variants within the tumor mass, hinting towards a more accurate choice of treatment modalities. Citation Format: Anna Fomitcheva Khartchenko, Peter Schraml, Govind V. Kaigala. Local quantification of protein expression on frozen tissue sections to evaluate tumor heterogeneity [abstract]. In: Proceedings of the AACR Virtual Special Conference on Tumor Heterogeneity: From Single Cells to Clinical Impact; 2020 Sep 17-18. Philadelphia (PA): AACR; Cancer Res 2020;80(21 Suppl):Abstract nr PO-086.

Interpretable multimodal deep learning for real-time pan-tissue pan-disease pathology search on social media

Pathologists are responsible for rapidly providing a diagnosis on critical health issues. Challenging cases benefit from additional opinions of pathologist colleagues. In addition to on-site colleagues, there is an active worldwide community of pathologists on social media for complementary opinions. Such access to pathologists worldwide has the capacity to improve diagnostic accuracy and generate broader consensus on next steps in patient care. From Twitter we curate 13,626 images from 6,351 tweets from 25 pathologists from 13 countries. We supplement the Twitter data with 113,161 images from 1,074,484 PubMed articles. We develop machine learning and deep learning models to (i) accurately identify histopathology stains, (ii) discriminate between tissues, and (iii) differentiate disease states. Area Under Receiver Operating Characteristic (AUROC) is 0.805–0.996 for these tasks. We repurpose the disease classifier to search for similar disease states given an image and clinical covariates. We report precision@k = 1 = 0.7618 ± 0.0018 (chance 0.397 ± 0.004, mean ±stdev ). The classifiers find that texture and tissue are important clinico-visual features of disease. Deep features trained only on natural images (e.g., cats and dogs) substantially improved search performance, while pathology-specific deep features and cell nuclei features further improved search to a lesser extent. We implement a social media bot (@pathobot on Twitter) to use the trained classifiers to aid pathologists in obtaining real-time feedback on challenging cases. If a social media post containing pathology text and images mentions the bot, the bot generates quantitative predictions of disease state (normal/artifact/infection/injury/nontumor, preneoplastic/benign/low-grade-malignant-potential, or malignant) and lists similar cases across social media and PubMed. Our project has become a globally distributed expert system that facilitates pathological diagnosis and brings expertise to underserved regions or hospitals with less expertise in a particular disease. This is the first pan-tissue pan-disease (i.e., from infection to malignancy) method for prediction and search on social media, and the first pathology study prospectively tested in public on social media. We will share data through http://pathobotology.org. We expect our project to cultivate a more connected world of physicians and improve patient care worldwide.

Relative efficacy and safety of iguratimod monotherapy for the treatment of patients with rheumatoid arthritis: a systematic review and meta-analysis.

This study aims to compare the efficacy and the safety of the iguratimod with placebo and other disease-modifying antirheumatic drugs (DMARDs) in adults with rheumatoid arthritis. Two authors independently searched and selected randomized controlled trials from Cochrane library, Medline (through Pubmed), and Chinese databases, and then assessed the risk of bias (using ROB 2 tool), and graded the certainty of evidence (using the GRADEpro GDT software). We applied the RevMan 5 software for performing meta-analyses of the final consensus data. We identified 12 trials involving 1938 participants. Ten trials had an overall high risk of bias. Although iguratimod had superior efficacy than placebo, the incidence of adverse events was also higher. Inferring to non-inferiority analysis with other DMARD therapy (primarily comprising methotrexate), iguratimod is likely to result in similar treatment response (20% (OR 1.04, 95% CI 0.79 to 1.36), 50% and 70% improvement in American College of Rheumatology criteria) and functional ability at 24weeks. Although the disease state was slightly better with iguratimod (MD - 0.55, 95% CI - 0.85 to - 0.25), a clinically important improvement was not achieved. Iguratimod may have lower C-reactive protein and erythrocyte sedimentation rate values. Swollen joint count, tender joint count, pain intensity, and patient's and physician's global assessment of disease state may be comparable between the therapies. Both the therapies are likely to have similar odds (OR 0.91, 95% CI 0.67 to 1.26) of adverse events. Our evidence suggests that iguratimod may be considered a potential alternative to methotrexate to treat rheumatoid arthritis.Key Points• The Asia Pacific League of Association for Rheumatology (APLAR) has recommended that iguratimod may be used a first-line drug for rheumatoid arthritis in specific cases.• Patients on iguratimod may have similar treatment response, functional ability, disease state, and adverse event profile at 24weeks compared with those on methotrexate.• Iguratimod may be considered a better alternative to methotrexate in RA patients having high CRP and ESR values.• Future clinical trials in diverse population comparing the efficacy and safety of iguratimod in monotherapy or combination therapy with DMARDs (other than methotrexate) are warranted.

Correlations in Trends of Sinusitis-Related Online Google Search Queries in the United States

Online search query trends have been shown to correlate with real-life epidemiologic phenomena. The aim of this study was to analyze correlations in trends in Google online search volumes of sinusitis-related terms, including symptomatology and similar disease states. Terms clinically associated with "sinusitis" were determined by consensus. Terms of symptomatology were derived from the validated 22-item sinonasal outcome test (SNOT-22) as well as terminology encountered with the authors' clinical experience. Terms of disease states that could overlap in symptomatology with sinusitis were then chosen. Google Trends, an online tool for extracting relative frequencies from a public database of search queries, was used to query normalized monthly volumes in the United States from January 2004 to September 2017 of searches related to the topics decided upon by consensus. Bivariate Pearson correlation was used to compare the search queries. Online search volumes of "sinusitis" have a distinct seasonal variation, with consistent annual peaks and troughs. In terms of symptomatology, "postnasal drip," "nasal congestion," "cough," "rhinorrhea," and "sore throat" most highly correlated with "sinusitis" search volumes with statistical significance. "sinusitis" search query volume had a higher positive correlation with "common cold" and "acute sinusitis" than "chronic sinusitis" with regard to disease states. Trends in Google online search volumes over time of "sinusitis" symptomatology mimic real-world clinical phenomena and provide insight into the issues affecting the general population.

Incidence of comorbidities on death certificate data in women with gynecological cancers.

e17127 Background: Risk factors for the development of gynecological cancers are known to vary; however, the effect on mortality is uncertain. Methods: Using CDC Wonder Database, death certificate data, all women who died of cancer in the United States from 2003-2016 were included if a cancer diagnosis was listed as a primary or contributing cause. Cases were then divided into gynecological (GYN) cancer or not, and thereafter characterized as cervical, ovarian, uterine, or other. Other causes contributing to death (comorbidities were then captured via ICD10 codes listed on death certificates and grouped into similar disease states. Chi-squared and Mann-Whitney U tests were performed for statistical significance. Results: Of 35,324,091 death certificates, 4,177,823 deaths were women with cancer listed as a cause of death. 440,792 were gynecological cancer, with cervical cancer in 62,807; uterine cancer in 134,420; ovarian cancer in 221,119; and other gynecological cancers in 25,946. Women with GYN cancers were more likely to have intestinal obstruction (2.7% vs 0.7%, OR 4.05), ascites (0.7% vs 0.2%, OR 3.30), HIV (0.1% vs 0.05%, OR 1.53), kidney disease (4.7% vs 3.6%, OR 1.33), and thrombo-embolic disorders (4.3% vs 3.5%, OR 1.25) when compared to non-GYN cancers (all p &lt; 0.00001). Uterine cancer was more likely to have diabetes-related comorbidities (6.4% vs 3.8%, OR 1.75), metabolic syndrome (9.5% vs 6.1%, OR 1.60), thrombo-embolic disorders (5.5% vs 3.8%, OR 1.48), ischemic heart disease (5.0% vs 3.6%, OR 1.43), than other GYN cancers (all p &lt; 0.00001). Cervical cancer was more associated with HIV (0.3% vs 0.03%, OR 10.73) and tobacco-related disorders (4.7% vs 1.9%, OR 2.56). Ovarian cancer was more associated with ascites (1.1% vs 0.4%, OR 2.93), intestinal obstruction (3.5% vs 1.8%, OR 2.04), and pleural effusion (1.1% vs 0.6%, OR 1.92) (all p &lt; 0.00001). Conclusions: Comorbidities contributing to death significantly differ amongst gynecological cancers and non-gynecological cancers, which has relevance to strategies that can be differentially employed to reduce mortality. In particular, women with uterine cancer may benefit from reduction in risk of diabetes, ischemic heart disease, and embolic disorders; those with cervical cancer may benefit from smoking cessation and optimal HIV management.

Understanding Classification Systems in Spine Deformity.

Classification systems provide organization for pathologic conditions and guide treatment for similar disease states. Spine deformity is a growing field with newer classification systems being developed as our surgical techniques and clinical understanding advances. The evolution of these classification systems reflects our current knowledge and are used to better understand the evolving field of spine surgery. Currently, classification systems in spine surgery range from describing deformity in different age groups and different regions of the spine to describing various osteotomies and the severity of proximal junctional kyphosis. This paper will describe what makes a successful classification system in spine deformity. Old classification systems will be briefly described and their limitations that necessitated the need for newer classification systems. Newer systems will also be reviewed and the importance of specific radiographic parameters. Finishing this review, clinicians will be able to pick which systems are ideal for their practice.

Nutrition Implications for Long-Term Survivors of Pancreatic Cancer Surgery.

With slowly increasing survival rates in pancreatic cancer and international consensus guidelines recommending surgical resection of premalignant lesions, survival post-pancreatic resection is increasing. With longer survival time, the significant comorbidities of such major surgery have far-reaching effects on the nutrition status of the survivor of pancreatic cancer. This review describes the many nutrition-related side effects of pancreatic surgery, including the development of pancreatic enzyme insufficiency, micronutrient deficiencies, diabetes, fatty liver, and metabolic bone disease. Beyond causing additional medical problems, each of these can have significant effects on quality of life and functional status. The potential mechanisms, diagnosis criteria, and potential treatments of these conditions are described. Overall, little literature exists to fully describe the effects of these comorbidities, and even less is able to guide effective treatments for this population. Clinicians caring for these patients should begin incorporating goals for promotion of long-term health and reduction of these known and reported comorbidities in patients who have undergone pancreatic surgery. Treatment plans in this population remain understudied, and clinicians may need to consider recommendations for similar disease states when developing interventions for these patients.

Estimation of EuroQol 5-Dimensions health status utility values in hereditary angioedema.

ObjectiveTo estimate health status utility (preference) weights for hereditary angioedema (HAE) during an attack and between attacks using data from the Hereditary Angioedema Burden of Illness Study in Europe (HAE-BOIS-Europe) survey. Utility measures quantitatively describe the net impact of a condition on a patient’s life; a score of 0.0 reflects death and 1.0 reflects full health.Study design and methodsThe HAE-BOIS-Europe was a cross-sectional survey conducted in Spain, Germany, and Denmark to assess the real-world experience of HAE from the patient perspective. Survey items that overlapped conceptually with the EuroQol 5-Dimensions (EQ-5D) domains (pain/discomfort, mobility, self-care, usual activities, and anxiety/depression) were manually crosswalked to the corresponding UK population-based EQ-5D utility weights. EQ-5D utilities were computed for each respondent in the HAE-BOIS-Europe survey for acute attacks and between attacks.ResultsOverall, a total of 111 HAE-BOIS-Europe participants completed all selected survey items and thus allowed for computation of EQ-5D-based utilities. The mean utilities for an HAE attack and between attacks were 0.44 and 0.72, respectively. Utilities for an acute attack were dependent on the severity of pain of the last attack (0.61 for no pain or mild pain, 0.47 for moderate pain, and 0.08 for severe pain). There were no significant differences across countries. Mean utilities derived from the study approach compare sensibly with other disease states for both acute attacks and between attacks.ConclusionThe impacts of HAE translate into substantial health status disutilities associated with acute attacks as well as between attacks, documenting that the detrimental effects of HAE are meaningful from the patient perspective. Results were consistent across countries with regard to pain severity and in comparison to similar disease states. The results can be used to raise awareness of HAE as a serious disease with wide-ranging personal and social impacts.

Examining KIT, EDNRB, MITF, PMEL17, MLANA Genes to Determine the Genetic Basis of Equine Deafness

There appears to be a correlation between congenital deafness and lack of pigmentation in a small pedigree of Spanish Mustangs. At this time, no hereditary explanation has been presented for this pattern as it appears in horses. Based on the previously determined correlation of melanocyte function and/or migration to deafness in humans, we have conducted a candidate gene study to attempt to link the white coat pigmentation, blue eyes and cochleosaccular deafness within this pedigree. Candidate genes were chosen on the basis of correlation to similar disease states in humans, dogs and cats, including KIT, EDNRB, MITF, and MLANA. KIT, a receptor tyrosine kinase in the pigment production pathway, has been implicated in multiple mammalian deafness studies to date. A mutation (KI16 + 1037A) in the 5′ splice junction of exon 17 is known to cause sabino coloration, but was determined to not be linked to deafness. Alternatively, a heterozygous glycine to serine non‐synonymous mutation was identified within exon 6 of MITF, a transcription factor necessary for proper melanocyte differentiation, but doesn't appear to correlate to deafness. In addition, no mutations within exons were found in neither EDNRB (endothelin receptor type B) nor MLANA, also known as melan A or MART1 (melanoma antigen recognized by t‐cells). Further analysis of additional candidate genes, including PMEL17 and SOX10, and the inclusion of intronic sequence analysis for all candidate genes, will be necessary to determine the cause of cochleosaccular deafness, through mutations in these genes or others.

Maladaptive Remodeling Is Associated With Impaired Survival in Women But Not in Men After Aortic Valve Replacement

ObjectivesThe purpose of this study was to test whether adaptive or maladaptive remodeling is associated with survival in women and men after aortic valve replacement (AVR). BackgroundWomen with isolated aortic valve stenosis (AS) develop more concentric left ventricular hypertrophy (LVH) than men in similar disease states. We recently reported less up-regulation of profibrotic genes at AVR and faster LVH regression post-operatively in women than in men, suggesting that there are sex differences in the adaptation to pressure overload and its regression. MethodsThe study cohort included 128 patients (age 70.0 ± 9.6 years, 49% women) undergoing AVR for AS. Echocardiography was obtained before and 4.0 ± 1.6 years after surgery. Factor analysis was used to classify LVH as adaptive (combining smaller left ventricular [LV] mass/diameters and greater relative wall thicknesses) or maladaptive. Myocardial tissue samples from the LV septum were obtained during AVR to analyze cardiac fibrosis and associated key molecular regulators. ResultsBefore AVR, LVH was classified as adaptive in 62% of women and 45% of men (p < 0.050). Four years after AVR, adaptive LVH was observed in 75% of women and 49% of men (p < 0.031). At surgery, more cardiac fibrosis was present in men compared with women (p < 0.05). Higher levels of transforming growth factor beta 1 (p < 0.01), SMAD2 phosphorylation (p < 0.001), and periostin expression (p < 0.05) were found in men than in women. Women with maladaptive LVH had worse survival than women with adaptive LVH (p < 0.050), whereas the pattern of LVH did not affect survival in men (p < 0.307). ConclusionsWomen more frequently exhibit adaptive LV remodeling with less fibrosis than men. Maladaptive LVH is associated with worse survival in women. Thus, sex should be considered as a strong modulating factor when management of patients with AS is discussed.