Hematologic and Inflammatory Predictors of Outcome in Patients with Brain Arteriovenous Malformations

OBJECTIVE

This study investigated the prognostic value of admission blood counts for arteriovenous malformation (AVM) outcomes and compared admission blood counts for patients with ruptured and unruptured AVMs.

METHODS

A retrospective analysis of patients who underwent surgical treatment for a ruptured cerebral AVM between February 1, 2014, and March 31, 2020, was conducted. The primary outcome was poor neurologic outcome, defined as a modified Rankin Scale score ≥2 in patients with unruptured AVMs or >2 in patients with ruptured AVMs.

RESULTS

Of 235 included patients, 80 (34%) had ruptured AVMs. At admission, patients with ruptured AVMs had a significantly lower mean (SD) hemoglobin level (12.78 [2.07] g/dL vs. 13.71 [1.60] g/dL, P < 0.001), hematocrit (38.1% [5.9%] vs. 40.7% [4.6%], P < 0.001), lymphocyte count (16% [11%] vs. 26% [10%], P < 0.001), and absolute lymphocyte count (1.41 [0.72] × 103/μL vs. 1.79 [0.68] × 103/μL, P < 0.001), and they had a significantly higher mean (SD) white blood cell count (10.4 [3.8] × 103/μL vs. 7.6 [2.3] × 103/μL, P < 0.001), absolute neutrophil count (7.8 [3.8] × 103/μL vs. 5.0 [2.5] × 103/μL, P < 0.001), and neutrophil count (74% [14%] vs. 64% [13%], P < 0.001). Among patients with unruptured AVMs, white blood cell count ≥6.4 × 103/μL and absolute neutrophil count ≥3.4 × 103/μL were associated with a favorable neurologic outcome, whereas hemoglobin level ≥13.4 g/dL was associated with an unfavorable outcome. Among patients with ruptured AVMs, hypertension was associated with a 3-fold increase in odds of a poor neurologic outcome.

CONCLUSIONS

Patients with ruptured and unruptured AVMs present with characteristic profiles of hematologic and inflammatory parameters evident in their admission blood work.

Clinical utility of polygenic risk scores: a critical 2023 appraisal

Since their first appearance in the context of schizophrenia and bipolar disorder in 2009, polygenic risk scores (PRSs) have been described for a large number of common complex diseases. However, the clinical utility of PRSs in disease risk assessment or therapeutic decision making is likely limited because PRSs usually only account for the heritable component of a trait and ignore the etiological role of environment and lifestyle. We surveyed the current state of PRSs for various diseases, including breast cancer, diabetes, prostate cancer, coronary artery disease, and Parkinson disease, with an extra focus upon the potential improvement of clinical scores by their combination with PRSs. We observed that the diagnostic and prognostic performance of PRSs alone is consistently low, as expected. Moreover, combining a PRS with a clinical score at best led to moderate improvement of the power of either risk marker. Despite the large number of PRSs reported in the scientific literature, prospective studies of their clinical utility, particularly of the PRS-associated improvement of standard screening or therapeutic procedures, are still rare. In conclusion, the benefit to individual patients or the health care system in general of PRS-based extensions of existing diagnostic or treatment regimens is still difficult to judge.

MicroRNA targeting: A novel therapeutic intervention for ovarian cancer

Ovarian cancer, a perilous form of cancer affecting the female reproductive system, exhibits intricate communication networks that contribute to its progression. This study aims to identify crucial molecular abnormalities linked to the disease to enhance diagnostic and therapeutic strategies. In particular, we investigate the role of microRNAs (miRNAs) as diagnostic biomarkers and explore their potential in treating ovarian cancer. By targeting miRNAs, which can influence multiple pathways and genes, substantial therapeutic benefits can be attained. In this review we want to shed light on the promising application of miRNA-based interventions and provide insights into the specific miRNAs implicated in ovarian cancer pathogenesis.

Epigenetics regulation of prostate cancer: Biomarker and therapeutic potential

Prostate cancer (CaP) is the second leading cause of cancer death and displays a broad range of clinical behavior from relatively indolent to aggressive metastatic disease. The etiology of most cases of CaP is not understood completely, which makes it imperative to search for the molecular basis of CaP and markers for early diagnosis. Epigenetic modifications, including changes in DNA methylation patterns, histone modifications, miRNAs, and lncRNAs are key drivers of prostate tumorigenesis. These epigenetic defects might be due to deregulated expression of the epigenetic machinery, affecting the expression of several important genes like GSTP1, RASSF1, CDKN2, RARRES1, IGFBP3, RARB, TMPRSS2-ERG, ITGB4, AOX1, HHEX, WT1, HSPE, PLAU, FOXA1, ASC, GPX3, EZH2, LSD1, etc. In this review, we highlighted the most important epigenetic gene alterations and their variations as a diagnostic marker and target for therapeutic intervention of CaP in the future. Characterization of epigenetic changes involved in CaP is obscure and adequate validation studies are still required to corroborate the present results that would be the impending future of transforming basic research settings into clinical practice.

Recursive zonotopic set-membership approach for system-level prognostics with application to linear parameter-varying systems

A robust recursive zonotopic set-membership approach for remaining useful life forecasting with application to linear parameter-varying systems is proposed in this paper. The proposed approach addresses systems with degraded components formulated as a system-level prognostics problem. Thus, the critical degraded components of the system are augmented to the states resulting a nonlinear system that is reformulated as a linear parameter-varying model. Hence, joint estimation of states and parameters is adopted in a zonotopic set-membership scheme with an optimal linear matrix inequality-based tuning and assuming unknown-but-bounded noises and uncertainties. As a result, a recursive zonotopic set-membership approach is proposed for remaining useful life forecasting based on the prediction of the failure precursors of degraded systems. Finally, this approach is tested on a DC-DC converter case study with unknown degradation behaviors, and the obtained results show the estimation and the forecasting accuracy of this methodology.

Heart rate and QRS duration as biomarkers predict the immediate outcome from pulseless electrical activity

INTRODUCTION

Pulseless electrical activity (PEA) is commonly observed in in-hospital cardiac arrest (IHCA). Universally available ECG characteristics such as QRS duration (QRSd) and heart rate (HR) may develop differently in patients who obtain ROSC or not. The aim of this study was to assess prospectively how QRSd and HR as biomarkers predict the immediate outcome of patients with PEA.

METHOD

We investigated 327 episodes of IHCA in 298 patients at two US and one Norwegian hospital. We assessed the ECG in 559 segments of PEA nested within episodes, measuring QRSd and HR during pauses of compressions, and noted the clinical state that immediately followed PEA. We investigated the development of HR, QRSd, and transitions to ROSC or no-ROSC (VF/VT, asystole or death) in a joint longitudinal and competing risks statistical model.

RESULTS

Higher HR, and a rising HR, reflect a higher transition intensity ("hazard") to ROSC (p < 0.001), but HR was not associated with the transition intensity to no-ROSC. A lower QRSd and a shrinking QRSd reflect an increased transition intensity to ROSC (p = 0.023) and a reduced transition intensity to no-ROSC (p = 0.002).

CONCLUSION

HR and QRSd convey information of the immediateoutcome during resuscitation from PEA. These universally available and promising biomarkers may guide the emergency team in tailoring individual treatment.

Whole-blood methylation signatures are associated with and accurately classify multiple sclerosis disease severity

BACKGROUND

The variation in multiple sclerosis (MS) disease severity is incompletely explained by genetics, suggesting genetic and environmental interactions are involved. Moreover, the lack of prognostic biomarkers makes it difficult for clinicians to optimise care. DNA methylation is one epigenetic mechanism by which gene-environment interactions can be assessed. Here, we aimed to identify DNA methylation patterns associated with mild and severe relapse-onset MS (RMS) and to test the utility of methylation as a predictive biomarker.

METHODS

We conducted an epigenome-wide association study between 235 females with mild (n = 119) or severe (n = 116) with RMS. Methylation was measured with the Illumina methylationEPIC array and analysed using logistic regression. To generate hypotheses about the functional consequence of differential methylation, we conducted gene set enrichment analysis using ToppGene. We compared the accuracy of three machine learning models in classifying disease severity: (1) clinical data available at baseline (age at onset and first symptoms) built using elastic net (EN) regression, (2) methylation data using EN regression and (3) a weighted methylation risk score of differentially methylated positions (DMPs) from the main analysis using logistic regression. We used a conservative 70:30 test:train split for classification modelling. A false discovery rate threshold of 0.05 was used to assess statistical significance.

RESULTS

Females with mild or severe RMS had 1472 DMPs in whole blood (839 hypermethylated, 633 hypomethylated in the severe group). Differential methylation was enriched in genes related to neuronal cellular compartments and processes, and B-cell receptor signalling. Whole-blood methylation levels at 1708 correlated CpG sites classified disease severity more accurately (machine learning model 2, AUC = 0.91) than clinical data (model 1, AUC = 0.74) or the wMRS (model 3, AUC = 0.77). Of the 1708 selected CpGs, 100 overlapped with DMPs from the main analysis at the gene level. These overlapping genes were enriched in neuron projection and dendrite extension, lending support to our finding that neuronal processes, rather than immune processes, are implicated in disease severity.

CONCLUSION

RMS disease severity is associated with whole-blood methylation at genes related to neuronal structure and function. Moreover, correlated whole-blood methylation patterns can assign disease severity in females with RMS more accurately than clinical data available at diagnosis.

Multiplex substrate profiling by mass spectrometry for proteases

Proteolysis is a central regulator of many biological pathways and the study of proteases has had a significant impact on our understanding of both native biology and disease. Proteases are key regulators of infectious disease and misregulated proteolysis in humans contributes to a variety of maladies, including cardiovascular disease, neurodegeneration, inflammatory diseases, and cancer. Central to understanding a protease's biological role, is characterizing its substrate specificity. This chapter will facilitate the characterization of individual proteases and complex, heterogeneous proteolytic mixtures and provide examples of the breadth of applications that leverage the characterization of misregulated proteolysis. Here we present the protocol of Multiplex Substrate Profiling by Mass Spectrometry (MSP-MS), a functional assay that quantitatively characterizes proteolysis using a synthetic library of physiochemically diverse, model peptide substrates, and mass spectrometry. We present a detailed protocol as well as examples of the use of MSP-MS for the study of disease states, for the development of diagnostic and prognostic tests, for the generation of tool compounds, and for the development of protease-targeted drugs.

Theranostic roles of machine learning in clinical management of kidney stone disease

Kidney stone disease (KSD) is a common illness caused by deposition of solid minerals formed inside the kidney. The disease prevalence varies, based on sociodemographic, lifestyle, dietary, genetic, gender, age, environmental and climatic factors, but has been continuously increasing worldwide. KSD is a highly recurrent disease, and the recurrence rate is about 11% within two years after the stone removal. Recently, machine learning has been widely used for KSD detection, stone type prediction, determination of appropriate treatment modality and prediction of therapeutic outcome. This review provides a brief overview of KSD and discusses how machine learning can be applied to diagnostics, therapeutics and prognostics in clinical management of KSD for better therapeutic outcome.

Cardiac Characteristics of the First Two Waves of COVID-19 in Denmark and the Prognostic Value of Echocardiography: The ECHOVID-19 Study

INTRODUCTION

COVID-19 has spread globally in waves, and Danish treatment guidelines have been updated following the first wave. We sought to investigate whether the prognostic values of echocardiographic parameters changed with updates in treatment guidelines and the emergence of novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants, 20E (EU1) and alpha (B.1.1.7), and further to compare cardiac parameters between patients from the first and second wave.

METHODS

A total of 305 patients hospitalized with COVID-19 were prospectively included, 215 and 90 during the first and second wave, respectively. Treatment in the study was defined as treatment with remdesivir, dexamethasone, or both. Patients were assumed to be infected with the dominant SARS-CoV-2 variant at the time of their hospitalization.

RESULTS

Mean age for the first versus second wave was 68.7 ± 13.6 versus 69.7 ± 15.8 years, and 55% versus 62% were males. Left ventricular (LV) systolic and diastolic function was worse in patients hospitalized during the second wave (LV ejection fraction [LVEF] for first vs. second wave = 58.5 ± 8.1% vs. 52.4 ± 10.6%, p < 0.001; and global longitudinal strain [GLS] = 16.4 ± 4.3% vs. 14.2 ± 4.3%, p < 0.001). In univariable Cox regressions, reduced LVEF (hazard ratio [HR] = 1.07 per 1% decrease, p = 0.002), GLS (HR = 1.21 per 1% decrease, p < 0.001), and tricuspid annular plane systolic excursion (HR = 1.18 per 1 mm decrease, p < 0.001) were associated with COVID-related mortality, but only GLS remained significant in fully adjusted analysis (HR = 1.14, p = 0.02).

CONCLUSION

Reduced GLS was associated with COVID-related mortality independently of wave, treatment, and the SARS-CoV-2 variant. LV function was significantly impaired in patients hospitalized during the second wave.

The role of mental disorders in precision medicine for diabetes: a narrative review

This narrative review aims to examine the value of addressing mental disorders as part of the care of people with type 1 and type 2 diabetes in terms of four components of precision medicine. First, we review the empirical literature on the role of common mental disorders in the development and outcomes of diabetes (precision prevention and prognostics). We then review interventions that can address mental disorders in individuals with diabetes or at risk of diabetes (precision treatment) and highlight recent studies that have used novel methods to individualise interventions, in person and through applications, based on mental disorders. Additionally, we discuss the use of detailed assessment of mental disorders using, for example, mobile health technologies (precision monitoring). Finally, we discuss future directions in research and practice and challenges to addressing mental disorders as a factor in precision medicine for diabetes. This review shows that several mental disorders are associated with a higher risk of type 2 diabetes and its complications, while there is suggestive evidence indicating that treating some mental disorders could contribute to the prevention of diabetes and improve diabetes outcomes. Using technologically enabled solutions to identify mental disorders could help individuals who stand to benefit from particular treatments. However, there are considerable gaps in knowledge and several challenges to be met before we can stratify treatment recommendations based on mental disorders. Overall, this review demonstrates that addressing mental disorders as a facet of precision medicine could have considerable value for routine diabetes care and has the potential to improve diabetes outcomes.

Data structuring may prevent ambiguity and improve personalized medical prognosis

Topics expected to influence personalized medicine (PM), where medical decisions, practices, and treatments are tailored to the individual patient, are reviewed. Lack of discrimination due to different biological conditions that express similar values of numerical variables (ambiguity) is regarded to be a major potential barrier for PM. This material explores possible causes and sources of ambiguity and offers suggestions for mitigating the impacts of uncertainties. Three causes of ambiguity are identified: (1) delayed adoption of innovations, (2) inadequate emphases, and (3) inadequate processes used when new medical practices are developed and validated. One example of the first problem is the relative lack of medical research on "compositional data" -the type that characterizes leukocyte data. This omission results in erroneous use of data abundantly utilized in medicine, such as the blood cell differential. Emphasis on data output ‒not biomedical interpretation that facilitates the use of clinical data‒ exemplifies the second type of problems. Reliance on tools generated in other fields (but not validated within biomedical contexts) describes the last limitation. Because reductionism is associated with these problems, non-reductionist alternatives are reviewed as potential remedies. Data structuring (converting data into information) is considered a key element that may promote PM. To illustrate a process that includes data-information-knowledge and decision-making, previously published data on COVID-19 are utilized. It is suggested that ambiguity may be prevented or ameliorated. Provided that validations are grounded on biomedical knowledge, approaches that describe certain criteria - such as non-overlapping data intervals of patients that experience different outcomes, immunologically interpretable data, and distinct graphic patterns - can inform, at personalized bases, earlier and/or with fewer observations.

An effective method for remaining useful life estimation of bearings with elbow point detection and adaptive regression models

Bearing is one of the critical components in rotating equipment. Therefore, accurate estimation of the remaining useful life (RUL) of bearings plays a vital role in reducing the costly unplanned maintenance and increasing the reliability of machines. This paper proposes a method for bearing prognostics that uses iteratively updated degradation regression models to capture the degradation trend in bearing's health indicator (HI), and the models are utilized to predict the degradation trajectory of HI and to estimate the RUL of bearings. The importance of determining the time to start prediction by elbow point is explained, which is often overlooked in prognostics. To improve the prognostic performance, an adaptive approach for elbow point detection is designed based on the gradient change of HIs, and a new smooth approach is applied to reduce spurious fluctuations in degradation trajectory. The effectiveness of the proposed method is validated on two publicly available data sets, i.e., IMS and FEMTO bearing prognostics data set, and its prognostic performance is compared with that of three state-of-the-art methods. The obtained results demonstrate that the proposed method can effectively detect elbow point and determine the time to start prediction, and can calibrate the degradation regression model dynamically according to the evolving degradation trend in the HI, which validates its superior prognostic performance.

Functions of three ubiquitin-conjugating enzyme 2 genes in hepatocellular carcinoma diagnosis and prognosis

BACKGROUND

Liver cancer ranks the third cause of cancer-related death worldwide. The most common type of liver cancer is hepatocellular carcinoma (HCC). The survival time for HCC patients is very limited by years due to the lack of efficient treatment, failure of early diagnosis, and poor prognosis. Ubiquitination plays an essential role in the biochemical processes of a variety of cellular functions.

AIM

To investigate three ubiquitination-associated genes in HCC.

METHODS

Herein, the expression levels of ubiquitin-conjugating enzymes 2 (UBE2) including UBE2C, UBE2T, and UBE2S in tumor samples of HCC patients and non-tumor controls at the Cancer Genome Atlas (TCGA) database, was comprehensively analyzed. The relationship of UBE2 gene expression level with cancer stage, prognostic outcome, and TP53 mutant status was studied.

RESULTS

Our results showed that UBE2C, UBE2T, and UBE2S genes were overexpressed in HCC samples compared to non-tumor tissues. Dependent on the cancer progression stage, three UBE2 genes showed higher expression in tumor tissues at all four stages compared to non-tumor control samples. Furthermore, a significantly higher expression of these genes was found in stage 2 and stage 3 cancers compared to stage 1 cancer. Additionally, overexpression of those genes was negatively associated with prognostic outcome and overall survival time. Patients with TP53 mutation showed a higher expression level of three UBE2 genes, indicating an association between UBE2 expression with p53 function.

CONCLUSION

In summary, this study shed light on the potential roles of UBE2C, UBE2T, UBE2S on diagnostic and prognostic biomarkers for HCC. Moreover, based on our findings, it is appealing to further explore the correlation of those genes with TP53 mutation in HCC and the related mechanisms.

Ligase 1 is a predictor of platinum resistance and its blockade is synthetically lethal in XRCC1 deficient epithelial ovarian cancers

Rationale: The human ligases (LIG1, LIG3 and LIG4) are essential for the maintenance of genomic integrity by catalysing the formation of phosphodiester bonds between adjacent 5′-phosphoryl and 3′-hydroxyl termini at single and double strand breaks in duplex DNA molecules generated either directly by DNA damage or during replication, recombination, and DNA repair. Whether LIG1, LIG3 and LIG4 can influence ovarian cancer pathogenesis and therapeutics is largely unknown.

Methods: We investigated LIG1, LIG3 and LIG4 expression in clinical cohorts of epithelial ovarian cancers [protein level (n=525) and transcriptional level (n=1075)] and correlated to clinicopathological features and survival outcomes. Pre-clinically, platinum sensitivity was investigated in LIG1 depleted ovarian cancer cells. A small molecule inhibitor of LIG1 (L82) was tested for synthetic lethality application in XRCC1, BRCA2 or ATM deficient cancer cells.

Results: LIG1 and LIG3 overexpression linked with aggressive phenotypes, platinum resistance and poor progression free survival (PFS). In contrast, LIG4 deficiency was associated with platinum resistance and worse PFS. In a multivariate analysis, LIG1 was independently associated with adverse outcome. In ovarian cancer cell lines, LIG1 depletion increased platinum cytotoxicity. L82 monotherapy was synthetically lethal in XRCC1 deficient ovarian cancer cells and 3D-spheroids. Increased cytotoxicity was linked with accumulation of DNA double strand breaks (DSBs), S-phase cell cycle arrest and increased apoptotic cells. L82 was also selectively toxic in BRCA2 deficient or ATM deficient cancer cells and 3D-spheroids.

Conclusions: We provide evidence that LIG1 is an attractive target for personalization of ovarian cancer therapy.

Dynamic repair priority rule based on remaining useful life predictions

In this paper, we propose a novel repair priority rule for spare parts in a repair station with limited repair capacity to minimize total inventory cost per time unit. Inventory cost is composed of holding costs and backorder costs. The proposed rule uses Remaining Useful Life (RUL) predictions of functioning machines obtained from a Prognostics and Health Monitoring (PHM) system. An inventory system comprising a finite number of machines, one warehouse, and one single-server repair station is considered. Numerical experiments were conducted to compare the performance of the proposed model with the performance of three existing priority rules: a first-come, first-served (FCFS) rule, a static priority rule, and a dynamic priority rule. A testbed with 20 instances of the problem was considered. The results showed that the proposed PHM-based rule consistently reduces the inventory system cost.

Hybridized neural networks for non-invasive and continuous mortality risk assessment in neonates

Premature birth is the primary risk factor in neonatal deaths, with the majority of extremely premature babies cared for in neonatal intensive care units (NICUs). Mortality risk prediction in this setting can greatly improve patient outcomes and resource utilization. However, existing schemes often require laborious medical testing and calculation, and are typically only calculated once at admission. In this work, we propose a shallow hybrid neural network for the prediction of mortality risk in 3-day, 7-day, and 14-day risk windows using only birthweight, gestational age, sex, and heart rate (HR) and respiratory rate (RR) information from a 12-h window. As such, this scheme is capable of continuously updating mortality risk assessment, enabling analysis of health trends and responses to treatment. The highest performing scheme was the network that considered mortality risk within 3 days, with this scheme outperforming state-of-the-art works in the literature and achieving an area under the receiver-operator curve (AUROC) of 0.9336 with standard deviation of 0.0337 across 5 folds of cross-validation. As such, we conclude that our proposed scheme could readily be used for continuously-updating mortality risk prediction in NICU environments.

A data-driven prognostics method for explicit health index assessment and improved remaining useful life prediction of bearings

Although bearings offer a broad extent of applications and rank among the most-used elements in rotating machinery they also are the most vulnerable to failure. Consequently, "prognostics and health management (PHM)" of bearings has gained awareness in both academia and industry. As it aims to predict future failure events, "remaining useful life (RUL)" prediction is an important process to ensure a reliable and safe operation of bearings in the course of their degradation. However, accurate RUL prediction can hardly be carried out without an explicit health index that fully reflects the bearing's dynamic performance degradation process. Thus, obtaining an explicit health index is a major concern. This paper advocates a novel method to solve this issue. The "proposed method" is based on the ensemble of "deep autoencoder (DAE)" and "locally linear embedding (LLE)". To begin with, secondary features are extracted from the original unprocessed data obtained from sensors. These secondary features are used as inputs to the DAE where they become compressed to a more compact, lower-dimension form. Accordingly, the dimensionally reduced features are evaluated based on a trend factor with which higher-trend features are selected to enhance the accuracy and computational efficiency of the subsequent RUL prediction. The selected features are used as inputs for the LLE algorithm to determine a truly representative explicit health index which fully reflects the bearing's dynamic performance degradation. Having obtained the health index by the "proposed method", the RUL is finally predicted by employing the "long short-term memory (LSTM)" neural network. The obtained results from the experiment, authenticates the "effectiveness and superiority" of the "proposed method".

Evolutionary algorithms and decision trees for predicting poor outcome after endovascular treatment for acute ischemic stroke

Despite the large overall beneficial effects of endovascular treatment in patients with acute ischemic stroke, severe disability or death still occurs in almost one-third of patients. These patients, who might not benefit from treatment, have been previously identified with traditional logistic regression models, which may oversimplify relations between characteristics and outcome, or machine learning techniques, which may be difficult to interpret. We developed and evaluated a novel evolutionary algorithm for fuzzy decision trees to accurately identify patients with poor outcome after endovascular treatment, which was defined as having a modified Rankin Scale score (mRS) higher or equal to 5. The created decision trees have the benefit of being comprehensible, easily interpretable models, making its predictions easy to explain to patients and practitioners. Insights in the reason for the predicted outcome can encourage acceptance and adaptation in practice and help manage expectations after treatment. We compared our proposed method to CART, the benchmark decision tree algorithm, on classification accuracy and interpretability. The fuzzy decision tree significantly outperformed CART: using 5-fold cross-validation with on average 1090 patients in the training set and 273 patients in the test set, the fuzzy decision tree misclassified on average 77 (standard deviation of 7) patients compared to 83 (±7) using CART. The mean number of nodes (decision and leaf nodes) in the fuzzy decision tree was 11 (±2) compared to 26 (±1) for CART decision trees. With an average accuracy of 72% and much fewer nodes than CART, the developed evolutionary algorithm for fuzzy decision trees might be used to gain insights into the predictive value of patient characteristics and can contribute to the development of more accurate medical outcome prediction methods with improved clarity for practitioners and patients.

Measurement and Evaluation for Prognostics and Health Management (PHM) for Manufacturing Operations - Summary of an Interactive Workshop Highlighting PHM Trends

Personnel from the National Institute of Standards and Technology (NIST) organized and led a Measurement and Evaluation for Prognostics and Health Management for Manufacturing Operations (ME4PHM) workshop at the 2019 Annual Conference of the Prognostics and Health Management Society held on September 23rd, 2019 in Scottsdale, Arizona. This event featured panel presentations and discussions from industry, government, and academic participants who are focused in advancing monitoring, diagnostic, and prognostic (collectively known as prognostic and health management (PHM)) capabilities within manufacturing operations. The participants represented a diverse cross-section of technology developers, integrators, end-users/manufacturers (from small to large), and researchers. These contributors discussed 1) what works well, 2) common challenges that need to be addressed, 3) where the community's priorities should be focused, and 4) how PHM technological adoption can be sped in a cost-effective manner. This report summarizes the workshop and offers lessons learned regarding the current state of PHM. Based upon the discussions, recommended next steps to advance this technological domain are also presented.

Predicting onset of secondary-progressive multiple sclerosis using genetic and non-genetic factors

BACKGROUND

Predicting the transition from relapsing-remitting (RR) to secondary-progressive (SP) multiple sclerosis (MS) from early in the disease course is challenging.

OBJECTIVE

To construct prediction models for SPMS using sociodemographic and self-reported clinical measures that would be available at/near MS onset, with specific considerations for MS genetic risk factors.

METHODS

We conducted a retrospective cross-sectional study based on 1295 white, non-Hispanic individuals. Cox proportional hazard prediction models were generated for three censored SPMS outcomes (ever transitioning, transitioning within 10 years, and transitioning within 20 years) using sociodemographic, comorbid health information, symptomatology, and other measures of early disease activity. HLADRB1*15:01 and HLA-A*02:01, as well as a genetic risk score, were iteratively considered in each model. We also explored the relationships for all 200 MS risk variants located outside the major histocompatibility complex. Nomograms were generated for the final prediction models.

RESULTS

An older age of MS onset and being male predicted a short latency to SPMS, while a longer interval between the first two relapses predicted a much longer latency. Comorbid conditions and onset symptomatology variably predicted the risk for transitioning to SPMS for each censored outcome. The most notable observation was that HLA-A*02:01, which confers decreased risk for MS, also contributed to decreased hazards for SPMS.

CONCLUSIONS

These results have the potential to advance prognostication for a person with MS using information available at or near onset, potentially improving care and quality of life for those who live with MS.

The MemClin project: a prospective multi memory clinics study targeting early stages of cognitive impairment

Background

There remains a lack of large-scale clinical studies of cognitive impairment that aim to increase diagnostic and prognostic accuracy as well as validate previous research findings. The MemClin project will amass large quantities of cross-disciplinary data allowing for the construction of robust models to improve diagnostic accuracy, expand our knowledge on differential diagnostics, strengthen longitudinal prognosis, and harmonise examination protocols across centres. The current article describes the Memory Clinic (MemClin) project’s study-design, materials and methods, and patient characteristics. In addition, we present preliminary descriptive data from the ongoing data collection.

Methods

Nine out of ten memory clinics in the greater Stockholm area, which largely use the same examination methods, are included. The data collection of patients with different stages of cognitive impairment and dementia is coordinated centrally allowing for efficient and secure large-scale database construction. The MemClin project rest directly on the memory clinics examinations with cognitive measures, health parameters, and biomarkers.

Results

Currently, the MemClin project has informed consent from 1543 patients. Herein, we present preliminary data from 835 patients with confirmed cognitive diagnosis and neuropsychological test data available. Of those, 239 had dementia, 487 mild cognitive impairment (MCI), and 104 subjective cognitive impairment (SCI). In addition, we present descriptive data on visual ratings of brain atrophy and cerebrospinal fluid markers.

Conclusions

Based on our current progress and preliminary data, the MemClin project has a high potential to provide a large-scale database of 1200–1500 new patients annually. This coordinated data collection will allow for the construction of improved diagnostic and prognostic models for neurodegenerative disorders and other cognitive conditions in their naturalistic setting.

Bearing remaining useful life prediction using support vector machine and hybrid degradation tracking model

Rolling element bearing is one of the critical components in rotating machines, and its running state determines machinery Remaining Useful Life (RUL). Estimating impending failure and predicting RUL of bearing is beneficial to schedule maintenance strategy and avoid abrupt shutdowns. This paper presents a novel method of RUL prediction of bearings, which can evaluate the degradation stage of bearings through dimensionless measurements and exploit the optimal RUL prediction through hybrid degradation tracing model in degradation stage. Two new measurements reflect the vibration intensity of bearings regarding normal vibration value. They can eliminate individual differences of bearings, improve sensitivity to the incipient defect of bearings, and reduce fluctuation. Moreover, they are helpful to detect the time to start prediction and set dimensionless failure threshold. SVM classifier is used to assess the degradation stage of bearing, which shows a high classification accuracy because of its excellent generalization ability and mathematical foundation. As input, the fitted measurements based on the generalized degradation model are used to train the SVM classifier. As output, five degradation stages are defined. However, actual measurements are used as inputs in the prediction process. According to the classification results, a hybrid degradation tracing model is utilized to exploit the optimal RUL prediction by tracking the degradation process of bearings. The proposed method is validated on the public IMS and PRONOSTIA bearing datasets, and its performance is compared with other methods on PRONOSTIA bearing datasets. The results show that the proposed approach is an effective way for RUL prediction of bearings within the prescribed error range. Given that the proposed measurements are dimensionless, this method can be applied under different operating conditions.

Mapping actionable pathways and mutations in brain tumours using targeted RNA next generation sequencing

Many biology-based precision drugs are available that neutralize aberrant molecular pathways in cancer. Molecular heterogeneity and the lack of reliable companion diagnostic biomarkers for many drugs makes targeted treatment of cancer inaccurate for many individuals. Identifying actionable hyperactive biological pathways in individual cancers may improve this situation.

To achieve this we applied a novel targeted RNA next generation sequencing (t/RNA-NGS) technique to surgically obtained glioma tissues. The test combines mutation detection with analysis of biological pathway activities that are involved in tumour behavior in many cancer types (e.g. tyrosine kinase signaling, angiogenesis signaling, immune response, metabolism), via quantitative measurement of transcript levels and splice variants of hundreds of genes. We here present proof of concept that the technique, which uses molecular inversion probes, generates a histology-independent molecular diagnosis and identifies classifiers that are strongly associated with conventional histopathology diagnoses and even with patient prognosis. The test not only confirmed known glioma-associated molecular aberrations but also identified aberrant expression levels of actionable genes and mutations that have so far been considered not to be associated with glioma, opening up the possibility of drug repurposing for individual patients. Its cost-effectiveness makes t/RNA-NGS to an attractive instrument to aid oncologists in therapy decision making.

Data-efficient deep learning of radiological image data for outcome prediction after endovascular treatment of patients with acute ischemic stroke

Treatment selection is becoming increasingly more important in acute ischemic stroke patient care. Clinical variables and radiological image biomarkers (old age, pre-stroke mRS, NIHSS, occlusion location, ASPECTS, among others) have an important role in treatment selection and prognosis. Radiological biomarkers require expert annotation and are subject to inter-observer variability. Recently, Deep Learning has been introduced to reproduce these radiological image biomarkers. Instead of reproducing these biomarkers, in this work, we investigated Deep Learning techniques for building models to directly predict good reperfusion after endovascular treatment (EVT) and good functional outcome using CT angiography images. These models do not require image annotation and are fast to compute. We compare the Deep Learning models to Machine Learning models using traditional radiological image biomarkers. We explored Residual Neural Network (ResNet) architectures, adapted them with Structured Receptive Fields (RFNN) and auto-encoders (AE) for network weight initialization. We further included model visualization techniques to provide insight into the network's decision-making process. We applied the methods on the MR CLEAN Registry dataset with 1301 patients. The Deep Learning models outperformed the models using traditional radiological image biomarkers in three out of four cross-validation folds for functional outcome (average AUC of 0.71) and for all folds for reperfusion (average AUC of 0.65). Model visualization showed that the arteries were relevant features for functional outcome prediction. The best results were obtained for the ResNet models with RFNN. Auto-encoder initialization often improved the results. We concluded that, in our dataset, automated image analysis with Deep Learning methods outperforms radiological image biomarkers for stroke outcome prediction and has the potential to improve treatment selection.

Intra-night variation in apnea-hypopnea index affects diagnostics and prognostics of obstructive sleep apnea

Background

Diagnostics of obstructive sleep apnea (OSA) is based on apnea-hypopnea index (AHI) determined as full-night average of occurred events. We investigate our hypothesis that intra-night variation in the frequency of obstructive events affects diagnostics and prognostics of OSA and should therefore be considered in clinical practice.

Methods

Polygraphic recordings of 1989 patients (mean follow-up 18.3 years) with suspected OSA were analyzed. Number and severity of individual obstructive events were calculated hourly for the first 6 h of sleep. OSA severity was determined based on the full-night AHI and AHI for the 2 h when the obstructive event frequency was highest (AHI_2h). Hazard ratios for all-cause, cardiovascular, and non-cardiovascular mortalities were calculated for different OSA severity categories based on the full-night AHI and AHI_2h.

Results

Frequency and duration of obstructive events varied hour-by-hour increasing towards morning. Using AHI_2h led to a statistically significant rearrangement of patients between the OSA severity categories. The use of AHI_2h for severity classification showed clearer relationship between the OSA severity and mortality than the full-night AHI.

Conclusions

Currently, the intra-night variation in frequency and severity of obstructive events is completely ignored by conventional, full-night AHI and considering this information could improve the diagnostics of OSA.

A neural network-evolutionary computational framework for remaining useful life estimation of mechanical systems

This paper presents a framework for estimating the remaining useful life (RUL) of mechanical systems. The framework consists of a multi-layer perceptron and an evolutionary algorithm for optimizing the data-related parameters. The framework makes use of a strided time window along with a piecewise linear model to estimate the RUL for each mechanical component. Tuning the data-related parameters in the optimization framework allows for the use of simple models, e.g. neural networks with few hidden layers and few neurons at each layer, which may be deployed in environments with limited resources such as embedded systems. The proposed method is evaluated on the publicly available C-MAPSS dataset. The accuracy of the proposed method is compared against other state-of-the art methods in the literature. The proposed method is shown to perform better than the compared methods while making use of a compact model.

A review of diagnostic and prognostic capabilities and best practices for manufacturing

Prognostics and health management (PHM) technologies reduce time and costs for maintenance of products or processes through efficient and cost-effective diagnostic and prognostic activities. PHM systems use real-time and historical state information of subsystems and components to provide actionable information, enabling intelligent decision-making for improved performance, safety, reliability, and maintainability. However, PHM is still an emerging field, and much of the published work has been either too exploratory or too limited in scope. Future smart manufacturing systems will require PHM capabilities that overcome current challenges, while meeting future needs based on best practices, for implementation of diagnostics and prognostics. This paper reviews the challenges, needs, methods, and best practices for PHM within manufacturing systems. This includes PHM system development of numerous areas highlighted by diagnostics, prognostics, dependability analysis, data management, and business. Based on current capabilities, PHM systems are shown to benefit from open-system architectures, cost-benefit analyses, method verification and validation, and standards.

Quantitative radiomics: Validating image textural features for oncological PET in lung cancer

BACKGROUND AND PURPOSE

Radiomics textural features derived from PET imaging are of broad and current interest due to recent evidence of their prognostic value during cancer management. An inherent assumption is the link between these imaging features and the underlying tumoral phenotypic spatial heterogeneity. The purpose of this work was to validate this assumption for tumors within the lung through a comparison of image based textural features and the ground truth activity distribution from which the images were created. A second purpose was to assess the level at which PET imaging introduces spatial texture not present in the associated ground truth activity distribution.

MATERIALS AND METHODS

25 lung lesions were created using an anthropomorphic phantom. Ten of the lesions had a spherical shape with a uniform activity distribution. The remaining 15 had an irregular shape with a heterogeneous activity distribution. PET images were created for each lesion using Monte Carlo simulation. 79 textural features related to the gray-level intensity histograms, co-occurrence matrices, neighborhood difference matrices, run length, and size zone matrices were derived from both the simulated PET images and ground truth activity maps. A comparison was made between the two datasets using statistical analysis.

RESULTS

For homogenous lesions, features extracted from the PET images were largely irrelevant to the underlying uniform activity distribution. Additionally, the majority of these features assumed substantial values implying that an extensive amount of spatial texture had been introduced into the final imaging data. For heterogeneous lesions, complex trends were observed in the deviation between features extracted from PET images and those extracted from the ground truth activity maps. Moreover, the extent of both the deviation and the associated dynamic range was seen to be greatly feature-dependent.

CONCLUSION

The use of image based textural features as a surrogate for tumoral phenotypic spatial heterogeneity could not be clearly validated. The association between the two is complex and a significant amount of uncertainty exist due to the introduction of incidental texture during image acquisition and reconstruction.

The course of pain hypersensitivity according to painDETECT in patients with rheumatoid arthritis initiating treatment: results from the prospective FRAME-cohort study

BACKGROUND

Evidence is emerging that pain in rheumatoid arthritis (RA) exists without underlying inflammation. Our objective was to evaluate the prognostic value of pain classification at treatment initiation using the painDETECT questionnaire (PDQ). Outcomes were change in DAS28-CRP and RAMRIS synovitis score.

METHODS

RA patients initiating a disease-modifying anti-rheumatic drug (DMARD) or initiating/ switching a biological agent were included. Follow-up time was 4 months. Clinical examination, imaging (MRI, dynamic contrast-enhanced MRI (DCE-MRI)), and patient-reported outcomes were undertaken. The PDQ was used to differentiate pain mechanisms. Mean change (95% CI) was calculated using ANCOVA. Multivariable regression models were used to determine a prognostic value.

RESULTS

A total of 102 patients were included; 75 were enrolled for MRI. Mean changes in baseline variables were greatest in the high PDQ classification group (> 18), while limited in the intermediate group (13-18). The 12 patients with high baseline PDQ score all changed pain classification group. No prognostic value of PDQ pain classification was found in relation to change of DAS28-CRP, RAMRIS score, or VAS pain. In the unadjusted model, RAMRIS score at baseline was associated with change in DAS28-CRP. The exploratory variables of DCE-MRI did not differ from other inflammatory variables.

CONCLUSIONS

In RA patients a high PDQ score (non-nociceptive pain) at baseline was not associated with worse outcomes, in fact these patients had numerically greater improvement in DAS28-CRP. However, pain classification by PDQ was not independently associated with change in DAS28-CRP, RAMRIS score, or VAS pain in the prognostic models. Furthermore, patients classified with a high baseline PDQ score changed pain classification group. Patients with unclear pain mechanism had reduced numerically treatment response.

TRIAL REGISTRATION

The study was approved by the Regional Ethics Committee of the Capital of Denmark April 18 2013; identification number H-3-2013-049 .

Targeting invadopodia for blocking breast cancer metastasis

Dissemination of cancer cells from the primary tumor and their spread to distant sites of the body is the leading cause of mortality in metastatic cancer patients. Metastatic cancer cells invade surrounding tissues and blood vessels by forming F-actin-rich protrusions known as invadopodia, which degrade the extracellular matrix and enable invasion of tumor cells through it. Invadopodia have now been observed in vivo, and recent evidence demonstrates direct molecular links between assembly of invadopodia and cancer metastasis in both mouse models and in human patients. While significant progress has been achieved in the last decade in understanding the molecular mechanisms and signaling pathways regulating invadopodia formation and function, the application of this knowledge to development of prognostic and therapeutic approaches for cancer metastasis has not been discussed before. Here, we provide a detailed overview of current prognostic markers and tests for cancer metastasis and discuss their advantages, disadvantages, and their predicted efficiency. Using bioinformatic patient database analysis, we demonstrate, for the first time, a significant correlation between invadopodia-associated genes to breast cancer metastasis, suggesting that invadopodia could be used as both a prognostic marker and as a therapeutic target for blocking cancer metastasis. We include here a novel network interaction map of invadopodia-associated proteins with currently available inhibitors, demonstrating a central role for the recently identified EGFR-Pyk2-Src-Arg-cortactin invadopodial pathway, to which re-purposing of existent inhibitors could be used to block breast cancer metastasis. We then present an updated overview of current cancer-related clinical trials, demonstrating the negligible number of trials focusing on cancer metastasis. We also discuss the difficulties and complexity of performing cancer metastasis clinical trials, and the possible development of anti-metastasis drug resistance when using a prolonged preventive treatment with invadopodia inhibitors. This review presents a new perspective on invadopodia-mediated tumor invasiveness and may lead to the development of novel prognostic and therapeutic approaches for cancer metastasis.

Along with its favorable prognostic role, CLCA2 inhibits growth and metastasis of nasopharyngeal carcinoma cells via inhibition of FAK/ERK signaling

Background

CLCA2 was reported as a tumor suppressor and disregulated in breast cancer. However, its function in tumor growth and metastasis in NPC has rarely been reported. In this study, we investigated the functional and molecular mechanisms by which CLCA2 influences NPC.

Methods

CLCA2 expression in human NPC cell lines and tissues was examined via real-time PCR (RT-PCR), Western blot and IHC. The biological roles of CLCA2 in proliferative, migration and invasion of NPC cell lines was evaluated in 5-8F, S18, S26 and SUNE-1 cells. Cell viability, migration and invasion were assessed in vitro by MTS, colony formation and transwell assay, respectively. CLCA2 in growth and metastasis of NPC were evaluated in vivo through NPC xenograft tumor growth, lung metastatic mice model and popliteal lymph node (LN) metastasis model.

Results

Overexpression of CLCA2 significantly decreased proliferation, migration and invasion of NPC cells. In contrast, knockdown of CLCA2 elicited the opposite effects. CLCA2 overexpression suppressed xenograft tumor growth and lung, popliteal lymph node (LN) metastasis in vivo. CLCA2 inhibited tumor metastasis through suppressing epithelial-Mesenchymal transition (EMT) and in-activating FAK/ERK1/2 signaling pathway in NPC cells. Immunohistochemical staining of 143 NPC samples revealed that CLCA2 expression was an independent, favorable prognostic factor for overall survival and distant metastasis-free survival of patients. In addition, inhibition of FAK and ERK1/2 reversed CLCA2 silencing-induced tumor cell migration. Furthermore, inhibitors against chloride channels suppressed NPC cellular migration which could have been enhanced by the presence of CLCA2.

Conclusion

CLCA2 suppress NPC proliferation, migration, invasion and epithelial-mesenchymal transition through inhibiting FAK/ERK signaling.

Electronic supplementary material

The online version of this article (10.1186/s13046-018-0692-8) contains supplementary material, which is available to authorized users.

Developing a hierarchical decomposition methodology to increase manufacturing process and equipment health awareness

Manufacturing systems are becoming increasingly complex as more advanced and emerging technologies are integrated into the factory floor to yield new processes or increase the efficiency of existing processes. As greater complexity is formed across the factory, new relationships are often generated that can lead to advanced capabilities, yet produce unforeseen faults and failures. Industrial robot arm work cells within the manufacturing environment present increasing complexity, emergent technologies, new relationships, and unpredicted faults/failures. To maintain required levels of productivity, process quality, and asset availability, manufacturers must reconcile this complexity to understand how the health degradation of constituent physical elements and functional tasks impact one another through the monitoring of critical informative measures and metrics. This article presents the initial efforts in developing a novel hierarchical decomposition methodology. The innovation in this method is that it provides the manufacturer with sufficient discretion to physically deconstruct their system and functionally decompose their process to user-defined levels based upon desired monitoring, maintenance, and control levels. This enables the manufacturer to specify relationships within and across the physical, functional, and information domains to identify impactful health degradations without having to know all possible failure modes. The hierarchical decomposition methodology will advance the state of the art in terms of improving machine health by highlighting how health degradations propagate through the relationship network prior to a piece of equipment compromising the productivity or quality of a process. The first two steps of the methodology, physical decomposition and functional decomposition, are defined in detail and applied to a multi-robot work cell use case.

MicroRNAs: Biomarkers, Diagnostics, and Therapeutics

MicroRNAs (miRNAs) are small noncoding RNAs (21-23 nucleotides in length) that regulate gene expression at translational or posttranslational levels. The major regulatory mechanisms include translational repression or mRNA degradation (Filipowicz et al., Curr Opin Struct Biol 15:331-341, 2005).Aberrant expression of miRNAs has been found to be associated with a variety of human diseases such as cancers/tumors, diabetes, viral infections, cardiovascular diseases, neurodegenerative diseases, and other diseases (Wang et al., J Cell Physiol 23:25-30, 2016; Lawrie, MicroRNAs in medicine, 2013). The expression of miRNAs is tissue specific and can be used to identify tumor type and its origin (Mishra and Merlino, J Clin Invest 119:2119-2123, 2009). Many investigations suggest that the miRNA-expression profiles are novel diagnostic and prognostic biomarkers for multiple human diseases. Manipulating relevant miRNA expression or function may serve as potential therapeutic strategies for different diseases.

Recent scenario of microRNA as diagnostic and prognostic biomarkers of prostate cancer

Prostate cancer (CaP) is a leading cause of cancer death and displays a broad range of clinical behavior from relatively indolent to aggressive metastatic disease. Due to the alteration and incomplete characterization of the CaP genomic markers, the quest for novel cellular metabolic regulatory molecules like micro RNA (miRNA) as a biomarker could be considered for the prognosis and treatment of CaP in future. In this article, we review the existing literature pertaining to CaP. Study provides a comprehensive miRNA profile expressed in CaP. Beside the miRNA expressed in the tumor tissue, circulating miRNAs have been found highly stable and are both detectable and quantifiable in a range of accessible bio fluids; therefore, miRNA has the potential to be useful diagnostic, prognostic and predictive biomarker. Along with being an important molecule in modulation of CaP progression, the miRNA have certain limitations such as lack of stable expression of multiple target genes and often disrupt entire signaling networks of cellular metabolic pathways. We conclude that: The alteration of miRNA and their role played in cellular regulatory networks would be the next target of basic research in CaP. The miRNAs identified may be validated and modeled to understand their role in CaP, using bioinformatics. There is an immediate unmet need in the translational approach of identified miRNAs. The characterization of miRNAs involved in CaP is still incomplete: adequate validation studies are required to corroborate current results.

Evidence base and future research directions in the management of low back pain

Low back pain (LBP) is a prevalent and costly condition. Awareness of valid and reliable patient history taking, physical examination and clinical testing is important for diagnostic accuracy. Stratified care which targets treatment to patient subgroups based on key characteristics is reliant upon accurate diagnostics. Models of stratified care that can potentially improve treatment effects include prognostic risk profiling for persistent LBP, likely response to specific treatment based on clinical prediction models or suspected underlying causal mechanisms. The focus of this editorial is to highlight current research status and future directions for LBP diagnostics and stratified care.

Risk Stratification and Prognosis in Sepsis: What Have We Learned from Microarrays?

Sepsis mortality rates have decreased in recent years but remain unacceptably high. Risk stratification and prognostication is of particular importance because high-risk patients may benefit from earlier clinical interventions, whereas low-risk patients may benefit from not undergoing unnecessary procedures. Prognostication is currently done mostly via clinical criteria and blood lactate levels. This article summarizes the literature on the complexity of changes at the molecular level for the casual reader.

Differences in Bordetella pertussis DNA load according to clinical and epidemiological characteristics of patients with whooping cough

OBJECTIVE

To identify associations between nasopharyngeal Bordetella pertussis DNA load and clinical and epidemiological characteristics and evaluate DNA load prognostic value in pertussis severity.

METHODS

Prospective observational multi-centre study including nasopharyngeal samples positive to pertussis DNA by real-time PCR collected from children and adult patients in more than 200 health centres of Catalonia (Spain) during 2012-2013.

RESULTS

B. pertussis load was inversely correlated with age (rho = -0.32, p < 0.001), time to diagnosis (rho = -0.33, p < 0.001) and number of symptoms (rho = 0.13, p = 0.002). Median bacterial load was significantly higher in inpatients versus outpatients (4.91 vs. 2.55 log10 CFU/mL, p < 0.001), patients with complications versus those without (6.05 vs. 2.82 log10 CFU/mL, p < 0.001), disease incidence in summer and autumn versus spring and winter (3.50 vs. 2.21 log10 CFU/mL, p = 0.002), and unvaccinated-partially vaccinated patients versus vaccinated (4.20 vs. 2.76 log10 CFU/mL, p = 0.004). A logistic regression model including bacterial load and other candidate prognostic factors showed good prediction for hospital care (AUC = 0.94) although only age and unvaccinated status were found to be prognostic factors.

CONCLUSIONS

We observed strong positive associations of nasopharyngeal bacterial load with severity outcomes of hospitalisation and occurrence of complications. Bacterial load and other independent variables contributed to an accurate prognostic model for hospitalisation.

Can the painDETECT Questionnaire score and MRI help predict treatment outcome in rheumatoid arthritis: protocol for the Frederiksberg hospital's Rheumatoid Arthritis, pain assessment and Medical Evaluation (FRAME-cohort) study

INTRODUCTION

Pain in rheumatoid arthritis (RA) is traditionally considered to be of inflammatory origin. Despite better control of inflammation, some patients still report pain as a significant concern, even when being in clinical remission. This suggests that RA may prompt central sensitisation-one aspect of chronic pain. In contrast, other patients report good treatment response, although imaging shows signs of inflammation, which could indicate a possible enhancement of descending pain inhibitory mechanisms. When assessing disease activity in patients with central sensitisation, the commonly used disease activity scores (eg, DAS28-CRP (C reactive protein)) will yield constant high total scores due to high tender joint count and global health assessments, whereas MRI provides an isolated estimate of inflammation. The objective of this study is, in patients with RA initiating anti-inflammatory treatment, to explore the prognostic value of a screening questionnaire for central sensitisation, hand inflammation assessed by conventional MRI, and the interaction between them regarding treatment outcome evaluated by clinical status (DAS28-CRP). For the purpose of further exploratory analyses, dynamic contrast-enhanced MRI (DCE-MRI) is performed.

METHOD AND ANALYSIS

The painDETECT Questionnaire (PDQ), originally developed to screen for a neuropathic pain component, is applied to indicate the presence of central sensitisation. Adults diagnosed with RA are included when either (A) initiating disease-modifying antirheumatic drug treatment, or (B) initiating or switching to biological therapy. We anticipate that 100 patients will be enrolled, tested and reassessed after 4 months of treatment.

DATA COLLECTION INCLUDES

Clinical data, conventional MRI, DCE-MRI, blood samples and patient-reported outcomes.

ETHICS AND DISSEMINATION

This study aims at supporting rheumatologists to define strategies to reach optimal treatment outcomes in patients with RA based on chronic pain prognostics. The study has been approved by The Capital region of Denmark's Ethics Committee; identification number H-3-2013-049. The results will be published in international peer-reviewed journals.

Future directions in inflammatory bowel disease management

BACKGROUND AND AIMS

Clinical management of inflammatory bowel diseases (IBD), new treatment modalities and the potential impact of personalised medicine remain topics of intense interest as our understanding of the pathophysiology of IBD expands.

METHODS

Potential future strategies for IBD management are discussed, based on recent preclinical and clinical research.

RESULTS

A top-down approach to medical therapy is increasingly being adopted for patients with risk factors for severe inflammation or an unfavourable disease course in an attempt to halt the inflammatory process as early as possible, prevent complications and induce mucosal healing. In the future, biological therapies for IBD are likely to be used more selectively based on personalised benefit/risk assessment, determined through reliable biomarkers and tissue signatures, and will probably be optimised throughout the course of treatment. Biologics with different mechanisms of action will be available; when one drug fails, patients will be able to switch to another and even combination biologics may become a reality. The role of biotherapeutic products that are similar to currently licensed biologics in terms of quality, safety and efficacy - i.e. biosimilars - is at an early stage and requires further experience. Other therapeutic strategies may involve manipulation of the microbiome using antibiotics, probiotics, prebiotics, diet and combinations of all these approaches. Faecal microbiota transplantation is also a potential option in IBD although controlled data are lacking.

CONCLUSIONS

The future of classifying, prognosticating and managing IBD involves an outcomes-based approach to identify biomarkers reflecting various biological processes that can be matched with clinically important endpoints.