A review of approaches to identifying patient phenotype cohorts using electronic health records

Objective

To summarize literature describing approaches aimed at automatically identifying patients with a common phenotype.

Materials and methods

We performed a review of studies describing systems or reporting techniques developed for identifying cohorts of patients with specific phenotypes. Every full text article published in (1) Journal of American Medical Informatics Association, (2) Journal of Biomedical Informatics, (3) Proceedings of the Annual American Medical Informatics Association Symposium, and (4) Proceedings of Clinical Research Informatics Conference within the past 3 years was assessed for inclusion in the review. Only articles using automated techniques were included.

Results

Ninety-seven articles met our inclusion criteria. Forty-six used natural language processing (NLP)-based techniques, 24 described rule-based systems, 41 used statistical analyses, data mining, or machine learning techniques, while 22 described hybrid systems. Nine articles described the architecture of large-scale systems developed for determining cohort eligibility of patients.

Discussion

We observe that there is a rise in the number of studies associated with cohort identification using electronic medical records. Statistical analyses or machine learning, followed by NLP techniques, are gaining popularity over the years in comparison with rule-based systems.

Conclusions

There are a variety of approaches for classifying patients into a particular phenotype. Different techniques and data sources are used, and good performance is reported on datasets at respective institutions. However, no system makes comprehensive use of electronic medical records addressing all of their known weaknesses.

Characterization of subgroups of heart failure patients with preserved ejection fraction with possible implications for prognosis and treatment response

BACKGROUND

Patients with heart failure and preserved ejection fraction (HFpEF) have a poor prognosis, and no therapies have been proven to improve outcomes. It has been proposed that heart failure, including HFpEF, represents overlapping syndromes that may have different prognoses. We present an exploratory study of patients enrolled in the Irbesartan in Heart Failure with Preserved Ejection Fraction Study (I-PRESERVE) using latent class analysis (LCA) with validation using the Candesartan in Heart failure: Assessment of Reduction in Mortality and morbidity (CHARM)-Preserved study to identify HFpEF subgroups.

METHODS AND RESULTS

In total, 4113 HFpEF patients randomized to irbesartan or placebo were characterized according to 11 clinical features. The HFpEF subgroups were identified using LCA. Event-free survival and effect of irbesartan on the composite of all-cause mortality and cardiovascular hospitalization were determined for each subgroup. Subgroup definitions were applied to 3203 patients enrolled in CHARM-Preserved to validate observations regarding prognosis and treatment response. Six subgroups were identified with significant differences in event-free survival (P < 0.001). Clinical profiles and prognoses of the six subgroups were similar in CHARM-Preserved. The two subgroups with the worst event-free survival in both studies were characterized by a high prevalence of obesity, hyperlipidaemia, diabetes mellitus, anaemia, and renal insufficiency (Subgroup C) and by female predominance, advanced age, lower body mass index, and high rates of atrial fibrillation, valvular disease, renal insufficiency, and anaemia (Subgroup F).

CONCLUSION

Using a data-driven approach, we identified HFpEF subgroups with significantly different prognoses. Further development of this approach for characterizing HFpEF subgroups is warranted.

Toward harmonized phenotyping of human myeloid-derived suppressor cells by flow cytometry: results from an interim study

There is an increasing interest for monitoring circulating myeloid-derived suppressor cells (MDSCs) in cancer patients, but there are also divergences in their phenotypic definition. To overcome this obstacle, the Cancer Immunoguiding Program under the umbrella of the Association of Cancer Immunotherapy is coordinating a proficiency panel program that aims at harmonizing MDSC phenotyping. After a consultation period, a two-stage approach was designed to harmonize MDSC phenotype. In the first step, an international consortium of 23 laboratories immunophenotyped 10 putative MDSC subsets on pretested, peripheral blood mononuclear cells of healthy donors to assess the level of concordance and define robust marker combinations for the identification of circulating MDSCs. At this stage, no mandatory requirements to standardize reagents or protocols were introduced. Data analysis revealed a small intra-laboratory, but very high inter-laboratory variance for all MDSC subsets, especially for the granulocytic subsets. In particular, the use of a dead-cell marker altered significantly the reported percentage of granulocytic MDSCs, confirming that these cells are especially sensitive to cryopreservation and/or thawing. Importantly, the gating strategy was heterogeneous and associated with high inter-center variance. Overall, our results document the high variability in MDSC phenotyping in the multicenter setting if no harmonization/standardization measures are applied. Although the observed variability depended on a number of identified parameters, the main parameter associated with variation was the gating strategy. Based on these findings, we propose further efforts to harmonize marker combinations and gating parameters to identify strategies for a robust enumeration of MDSC subsets.

VarElect: the phenotype-based variation prioritizer of the GeneCards Suite

Background

Next generation sequencing (NGS) provides a key technology for deciphering the genetic underpinnings of human diseases. Typical NGS analyses of a patient depict tens of thousands non-reference coding variants, but only one or very few are expected to be significant for the relevant disorder. In a filtering stage, one employs family segregation, rarity in the population, predicted protein impact and evolutionary conservation as a means for shortening the variation list. However, narrowing down further towards culprit disease genes usually entails laborious seeking of gene-phenotype relationships, consulting numerous separate databases. Thus, a major challenge is to transition from the few hundred shortlisted genes to the most viable disease-causing candidates.

Results

We describe a novel tool, VarElect (http://ve.genecards.org), a comprehensive phenotype-dependent variant/gene prioritizer, based on the widely-used GeneCards, which helps rapidly identify causal mutations with extensive evidence. The GeneCards suite offers an effective and speedy alternative, whereby >120 gene-centric automatically-mined data sources are jointly available for the task. VarElect cashes on this wealth of information, as well as on GeneCards’ powerful free-text Boolean search and scoring capabilities, proficiently matching variant-containing genes to submitted disease/symptom keywords. The tool also leverages the rich disease and pathway information of MalaCards, the human disease database, and PathCards, the unified pathway (SuperPaths) database, both within the GeneCards Suite. The VarElect algorithm infers direct as well as indirect links between genes and phenotypes, the latter benefitting from GeneCards’ diverse gene-to-gene data links in GenesLikeMe. Finally, our tool offers an extensive gene-phenotype evidence portrayal (“MiniCards”) and hyperlinks to the parent databases.

Conclusions

We demonstrate that VarElect compares favorably with several often-used NGS phenotyping tools, thus providing a robust facility for ranking genes, pointing out their likelihood to be related to a patient’s disease. VarElect’s capacity to automatically process numerous NGS cases, either in stand-alone format or in VCF-analyzer mode (TGex and VarAnnot), is indispensable for emerging clinical projects that involve thousands of whole exome/genome NGS analyses.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-2722-2) contains supplementary material, which is available to authorized users.

Electronic health records based phenotyping in next-generation clinical trials: a perspective from the NIH Health Care Systems Collaboratory

Widespread sharing of data from electronic health records and patient-reported outcomes can strengthen the national capacity for conducting cost-effective clinical trials and allow research to be embedded within routine care delivery. While pragmatic clinical trials (PCTs) have been performed for decades, they now can draw on rich sources of clinical and operational data that are continuously fed back to inform research and practice. The Health Care Systems Collaboratory program, initiated by the NIH Common Fund in 2012, engages healthcare systems as partners in discussing and promoting activities, tools, and strategies for supporting active participation in PCTs. The NIH Collaboratory consists of seven demonstration projects, and seven problem-specific working group 'Cores', aimed at leveraging the data captured in heterogeneous 'real-world' environments for research, thereby improving the efficiency, relevance, and generalizability of trials. Here, we introduce the Collaboratory, focusing on its Phenotype, Data Standards, and Data Quality Core, and present early observations from researchers implementing PCTs within large healthcare systems. We also identify gaps in knowledge and present an informatics research agenda that includes identifying methods for the definition and appropriate application of phenotypes in diverse healthcare settings, and methods for validating both the definition and execution of electronic health records based phenotypes.

The 2013 EAU guidelines on chronic pelvic pain: is management of chronic pelvic pain a habit, a philosophy, or a science? 10 years of development

CONTEXT

Progress in the science of pain has led pain specialists to move away from an organ-centred understanding of pain located in the pelvis to an understanding based on the mechanism of pain and integrating, as far as possible, psychological, social, and sexual dimensions of the problem. This change is reflected in all areas, from taxonomy through treatment. However, deciding what is adequate investigation to rule out treatable disease before moving to this way of engaging with the patient experiencing pain is a complex process, informed by pain expertise as much as by organ-based medical knowledge.

OBJECTIVE

To summarise the evolving changes in the management of patients with chronic pelvic pain by referring to the 2012 version of the European Association of Urology (EAU) guidelines on chronic pelvic pain.

EVIDENCE ACQUISITION

The working panel highlights some of the most important aspects of the management of patients with chronic pelvic pain emerging in recent years in the context of the EAU guidelines on chronic pelvic pain. The guidelines were completely updated in 2012 based on a systematic review of the literature from online databases from 1995 to 2011. According to this review, levels of evidence and grades of recommendation were added to the text. A full version of the guidelines is available at the EAU office or Web site (www.uroweb.org).

EVIDENCE SYNTHESIS

The previously mentioned issues are explored in this paper, which refers throughout to dilemmas for the physician and treatment team as well as to the need to inform and engage the patient in a collaborative empirical approach to pain relief and rehabilitation. These issues are exemplified in two case histories.

CONCLUSIONS

Chronic pelvic pain persisting after appropriate treatment requires a different approach focussing on pain. This approach integrates the medical, psychosocial, and sexual elements of care to engage the patient in a collaborative journey towards self-management.

Deep machine learning provides state-of-the-art performance in image-based plant phenotyping

In plant phenotyping, it has become important to be able to measure many features on large image sets in order to aid genetic discovery. The size of the datasets, now often captured robotically, often precludes manual inspection, hence the motivation for finding a fully automated approach. Deep learning is an emerging field that promises unparalleled results on many data analysis problems. Building on artificial neural networks, deep approaches have many more hidden layers in the network, and hence have greater discriminative and predictive power. We demonstrate the use of such approaches as part of a plant phenotyping pipeline. We show the success offered by such techniques when applied to the challenging problem of image-based plant phenotyping and demonstrate state-of-the-art results (>97% accuracy) for root and shoot feature identification and localization. We use fully automated trait identification using deep learning to identify quantitative trait loci in root architecture datasets. The majority (12 out of 14) of manually identified quantitative trait loci were also discovered using our automated approach based on deep learning detection to locate plant features. We have shown deep learning-based phenotyping to have very good detection and localization accuracy in validation and testing image sets. We have shown that such features can be used to derive meaningful biological traits, which in turn can be used in quantitative trait loci discovery pipelines. This process can be completely automated. We predict a paradigm shift in image-based phenotyping bought about by such deep learning approaches, given sufficient training sets.

Chronic inflammation: key player and biomarker-set to predict and prevent cancer development and progression based on individualized patient profiles

A strong relationship exists between tumor and inflammation, which is the hot point in cancer research. Inflammation can promote the occurrence and development of cancer by promoting blood vessel growth, cancer cell proliferation, and tumor invasiveness, negatively regulating immune response, and changing the efficacy of certain anti-tumor drugs. It has been demonstrated that there are a large number of inflammatory factors and inflammatory cells in the tumor microenvironment, and tumor-promoting immunity and anti-tumor immunity exist simultaneously in the tumor microenvironment. The typical relationship between chronic inflammation and tumor has been presented by the relationships between Helicobacter pylori, chronic gastritis, and gastric cancer; between smoking, development of chronic pneumonia, and lung cancer; and between hepatitis virus (mainly hepatitis virus B and C), development of chronic hepatitis, and liver cancer. The prevention of chronic inflammation is a factor that can prevent cancer, so it effectively inhibits or blocks the occurrence, development, and progression of the chronic inflammation process playing important roles in the prevention of cancer. Monitoring of the causes and inflammatory factors in chronic inflammation processes is a useful way to predict cancer and assess the efficiency of cancer prevention. Chronic inflammation-based biomarkers are useful tools to predict and prevent cancer.

The use of plant models in deep learning: an application to leaf counting in rosette plants

Deep learning presents many opportunities for image-based plant phenotyping. Here we consider the capability of deep convolutional neural networks to perform the leaf counting task. Deep learning techniques typically require large and diverse datasets to learn generalizable models without providing a priori an engineered algorithm for performing the task. This requirement is challenging, however, for applications in the plant phenotyping field, where available datasets are often small and the costs associated with generating new data are high. In this work we propose a new method for augmenting plant phenotyping datasets using rendered images of synthetic plants. We demonstrate that the use of high-quality 3D synthetic plants to augment a dataset can improve performance on the leaf counting task. We also show that the ability of the model to generate an arbitrary distribution of phenotypes mitigates the problem of dataset shift when training and testing on different datasets. Finally, we show that real and synthetic plants are significantly interchangeable when training a neural network on the leaf counting task.

Learning probabilistic phenotypes from heterogeneous EHR data

We present the Unsupervised Phenome Model (UPhenome), a probabilistic graphical model for large-scale discovery of computational models of disease, or phenotypes. We tackle this challenge through the joint modeling of a large set of diseases and a large set of clinical observations. The observations are drawn directly from heterogeneous patient record data (notes, laboratory tests, medications, and diagnosis codes), and the diseases are modeled in an unsupervised fashion. We apply UPhenome to two qualitatively different mixtures of patients and diseases: records of extremely sick patients in the intensive care unit with constant monitoring, and records of outpatients regularly followed by care providers over multiple years. We demonstrate that the UPhenome model can learn from these different care settings, without any additional adaptation. Our experiments show that (i) the learned phenotypes combine the heterogeneous data types more coherently than baseline LDA-based phenotypes; (ii) they each represent single diseases rather than a mix of diseases more often than the baseline ones; and (iii) when applied to unseen patient records, they are correlated with the patients' ground-truth disorders. Code for training, inference, and quantitative evaluation is made available to the research community.

The impact of various preanalytical treatments on the phenotype of small extracellular vesicles in blood analyzed by protein microarray

The research field of extracellular vesicles (EVs) is increasing immensely and the potential uses of EVs seem endless. They are found in large numbers in various body fluids, and blood samples may well serve as liquid biopsies. However, these small membrane-derived entities of cellular origin are not straightforward to work with in regard to isolation and characterization. A broad range of relevant preanalytical issues was tested, with a focus on the phenotypic impact of smaller EVs. The influences of the i) blood collection tube used, ii) incubation time before the initial centrifugation, iii) transportation/physical stress, iv) storage temperature and time (short term and long term), v) choice of centrifugation protocol, vi) freeze-thaw cycles, and vii) exosome isolation procedure (ExoQuick™) were examined. To identify the impact of the preanalytical treatments, the relative amounts (detected signal intensities of CD9-, CD63- and/or CD81-positive) and phenotypes of small EVs were analyzed using the multiplexed antibody-based microarray technology, termed the EV Array. The analysis encompassed 15 surface- or surface-related markers, including CD9, CD63, CD81, CD142, and Annexin V. This study revealed that samples collected in different blood collection tubes suffered to varying degrees from the preanalytical treatments tested here. There is no unequivocal answer to the questions asked. However, in general, the period of time and prospective transportation before the initial centrifugation, choice of centrifugation protocol, and storage temperature were observed to have major impacts on the samples. On the contrary, long-term storage and freeze-thawing seemed to not have a critical influence. Hence, there are pros and cons of any choice regarding sample collection and preparation and may very well be analysis dependent. However, to compare samples and results, it is important to ensure that all samples are of the same type and have been handled similarly.

Identification of distinct phenotypic clusters in heart failure with preserved ejection fraction

AIMS

We aimed to derive and validate clinically useful clusters of patients with heart failure with preserved ejection fraction (HFpEF; left ventricular ejection fraction ≥50%).

METHODS AND RESULTS

We derived a cluster model from 6909 HFpEF patients from the Swedish Heart Failure Registry (SwedeHF) and externally validated this in 2153 patients from the Chronic Heart Failure ESC-guideline based Cardiology practice Quality project (CHECK-HF) registry. In SwedeHF, the median age was 80 [interquartile range 72-86] years, 52% of patients were female and most frequent comorbidities were hypertension (82%), atrial fibrillation (68%), and ischaemic heart disease (48%). Latent class analysis identified five distinct clusters: cluster 1 (10% of patients) were young patients with a low comorbidity burden and the highest proportion of implantable devices; cluster 2 (30%) patients had atrial fibrillation, hypertension without diabetes; cluster 3 (25%) patients were the oldest with many cardiovascular comorbidities and hypertension; cluster 4 (15%) patients had obesity, diabetes and hypertension; and cluster 5 (20%) patients were older with ischaemic heart disease, hypertension and renal failure and were most frequently prescribed diuretics. The clusters were reproduced in the CHECK-HF cohort. Patients in cluster 1 had the best prognosis, while patients in clusters 3 and 5 had the worst age- and sex-adjusted prognosis.

CONCLUSIONS

Five distinct clusters of HFpEF patients were identified that differed in clinical characteristics, heart failure drug therapy and prognosis. These results confirm the heterogeneity of HFpEF and form a basis for tailoring trial design to individualized drug therapy in HFpEF patients.

Modelling strategies for assessing and increasing the effectiveness of new phenotyping techniques in plant breeding

New types of phenotyping tools generate large amounts of data on many aspects of plant physiology and morphology with high spatial and temporal resolution. These new phenotyping data are potentially useful to improve understanding and prediction of complex traits, like yield, that are characterized by strong environmental context dependencies, i.e., genotype by environment interactions. For an evaluation of the utility of new phenotyping information, we will look at how this information can be incorporated in different classes of genotype-to-phenotype (G2P) models. G2P models predict phenotypic traits as functions of genotypic and environmental inputs. In the last decade, access to high-density single nucleotide polymorphism markers (SNPs) and sequence information has boosted the development of a class of G2P models called genomic prediction models that predict phenotypes from genome wide marker profiles. The challenge now is to build G2P models that incorporate simultaneously extensive genomic information alongside with new phenotypic information. Beyond the modification of existing G2P models, new G2P paradigms are required. We present candidate G2P models for the integration of genomic and new phenotyping information and illustrate their use in examples. Special attention will be given to the modelling of genotype by environment interactions. The G2P models provide a framework for model based phenotyping and the evaluation of the utility of phenotyping information in the context of breeding programs.

Breeder friendly phenotyping

The word phenotyping can nowadays invoke visions of a drone or phenocart moving swiftly across research plots collecting high-resolution data sets on a wide array of traits. This has been made possible by recent advances in sensor technology and data processing. Nonetheless, more comprehensive often destructive phenotyping still has much to offer in breeding as well as research. This review considers the 'breeder friendliness' of phenotyping within three main domains: (i) the 'minimum data set', where being 'handy' or accessible and easy to collect and use is paramount, visual assessment often being preferred; (ii) the high throughput phenotyping (HTP), relatively new for most breeders, and requiring significantly greater investment with technical hurdles for implementation and a steeper learning curve than the minimum data set; (iii) detailed characterization or 'precision' phenotyping, typically customized for a set of traits associated with a target environment and requiring significant time and resources. While having been the subject of debate in the past, extra investment for phenotyping is becoming more accepted to capitalize on recent developments in crop genomics and prediction models, that can be built from the high-throughput and detailed precision phenotypes. This review considers different contexts for phenotyping, including breeding, exploration of genetic resources, parent building and translational research to deliver other new breeding resources, and how the different categories of phenotyping listed above apply to each. Some of the same tools and rules of thumb apply equally well to phenotyping for genetic analysis of complex traits and gene discovery.

Sensory, psychological, and metabolic dysfunction in HIV-associated peripheral neuropathy: A cross-sectional deep profiling study

HIV-associated sensory neuropathy (HIV-SN) is a frequent complication of HIV infection and a major source of morbidity. A cross-sectional deep profiling study examining HIV-SN was conducted in people living with HIV in a high resource setting using a battery of measures which included the following: parameters of pain and sensory symptoms (7day pain diary, Neuropathic Pain Symptom Inventory [NPSI] and Brief Pain Inventory [BPI]), sensory innervation (structured neurological examination, quantitative sensory testing [QST] and intraepidermal nerve fibre density [IENFD]), psychological state (Pain Anxiety Symptoms Scale-20 [PASS-20], Depression Anxiety and Positive Outlook Scale [DAPOS], and Pain Catastrophizing Scale [PCS], insomnia (Insomnia Severity Index [ISI]), and quality of life (Short Form (36) Health Survey [SF-36]). The diagnostic utility of the Brief Peripheral Neuropathy Screen (BPNS), Utah Early Neuropathy Scale (UENS), and Toronto Clinical Scoring System (TCSS) were evaluated. Thirty-six healthy volunteers and 66 HIV infected participants were recruited. A novel triumvirate case definition for HIV-SN was used that required 2 out of 3 of the following: 2 or more abnormal QST findings, reduced IENFD, and signs of a peripheral neuropathy on a structured neurological examination. Of those with HIV, 42% fulfilled the case definition for HIV-SN (n=28), of whom 75% (n=21) reported pain. The most frequent QST abnormalities in HIV-SN were loss of function in mechanical and vibration detection. Structured clinical examination was superior to QST or IENFD in HIV-SN diagnosis. HIV-SN participants had higher plasma triglyceride, concentrations depression, anxiety and catastrophizing scores, and prevalence of insomnia than HIV participants without HIV-SN.

Functional Annotation of All Salmonid Genomes (FAASG): an international initiative supporting future salmonid research, conservation and aquaculture

We describe an emerging initiative - the 'Functional Annotation of All Salmonid Genomes' (FAASG), which will leverage the extensive trait diversity that has evolved since a whole genome duplication event in the salmonid ancestor, to develop an integrative understanding of the functional genomic basis of phenotypic variation. The outcomes of FAASG will have diverse applications, ranging from improved understanding of genome evolution, to improving the efficiency and sustainability of aquaculture production, supporting the future of fundamental and applied research in an iconic fish lineage of major societal importance.

A review of blood sample handling and pre-processing for metabolomics studies

Metabolomics has been found to be applicable to a wide range of clinical studies, bringing a new era for improving clinical diagnostics, early disease detection, therapy prediction and treatment efficiency monitoring. A major challenge in metabolomics, particularly untargeted studies, is the extremely diverse and complex nature of biological specimens. Despite great advances in the field there still exist fundamental needs for considering pre-analytical variability that can introduce bias to the subsequent analytical process and decrease the reliability of the results and moreover confound final research outcomes. Many researchers are mainly focused on the instrumental aspects of the biomarker discovery process, and sample related variables sometimes seem to be overlooked. To bridge the gap, critical information and standardized protocols regarding experimental design and sample handling and pre-processing are highly desired. Characterization of a range variation among sample collection methods is necessary to prevent results misinterpretation and to ensure that observed differences are not due to an experimental bias caused by inconsistencies in sample processing. Herein, a systematic discussion of pre-analytical variables affecting metabolomics studies based on blood derived samples is performed. Furthermore, we provide a set of recommendations concerning experimental design, collection, pre-processing procedures and storage conditions as a practical review that can guide and serve for the standardization of protocols and reduction of undesirable variation.

Ambient temperature and genotype differentially affect developmental and phenotypic plasticity in Arabidopsis thaliana

BACKGROUND

Global increase in ambient temperatures constitute a significant challenge to wild and cultivated plant species. Forward genetic analyses of individual temperature-responsive traits have resulted in the identification of several signaling and response components. However, a comprehensive knowledge about temperature sensitivity of different developmental stages and the contribution of natural variation is still scarce and fragmented at best.

RESULTS

Here, we systematically analyze thermomorphogenesis throughout a complete life cycle in ten natural Arabidopsis thaliana accessions grown under long day conditions in four different temperatures ranging from 16 to 28 °C. We used Q₁₀, GxE, phenotypic divergence and correlation analyses to assess temperature sensitivity and genotype effects of more than 30 morphometric and developmental traits representing five phenotype classes. We found that genotype and temperature differentially affected plant growth and development with variing strengths. Furthermore, overall correlations among phenotypic temperature responses was relatively low which seems to be caused by differential capacities for temperature adaptations of individual accessions.

CONCLUSION

Genotype-specific temperature responses may be attractive targets for future forward genetic approaches and accession-specific thermomorphogenesis maps may aid the assessment of functional relevance of known and novel regulatory components.

A comparison between water uptake and root length density in winter wheat: effects of root density and rhizosphere properties

BACKGROUND AND AIMS

We aim to quantify the variation in root distribution in a set of 35 experimental wheat lines. We also compared the effect of variation in hydraulic properties of the rhizosphere on water uptake by roots.

METHODS

We measured the root length density and soil drying in 35 wheat lines in a field experiment. A 3D numerical model was used to predict soil drying profiles with the different root length distributions and compared with measured soil drying. The model was used to test different scenarios of the hydraulic properties of the rhizosphere.

RESULTS

We showed that wheat lines with no detectable differences in root length density can induce soil drying profiles with statistically significant differences. Our data confirmed that a root length density of at least 1 cm/cm³ is needed to drain all the available water in soil. In surface layers where the root length density was far greater than 1 cm/cm³ water uptake was independent of rooting density due to competition for water. However, in deeper layers where root length density was less than 1 cm/cm³, water uptake by roots was proportional to root density.

CONCLUSION

In a set of wheat lines with no detectable differences in the root length density we found significant differences in water uptake. This may be because small differences in root density at depth can result in larger differences in water uptake or that the hydraulic properties of the rhizosphere can greatly affect water uptake.

Phenotypic Features of Central Sensitization

PURPOSE

The current manuscript reviews approaches for phenotyping central sensitization (CS).

METHODS

The manuscript covers the concept of diagnostic phenotyping, use of endophenotypes, biomarkers, and symptom clusters. Specifically, the components of CS that include general sensory sensitivity (assessed by quantitative sensory testing) and a symptom cluster denoting sleep difficulties, pain, affect, cognitive difficulties, and low energy (S.P.A.C.E.).

RESULTS

Each of the assessment domains are described with reference to CS and their presence in chronic overlapping pain conditions (COPCs) - conditions likely influenced by CS.

CONCLUSIONS

COPCs likely represent clinical diagnostic phenotypes of CS. Components of CS can also be assessed using QST or self-report instruments designed to assess single elements of CS or more general composite indices.

Treatment of amblyopia as a function of age

Although historically, treatment of amblyopia has been recommended prior to closure of a critical window in visual development, the existence and duration of that critical window is currently unclear. Moreover, there is clear evidence, both from animal and human studies of deprivation amblyopia, that there are different critical windows for different visual functions and that monocular and binocular deprivation have different neural and behavioral consequences. In view of the spectrum of critical windows for different visual functions and for different types of amblyopia, combined with individual variability in these windows, treatment of amblyopia has been increasingly offered to older children and adults. Nevertheless, treatment beyond the age of 7 years tends to be, on average, less effective than in younger children, and the high degree of variability in treatment response suggests that age is only one of many factors determining treatment response. Newly emerging treatment modalities may hold promise for more effective treatment of amblyopia at older ages. Additional studies are needed to characterize amblyopia by using new and existing clinical tests, leading to improved clinical classification and better prediction of treatment response. Attention also needs to be directed toward characterizing and measuring the impact of amblyopia on the patients' functional vision and health-related quality of life.

Non-invasive imaging of plant roots in different soils using magnetic resonance imaging (MRI)

BACKGROUND

Root systems are highly plastic and adapt according to their soil environment. Studying the particular influence of soils on root development necessitates the adaptation and evaluation of imaging methods for multiple substrates. Non-invasive 3D root images in soil can be obtained using magnetic resonance imaging (MRI). Not all substrates, however, are suitable for MRI. Using barley as a model plant we investigated the achievable image quality and the suitability for root phenotyping of six commercially available natural soil substrates of commonly occurring soil textures. The results are compared with two artificially composed substrates previously documented for MRI root imaging.

RESULTS

In five out of the eight tested substrates, barley lateral roots with diameters below 300 µm could still be resolved. In two other soils, only the thicker barley seminal roots were detectable. For these two substrates the minimal detectable root diameter was between 400 and 500 µm. Only one soil did not allow imaging of the roots with MRI. In the artificially composed substrates, soil moisture above 70% of the maximal water holding capacity (WHCmax) impeded root imaging. For the natural soil substrates, soil moisture had no effect on MRI root image quality in the investigated range of 50-80% WHCmax.

CONCLUSIONS

Almost all tested natural soil substrates allowed for root imaging using MRI. Half of these substrates resulted in root images comparable to our current lab standard substrate, allowing root detection down to a diameter of 300 µm. These soils were used as supplied by the vendor and, in particular, removal of ferromagnetic particles was not necessary. With the characterization of different soils, investigations such as trait stability across substrates are now possible using noninvasive MRI.

A comprehensive iterative approach is highly effective in diagnosing individuals who are exome negative

PURPOSE

Sixty to seventy-five percent of individuals with rare and undiagnosed phenotypes remain undiagnosed after exome sequencing (ES). With standard ES reanalysis resolving 10-15% of the ES negatives, further approaches are necessary to maximize diagnoses in these individuals.

METHODS

In 38 ES negative patients an individualized genomic-phenotypic approach was employed utilizing (1) phenotyping; (2) reanalyses of FASTQ files, with innovative bioinformatics; (3) targeted molecular testing; (4) genome sequencing (GS); and (5) conferring of clinical diagnoses when pathognomonic clinical findings occurred.

RESULTS

Certain and highly likely diagnoses were made in 18/38 (47%) individuals, including identifying two new developmental disorders. The majority of diagnoses (>70%) were due to our bioinformatics, phenotyping, and targeted testing identifying variants that were undetected or not prioritized on prior ES. GS diagnosed 3/18 individuals with structural variants not amenable to ES. Additionally, tentative diagnoses were made in 3 (8%), and in 5 individuals (13%) candidate genes were identified. Overall, diagnoses/potential leads were identified in 26/38 (68%).

CONCLUSIONS

Our comprehensive approach to ES negatives maximizes the ES and clinical data for both diagnoses and candidate gene identification, without GS in the majority. This iterative approach is cost-effective and is pertinent to the current conundrum of ES negatives.

The occurrence of ESBL-producing Escherichia coli carrying aminoglycoside resistance genes in urinary tract infections in Saudi Arabia

Background

The infection and prevalence of extended-spectrum β-lactamases (ESBLs) is a worldwide problem, and the presence of ESBLs varies between countries. In this study, we investigated the occurrence of plasmid-mediated ESBL/AmpC/carbapenemase/aminoglycoside resistance gene expression in Escherichia coli using phenotypic and genotypic techniques.

Methods

A total of 58 E. coli isolates were collected from hospitals in the city of Makkah and screened for the production of ESBL/AmpC/carbapenemase/aminoglycoside resistance genes. All samples were subjected to phenotypic and genotypic analyses. The antibiotic susceptibility of the E. coli isolates was determined using the Vitek-2 system and the minimum inhibitory concentration (MIC) assay. Antimicrobial agents tested using the Vitek 2 system and MIC assay included the expanded-spectrum (or third-generation) cephalosporins (e.g., cefoxitin, cefepime, aztreonam, cefotaxime, ceftriaxone, and ceftazidime) and carbapenems (meropenem and imipenem). Reported positive isolates were investigated using genotyping technology (oligonucleotide microarray-based assay and PCR). The genotyping investigation was focused on ESBL variants and the AmpC, carbapenemase and aminoglycoside resistance genes. E. coli was phylogenetically grouped, and the clonality of the isolates was studied using multilocus sequence typing (MLST).

Results

Our E. coli isolates exhibited different levels of resistance to ESBL drugs, including ampicillin (96.61%), cefoxitin (15.25%), ciprofloxacin (79.66%), cefepime (75.58%), aztreonam (89.83%), cefotaxime (76.27%), ceftazidime (81.36%), meropenem (0%) and imipenem (0%). Furthermore, the distribution of ESBL-producing E. coli was consistent with the data obtained using an oligonucleotide microarray-based assay and PCR genotyping against genes associated with β-lactam resistance. ST131 was the dominant sequence type lineage of the isolates and was the most uropathogenic E. coli lineage. The E. coli isolates also carried aminoglycoside resistance genes.

Conclusions

The evolution and prevalence of ESBL-producing E. coli may be rapidly accelerating in Saudi Arabia due to the high visitation seasons (especially to the city of Makkah). The health authority in Saudi Arabia should monitor the level of drug resistance in all general hospitals to reduce the increasing trend of microbial drug resistance and the impact on patient therapy.

A collaborative approach to developing an electronic health record phenotyping algorithm for drug-induced liver injury

OBJECTIVE

To describe a collaborative approach for developing an electronic health record (EHR) phenotyping algorithm for drug-induced liver injury (DILI).

METHODS

We analyzed types and causes of differences in DILI case definitions provided by two institutions-Columbia University and Mayo Clinic; harmonized two EHR phenotyping algorithms; and assessed the performance, measured by sensitivity, specificity, positive predictive value, and negative predictive value, of the resulting algorithm at three institutions except that sensitivity was measured only at Columbia University.

RESULTS

Although these sites had the same case definition, their phenotyping methods differed by selection of liver injury diagnoses, inclusion of drugs cited in DILI cases, laboratory tests assessed, laboratory thresholds for liver injury, exclusion criteria, and approaches to validating phenotypes. We reached consensus on a DILI phenotyping algorithm and implemented it at three institutions. The algorithm was adapted locally to account for differences in populations and data access. Implementations collectively yielded 117 algorithm-selected cases and 23 confirmed true positive cases.

DISCUSSION

Phenotyping for rare conditions benefits significantly from pooling data across institutions. Despite the heterogeneity of EHRs and varied algorithm implementations, we demonstrated the portability of this algorithm across three institutions. The performance of this algorithm for identifying DILI was comparable with other computerized approaches to identify adverse drug events.

CONCLUSIONS

Phenotyping algorithms developed for rare and complex conditions are likely to require adaptive implementation at multiple institutions. Better approaches are also needed to share algorithms. Early agreement on goals, data sources, and validation methods may improve the portability of the algorithms.