Hydroxychloroquine Improves Low Complement Levels.

OBJECTIVE: Having a low complement level is associated with clinical systemic lupus erythematosus (SLE) disease activity and future organ damage. We studied the association of hydroxychloroquine (HCQ) whole blood levels with changes in complement level. METHODS: We performed two analyses on data prospectively collected from an SLE cohort. In the first (a "new starts on HCQ" analysis), we compared changes in complement level between those starting HCQ and those not starting it. The second analysis evaluated the association between HCQ whole blood levels and low complement level in all cohort visits using conditional logistic regression. RESULTS: In the "new starts on HCQ" analysis, a higher percentage of patients starting HCQ (as reflected in HCQ blood levels >50) experienced a normalization of C4 level compared to those not starting HCQ (23 of 57 [40%] vs. 9 of 56 [13%]; P = 0.011), as well as a significantly greater increase in both C3 and C4 level (P = 0.048 and P = 0.017, respectively). In the "all cohort visits" analysis, there was a statistically significant higher probability of having normal C4 levels in visits with higher HCQ whole blood levels (odds ratio 1.8-2.6 depending on the levels). This relationship was most pronounced for whole blood HCQ levels of 200 ng/mL or more. CONCLUSION: We observed significant improvement in complement levels when HCQ was started and among those with higher whole blood levels of HCQ, particularly with respect to C4. Modulating the pathogenic mechanisms that lead to complement consumption may be one mode by which HCQ prevents poor outcomes in SLE.

Functional and Cutaneous Treatment Outcomes With Intravenous Immunoglobulin for Eosinophilic Fasciitis: A Retrospective Study.

BACKGROUND: Eosinophilic fasciitis (EF) is a rare subtype of deep morphea with an elevated risk of functional impairment. No treatment algorithm has been established for adults with EF refractory to traditional corticosteroid or immunomodulatory treatments. Research on cutaneous and functional outcomes of alternative therapies, such as intravenous immunoglobulin (IVIG), remains scarce.  Objective: To describe the functional and cutaneous outcomes associated with IVIG in adults with treatment-refractory EF at a tertiary referral center. METHODS: We performed a retrospective chart review of 18 consecutive patients with EF identified through a billing code search seen within the UCSF Department of Dermatology between 2015 and 2022.  Results: Seven patients (41.2%) underwent at least one course of intravenous immunoglobulins (IVIG) during the study period. Of 6 patients with available follow-up data, 5 patients (83.3%) achieved both sustained cutaneous and functional improvement. In the IVIG cohort, 1 patient (16.7%) achieved complete response with relapse, 4 (66.7%) were partial responders, and 1 (16.7%) was a non-responder who required treatment with mepolizumab. CONCLUSION: Adverse effects of IVIG included headaches in 1 patient (14.3%) and rash in 2 patients (28.6%). There were no reported veno-occlusive or thromboembolic events associated with IVIG.  J Drugs Dermatol. 2024;23(4):8017.    doi:10.36849/JDD.8017e.

Clinical and therapeutic course in head variants of linear morphea in adults: a retrospective review.

Parry Romberg Syndrome (PRS) and en coup de sabre (ECDS) are head variants of linear morphea with functional and structural implications. This study describes the clinical course, autoimmune co-morbidities, complications, and treatment of adults with PRS/ECDS at a tertiary referral center. We retrospectively reviewed the records of all 34 adult patients with PRS/ECDS identified through billing code search and seen by dermatologists at our institution between 2015 and 2021. Eight patients (23.5%) had ECDS, 8 (23.5%) had PRS, and 18 (52.9%) had overlap. Twenty-six patients (76.5%) reported ocular, oral, and/or neurologic symptoms, and 8 (23.5%) had concomitant autoimmune/inflammatory conditions. Sixteen patients (47.1%) had a skin biopsy, and 25 (73.5%) had imaging. Forty-six MRIs were obtained, of which 6 (13.0%) reported intracranial findings and 25 (54.3%) reported disease-related connective tissue damage. Twenty-four patients (70.6%) underwent systemic treatment during their disease course per available clinical records. Seventeen patients (70.8%) had improved or stable disease upon treatment completion, with an average duration of 22.2 months. Ten patients (41.7%) reported recurrence of disease following the treatment course. To address changes to facial contour, 6 patients (17.6%) opted for procedural treatments. One patient (16.7%) experienced morphea reactivation following a filler injection performed off-immunosuppression. Compared to findings in children, our study suggests adults with PRS/ECDS are more likely to have oral and ocular complications but experience less severe neurologic symptoms. While systemic treatments appear beneficial in most adult patients with PRS/ECDS, disease may recur following discontinuation.

Model to estimate abdominal wall thickness in children undergoing placement or replacement of gastrostomy devices.

OBJECTIVES: Abdominal wall thickness (AWT) is a key measurement when placing or replacing low profile gastrostomy devices. This measurement varies, depending on nutritional status and body habitus. We developed a mathematical model to estimate AWT using a compendium of body measurements. METHODS: Ultrasonography was used to measure AWT at the initial gastrostomy site in subjects aged 22 days to 24 years old. Other body measurements (height, weight, waist circumference and distance from xiphisternum to pubis) were also obtained. Multiple linear regression was used to develop two separate models using age of 2 years to separate the groups. For analysis, AWT is log transformed. RESULTS: Data from 97 subjects were used for analysis. The final model for those ≤24 months old is the following: ln(Estimated AWT) = -1.255 + 0.082*(1 if age 3-24 months, 0 if <3 months) + 0.022*(waist circumference in cm). The final model for those >24 months old is the following: ln(Estimated AWT) = -1.335 + 0.271*(1 if age >84 months, 0 if 24-84 months) + 0.082*(BMI) CONCLUSION: This model to estimate AWT is useful for determining the length of a gastrostomy device at initial placement and with subsequent changes. More data are needed to refine and further validate the model. LEVEL OF EVIDENCE: Level IV, study of prognostic test.

Bowel management program in patients with spina bifida.

PURPOSE: Our center has been successfully implementing a bowel management program (BMP) for fecal incontinence consecutive to anorectal malformation and Hirschsprung disease. Recently, the number of patients with spina bifida requiring management for fecal incontinence has increased. The purpose of this study was to review the results of bowel management in patients with spina bifida and the challenges unique to this population. METHODS: A retrospective chart review was performed including all patients with spina bifida who attended our BMP from February 2016 until April 2018. Data collection included: prenatal intervention, gender, age, characteristics of contrast enema, success rateand challenges faced. RESULTS: Twenty-two patients met inclusion criteria 13 of which were females. Three patients had their myelomeningocele repaired prenatally, the remaining were repaired postnatally. Patient ages ranged from 2 to 24 years. Only nine patients were referred to BMP at proper toilet training age. Three patients came to BMP status post an antegrade enema procedure with reported "accidents" on their current regimen. The colon in the contrast enema was non-dilated in all patients and two behaved as hypermotile requiring loperamide. Seventeen patients (77%) were clean of stool and considered successful. Solution leakage during enema administration was the most common challenge and was corrected by increasing the Foley balloon fill volume. CONCLUSIONS: Our bowel management program with enemas is effective for patients with a history of spina bifida. The data support specific considerations for this population including frequent adjustments, close follow-up and specific administration techniques.

The role of skin ulceration in breast carcinoma staging and outcome.

Breast carcinoma with skin ulceration (SU) is considered a locally advanced disease. The purpose of the study is to investigate if SU is an independent adverse factor. Breast carcinoma patients with SU (n=111) were included in the study. A subset (n=38, study cohort) was matched with cases that had no SU (n=38, matched cohort); the survival analyses were compared between these groups. Then, cases (n=80) were staged independent from SU into stage I, II or III. Disease free survival (DFS) and overall survival (OS) were analyzed. Patients with larger tumors tended to present with distant metastases more often than patients with smaller tumors (P=.004). In the matched cases, the 5-year DFS probability was 53% for the study cohort and 58% for the matched cohort; and for OS 75% for the study cohort and 84% for the matched cohort with no statistical significant difference. However, there was a trend towards worse DFS for the patients whose tumors had SU. When the cases were staged based on tumor size and node status (I, II or III), the OS was statistically significant (P=.047) but not the DFS (P=.195). Relatively small tumors with SU had an extent of disease similar to that observed in patients with early stages disease. The survival analysis suggests that SU may not be an adverse factor. However, more cases are needed to further examine this finding.

DeepPhe: A Natural Language Processing System for Extracting Cancer Phenotypes from Clinical Records.

Precise phenotype information is needed to understand the effects of genetic and epigenetic changes on tumor behavior and responsiveness. Extraction and representation of cancer phenotypes is currently mostly performed manually, making it difficult to correlate phenotypic data to genomic data. In addition, genomic data are being produced at an increasingly faster pace, exacerbating the problem. The DeepPhe software enables automated extraction of detailed phenotype information from electronic medical records of cancer patients. The system implements advanced Natural Language Processing and knowledge engineering methods within a flexible modular architecture, and was evaluated using a manually annotated dataset of the University of Pittsburgh Medical Center breast cancer patients. The resulting platform provides critical and missing computational methods for computational phenotyping. Working in tandem with advanced analysis of high-throughput sequencing, these approaches will further accelerate the transition to precision cancer treatment. Cancer Res; 77(21); e115-8. ©2017 AACR.

Automated annotation and classification of BI-RADS assessment from radiology reports.

The Breast Imaging Reporting and Data System (BI-RADS) was developed to reduce variation in the descriptions of findings. Manual analysis of breast radiology report data is challenging but is necessary for clinical and healthcare quality assurance activities. The objective of this study is to develop a natural language processing (NLP) system for automated BI-RADS categories extraction from breast radiology reports. We evaluated an existing rule-based NLP algorithm, and then we developed and evaluated our own method using a supervised machine learning approach. We divided the BI-RADS category extraction task into two specific tasks: (1) annotation of all BI-RADS category values within a report, (2) classification of the laterality of each BI-RADS category value. We used one algorithm for task 1 and evaluated three algorithms for task 2. Across all evaluations and model training, we used a total of 2159 radiology reports from 18 hospitals, from 2003 to 2015. Performance with the existing rule-based algorithm was not satisfactory. Conditional random fields showed a high performance for task 1 with an F-1 measure of 0.95. Rules from partial decision trees (PART) algorithm showed the best performance across classes for task 2 with a weighted F-1 measure of 0.91 for BIRADS 0-6, and 0.93 for BIRADS 3-5. Classification performance by class showed that performance improved for all classes from Naïve Bayes to Support Vector Machine (SVM), and also from SVM to PART. Our system is able to annotate and classify all BI-RADS mentions present in a single radiology report and can serve as the foundation for future studies that will leverage automated BI-RADS annotation, to provide feedback to radiologists as part of a learning health system loop.

TCGA Expedition: A Data Acquisition and Management System for TCGA Data.

BACKGROUND: The Cancer Genome Atlas Project (TCGA) is a National Cancer Institute effort to profile at least 500 cases of 20 different tumor types using genomic platforms and to make these data, both raw and processed, available to all researchers. TCGA data are currently over 1.2 Petabyte in size and include whole genome sequence (WGS), whole exome sequence, methylation, RNA expression, proteomic, and clinical datasets. Publicly accessible TCGA data are released through public portals, but many challenges exist in navigating and using data obtained from these sites. We developed TCGA Expedition to support the research community focused on computational methods for cancer research. Data obtained, versioned, and archived using TCGA Expedition supports command line access at high-performance computing facilities as well as some functionality with third party tools. For a subset of TCGA data collected at University of Pittsburgh, we also re-associate TCGA data with de-identified data from the electronic health records. Here we describe the software as well as the architecture of our repository, methods for loading of TCGA data to multiple platforms, and security and regulatory controls that conform to federal best practices. RESULTS: TCGA Expedition software consists of a set of scripts written in Bash, Python and Java that download, extract, harmonize, version and store all TCGA data and metadata. The software generates a versioned, participant- and sample-centered, local TCGA data directory with metadata structures that directly reference the local data files as well as the original data files. The software supports flexible searches of the data via a web portal, user-centric data tracking tools, and data provenance tools. Using this software, we created a collaborative repository, the Pittsburgh Genome Resource Repository (PGRR) that enabled investigators at our institution to work with all TCGA data formats, and to interrogate these data with analysis pipelines, and associated tools. WGS data are especially challenging for individual investigators to use, due to issues with downloading, storage, and processing; having locally accessible WGS BAM files has proven invaluable. CONCLUSION: Our open-source, freely available TCGA Expedition software can be used to create a local collaborative infrastructure for acquiring, managing, and analyzing TCGA data and other large public datasets.

An information model for computable cancer phenotypes.

BACKGROUND: Standards, methods, and tools supporting the integration of clinical data and genomic information are an area of significant need and rapid growth in biomedical informatics. Integration of cancer clinical data and cancer genomic information poses unique challenges, because of the high volume and complexity of clinical data, as well as the heterogeneity and instability of cancer genome data when compared with germline data. Current information models of clinical and genomic data are not sufficiently expressive to represent individual observations and to aggregate those observations into longitudinal summaries over the course of cancer care. These models are acutely needed to support the development of systems and tools for generating the so called clinical "deep phenotype" of individual cancer patients, a process which remains almost entirely manual in cancer research and precision medicine. METHODS: Reviews of existing ontologies and interviews with cancer researchers were used to inform iterative development of a cancer phenotype information model. We translated a subset of the Fast Healthcare Interoperability Resources (FHIR) models into the OWL 2 Description Logic (DL) representation, and added extensions as needed for modeling cancer phenotypes with terms derived from the NCI Thesaurus. Models were validated with domain experts and evaluated against competency questions. RESULTS: The DeepPhe Information model represents cancer phenotype data at increasing levels of abstraction from mention level in clinical documents to summaries of key events and findings. We describe the model using breast cancer as an example, depicting methods to represent phenotypic features of cancers, tumors, treatment regimens, and specific biologic behaviors that span the entire course of a patient's disease. CONCLUSIONS: We present a multi-scale information model for representing individual document mentions, document level classifications, episodes along a disease course, and phenotype summarization, linking individual observations to high-level summaries in support of subsequent integration and analysis.

Using Nonexperts for Annotating Pharmacokinetic Drug-Drug Interaction Mentions in Product Labeling: A Feasibility Study.

BACKGROUND: Because vital details of potential pharmacokinetic drug-drug interactions are often described in free-text structured product labels, manual curation is a necessary but expensive step in the development of electronic drug-drug interaction information resources. The use of nonexperts to annotate potential drug-drug interaction (PDDI) mentions in drug product label annotation may be a means of lessening the burden of manual curation. OBJECTIVE: Our goal was to explore the practicality of using nonexpert participants to annotate drug-drug interaction descriptions from structured product labels. By presenting annotation tasks to both pharmacy experts and relatively naïve participants, we hoped to demonstrate the feasibility of using nonexpert annotators for drug-drug information annotation. We were also interested in exploring whether and to what extent natural language processing (NLP) preannotation helped improve task completion time, accuracy, and subjective satisfaction. METHODS: Two experts and 4 nonexperts were asked to annotate 208 structured product label sections under 4 conditions completed sequentially: (1) no NLP assistance, (2) preannotation of drug mentions, (3) preannotation of drug mentions and PDDIs, and (4) a repeat of the no-annotation condition. Results were evaluated within the 2 groups and relative to an existing gold standard. Participants were asked to provide reports on the time required to complete tasks and their perceptions of task difficulty. RESULTS: One of the experts and 3 of the nonexperts completed all tasks. Annotation results from the nonexpert group were relatively strong in every scenario and better than the performance of the NLP pipeline. The expert and 2 of the nonexperts were able to complete most tasks in less than 3 hours. Usability perceptions were generally positive (3.67 for expert, mean of 3.33 for nonexperts). CONCLUSIONS: The results suggest that nonexpert annotation might be a feasible option for comprehensive labeling of annotated PDDIs across a broader range of drug product labels. Preannotation of drug mentions may ease the annotation task. However, preannotation of PDDIs, as operationalized in this study, presented the participants with difficulties. Future work should test if these issues can be addressed by the use of better performing NLP and a different approach to presenting the PDDI preannotations to users during the annotation workflow.

NOBLE - Flexible concept recognition for large-scale biomedical natural language processing.

BACKGROUND: Natural language processing (NLP) applications are increasingly important in biomedical data analysis, knowledge engineering, and decision support. Concept recognition is an important component task for NLP pipelines, and can be either general-purpose or domain-specific. We describe a novel, flexible, and general-purpose concept recognition component for NLP pipelines, and compare its speed and accuracy against five commonly used alternatives on both a biological and clinical corpus. NOBLE Coder implements a general algorithm for matching terms to concepts from an arbitrary vocabulary set. The system's matching options can be configured individually or in combination to yield specific system behavior for a variety of NLP tasks. The software is open source, freely available, and easily integrated into UIMA or GATE. We benchmarked speed and accuracy of the system against the CRAFT and ShARe corpora as reference standards and compared it to MMTx, MGrep, Concept Mapper, cTAKES Dictionary Lookup Annotator, and cTAKES Fast Dictionary Lookup Annotator. RESULTS: We describe key advantages of the NOBLE Coder system and associated tools, including its greedy algorithm, configurable matching strategies, and multiple terminology input formats. These features provide unique functionality when compared with existing alternatives, including state-of-the-art systems. On two benchmarking tasks, NOBLE's performance exceeded commonly used alternatives, performing almost as well as the most advanced systems. Error analysis revealed differences in error profiles among systems. CONCLUSION: NOBLE Coder is comparable to other widely used concept recognition systems in terms of accuracy and speed. Advantages of NOBLE Coder include its interactive terminology builder tool, ease of configuration, and adaptability to various domains and tasks. NOBLE provides a term-to-concept matching system suitable for general concept recognition in biomedical NLP pipelines.