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Results: 1 to 10 of 91

Publication Record


Using an artificial neural network to predict traumatic brain injury.
Hale AT, Stonko DP, Lim J, Guillamondegui OD, Shannon CN, Patel MB
(2018) J Neurosurg Pediatr 23: 219-226
MeSH Terms: Adolescent, Algorithms, Area Under Curve, Brain Injuries, Traumatic, Child, False Positive Reactions, Female, Humans, Male, Neural Networks, Computer, Predictive Value of Tests, Sensitivity and Specificity, Tomography, X-Ray Computed
Show Abstract · Added January 23, 2019
In BriefPediatric traumatic brain injury (TBI) is common, but not all injuries require hospitalization. A computational tool for ruling-in patients who will have clinically relevant TBI (CRTBI) would be valuable, providing an evidence-based mechanism for safe discharge. Here, using data from 12,902 patients from the Pediatric Emergency Care Applied Research Network (PECARN) TBI data set, the authors utilize artificial intelligence to predict CRTBI using radiologist-interpreted CT information with > 99% sensitivity and an AUC of 0.99.
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13 MeSH Terms
Outpatient Engagement and Predicted Risk of Suicide Attempts in Fibromyalgia.
McKernan LC, Lenert MC, Crofford LJ, Walsh CG
(2019) Arthritis Care Res (Hoboken) 71: 1255-1263
MeSH Terms: Adult, Area Under Curve, Case-Control Studies, Female, Fibromyalgia, Humans, Incidence, Male, Middle Aged, Odds Ratio, Outpatients, Predictive Value of Tests, Reproducibility of Results, Retrospective Studies, Risk Assessment, Suicidal Ideation, Suicide, Attempted, Survival Analysis, Young Adult
Show Abstract · Added March 25, 2020
OBJECTIVE - Patients with fibromyalgia (FM) are 10 times more likely to die by suicide than the general population. The purpose of this study was to externally validate published models predicting suicidal ideation and suicide attempts in patients with FM and to identify interpretable risk and protective factors for suicidality unique to FM.
METHODS - This was a case-control study of large-scale electronic health record data collected from 1998 to 2017, identifying FM cases with validated Phenotype KnowledgeBase criteria. Model performance was measured through discrimination, including the receiver operating area under the curve (AUC), sensitivity, and specificity, and through calibration, including calibration plots. Risk factors were selected by L1 penalized regression with bootstrapping for both outcomes. Secondary utilization analyses converted time-based billing codes to equivalent minutes to estimate face-to-face provider contact.
RESULTS - We identified 8,879 patients with FM, with 34 known suicide attempts and 96 documented cases of suicidal ideation. External validity was good for both suicidal ideation (AUC 0.80) and attempts (AUC 0.82) with excellent calibration. Risk factors specific to suicidal ideation included polysomatic symptoms such as fatigue (odds ratio [OR] 1.29 [95% confidence interval (95% CI) 1.25-1.32]), dizziness (OR 1.25 [95% CI 1.22-1.28]), and weakness (OR 1.17 [95% CI 1.15-1.19]). Risk factors specific to suicide attempt included obesity (OR 1.18 [95% CI 1.10-1.27]) and drug dependence (OR 1.15 [95% CI 1.12-1.18]). Per utilization analyses, those patients with FM and no suicidal ideation spent 3.5 times more time in follow-up annually, and those without documented suicide attempts spent more than 40 times more time face-to-face with providers annually.
CONCLUSION - This is the first study to successfully apply machine learning to reliably detect suicidality in patients with FM, identifying novel risk factors for suicidality and highlighting outpatient engagement as a protective factor against suicide.
© 2018, American College of Rheumatology.
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MeSH Terms
Three-dimensional spatial analysis of missense variants in RTEL1 identifies pathogenic variants in patients with Familial Interstitial Pneumonia.
Sivley RM, Sheehan JH, Kropski JA, Cogan J, Blackwell TS, Phillips JA, Bush WS, Meiler J, Capra JA
(2018) BMC Bioinformatics 19: 18
MeSH Terms: Algorithms, Area Under Curve, DNA Helicases, Humans, Lung Diseases, Interstitial, Mutation, Missense, Protein Structure, Tertiary, ROC Curve, Spatial Analysis
Show Abstract · Added March 14, 2018
BACKGROUND - Next-generation sequencing of individuals with genetic diseases often detects candidate rare variants in numerous genes, but determining which are causal remains challenging. We hypothesized that the spatial distribution of missense variants in protein structures contains information about function and pathogenicity that can help prioritize variants of unknown significance (VUS) and elucidate the structural mechanisms leading to disease.
RESULTS - To illustrate this approach in a clinical application, we analyzed 13 candidate missense variants in regulator of telomere elongation helicase 1 (RTEL1) identified in patients with Familial Interstitial Pneumonia (FIP). We curated pathogenic and neutral RTEL1 variants from the literature and public databases. We then used homology modeling to construct a 3D structural model of RTEL1 and mapped known variants into this structure. We next developed a pathogenicity prediction algorithm based on proximity to known disease causing and neutral variants and evaluated its performance with leave-one-out cross-validation. We further validated our predictions with segregation analyses, telomere lengths, and mutagenesis data from the homologous XPD protein. Our algorithm for classifying RTEL1 VUS based on spatial proximity to pathogenic and neutral variation accurately distinguished 7 known pathogenic from 29 neutral variants (ROC AUC = 0.85) in the N-terminal domains of RTEL1. Pathogenic proximity scores were also significantly correlated with effects on ATPase activity (Pearson r = -0.65, p = 0.0004) in XPD, a related helicase. Applying the algorithm to 13 VUS identified from sequencing of RTEL1 from patients predicted five out of six disease-segregating VUS to be pathogenic. We provide structural hypotheses regarding how these mutations may disrupt RTEL1 ATPase and helicase function.
CONCLUSIONS - Spatial analysis of missense variation accurately classified candidate VUS in RTEL1 and suggests how such variants cause disease. Incorporating spatial proximity analyses into other pathogenicity prediction tools may improve accuracy for other genes and genetic diseases.
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9 MeSH Terms
Quantitative Structure-Activity Relationship Modeling of Kinase Selectivity Profiles.
Kothiwale S, Borza C, Pozzi A, Meiler J
(2017) Molecules 22:
MeSH Terms: Adenosine Triphosphate, Area Under Curve, Binding Sites, Databases, Pharmaceutical, Drug Discovery, Models, Molecular, Neural Networks, Computer, Protein Binding, Protein Conformation, Protein Kinase Inhibitors, Protein-Tyrosine Kinases, Quantitative Structure-Activity Relationship, ROC Curve, Software
Show Abstract · Added November 2, 2017
The discovery of selective inhibitors of biological target proteins is the primary goal of many drug discovery campaigns. However, this goal has proven elusive, especially for inhibitors targeting the well-conserved orthosteric adenosine triphosphate (ATP) binding pocket of kinase enzymes. The human kinome is large and it is rather difficult to profile early lead compounds against around 500 targets to gain an upfront knowledge on selectivity. Further, selectivity can change drastically during derivatization of an initial lead compound. Here, we have introduced a computational model to support the profiling of compounds early in the drug discovery pipeline. On the basis of the extensive profiled activity of 70 kinase inhibitors against 379 kinases, including 81 tyrosine kinases, we developed a quantitative structure-activity relation (QSAR) model using artificial neural networks, to predict the activity of these kinase inhibitors against the panel of 379 kinases. The model's performance in predicting activity ranges from 0.6 to 0.8 depending on the kinase, from the area under the curve (AUC) of the receiver operating characteristics (ROC). The profiler is available online at http://www.meilerlab.org/index.php/servers/show?s_id=23.
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14 MeSH Terms
Evaluating the American College of Surgeons National Surgical Quality Improvement project risk calculator: results from the U.S. Extrahepatic Biliary Malignancy Consortium.
Beal EW, Lyon E, Kearney J, Wei L, Ethun CG, Black SM, Dillhoff M, Salem A, Weber SM, Tran TB, Poultsides G, Shenoy R, Hatzaras I, Krasnick B, Fields RC, Buttner S, Scoggins CR, Martin RCG, Isom CA, Idrees K, Mogal HD, Shen P, Maithel SK, Pawlik TM, Schmidt CR
(2017) HPB (Oxford) 19: 1104-1111
MeSH Terms: Academic Medical Centers, Adult, Aged, Aged, 80 and over, Area Under Curve, Bile Duct Neoplasms, Biliary Tract Surgical Procedures, Cholangiocarcinoma, Databases, Factual, Decision Support Techniques, Female, Gallbladder Neoplasms, Hepatectomy, Humans, Male, Middle Aged, Pancreaticoduodenectomy, Patient Readmission, Postoperative Complications, Predictive Value of Tests, ROC Curve, Reoperation, Retrospective Studies, Risk Assessment, Risk Factors, Time Factors, Treatment Outcome, United States, Young Adult
Show Abstract · Added April 10, 2018
BACKGROUND - The objective of this study is to evaluate use of the American College of Surgeons (ACS) National Surgical Quality Improvement Program (NSQIP) online risk calculator for estimating common outcomes after operations for gallbladder cancer and extrahepatic cholangiocarcinoma.
METHODS - Subjects from the United States Extrahepatic Biliary Malignancy Consortium (USE-BMC) who underwent operation between January 1, 2000 and December 31, 2014 at 10 academic medical centers were included in this study. Calculator estimates of risk were compared to actual outcomes.
RESULTS - The majority of patients underwent partial or major hepatectomy, Whipple procedures or extrahepatic bile duct resection. For the entire cohort, c-statistics for surgical site infection (0.635), reoperation (0.680) and readmission (0.565) were less than 0.7. The c-statistic for death was 0.740. For all outcomes the actual proportion of patients experiencing an event was much higher than the median predicted risk of that event. Similarly, the group of patients who experienced an outcome did have higher median predicted risk than those who did not.
CONCLUSIONS - The ACS NSQIP risk calculator is easy to use but requires further modifications to more accurately estimate outcomes for some patient populations and operations for which validation studies show suboptimal performance.
Copyright © 2017 International Hepato-Pancreato-Biliary Association Inc. Published by Elsevier Ltd. All rights reserved.
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29 MeSH Terms
Predicting Negative Events: Using Post-discharge Data to Detect High-Risk Patients.
Sulieman L, Fabbri D, Wang F, Hu J, Malin BA
(2016) AMIA Annu Symp Proc 2016: 1169-1178
MeSH Terms: Acute Disease, Area Under Curve, Chronic Disease, Electronic Health Records, Heart Failure, Hip Fractures, Humans, Models, Statistical, Patient Discharge, Patient Readmission, Prognosis
Show Abstract · Added April 10, 2018
Predicting negative outcomes, such as readmission or death, and detecting high-risk patients are important yet challenging problems in medical informatics. Various models have been proposed to detect high-risk patients; however, the state of the art relies on patient information collected before or at the time of discharge to predict future outcomes. In this paper, we investigate the effect of including data generated post discharge to predict negative outcomes. Specifically, we focus on two types of patients admitted to the Vanderbilt University Medical Center between 2010-2013: i) those with an acute event - 704 hip fractures and ii) those with chronic problems - 5250 congestive heart failure (CHF) patients. We show that the post-discharge model improved the AUC of the LACE index, a standard readmission scoring function, by 20 - 30%. Moreover, the new model resulted in higher AUCs by 15 - 27% for hip fracture and 10 - 12% for CHF compared to standard models.
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Mucosal Expression of Type 2 and Type 17 Immune Response Genes Distinguishes Ulcerative Colitis From Colon-Only Crohn's Disease in Treatment-Naive Pediatric Patients.
Rosen MJ, Karns R, Vallance JE, Bezold R, Waddell A, Collins MH, Haberman Y, Minar P, Baldassano RN, Hyams JS, Baker SS, Kellermayer R, Noe JD, Griffiths AM, Rosh JR, Crandall WV, Heyman MB, Mack DR, Kappelman MD, Markowitz J, Moulton DE, Leleiko NS, Walters TD, Kugathasan S, Wilson KT, Hogan SP, Denson LA
(2017) Gastroenterology 152: 1345-1357.e7
MeSH Terms: Adolescent, Area Under Curve, Case-Control Studies, Child, Colitis, Ulcerative, Colon, Crohn Disease, Female, Gene Expression, Humans, Immunity, Mucosal, Interleukin-13, Interleukin-13 Receptor alpha2 Subunit, Interleukin-17, Interleukin-23, Interleukin-5, Interleukins, Intestinal Mucosa, Male, Predictive Value of Tests, Prognosis, Prospective Studies, RNA, Messenger, ROC Curve, Rectum, Transcriptome, Up-Regulation
Show Abstract · Added January 31, 2017
BACKGROUND & AIMS - There is controversy regarding the role of the type 2 immune response in the pathogenesis of ulcerative colitis (UC)-few data are available from treatment-naive patients. We investigated whether genes associated with a type 2 immune response in the intestinal mucosa are up-regulated in treatment-naive pediatric patients with UC compared with patients with Crohn's disease (CD)-associated colitis or without inflammatory bowel disease (IBD), and whether expression levels are associated with clinical outcomes.
METHODS - We used a real-time reverse-transcription quantitative polymerase chain reaction array to analyze messenger RNA (mRNA) expression patterns in rectal mucosal samples from 138 treatment-naive pediatric patients with IBD and macroscopic rectal disease, as well as those from 49 children without IBD (controls), enrolled in a multicenter prospective observational study from 2008 to 2012. Results were validated in real-time reverse-transcription quantitative polymerase chain reaction analyses of rectal RNA from an independent cohort of 34 pediatric patients with IBD and macroscopic rectal disease and 17 controls from Cincinnati Children's Hospital Medical Center.
RESULTS - We measured significant increases in mRNAs associated with a type 2 immune response (interleukin [IL]5 gene, IL13, and IL13RA2) and a type 17 immune response (IL17A and IL23) in mucosal samples from patients with UC compared with patients with colon-only CD. In a regression model, increased expression of IL5 and IL17A mRNAs distinguished patients with UC from patients with colon-only CD (P = .001; area under the receiver operating characteristic curve, 0.72). We identified a gene expression pattern in rectal tissues of patients with UC, characterized by detection of IL13 mRNA, that predicted clinical response to therapy after 6 months (odds ratio [OR], 6.469; 95% confidence interval [CI], 1.553-26.94), clinical response after 12 months (OR, 6.125; 95% CI, 1.330-28.22), and remission after 12 months (OR, 5.333; 95% CI, 1.132-25.12).
CONCLUSIONS - In an analysis of rectal tissues from treatment-naive pediatric patients with IBD, we observed activation of a type 2 immune response during the early course of UC. We were able to distinguish patients with UC from those with colon-only CD based on increased mucosal expression of genes that mediate type 2 and type 17 immune responses. Increased expression at diagnosis of genes that mediate a type 2 immune response is associated with response to therapy and remission in pediatric patients with UC.
Copyright © 2017 AGA Institute. Published by Elsevier Inc. All rights reserved.
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27 MeSH Terms
Reporting Surgical Resection Margin Status for Osteosarcoma: Comparison of the AJCC, MSTS, and Margin Distance Methods.
Cates JM
(2017) Am J Surg Pathol 41: 633-642
MeSH Terms: Adolescent, Adult, Area Under Curve, Bone Neoplasms, Decision Support Techniques, Disease-Free Survival, Female, Humans, Kaplan-Meier Estimate, Logistic Models, Male, Margins of Excision, Multivariate Analysis, Neoplasm Grading, Neoplasm Recurrence, Local, Neoplasm, Residual, Osteosarcoma, Osteotomy, Predictive Value of Tests, Proportional Hazards Models, ROC Curve, Risk Assessment, Risk Factors, Time Factors, Treatment Outcome, Young Adult
Show Abstract · Added November 1, 2018
Multiple different schemes are used to assess surgical resection margins in orthopedic pathology, but which is optimal for reporting resection margin status of osteosarcoma is uncertain. Moreover, the minimum tumor clearance (metric width of resection margin) necessary for local control is not well defined. In this investigation, the American Joint Committee on Cancer (AJCC) R system, Musculoskeletal Tumor Society (MSTS) system, and margin distance method for reporting resection margin status were compared in a series of 186 high-grade osteosarcomas. Hazard ratios for local recurrence for each resection margin category were compared with other categories within each margin assessment scheme to assess discriminatory ability. Cross-model comparisons of regression coefficients from parametric survival and logistic regression models were also performed. Predictive accuracy of each margin assessment scheme for determining 2-year local recurrence-free survival was evaluated by comparing the areas under receiver-operating characteristic curves generated from logistic regression analyses. Concordance with clinical outcomes was also calculated. Both the MSTS and margin distance schemes showed significantly greater predictive accuracy and concordance with observed outcomes than the AJCC R system. A margin distance of ≥2 mm significantly decreased the risk of local recurrence. Results were similar after adjustment for confounding prognostic factors (anatomic site, macroscopic lymphovascular invasion, and chemotherapy status). Therefore, surgical resection margins for osteosarcoma should be reported using either the MSTS or margin distance method instead of the AJCC R system.
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MeSH Terms
MAGI-2 in prostate cancer: an immunohistochemical study.
Goldstein J, Borowsky AD, Goyal R, Roland JT, Arnold SA, Gellert LL, Clark PE, Hameed O, Giannico GA
(2016) Hum Pathol 52: 83-91
MeSH Terms: Adaptor Proteins, Signal Transducing, Adenocarcinoma, Adult, Aged, Aged, 80 and over, Area Under Curve, Biomarkers, Tumor, Biopsy, Carrier Proteins, Guanylate Kinases, Humans, Immunohistochemistry, In Situ Hybridization, Fluorescence, Male, Middle Aged, Neoplasm Grading, PTEN Phosphohydrolase, Predictive Value of Tests, Prostatic Hyperplasia, Prostatic Intraepithelial Neoplasia, Prostatic Neoplasms, ROC Curve, Tissue Array Analysis, Transcription Factors, Tumor Suppressor Proteins, Up-Regulation
Show Abstract · Added April 18, 2017
Membrane-associated guanylate kinase, WW and PDZ domain-containing protein 2 (MAGI-2) is a scaffolding protein that links cell adhesion molecules, receptors, and signaling molecules to the cytoskeleton and maintains the architecture of cell junctions. MAGI-2 gene rearrangements have recently been described in prostate cancer. We studied the immunohistochemical expression of MAGI-2 protein in prostate tissue. Seventy-eight radical prostatectomies were used to construct 3 tissue microarrays consisting of 512 cores, including benign tissue, benign prostatic hyperplasia, high-grade prostatic intraepithelial neoplasia (HGPIN), and adenocarcinoma, Gleason patterns 3 to 5. Immunohistochemistry for phosphatase and tensin homologue (PTEN) and double-stain MAGI-2/p63 was performed and analyzed by visual and image analysis, the latter as percent of analyzed area (%AREA), and mean optical density multiplied by %AREA (STAIN). By visual and image analysis, MAGI-2 was significantly higher in adenocarcinoma and HGPIN compared with benign (benign versus HGPIN P < .001; benign versus adenocarcinoma, P < .001). HGPIN and adenocarcinoma did not significantly differ by either modality. Using visual intensity to distinguish benign tissue and adenocarcinoma, a receiver operating curve yielded an area under the curve of 0.902. A STAIN threshold of 1470 yielded a sensitivity of 0.66 and specificity of 0.96. There was a significant correlation between PTEN and MAGI-2 staining for normal and benign prostatic hyperplasia, but this was lost in HGPIN and cancer. We conclude that MAGI-2 immunoreactivity is elevated in prostate cancer and HGPIN compared with normal tissue, and suggest that MAGI-2 may contribute to prostate carcinogenesis. This is the first report of MAGI-2 staining by immunohistochemistry in prostate cancer.
Copyright © 2016 Elsevier Inc. All rights reserved.
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26 MeSH Terms
The influence of non-steroidal anti-inflammatory drugs on the gut microbiome.
Rogers MAM, Aronoff DM
(2016) Clin Microbiol Infect 22: 178.e1-178.e9
MeSH Terms: Adult, Aged, Aged, 80 and over, Anti-Inflammatory Agents, Non-Steroidal, Area Under Curve, Bacteria, Feces, Female, Gastrointestinal Microbiome, Humans, Male, Middle Aged
Show Abstract · Added June 2, 2017
The composition of the gut microbiome with the use of non-steroidal anti-inflammatory drugs (NSAIDs) has not been fully characterized. Drug use within the past 30 days was ascertained in 155 adults, and stool specimens were submitted for analysis. Area under the receiver operating characteristic curve (AUC) was calculated in logit models to distinguish the relative abundance of operational taxonomic units (OTUs) by medication class. The type of medication had a greater influence on the gut microbiome than the number of medications. NSAIDs were particularly associated with distinct microbial populations. Four OTUs (Prevotella species, Bacteroides species, family Ruminococcaceae, and Barnesiella species) discriminated aspirin users from those using no medication (AUC = 0.96; 95% CI 0.84-1.00). The microbiome profile of celecoxib users was similar to that of ibuprofen users, with both showing enrichment of Acidaminococcaceae and Enterobacteriaceae. Bacteria from families Propionibacteriaceae, Pseudomonadaceae, Puniceicoccaceae and Rikenellaceae were more abundant in ibuprofen users than in controls or naproxen users. Bacteroides species and Erysipelotrichaceae species discriminated individuals using NSAIDs plus proton-pump inhibitors from those using NSAIDs alone (AUC = 0.96; 95% CI 0.87-1.00). Bacteroides species and a bacterium of family Ruminococcaceae discriminated individuals using NSAIDs in combination with antidepressants and laxatives from those using NSAIDs alone (AUC = 0.98; 95% CI 0.93-1.00). In conclusion, bacteria in the gastrointestinal tract reflect the combinations of medications that people ingest. The bacterial composition of the gut varied with the type of NSAID ingested.
Copyright © 2015 European Society of Clinical Microbiology and Infectious Diseases. Published by Elsevier Ltd. All rights reserved.
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12 MeSH Terms