scholarly journals Recurrent Clostridioides difficile infection can be predicted using inflammatory mediator and toxin activity levels

2020 ◽  
Vol 41 (S1) ◽  
pp. s77-s78
Author(s):  
Jonathan Motyka ◽  
Aline Penkevich ◽  
Vincent Young ◽  
Krishna Rao
Keyword(s):  
Machine Learning ◽  
Inflammatory Mediators ◽  
Inflammatory Mediator ◽  
Characteristic Curve ◽  
Receiver Operator Characteristic Curve ◽  
Sensitivity Analyses ◽  
Activity Levels ◽  
Clinical Criteria ◽  
Clostridioides Difficile ◽  
Potential Biomarker

Background:Clostridioides difficile infection (CDI) frequently recurs after initial treatment. Predicting recurrent CDI (rCDI) early in the disease course can assist clinicians in their decision making and improve outcomes. However, predictions based on clinical criteria alone are not accurate and/or do not validate other results. Here, we tested the hypothesis that circulating and stool-derived inflammatory mediators predict rCDI. Methods: Consecutive subjects with available specimens at diagnosis were included if they tested positive for toxigenic C. difficile (+enzyme immunoassay [EIA] for glutamate dehydrogenase and toxins A/B, with reflex to PCR for the tcdB gene for discordants). Stool was thawed on ice, diluted 1:1 in PBS with protease inhibitor, centrifuged, and used immediately. A 17-plex panel of inflammatory mediators was run on a Luminex 200 machine using a custom antibody-linked bead array. Prior to analysis, all measurements were normalized and log-transformed. Stool toxin activity levels were quantified using a custom cell-culture assay. Recurrence was defined as a second episode of CDI within 100 days. Ordination characterized variation in the panel between outcomes, tested with a permutational, multivariate ANOVA. Machine learning via elastic net regression with 100 iterations of 5-fold cross validation selected the optimal model and the area under the receiver operator characteristic curve (AuROC) was computed. Sensitivity analyses excluding those that died and/or lived >100 km away were performed. Results: We included 186 subjects, with 95 women (51.1%) and average age of 55.9 years (±20). More patients were diagnosed by PCR than toxin EIA (170 vs 55, respectively). Death, rCDI, and no rCDI occurred in 32 (17.2%), 36 (19.4%), and 118 (63.4%) subjects, respectively. Ordination revealed that the serum panel was associated with rCDI (P = .007) but the stool panel was not. Serum procalcitonin, IL-8, IL-6, CCL5, and EGF were associated with recurrence. The machine-learning models using the serum panel predicted rCDI with AuROCs between 0.74 and 0.8 (Fig. 1). No stool inflammatory mediators independently predicted rCDI. However, stool IL-8 interacted with toxin activity to predict rCDI (Fig. 2). These results did not change significantly upon sensitivity analysis. Conclusions: A panel of serum inflammatory mediators predicted rCDI with up to 80% accuracy, but the stool panel alone was less successful. Incorporating toxin activity levels alongside inflammatory mediator measurements is a novel, promising approach to studying stool-derived biomarkers of rCDI. This approach revealed that stool IL-8 is a potential biomarker for rCDI. These results need to be confirmed both with a larger dataset and after adjustment for clinical covariates.Funding: NoneDisclosure: Vincent Young is a consultant for Bio-K+ International, Pantheryx, and Vedanta Biosciences.

scholarly journals 2236. Stool-Derived Inflammatory Mediators Serve as Biomarkers of Severity in Clostridium difficile Infection

2019 ◽  
Vol 6 (Supplement_2) ◽  
pp. S764-S765
Author(s):  
Jonathan Motyka ◽  
Aline Penkevich ◽  
D Alex Perry ◽  
Shayna Weiner ◽  
Alexandra Standke ◽  
...  
Keyword(s):  
Clostridium Difficile ◽  
Clostridium Difficile Infection ◽  
Inflammatory Mediators ◽  
Characteristic Curve ◽  
Severe Disease ◽  
Receiver Operator Characteristic Curve ◽  
Health Concern ◽  
Clinical Microbiology Laboratory ◽  
Clinical Criteria ◽  
Clinical Covariates

Abstract Background Clostridium difficile infection (CDI) is a major public health concern and frequently results in severe disease, including death. Predicting subsequent complications early in the course can help optimize treatments and improve outcomes. However, models based on clinical criteria alone are not accurate and/or do not validate. We hypothesized that inflammatory mediators from the stool would be biomarkers for severity and complications. Methods Subjects were included after testing positive for toxigenic C. difficile by the clinical microbiology laboratory via enzyme immunoassay (EIA) for glutamate dehydrogenase and toxins A/B, with reflex to tcdB gene PCR for discordants. Stool was thawed on ice, diluted 1:1 with PBS and protease inhibitor, centrifuged, and the supernatant was analyzed by a custom antibody-linked bead array with 17 inflammatory mediators. Measurements were normalized and log-transformed. IDSA severity was defined by serum white blood cell count > 15000 cells/µL or creatinine 1.5-fold above baseline. Primary 30-day outcomes were all-cause mortality and attributable disease-related complications (DRC): ICU admission, colectomy, and/or death. Analyses included principal components, permutational multivariate ANOVA (PERMANOVA), and logistic regression ± L1 regularization and 5-fold cross validation. The area under the receiver operator characteristic curve (AuROC) was computed. Results We included 225 subjects, with 124 females (55.1%), average age 58.5 (±17), and more PCR+ than toxin EIA+ (170 vs. 55, respectively). IDSA severity, death, and DRCs occurred in 79 (35.1%), 14 (6.2%), and 12 (5.3%) subjects, respectively. PCA and PERMANOVA showed IDSA severity (P = 0.009) but not death or DRCs associated with the panel (figure). Several inflammatory mediators associated with IDSA severity and death (table). Machine learning models had AuROCs of 0.77 (IDSA severity), 0.84 (death), and 0.7 (DRCs). Conclusion We found that specific inflammatory mediators from the stool of patients with CDI associate with severity and complications. These results are promising, but need replication in a larger dataset and should be incorporated into models that include clinical covariates prior to deployment. Disclosures All authors: No reported disclosures.

scholarly journals 2409. External Validation and Comparison of Clostridioides difficile Severity Scoring Systems

2019 ◽  
Vol 6 (Supplement_2) ◽  
pp. S831-S832
Author(s):  
Donald A Perry ◽  
Daniel Shirley ◽  
Dejan Micic ◽  
Rosemary K B Putler ◽  
Pratish Patel ◽  
...  
Keyword(s):  
Binary Classification ◽  
Characteristic Curve ◽  
External Validation ◽  
Receiver Operator Characteristic Curve ◽  
Adverse Outcomes ◽  
Risk Scores ◽  
Composite Outcome ◽  
Clostridioides Difficile ◽  
Severity Scores ◽  
Primary Composite Outcome

Abstract Background Annually in the US alone, Clostridioides difficile infection (CDI) afflicts nearly 500,000 patients causing 29,000 deaths. Since early and aggressive interventions could save lives but are not optimally deployed in all patients, numerous studies have published predictive models for adverse outcomes. These models are usually developed at a single institution, and largely are not externally validated. This aim of this study was to validate the predictability for severe CDI with previously published risk scores in a multicenter cohort of patients with CDI. Methods We conducted a retrospective study on four separate inpatient cohorts with CDI from three distinct sites: the Universities of Michigan (2010–2012 and 2016), Chicago (2012), and Wisconsin (2012). The primary composite outcome was admission to an intensive care unit, colectomy, and/or death attributed to CDI within 30 days of positive test. Structured query and manual chart review abstracted data from the medical record at each site. Published CDI severity scores were assessed and compared with each other and the IDSA guideline definition of severe CDI. Sensitivity, specificity, area under the receiver operator characteristic curve (AuROC), precision-recall curves, and net reclassification index (NRI) were calculated to compare models. Results We included 3,775 patients from the four cohorts (Table 1) and evaluated eight severity scores (Table 2). The IDSA (baseline comparator) model showed poor performance across cohorts(Table 3). Of the binary classification models, including those that were most predictive of the primary composite outcome, Jardin, performed poorly with minimal to no NRI improvement compared with IDSA. The continuous score models, Toro and ATLAS, performed better, but the AuROC varied by site by up to 17% (Table 3). The Gujja model varied the most: from most predictive in the University of Michigan 2010–2012 cohort to having no predictive value in the 2016 cohort (Table 3). Conclusion No published CDI severity score showed stable, acceptable predictive ability across multiple cohorts/institutions. To maximize performance and clinical utility, future efforts should focus on a multicenter-derived and validated scoring system, and/or incorporate novel biomarkers. Disclosures All authors: No reported disclosures.

scholarly journals Prediction of Diabetic Sensorimotor Polyneuropathy Using Machine Learning Techniques

2021 ◽  
Vol 10 (19) ◽  
pp. 4576
Author(s):  
Dae Youp Shin ◽  
Bora Lee ◽  
Won Sang Yoo ◽  
Joo Won Park ◽  
Jung Keun Hyun
Keyword(s):  
Machine Learning ◽  
Clinical Chemistry ◽  
Characteristic Curve ◽  
Linear Regression Analysis ◽  
Receiver Operator Characteristic Curve ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Sensorimotor Polyneuropathy ◽  
Learning Techniques ◽  
Electrophysiological Findings

Diabetic sensorimotor polyneuropathy (DSPN) is a major complication in patients with diabetes mellitus (DM), and early detection or prediction of DSPN is important for preventing or managing neuropathic pain and foot ulcer. Our aim is to delineate whether machine learning techniques are more useful than traditional statistical methods for predicting DSPN in DM patients. Four hundred seventy DM patients were classified into four groups (normal, possible, probable, and confirmed) based on clinical and electrophysiological findings of suspected DSPN. Three ML methods, XGBoost (XGB), support vector machine (SVM), and random forest (RF), and their combinations were used for analysis. RF showed the best area under the receiver operator characteristic curve (AUC, 0.8250) for differentiating between two categories—criteria by clinical findings (normal, possible, and probable groups) and those by electrophysiological findings (confirmed group)—and the result was superior to that of linear regression analysis (AUC = 0.6620). Average values of serum glucose, International Federation of Clinical Chemistry (IFCC), HbA1c, and albumin levels were identified as the four most important predictors of DSPN. In conclusion, machine learning techniques, especially RF, can predict DSPN in DM patients effectively, and electrophysiological analysis is important for identifying DSPN.

scholarly journals Machine learning-based prediction of activity and substrate specificity for OleA enzymes in the thiolase superfamily

2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Serina L Robinson ◽  
Megan D Smith ◽  
Jack E Richman ◽  
Kelly G Aukema ◽  
Lawrence P Wackett
Keyword(s):  
Machine Learning ◽  
Enzyme Activity ◽  
Substrate Specificity ◽  
Xanthomonas Campestris ◽  
Membrane Lipids ◽  
Characteristic Curve ◽  
Structural Features ◽  
Activity Levels ◽  
Enzyme Substrate ◽  
Carbon Carbon Bond

Abstract Enzymes in the thiolase superfamily catalyze carbon–carbon bond formation for the biosynthesis of polyhydroxyalkanoate storage molecules, membrane lipids and bioactive secondary metabolites. Natural and engineered thiolases have applications in synthetic biology for the production of high-value compounds, including personal care products and therapeutics. A fundamental understanding of thiolase substrate specificity is lacking, particularly within the OleA protein family. The ability to predict substrates from sequence would advance (meta)genome mining efforts to identify active thiolases for the production of desired metabolites. To gain a deeper understanding of substrate scope within the OleA family, we measured the activity of 73 diverse bacterial thiolases with a library of 15 p-nitrophenyl ester substrates to build a training set of 1095 unique enzyme–substrate pairs. We then used machine learning to predict thiolase substrate specificity from physicochemical and structural features. The area under the receiver operating characteristic curve was 0.89 for random forest classification of enzyme activity, and our regression model had a test set root mean square error of 0.22 (R2 = 0.75) to quantitatively predict enzyme activity levels. Substrate aromaticity, oxygen content and molecular connectivity were the strongest predictors of enzyme–substrate pairing. Key amino acid residues A173, I284, V287, T292 and I316 in the Xanthomonas campestris OleA crystal structure lining the substrate binding pockets were important for thiolase substrate specificity and are attractive targets for future protein engineering studies. The predictive framework described here is generalizable and demonstrates how machine learning can be used to quantitatively understand and predict enzyme substrate specificity.

scholarly journals Using Machine Learning and the Electronic Health Record to Predict Complicated Clostridium difficile Infection

2019 ◽  
Vol 6 (5) ◽  
Author(s):  
Benjamin Y Li ◽  
Jeeheh Oh ◽  
Vincent B Young ◽  
Krishna Rao ◽  
Jenna Wiens
Keyword(s):  
Machine Learning ◽  
Electronic Health Record ◽  
Characteristic Curve ◽  
Model Performance ◽  
Health Record ◽  
Data Set ◽  
Icu Admission ◽  
Clostridioides Difficile ◽  
Electronic Health ◽  
Health Care Associated Infection

Abstract Background Clostridium (Clostridioides) difficile infection (CDI) is a health care–associated infection that can lead to serious complications. Potential complications include intensive care unit (ICU) admission, development of toxic megacolon, need for colectomy, and death. However, identifying the patients most likely to develop complicated CDI is challenging. To this end, we explored the utility of a machine learning (ML) approach for patient risk stratification for complications using electronic health record (EHR) data. Methods We considered adult patients diagnosed with CDI between October 2010 and January 2013 at the University of Michigan hospitals. Cases were labeled complicated if the infection resulted in ICU admission, colectomy, or 30-day mortality. Leveraging EHR data, we trained a model to predict subsequent complications on each of the 3 days after diagnosis. We compared our EHR-based model to one based on a small set of manually curated features. We evaluated model performance using a held-out data set in terms of the area under the receiver operating characteristic curve (AUROC). Results Of 1118 cases of CDI, 8% became complicated. On the day of diagnosis, the model achieved an AUROC of 0.69 (95% confidence interval [CI], 0.55–0.83). Using data extracted 2 days after CDI diagnosis, performance increased (AUROC, 0.90; 95% CI, 0.83–0.95), outperforming a model based on a curated set of features (AUROC, 0.84; 95% CI, 0.75–0.91). Conclusions Using EHR data, we can accurately stratify CDI cases according to their risk of developing complications. Such an approach could be used to guide future clinical studies investigating interventions that could prevent or mitigate complicated CDI.

scholarly journals Developing machine learning models for predicting intensive care unit resource use during the COVID-19 pandemic

2021 ◽  
Author(s):  
Stephan Slot Lorenzen ◽  
Mads Nielsen ◽  
Espen Jimenez-Solem ◽  
Tonny Studsgaard Petersen ◽  
Anders Perner ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intensive Care ◽  
Random Forest ◽  
Characteristic Curve ◽  
Receiver Operator Characteristic Curve ◽  
Coefficient Of Determination ◽  
Icu Admission ◽  
Future Design ◽  
Forecasting Models ◽  
Regional Area

ABSTRACT Importance: The COVID-19 pandemic has put massive strains on hospitals, and tools to guide hospital planners in resource allocation during the ebbs and flows of the pandemic are urgently needed. Objective: We investigate whether Machine Learning (ML) can be used for predictions of intensive care requirements 5 and 10 days into the future. Design: Retrospective design where health Records from 34,012 SARS-CoV-2 positive patients was extracted. Random Forest (RF) models were trained to predict risk of ICU admission and use of mechanical ventilation after n days (n = 5, 10). Setting: Two Danish regions, encompassing approx. 2.5 million citizens. Participants: All patients from the bi-regional area with a registered positive SARS-CoV-2 test from March 2020 to January 2021. Main outcomes: Prediction of future 5- and 10-day requirements of ICU admission and ventilator use. Mortality was also predicted. Results. Models predicted 5-day risk of ICU admission with an area under the receiver operator characteristic curve (ROC-AUC) of 0.986 and 5-day risk of use of ventilation with an ROC-AUC of 0.995. The corresponding 5-day forecasting models predicted the needed ICU capacity with a coefficient of determination (R2) of 0.930 and use of ventilation with an R2 of 0.934. Performance was comparable but slightly reduced for 10-day forecasting models. Conclusions. Random Forest-based modelling can be used for accurate 5- and 10-day forecasting predictions of ICU resource requirements.

External Validation and Comparison of Clostridioides difficile Severity Scoring Systems

2021 ◽  
Author(s):  
D Alexander Perry ◽  
Daniel Shirley ◽  
Dejan Micic ◽  
C Pratish Patel ◽  
Rosemary Putler ◽  
...  
Keyword(s):  
Characteristic Curve ◽  
External Validation ◽  
Predictive Ability ◽  
Scoring Systems ◽  
Receiver Operator Characteristic Curve ◽  
Adverse Outcomes ◽  
Risk Scores ◽  
Clostridioides Difficile ◽  
Multicenter Cohort ◽  
Severity Scores

Abstract Background Many models have been developed to predict severe outcomes from Clostridioides difficile infection. These models are usually developed at a single institution and largely are not externally validated. This aim of this study was to validate previously published risk scores in a multicenter cohort of patients with CDI. Methods Retrospective study on four separate inpatient cohorts with CDI from three distinct sites: The Universities of Michigan (2010-2012 and 2016), Chicago (2012), and Wisconsin (2012). The primary composite outcome was admission to an intensive care unit, colectomy, and/or death attributed to CDI within 30 days of positive testing. Both within each cohort and combined across all cohorts, published CDI severity scores were assessed and compared to each other and the IDSA guideline definitions of severe and fulminant CDI. Results A total of 3,646 patients were included for analysis. Including the two IDSA guideline definitions, fourteen scores were assessed. Performance of scores varied within each cohort and in the combined set (mean area under the receiver operator characteristic curve(AUC 0.61, range 0.53-0.66). Only half of the scores had performance at or better than IDSA severe and fulminant definitions (AUCs 0.64 and 0.63, respectively). Most of the scoring systems had more false than true positives in the combined set (mean: 81.5%, range:0-91.5%). Conclusions No published CDI severity score showed stable, good predictive ability for adverse outcomes across multiple cohorts/institutions or in a combined multicenter cohort.

scholarly journals Interpretable Deep Learning for Spatial Analysis of Severe Hailstorms

2019 ◽  
Vol 147 (8) ◽  
pp. 2827-2845 ◽  
Author(s):  
David John Gagne II ◽  
Sue Ellen Haupt ◽  
Douglas W. Nychka ◽  
Gregory Thompson
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Characteristic Curve ◽  
Weather Prediction ◽  
Principal Component ◽  
Receiver Operator Characteristic Curve ◽  
Learning Approaches ◽  
Learning Models

Abstract Deep learning models, such as convolutional neural networks, utilize multiple specialized layers to encode spatial patterns at different scales. In this study, deep learning models are compared with standard machine learning approaches on the task of predicting the probability of severe hail based on upper-air dynamic and thermodynamic fields from a convection-allowing numerical weather prediction model. The data for this study come from patches surrounding storms identified in NCAR convection-allowing ensemble runs from 3 May to 3 June 2016. The machine learning models are trained to predict whether the simulated surface hail size from the Thompson hail size diagnostic exceeds 25 mm over the hour following storm detection. A convolutional neural network is compared with logistic regressions using input variables derived from either the spatial means of each field or principal component analysis. The convolutional neural network statistically significantly outperforms all other methods in terms of Brier skill score and area under the receiver operator characteristic curve. Interpretation of the convolutional neural network through feature importance and feature optimization reveals that the network synthesized information about the environment and storm morphology that is consistent with our understanding of hail growth, including large lapse rates and a wind shear profile that favors wide updrafts. Different neurons in the network also record different storm modes, and the magnitude of the output of those neurons is used to analyze the spatiotemporal distributions of different storm modes in the NCAR ensemble.

scholarly journals Using machine learning for predicting intensive care unit resource use during the COVID-19 pandemic in Denmark

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Stephan Sloth Lorenzen ◽  
Mads Nielsen ◽  
Espen Jimenez-Solem ◽  
Tonny Studsgaard Petersen ◽  
Anders Perner ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intensive Care ◽  
Random Forest ◽  
Characteristic Curve ◽  
Fixed Number ◽  
Receiver Operator Characteristic Curve ◽  
Coefficient Of Determination ◽  
Icu Admission ◽  
Forecasting Models ◽  
The Future

AbstractThe COVID-19 pandemic has put massive strains on hospitals, and tools to guide hospital planners in resource allocation during the ebbs and flows of the pandemic are urgently needed. We investigate whether machine learning (ML) can be used for predictions of intensive care requirements a fixed number of days into the future. Retrospective design where health Records from 42,526 SARS-CoV-2 positive patients in Denmark was extracted. Random Forest (RF) models were trained to predict risk of ICU admission and use of mechanical ventilation after n days (n = 1, 2, …, 15). An extended analysis was provided for n = 5 and n = 10. Models predicted n-day risk of ICU admission with an area under the receiver operator characteristic curve (ROC-AUC) between 0.981 and 0.995, and n-day risk of use of ventilation with an ROC-AUC between 0.982 and 0.997. The corresponding n-day forecasting models predicted the needed ICU capacity with a coefficient of determination (R2) between 0.334 and 0.989 and use of ventilation with an R2 between 0.446 and 0.973. The forecasting models performed worst, when forecasting many days into the future (for large n). For n = 5, ICU capacity was predicted with ROC-AUC 0.990 and R2 0.928, and use of ventilator was predicted with ROC-AUC 0.994 and R2 0.854. Random Forest-based modelling can be used for accurate n-day forecasting predictions of ICU resource requirements, when n is not too large.

scholarly journals The Kynurenine/Tryptophan Ratio Is a Sensitive Biomarker for the Diagnosis of Pediatric Tuberculosis Among Indian Children

2022 ◽  
Vol 12 ◽  
Author(s):  
Jeffrey A. Tornheim ◽  
Mandar Paradkar ◽  
Henry Zhao ◽  
Vandana Kulkarni ◽  
Neeta Pradhan ◽  
...  
Keyword(s):  
Treatment Response ◽  
Characteristic Curve ◽  
Receiver Operator Characteristic Curve ◽  
Group Differences ◽  
Indian Children ◽  
Tb Treatment ◽  
Potential Biomarker ◽  
Pediatric Tb ◽  
Metabolome Profiling ◽  
Pediatric Tuberculosis

ObjectivesPediatric tuberculosis (TB) remains difficult to diagnose. The plasma kynurenine to tryptophan ratio (K/T ratio) is a potential biomarker for TB diagnosis and treatment response but has not been assessed in children.MethodsWe performed a targeted diagnostic accuracy analysis of four biomarkers: kynurenine abundance, tryptophan abundance, the K/T ratio, and IDO-1 gene expression. Data were obtained from transcriptome and metabolome profiling of children with confirmed tuberculosis and age- and sex-matched uninfected household contacts of pulmonary tuberculosis patients. Each biomarker was assessed as a baseline diagnostic and in response to successful TB treatment.ResultsDespite non-significant between-group differences in unbiased analysis, the K/T ratio achieved an area under the receiver operator characteristic curve (AUC) of 0.667 and 81.5% sensitivity for TB diagnosis. Kynurenine, tryptophan, and IDO-1 demonstrated diagnostic AUCs of 0.667, 0.602, and 0.463, respectively. None of these biomarkers demonstrated high AUCs for treatment response. The AUC of the K/T ratio was lower than biomarkers identified in unbiased analysis, but improved sensitivity over existing commercial assays for pediatric TB diagnosis.ConclusionsPlasma kynurenine and the K/T ratio may be useful biomarkers for pediatric TB. Ongoing studies in geographically diverse populations will determine optimal use of these biomarkers worldwide.

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