scholarly journals Multiomics Longitudinal Modeling of Preeclamptic Pregnancies

2021 ◽  
Author(s):  
Ivana Maric ◽  
Kévin Contrepois ◽  
Mira Moufarrej ◽  
Ina Stelzer ◽  
Dorien Feyaerts ◽  
...  
Keyword(s):  
Complex Disease ◽  
Prediction Models ◽  
Operating Characteristics ◽  
Urine Metabolites ◽  
Simultaneous Application ◽  
Physiological Alterations ◽  
Receiver Operating Characteristics Curve ◽  
Specific Complex ◽  
Urine Metabolome ◽  
Independent Cohort

Abstract Preeclampsia is a complex disease of pregnancy whose physiopathology remains unclear and that poses a threat to both mothers and infants. Specific complex changes in women's physiology precede a diagnosis of preeclampsia. Understanding multiple aspects of such a complex changes at different levels of biology, can be enabled by simultaneous application of multiple assays. We developed prediction models for preeclampsia risk by analyzing six omics datasets from a longitudinal cohort of pregnant women. A machine learning-based multiomics model had high accuracy (area under the receiver operating characteristics curve (AUC) of 0.94, 95% confidence intervals (CI): [0.90, 0.99]). A prediction model using only ten urine metabolites provided an accuracy of the whole metabolomic dataset and was validated using an independent cohort of 16 women (AUC = 0.87, 95% CI: [0.76, 0.99]). Integration with clinical variables further improved prediction accuracy of the urine metabolome model (AUC = 0.90, 95% CI: [0.80, 0.99], urine metabolome, validated). We identified several biological pathways to be associated with preeclampsia. The findings derived from models were integrated with immune system cytometry data, confirming known physiological alterations associated with preeclampsia and suggesting novel associations between the immune and proteomic dynamics. While further validation in larger populations is necessary, these encouraging results will serve as a basis for a simple, early diagnostic test for preeclampsia.

scholarly journals Construction of the Metabolomics-Based Prognosis-Prediction Models for ICU Septic Patients

2021 ◽  
Author(s):  
Xianfei Ding ◽  
Ran Tong ◽  
Heng Song ◽  
Guiying Sun ◽  
Dong Wang ◽  
...  
Keyword(s):  
Heart Rate ◽  
Acid Metabolism ◽  
Prediction Models ◽  
Respiratory Support ◽  
Day Hospital ◽  
Operating Characteristics ◽  
Clinical Indicators ◽  
Logistic Regression Models ◽  
Receiver Operating Characteristics Curve ◽  
Death Group

Abstract Background: Global mortality related to sepsis remains unacceptably high in intensive care units (ICUs). Accurate prognostic evaluation of sepsis could effectively reduce the mortality of septic patients. Our goal is to present an effective and rapid method to assess the prognosis of sepsis.Methods: We included 96 septic patients according to the sepsis 3.0 in ICU, who were grouped into survival and death groups according to 28-day, hospital, and 90-day prognosis. Liquid chromatography/mass spectrometry was performed to detect the metabolite changes in plasma. Multivariate logistic regression models, using differential metabolites and clinical indicators within 24 h after diagnosis of sepsis, were used to construct the prediction models for 28-day, hospital, and 90-day prognosis in sepsis.Results: Metabolic profiles related to 28-day, hospital, and 90-day prognosis were significantly different between the survival and the death group. Specifically, 13, 4, and 29 primary differential metabolites related to amino acid metabolism and fatty acid metabolism were identified between the survival and death group at 28-day, hospital, and 90-day prognosis, respectively. Further, we found that model 1 including indoleacetic acid, 3-methylene-indolenine, heart rate, respiratory support, and application of pressure drugs; model 2 including lymphocyte count, alkaline phosphatase, SOFA, and L-alpha-amino-1H-pyrrole-1-hexanoic acid; model 3 including pyrrolidine, dopamine, heart rate, respiratory support, and application of pressure drugs, could predict 28-day, hospital, and 90-day prognosis of sepsis with a sensitivity of 75.51%, 73.58%, and 83.33%, specificity of 78.72%, 72.09%, and 76.19%, the area under the receiver operating characteristics curve of 0.881, 0.830, 0.892, respectively.Conclusions: This research could be used to predict the 28-day, hospital, and 90-day prognosis of septic patients based on differential metabolites and clinical parameters, and could also be used to develop novel sepsis-treatment methods.

scholarly journals Etiological Work-Up for Adults with Bronchiectasis: A Predictive Diagnostic Score for Primary Ciliary Dyskinesia and Cystic Fibrosis

2021 ◽  
Vol 10 (16) ◽  
pp. 3478
Author(s):  
Frederic Schlemmer ◽  
Agnes Hamzaoui ◽  
Sonia Zebachi ◽  
Aurelie Le Thuaut ◽  
Gilles Mangiapan ◽  
...  
Keyword(s):  
Cystic Fibrosis ◽  
Primary Ciliary Dyskinesia ◽  
Clinical Score ◽  
Adult Patients ◽  
Operating Characteristics ◽  
Diagnostic Score ◽  
Receiver Operating Characteristics Curve ◽  
Diagnostic Work ◽  
Work Up ◽  
Ciliary Dyskinesia

Background: etiological investigations are not done for all adult patients with bronchiectasis because of the availability and interpretation of tests. The aim of the study was to elaborate a score to identify patients at high risk of having cystic fibrosis or primary ciliary dyskinesia (CF/PCD), which require appropriate management. Methods: diagnostic work-ups were carried out on a French monocenter cohort, and results were subjected to logistic-regression analyses to identify the independent factors associated with CF/PCD diagnosis and, thereby, elaborate a score to validate in a second cohort. Results: among 188 patients, 158 had no obvious diagnosis and were enrolled in the algorithm-construction group. In multivariate analyses, age at symptom onset (8.69 (2.10–35.99); p = 0.003), chronic ENT symptoms or diagnosed sinusitis (10.53 (1.26–87.57); p = 0.03), digestive symptoms or situs inversus (5.10 (1.23–21.14); p = 0.025), and Pseudomonas. aeruginosa and/or Staphylococcus aureus isolated from sputum (11.13 (1.34–92.21); p = 0.02) are associated with CF or PCD. Receiver operating characteristics curve analysis, using a validation group of 167 patients with bronchiectasis, confirmed the score’s performance with AUC 0.92 (95% CI: 0.84–0.98). Conclusions: a clinical score may help identify adult patients with bronchiectasis at higher risk of having CF or PCD.

Validation of Michigan risk score and D-dimer to predict peripherally inserted central catheter-related thrombosis: A study of 206,132 catheter days

2021 ◽  
pp. 112972982110087
Author(s):  
Junren Kang ◽  
Wenyan Sun ◽  
Hailong Li ◽  
En ling Ma ◽  
Wei Chen
Keyword(s):  
Sensitivity And Specificity ◽  
Risk Score ◽  
Primary Objective ◽  
Class Iii ◽  
Operating Characteristics ◽  
Central Venous ◽  
Sensitivity Specificity ◽  
Catheter Related Thrombosis ◽  
D Dimer ◽  
Independent Cohort

Background: The Michigan Risk Score (MRS) was the only predicted score for peripherally inserted central venous catheters (PICC) associated upper extremity venous thrombosis (UEVT). Age-adjusted D-dimer increased the efficiency for UEVT. There were no external validations in an independent cohort. Method: A retrospective study of adult patients with PICC insertion was performed. The primary objective was to evaluate the performance of the MRS and age-adjusted D-dimer in estimating risk of PICC-related symptomatic UEVT. The sensitivity, specificity and areas under the receiver operating characteristics (ROC) of MRS and age-adjusted D-dimer were calculated. Results: Two thousand one hundred sixty-three patients were included for a total of 206,132 catheter days. Fifty-six (2.6%) developed PICC-UEVT. The incidences of PICC-UEVT were 4.9% for class I, 7.5% for class II, 2.2% for class III, 0% for class IV of MRS ( p = 0.011). The incidences of PICC-UEVT were 4.5% for D-dimer above the age-adjusted threshold and 1.5% for below the threshold ( p = 0.001). The areas under ROC of MRS and age-adjusted D-dimer were 0.405 (95% confidence interval (CI) 0.303–0.508) and 0.639 (95% CI 0.547–0.731). The sensitivity and specificity of MRS were 0.82 (95% CI, 0.69–0.91), 0.09 (95% CI, 0.08–0.11), respectively. The sensitivity and specificity of age-adjusted D-dimer were 0.64 (95% CI, 0.46–0.79) and 0.64 (95% CI, 0.61–0.66), respectively. Conclusions: MRS and age-adjusted D-dimer have low accuracy to predict PICC-UEVT. Further studies are needed.

Performance of the Surprise Question Compared to Prediction Models in Hemodialysis Patients: A Prospective Study

2017 ◽  
Vol 46 (5) ◽  
pp. 390-396 ◽  
Author(s):  
Rakesh Malhotra ◽  
Xia Tao ◽  
Yuedong Wang ◽  
Yuqi Chen ◽  
Rebecca H. Apruzzese ◽  
...  
Keyword(s):  
Linear Models ◽  
Prediction Models ◽  
Clinical Laboratory ◽  
Mortality Prediction ◽  
Operating Characteristics ◽  
Dialysis Treatment ◽  
Mortality Probability ◽  
A Prospective Study ◽  
Surprise Question

Background: The surprise question (SQ) (“Would you be surprised if this patient were still alive in 6 or 12 months?”) is used as a mortality prognostication tool in hemodialysis (HD) patients. We compared the performance of the SQ with that of prediction models (PMs) for 6- and 12-month mortality prediction. Methods: Demographic, clinical, laboratory, and dialysis treatment indicators were used to model 6- and 12-month mortality probability in a HD patients training cohort (n = 6,633) using generalized linear models (GLMs). A total of 10 nephrologists from 5 HD clinics responded to the SQ in 215 patients followed prospectively for 12 months. The performance of PM was evaluated in the validation (n = 6,634) and SQ cohorts (n = 215) using the areas under receiver operating characteristics curves. We compared sensitivities and specificities of PM and SQ. Results: The PM and SQ cohorts comprised 13,267 (mean age 61 years, 55% men, 54% whites) and 215 (mean age 62 years, 59% men, 50% whites) patients, respectively. During the 12-month follow-up, 1,313 patients died in the prediction model cohort and 22 in the SQ cohort. For 6-month mortality prediction, the GLM had areas under the curve of 0.77 in the validation cohort and 0.77 in the SQ cohort. As for 12-month mortality, areas under the curve were 0.77 and 0.80 in the validation and SQ cohorts, respectively. The 6- and 12-month PMs had sensitivities of 0.62 (95% CI 0.35–0.88) and 0.75 (95% CI 0.56–0.94), respectively. The 6- and 12-month SQ sensitivities were 0.23 (95% CI 0.002–0.46) and 0.35 (95% CI 0.14–0.56), respectively. Conclusion: PMs exhibit superior sensitivity compared to the SQ for mortality prognostication in HD patients.

scholarly journals Evaluating the Risk of Tumors Diseases Based on Measurement of Urinary and Serumal Antioxidants Using the New Agar Diffusion Methods

2017 ◽  
Vol 2017 ◽  
pp. 1-6 ◽  
Author(s):  
Ying Zhou ◽  
Jing Chen ◽  
Zhen Wang ◽  
Hui Liu
Keyword(s):  
Antioxidant Capacity ◽  
Healthy People ◽  
Healthy Individuals ◽  
Operating Characteristics ◽  
Tumor Patients ◽  
Tumor Diseases ◽  
Receiver Operating Characteristics Curve ◽  
Age Range ◽  
Urine And Serum ◽  
Effect Of Age

Objectives. To discuss the characteristics of the amount of urinary total antioxidants in tumor diseases and the possibility of utilizing the changing regulation of urinary antioxidants to diagnose tumor diseases.Method. Urine and serum specimens from 130 healthy people were used to investigate the variation of antioxidant capacity against age. Urine and serum specimens from 44 unselected patients with tumors and 44 healthy people with same age background were used to explore the significance of urinary antioxidant capacity in clinic to diagnose tumor diseases. Potassium permanganate agar method and iodine starch method were used to determine the amount of total antioxidants.Results. In healthy people, more antioxidants in urine were measured in older people, while the results were opposite in serum. More antioxidants were found in urine of tumor patients than in healthy people with same age-range.Conclusions. According to the results of 130 measurements, the amount of antioxidants in urine varies by age. By using agar methods to measure antioxidants, the effect of age is required to be considered. Antioxidants levels from tumor patients were significantly higher than healthy individuals in urine. The combination of urine and serum to determine total antioxidants can better diagnose tumor diseases based on iodine starch method, with area under the receiver operating characteristics curve at 0.787.

scholarly journals Building prediction models for coronary heart disease by synthesizing multiple longitudinal research findings

2005 ◽  
Vol 12 (5) ◽  
pp. 459-464 ◽  
Author(s):  
Guizhou Hu ◽  
Martin M. Root
Keyword(s):  
Coronary Heart Disease ◽  
Heart Disease ◽  
Prediction Model ◽  
Empirical Model ◽  
Complex Disease ◽  
Prediction Models ◽  
Longitudinal Research ◽  
Study Data ◽  
Individual Risk ◽  
Data Set

Background No methodology is currently available to allow the combining of individual risk factor information derived from different longitudinal studies for a chronic disease in a multivariate fashion. This paper introduces such a methodology, named Synthesis Analysis, which is essentially a multivariate meta-analytic technique. Design The construction and validation of statistical models using available data sets. Methods and results Two analyses are presented. (1) With the same data, Synthesis Analysis produced a similar prediction model to the conventional regression approach when using the same risk variables. Synthesis Analysis produced better prediction models when additional risk variables were added. (2) A four-variable empirical logistic model for death from coronary heart disease was developed with data from the Framingham Heart Study. A synthesized prediction model with five new variables added to this empirical model was developed using Synthesis Analysis and literature information. This model was then compared with the four-variable empirical model using the first National Health and Nutrition Examination Survey (NHANES I) Epidemiologic Follow-up Study data set. The synthesized model had significantly improved predictive power ( x2 = 43.8, P < 0.00001). Conclusions Synthesis Analysis provides a new means of developing complex disease predictive models from the medical literature.

scholarly journals DeepIsoFun: a deep domain adaptation approach to predict isoform functions

2018 ◽  
Vol 35 (15) ◽  
pp. 2535-2544 ◽  
Author(s):  
Dipan Shaw ◽  
Hao Chen ◽  
Tao Jiang
Keyword(s):  
Network Architecture ◽  
Domain Adaptation ◽  
Multiple Instance Learning ◽  
Training Data ◽  
Supplementary Information ◽  
Operating Characteristics ◽  
Neural Network Architecture ◽  
Receiver Operating Characteristics Curve ◽  
Gene Functions ◽  
Better Than

AbstractMotivationIsoforms are mRNAs produced from the same gene locus by alternative splicing and may have different functions. Although gene functions have been studied extensively, little is known about the specific functions of isoforms. Recently, some computational approaches based on multiple instance learning have been proposed to predict isoform functions from annotated gene functions and expression data, but their performance is far from being desirable primarily due to the lack of labeled training data. To improve the performance on this problem, we propose a novel deep learning method, DeepIsoFun, that combines multiple instance learning with domain adaptation. The latter technique helps to transfer the knowledge of gene functions to the prediction of isoform functions and provides additional labeled training data. Our model is trained on a deep neural network architecture so that it can adapt to different expression distributions associated with different gene ontology terms.ResultsWe evaluated the performance of DeepIsoFun on three expression datasets of human and mouse collected from SRA studies at different times. On each dataset, DeepIsoFun performed significantly better than the existing methods. In terms of area under the receiver operating characteristics curve, our method acquired at least 26% improvement and in terms of area under the precision-recall curve, it acquired at least 10% improvement over the state-of-the-art methods. In addition, we also study the divergence of the functions predicted by our method for isoforms from the same gene and the overall correlation between expression similarity and the similarity of predicted functions.Availability and implementationhttps://github.com/dls03/DeepIsoFun/Supplementary informationSupplementary data are available at Bioinformatics online.

scholarly journals Reduced Superficial Capillary Density in Cerebral Infarction Is Inversely Correlated With the NIHSS Score

2021 ◽  
Vol 13 ◽  
Author(s):  
William Robert Kwapong ◽  
Yuying Yan ◽  
Zilong Hao ◽  
Bo Wu
Keyword(s):  
Cerebral Infarction ◽  
Capillary Density ◽  
Foveal Avascular Zone ◽  
Healthy Controls ◽  
Operating Characteristics ◽  
Microvascular Changes ◽  
Receiver Operating Characteristics Curve ◽  
Capillary Plexus ◽  
Magnetic Resonance Imaging Mri

Purpose: The retina and the brain share similar neuronal and microvascular features, therein we aimed to assess the structural and microvascular changes in the macula and choriocapillaris (CC) in patients with cerebral infarction when compared with healthy controls using optical coherence tomography angiography (OCTA).Methods: OCTA was used to image and measure the capillary density in the radial peripapillary capillaries (RPC), superficial capillary plexus (SCP), deep capillary plexus (DCP), choriocapillaris (CC), and mean area of the foveal avascular zone (FAZ) in all participants. Twenty-two cerebral infarction patients based on their magnetic resonance imaging (MRI) and 25 healthy controls were included in our study.Results: Density of the RPC (P &lt; 0.001), SCP (P = 0.001), DCP (P &lt; 0.001) and CC (P &lt; 0.001) were significantly reduced in cerebral infarction patients when compared with healthy controls, respectively. Retinal thickness measurements (P &lt; 0.05) were significantly reduced in cerebral infarction patients when compared with healthy controls. The mean FAZ area was significantly larger (P = 0.012) in cerebral infarction patients when compared with healthy controls. National Institute of HealthStroke Scale (NIHSS) inversely correlated with SCP density in cerebral infarction patients (Rho = −0.409, P = 0.001). Receiver operating characteristics curve analysis showed that the blood flow of the choriocapillaris had the highest index [area under the receiver operatingcharacteristic (AUROC) = 0.964] to discriminate cerebral infarction patients from the healthy controls.Conclusions: Our study suggests that cerebral microcirculation dysfunction which occurs in cerebral infarction is mirrored in the macula and choroidal microcirculation. OCTA has the potential to non-invasively characterize the macula and choroidal changes in cerebral infarction in vivo.

scholarly journals A pilot study of discovery and validation of peritoneal endometriosis biomarkers in peritoneal fluid and serum

2020 ◽  
Author(s):  
See Ling Loy ◽  
Jieliang Zhou ◽  
Liang Cui ◽  
Tse Yeun Tan ◽  
Tat Xin Ee ◽  
...  
Keyword(s):  
Peritoneal Fluid ◽  
Liquid Chromatography Mass Spectrometry ◽  
Operating Characteristics ◽  
Targeted Metabolomics ◽  
Serum Samples ◽  
Pilot Studies ◽  
Potential Biomarker ◽  
Receiver Operating Characteristics Curve ◽  
Peritoneal Endometriosis ◽  
Validation Set

ObjectiveTo identify potential serum biomarkers in women with peritoneal endometriosis (PE) by first looking at its source in the peritoneal fluid (PF).DesignCase-control pilot studies, comprising independent discovery and validation sets.SettingKK Women’s and Children’s Hospital, Singapore.Patient(s)Women with laparoscopically confirmed PE and absence of endometriosis (control).Intervention(s)None.Main Outcome Measure(s)In the discovery set, we used untargeted liquid chromatography-mass spectrometry (LC-MS/MS) metabolomics, multivariable and univariable analyses to generate global metabolomic profiles of PF for endometriosis and to identify potential metabolites that could distinguish PE (n=10) from controls (n=31). Using targeted metabolomics, we validated the identified metabolites in PF and sera of cases (n=16 PE) and controls (n=19). We performed the area under the receiver-operating characteristics curve (AUC) analysis to evaluate the diagnostic performance of PE metabolites.Result(s)In the discovery set, PF phosphatidylcholine (34:3) and phenylalanyl-isoleucine were significantly increased in PE than controls groups, with AUC 0.77 (95% confidence interval 0.61-0.92; p=0.018) and AUC 0.98 (0.95-1.02; p<0.001), respectively. In the validation set, phenylalanyl-isoleucine retained discriminatory performance to distinguish PE from controls in both PF (AUC 0.77; 0.61-0.92; p=0.006) and serum samples (AUC 0.81; 0.64-0.99; p=0.004).Conclusion(s)Our preliminary results propose phenylalanyl-isoleucine as a potential biomarker of PE, which may be used as a minimally-invasive diagnostic biomarker of PE.

scholarly journals Predicting bloodstream infection outcome using machine learning

2021 ◽  
Author(s):  
Yazeed Zoabi ◽  
Orli Kehat ◽  
Dan Lahav ◽  
Ahuva Weiss-Meilik ◽  
Amos Adler ◽  
...  
Keyword(s):  
Electronic Medical Records ◽  
Patient Outcomes ◽  
Medical Records ◽  
Bloodstream Infections ◽  
Operating Characteristics ◽  
Is Implementation ◽  
Adverse Health Outcomes ◽  
Receiver Operating Characteristics Curve ◽  
History Of ◽  
Poor Outcomes

Bloodstream infections (BSI) are a main cause of infectious disease morbidity and mortality worldwide. Early prediction of patients at high risk of poor outcomes of BSI is important for earlier decision making and effective patient stratification. We developed electronic medical record-based ma-chine learning models that predict patient outcomes of BSI. The area under the receiver-operating characteristics curve was 0.82 for a full featured inclusive model, and 0.81 for a compact model using only 25 features. Our models were trained, using electronic medical records that include demographics, blood tests, and the medical and diagnosis history of 7,889 hospitalized patients diagnosed with BSI. Among the implications of this work is implementation of the models as a basis for selective rapid microbiological identification, toward earlier administration of appropriate antibiotic therapy. Additionally, our models may help reduce the development of BSI and its associated adverse health outcomes and complications.

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