scholarly journals Prediction of preeclampsia risk in first time pregnant women: Metabolite biomarkers for a clinical test

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0244369
Author(s):  
Louise C. Kenny ◽  
Grégoire Thomas ◽  
Lucilla Poston ◽  
Jenny E. Myers ◽  
Nigel A. B. Simpson ◽  
...  
Keyword(s):  
Risk Factors ◽  
Risk Assessment ◽  
Pregnant Women ◽  
Prediction Models ◽  
Clinical Test ◽  
Large Set ◽  
Screening Assay ◽  
Liquid Chromatography Tandem Mass ◽  
Selection Step ◽  
First Time

Preeclampsia remains a leading cause of maternal and perinatal morbidity and mortality. Accurate prediction of preeclampsia risk would enable more effective, risk-based prenatal care pathways. Current risk assessment algorithms depend on clinical risk factors largely unavailable for first-time pregnant women. Delivering accurate preeclampsia risk assessment to this cohort of women, therefore requires for novel biomarkers. Here, we evaluated the relevance of metabolite biomarker candidates for their selection into a prototype rapid, quantitative Liquid Chromatography-tandem Mass Spectrometry (LC-MS/MS) based clinical screening assay. First, a library of targeted LC-MS/MS assays for metabolite biomarker candidates was developed, using a medium-throughput translational metabolomics workflow, to verify biomarker potential in the Screening-for-Pregnancy-Endpoints (SCOPE, European branch) study. A variable pre-selection step was followed by the development of multivariable prediction models for pre-defined clinical use cases, i.e., prediction of preterm preeclampsia risk and of any preeclampsia risk. Within a large set of metabolite biomarker candidates, we confirmed the potential of dilinoleoyl-glycerol and heptadecanoyl-2-hydroxy-sn-glycero-3-phosphocholine to effectively complement Placental Growth Factor, an established preeclampsia biomarker, for the prediction of preeclampsia risk in first-time pregnancies without overt risk factors. These metabolites will be considered for integration in a prototype rapid, quantitative LC-MS/MS assay, and subsequent validation in an independent cohort.

scholarly journals A Review on Automatic Mammographic Density and Parenchymal Segmentation

2015 ◽  
Vol 2015 ◽  
pp. 1-31 ◽  
Author(s):  
Wenda He ◽  
Arne Juette ◽  
Erika R. E. Denton ◽  
Arnau Oliver ◽  
Robert Martí ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
Risk Assessment ◽  
Risk Prediction ◽  
Prediction Models ◽  
Prevention Measures ◽  
Clinical Environment ◽  
Tissue Segmentation ◽  
Risk Prediction Models ◽  
The Impact

Breast cancer is the most frequently diagnosed cancer in women. However, the exact cause(s) of breast cancer still remains unknown. Early detection, precise identification of women at risk, and application of appropriate disease prevention measures are by far the most effective way to tackle breast cancer. There are more than 70 common genetic susceptibility factors included in the current non-image-based risk prediction models (e.g., the Gail and the Tyrer-Cuzick models). Image-based risk factors, such as mammographic densities and parenchymal patterns, have been established as biomarkers but have not been fully incorporated in the risk prediction models used for risk stratification in screening and/or measuring responsiveness to preventive approaches. Within computer aided mammography, automatic mammographic tissue segmentation methods have been developed for estimation of breast tissue composition to facilitate mammographic risk assessment. This paper presents a comprehensive review of automatic mammographic tissue segmentation methodologies developed over the past two decades and the evidence for risk assessment/density classification using segmentation. The aim of this review is to analyse how engineering advances have progressed and the impact automatic mammographic tissue segmentation has in a clinical environment, as well as to understand the current research gaps with respect to the incorporation of image-based risk factors in non-image-based risk prediction models.

scholarly journals Modeling of the cave-ins occurrence using AHD and GIS

2013 ◽  
Vol 1 (6) ◽  
pp. 7473-7495
Author(s):  
A. A. Malinowska ◽  
K. Dziarek
Keyword(s):  
Risk Factors ◽  
Risk Assessment ◽  
Prediction Models ◽  
Qualitative And Quantitative ◽  
Discontinuous Deformation ◽  
Qualitative Factors ◽  
Predicted Values ◽  
Risk Potential ◽  
Very High ◽  
Sinkhole Hazard

Abstract. The analysis of mining-induced sinkholes occurrence is a very important problem as far as the spatial development optimization is concerned. Research conducted within this paper was oriented to revealing the applicability of GIS and the associated AHD method for estimating the risk of discontinuous deformation occurrence on the surface. The qualitative factors were accounted for in the sinkhole risk assessment, thus creating bases for the research. These elements play an important role in the process of sinkholes formation; however they were not used in prediction models. Another assumption lied in minimizing the number of variables in the model. Accordingly, the most important qualitative and quantitative risk factors were finally selected, on the basis of which the risk of cave-ins occurrence on the surface could be calculated. The results of estimations of zones with sinkholes potential were verified. The places of actual and high-risk potential discontinuous deformations were compared. The congruence between predicted values and actual observations of sinkholes was very high. The results of presented research prove the necessity to evaluate the sinkhole hazard in view of qualitative factors.

scholarly journals Do nuclear magnetic resonance (NMR)-based metabolomics improve the prediction of pregnancy-related disorders?

2020 ◽  
Author(s):  
Nancy McBride ◽  
Sara L. White ◽  
Lucilla Poston ◽  
Diane Farrar ◽  
Jane West ◽  
...  
Keyword(s):  
Risk Factors ◽  
Pregnant Women ◽  
Gestational Age ◽  
Prediction Models ◽  
External Validation ◽  
Area Under The Curve ◽  
Large For Gestational Age ◽  
List Type ◽  
Independent Validation ◽  
Pregnancy Smoking

AbstractBackgroundPrediction of pregnancy-related disorders is mostly done based on established and easily measured risk factors. However, these measures are at best moderate at discriminating between high and low risk women. Recent advances in metabolomics may provide earlier and more accurate prediction of women at risk of pregnancy-related disorders.Methods and FindingsWe used data collected from women in the Born in Bradford (BiB; n=8,212) and UK Pregnancies Better Eating and Activity Trial (UPBEAT; n=859) studies to create and validate prediction models for pregnancy-related disorders. These were gestational diabetes mellitus (GDM), hypertensive disorders of pregnancy (HDP), small for gestational age (SGA), large for gestational age (LGA) and preterm birth (PTB). We used ten-fold cross-validation and penalised regression to create prediction models. We compared the predictive performance of 1) risk factors (maternal age, pregnancy smoking status, body mass index, ethnicity and parity) to 2) nuclear magnetic resonance-derived metabolites (N = 156 quantified metabolites, collected at 24-28 weeks gestation) and 3) risk factors and metabolites combined. The multi-ethnic BiB cohort was used for training and testing the models, with independent validation conducted in UPBEAT, a study of obese pregnant women of multiple ethnicities.In BiB, discrimination for GDM, HDP, LGA and SGA was improved with the addition of metabolites to the risk factors only model. Risk factors area under the curve (AUC 95% confidence interval (CI)): GDM (0.69 (0.64, 0.73)), HDP (0.74 (0.70, 0.78)) and LGA (0.71 (0.66, 0.75)), and SGA (0.59 (0.56,0.63)). Combined AUC 95% (CI)): GDM (0.78 (0.74, 0.81)), HDP (0.76 (0.73, 0.79)) and LGA (0.75 (0.70, 0.79)), and SGA (0.66 (0.63,0.70)). For GDM, HDP, LGA, but not SGA, calibration was good for a combined risk factor and metabolite model. Prediction of PTB was poor for all models. Independent validation in UPBEAT at 24-28 weeks and 15-18 weeks gestation confirmed similar patterns of results, but AUC were attenuated. A key limitation was our inability to identify a large general pregnancy population for independent validation.ConclusionsOur results suggest metabolomics combined with established risk factors improves prediction GDM, HDP and LGA, when compared to risk factors alone. They also highlight the difficulty of predicting PTB, with all models performing poorly.Author SummaryBackgroundCurrent methods used to predict pregnancy-related disorders exhibit modest discrimination and calibration.Metabolomics may enable improved prediction of pregnancy-related disorders.Why Was This Study Done?We require tools to identify women with high-risk pregnancies earlier on, so that antenatal care can be more appropriately targeted at women who need it most and tailored to women’s needs and to facilitate early intervention.It has been suggested that metabolomic markers might improve prediction of future pregnancy-related disorders. Previous studies tend to be small and rarely undertake external validation.What Did the Researchers Do and Find?Using BiB (8,212 pregnant women of multiple ethnicities), we created prediction models, using established risk factors and 156 NMR-derived metabolites, for five pregnancy-related disorders. These were gestational diabetes mellitus (GDM), hypertensive disorders of pregnancy (HDP), small for gestational age (SGA), large for gestational age (LGA) and preterm birth (PTB). We sought external validation in UPBEAT (859 obese pregnant women).We compared the predictive discrimination (area under the curve - AUC) and calibration (calibration slopes) of the models. The prediction models we compared were 1) established risk factors (pregnancy smoking, maternal age, body mass index (BMI), maternal ethnicity and parity) 2) NMR-derived metabolites measured in the second trimester and 3) a combined model of risk factors and metabolites.Inclusion of metabolites with risk factors improved prediction of GDM, HDP, LGA and SGA in BiB. Prediction of PTB was poor with all models. Result patterns were similar in validation using UPBEAT, particularly for GDM and HDP, but AUC were attenuated.What Do These Findings Mean?These findings indicate that combining current risk factor and metabolomic data could improve the prediction of GDM, HDP, LGA and SGA. These findings need to be validated in larger, general populations of pregnant women.

scholarly journals Validity Study of the Roll-Over Test and Risk Factors for Preeclampsia in Four Health Districts of Surigao City, Philippines

2019 ◽  
Vol 53 (4) ◽  
Author(s):  
Lovella C. Condeza ◽  
Christopher B. Arguelles ◽  
Cris F. Velez ◽  
Glenda M. Labadan ◽  
Emmanuel S. Baja ◽  
...  
Keyword(s):  
Risk Factors ◽  
Pregnant Women ◽  
Health Workers ◽  
Cross Sectional Study ◽  
The Philippines ◽  
Clinical Test ◽  
Cross Sectional ◽  
History Of ◽  
Low Sensitivity ◽  
Health Districts

Background. The roll-over test is a simple, easily available clinical test using the sphygmomanometer to predict pregnancy-induced hypertension starting at 20 weeks age of gestation (AOG). However, the roll-over test is not part of the standard prenatal care in government health facilities even if health workers can easily perform the test. Objectives. To validate the roll-over test at the 20th weeks of gestation and to determine the risk factors for preeclampsia in 4 health districts of Surigao City, a province in the CARAGA Region of the Philippines. Methods. A total of 190 pregnant women without history of hypertension during their previous pregnancies from 4 health districts in Surigao City, Philippines were consecutively enrolled from May 2014 to October 2014 in a cross-sectional study design. The roll-over test was done using the method described in Williams Obstetrics and the validity of the roll-over test was measured. Bivariate and multivariate analyses were done to determine risk factors for preeclampsia. Results. Seven of the 190 women developed preeclampsia. The sensitivity of the rollover test done at 20 weeks AOG was 43% and the specificity was 81%. Maternal age, history of urinary tract infection (UTI) and a positive rollover test were associated with preeclampsia. Conclusion. Pregnant women whose ages are <20 years or >29 years old, or who had a history of UTI, or a positive roll-over test are more likely to develop preeclampsia. Roll-over test has a low sensitivity at 20 weeks AOG. More studies are warranted to explore the improvement of its predictive value in other AOG.

scholarly journals Assessing Risk of Breast Cancer: A Review of Risk Prediction Models

2021 ◽  
Author(s):  
Geunwon Kim ◽  
Manisha Bahl
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
Risk Assessment ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Risk Prediction ◽  
Prediction Models ◽  
Cancer Risk Assessment ◽  
Risk Prediction Models ◽  
Breast Cancer Risk Assessment

Abstract Accurate and individualized breast cancer risk assessment can be used to guide personalized screening and prevention recommendations. Existing risk prediction models use genetic and nongenetic risk factors to provide an estimate of a woman’s breast cancer risk and/or the likelihood that she has a BRCA1 or BRCA2 mutation. Each model is best suited for specific clinical scenarios and may have limited applicability in certain types of patients. For example, the Breast Cancer Risk Assessment Tool, which identifies women who would benefit from chemoprevention, is readily accessible and user-friendly but cannot be used in women under 35 years of age or those with prior breast cancer or lobular carcinoma in situ. Emerging research on deep learning-based artificial intelligence (AI) models suggests that mammographic images contain risk indicators that could be used to strengthen existing risk prediction models. This article reviews breast cancer risk factors, describes the appropriate use, strengths, and limitations of each risk prediction model, and discusses the emerging role of AI for risk assessment.

SEISMOLOGICAL, AND GEOMORPHIC HYDROGRAPHIC TSUNAMI RISK FACTORS FOR FAR EAST COAST OF RUSSIA

2018 ◽  
Author(s):  
Г. Кофф ◽  
G. Koff ◽  
И. Чеснокова ◽  
I. Chesnokova ◽  
О. Борсукова ◽  
...  
Keyword(s):  
Risk Factors ◽  
Risk Assessment ◽  
Far East ◽  
Federal District ◽  
Tsunami Waves ◽  
Marine Terraces ◽  
Tsunami Risk ◽  
Local Risk ◽  
River Valleys ◽  
First Time

The problems of formation of the risk of a tsunami on the coast of the southern regions of the Far East Federal District. As the risk factors used features such as the relative position of the epicenters of tsunamigenic earthquakes and the coast, the underwater terrain coast, the degree of closeness of the studied bays, the presence or absence of wide beach and the first marine terraces, flowing into the characteristics of the studied coast estuaries. The factors subjected to expert estimates, taking into account their influence on the formation of the risk of a tsunami. The characteristics of the underwater topography, location in relation to the shores and bays of the epicenters of tsunamigenic earthquakes are estimated as zonal factors. Characteristics of marine terraces and river valleys are treated as local risk factors. For the first time for the coasts of Primorye and Khabarovsk area made of consequences of historical tsunamis: the presence of the devastation on the shores endured the tsunami waves from the open sea into the rivers and beaches ships, coast erosion, as well as descriptions of eyewitnesses. Characteristics of risk assessments are preceded by the WHO as areas of possible foci of tsunamigenic earthquakes, fault description and characteristics of buildings coasts. Risk assessment of the tsunami produces by the method proposed by G. Koff et al. The following characteristics was taken into account: the nature of the underwater coastal slope, exposure, banks in relation to the tsunami, the presence of the beach or I marine terrace, the presence of river valleys in the rear of the bay, the degree of openness of the bay. Overall, Nakhodka Bay and the Vostok Bay have the same performance tsunami -28,2 points. However, within these bays be provided with separate bays, with a higher risk assessment tsunami. Tsunami response coasts was made for these areas for the first time. Analysis of the materials allowed to identify the most tsunami zone, and to recommend the use of certain sections of the proposed development.

Psychological Resilience Provides No Independent Protection From Suicidal Risk

Crisis ◽  
2016 ◽  
Vol 37 (2) ◽  
pp. 130-139 ◽  
Author(s):  
Danica W. Y. Liu ◽  
A. Kate Fairweather-Schmidt ◽  
Richard Burns ◽  
Rachel M. Roberts ◽  
Kaarin J. Anstey
Keyword(s):  
Risk Factors ◽  
Well Being ◽  
Current Status ◽  
Psychological Resilience ◽  
Cross Sectional ◽  
Life Project ◽  
Resilience Factors ◽  
First Time

Abstract. Background: Little is known about the role of resilience in the likelihood of suicidal ideation (SI) over time. Aims: We examined the association between resilience and SI in a young-adult cohort over 4 years. Our objectives were to determine whether resilience was associated with SI at follow-up or, conversely, whether SI was associated with lowered resilience at follow-up. Method: Participants were selected from the Personality and Total Health (PATH) Through Life Project from Canberra and Queanbeyan, Australia, aged 28–32 years at the first time point and 32–36 at the second. Multinomial, linear, and binary regression analyses explored the association between resilience and SI over two time points. Models were adjusted for suicidality risk factors. Results: While unadjusted analyses identified associations between resilience and SI, these effects were fully explained by the inclusion of other suicidality risk factors. Conclusion: Despite strong cross-sectional associations, resilience and SI appear to be unrelated in a longitudinal context, once risk/resilience factors are controlled for. As independent indicators of psychological well-being, suicidality and resilience are essential if current status is to be captured. However, the addition of other factors (e.g., support, mastery) makes this association tenuous. Consequently, resilience per se may not be protective of SI.

Risk Factors in Diabetes and Pregnant Women with Urinary Tract Infections Compared to Younger Aged Female.

2018 ◽  
Vol 1 (3) ◽  
pp. 26-38
Author(s):  
Abdulghani Mohamed Alsamarai ◽  
Shler Ali Khorshed
Keyword(s):  
Risk Factors ◽  
Urinary Tract Infection ◽  
Urinary Tract ◽  
Tract Infection ◽  
Pregnant Women ◽  
Female Student ◽  
Mean Value ◽  
Infected Female ◽  
Significant Difference ◽  
Tract Infections

Background: Urinary tract infection is common with health impact in women and characterised by failure to treatment and recurrent episodes. Aim: This study was conducted to determine the risk factors for the development of urinary tract infection in diabetic and pregnant women in comparison to student female. Materials and methods: A prospective cross-sectional study conducted during the period from 1st of June 2015 to the end of January 2016. The population included in the study are 563 women, of them 425 were outpatients, and 138 were inpatients. Their age range between 18 and 80 years, with a mean age of 33.59±15.29 years. Urine samples collected and cultured on blood agar and MacConkey agar by spread plate technique. Bacterial colonies with different morphology were selected, purified and identified according to their biochemical characteristics using conventional standard methods. Results: In diabetic women, there were no significant difference in mean age and BMI values between culture positive and culture negative groups. However, pus cell mean scale was significantly higher [P=0.000] in women with urinary tract infection [1.76±1.25] than in those with negative culture [0.69±1.00]. In pregnant women, BMI mean value was significantly [P=0.013] lower in pregnant women with UTI [26.14] as compared to those without infection [26.99]. Pus cell scale mean value was significantly [P=0.000] higher in pregnant women with UTI [1.55] than women with negative UTI [0.85]. While there was no significant difference in mean age between UTI positive and negative pregnant women. In female student, there was a significant difference between UTI infected and non-infected in mean age [P=0.041] and pus cell scale [P=0.000]. However, BMI was not significantly different between infected and non-infected female student. Other risk factors association are variables in the 3 groups when analysed using X2, while AUC and OR show different trends of association between risk factors and UTI. Conclusion: BMI, pus cell scale, child number, delivery method, operation history and hospital setting were significantly associated with culture positivity in the 3 studied groups as determined by AUC. While OR confirmed association with pus sale scale in the 3 groups.

4 Pattern Recognition for Healing Burns to Compute Evidence: Space Syntax and Machine Learning Analysis of Burns Center Karachi

2013 ◽  
Vol 1 (1) ◽  
pp. 13
Author(s):  
Javaria Manzoor Shaikh ◽  
JaeSeung Park
Keyword(s):  
Pattern Recognition ◽  
Prediction Models ◽  
Uv Light ◽  
Healing Process ◽  
Space Syntax ◽  
Depth Map ◽  
Routine Tasks ◽  
Hyper Plane ◽  
First Time ◽  
Learning Analysis

Usually elongated hospitalization is experienced byBurn patients, and the precise forecast of the placement of patientaccording to the healing acceleration has significant consequenceon healthcare supply administration. Substantial amount ofevidence suggest that sun light is essential to burns healing andcould be exceptionally beneficial for burned patients andworkforce in healthcare building. Satisfactory UV sunlight isfundamental for a calculated amount of burn to heal; this delicaterather complex matrix is achieved by applying patternclassification for the first time on the space syntax map of the floorplan and Browder chart of the burned patient. On the basis of thedata determined from this specific healthcare learning technique,nurse can decide the location of the patient on the floor plan, hencepatient safety first is the priority in the routine tasks by staff inhealthcare settings. Whereas insufficient UV light and vitamin Dcan retard healing process, hence this experiment focuses onmachine learning design in which pattern recognition andtechnology supports patient safety as our primary goal. In thisexperiment we lowered the adverse events from 2012- 2013, andnearly missed errors and prevented medical deaths up to 50%lower, as compared to the data of 2005- 2012 before this techniquewas incorporated.In this research paper, three distinctive phases of clinicalsituations are considered—primarily: admission, secondly: acute,and tertiary: post-treatment according to the burn pattern andhealing rate—and be validated by capable AI- origin forecastingtechniques to hypothesis placement prediction models for eachclinical stage with varying percentage of burn i.e. superficialwound, partial thickness or full thickness deep burn. Conclusivelywe proved that the depth of burn is directly proportionate to thedepth of patient’s placement in terms of window distance. Ourfindings support the hypothesis that the windowed wall is mosthealing wall, here fundamental suggestion is support vectormachines: which is most advantageous hyper plane for linearlydivisible patterns for the burns depth as well as the depth map isused.

FETAL GROWTH RESTRICTION WITH EARLY AND LATE MANIFESTATION, PROGNOSTIC MARKERS

2020 ◽  
pp. 27-30
Author(s):  
Yakubova D.I.
Keyword(s):  
Risk Factors ◽  
Pregnant Women ◽  
Fetal Growth ◽  
Fetal Growth Restriction ◽  
Prenatal Screening ◽  
Prognostic Markers ◽  
Growth Restriction ◽  
Late Manifestation ◽  
Late Form ◽  
Early Manifestation

Objective of the study: Comprehensive assessment of risk factors, the implementation of which leads to FGR with early and late manifestation. To evaluate the results of the first prenatal screening: PAPP-A, B-hCG, made at 11-13 weeks. Materials and Methods: A retrospective study included 110 pregnant women. There were 48 pregnant women with early manifestation of fetal growth restriction, 62 pregnant women with late manifestation among them. Results of the study: The risk factors for the formation of the FGR are established. Statistically significant differences in the indicators between groups were not established in the analyses of structures of extragenital pathology. According to I prenatal screening, there were no statistical differences in levels (PAPP-A, b-hCG) in the early and late form of FGR.

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