Prediction of Preeclampsia and Delivery of Small for Gestational Age Babies Based on a Combination of Clinical Risk Factors in High-Risk Women

2010 ◽  
Vol 30 (1) ◽  
pp. 58-73 ◽  
Author(s):  
Paul T. Seed ◽  
Lucy C. Chappell ◽  
Michael A. Black ◽  
Katrina K. Poppe ◽  
Yuan-Chun Hwang ◽  
...  
Keyword(s):  
Risk Factors ◽  
High Risk ◽  
Gestational Age ◽  
Small For Gestational Age ◽  
Clinical Risk Factors ◽  
High Risk Women ◽  
Clinical Risk

scholarly journals Simple electrocardiographic measures improve sudden arrhythmic death prediction in coronary disease

2020 ◽  
Vol 41 (21) ◽  
pp. 1988-1999 ◽  
Author(s):  
Neal A Chatterjee ◽  
Jani T Tikkanen ◽  
Gopi K Panicker ◽  
Dhiraj Narula ◽  
Daniel C Lee ◽  
...  
Keyword(s):  
Risk Factors ◽  
High Risk ◽  
Risk Stratification ◽  
Validation Cohort ◽  
Clinical Risk Factors ◽  
Left Ventricular Ejection ◽  
Derivation Cohort ◽  
Arrhythmic Death ◽  
Clinical Risk ◽  
Ecg Score

Abstract Aims To determine whether the combination of standard electrocardiographic (ECG) markers reflecting domains of arrhythmic risk improves sudden and/or arrhythmic death (SAD) risk stratification in patients with coronary heart disease (CHD). Methods and results The association between ECG markers and SAD was examined in a derivation cohort (PREDETERMINE; N = 5462) with adjustment for clinical risk factors, left ventricular ejection fraction (LVEF), and competing risk. Competing outcome models assessed the differential association of ECG markers with SAD and competing mortality. The predictive value of a derived ECG score was then validated (ARTEMIS; N = 1900). In the derivation cohort, the 5-year cumulative incidence of SAD was 1.5% [95% confidence interval (CI) 1.1–1.9] and 6.2% (95% CI 4.5–8.3) in those with a low- and high-risk ECG score, respectively (P for Δ < 0.001). A high-risk ECG score was more strongly associated with SAD than non-SAD mortality (adjusted hazard ratios = 2.87 vs. 1.38 respectively; P for Δ = 0.003) and the proportion of deaths due to SAD was greater in the high vs. low risk groups (24.9% vs. 16.5%, P for Δ = 0.03). Similar findings were observed in the validation cohort. The addition of ECG markers to a clinical risk factor model inclusive of LVEF improved indices of discrimination and reclassification in both derivation and validation cohorts, including correct reclassification of 28% of patients in the validation cohort [net reclassification improvement 28 (7–49%), P = 0.009]. Conclusion For patients with CHD, an externally validated ECG score enriched for both absolute and proportional SAD risk and significantly improved risk stratification compared to standard clinical risk factors including LVEF. Clinical Trial Registration https://clinicaltrials.gov/ct2/show/NCT01114269. ClinicalTrials.gov ID NCT01114269.

Predicting Risk of Preterm Birth: The Roles of Stress, Clinical Risk Factors, and Corticotropin-Releasing Hormone

2002 ◽  
Vol 4 (1) ◽  
pp. 54-64 ◽  
Author(s):  
R. Jeanne Ruiz ◽  
Judith Fullerton ◽  
Charles E. L. Brown ◽  
Donald J. Dudley
Keyword(s):  
Risk Factors ◽  
Preterm Birth ◽  
Perceived Stress ◽  
Gestational Age ◽  
Maternal Plasma ◽  
Clinical Risk Factors ◽  
Corticotropin Releasing Hormone ◽  
Releasing Hormone ◽  
Clinical Risk ◽  
Prospective Longitudinal

The relationships and predictive abilities of perceived stress, selected clinical risk factors, and corticotropin-releasing hormone (CRH) levels in maternal plasma were investigated for their association with preterm labor (PTL), preterm birth, and gestational age at delivery. An exploratory, prospective, longitudinal research design was used to measure CRH 4 times over pregnancy, perceived stress at 24 and 32 weeks of pregnancy, clinical risk factors, and genitourinary infections in low-income women. Multiple regression analyses revealed that a model of measurement of perceived stress at 24 or 32 weeks, CRH at 24 or 32 weeks, and PTL (indicated by a diagnosis by the physicians on the medical record and greater than 5 contractions per hour on the fetal monitor) was predictive of as much as 0.23 to 0.27 of the variance in gestational age at birth. Entering ethnicity as a variable into a model did not improve the predictive value. An analysis of variance between Caucasian and Hispanic ethnic groups revealed differences in CRH levels. However, simple regression analysis of ethnicity as a predictor showed a weak r 2 with no significance for prediction. There was some evidence of an association of smoking with stress and both PTL and preterm birth. The measurement of stress combined with the measurement of CRH from maternal plasma may improve the prediction of which pregnant women are at risk for preterm birth. The measurement of CRH has potential as an early biological marker of preterm birth.

scholarly journals Integration of genetic and clinical risk factors improves prognostication in relapsed childhood B-cell precursor acute lymphoblastic leukemia

Blood ◽  
2016 ◽  
Vol 128 (7) ◽  
pp. 911-922 ◽  
Author(s):  
Julie A. E. Irving ◽  
Amir Enshaei ◽  
Catriona A. Parker ◽  
Rosemary Sutton ◽  
Roland P. Kuiper ◽  
...  
Keyword(s):  
Risk Factors ◽  
Acute Lymphoblastic Leukemia ◽  
High Risk ◽  
Chromosomal Abnormalities ◽  
Lymphoblastic Leukemia ◽  
Clinical Risk Factors ◽  
Clinical High Risk ◽  
Cell Precursor ◽  
Clinical Risk ◽  
Key Points

Key Points Chromosomal abnormalities predict outcome after relapse in BCP-ALL, and high-risk cytogenetics takes precedence over clinical risk factors. Patients with mutations or deletions targeting TP53, NR3C1, BTG1, and NRAS were associated with clinical high risk and an inferior outcome.

scholarly journals A Nomogram For Predicting HCC Patients’ Overall Survival Based On Double Hub Genes and Other Clinical Risk Factors.

2021 ◽  
Author(s):  
Jianfei Chen ◽  
Zhong-liang Liu ◽  
Song Su ◽  
Jun Fan ◽  
Shun-de Tan ◽  
...  
Keyword(s):  
Risk Factors ◽  
Overall Survival ◽  
High Risk ◽  
Evaluation System ◽  
Assessment Model ◽  
Clinical Risk Factors ◽  
Differentially Expressed ◽  
Biological Behavior ◽  
Hub Genes ◽  
Clinical Risk

Abstract Background: Hepatocellular carcinoma (HCC) is one of the series malignant aggressive disease which shows elusive biological behavior and terrible prognosis. It is inadequate for the single evaluation system such as tumor-node-metastasis (TNM) staging system to predict the overall survival (OS) in HCC patients. Here, we conducted this study to identify prognosis-related genes, so as to form a reliable prognostic assessment model.Results: From three datasets in the GEO database, we identified two hub genes cytoskeleton associated protein 2 (CKAP2) and forkhead box M1 (FOXM1) among 348 differentially expressed genes (DGEs) that were differentially expressed between HCC and normal samples. The function analysis of those DEGs are enriched in cell division process (e.g., mitotic nuclear division, nuclear chromosome segregation) and metabolic process (e.g., organic acid catabolic process). Then, we established a two-gene model, that extremely distinguished the population at risk of liver cancer to high-risk and low-risk and was even viable in the TNM stage i-ii and iii-iv, vascular invasion and non-invasion subgroups (all P<0.05). Next, a nomogram was set out combined with the two hub genes and clinical risk factors, and the predictive power of the nomogram performed more outstanding than the gene expression or clinical parameters alone.Conclusions: Our two-gene-based evaluation system effectively filtered out the high-risk HCC patients, and could potentially be used for clinical decision-making and individualized management of particular HCC patients.

scholarly journals A risk model for relapsed/refractory aggressive NHL integrating clinical risk factors and pretransplant Deauville score

2020 ◽  
Vol 4 (22) ◽  
pp. 5762-5771
Author(s):  
Ho-Young Yhim ◽  
Yael Eshet ◽  
Ur Metser ◽  
Chae-Hong Lim ◽  
Katherine Lajkosz ◽  
...  
Keyword(s):  
Risk Factors ◽  
High Risk ◽  
International Prognostic Index ◽  
Prognostic Index ◽  
Salvage Chemotherapy ◽  
Low Risk ◽  
Clinical Risk Factors ◽  
Intermediate Risk ◽  
Clinical Risk ◽  
Deauville Score

Abstract There are limited data regarding the combined value of the pretransplant Deauville score (DS) from a positron emission tomography scan and clinical risk factors in patients with relapsed/refractory aggressive non-Hodgkin lymphoma (NHL). We performed a retrospective analysis to assess the prognostic role of pretransplant DS in patients with relapsed/refractory aggressive NHL who underwent salvage chemotherapy and autologous stem cell transplantation (ASCT). We identified 174 eligible patients between January 2013 and March 2019. In multivariable analysis, pretransplant DS, B symptoms, and secondary International Prognostic Index (sIPI) were independent risk factors for event-free survival (EFS). These variables were used to derive an integrated risk score that categorized 166 patients with available information for all risk factors into 3 groups: low (n = 92; 55.4%), intermediate (n = 48; 28.9%), and high (n = 26; 15.7%). The new prognostic index showed a strong association with EFS (low-risk vs intermediate-risk hazard ratio [HR], 3.94; 95% confidence interval [CI], 2.16-7.17; P &lt; .001; low-risk vs high-risk HR, 10.83; 95% CI, 5.81-20.19; P &lt; .001) and outperformed models based on clinical risk factors or DS alone. These results were validated in 60 patients from an independent external cohort (low-risk vs intermediate-risk HR, 4.04; 95% CI, 1.51-10.82; P = .005; low-risk vs high-risk HR, 10.49; 95% CI, 4.11-26.73; P &lt; .001). We propose and validate a new prognostic index that risk-stratifies patients undergoing salvage chemotherapy followed by ASCT, thereby identifying patients at high risk for posttransplant treatment failure.

P6162Difference in progression to obstructive lesions according to the presence of high-risk plaque features

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
S E Lee ◽  
G Pontone ◽  
I Gottlieb ◽  
M Hadamitzky ◽  
J A Leipsic ◽  
...  
Keyword(s):  
Risk Factors ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
High Risk ◽  
Obstructive Coronary Artery Disease ◽  
Clinical Risk Factors ◽  
Clinical Risk ◽  
Obstructive Lesion ◽  
History Of ◽  
Artery Disease

Abstract Background It is still debatable whether the so-called high-risk plaque (HRP) simply represents a certain phase during the natural history of coronary atherosclerotic plaques or the disease progression would differ according to the presence of HRP. Purpose We determined whether the pattern of non-obstructive lesion progression into obstructive lesions would differ according to the presence of HRP. Methods Patients with non-obstructive coronary artery disease, defined as % diameter stenosis (%DS) ≥50%, were enrolled from a prospective, multinational registry of consecutive patients who underwent serial coronary computed tomography angiography at an inter-scan interval of ≥2 years. HRP was defined as lesions with ≥2 of positive remodelling, spotty calcification, and low-attenuation plaque. The total and compositional percent atheroma volume (PAV) at baseline and annualized PAV change were compared between non-HRP and HRP lesions. Results A total of 1,115 non-obstructive lesions were identified from 327 patients (61.1±8.9 years old, 66.0% male). There were 690 non-HRP and 425 HRP lesions. HRP lesions possessed greater PAV and %DS at baseline compared to non-HRP lesions. However, the annualized total and non-calcified PAV change were greater in non-HRP lesions than in HRP lesions. On multivariate analysis, addition of baseline PAV and %DS to clinical risk factors improved the predictive power of the model (Table). When clinical risk factors, PAV, %DS, and HRP were all adjusted on Model 3, only baseline PAV and %DS independently predicted the development of obstructive lesions (hazard ratio (HR) 1.046 [95% confidence interval (CI): 1.026–1.066] and HR 1.087 [95% CI: 1.055–1.119], respectively, all p<0.001), while HRP did not (p>0.05). Comparison of C-statistics of per-lesion analysis to predict progression to obstructive lesion C-statistics (95% CI) P Model 1: Baseline PAV 0.880 (0.879–0.884) – Model 2: Model 1 + baseline %DS 0.938 (0.937–0.939) vs. Model 1: <0.001 Model 3: Model 2 + HRP 0.935 (0.934–0.937) vs. Model 2: 0.004 Adjusted for age, male sex, hypertension, diabetes mellitus, hyperlipidemia, family history of coronary artery disease, smoking, body mass index, and statin use. Conclusion The pattern of individual coronary atherosclerotic plaque progression differed according to the presence of HRP. Baseline PAV was the most important predictor for lesions developing into obstructive lesions rather than the presence of HRP features at baseline. Acknowledgement/Funding This work was supported by the National Research Foundation of Korea funded by the Ministry of Science and ICT (Grant No. 2012027176).

Pharmacogenomic Prediction of Anthracycline-Induced Cardiotoxicity in Children

2012 ◽  
Vol 30 (13) ◽  
pp. 1422-1428 ◽  
Author(s):  
Henk Visscher ◽  
Colin J.D. Ross ◽  
S. Rod Rassekh ◽  
Amina Barhdadi ◽  
Marie-Pierre Dubé ◽  
...  
Keyword(s):  
Risk Factors ◽  
High Risk ◽  
Genetic Variants ◽  
Risk Group ◽  
Treatment Options ◽  
Risk Groups ◽  
High Risk Group ◽  
Clinical Risk Factors ◽  
Nucleotide Polymorphisms ◽  
Clinical Risk

Purpose Anthracycline-induced cardiotoxicity (ACT) is a serious adverse drug reaction limiting anthracycline use and causing substantial morbidity and mortality. Our aim was to identify genetic variants associated with ACT in patients treated for childhood cancer. Patients and Methods We carried out a study of 2,977 single-nucleotide polymorphisms (SNPs) in 220 key drug biotransformation genes in a discovery cohort of 156 anthracycline-treated children from British Columbia, with replication in a second cohort of 188 children from across Canada and further replication of the top SNP in a third cohort of 96 patients from Amsterdam, the Netherlands. Results We identified a highly significant association of a synonymous coding variant rs7853758 (L461L) within the SLC28A3 gene with ACT (odds ratio, 0.35; P = 1.8 × 10−5 for all cohorts combined). Additional associations (P < .01) with risk and protective variants in other genes including SLC28A1 and several adenosine triphosphate–binding cassette transporters (ABCB1, ABCB4, and ABCC1) were present. We further explored combining multiple variants into a single-prediction model together with clinical risk factors and classification of patients into three risk groups. In the high-risk group, 75% of patients were accurately predicted to develop ACT, with 36% developing this within the first year alone, whereas in the low-risk group, 96% of patients were accurately predicted not to develop ACT. Conclusion We have identified multiple genetic variants in SLC28A3 and other genes associated with ACT. Combined with clinical risk factors, genetic risk profiling might be used to identify high-risk patients who can then be provided with safer treatment options.

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