The predictive value of lncRNA MIR31HG expression on clinical outcomes in patients with solid malignant tumors

Abstract Background Post hoc analyses assessed the prognostic and predictive value of baseline alpha-fetoprotein (AFP), as well as clinical outcomes by AFP response or progression, during treatment in two placebo-controlled trials (REACH, REACH-2). Methods Serum AFP was measured at baseline and every three cycles. The prognostic and predictive value of baseline AFP was assessed by Cox regression models and Subpopulation Treatment Effect Pattern Plot method. Associations between AFP (≥ 20% increase) and radiographic progression and efficacy were assessed. Results Baseline AFP was confirmed as a continuous (REACH, REACH-2; p < 0.0001) and dichotomous (≥400 vs. <400 ng/ml; REACH, p < 0.01) prognostic factor, and was predictive for ramucirumab survival benefit in REACH (p = 0.0042 continuous; p < 0.0001 dichotomous). Time to AFP (hazard ratio [HR] 0.513; p < 0.0001) and radiographic (HR 0.549; p < 0.0001) progression favoured ramucirumab. Association between AFP and radiographic progression was shown for up to 6 (odds ratio [OR] 5.1; p < 0.0001) and 6–12 weeks (OR 1.8; p = 0.0065). AFP response was higher with ramucirumab vs. placebo (p < 0.0001). Survival was longer in patients with an AFP response than patients without (13.6 vs. 5.6 months, HR 0.451; 95% confidence interval, 0.354–0.574; p < 0.0001). Conclusions AFP is an important prognostic factor and a predictive biomarker for ramucirumab survival benefit. AFP ≥ 400 ng/ml is an appropriate selection criterion for ramucirumab. Clinical Trial Registration ClinicalTrials.gov, REACH (NCT01140347) and REACH-2 (NCT02435433).

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Development and validation of a prognostic COVID-19 severity assessment (COSA) score and machine learning models for patient triage at a tertiary hospital

Journal of Translational Medicine ◽

10.1186/s12967-021-02720-w ◽

2021 ◽

Vol 19 (1) ◽

Cited By ~ 2

Author(s):

Verena Schöning ◽

Evangelia Liakoni ◽

Christine Baumgartner ◽

Aristomenis K. Exadaktylos ◽

Wolf E. Hautz ◽

...

Keyword(s):

Machine Learning ◽

Risk Stratification ◽

Clinical Outcomes ◽

Predictive Value ◽

Validation Cohort ◽

Tertiary Hospital ◽

Learning Models ◽

Clinical Risk ◽

Machine Learning Models ◽

Risk Stratification Score

Abstract Background Clinical risk scores and machine learning models based on routine laboratory values could assist in automated early identification of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) patients at risk for severe clinical outcomes. They can guide patient triage, inform allocation of health care resources, and contribute to the improvement of clinical outcomes. Methods In- and out-patients tested positive for SARS-CoV-2 at the Insel Hospital Group Bern, Switzerland, between February 1st and August 31st (‘first wave’, n = 198) and September 1st through November 16th 2020 (‘second wave’, n = 459) were used as training and prospective validation cohort, respectively. A clinical risk stratification score and machine learning (ML) models were developed using demographic data, medical history, and laboratory values taken up to 3 days before, or 1 day after, positive testing to predict severe outcomes of hospitalization (a composite endpoint of admission to intensive care, or death from any cause). Test accuracy was assessed using the area under the receiver operating characteristic curve (AUROC). Results Sex, C-reactive protein, sodium, hemoglobin, glomerular filtration rate, glucose, and leucocytes around the time of first positive testing (− 3 to + 1 days) were the most predictive parameters. AUROC of the risk stratification score on training data (AUROC = 0.94, positive predictive value (PPV) = 0.97, negative predictive value (NPV) = 0.80) were comparable to the prospective validation cohort (AUROC = 0.85, PPV = 0.91, NPV = 0.81). The most successful ML algorithm with respect to AUROC was support vector machines (median = 0.96, interquartile range = 0.85–0.99, PPV = 0.90, NPV = 0.58). Conclusion With a small set of easily obtainable parameters, both the clinical risk stratification score and the ML models were predictive for severe outcomes at our tertiary hospital center, and performed well in prospective validation.

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The emerging role of circular RNAs in common solid malignant tumors in children

Cancer Cell International ◽

10.1186/s12935-021-01998-6 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Jiabin Yu ◽

Li Yang ◽

Hongting Lu

Keyword(s):

Epithelial Mesenchymal Transition ◽

Malignant Tumors ◽

Circular Rnas ◽

Development Research ◽

Physical And Mental Health ◽

Invasive Ability ◽

Mesenchymal Transition ◽

Circular Structure ◽

Solid Malignant Tumors

AbstractMalignant tumors are one of the fatal diseases that threaten children’s physical and mental health and affect their development. Research has shown that the occurrence and development of malignant tumors are associated with the abnormal expression and regulation of genes. Circular RNAs (circRNAs) are noncoding RNAs that have a closed circular structure, with a relatively stable expression, and do not undergo exonuclease-mediated degradation readily. Recent studies have shown that circRNA plays an important role in the occurrence, metastasis, and invasion of solid malignant tumors (SMTs) in children. Thus, circRNA is being considered as a breakthrough in the treatment of SMTs in children. In this review, we describe the functions and mechanisms of circRNAs involved in SMTs in children oncogenesis, and summarize the roles of circRNAs in regulating cell proliferation, cell apoptotic death, the cell cycle, cell migrative and invasive ability, epithelial-mesenchymal transition (EMT), cancer stem cells and drug resistance in SMTs in children. In addition, we also discuss the role of circRNAs in the early diagnosis, pathological grading, targeted therapy, and prognosis evaluation of common SMTs in children. CircRNAs are likely to provide a novel direction in therapy in SMTs of children.

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Pathogenetic and predictive value of biomarkers in patients with ALI and lower severity of illness: results from two clinical trials

AJP Lung Cellular and Molecular Physiology ◽

10.1152/ajplung.00195.2012 ◽

2012 ◽

Vol 303 (8) ◽

pp. L634-L639 ◽

Cited By ~ 42

Author(s):

Ashish Agrawal ◽

Hanjing Zhuo ◽

Sandra Brady ◽

Joseph Levitt ◽

Jay Steingrub ◽

...

Keyword(s):

Clinical Trials ◽

Clinical Outcomes ◽

Predictive Value ◽

Secondary Analysis ◽

Activated Protein C ◽

Oxygenation Index ◽

Severity Of Illness ◽

Plasminogen Activator Inhibitor ◽

High Plasma ◽

Plasminogen Activator Inhibitor 1

Plasma and bronchoalveolar lavage (BAL) biomarkers related to the pathogenesis of acute lung injury (ALI) have previously been associated with poorer clinical outcomes and increased disease severity among patients with ALI. Whether these biomarkers have predictive value in a less severely ill population that excludes septic patients with high APACHE II scores is currently unknown. We tested the association of plasma and BAL biomarkers with physiological markers of ALI severity or clinically relevant outcomes in a secondary analysis of a clinical trial of activated protein C for the treatment of ALI. Plasma plasminogen activator inhibitor-1 (PAI-1) and mini-BAL protein were both significantly associated with increased oxygenation index ( P = 0.02 and 0.01, respectively), whereas there was a trend toward an association between IL-6 and oxygenation index ( P = 0.057). High plasma IL-6, thrombomodulin, and mini-BAL protein were all significantly associated with fewer ventilator-free days (VFDs) ( P = 0.01, 0.01, and 0.05, respectively); no markers were associated with mortality, but we hypothesized that this was due to the small size of our cohort and the low death rate. To confirm these associations in a larger sample, we identified a restricted cohort of patients from the ARDS Network ALVEOLI study with similar baseline characteristics. We retested the associations of the significant biomarkers with markers of severity and clinical outcomes and studied IL-8 as an additional biomarker given its important predictive value in prior studies. In this restricted cohort, IL-6 was significantly associated with oxygenation index ( P = 0.02). Both IL-6 and IL-8 were associated with decreased VFDs and increased 28-day mortality. Future studies should be focused on examining larger numbers of patients with less severe ALI to further test the relative predictive value of plasma and mini-BAL biomarkers for clinically relevant outcomes, including VFDs and mortality, and for their prospective utility in risk stratification for future clinical trials.

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Predictive value of SYNTAX score II for clinical outcomes in cardiogenic shock underwent primary percutaneous coronary intervention; a pilot study

The International Journal of Cardiovascular Imaging ◽

10.1007/s10554-017-1241-9 ◽

2017 ◽

Vol 34 (3) ◽

pp. 329-336 ◽

Cited By ~ 8

Author(s):

Mert İlker Hayıroğlu ◽

Muhammed Keskin ◽

Ahmet Okan Uzun ◽

Emrah Bozbeyoğlu ◽

Özlem Yıldırımtürk ◽

...

Keyword(s):

Percutaneous Coronary Intervention ◽

Pilot Study ◽

Cardiogenic Shock ◽

Clinical Outcomes ◽

Primary Percutaneous Coronary Intervention ◽

Predictive Value ◽

Coronary Intervention ◽

Syntax Score ◽

Syntax Score Ii ◽

Percutaneous Coronary

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Which Clinical Scoring Systems Are Most Useful in Showing Severity in COVID-19 Patients?

10.21203/rs.3.rs-362665/v1 ◽

2021 ◽

Author(s):

Ishak San ◽

Emin Gemcioglu ◽

Salih Baser ◽

Nuray Yilmaz Cakmak ◽

Abdulsamet Erden ◽

...

Keyword(s):

High Risk ◽

Clinical Outcomes ◽

Predictive Value ◽

Independent Predictor ◽

Severe Disease ◽

Scoring Systems ◽

High Risk Group ◽

World Health ◽

Severe Group ◽

Clinical Scoring

Abstract IntroductionIn this study, we compare the predictive value of clinical scoring systems that are already in use in patients with COVID-19, including the BCRSS, qSOFA, SOFA, MuLBSTA and HScore, for determining the severity of the disease. Our aim in this study is to determine which scoring system is most useful in determining disease severity and to guide clinicians.Materials and MethodsWe classified the patients into two groups according to the stage of the disease (severe and non-severe) by using the slightly modified and adopted interim guidance of the World Health Organization. Severe cases were divided into a group of surviving patients and a deceased group according to the prognosis. According to admission values, the BCRSS, qSOFA, SOFA, MuLBSTA, and HScore were evaluated at admission using the worst parameters available in the first 24 hours.ResultsOf the 417 patients included in our study, 46 (11%) were in the severe group, while 371 (89%) were in the non-severe group. Of these 417 patients, 230 (55.2%) were men. The median (IQR) age of all patients was 44 (25) years. In multivariate logistic regression analyses, BRCSS in the highest tertile (HR: 6.1, 95% CI: 2.105–17.674, p = 0.001) was determined as an independent predictor of severe disease in cases of COVID-19. In multivariate analyses, qSOFA was also found to be an independent predictor of severe COVID-19 (HR: 4.757, 95% CI: 1.438–15.730, p = 0.011). The area under the curve (AUC) of the BRCSS, qSOFA, SOFA, MuLBSTA, and HScore was 0.977, 0.961, 0.958, 0.860, and 0.698, respectively.ConclusionCalculation of the BRCSS and qSOFA at the time of hospital admission can predict critical clinical outcomes in patients with COVID-19, and their predictive value is superior to that of HScore, MuLBSTA, and SOFA. With early identification of the high-risk group using BRCSS and qSOFA, early interventions for high-risk patients can improve clinical outcomes in COVID-19.

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Comparison of RMI 3 and RMI 4 in pre-operative evaluation of ovarian masses

International Journal of Reproduction Contraception Obstetrics and Gynecology ◽

10.18203/2320-1770.ijrcog20193782 ◽

2019 ◽

Vol 8 (9) ◽

pp. 3602

Author(s):

Badugu Rao Bahadur ◽

Gangadhara Rao Koneru ◽

Prabha Devi Kodey ◽

Jyothi Melam

Keyword(s):

Positive Predictive Value ◽

Negative Predictive Value ◽

Predictive Value ◽

Malignant Tumors ◽

Clinical Approach ◽

Ovarian Mass ◽

Medical College ◽

Operative Evaluation ◽

Sensitivity Specificity ◽

Final Confirmation

Background: To differentiate ovarian mass as benign or malignant could change clinical approach. Finding a screening and diagnostic method for ovarian cancer is challenging due to high mortality and insidious symptoms. Risk malignancy index (RMI) has the advantage of rapid and exact triage of patients with ovarian mass.Methods: Prospective study carried for 2 years at NRI Medical College and General Hospital, Chinakakani, Mangalagiri, Andhra Pradesh, India. 79 patients with ovarian mass were investigated and risk malignancy index (RMI-3 and RMI-4) calculated. Final confirmation was done based on histopathological report. Sensitivity, specificity, positive predictive value and negative predictive value were calculated for RMI 3 and RMI 4 taking histopathology as control and comparison was done.Results: (n=79); 50 (63.29%) cases were benign and 29 (36.70%) were malignant based on histopathology. RMI 4 is more sensitive (68.96%) than RMI 3 (62.06%), but RMI 3 is more specific (94%) than RMI 4 (92%).The positive predictive value of RMI-3 and RMI-4 were 85.71% and 83.33% respectively. The negative predictive value for RMI-4 and RMI-3 were 83.63% and 81.03% respectively.Conclusions: With increasing age, chance of malignancy increases. RMI 4 was more sensitive than RMI-3, however less specific than RMI 3 in differentiating benign and malignant tumors. The positive predictive value is slightly more for RMI 3, than RMI 4. Negative predictive value is slightly more for RMI 4, than RMI 3.

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New technologies in breast cancer detection – contrast enhanced dual-energy spectral mammography

Medical Visualization ◽

10.24835/1607-0763-2019-2-49-61 ◽

2019 ◽

pp. 49-61

Author(s):

A. V. Chernaya ◽

S. N. Novikov ◽

P. V. Krivorotko ◽

R. Kh. Ulyanova ◽

V. V. Danilov

Keyword(s):

Breast Cancer ◽

Digital Mammography ◽

Predictive Value ◽

New Technologies ◽

Malignant Tumors ◽

Dual Energy ◽

Post Contrast ◽

Morphological Studies ◽

Contrast Enhanced ◽

Sensitivity Specificity

Purpose: to study the possibilities of contrast enhanced dual-energy spectral mammography (CESM) in the diagnostics of malignant tumors in the breast.Material and methods. Forty-seven patients with suspicious for breast cancer (BC) lesions underwent CESM. Digital mammography (MMG) and post-contrast images were correlated with the results of path morphological studies after surgery or puncture biopsy was performed.Results. Sensitivity, specificity and overall accuracy in the diagnostics of breast cancer were 83.3%, 85.7%, 85.1% for digital mammography and 91.6%, 91.4%, 91.4% for CESM, respectively. The positive predictive value was 66.6% for digital MMG and 78.5% for CESM. The negative predictive value (NPV) was 96.9% for the CESM and exceeded NPV of the digital MMG, which was 93.7%.Conclusion. Thus, these findings suggest that CESM is an effective method for the diagnostics of malignant tumors in the breast.

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