Accuracy of commercially available urine bladder tumour markers in the diagnosis of bladder cancer: a meta-analysis with summary ROC curve analysis

Bladder cancer is a common type of tumor among elderly male population; it causes intraocular metastasis (IOM). The study investigated the differences between elderly male bladder cancer patients with and without IOM, and identified risk factors for IOM. In this study, 749 elderly male patients (aged ≥50 years) with bladder cancer were included from November 2003 to December 2016. Differences between the IOM and non-IOM (NIOM) groups were evaluated by chi-square test and Student’s t-test. The binary logistic regression analysis calculates the risk factors. Receiver operating characteristic (ROC) curve analysis was used to assess the diagnostic value of IOM in elderly male patients with bladder cancer. The incidence of IOM in patients with bladder cancer was 1.7%. No significant differences were detected in age and histopathology between the IOM and NIOM groups. According to the study, the IOM group had higher ALP and Cyfra21-1. Binary logistic regression indicated that ALP and Cyfra21-1 were risk factors for IOM in elderly male bladder cancer patients ( p < .05). ROC curve analysis revealed area under the curve values for ALP and Cyfra21-1 of 0.913 and 0.814, using cutoff values of 9.65 and 83.5 U/L, respectively. The sensitivity and specificity values for ALP were 61.5% and 95.8%, respectively, while those for Cyfra21-1 were 84.6% and 73.3%. The investigation indicates that ALP and Cyfra21-1 are risk factors for IOM in elderly male patients with bladder cancer and ALP is more reliable at distinguishing IOM from NIOM in elderly male patients with bladder cancer.

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Identification of a Novel Glycolysis-Related LncRNA Signature for Predicting Overall Survival in Patients With Bladder Cancer

Frontiers in Genetics ◽

10.3389/fgene.2021.720421 ◽

2021 ◽

Vol 12 ◽

Author(s):

Zhenming Zheng ◽

Cong Lai ◽

Wenshuang Li ◽

Caixia Zhang ◽

Kaiqun Ma ◽

...

Keyword(s):

Bladder Cancer ◽

Roc Curve ◽

Prognostic Model ◽

Prognostic Significance ◽

Enrichment Analysis ◽

Curve Analysis ◽

Gene Set Enrichment Analysis ◽

Risk Scores ◽

Roc Curve Analysis ◽

Gene Set Enrichment

BackgroundBoth lncRNAs and glycolysis are considered to be key influencing factors in the progression of bladder cancer (BCa). Studies have shown that glycolysis-related lncRNAs are an important factor affecting the overall survival and prognosis of patients with bladder cancer. In this study, a prognostic model of BCa patients was constructed based on glycolysis-related lncRNAs to provide a point of reference for clinical diagnosis and treatment decisions.MethodsThe transcriptome, clinical data, and glycolysis-related pathway gene sets of BCa patients were obtained from The Cancer Genome Atlas (TCGA) database and the Gene Set Enrichment Analysis (GSEA) official website. Next, differentially expressed glycolysis-related lncRNAs were screened out, glycolysis-related lncRNAs with prognostic significance were identified through LASSO regression analysis, and a risk scoring model was constructed through multivariate Cox regression analysis. Then, based on the median of the risk scores, all BCa patients were divided into either a high-risk or low-risk group. Kaplan-Meier (KM) survival analysis and the receiver operating characteristic (ROC) curve were used to evaluate the predictive power of the model. A nomogram prognostic model was then constructed based on clinical indicators and risk scores. A calibration chart, clinical decision curve, and ROC curve analysis were used to evaluate the predictive performance of the model, and the risk score of the prognostic model was verified using the TCGA data set. Finally, Gene Set Enrichment Analysis (GSEA) was performed on glycolysis-related lncRNAs.ResultsA total of 59 differentially expressed glycolysis-related lncRNAs were obtained from 411 bladder tumor tissues and 19 pericarcinomatous tissues, and 9 of those glycolysis-related lncRNAs (AC099850.3, AL589843.1, MAFG-DT, AC011503.2, NR2F1-AS1, AC078778.1, ZNF667-AS1, MNX1-AS1, and AC105942.1) were found to have prognostic significance. A signature was then constructed for predicting survival in BCa based on those 9 glycolysis-related lncRNAs. ROC curve analysis and a nomogram verified the accuracy of the signature.ConclusionThrough this study, a novel prognostic prediction model for BCa was established based on 9 glycolysis-related lncRNAs that could effectively distinguish high-risk and low-risk BCa patients, and also provide a new point of reference for clinicians to make individualized treatment and review plans for patients with different levels of risk.

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Acute heart failure: predicting early in-hospital outcomes

European Journal of Preventive Cardiology ◽

10.1093/eurjpc/zwab061.019 ◽

2021 ◽

Vol 28 (Supplement_1) ◽

Author(s):

M Santos ◽

S Paula ◽

I Almeida ◽

H Santos ◽

H Miranda ◽

...

Keyword(s):

Heart Failure ◽

Logistic Regression ◽

Acute Heart Failure ◽

Roc Curve ◽

Real Life ◽

Study Data ◽

Curve Analysis ◽

Older Age ◽

Invasive Ventilation ◽

Roc Curve Analysis

Abstract Funding Acknowledgements Type of funding sources: None. Introduction Patients (P) with acute heart failure (AHF) are a heterogeneous population. Risk stratification at admission may help predict in-hospital complications and needs. The Get With The Guidelines Heart Failure score (GWTG-HF) predicts in-hospital mortality (M) of P admitted with AHF. ACTION ICU score is validated to estimate the risk of complications requiring ICU care in non-ST elevation acute coronary syndromes. Objective To validate ACTION-ICU score in AHF and to compare ACTION-ICU to GWTG-HF as predictors of in-hospital M (IHM), early M [1-month mortality (1mM)] and 1-month readmission (1mRA), using real-life data. Methods Based on a single-center retrospective study, data collected from P admitted in the Cardiology department with AHF between 2010 and 2017. P without data on previous cardiovascular history or uncompleted clinical data were excluded. Statistical analysis used chi-square, non-parametric tests, logistic regression analysis and ROC curve analysis. Results Among the 300 P admitted with AHF included, mean age was 67.4 ± 12.6 years old and 72.7% were male. Systolic blood pressure (SBP) was 131.2 ± 37.0mmHg, glomerular filtration rate (GFR) was 57.1 ± 23.5ml/min. 35.3% were admitted in Killip-Kimball class (KKC) 4. ACTION-ICU score was 10.4 ± 2.3 and GWTG-HF was 41.7 ± 9.6. Inotropes’ usage was necessary in 32.7% of the P, 11.3% of the P needed non-invasive ventilation (NIV), 8% needed invasive ventilation (IV). IHM rate was 5% and 1mM was 8%. 6.3% of the P were readmitted 1 month after discharge. Older age (p < 0.001), lower SBP (p = 0,035) and need of inotropes (p < 0.001) were predictors of IHM in our population. As expected, patients presenting in KKC 4 had higher IHM (OR 8.13, p < 0.001). Older age (OR 1.06, p = 0.002, CI 1.02-1.10), lower SBP (OR 1.01, p = 0.05, CI 1.00-1.02) and lower left ventricle ejection fraction (LVEF) (OR 1.06, p < 0.001, CI 1.03-1.09) were predictors of need of NIV. None of the variables were predictive of IV. LVEF (OR 0.924, p < 0.001, CI 0.899-0.949), lower SBP (OR 0.80, p < 0.001, CI 0.971-0.988), higher urea (OR 1.01, p < 0.001, CI 1.005-1.018) and lower sodium (OR 0.92, p = 0.002, CI 0.873-0.971) were predictors of inotropes’ usage. Logistic regression showed that GWTG-HF predicted IHM (OR 1.12, p < 0.001, CI 1.05-1.19), 1mM (OR 1.10, p = 1.10, CI 1.04-1.16) and inotropes’s usage (OR 1.06, p < 0.001, CI 1.03-1.10), however it was not predictive of 1mRA, need of IV or NIV. Similarly, ACTION-ICU predicted IHM (OR 1.51, p = 0.02, CI 1.158-1.977), 1mM (OR 1.45, p = 0.002, CI 1.15-1.81) and inotropes’ usage (OR 1.22, p = 0.002, CI 1.08-1.39), but not 1mRA, the need of IV or NIV. ROC curve analysis revealed that GWTG-HF score performed better than ACTION-ICU regarding IHM (AUC 0.774, CI 0.46-0-90 vs AUC 0.731, CI 0.59-0.88) and 1mM (AUC 0.727, CI 0.60-0.85 vs AUC 0.707, CI 0.58-0.84). Conclusion In our population, both scores were able to predict IHM, 1mM and inotropes’s usage.

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Inhibition of Androgen Signalling Improves the Outcomes of Therapies for Bladder Cancer: Results from a Systematic Review of Preclinical and Clinical Evidence and Meta-Analysis of Clinical Studies

Diagnostics ◽

10.3390/diagnostics11020351 ◽

2021 ◽

Vol 11 (2) ◽

pp. 351

Author(s):

Massimiliano Creta ◽

Giuseppe Celentano ◽

Luigi Napolitano ◽

Roberto La Rocca ◽

Marco Capece ◽

...

Keyword(s):

Systematic Review ◽

Bladder Cancer ◽

Relative Risk ◽

Clinical Studies ◽

Clinical Evidence ◽

Meta Analysis ◽

Bladder Tumour ◽

Signalling Pathways ◽

Reductase Inhibitors ◽

The Impact

Bladder cancer (BCa) is an endocrine-related tumour and the activation of androgen signalling pathways may promote bladder tumorigenesis. We summarized the available preclinical and clinical evidence on the implications of the manipulation of androgen signalling pathways on the outcomes of BCa therapies. A systematic review was performed in December 2020. We included papers that met the following criteria: original preclinical and clinical research; evaluating the impact of androgen signalling modulation on the outcomes of BCa therapies. Six preclinical and eight clinical studies were identified. The preclinical evidence demonstrates that the modulation of androgen receptor-related pathways has the potential to interfere with the activity of the Bacillus Calmette Guerin, doxorubicin, cisplatin, gemcitabine, and radiotherapy. The relative risk of BCa recurrence after transurethral resection of the bladder tumour (TURBT) is significantly lower in patients undergoing therapy with 5 alpha reductase inhibitors (5-ARIs) or androgen deprivation therapy (ADT) (Relative risk: 0.50, 95% CI: 0.30–0.82; p = 0.006). Subgroup analysis in patients receiving 5-ARIs revealed a relative risk of BCa recurrence of 0.46 (95% CI: 0.22–0.95; p = 0.040). A significant negative association between the ratio of T1 BCa patients in treated/control groups and the relative risk of BCa recurrence was observed. Therapy with 5-ARIs may represent a potential strategy aimed at reducing BCa recurrence rate, mainly in patients with low stage disease. Further studies are needed to confirm these preliminary data.

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Presepsin values and prognostic nutritional index predict mortality in intensive care unit patients with sepsis: a pilot study

BMC Research Notes ◽

10.1186/s13104-021-05659-9 ◽

2021 ◽

Vol 14 (1) ◽

Author(s):

Yuichiro Shimoyama ◽

Osamu Umegaki ◽

Noriko Kadono ◽

Toshiaki Minami

Keyword(s):

Multivariate Analysis ◽

Roc Curve ◽

Operating Characteristic ◽

Prognostic Nutritional Index ◽

Early Mortality ◽

Curve Analysis ◽

Rank Test ◽

Roc Curve Analysis ◽

Log Rank Test ◽

Nutritional Index

Abstract Objective Sepsis is a major cause of mortality for critically ill patients. This study aimed to determine whether presepsin values can predict mortality in patients with sepsis. Results Receiver operating characteristic (ROC) curve analysis, Log-rank test, and multivariate analysis identified presepsin values and Prognostic Nutritional Index as predictors of mortality in sepsis patients. Presepsin value on Day 1 was a predictor of early mortality, i.e., death within 7 days of ICU admission; ROC curve analysis revealed an AUC of 0.84, sensitivity of 89%, and specificity of 77%; and multivariate analysis showed an OR of 1.0007, with a 95%CI of 1.0001–1.0013 (p = 0.0320).

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Soluble cytotoxic T-lymphocyte–associated antigen 4 (sCTLA-4) as a potential biomarker for diagnosis and evaluation of the prognosis in Glioma

BMC Immunology ◽

10.1186/s12865-021-00422-y ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Jiajia Liu ◽

Xiaoyi Tian ◽

Yan Wang ◽

Xixiong Kang ◽

Wenqi Song

Keyword(s):

T Lymphocyte ◽

Roc Curve ◽

Cytotoxic T Lymphocyte ◽

Tumor Grade ◽

Curve Analysis ◽

Enzyme Linked Immunosorbent Assay ◽

Low Grade ◽

High Grade ◽

Healthy Individuals ◽

Roc Curve Analysis

Abstract Background The cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) is widely considered as a pivotal immune checkpoint molecule to suppress antitumor immunity. However, the significance of soluble CTLA-4 (sCTLA-4) remains unclear in the patients with brain glioma. Here we aimed to investigate the significance of serum sCTLA-4 levels as a noninvasive biomarker for diagnosis and evaluation of the prognosis in glioma patients. Methods In this study, the levels of sCTLA-4 in serum from 50 patients diagnosed with different grade gliomas including preoperative and postoperative, and 50 healthy individuals were measured by an enzyme-linked immunosorbent assay (ELISA). And then ROC curve analysis and survival analyses were performed to explore the clinical significance of sCTLA-4. Results Serum sCTLA-4 levels were significantly increased in patients with glioma compared to that of healthy individuals, and which was also positively correlated with the tumor grade. ROC curve analysis showed that the best cutoff value for sCTLA-4 for glioma is 112.1 pg/ml, as well as the sensitivity and specificity with 82.0 and 78.0%, respectively, and a cut-off value of 220.43 pg/ml was best distinguished in patients between low-grade glioma group and high-grade glioma group with sensitivity 73.1% and specificity 79.2%. Survival analysis revealed that the patients with high sCTLA-4 levels (> 189.64 pg/ml) had shorter progression-free survival (PFS) compared to those with low sCTLA-4 levels (≤189.64 pg/ml). In the univariate analysis, elder, high-grade tumor, high sCTLA-4 levels and high Ki-67 index were significantly associated with shorter PFS. In the multivariate analysis, sCTLA-4 levels and tumor grade remained an independent prognostic factor. Conclusion These findings indicated that serum sCTLA-4 levels play a critical role in the pathogenesis and development of glioma, which might become a valuable predictive biomarker for supplementary diagnosis and evaluation of the progress and prognosis in glioma.

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Predictive CT features for the diagnosis of primary pulmonary mucoepidermoid carcinoma: comparison with squamous cell carcinomas and adenocarcinomas

Cancer Imaging ◽

10.1186/s40644-020-00375-2 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Xiaohua Ban ◽

Xinping Shen ◽

Huijun Hu ◽

Rong Zhang ◽

Chuanmiao Xie ◽

...

Keyword(s):

Logistic Regression ◽

Roc Curve ◽

Multinomial Logistic Regression ◽

Univariate Analysis ◽

Ct Imaging ◽

Squamous Cell Carcinomas ◽

Curve Analysis ◽

Imaging Features ◽

Roc Curve Analysis ◽

Ct Features

Abstract Background To determine the predictive CT imaging features for diagnosis in patients with primary pulmonary mucoepidermoid carcinomas (PMECs). Materials and methods CT imaging features of 37 patients with primary PMECs, 76 with squamous cell carcinomas (SCCs) and 78 with adenocarcinomas were retrospectively reviewed. The difference of CT features among the PMECs, SCCs and adenocarcinomas was analyzed using univariate analysis, followed by multinomial logistic regression and receiver operating characteristic (ROC) curve analysis. Results CT imaging features including tumor size, location, margin, shape, necrosis and degree of enhancement were significant different among the PMECs, SCCs and adenocarcinomas, as determined by univariate analysis (P < 0.05). Only lesion location, shape, margin and degree of enhancement remained independent factors in multinomial logistic regression analysis. ROC curve analysis showed that the area under curve of the obtained multinomial logistic regression model was 0.805 (95%CI: 0.704–0.906). Conclusion The prediction model derived from location, margin, shape and degree of enhancement can be used for preoperative diagnosis of PMECs.

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Mortality risk prediction in lupus patients complicated with invasive infection in the emergency department: LUPHAS score

Therapeutic Advances in Musculoskeletal Disease ◽

10.1177/1759720x19885559 ◽

2019 ◽

Vol 11 ◽

pp. 1759720X1988555 ◽

Cited By ~ 1

Author(s):

Wanlong Wu ◽

Jun Ma ◽

Yuhong Zhou ◽

Chao Tang ◽

Feng Zhao ◽

...

Keyword(s):

Emergency Department ◽

Prediction Model ◽

Risk Score ◽

Roc Curve ◽

Curve Analysis ◽

Multivariable Logistic Regression Analysis ◽

Invasive Infection ◽

Roc Curve Analysis ◽

All Cause Mortality ◽

Qsofa Score

Background: Infection remains a major cause of morbidity and mortality in patients with systemic lupus erythematosus (SLE). This study aimed to establish a clinical prediction model for the 3-month all-cause mortality of invasive infection events in patients with SLE in the emergency department. Methods: SLE patients complicated with invasive infection admitted into the emergency department were included in this study. Patient’s demographic, clinical, and laboratory characteristics on admission were retrospectively collected as baseline data and compared between the deceased and the survivors. Independent predictors were identified by multivariable logistic regression analysis. A prediction model for all-cause mortality was established and evaluated by receiver operating characteristic (ROC) curve analysis. Results: A total of 130 eligible patients were collected with a cumulative 38.5% 3-month mortality. Lymphocyte count <800/ul, urea >7.6mmol/l, maximum prednisone dose in the past ⩾60 mg/d, quick Sequential Organ Failure Assessment (qSOFA) score, and age at baseline were independent predictors for all-cause mortality (LUPHAS). In contrast, a history of hydroxychloroquine use was protective. In a combined, odds ratio-weighted LUPHAS scoring system (score 3–22), patients were categorized to three groups: low-risk (score 3–9), medium-risk (score 10–15), and high-risk (score 16–22), with mortalities of 4.9% (2/41), 45.9% (28/61), and 78.3% (18/23) respectively. ROC curve analysis indicated that a LUPHAS score could effectively predict all-cause mortality [area under the curve (AUC) = 0.86, CI 95% 0.79–0.92]. In addition, LUPHAS score performed better than the qSOFA score alone (AUC = 0.69, CI 95% 0.59–0.78), or CURB-65 score (AUC = 0.69, CI 95% 0.59–0.80) in the subgroup of lung infections ( n = 108). Conclusions: Based on a large emergency cohort of lupus patients complicated with invasive infection, the LUPHAS score was established to predict the short-term all-cause mortality, which could be a promising applicable tool for risk stratification in clinical practice.

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Right choice of a method for determination of cut-off values: A statistical tool for a diagnostic test

Asian Journal of Medical Sciences ◽

10.3126/ajms.v5i3.9296 ◽

2014 ◽

Vol 5 (3) ◽

pp. 30-34 ◽

Cited By ~ 19

Author(s):

Balkishan Sharma ◽

Ravikant Jain

Keyword(s):

Logistic Regression ◽

Discriminant Analysis ◽

Diagnostic Test ◽

Roc Curve ◽

Traditional Method ◽

Statistical Technique ◽

Curve Analysis ◽

Operating Characteristics ◽

Roc Curve Analysis

Objective: The clinical diagnostic tests are generally used to identify the presence of a disease. The cutoff value of a diagnostic test should be chosen to maximize the advantage that accrues from testing a population of human and others. When a diagnostic test is to be used in a clinical condition, there may be an opportunity to improve the test by changing the cutoff value. To enhance the accuracy of diagnosis is to develop new tests by using a proper statistical technique with optimum sensitivity and specificity. Method: Mean±2SD method, Logistic Regression Analysis, Receivers Operating Characteristics (ROC) curve analysis and Discriminant Analysis (DA) have been discussed with their respective applications. Results: The study highlighted some important methods to determine the cutoff points for a diagnostic test. The traditional method is to identify the cut-off values is Mean±2SD method. Logistic Regression Analysis, Receivers Operating Characteristics (ROC) curve analysis and Discriminant Analysis (DA) have been proved to be beneficial statistical tools for determination of cut-off points.Conclusion: There may be an opportunity to improve the test by changing the cut-off value with the help of a correctly identified statistical technique in a clinical condition when a diagnostic test is to be used. The traditional method is to identify the cut-off values is Mean ± 2SD method. It was evidenced in certain conditions that logistic regression is found to be a good predictor and the validity of the same can be confirmed by identifying the area under the ROC curve. Abbreviations: ROC-Receiver operating characteristics and DA-Discriminant Analysis. Asian Journal of Medical Science, Volume-5(3) 2014: 30-34 http://dx.doi.org/10.3126/ajms.v5i3.9296

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