scholarly journals Predictive value of a novel lncRNA LINC02518 in evaluating the prognosis of patients with hepatocellular carcinoma

2021 ◽  
Author(s):  
Wei Cui ◽  
Jingzhi Huang ◽  
Ruiqi Wang ◽  
Yu Wang ◽  
Xiaoming Chen ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Predictive Value ◽  
Long Noncoding Rna ◽  
Noncoding Rna ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Operating Characteristic Curve ◽  
Receiver Operating

Aim: The potential of long noncoding RNA in hepatocellular carcinoma (HCC) has led to promising insights into therapeutic intervention. The clinical significance of LINC02518 in HCC is unclear. This study aimed to evaluate the predictive value of a novel long noncoding RNA, LINC02518, for the prognosis of patients with HCC. Methods: Between December 2005 and November 2011, 125 and 75 HCC patients in the training and validation groups, respectively, who underwent liver surgery were included in our study. The LINC02518 expression of HCC and corresponding nontumor liver tissues was detected using microarray and reverse transcription quantitative polymerase chain reaction (RT-qPCR). These HCC patients were assigned into high and low LINC02518 expression groups based on the threshold of the receiver operating characteristic curve. Kaplan-Meier analysis was performed to determine the prognosis of HCC patients. Results: LINC02518 expression was upregulated in paired tumor samples compared with corresponding nontumor samples in the two groups. The area under the receiver operating characteristic curve for the levels of LINC02518 in the diagnosis of HCC was 0.66, 95% CI: 0.59–0.73. HCC patients with high LINC02518 expression had significantly worse tumor recurrence-free, metastasis-free, disease-free and overall survival than those with low LINC02518 expression. Conclusion: LINC02518 is negatively correlated with the prognosis of HCC and provides a promising strategy for the treatment and prognosis of HCC.

scholarly journals Predictive Value of A Novel lncRNA LINC02518 in Evaluating the Prognosis of Patients With Hepatocellular Carcinoma

2020 ◽  
Author(s):  
Wei Cui ◽  
Jingzhi Huang ◽  
Ruiqi Wang ◽  
Yu Wang ◽  
Xiaoming Chen ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Predictive Value ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Training Group ◽  
Kaplan Meier ◽  
Operating Characteristic Curve ◽  
Receiver Operating

Abstract BACKGROUND: The potential of lncRNA in hepatocellular carcinoma (HCC) has led to promising insights in therapeutic intervention. The clinical significance of LINC02518 in HCC is unclear. This study aimed to evaluate the predictive value of a novel long non-coding RNA LINC02518 for the prognosis of patients with HCC. METHODS: Between December 2005 and November 2011, 125 HCC patients in training group and 75 HCC patients in validation group who underwent liver surgery were involved in our study. The LINC02518 expression of HCC and corresponding non-tumor liver tissues was detected by microarray and qRT-PCR. These HCC patients were divided into high and low LINC02518 expression groups based on the threshold of the receiver operating characteristic curve. Kaplan-Meier analysis was performed to determine the prognosis of HCC patients.RESULTS: LINC02518 expression was upregulated in paired tumor samples compared to that in corresponding non-tumor samples in two groups. The areas under the receiver operating characteristic curve for the levels of LINC02518 in the diagnosis of HCC was 0.66, 95% CI: 0.59–0.73. HCC patients with high LINC02518 expression had significantly worse tumor recurrence-free, metastasis-free, disease-free, and overall survival than those with low LINC02518 expression.CONCLUSIONS: LINC02518 is negatively correlated with the prognosis of HCC and provides a promising strategy for the treatment and prognosis of HCC.

scholarly journals Predictive Value of 5 Early Warning Scores for Critical COVID-19 Patients

2020 ◽  
pp. 1-8
Author(s):  
Hai Hu ◽  
Ni Yao ◽  
Yanru Qiu
Keyword(s):  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Early Warning ◽  
Predictive Value ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Early Warning Score ◽  
Early Warning Scores ◽  
Operating Characteristic Curve ◽  
Receiver Operating

ABSTRACT Objectives: A simple evaluation tool for patients with novel coronavirus disease 2019 (COVID-19) could assist the physicians to triage COVID-19 patients effectively and rapidly. This study aimed to evaluate the predictive value of 5 early warning scores based on the admission data of critical COVID-19 patients. Methods: Overall, medical records of 319 COVID-19 patients were included in the study. Demographic and clinical characteristics on admission were used for calculating the Standardized Early Warning Score (SEWS), National Early Warning Score (NEWS), National Early Warning Score2 (NEWS2), Hamilton Early Warning Score (HEWS), and Modified Early Warning Score (MEWS). Data on the outcomes (survival or death) were collected for each case and extracted for overall and subgroup analysis. Receiver operating characteristic curve analyses were performed. Results: The area under the receiver operating characteristic curve for the SEWS, NEWS, NEWS2, HEWS, and MEWS in predicting mortality were 0.841 (95% CI: 0.765-0.916), 0.809 (95% CI: 0.727-0.891), 0.809 (95% CI: 0.727-0.891), 0.821 (95% CI: 0.748-0.895), and 0.670 (95% CI: 0.573-0.767), respectively. Conclusions: SEWS, NEWS, NEWS2, and HEWS demonstrated moderate discriminatory power and, therefore, offer potential utility as prognostic tools for screening severely ill COVID-19 patients. However, MEWS is not a good prognostic predictor for COVID-19.

scholarly journals Risk stratification of patients admitted to hospital with covid-19 using the ISARIC WHO Clinical Characterisation Protocol: development and validation of the 4C Mortality Score

BMJ ◽  
2020 ◽  
pp. m3339 ◽  
Author(s):  
Stephen R Knight ◽  
Antonia Ho ◽  
Riinu Pius ◽  
Iain Buchan ◽  
Gail Carson ◽  
...  
Keyword(s):  
Mortality Rate ◽  
Risk Stratification ◽  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Predictive Value ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Validation Dataset ◽  
Operating Characteristic Curve ◽  
Receiver Operating

Abstract Objective To develop and validate a pragmatic risk score to predict mortality in patients admitted to hospital with coronavirus disease 2019 (covid-19). Design Prospective observational cohort study. Setting International Severe Acute Respiratory and emerging Infections Consortium (ISARIC) World Health Organization (WHO) Clinical Characterisation Protocol UK (CCP-UK) study (performed by the ISARIC Coronavirus Clinical Characterisation Consortium—ISARIC-4C) in 260 hospitals across England, Scotland, and Wales. Model training was performed on a cohort of patients recruited between 6 February and 20 May 2020, with validation conducted on a second cohort of patients recruited after model development between 21 May and 29 June 2020 . Participants Adults (age ≥18 years) admitted to hospital with covid-19 at least four weeks before final data extraction. Main outcome measure In-hospital mortality. Results 35 463 patients were included in the derivation dataset (mortality rate 32.2%) and 22 361 in the validation dataset (mortality rate 30.1%). The final 4C Mortality Score included eight variables readily available at initial hospital assessment: age, sex, number of comorbidities, respiratory rate, peripheral oxygen saturation, level of consciousness, urea level, and C reactive protein (score range 0-21 points). The 4C Score showed high discrimination for mortality (derivation cohort: area under the receiver operating characteristic curve 0.79, 95% confidence interval 0.78 to 0.79; validation cohort: 0.77, 0.76 to 0.77) with excellent calibration (validation: calibration-in-the-large=0, slope=1.0). Patients with a score of at least 15 (n=4158, 19%) had a 62% mortality (positive predictive value 62%) compared with 1% mortality for those with a score of 3 or less (n=1650, 7%; negative predictive value 99%). Discriminatory performance was higher than 15 pre-existing risk stratification scores (area under the receiver operating characteristic curve range 0.61-0.76), with scores developed in other covid-19 cohorts often performing poorly (range 0.63-0.73). Conclusions An easy-to-use risk stratification score has been developed and validated based on commonly available parameters at hospital presentation. The 4C Mortality Score outperformed existing scores, showed utility to directly inform clinical decision making, and can be used to stratify patients admitted to hospital with covid-19 into different management groups. The score should be further validated to determine its applicability in other populations. Study registration ISRCTN66726260

A validated novel preoperative index to predict the extent of intraperitoneal contamination in patients with acute abdominal pathology: A cohort study

2019 ◽  
Vol 30 (7-8) ◽  
pp. 221-228
Author(s):  
Shahab Hajibandeh ◽  
Shahin Hajibandeh ◽  
Nicholas Hobbs ◽  
Jigar Shah ◽  
Matthew Harris ◽  
...  
Keyword(s):  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Validation Cohort ◽  
Characteristic Curve ◽  
Curve Analysis ◽  
Emergency Laparotomy ◽  
Contamination Index ◽  
Operating Characteristic Curve ◽  
Receiver Operating

Aims To investigate whether an intraperitoneal contamination index (ICI) derived from combined preoperative levels of C-reactive protein, lactate, neutrophils, lymphocytes and albumin could predict the extent of intraperitoneal contamination in patients with acute abdominal pathology. Methods Patients aged over 18 who underwent emergency laparotomy for acute abdominal pathology between January 2014 and October 2018 were randomly divided into primary and validation cohorts. The proposed intraperitoneal contamination index was calculated for each patient in each cohort. Receiver operating characteristic curve analysis was performed to determine discrimination of the index and cut-off values of preoperative intraperitoneal contamination index that could predict the extent of intraperitoneal contamination. Results Overall, 468 patients were included in this study; 234 in the primary cohort and 234 in the validation cohort. The analyses identified intraperitoneal contamination index of 24.77 and 24.32 as cut-off values for purulent contamination in the primary cohort (area under the curve (AUC): 0.73, P < 0.0001; sensitivity: 84%, specificity: 60%) and validation cohort (AUC: 0.83, P < 0.0001; sensitivity: 91%, specificity: 69%), respectively. Receiver operating characteristic curve analysis also identified intraperitoneal contamination index of 33.70 and 33.41 as cut-off values for feculent contamination in the primary cohort (AUC: 0.78, P < 0.0001; sensitivity: 87%, specificity: 64%) and validation cohort (AUC: 0.79, P < 0.0001; sensitivity: 86%, specificity: 73%), respectively. Conclusions As a predictive measure which is derived purely from biomarkers, intraperitoneal contamination index may be accurate enough to predict the extent of intraperitoneal contamination in patients with acute abdominal pathology and to facilitate decision-making together with clinical and radiological findings.

A conditional approach for the receiver operating characteristic curve construction to evaluate diagnostic test performance in a family-matched case–control design

2021 ◽  
pp. 096228022199595
Author(s):  
Yalda Zarnegarnia ◽  
Shari Messinger
Keyword(s):  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Case Control ◽  
Paired Data ◽  
Characteristic Curves ◽  
Receiver Operating Characteristic Curves ◽  
Operating Characteristic Curve ◽  
Receiver Operating

Receiver operating characteristic curves are widely used in medical research to illustrate biomarker performance in binary classification, particularly with respect to disease or health status. Study designs that include related subjects, such as siblings, usually have common environmental or genetic factors giving rise to correlated biomarker data. The design could be used to improve detection of biomarkers informative of increased risk, allowing initiation of treatment to stop or slow disease progression. Available methods for receiver operating characteristic construction do not take advantage of correlation inherent in this design to improve biomarker performance. This paper will briefly review some developed methods for receiver operating characteristic curve estimation in settings with correlated data from case–control designs and will discuss the limitations of current methods for analyzing correlated familial paired data. An alternative approach using conditional receiver operating characteristic curves will be demonstrated. The proposed approach will use information about correlation among biomarker values, producing conditional receiver operating characteristic curves that evaluate the ability of a biomarker to discriminate between affected and unaffected subjects in a familial paired design.

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