Nomogram Based on Cytokines for Cardiovascular Diseases in Xinjiang Kazakhs

Background. This study involved the development of a predictive 5-year morbidity nomogram for cardiovascular diseases (CVD) in Xinjiang Kazakhs based on cytokine levels. Methods. The nomogram was based on a baseline survey of the town of Nalati in the Kazakh Autonomous Prefecture of Xinjiang from 2009 to 2013. By 2016, we had monitored 1508 people for a median time of 5.17 years and identified CVD events in the study population by collecting case information from local hospitals. The study population was divided into the training (n=1005) and validation cohorts (n=503) in a 2 : 1 ratio. The area under the receiver operating characteristic curve (AUC) was used to verify the predictive accuracy of the nomogram. The result was assessed in a validation cohort. Results. At the end of the study, the incidence of CVD in Xinjiang Kazakhs was found to be 11.28%. We developed a new nomogram to predict the 5-year incidence of CVD based on age, interleukin-6 (IL-6), and adiponectin (APN) levels, diastolic blood pressure, and dyslipidemia. The AUC for the predictive accuracy of the nomogram was 0.836 (95% confidence interval: 0.802–0.869), which was higher than that for IL-6 and APN. These results were supported by validation studies. Conclusions. The nomogram model can more directly assess the risk of CVD in Kazakhs and can be used for CVD risk assessment.

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

Journal of Perioperative Practice ◽

10.1177/1750458919875592 ◽

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.

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Upregulation of the RNF8 gene can predict the presence of sperm in azoospermic individuals

Clinical and Experimental Reproductive Medicine ◽

10.5653/cerm.2019.03111 ◽

2020 ◽

Vol 47 (1) ◽

pp. 61-67

Author(s):

Majid Nazari ◽

Emad Babakhanzadeh ◽

S. Mohsen Aghaei Zarch ◽

Mehrdad Talebi ◽

Nima Narimani ◽

...

Keyword(s):

Operating Characteristic ◽

Characteristic Curve ◽

Area Under The Curve ◽

Testicular Biopsy ◽

Negative Group ◽

Nonobstructive Azoospermia ◽

Entire Study ◽

Biopsy Specimens ◽

Study Population ◽

Polymerase Chain

Objective: In this study, specimens from testicular biopsies of men with nonobstructive azoospermia (NOA) were used to investigate whether RNF8 gene could serve as a biomarker to predict the presence of sperm in these patients.Methods: Testicular biopsy specimens from 47 patients were classified according to the presence of sperm (positive vs. negative groups) and investigated for the expression of RNF8. The level of RNF8 gene expression in the testes was compared between these groups using reverse-transcription polymerase chain reaction.Results: The expression level of RNF8 was significantly higher in testicular samples from the positive group than in those from the negative group. Moreover, the area under the curve of RNF8 expression for the entire study population was 0.84, showing the discriminatory power of RNF8 expression in differentiating between the positive and negative groups of men with NOA. A receiver operating characteristic curve analysis showed that RNF8 expression had a sensitivity of 81% and a specificity of 84%, with a cutoff level of 1.76.Conclusion: This study points out a significant association between the expression of RNF8 and the presence of sperm in NOA patients, which suggests that quantified RNF8 expression in testicular biopsy samples may be a valuable biomarker for predicting the presence of spermatozoa in biopsy samples.

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Validation Study of the Accuracy of a Postoperative Nomogram for Recurrence After Radical Prostatectomy for Localized Prostate Cancer

Journal of Clinical Oncology ◽

10.1200/jco.2002.20.4.951 ◽

2002 ◽

Vol 20 (4) ◽

pp. 951-956 ◽

Cited By ~ 37

Author(s):

Markus Graefen ◽

Pierre I. Karakiewicz ◽

Ilias Cagiannos ◽

Eric Klein ◽

Patrick A. Kupelian ◽

...

Keyword(s):

Prostate Cancer ◽

Radical Prostatectomy ◽

Operating Characteristic ◽

Predictive Accuracy ◽

Characteristic Curve ◽

Extracapsular Extension ◽

Validation Data ◽

Data Set ◽

Time Period

PURPOSE: A postoperative nomogram for prostate cancer was developed at Baylor College of Medicine. This nomogram uses readily available clinical and pathologic variables to predict 7-year freedom from recurrence after radical prostatectomy. We evaluated the predictive accuracy of the nomogram when applied to patients of four international institutions. PATIENTS AND METHODS: Clinical and pathologic data of 2,908 patients were supplied for validation, and 2,465 complete records were used. Nomogram-predicted probabilities of 7-year freedom from recurrence were compared with actual follow-up in two ways. First, the area under the receiver operating characteristic curve (AUC) was calculated for all patients and stratified by the time period of surgery. Second, calibration of the nomogram was achieved by comparing the predicted freedom from recurrence with that of an ideal nomogram. For patients in whom the pathologic report does not distinguish between focal and established extracapsular extension (an input variable of the nomogram), two separate calculations were performed assuming one or the other. RESULTS: The overall AUC was 0.80 when applied to the validation data set, with individual institution AUCs ranging from 0.77 to 0.82. The predictive accuracy of the nomogram was apparently higher in patients who were operated on between 1997 and 2000 (AUC, 0.83) compared with those treated between 1987 and 1996 (AUC, 0.78). Nomogram predictions of 7-year freedom from recurrence were within 10% of an ideal nomogram. CONCLUSION: The postoperative Baylor nomogram was accurate when applied at international treatment institutions. Our results suggest that accurate predictions may be expected when using this nomogram across different patient populations.

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Identification and Validation of a Five-lncRNA Signature Related to Glioma Using Bioinformatics Analysis

10.21203/rs.3.rs-70707/v1 ◽

2020 ◽

Author(s):

Daofeng Tian ◽

Haitao Liu ◽

Pengfei Xu ◽

Liguo Ye ◽

Long Wang ◽

...

Keyword(s):

Operating Characteristic ◽

Predictive Accuracy ◽

Characteristic Curve ◽

Prognostic Indicator ◽

High Risk Group ◽

Survival Prediction ◽

Kaplan Meier ◽

Clinical Covariates ◽

Non Coding Rnas ◽

Cox Analysis

Abstract BackgroundTo accurately predict the prognosis of glioma patients. Methods and ResultsA total of 541 samples from the TCGA cohort and 181 observations from the CGGA database were included in our study. By weighted gene co-expression network analysis (WGCNA), 14 long non-coding RNAs (lncRNAs) associated with glioma grade were identified. Using univariate and multivariate Cox analysis Five lncRNAs (CYTOR, MIR155HG, LINC00641, AC120036.4 and PWAR6) were selected to develop the prognostic signature. The Kaplan-Meier curve depicted that the patients in high risk group had poor prognosis in both cohorts. The areas under the receiver operating characteristic curve of the signature in predicting the survival of glioma patients at 1, 3, and 5 years were 0.84, 0.92, and 0.90 in the CGGA cohort and 0.8, 0.85 and 0.77 in the TCGA set. Multivariate Cox analysis demonstrated that the five-lncRNA signature was an independent prognostic indicator in both sets (HR = 2.002, p < 0.001; HR = 1.243, p = 0.007, respectively). A nomogram including the lncRNAs signature and clinical covariates was constructed and demonstrated high predictive accuracy in predicting 1-, 3- and 5-year survival probability of glioma patients. ConclusionWe established a five-lncRNA signature as a potentially reliable tool for survival prediction of glioma patients.

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Clinical value and potential mechanisms of LINC00221 in hepatocellular carcinoma based on integrated analysis

Epigenomics ◽

10.2217/epi-2020-0363 ◽

2021 ◽

Author(s):

Yanlin Feng ◽

Souraka Tapara Dramani Maman ◽

Xinyu Zhu ◽

Xuefang Liu ◽

Christian Cedric Bongolo ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Operating Characteristic ◽

Bioinformatics Analysis ◽

Validation Cohort ◽

Characteristic Curve ◽

Integrated Analysis ◽

Clinical Value ◽

Serum Samples ◽

Functional Roles

This study aimed to unveil the functional roles of LINC00221 in hepatocellular carcinoma (HCC). A discovery cohort and a validation cohort were respectively used to identify and verify the clinical value of LINC00221 in HCC. Bioinformatics analysis was performed to explore its potential mechanisms. LINC00221 was upregulated in HCC tissues and serum samples. Survival analysis and receiver operating characteristic curve further revealed its prognostic and diagnostic roles. Exploration of the mechanism showed that LINC00221 might exert a pro-cancer role via the lncRNA–miRNA–mRNA network. Our study reveals that upregulated LINC00221 can serve as a potential diagnostic and prognostic biomarker and provides novel clues as to the role of LINC00221 in HCC.

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Cerebellar Arteriovenous Malformations

Neurosurgery ◽

10.1227/neu.0b013e318271c081 ◽

2012 ◽

Vol 71 (6) ◽

pp. 1111-1124 ◽

Cited By ~ 52

Author(s):

◽

Ana Rodríguez-Hernández ◽

Helen Kim ◽

Tony Pourmohamad ◽

William L. Young ◽

...

Keyword(s):

Arteriovenous Malformations ◽

Operating Characteristic ◽

Predictive Accuracy ◽

Characteristic Curve ◽

Venous Drainage ◽

Martin Grade ◽

Operating Characteristic Curve ◽

Cerebral Avms ◽

Cerebellar Anatomy ◽

Better Than

ABSTRACT BACKGROUND: Anatomic diversity among cerebellar arteriovenous malformations (AVMs) calls for a classification that is intuitive and surgically informative. Selection tools like the Spetzler-Martin grading system are designed to work best with cerebral AVMs but have shortcomings with cerebellar AVMs. OBJECTIVE: To define subtypes of cerebellar AVMs that clarify anatomy and surgical management, to determine results according to subtypes, and to compare predictive accuracies of the Spetzler-Martin and supplementary systems. METHODS: From a consecutive surgical series of 500 patients, 60 had cerebellar AVMs, 39 had brainstem AVMs and were excluded, and 401 had cerebral AVMs. RESULTS: Cerebellar AVM subtypes were as follows: 18 vermian, 13 suboccipital, 12 tentorial, 12 petrosal, and 5 tonsillar. Patients with tonsillar and tentorial AVMs fared best. Cerebellar AVMs presented with hemorrhage more than cerebral AVMs (P < .001). Cerebellar AVMs were more likely to drain deep (P = .04) and less likely to be eloquent (P < .001). The predictive accuracy of the supplementary grade was better than that of the Spetzler-Martin grade with cerebellar AVMs (areas under the receiver-operating characteristic curve, 0.74 and 0.59, respectively). The predictive accuracy of the supplementary system was consistent for cerebral and cerebellar AVMs, whereas that of the Spetzler-Martin system was greater with cerebral AVMs. CONCLUSION: Patients with cerebellar AVMs present with hemorrhage more often than patients with cerebral AVMs, justifying an aggressive treatment posture. The supplementary system is better than the Spetzler-Martin system at predicting outcomes after cerebellar AVM resection. Key components of the Spetzler-Martin system such as venous drainage and eloquence are distorted by cerebellar anatomy in ways that components of the supplementary system are not.

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Accuracy of Fibrosis-4 Index in Identification of Patients with Cirrhosis Who Could Potentially Avoid Variceal Screening Endoscopy

Journal of Clinical Medicine ◽

10.3390/jcm9113510 ◽

2020 ◽

Vol 9 (11) ◽

pp. 3510

Author(s):

Koji Ishida ◽

Tadashi Namisaki ◽

Koji Murata ◽

Yuki Fujimoto ◽

Souichi Takeda ◽

...

Keyword(s):

High Risk ◽

Operating Characteristic ◽

Validation Cohort ◽

Characteristic Curve ◽

Protein Glycosylation ◽

Fibrosis Score ◽

Procollagen Type ◽

Screening Endoscopy ◽

Cirrhotic Patients ◽

Index Value

A potential restriction of the Baveno VI consensus, which helps to avoid unnecessary endoscopies, is the limited availability of FibroScan. We aimed to identify serum fibrosis indices that might aid in ruling out the presence of high-risk varices in cirrhotic patients. This retrospective study included 541 consecutive patients with cirrhosis who underwent endoscopy and had data available for nine serum fibrosis indices, including platelet count, hyaluronic acid, 7S fragment of type 4 collagen, procollagen type III N-terminal peptide, tissue inhibitor of metalloproteinases 1, Mac-2 binding protein glycosylation isomer, fibrosis index based on four factors (FIB-4), aspartate transaminase/platelet ratio index and enhanced liver fibrosis score. Optimal index cutoffs for predicting high-risk varices were calculated in an estimation cohort (n = 127) and evaluated in a validation cohort (n = 351). The diagnostic performance of the indices was assessed by receiver operating characteristic curve analysis. In the estimation cohort, a FIB-4 cutoff of 2.78 provided the greatest diagnostic accuracy in predicting both all-grade and high-risk varices. FIB-4 had a negative predictive value of 1.00 for high-risk varices in both cohorts, and 21.3% (27/127) and 14.8% (52/351) of the estimation and validation cohorts, respectively, avoided esophagogastroduodenoscopy; no high-risk varices were missed in either cohort. FIB-4 correctly identifies the absence of high-risk varices in patients with cirrhosis. Therefore, those with a FIB-4 of ≥2.78 should undergo esophagogastroduodenoscopy, and FIB-4 determination should be recommended every 6–12 months concurrently with the other blood tests until the index value reaches 2.78 in those with a FIB-4 of <2.78.

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2476

Journal of Clinical and Translational Science ◽

10.1017/cts.2017.79 ◽

2017 ◽

Vol 1 (S1) ◽

pp. 19-19

Author(s):

Samuel David Zetumer ◽

Hobart Harris

Keyword(s):

Operating Characteristic ◽

Learning Algorithm ◽

Characteristic Curve ◽

High Sensitivity ◽

Exploratory Laparotomy ◽

Machine Learning Algorithm ◽

Regression Algorithms ◽

Study Population ◽

Operating Characteristic Curve ◽

Better Than

OBJECTIVES/SPECIFIC AIMS: Historically, logistic regression algorithms (LRAs) have failed to differentiate strangulated small bowel obstructions (SBOs) from nonstrangulated SBOs. Our hypothesis is that a machine learning algorithm (MLA) can differentiate strangulated from simple SBOs better than an LRA can. METHODS/STUDY POPULATION: We used records of patients presenting with acute SBO and managed with exploratory laparotomy to test and train algorithms. We compared MLA to LRA via area under the receiver operating characteristic curve (AUROC) and cut-off points maximizing sensitivity and specificity. RESULTS/ANTICIPATED RESULTS: With 192 patient records, the AUROC of the MLA was 0.85. At the sensitivity cutoff, the MLA had 100% sensitivity and 55% specificity. At the specificity cutoff, the MLA had 45% sensitivity and 100% specificity. We anticipate improvements as more records are incorporated, and that LRA will underperform MLA across all measures. DISCUSSION/SIGNIFICANCE OF IMPACT: Our MLA represents a significant improvement over past LRAs, and may provide decision assistance to surgeons managing SBO. If this MLA maintains its high sensitivity, it may be used in the future to prevent unnecessary surgeries.

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A Nomogram for Predicting Acute Respiratory Failure After Cervical Traumatic Spinal Cord Injury Based on Admission Clinical Findings

Neurocritical Care ◽

10.1007/s12028-021-01302-4 ◽

2021 ◽

Author(s):

Yongfan Xie ◽

Yongyi Wang ◽

Yong Zhou ◽

Mingxing Liu ◽

Shengli Li ◽

...

Keyword(s):

Spinal Cord ◽

Operating Characteristic ◽

Predictive Accuracy ◽

Characteristic Curve ◽

Traumatic Spinal Cord Injury ◽

Concordance Index ◽

Platelet To Lymphocyte Ratio ◽

Training Cohort ◽

Cord Injury ◽

Operating Characteristic Curve

Abstract Objectives Acute respiratory failure (ARF) is a common medical complication in patients with cervical traumatic spinal cord injury (TSCI). To identify independent predictors for ARF onset in patients who underwent cervical TSCI without premorbid respiratory diseases and to apply appropriate medical supports based on accurate prediction, a nomogram relating admission clinical information was developed for predicting ARF during acute care period. Methods We retrospectively reviewed clinical profiles of patients who suffered cervical TSCI and were emergently admitted to Qingdao Municipal Hospital from 2014 to 2020 as the training cohort. Univariate analysis was performed using admission clinical variables to estimate associated factors and a nomogram for predicting ARF occurrence was generated based on the independent predictors from multivariate logistic regression analysis. This nomogram was assessed by concordance index for discrimination and calibration curve with internal-validated bootstrap strategy. Receiver operating characteristic curve was conducted to compare the predictive accuracy between the nomogram and the traditional gold standard, which combines neuroimaging and neurological measurements by using area under the receiver operating characteristic curve (AUC). An additional 56-patient cohort from another medical center was retrospectively reviewed as the test cohort for external validation of the nomogram. Results 162 patients were eligible for this study and were included in the training cohort, among which 25 individuals developed ARF and were recorded to endure more complications. Despite the aggressive treatments and prolonged intensive care unit cares, 14 patients insulted with ARF died. Injury level, American Spinal Injury Association Impairment Scale (AIS) grade, admission hemoglobin (Hb), platelet to lymphocyte ratio, and neutrophil percentage to albumin ratio (NPAR) were independently associated with ARF onset. The concordance index of the nomogram incorporating these predictors was 0.933 in the training cohort and 0.955 in the test cohort, although both calibrations were good. The AUC of the nomogram was equal to concordance index, which presented better predictive accuracy compared with previous measurements using neuroimaging and AIS grade (AUC 0.933 versus 0.821, Delong’s test p < 0.001). Similar significant results were also found in the test cohort (AUC 0.955 versus 0.765, Delong’s test p = 0.034). In addition, this nomogram was translated to a Web-based calculator that could generate individual probability for ARF in a visualized form. Conclusions The nomogram incorporating the injury level, AIS grade, admission Hb, platelet to lymphocyte ratio, and NPAR is a promising model to predict ARF in patients with cervical TSCI who are absent from previous respiratory dysfunction. This nomogram can be offered to clinicians to stratify patients, strengthen evidence-based decision-making, and apply appropriate individualized treatment in the field of acute clinical care.

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CANPT Score: A Tool to Predict Severe COVID-19 on Admission

Frontiers in Medicine ◽

10.3389/fmed.2021.608107 ◽

2021 ◽

Vol 8 ◽

Author(s):

Yuanyuan Chen ◽

Xiaolin Zhou ◽

Huadong Yan ◽

Huihong Huang ◽

Shengjun Li ◽

...

Keyword(s):

Risk Factors ◽

Operating Characteristic ◽

Total Bilirubin ◽

Predictive Accuracy ◽

Characteristic Curve ◽

Severe Disease ◽

Multivariate Logistic Regression ◽

Independent Risk Factors ◽

Operating Characteristic Curve ◽

Cr Creatine

Background and Aims: Patients with critical coronavirus disease 2019 (COVID-19) have a mortality rate higher than 50%. The purpose of this study was to establish a model for the prediction of the risk of severe disease and/or death in patients with COVID-19 on admission.Materials and Methods: Patients diagnosed with COVID-19 in four hospitals in China from January 22, 2020 to April 15, 2020 were retrospectively enrolled. The demographic, laboratory, and clinical data of the patients with COVID-19 were collected. The independent risk factors related to the severity of and death due to COVID-19 were identified with a multivariate logistic regression; a nomogram and prediction model were established. The area under the receiver operating characteristic curve (AUROC) and predictive accuracy were used to evaluate the model's effectiveness.Results: In total, 582 patients with COVID-19, including 116 patients with severe disease, were enrolled. Their comorbidities, body temperature, neutrophil-to-lymphocyte ratio (NLR), platelet (PLT) count, and levels of total bilirubin (Tbil), creatinine (Cr), creatine kinase (CK), and albumin (Alb) were independent risk factors for severe disease. A nomogram was generated based on these eight variables with a predictive accuracy of 85.9% and an AUROC of 0.858 (95% CI, 0.823–0.893). Based on the nomogram, the CANPT score was established with cut-off values of 12 and 16. The percentages of patients with severe disease in the groups with CANPT scores <12, ≥12, and <16, and ≥16 were 4.15, 27.43, and 69.64%, respectively. Seventeen patients died. NLR, Cr, CK, and Alb were independent risk factors for mortality, and the CAN score was established to predict mortality. With a cut-off value of 15, the predictive accuracy was 97.4%, and the AUROC was 0.903 (95% CI 0.832, 0.974).Conclusions: The CANPT and CAN scores can predict the risk of severe disease and mortality in COVID-19 patients on admission.

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