Nomogram: An analogue tool to deliver digital knowledge

Park, Seo Young

doi:10.1016/j.jtcvs.2017.12.107

Cited by 304 publications

(179 citation statements)

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“…Nomogram is an effective method for predicting the prognosis of cancer patients by simplifying the complex statistical prediction model into a profile chart for assessing the probability of OS in individual patients (Park, 2018). In this study, we included all independent clinical pathological prognostic factors selected from Cox regression analysis to construct a nomogram which can assess the OS probability of 1, 3, and 5 years in HCC patients.…”

Section: Establishment and Evaluation Of The Nomograms For Hcc Survivmentioning

confidence: 99%

An Integrated Model Based on a Six-Gene Signature Predicts Overall Survival in Patients With Hepatocellular Carcinoma

et al. 2020

Front. Genet.

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Section: Establishment and Evaluation Of The Nomograms For Hcc Survivmentioning

confidence: 99%

An Integrated Model Based on a Six-Gene Signature Predicts Overall Survival in Patients With Hepatocellular Carcinoma

et al. 2020

Front. Genet.

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“…To assess the consistency between actual and predicted survival time, calibration curves were performed for 3-year and 5-year survival. Besides, concordance index, that is C-index, was calculated to evaluate whether the nomogram model had excellent performance for predicting survival time [31].…”

Section: Construction Of Nomogrammentioning

confidence: 99%

Comprehensive analysis of alternative splicing and transcriptome related to the prognosis of cervical squamous cell cancer

Yan

Chen

et al. 2020

Preprint

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Background: Cervical squamous cell cancer (CESC) is a common malignancy tumor with high incidence and mortality in women globally. Increasing studies have indicated that there was an indivisible association between alternative mRNA splicing (AS) and multiple types of cancer. However, comprehensive analysis of alternative mRNA splicing events is scarce in CESC. Methods: In this study, alternative mRNA splicing events data and clinical information of 216 CESC patients were downloaded from TCGA SpliceSeq database and TCGA website. We used identified survival-associated splicing events (SASEs) to construct prognostic signatures. Kaplan-Meier survival analysis and receiver operating characteristic (ROC) curve were performed to evaluate the clinical value of prognostic signatures. A nomogram was carried out to quantitatively predict individuals’ survival probability. Regulatory network between splicing factors (SFs) and SASE was analyzed to explore the upstream regulators of a certain SASE. Additionally, we explored potential downstream pathways of a certain SASE. Results: A total of 41776 alternative splicing events in 9961 genes were collected. After sorting out SASEs, multivariable regression analysis was used to acquire 70 SASEs that could be independent prognostic factors for overall survival in CESC. Kaplan-Meier survival analyses showed that the difference was statistically significant (P＜0.01). The area under ROC curve (AUC) for all AS pattern was 0.932. A nomogram was constructed with a concordance index of 0.82 to predict individuals’ survival probability. EIF3A positively regulated SEC23A-27346-AP with highest correlation coefficient (P＜0.001, R=0.69). Besides, the most significant SASEs and KEGG pathways were SEC23A-27346-AP and adherens junction pathway (P=0.02, R=0.65). Conclusions: Prognostic signatures of survival-associated splicing events were independent prognostic factors for overall survival among CESC patients. A nomogram could quantitatively predict individuals’ survival probability. Moreover, we proposed that splicing factor EIF3A positively regulated SEC23A-27346-AP, and adherens junction pathway may be the downstream pathway of SEC23A-27346-AP. The aforementioned signaling pathway could play a crucial role in the development of CESC.

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“…We further carried out univariate logistic regression analysis on the research variables corresponding to the 1-SE criteria, and included the research variables with statistical differences as the nal model construction variables. Multivariate logistic regression analysis was used to construct the scoring model of WCH, and the corresponding nomogram was drawn [28]. The R language packets used mainly include "readxl", "glmnet", "informationValue", "rms", "pROC" and so on.…”

Section: Model Constructionmentioning

confidence: 99%

How to Identify White Coat Hypertension at The First Diagnosis: Establishment of A Scoring Model for The Differential Diagnosis of White Coat Hypertension and Sustained Hypertension

Cai

Yan

Chen

et al. 2020

Preprint

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Background: To establish a scoring model for the differential diagnosis of white coat hypertension (WCH) and sustained hypertension (SHT). Methods: 553 adults with elevated office blood pressure, normal renal function and no antihypertensive drugs were included in this study. 17 parameters, such as gender and age, were obtained by questionnaire investigation and biochemical index detection. WCH and SHT were distinguished by 24-hour ambulatory blood pressure monitoring. Participants were randomly divided into a training set (445 cases) and a verification set (108 cases). In the training set, the above parameters were screened by LASSO regression and univariate logistic regression analysis, then, the scoring model was constructed through multivariate logistic regression analysis. ROC curve and calibration curve were used to discuss the discrimination and calibration of this scoring model respectivelyResults: 6 parameters were finally selected, namely isolated systolic hypertension, systolic blood pressure, diastolic blood pressure, triglyceride, serum creatinine, and cardiovascular and cerebrovascular diseases. Multivariate logistic regression was used to establish the scoring model. The R2 and AUC of the scoring model in the training set were 0.163 and 0.705, respectively. In the verification set, the R2 of the scoring model was 0.206, and AUC was 0.718. The calibration test results showed that the scoring model had good stability in both training set and verification set (MSE=0. 001, MAE=0. 014; MSE=0. 001, MAE=0. 025, respectively).Conclusion: A stable scoring model for distinguishing WCH can be established, which can assist clinical medical workers to identify WCH at the first diagnosis.

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Nomogram: An analogue tool to deliver digital knowledge

Cited by 304 publications

References 1 publication

An Integrated Model Based on a Six-Gene Signature Predicts Overall Survival in Patients With Hepatocellular Carcinoma

An Integrated Model Based on a Six-Gene Signature Predicts Overall Survival in Patients With Hepatocellular Carcinoma

Comprehensive analysis of alternative splicing and transcriptome related to the prognosis of cervical squamous cell cancer

How to Identify White Coat Hypertension at The First Diagnosis: Establishment of A Scoring Model for The Differential Diagnosis of White Coat Hypertension and Sustained Hypertension

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