Development and Validation of Nomograms Predicting the Overall and the Cancer-Specific Survival in Endometrial Cancer Patients

Li, Xingchen; Dong, Yangyang; Cheng, Yongjing; Zhou, Jingyi; Wu, Xiangsong; Shen, Boqiang; Yang, Xiao; Wang, Zhiqi; Li, Xiaoping; Wang, Jianliu

doi:10.21203/rs.3.rs-68463/v1

Cited by 3 publications

(2 citation statements)

References 16 publications

(17 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The nomogram as a visualization tool has been widely used in clinical prognosis research on critical patient and cancer patient survival studies (12)(13)(14). The primary aim of the present study is to develop a novel prediction nomogram for the 28-day mortality risk in SIC patients.…”

Section: Introductionmentioning

confidence: 99%

Development of a Nomogram to Predict 28-Day Mortality of Patients With Sepsis-Induced Coagulopathy: An Analysis of the MIMIC-III Database

Zhang

Hong

et al. 2021

Front. Med.

View full text Add to dashboard Cite

Background: Sepsis-induced coagulopathy (SIC) is a common cause for inducing poor prognosis of critically ill patients in intensive care unit (ICU). However, currently there are no tools specifically designed for assessing short-term mortality in SIC patients. This study aimed to develop a practical nomogram to predict the risk of 28-day mortality in SIC patients.Methods: In this retrospective cohort study, we extracted patients from the Medical Information Mart for Intensive Care III (MIMIC-III) database. Sepsis was defined based on Sepsis 3.0 criteria and SIC based on Toshiaki Iba's criteria. Kaplan–Meier curves were plotted to compare the short survival time between SIC and non-SIC patients. Afterward, only SIC cohort was randomly divided into training or validation set. We employed univariate logistic regression and stepwise multivariate analysis to select predictive features. The proposed nomogram was developed based on multivariate logistic regression model, and the discrimination and calibration were verified by internal validation. We then compared model discrimination with other traditional severity scores and machine learning models.Results: 9432 sepsis patients in MIMIC III were enrolled, in which 3280 (34.8%) patients were diagnosed as SIC during the first ICU admission. SIC was independently associated with the 7- and 28-day mortality of ICU patients. K–M curve indicated a significant difference in 7-day (Log-Rank: P < 0.001 and P = 0.017) and 28-day survival (Log-Rank: P < 0.001 and P < 0.001) between SIC and non-SIC groups whether the propensity score match (PSM) was balanced or not. For nomogram development, a total of thirteen variables of 3,280 SIC patients were enrolled. When predicted the risk of 28-day mortality, the nomogram performed a good discrimination in training and validation sets (AUROC: 0.78 and 0.81). The AUROC values were 0.80, 0.81, 0.71, 0.70, 0.74, and 0.60 for random forest, support vector machine, sequential organ failure assessment (SOFA) score, logistic organ dysfunction score (LODS), simplified acute physiology II score (SAPS II) and SIC score, respectively, in validation set. And the nomogram calibration slope was 0.91, the Brier value was 0.15. As presented by the decision curve analyses, the nomogram always obtained more net benefit when compared with other severity scores.Conclusions: SIC is independently related to the short-term mortality of ICU patients. The nomogram achieved an optimal prediction of 28-day mortality in SIC patient, which can lead to a better prognostics assessment. However, the discriminative ability of the nomogram requires validation in external cohorts to further improve generalizability.

show abstract

Section: Introductionmentioning

confidence: 99%

Development of a Nomogram to Predict 28-Day Mortality of Patients With Sepsis-Induced Coagulopathy: An Analysis of the MIMIC-III Database

Zhang

Hong

et al. 2021

Front. Med.

View full text Add to dashboard Cite

show abstract

“…Nomograms, which can provide evidence-based, individualized, and highly accurate risk estimation, have been widely used for diferent types of cancer [11][12][13]. A nomogram is created by applying meaningful variables based on the results of regression analyses investigating potential prognostic factors, which contributed to better risk stratifcation and clinical decision-making [14][15][16]. Te above makes the nomogram quite practical and easy to popularize.…”

Section: Introductionmentioning

confidence: 99%

Development and Validation of a Prognostic Nomogram for Lung Adenocarcinoma: A Population-Based Study

Xie

Chen

Deng

et al. 2022

Journal of Healthcare Engineering

View full text Add to dashboard Cite

Purpose. To establish an effective and accurate prognostic nomogram for lung adenocarcinoma (LUAD). Patients and Methods. 62,355 LUAD patients from 1975 to 2016 enrolled in the Surveillance, Epidemiology, and End Results (SEER) database were randomly and equally divided into the training cohort (n = 31,179) and the validation cohort (n = 31,176). Univariate and multivariate Cox regression analyses screened the predictive effects of each variable on survival. The concordance index (C-index), calibration curves, receiver operating characteristic (ROC) curve, and area under the ROC curve (AUC) were used to examine and validate the predictive accuracy of the nomogram. Kaplan–Meier curves were used to estimate overall survival (OS). Results. 10 prognostic factors associated with OS were identified, including age, sex, race, marital status, American Joint Committee on Cancer (AJCC) TNM stage, tumor size, grade, and primary site. A nomogram was established based on these results. C-indexes of the nomogram model reached 0.777 (95% confidence interval (CI), 0.773 to 0.781) and 0.779 (95% CI, 0.775 to 0.783) in the training and validation cohorts, respectively. The calibration curves were well-fitted for both cohorts. The AUC for the 3- and 5-year OS presented great prognostic accuracy in the training cohort (AUC = 0.832 and 0.827, respectively) and validation cohort (AUC = 0.835 and 0.828, respectively). The Kaplan–Meier curves presented significant differences in OS among the groups. Conclusion. The nomogram allows accurate and comprehensive prognostic prediction for patients with LUAD.

show abstract

Prognostic Model of Primary Spinal Tumor

吴¹

2021

ACM

View full text Add to dashboard Cite

Development and Validation of Nomograms Predicting the Overall and the Cancer-Specific Survival in Endometrial Cancer Patients

Cited by 3 publications

References 16 publications

Development of a Nomogram to Predict 28-Day Mortality of Patients With Sepsis-Induced Coagulopathy: An Analysis of the MIMIC-III Database

Development of a Nomogram to Predict 28-Day Mortality of Patients With Sepsis-Induced Coagulopathy: An Analysis of the MIMIC-III Database

Development and Validation of a Prognostic Nomogram for Lung Adenocarcinoma: A Population-Based Study

Prognostic Model of Primary Spinal Tumor

Contact Info

Product

Resources

About