Prognostic nomograms for predicting cause-specific survival and overall survival of stage I–III colon cancer patients: a large population-based study

Zhou, Zheng; Mo, Shaobo; Dai, Weixing; Xiang, Wenqiang; Han, Lingyu; Li, Qingguo; Wang, Renjie; Liu, Lu; Zhang, Long; Cai, Sanjun; Cai, Guoxiang

doi:10.1186/s12935-019-1079-4

Cited by 37 publications

(35 citation statements)

References 26 publications

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“…Several studies have reported better performance of nomogram model than conventional staging systems and proposed it as a promising tool for prognosis evaluation [24][25][26]. The nomograms developed in our study also presented better prediction capacity than AJCC 7th staging system.…”

Section: Discussionsupporting

confidence: 50%

Development and Validation of Nomograms for Predicting Overall Survival and Cancer-Specific Survival in Patients with Ovarian Clear Cell Carcinoma

Chen

Wang

Lang

2020

Preprint

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Abstract Background Ovarian clear cell carcinoma (OCCC) is a rare histologic type of ovarian cancer. There is a lack of useful prognostic predictive tool for OCCC in clinical work. This study aimed to construct and validate nomograms for predicting the overall survival (OS) and cancer-specific survival (CSS) in patients with OCCC. Methods Data of patients with primary diagnosed OCCC in the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2016 was extracted. Prognostic factors were evaluated with LASSO COX regression and multivariate Cox regression analysis, which were applied to construct nomograms. The performance of the nomogram models was assessed by concordance index (C-index), calibration plots, decision curve analysis (DCA) and risk subgroup classification. The Kaplan-Meier curves were plotted to compare survival outcomes between subgroups. Results A total of 1541 patients from SEER registries were randomly divided into a training cohort (n = 1079) and a validation cohort (n = 462). Age, laterality, stage, lymph node (LN) dissected, organ metastasis and chemotherapy were independently and significantly associated with OS, while laterality, stage, LN dissected, organ metastasis and chemotherapy were independent risk factors for CSS. Nomograms were developed for prediction of 3- and 5‐year OS and CSS. The C-indexes for OS and CSS were 0.802[95% confidence interval (CI) 0.773–0.831] and 0.802 (0.769–0.835), respectively, in the training cohort, while 0.746 (0.691–0.801) and 0.770 (0.721–0.819), respectively, in the validation cohort. Calibration plots illustrated favorable consistency between the nomogram predicted and actual survival. C-index and DCA curves also indicated better performance of nomogram than the AJCC staging system. Significant differences were observed in survival curves of different risk subgroups. Conclusions We constructed predictive nomograms and a risk classification system to evaluate the OS and CSS of OCCC patients. They were validated to be of satisfactory predictive value, and could aid in future clinical practice.

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Section: Discussionsupporting

confidence: 50%

Development and Validation of Nomograms for Predicting Overall Survival and Cancer-Specific Survival in Patients with Ovarian Clear Cell Carcinoma

Chen

Wang

Lang

2020

Preprint

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“…In view of the WBC of patients with viral infection is usually not high[21,22], although it was unassociated in multivariate analysis, we still kept it in the process of establishing the diagnostic nomogram.Finally, the nomogram incorporates 4 items of the imaging features, epidemiological contact history and WBC count status. Nomogram is a visualization of regression analysis, which is more and more widely used in clinical disease diagnosis, prognosis evaluation and efficacy evaluation[23][24][25][26]. Our results show that the nomogram based on imaging features has good sensitivity and specificity in the diagnosis of COVID-19 pneumonia.…”

mentioning

confidence: 76%

Development and Validation of a Diagnostic Nomogram to Predict COVID-19 Pneumonia

Wang

Weng

et al. 2020

Preprint

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Background The COVID-19 virus is an emerging virus associated with severe respiratory illness first detected in December, 2019, and rapidly spread worldwide. The aim of this study was to establish an effective diagnostic nomogram for suspected COVID-19 pneumonia patients. Methods We used the LASSO aggression and multivariable logistic regression methods to explore the predictive factors associated with COVID-19 pneumonia, and established the diagnostic nomogram for COVID-19 pneumonia using multivariable regression. This diagnostic nomogram was assessed by the internal and external validation data set. Further, we plotted decision curves and clinical impact curve to evaluate the clinical usefulness of this diagnostic nomogram. Results The predictive factors including the epidemiological history, wedge-shaped or fan-shaped lesion parallel to or near the pleura, bilateral lower lobes, ground glass opacities, crazy paving pattern and white blood cell (WBC) count were contained in the nomogram. In the primary cohort, the C-statistic for predicting the probability of the COVID-19 pneumonia was 0.967, even higher than the C-statistic (0.961) in initial viral nucleic acid nomogram which was established using the univariable regression. The C-statistic was 0.848 in external validation cohort. Good calibration curves were observed for the prediction probability in the internal validation and external validation cohort. The nomogram both performed well in terms of discrimination and calibration. Moreover, decision curve and clinical impact curve were also beneficial for COVID-19 pneumonia patients. Conclusion Our nomogram can be used to predict COVID-19 pneumonia accurately and favourably.

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“…Nomogram is a visualization of regression analysis, which is widely used in clinical disease diagnosis and prognosis evaluation [9][10][11][12]. In this study, we developed and validated a novel nomogram to predict the mortality risk among elderly patients with ARF.…”

Section: Discussionmentioning

confidence: 99%

Development and Validation of a Nomogram to Predict the Mortality Risk in Elderly Patients With ARF：A Retrospective Study in the Mimic-III Database

Chen¹,

Weng²,

Hou³

et al. 2020

Preprint

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Background: To explore the risk factors of prognosis in elderly patients with acute respiratory failure (ARF), and to develop a nomogram model to predict the short-term mortality risk of ARF.Methods: A total of 1432 patients were included in this study from MIMIC-III database. 759 patients were categorized into the training set and 673 patients were categorized into the validation set. Demographical, laboratory variables, SOFA score and APS-III score were collected within the first 24 h after the ICU admission. The univariate and multivariate logistic regression were used to identify risk factors from the training data set. A nomogram model was developed to predict the mortality risk of ARF patients within 30 days according to the risk factors.Results: Multivariate logistic regression analysis showed that the heart rate, respiratory rate, systolic pressure, SPO2, albumin and 24 h urine output were independent prognostic factors for 30-day mortality in ARF patients. A nomogram was established based on above independent prognostic factors. This nomogram had C-index of 0.741 (95% CI: 0.7058–0.7766), and the C-index was 0.687 (95%CI: 0.6458-0.7272) in the validation set. The calibration curves both in training and validation set were close to the ideal model. The SOFA had a C-index of 0.653 and the APS-III had a C-index of 0.707 in predicting 30-day mortality. The predictive performance of our nomogram is better than the SOFA score and APS-III score. Conclusions: Our nomogram performed better than APS-III and SOFA scores and should be useful as decision support on the prediction of mortality risk in elderly patients with ARF.

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Prognostic nomograms for predicting cause-specific survival and overall survival of stage I–III colon cancer patients: a large population-based study

Cited by 37 publications

References 26 publications

Development and Validation of Nomograms for Predicting Overall Survival and Cancer-Specific Survival in Patients with Ovarian Clear Cell Carcinoma

Development and Validation of Nomograms for Predicting Overall Survival and Cancer-Specific Survival in Patients with Ovarian Clear Cell Carcinoma

Development and Validation of a Diagnostic Nomogram to Predict COVID-19 Pneumonia

Development and Validation of a Nomogram to Predict the Mortality Risk in Elderly Patients With ARF：A Retrospective Study in the Mimic-III Database

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