[Factors affecting survival prognosis of advanced gastric cancer and establishment of a nomogram predictive model]

[Establishment and validation of a predictive nomogram for liver fibrosis in patients with Wilson disease and abnormal lipid metabolism]

OBJECTIVE

To establish and validate predictive nomogram for liver fibrosis in patients with Wilson disease (WD) showing abnormal lipid metabolism.

METHODS

We retrospectively collected the clinical data of 500 patients with WD showing abnormalities in lipid metabolism, who were treated in the Department of Encephalopathy of the First Affiliated Hospital of Anhui University of Chinese Medicine from December, 2018 to December, 2021 and divided into modeling group and validation group. The independent risk factors of liver fibrosis in these patients were screened using LASSO regression and multivariate logistic regression analysis for establishment of the predictive nomogram. The area under the curve (AUC), calibration curve and decision curve of the receiver-operating characteristic curve (ROC) were used for internal and external verification of the nomogram in the modeling and validation group and evaluating the differentiation, calibration and clinical practicability of the model.

RESULTS

Triglycerides (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C) and apolipoprotein B (Apo-B) were independent risk factors for the development of liver fibrosis in patients with WD and abnormal lipid metabolism (P < 0.05). The predictive nomogram showed good discrimination, calibration and clinical utility in both the modeling and validation groups.

CONCLUSION

The established predictive nomogram in this study has a high accuracy for early identification and risk prediction of liver fibrosis in patients with WD having abnormal lipid metabolism.

Developing and validating nomograms for predicting the survival in patients with clinical local-advanced gastric cancer

Background

Many patients with gastric cancer are at a locally advanced stage during initial diagnosis. TNM staging is inaccurate in predicting survival. This study aims to develop two more accurate survival prediction models for patients with locally advanced gastric cancer (LAGC) and guide clinical decision-making.

Methods

We recruited 2794 patients diagnosed with LAGC (2010–2015) from the Surveillance, Epidemiology, and End Results (SEER) database and performed external validation using data from 115 patients with LAGC at Yantai Affiliated Hospital of Binzhou Medical University. Univariate and multifactorial survival analyses were screened for meaningful independent prognostic factors and were used to build survival prediction models. Concordance index (C-index), receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were evaluated for nomograms. Finally, the differences and relationships of survival and prognosis between the three different risk groups were described using the Kaplan–Meier method.

Results

Cox proportional risk regression model analysis identified independent prognostic factors for patients with LAGC, and variables associated with overall survival (OS) included age, race, marital status, T-stage, N-stage, grade, histologic type, surgery, and chemotherapy. Variables associated with cancer-specific survival (CSS) included age, race, T-stage, N-stage, grade, histological type, surgery, and chemotherapy. In the training cohort, C-index of nomogram for predicting OS was 0.722 (95% confidence interval [95% CI]: 0.708–0.736] and CSS was 0.728 (95% CI: 0.713–0.743). In the external validation cohort, C-index of nomogram for predicted OS was 0.728 (95% CI:0.672–0.784) and CSS was 0.727 (95% CI:0.668–0.786). The calibration curves showed good concordance between the predicted and actual results. C-index, ROC, and DCA results indicated that our nomograms could more accurately predict OS and CSS than TNM staging and had a higher clinical benefit. Finally, to facilitate clinical use, we set up two web servers based on nomograms.

Conclusion

The nomograms established in this study have better risk assessment ability than the clinical staging system, which can help clinicians predict the individual survival of LAGC patients more accurately and thus develop appropriate treatment strategies.