Prognostic Dynamic Nomogram Integrated with Inflammation-Based Factors for Non-Small Cell Lung Cancer Patients with Chronic Hepatitis B Viral Infection

Hepatitis B virus infection: An insight into the clinical connection and molecular interaction between hepatitis B virus and host extrahepatic cancer risk

The evidence for chronic hepatitis B virus (HBV) infection and hepatocellular carcinoma (HCC) occurrence is well established. The hepatocyte epithelium carcinogenesis caused by HBV has been investigated and reviewed in depth. Nevertheless, recent findings from preclinical and observational studies suggested that chronic HBV infection is equally important in extrahepatic cancer occurrence and survival, specifically gastrointestinal system-derived cancers. Immune microenvironment changes (immune-suppressive cytokine infiltration), epigenetic modification (N6-methyladenosine), molecular signaling pathways (PI3K-Akt and Wnt), and serum biomarkers such as hepatitis B virus X (HBx) protein are potential underlying mechanisms in chronic HBV infection-induced extrahepatic cancers. This narrative review aimed to comprehensively summarize the most recent advances in evaluating the association between chronic HBV infection and extrahepatic cancer risk and explore the potential underlying molecular mechanisms in the carcinogenesis induction of extrahepatic cancers in chronic HBV conditions.

An Externally Validated Dynamic Nomogram for Predicting Unfavorable Prognosis in Patients With Aneurysmal Subarachnoid Hemorrhage

Background: Aneurysmal subarachnoid hemorrhage (aSAH) leads to severe disability and functional dependence. However, no reliable method exists to predict the clinical prognosis after aSAH. Thus, this study aimed to develop a web-based dynamic nomogram to precisely evaluate the risk of poor outcomes in patients with aSAH.

Methods: Clinical patient data were retrospectively analyzed at two medical centers. One center with 126 patients was used to develop the model. Least absolute shrinkage and selection operator (LASSO) analysis was used to select the optimal variables. Multivariable logistic regression was applied to identify independent prognostic factors and construct a nomogram based on the selected variables. The C-index and Hosmer–Lemeshow p-value and Brier score was used to reflect the discrimination and calibration capacities of the model. Receiver operating characteristic curve and calibration curve (1,000 bootstrap resamples) were generated for internal validation, while another center with 84 patients was used to validate the model externally. Decision curve analysis (DCA) and clinical impact curves (CICs) were used to evaluate the clinical usefulness of the nomogram.

Results: Unfavorable prognosis was observed in 46 (37%) patients in the training cohort and 24 (29%) patients in the external validation cohort. The independent prognostic factors of the nomogram, including neutrophil-to-lymphocyte ratio (NLR) (p = 0.005), World Federation of Neurosurgical Societies (WFNS) grade (p = 0.002), and delayed cerebral ischemia (DCI) (p = 0.0003), were identified using LASSO and multivariable logistic regression. A dynamic nomogram (https://hu-ping.shinyapps.io/DynNomapp/) was developed. The nomogram model demonstrated excellent discrimination, with a bias-corrected C-index of 0.85, and calibration capacities (Hosmer–Lemeshow p-value, 0.412; Brier score, 0.12) in the training cohort. Application of the model to the external validation cohort yielded a C-index of 0.84 and a Brier score of 0.13. Both DCA and CIC showed a superior overall net benefit over the entire range of threshold probabilities.

Conclusion: This study identified that NLR on admission, WFNS grade, and DCI independently predicted unfavorable prognosis in patients with aSAH. These factors were used to develop a web-based dynamic nomogram application to calculate the precise probability of a poor patient outcome. This tool will benefit personalized treatment and patient management and help neurosurgeons make better clinical decisions.

Development of an Early Warning Model for Predicting the Death Risk of Coronavirus Disease 2019 Based on Data Immediately Available on Admission

Introduction: COVID-19 has overloaded worldwide medical facilities, leaving some potentially high-risk patients trapped in outpatient clinics without sufficient treatment. However, there is still a lack of a simple and effective tool to identify these patients early.

Methods: A retrospective cohort study was conducted to develop an early warning model for predicting the death risk of COVID-19. Seventy-five percent of the cases were used to construct the prediction model, and the remaining 25% were used to verify the prediction model based on data immediately available on admission.

Results: From March 1, 2020, to April 16, 2020, a total of 4,711 COVID-19 patients were included in our study. The average age was 63.37 ± 16.70 years, of which 1,148 (24.37%) died. Finally, age, SpO2, body temperature (T), and mean arterial pressure (MAP) were selected for constructing the model by univariate analysis, multivariate analysis, and a review of the literature. We used five common methods for constructing the model and finally found that the full model had the best specificity and higher accuracy. The area under the ROC curve (AUC), specificity, sensitivity, and accuracy of full model in train cohort were, respectively, 0.798 (0.779, 0.816), 0.804, 0.656, and 0.768, and in the validation cohort were, respectively, 0.783 (0.751, 0.815), 0.800, 0.616, and 0.755. Visualization tools of the prediction model included a nomogram and an online dynamic nomogram (https://wanghai.shinyapps.io/dynnomapp/).

Conclusion: We developed a prediction model that might aid in the early identification of COVID-19 patients with a high probability of mortality on admission. However, further research is required to determine whether this tool can be applied for outpatient or home-based COVID-19 patients.

A multi-parametric prognostic model based on clinical features and serological markers predicts overall survival in non-small cell lung cancer patients with chronic hepatitis B viral infection

BACKGROUND

To establish and validate a multi-parametric prognostic model based on clinical features and serological markers to estimate the overall survival (OS) in non-small cell lung cancer (NSCLC) patients with chronic hepatitis B viral (HBV) infection.

METHODS

The prognostic model was established by using Lasso regression analysis in the training cohort. The incremental predictive value of the model compared to traditional TNM staging and clinical treatment for individualized survival was evaluated by the concordance index (C-index), time-dependent ROC (tdROC) curve, and decision curve analysis (DCA). A prognostic model risk score based nomogram for OS was built by combining TNM staging and clinical treatment. Patients were divided into high-risk and low-risk subgroups according to the model risk score. The difference in survival between subgroups was analyzed using Kaplan-Meier survival analysis, and correlations between the prognostic model, TNM staging, and clinical treatment were analysed.

RESULTS

The C-index of the model for OS is 0.769 in the training cohorts and 0.676 in the validation cohorts, respectively, which is higher than that of TNM staging and clinical treatment. The tdROC curve and DCA show the model have good predictive accuracy and discriminatory power compare to the TNM staging and clinical treatment. The prognostic model risk score based nomogram show some net clinical benefit. According to the model risk score, patients are divided into low-risk and high-risk subgroups. The difference in OS rates is significant in the subgroups. Furthermore, the model show a positive correlation with TNM staging and clinical treatment.

CONCLUSIONS

The prognostic model showed good performance compared to traditional TNM staging and clinical treatment for estimating the OS in NSCLC (HBV+) patients.

Serum hepatitis B viral (HBV) DNA is a predictive biomarker for survival in non-small cell lung cancer patients with chronic HBV infection

Purpose: To study the association between pretreatment serum hepatitis B viral (HBV) DNA copy numbers and clinical outcome of non-small cell lung cancer (NSCLC) patients with chronic HBV infection.

Patients and methods: We retrospectively evaluated the medical records of NSCLC HBV (+) patients between January 2008 and December 2010. The HBV DNA copy numbers and other prognostic factors including albumin (ALB), C-reactive protein (CRP), platelet, neutrophil–lymphocyte ratio (NLR), platelet–lymphocyte ratio (PLR), and Glasgow Prognostic Score (GPS) were obtained before any antitumor treatment. Kaplan–Meier curves and the log-rank test were used to calculate prognostic significance. A multivariable Cox proportional hazard regression was modeled to analyze the independent prognostic factors for NSCLC HBV (+) patients. All independent prognostic factors in the Cox multivariable analysis were used to build a nomogram. The predictive accuracy of HBV DNA, TNM stage and nomogram was evaluated with the concordance index (C-index) and time-dependent receiver operating characteristics (ROC) curves, and simultaneously compared with traditional TNM staging system respectively.

Results: A total of 188 patients were recruited in this study; the median age was 56 years, and the median overall survival (OS) was 34 months. Cox multivariate analysis results showed independent factors for OS including TNM stage (P=0.028), treatment (P=0.002), HBV DNA (P<0.001), and GPS (P=0.026). The nomogram model for survival was built based on four prognostic factors. The C-index for HBV DNA was 0.67, 0.69 for TNM stage, and 0.76 for the nomogram model. There was no statistical difference between HBV DNA and TNM stage (P=0.48). However, the C-index values of nomogram model were statistically higher both than HBV DNA (0.76 vs 0.67, P<0.001), and TNM stage (0.76 vs 0.69, P<0.001).

Conclusion: Pretreatment serum HBV DNA copy numbers can act as a prognostic marker of survival for NSCLC patients with chronic HBV infection.