A sample model established by S-index predicting overall survival after curative resection of primary hepatocellular carcinoma

The role of carotid artery stenosis in predicting stroke after coronary artery bypass grafting in a Chinese cohort study

Current guidelines give priority to surgical treatment of carotid artery stenosis (CAS) before coronary artery bypass grafting (CABG), especially in symptomatic patients. Carotid artery stenting is an alternative treatment for narrowing of the carotid arteries. This study sought to demonstrate the role of severe CAS in predicting stroke after CABG and assess the efficacy of carotid artery stenting in preventing postoperative stroke in a Chinese cohort. From 2015 to 2021, 1799 consecutive patients undergoing isolated CABG surgery were retrospectively recruited in a Chinese cohort. The predictive value of severe CAS in postoperative stroke and carotid stenting in preventing postoperative stroke was statistically analyzed. The incidence of postoperative stroke was 1.67%. The incidence of CAS with stenosis ≥ 50% and ≥ 70% was 19.2% and 6.9%. After propensity matching, the incidence of stroke was 8.0% in the severe CAS group and 0% in the non-severe CAS group. We successfully established an optimal predictive nomogram for predicting severe CAS in patients undergoing CABG. Carotid artery stenting was found ineffective in preventing postoperative stroke. The present study provides the incidence of CAS and postoperative stroke in a Chinese cohort, identifies severe CAS as an independent risk factor for postoperative stroke after CABG, constructs a nomogram predicting the incidence of severe CAS, and evaluates the effectiveness of carotid artery stenting in preventing postoperative stroke after CABG.

Development and Validation of a Website to Guide Decision-Making for Disorders of Consciousness

Background

This study aimed to develop and validate a nomogram and present it on a website to be used to predict the overall survival at 16, 32, and 48 months in patients with prolonged disorder of consciousness (pDOC).

Methods

We retrospectively analyzed the data of 381 patients with pDOC at two centers. The data were randomly divided into training and validation sets using a ratio of 6:4. On the training set, Cox proportional hazard analyses were used to identify the predictive variables. In the training set, two models were screened by COX regression analysis, and based on clinical evidence, model 2 was eventually selected in the nomogram after comparing the receiver operating characteristic (ROC) of the two models. In the training and validation sets, ROC curves, calibration curves, and decision curve analysis (DCA) curves were utilized to measure discrimination, calibration, and clinical efficacy, respectively.

Results

The final model included age, Glasgow coma scale (GCS) score, serum albumin level, and computed tomography (CT) midline shift, all of which had a significant effect on survival after DOCs. For the 16-, 32-, and 48-month survival on the training set, the model had good discriminative power, with areas under the curve (AUCs) of 0.791, 0.760, and 0.886, respectively. For the validation set, the AUCs for the 16-, 32-, and 48-month survival predictions were 0.806, 0.789, and 0.867, respectively. Model performance was good for both the training and validation sets according to calibration plots and DCA.

Conclusion

We developed an accurate, efficient nomogram, and a corresponding website based on four correlated factors to help clinicians improve their assessment of patient outcomes and help personalize the treatment process and clinical decisions.

A Powerful Nomogram Based on the Novel D-Index to Predict Prognosis After Surgical Resection of Hepatocellular Carcinoma

Purpose

Conventional staging and scoring systems such as the Tumor, Node, and Metastasis; Cancer of the Liver Italian Program; Barcelona Clinic Liver Cancer; and Okuda have failed to predict overall survival (OS) in patients with resected primary hepatocellular carcinoma. Thus, we aimed to establish a novel D-index and nomogram to improve prognostic accuracy.

Patients and Methods

We selected 396 patients who underwent liver resection between January 2007 and February 2015 at the First Affiliated Hospital of Wenzhou Medical University. These patients were randomly divided into the training and validation groups in a ratio of 7:3.

Results

We generated a nomogram using five independent risk factors, including the D-index (calculated by total bilirubin × tumor size/the ratio of fat-to-muscle area ^0.5) in the training set. The predictive performance of the nomogram was similar in both the training and validation cohorts according to the concordance index. The nomogram demonstrated the strongest predictive power for 1-year, 3-year, and 5-year OS, with the area under the receiving operating characteristic curve being 0.8486, 0.7785, and 0.752, respectively. The calibration curves exhibited stable capabilities in both cohorts. The stratification of the Kaplan-Meier curve was significant (P < 0.001).

Conclusion

The associated nomogram of the D-index demonstrated a powerful and accurate predictive ability for OS in patients with primary hepatocellular carcinoma.

Model to Predict Overall Survival in Patients With Hepatocellular Carcinoma After Curative Hepatectomy

Background

The prognosis of patients with hepatocellular carcinoma (HCC) remains difficult to accurately predict. The purpose of this study was to establish a prognostic model for HCC based on a novel scoring system.

Methods

Five hundred and sixty patients who underwent a curative hepatectomy for treatment of HCC at our hospital between January 2007 and January 2014 were included in this study. Univariate and multivariate analyses were used to screen for prognostic risk factors. The nomogram construction was based on Cox proportional hazard regression models, and the development of the new scoring model was analyzed using receiver operating characteristic (ROC) curve analysis and then compared with other clinical indexes. The novel scoring system was then validated with an external dataset from a different medical institution.

Results

Multivariate analysis showed that tumor size, portal vein tumor thrombus (PVTT), invasion of adjacent tissues, microvascular invasion, and levels of fibrinogen and total bilirubin were independent prognostic factors. The new scoring model had higher area under the curve (AUC) values compared to other systems, and the C-index of the nomogram was highly consistent for evaluating the survival of HCC patients in the validation and training datasets, as well as the external validation dataset.

Conclusions

Based on serum markers and other clinical indicators, a precise model to predict the prognosis of patients with HCC was developed. This novel scoring system can be an effective tool for both surgeons and patients.

Prognostic Nomograms for Patients with Hepatocellular Carcinoma After Curative Hepatectomy, with a Focus on Recurrence Timing and Post-Recurrence Management

Background

Prognoses of patients with hepatocellular carcinoma (HCC) after curative hepatectomy remain unsatisfactory because of the high incidence of postoperative recurrence. Published predictive systems focus on pre-resection oncological characteristics, ignoring post-recurrence factors.

Purpose

This study aimed to develop prognostic nomograms for 3- and 5-year overall survival (OS) of patients with HCC after curative hepatectomy, focusing on potentially influential post-recurrence factors.

Patients and Methods

Clinicopathological and postoperative follow-up data were extracted from 494 patients with HCC who underwent curative hepatectomy between January 2012 and June 2019. Early recurrence (ER) and late recurrence (LR) were defined as recurrence at ≤2 and >2 years, respectively, after curative hepatectomy. Nomograms for the prediction of 3- and 5-year OS were established based on multivariate analysis. The areas under time-dependent receiver operating characteristic curves (AUCs) for the nomograms were calculated independently to verify predictive accuracy. The nomograms were internally validated based on 2000 bootstrap resampling of 75% of the original data.

Results

In total, 494 patients with HCC who underwent curative hepatectomy met the eligibility criteria. Cox proportional hazard regression analysis identified factors potentially influencing 3- and 5-year OS. Multivariate analysis indicated that patient age, Hong Kong Liver Cancer stage, γ-glutamyl transferase (γ-GGT) level, METAVIR inflammation activity grade, ER and post-recurrence treatment modality were influencing factors for 3-year OS (AUC, 0.891; 95% CI, 0.8364-0.9447). γ-GGT > 60 U/L, hepatectomy extent, LR and post-recurrence treatment modality were influencing factors for 5-year OS (AUC, 0.864; 95% CI, 0.8041-0.9237). Calibration plots showed satisfactory concordance between the predicted and actual observation cohorts.

Conclusion

We propose new prognostic nomograms for OS prediction with a focus on the differentiation of recurrence timing and post-recurrence management. These nomograms overcome the shortcomings of previous predictive nomograms and significantly improve predictive accuracy.

Development and validation of a risk score for predicting mortality after resection of primary hepatocellular carcinoma

Background: Primary hepatocellular carcinoma (PHCC) has a poor prognosis and high short-term mortality rate, even after resection. Thus, early diagnosis in PHCC cases can help improve quality of life via personalized management strategies.

Results: The risk score system (RSS) were classified as low risk (<5 points), medium risk (5–10 points), or high risk (>10 points). The areas under the receiver operating characteristic curves were 0.80 in the training cohort and 0.69 in the validation cohort, which indicated satisfactory prognostic performance. The Hosmer-Lemeshow goodness of fit test (P>0.05) revealed consistent performance in both groups. The concordance index (C-index: 0.663, 95% CI: 0.618–0.708) revealed excellent discrimination and good calibration in the validation cohort.

Conclusions: This simple RSS, which is based on clinical and laboratory data from patients undergoing resection of PHCC, might allow clinicians and medical staff to better manage PHCC.

Materials and Methods: A total of 672 PHCC cases were retrospectively obtained from the First Affiliated Hospital of Wenzhou Medical University between January 2007 and February 2015. Cox proportional hazard models were used to identify independent predictors of mortality. Kaplan-Meier curves and the log-rank test were used to examine the relationships between the prognostic factors and overall mortality.

A New Nomogram Based on Early Postoperative NLR for Predicting Infectious Complications After Gastrectomy

Purpose

Our study aimed to construct a visible model to evaluate the risk of infectious complications after gastrectomy.

Methods

The clinical data of 856 patients who underwent gastrectomy were used to retrieve medical records. Univariate and multivariate analyses were performed to correlate early postoperative NLR and operative variables with postoperative complications, and the construction of the nomogram was based on logistic regression. The concordance index and receiver operating characteristic curves were used to evaluate the model performance.

Results

The postoperative infectious and noninfectious complication rates after gastrectomy were 18.5% (158/856 cases) and 12.3% (105/856 cases) respectively. Postoperative NLR (within 24 h) independently predicted the development of postoperative infectious complication. Multivariate analysis revealed that age, diabetes, body mass index (BMI), intraoperative blood transfusion and postoperative NLR were independent risk factors. The nomogram model showed a good performance in terms of predicting infectious complications after gastrectomy (concordance index=0.718).

Conclusion

Age, diabetes, BMI, intraoperative blood transfusion and postoperative NLR were independent risk factors of postoperative infectious complications after gastrectomy, and a novel nomogram based on these results can be used to predict postoperative infection and has the advantages of simple application and easy access.

Use of a novel index, the A-index, and its associated nomogram to predict overall survival rates after resection of primary hepatocellular carcinoma

BACKGROUND

Several international staging or scoring systems don't accurately predict overall survival (OS) after resection of primary hepatocellular carcinoma (PHCC). Therefore, we attempted to overcome this limitation by constructing the A-index and its associated nomogram.

METHODS

We selected 672 patients who underwent curative resection of PHCC between January 2007 and February 2015 at the first affiliated hospital of the Wenzhou medical university. These subjects were randomly divided into the training (n = 470) and the validation group (n = 202) according to the ratio of 7:3.

RESULTS

We prepared the nomogram using eight independent risk factors including the A-index (calculated by 100 × aspartate transaminase /albumin /albumin) in the training cohort. The concordance index (C-index) of the nomogram for both training and validation set was similar in indicating the OS rate. The nomogram showed the strongest predictive power for the 1-year, 3-year, and 5-year OS, with the area under the ROC curve being 0.8182, 0.7892, and 0.7669, respectively. Correction curves showed consistent performance for both groups, stratification of the Kaplan-Meier curve was significant (P < 0.001), and decision curve analysis (DCA) showed the superiority of nomograms considering clinical effects.

CONCLUSIONS

The predictive power of the nomogram integrating the A-index for OS was optimal.

Development and validation of a prognostic nomogram for HIV/AIDS patients who underwent antiretroviral therapy: Data from a China population-based cohort

BACKGROUND

Accurate forecast of the death risk is crucial to the administration of people living with HIV/AIDS (PLHIV). We aimed to establish and validate an effective prognosis nomogram in PLHIV receiving antiretroviral therapy (ART).

METHODS

All the data were obtained from 2006 to 2018 in the Wenzhou area from China AIDS prevention and control information system. Factors included in the nomogram were determined by univariate and multiple Cox proportional hazard analysis based on the training set. The receiver operating characteristic (ROC) and calibration curves were used to assess its predictive accuracy and discriminative ability. Its clinical utility was also evaluated using decision curve analysis (DCA), X-tile analysis and Kaplan-Meier curve, respectively in an independent validation set.

FINDINGS

Independent prognostic factors including haemoglobin, viral load and CD4+ T-cell count were determined and contained in the nomogram. Good agreement between the prediction by nomogram and actual observation could be detected in the calibration curve for mortality, especially in the first year. In the training cohort, AUC (95% CI) and C-index (95% CI) were 0.93 (0.90, 0.96) and 0.90 (0.85, 0.96), respectively. In the validation set, the nomogram still revealed excellent discriminations [AUC (95% CI): 0.95 (0.91, 1.00)] and good calibration [C-index (95% CI): 0.92 (0.82-1.00)]. Moreover, DCA also demonstrated that the nomogram was clinical beneficial. Additionally, participants could be classified into three distinct (low, middle and high) risk groups by the nomogram.

INTERPRETATION

The nomogram presents accurate and favourable prognostic prediction for PLHIV who underwent ART.

FUNDING

This work was supported by Zhejiang Basic Public Welfare Research Project (LGF19H260011), Wenzhou Basic Public Welfare Research Project (Y20180201), the Initial Scientific Research Fund (KYQD170301), the Major Project of the Eye Hospital Wenzhou the Major Project of the Eye Hospital Wenzhou Medical University (YNZD201602). Part of this work was also funded by National Natural Science Foundation of China (81670777) and Science and Technology Innovation Activity Plan and New Talents Plan for College Students in Zhejiang Province (2019R413073). The funders had no roles in study design, data collection, data analysis, interpretation and writing of the report.