scholarly journals Tailored Prediction Model of Survival after Liver Transplantation for Hepatocellular Carcinoma

2021 ◽  
Vol 10 (13) ◽  
pp. 2869
Author(s):  
Indah Jamtani ◽  
Kwang-Woong Lee ◽  
Yun-Hee Choi ◽  
Young-Rok Choi ◽  
Jeong-Moo Lee ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Liver Transplantation ◽  
Prediction Model ◽  
Risk Group ◽  
Significant Risk ◽  
Risk Scores ◽  
Accurate Information ◽  
Survival Prediction ◽  
Good Prediction ◽  
Recurrence Rates

This study aimed to create a tailored prediction model of hepatocellular carcinoma (HCC)-specific survival after transplantation based on pre-transplant parameters. Data collected from June 2006 to July 2018 were used as a derivation dataset and analyzed to create an HCC-specific survival prediction model by combining significant risk factors. Separate data were collected from January 2014 to June 2018 for validation. The prediction model was validated internally and externally. The data were divided into three groups based on risk scores derived from the hazard ratio. A combination of patient demographic, laboratory, radiological data, and tumor-specific characteristics that showed a good prediction of HCC-specific death at a specific time (t) were chosen. Internal and external validations with Uno’s C-index were 0.79 and 0.75 (95% confidence interval (CI) 0.65–0.86), respectively. The predicted survival after liver transplantation for HCC (SALT) at a time “t” was calculated using the formula: [1 − (HCC-specific death(t’))] × 100. The 5-year HCC-specific death and recurrence rates in the low-risk group were 2% and 5%; the intermediate-risk group was 12% and 14%, and in the high-risk group were 71% and 82%. Our HCC-specific survival predictor named “SALT calculator” could provide accurate information about expected survival tailored for patients undergoing transplantation for HCC.

scholarly journals Long-Term Outcomes of Hepatic Resection versus Living Donor Liver Transplantation for Hepatocellular Carcinoma: A Propensity Score-Matching Study

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Toshimi Kaido ◽  
Satoshi Morita ◽  
Sachiko Tanaka ◽  
Kohei Ogawa ◽  
Akira Mori ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Liver Transplantation ◽  
Propensity Score ◽  
Hepatic Resection ◽  
Propensity Score Matching ◽  
Odds Ratio ◽  
Living Donor ◽  
Survival Rates ◽  
Controlled Study ◽  
Recurrence Rates

Hepatic resection (HR) and liver transplantation (LT) are surgical treatment options for hepatocellular carcinoma (HCC). However, it is clinically impossible to perform a randomized, controlled study to determine the usefulness of these treatments. The present study compared survival rates and recurrence rates of HR versus living donor LT (LDLT) for HCC by using the propensity score method. Between January 1999 and August 2012, 936 patients (732 HR, 204 LDLT) underwent surgical therapy for HCC in our center. Using the propensity score matching, 80 well-balanced patients were defined. The 1- and 5-year overall survival rates were 90% and 53% in the HR group and 82% and 63% in the LT group, respectively. They were not significantly different between the two groups. The odds ratio estimated using the propensity score matching analysis was 0.842 (P=0.613). The 1- and 5-year recurrence rates were significantly lower in the LT group (9% and 21%) than in the HR group (43% and 74%) (P<0.001), and the odds ratio was 0.214 (P=0.001). In conclusion, HR should be considered a valid alternative to LDLT taking into consideration the risk for the living donor based on the results of this propensity score-matching study.

Ischemic Stroke in Patients With Hypertrophic Cardiomyopathy According to Presence or Absence of Atrial Fibrillation

Stroke ◽  
2021 ◽  
Author(s):  
Laurent Fauchier ◽  
Arnaud Bisson ◽  
Alexandre Bodin ◽  
Julien Herbert ◽  
Pascal Spiesser ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Ischemic Stroke ◽  
Hypertrophic Cardiomyopathy ◽  
Significant Risk ◽  
Cardiovascular Death ◽  
Hazard Ratio ◽  
Clinical Usefulness ◽  
Risk Scores ◽  
Good Prediction ◽  
Good Calibration

Background and Purpose: Patients with hypertrophic cardiomyopathy (HCM) have high risk of ischemic stroke (IS), especially if atrial fibrillation (AF) is present. Improvements in risk stratification are needed to help identify those patients with HCM at higher risk of stroke, whether AF is present or not. Methods: This French longitudinal cohort study from the database covering hospital care from 2010 to 2019 analyzed adults hospitalized with isolated HCM. A logistic regression model was used to construct a French HCM score, which was compared with the HCM Risk-CVA and CHA 2 DS 2 -VASc scores using c-indexes and calibration analysis. Results: In 32 206 patients with isolated HCM, 12 498 (38.8%) had AF, and 2489 (7.7%) sustained an IS during follow-up. AF in patients with HCM was independently associated with a higher risk for death (hazard ratio, 1.129 [95% CI, 1.088–1.172]), cardiovascular death (hazard ratio, 1.254 [95% CI, 1.177–1.337]), IS (hazard ratio, 1.210 [95% CI, 1.111–1.317]), and other major cardiovascular events. Independent predictors of IS in HCM were older age, heart failure, AF, prior IS, smoking and poor nutrition (all P <0.05). For the HCM Risk-CVA score, CHA 2 DS 2 -VASc score and a French HCM score, all c-indexes were 0.65 to 0.70, with good calibration. Among patients with AF, the CHA 2 DS 2 -VASc score had marginal improvement over the HCM Risk-CVA score but was less predictive compared with the French HCM score ( P =0.001). In patients without AF, both HCM Risk-CVA score and the French HCM score had significantly better prediction compared with CHA 2 DS 2 -VASc (both P <0.0001). Decision curve analysis demonstrated that the French HCM score had the best clinical usefulness of the 3 tested risk scores. Conclusions: Patients with HCM have a high prevalence of AF and a significant risk of IS, and the presence of AF in patients with HCM was independently associated with worse outcomes. A simple French HCM score shows good prediction of IS in patients with HCM and clinical usefulness, with good calibration.

scholarly journals Model-based optimization of subgroup weights for survival analysis

2019 ◽  
Vol 35 (14) ◽  
pp. i484-i491
Author(s):  
Jakob Richter ◽  
Katrin Madjar ◽  
Jörg Rahnenführer
Keyword(s):  
Survival Analysis ◽  
Prediction Model ◽  
Model Building ◽  
Prediction Models ◽  
Survival Prediction ◽  
Good Prediction ◽  
Similar Data ◽  
Weighted Likelihood ◽  
Model Based ◽  
Specific Subgroup

AbstractMotivationTo obtain a reliable prediction model for a specific cancer subgroup or cohort is often difficult due to limited sample size and, in survival analysis, due to potentially high censoring rates. Sometimes similar data from other patient subgroups are available, e.g. from other clinical centers. Simple pooling of all subgroups can decrease the variance of the predicted parameters of the prediction models, but also increase the bias due to heterogeneity between the cohorts. A promising compromise is to identify those subgroups with a similar relationship between covariates and target variable and then include only these for model building.ResultsWe propose a subgroup-based weighted likelihood approach for survival prediction with high-dimensional genetic covariates. When predicting survival for a specific subgroup, for every other subgroup an individual weight determines the strength with which its observations enter into model building. MBO (model-based optimization) can be used to quickly find a good prediction model in the presence of a large number of hyperparameters. We use MBO to identify the best model for survival prediction of a specific subgroup by optimizing the weights for additional subgroups for a Cox model. The approach is evaluated on a set of lung cancer cohorts with gene expression measurements. The resulting models have competitive prediction quality, and they reflect the similarity of the corresponding cancer subgroups, with both weights close to 0 and close to 1 and medium weights.Availability and implementationmlrMBO is implemented as an R-package and is freely available at http://github.com/mlr-org/mlrMBO.

scholarly journals Survival prediction model for postoperative hepatocellular carcinoma patients

Medicine ◽  
2017 ◽  
Vol 96 (37) ◽  
pp. e7902 ◽  
Author(s):  
Zhihui Ren ◽  
Shasha He ◽  
Xiaotang Fan ◽  
Fangping He ◽  
Wei Sang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Prediction Model ◽  
Survival Prediction

scholarly journals A Seven-Gene Signature to Predict Prognosis of Patients With Hepatocellular Carcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Junli Wang ◽  
Qi Zhang ◽  
Fukang Shi ◽  
Dipesh Kumar Yadav ◽  
Zhengtao Hong ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
High Risk ◽  
Risk Group ◽  
Radical Resection ◽  
Tissue Microarrays ◽  
Gene Signature ◽  
High Risk Group ◽  
Prognostic Models ◽  
Risk Scores

Purpose: Hepatocellular carcinoma (HCC) is one of the most prevalent malignant diseases worldwide and has a poor prognosis. Gene-based prognostic models have been reported to predict the overall survival of patients with HCC. Unfortunately, most of the genes used in earlier prognostic models lack prospective validation and, thus, cannot be used in clinical practice.Methods: Candidate genes were selected from GEPIA (Gene Expression Profiling Interactive Analysis), and their associations with patients’ survival were confirmed by RT-PCR using cDNA tissue microarrays established from patients with HCC after radical resection. A multivariate Cox proportion model was used to calculate the coefficient of corresponding gene. The expression of seven genes of interest (MKI67, AR, PLG, DNASE1L3, PTTG1, PPP1R1A, and TTR) with two reference genes was defined to calculate a risk score which determined groups of different risks.Results: Our risk scoring efficiently classified patients (n = 129) with HCC into a low-, intermediate-, and high-risk group. The three groups showed meaningful distinction of 3-year overall survival rate, i.e., 88.9, 74.5, and 20.6% for the low-, intermediate-, and high-risk group, respectively. The prognostic prediction model of risk scores was subsequently verified using an independent prospective cohort (n = 77) and showed high accuracy.Conclusion: Our seven-gene signature model performed excellent long-term prediction power and provided crucially guiding therapy for patients who are not a candidate for surgery.

scholarly journals Natural disease progression and novel survival prediction model for hepatocellular carcinoma with spinal metastases: a 10-year single-center study

2020 ◽  
Author(s):  
Phichayut Phinyo ◽  
Chonmavadh Boonyanaruthee ◽  
Permsak Paholpak ◽  
Dumnoensun Pruksakorn ◽  
Areerak Phanphaisarn ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Prediction Model ◽  
Karnofsky Performance Status ◽  
External Validation ◽  
Multivariable Analysis ◽  
Spinal Metastases ◽  
Total Bilirubin Level ◽  
University Hospital ◽  
Survival Prediction ◽  
Chiang Mai

Abstract Background: Individual prediction of life expectancy in patients with spinal metastases from hepatocellular carcinoma (HCC) is key for optimal treatment selection, especially when identifying potential candidates for surgery. Most reported prognostic tools provide categorical predictions, and only a few include HCC-related factors. This study aimed to investigate the natural progression of the disease and develop a prognostic tool that is capable of providing individualized predictions.Methods: Patients with HCC-derived metastatic spinal disease were identified from a retrospective cohort of patients with spinal metastases who were diagnosed at Chiang Mai University Hospital between 2006 and 2015. Kaplain–Meier methods and log-rank tests were used to statistically evaluate potential factors. Significant predictors from univariable analysis were included in the flexible parametric survival regression for the development of a prognostic prediction model. Results: Of the 1,143 patients diagnosed with HCC, 69 (6%) had spinal metastases. The median survival time of patients with HCC after spinal metastases was 79 days. In the multivariable analysis, a total of 11 potential clinical predictors were included. After backward elimination, four final predictors remained: patients aged > 60 years, Karnofsky Performance Status, total bilirubin level, and multifocality of HCC. The model showed acceptable discrimination at C-statistics 0.73 (95% Confidence interval 0.68–0.79) and fair calibration. Conclusion: Four clinical parameters were used in the development of the individual survival prediction model for patients with HCC-derived spinal metastases of Chiang Mai University or HCC-SM CMU model. Prospective external validation studies in a larger population are required prior to the clinical implication of the model.

scholarly journals Natural disease progression and novel survival prediction model for hepatocellular carcinoma with spinal metastases: a 10-year single-center study

2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Phichayut Phinyo ◽  
Chonmavadh Boonyanaruthee ◽  
Permsak Paholpak ◽  
Dumneoensun Pruksakorn ◽  
Areerak Phanphaisarn ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Prediction Model ◽  
Disease Progression ◽  
Spinal Metastases ◽  
Survival Prediction ◽  
Single Center ◽  
Single Center Study ◽  
Center Study

A multi-institutional predicting nomogram for BCLC stage C hepatocellular carcinoma after stereotactic body radiotherapy.

2018 ◽  
Vol 36 (4_suppl) ◽  
pp. 353-353
Author(s):  
Wen-Yen Huang ◽  
Yu-Ju Lin ◽  
Jason C. Cheng ◽  
Jenny Que ◽  
Mei-Hsuan Lee ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Prediction Model ◽  
Stereotactic Body Radiotherapy ◽  
Proportional Hazards ◽  
Performance Status ◽  
Receiver Operating Curve ◽  
Risk Scores ◽  
Total Risk ◽  
Ecog Performance Status ◽  
Pugh Class

353 Background: The study aimed to develop a nomogram to predict overall survival (OS) among hepatocellular carcinoma (HCC) patients with BCLC stage C after stereotactic body radiotherapy (SBRT). Methods: Clinical data from the multicenter study consisted of 270 HCC patients with BCLC stage C. The patients underwent SBRT with a median dose of 40 Gy (range: 25-60 Gy) in 2-6 fractions. The model included age, gender, ECOG performance status, etiology (HBV, HCV, and non-B, non-C), number of tumor, tumor size, AFP level, presence of macrovascular invasion, Child-Pugh class, N stage, M stage, sorafinib use, and biologically effective dose. Cox’s proportional hazards models were utilized to estimate regression coefficients of death risk predictors and derive risk scores. The area under receiver operating curve (AUROC) was used to evaluate the performance of the risk models. Results: The median survival was 12.6 months, with 1-year and 2-year OS rates of 51.6% and 30.8%, respectively. Multiple regression showed age (with 5-year increase, HR = 0.89, p = 0.002), ECOG (1 vs. 0, HR = 1.72, p = 0.016; 2 vs. 0, HR = 2.64, p = 0.002), number and size of tumors ( > 5 tumors or > 8 cm vs. ≤5 tumors and ≤ 8 cm, HR = 1.83, p = 0.003; > 5 tumors and > 8cm vs. ≤5 tumors and ≤ 8 cm, HR = 2.04, p = 0.013), macrovascular invasion (yes vs. no, HR = 2.22, p < 0.001), Child-Pugh class (B vs. A, HR = 2.83, p < 0.001), and M stage (1 vs. 0, HR = 2.30, p = 0.001) are statistically significant predictors for OS. All of these significant predictors were included in the risk prediction model. The total risk score of the prediction model ranged from 0 to 48. Each score had its corresponding risk of OS. The AUROC for predicting 1-year OS was 0.820. Conclusions: The nomogram we generated among HCC patients undergoing SBRT had discrimination satisfactory predictability for OS. It can be used to stratify high-risk patients and select appropriate patients for future clinical trials.

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