Nomogram for surgical hepatocellular carcinoma: What have we missed?

Huo, Teh–Ia; Ho, Shinn‐Ying; Ko, Chih-Chieh

doi:10.1111/liv.15071

Cited by 6 publications

(6 citation statements)

References 6 publications

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“…However, there is no predictive model for patients with advanced HCC. In the recent years, clinical models based on nomogram have been applied widely for survival prediction of oncology patients due to its advantages of intuitiveness and simplicity (13)(14)(15). Such new models can not only effectively promote personalized medicine, but also facilitate clinicians to utilize them for prognosis prediction.…”

Section: Introductionmentioning

confidence: 99%

A Practical Nomogram and Risk Stratification System Predicting the Cancer-Specific Survival for Patients With Advanced Hepatocellular Carcinoma

Yang

Zhao

et al. 2022

Front. Oncol.

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BackgroundHepatocellular carcinoma (HCC) has the highest cancer-related mortality rate. This study aims to create a nomogram to predict the cancer-specific survival (CSS) in patients with advanced hepatocellular carcinoma.MethodsPatients diagnosed with advanced HCC (AJCC stage III and IV) during 1975 to 2018 were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Qualified patents were randomized into training cohort and validation cohort at a ratio of 7:3. The results of univariate and multivariate Cox regression analyses were used to construct the nomogram. Consistency index (C-index), area under the time-dependent receiver operating characteristic (ROC) curve [time-dependent area under the curve (AUC)], and calibration plots were used to identify and calibrate the nomogram. The net reclassification index (NRI), integrated discrimination improvement (IDI), and C-index, and decision curve analysis DCA were adopted to compare the nomogram’s clinical utility with the AJCC criteria.ResultsThe 3,103 patients with advanced hepatocellular carcinoma were selected (the training cohort: 2,175 patients and the validation cohort: 928 patients). The C-index in both training cohort and validation cohort were greater than 0.7. The AUC for ROC in the training cohort was 0.781, 0.771, and 0.791 at 1, 2, and 3 years CSS, respectively. Calibration plots showed good consistency between actual observations and the 1-, 2-, and 3-year CSS predicted by the nomogram. The 1-, 2-, and 3-year NRI were 0.77, 0.46, and 0.48, respectively. The 1-, 2-, and 3-year IDI values were 0.16, 0.15, and 0.12 (P < 0.001), respectively. DCA curves in both the training and validation cohorts demonstrated that the nomogram showed better predicted 1-, 2-, and 3-year CSS probabilities than AJCC criteria.ConclusionsThis study established a practical nomogram for predicting CSS in patients with advanced HCC and a risk stratification system that provided an applicable tool for clinical management.

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Section: Introductionmentioning

confidence: 99%

A Practical Nomogram and Risk Stratification System Predicting the Cancer-Specific Survival for Patients With Advanced Hepatocellular Carcinoma

Yang

Zhao

et al. 2022

Front. Oncol.

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“…It is still challenging how to build a bridge between basic hospitals and general hospitals that can promptly predict the risks of MC among chest trauma patients even in basic hospitals and thereby can achieve effective triage and prompt treatment [17,19]. Predictive models or diagnostic models are reduplicative and objective methods, and are already widely used to evaluate the diagnosis or prognosis of diseases because of their reliable prediction e cacy and diagnostic e cacy [7,9]. The key of modeling is to nd out the independent risk factors or predictors of MC occurring among thoracic trauma patients.…”

Section: Discussionmentioning

confidence: 99%

“…The inclusion criteria were: (1) patient of new rib fracture with medical history of obvious trauma; (2) age ≥ 18 years; (3) no severe immune function defect or drug abuse. The exclusion criteria were (1) multiple functional organ/system failure; (2) coagulapathy or oral administration of anticoagulant; (3) injury time > 24 h; (4) discharged or dead within 24 h; (5) emergency thoracotomy; (6) severe cognitive or language impairment; (7) occurrence of myocardial infarction or myocarditis within 2 weeks; (8) alcoholism or liver dysfunction before admission; (9) failure to complete 2 times of cardiac biomarker tests within 48 h after admission, or time duration between two tests > 24 h; (10) incomplete or repeated data (Fig. 1).…”

Section: Basic Datamentioning

confidence: 99%

“…Consequently, 7%−20% MC patients die from severe arrhythmia or myocardial infarction [5,6], which brings huge harms to patients and the society. In the face of such challenges, prediction models that are simple and visual evaluation methods are gradually applied into clinic, and their e cacy has been fully validated [7][8][9][10]. So far, however, the independent risk factors of MC among thoracic trauma patients are still unclear.…”

Section: Introductionmentioning

confidence: 99%

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Risk factors of Traumatic Myocardial Contusion and Establishment of Nomogram Prediction Model

Changyong

Song

Liu

et al. 2023

Preprint

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Objectives To clarify the risk factors of traumatic myocardial contusion (MC) and to build an MC prediction model and assess its clinical application values. Methods The clinical data of 370 rib fracture patients treated upon emergency call in the Department of Thoracic Surgery at Jiangsu Shengze Hospital Affiliated to Nanjing Medical University between January 2017 and December 2019 were retrospectively analyzed. Of them, 159 patients were diagnosed as MC. All patients were fully randomly divided at a 7:3 ratio to a training cohort (264 cases, 106 MC cases, 158 NMC cases) and a validation cohort (106 cases, 53 MC cases, 53 NMC cases). The related risk factors of MC in the training cohort were identified via univariate Logistics regression, then the optimal independent risk factors were screened out using LASSO regression and multivariate Logistics regression. A Nomogram model for MC prediction in the training cohort was built with the selected independent risk factors. The receiver's operating characteristic (ROC) curves and calibration curves in the two cohorts were plotted and used to analyze the prediction efficacy of the Nomogram model, and the clinical application value of the model was assessed using decision curve analysis (DCA) and clinical impact curves (CIC). Results The multivariate Logistics regression and LASSO regression analysis showed there were five independent risk factors of MC, including the upper chest anterolateral segment (UAL), the middle chest proximal spinal segment (MSS), sternal fracture (SF), Pneumothorax and aspartic transaminase (AST). The ROC curves showed the Nomogram model based on the C index had discrimination of 0.838 (95%CI, 0.790–0.886) and 0.846 (95%CI, 0.770–0.921) in the training cohort and the validation cohort respectively. The calibration curves showed there was high predictive precision between the actual probability and predicted probability in both cohorts. DCA showed at threshold probability > 0.1, the Nomogram model had significant clinical net benefits in both cohorts. CIC showed at the threshold probability > 0.6, the predicted number of positive patients was basically consistent with the actual number. Conclusions SF, UAL, MSS, Pneumothorax and AST are the independent risk factors and predictors of MC in rib fracture patients. The Nomogram model based on the 5 independent risk factors has high discrimination, calibration and clinical net benefits, and shows extensive prospects for clinical application in basic hospitals.

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“…A nomogram is a line chart that displays scales for the variables included in a formula so that the appropriate values for each variable lie in a line that intersects all the scales. Nomograms have been used as a prediction tool for the prognosis of multiple diseases [6][7][8]. The nomogram is a better and easier risk-prediction tool in clinical practice compared to other prediction tools that require computer software [9].…”

Section: Introductionmentioning

confidence: 99%

A nomogram for predicting the severity of COVID-19 infections among patients in Chengdu, China

Luo¹,

Zhang²,

Zheng³

et al. 2023

Radiology Science

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Introduction: Although most COVID-19 infections are currently mild, poor prognoses and fatalities continue to occur, which remain a threat to the safety of people in China. The goal of this study was to create an efficient model that combines the clinical characteristics with computed tomography (CT) scores at the time of admission to predict the severity of COVID-19. Methodology: A total of 346 COVID-19 patients in the current study, of whom 46 had severe infections and 300 had non-severe infections according to the clinal outcomes. Clinical, laboratory, CT findings, and CT scores at admission were collected. To identify the independent risk factors, univariable and multivariable logistic regression analyses were performed. A nomogram model was built with the extracted risk factors. The calibration curve and decision curve (DCA) operated to validate model performance. Results: The receiver operating characteristic curve indicated that the severity CT score had an area under the curve of 0.933 (95% CI, 0.901-0.965) and a cut-off value of 6.5 (sensitivity, 95.70%; specificity, 78%). The CT score, age, lactic dehydrogenase and hydroxybutyrate dehydrogenase levels, and hypertension were exacted for the nomogram. The nomogram had good calibration (P = 0.539) and excellent clinical value based on the DCA. Conclusions: The nomogram presented herein could be a valuable model to predict severe COVID-19 among patients in Chengdu, China.

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Nomogram for surgical hepatocellular carcinoma: What have we missed?

Cited by 6 publications

References 6 publications

A Practical Nomogram and Risk Stratification System Predicting the Cancer-Specific Survival for Patients With Advanced Hepatocellular Carcinoma

A Practical Nomogram and Risk Stratification System Predicting the Cancer-Specific Survival for Patients With Advanced Hepatocellular Carcinoma

Risk factors of Traumatic Myocardial Contusion and Establishment of Nomogram Prediction Model

A nomogram for predicting the severity of COVID-19 infections among patients in Chengdu, China

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