Development and validation of a clinical and laboratory-based nomogram to predict nonalcoholic fatty liver disease

Cen, Chao; Wang, Wenpu; Yu, Songfeng; Tang, Xiaofeng; Liu, Jimin; Liu, Yuanxing; Zhou, Lin; Yu, Jun; Zheng, Shusen

doi:10.1007/s12072-020-10065-7

Cited by 28 publications

(23 citation statements)

References 24 publications

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“…Of these articles, thirty-one were excluded with reasons as follows: (1) eleven articles were conference abstracts [ 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 ], (2) three articles were letters, editorials, and notes [ 45 , 46 , 47 ], (3) four did not involve NAFLD patients [ 48 , 49 , 50 , 51 ], (4) two studies involved HIV patients [ 52 , 53 ], (6) one study involved patients with gastroenteropancreatic neuroendocrine tumors [ 54 ], (7) two studies involved patients with polycystic ovarian syndrome [ 51 , 55 ], (8) six were interventional studies [ 56 , 57 , 58 , 59 , 60 , 61 ], and (9) two articles were published in Chinese and Portuguese [ 62 , 63 ]. The total number of articles included in the qualitative synthesis was twenty-four studies, out of which twenty-two studies were included in the quantitative synthesis [ 36 , 42 , 44 , 55 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 ,…”

Section: Resultsmentioning

confidence: 99%

“…Hepatic steatosis was assessed using ultrasonography for diagnosing NAFLD in most studies ( n = 15) [ 36 , 55 , 64 , 67 , 68 , 70 , 73 , 75 , 76 , 77 , 79 , 80 , 81 , 82 , 83 ], while five studies used a biopsy [ 42 , 44 , 65 , 66 , 78 ]. Moreover, two studies used both [ 69 , 72 ].…”

Section: Resultsmentioning

confidence: 99%

“…Figure 9 summarizes the results obtained in the meta-analysis. The pooled studies for the analysis evaluating the VAI in predicting NAFLD demonstrated an overall AUC of 0.767 with a 95% CI of 0.692–0.841, I 2 = 99.33, and p -value < 0.001 [ 64 , 71 , 75 , 80 , 82 , 83 ]. VAI predicted NASH with an overall AUC of 0.732 with a 95% CI of 0.669–0.795, I 2 = 0, and p -value of 0.375 [ 66 , 82 ].…”

Section: Resultsmentioning

confidence: 99%

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The Visceral Adiposity Index in Non-Alcoholic Fatty Liver Disease and Liver Fibrosis—Systematic Review and Meta-Analysis

et al. 2021

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(1) Background: In order to avoid a liver biopsy in non-alcoholic fatty liver disease (NAFLD), several noninvasive biomarkers have been studied lately. Therefore, we aimed to evaluate the visceral adiposity index (VAI) in NAFLD and liver fibrosis, in addition to its accuracy in predicting NAFLD and NASH. (2) Methods: We searched PubMed, Embase, Scopus, and Cochrane Library, identifying observational studies assessing the VAI in NAFLD and liver fibrosis. QUADAS-2 was used to evaluate the quality of included studies. The principal summary outcomes were mean difference (MD) and area under the curve (AUC). (3) Results: A total of 24 studies were included in our review. VAI levels were significantly increased in NAFLD (biopsy-proven and ultrasound-diagnosed), simple steatosis vs. controls, and severe steatosis vs. simple steatosis. However, no significant MD was found according to sex, liver fibrosis severity, simple vs. moderate and moderate vs. severe steatosis, pediatric NAFLD, and NASH patients. The VAI predicted NAFLD (AUC 0.767) and NASH (AUC 0.732). (4) Conclusions: The VAI has a predictive value in diagnosing NAFLD and NASH, with significantly increased values in adult NAFLD patients, simple steatosis compared to controls, and severe steatosis compared to simple steatosis.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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The Visceral Adiposity Index in Non-Alcoholic Fatty Liver Disease and Liver Fibrosis—Systematic Review and Meta-Analysis

et al. 2021

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“…These variables identified as risk factors for NAFLD are consistent with previous studies and are widely used in NAFLD risk scoring models. 23 , 26 , 27 In our prediction model, BMI is one of the main aspects of the NAFLD risk factor score. Numerous previous studies have confirmed obesity as a well-known risk factor for NAFLD.…”

Section: Discussionmentioning

confidence: 99%

Application of a Novel Prediction Model for Predicting 2-Year Risk of Non-Alcoholic Fatty Liver Disease in the Non-Obese Population with Normal Blood Lipid Levels: A Large Prospective Cohort Study from China

Ji¹,

Cai²,

Bai³

et al. 2021

IJGM

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Purpose The purpose of this study was to develop and validate a nomogram to better assess the 2-year risk of non-alcoholic fatty liver disease (NAFLD) in non-obese population with normal blood lipid levels. Patients and Methods This study was a secondary analysis of a prospective study. We included 3659 non-obese adults with normal blood lipid levels without NAFLD at baseline. A total of 2744 participants were included in the development cohort and 915 participants were included in the validation cohort. The least absolute contraction selection operator (LASSO) regression model was used to identify the best risk factors. Multivariate Cox regression analysis was used to construct the prediction model. The performance of the prediction model was assessed using Harrell’s consistency index (C-index), area under the receiver operating characteristic (AUROC) curve and calibration curve. Decision curve analysis was applied to evaluate the clinical usefulness of the prediction model. Results After LASSO regression analysis and multivariate Cox regression analysis on the development cohort, BMI, TG, DBIL, ALT and GGT were found to be risk predictors and were integrated into the nomogram. The C-index of development cohort and validation cohort was 0.819 (95% CI, 0.798 to 0.840) and 0.815 (95% CI, 0.781 to 0.849), respectively. The AUROC of 2-year NAFLD risk in the development cohort and validation cohort was 0.831 (95% CI, 0.811 to 0.851) and 0.797 (95% CI, 0.765 to 0.829), respectively. From calibration curves, the nomogram showed a good agreement between predicted and actual probabilities. The decision curve analysis indicated that application of the nomogram is more effective than the intervention-for-all-patients scheme. Conclusion We developed and validated a nomogram for predicting 2-year risk of NAFLD in the non-obese population with normal blood lipid levels.

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“…Nomograms are widely used as assessment or prediction models for the prevalence and incidence of diseases (Cen et al, 2020;Hu et al, 2019;Zhao et al, 2020). A nomogram prognostic model for Small Cell Lung Cancer (SCLC) patients validated the model by using an independent patient cohort with an integrated area under the curve of 0.79 (Wang et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Development of a preoperative index-based nomogram for the prediction of hypokalemia in patients with pituitary adenoma: a retrospective cohort study

Zeng

Han³

et al. 2021

PeerJ

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Objective To develop and validate a preoperative index-based nomogram for the prediction of hypokalemia in patients with pituitary adenoma (PA). Methods This retrospective cohort study included 205 patients with PAs between January 2013 and April 2020 in the Sun Yat-sen Memorial Hospital, Guangzhou, China. The patients were randomly classified into either a training set (N = 143 patients) and a validation set (N = 62 patients) at a ratio of 7:3. Variables, which were identified by using the LASSO regression model were included for the construction of a nomogram, and a logistic regression analysis was used to calculate odds ratios (ORs) and 95% confidence intervals (CIs) in the training set. The area under the curve (AUC) was used to evaluate the performance of the nomogram for predicting hypokalemia. Multivariate logistic regression analysis with a restricted cubic spline analysis was conducted to identify a potential nonlinear association between the preoperative index and hypokalemia. Results The incidence of hypokalemia was 38.05%. Seven preoperative indices were identified for the construction of the nomogram: age, type of PA, weight, activated partial thromboplastin time, urea, eosinophil percentage, and plateletocrit. The AUCs of the nomogram for predicting hypokalemia were 0.856 (95% CI [0.796–0.915]) and 0.652 (95% CI [0.514–0.790]) in the training and validation sets, respectively. Restricted cubic splines demonstrated that there was no nonlinear association between hypokalemia and the selected variables. Conclusion In this study, we constructed a preoperative indices-based nomogram that can assess the risk of hypokalemia after the surgical treatment of pituitary adenomas. This nomogram may also help to identify high risk patients who require close monitoring of serum potassium.

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Development and validation of a clinical and laboratory-based nomogram to predict nonalcoholic fatty liver disease

Cited by 28 publications

References 24 publications

The Visceral Adiposity Index in Non-Alcoholic Fatty Liver Disease and Liver Fibrosis—Systematic Review and Meta-Analysis

The Visceral Adiposity Index in Non-Alcoholic Fatty Liver Disease and Liver Fibrosis—Systematic Review and Meta-Analysis

Application of a Novel Prediction Model for Predicting 2-Year Risk of Non-Alcoholic Fatty Liver Disease in the Non-Obese Population with Normal Blood Lipid Levels: A Large Prospective Cohort Study from China

Development of a preoperative index-based nomogram for the prediction of hypokalemia in patients with pituitary adenoma: a retrospective cohort study

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