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Title: Comments on validation of conventional non-invasive fibrosis scoring systems in patients with metabolic associated fatty liver disease Xian Li1, Shengyu Yao1, Lunjie Yan1, Haichao Li1, Ziniu Ding1, Dongxu Wang1, Zhaoru Dong1, Jianguo Hong1*, Tao Li1* 1Department of General Surgery, Qilu Hospital of Shandong University, Jinan, China. *Corresponding authors at: Jianguo Hong, MD, Department of General Surgery, Qilu Hospital of Shandong University, 107 West Wen Hua Road, Jinan, 250012, Shandong, China. E-mail address: hongsdu@126.com; Tao Li, MD, Department of General Surgery, Qilu Hospital of Shandong University, 107 West Wen Hua Road, Jinan, 250012, Shandong, China. E-mail address: litao7709@126.com. The first two authors contributed to this work equally. Author contributions: Xian Li and Shengyu Yao designed and performed the research. Lunjie Yan, Haichao Li and Ziniu Ding analyzed the data. Tao Li and Jianguo Hong wrote the letter. Dongxu Wang and Zhaoru Dong revised the letter.   Abstract The diagnostic performances of several non-invasive scoring systems including aspartate aminotransferase to platelet ratio index (APRI), fibrosis-4 index (FIB-4), body mass index, aspartate aminotransferase/alanine aminotransferase ratio, diabetes score (BARD) and nonalcoholic fatty liver disease fibrosis score (NFS), have been widely evaluated in the patients with nonalcoholic fatty liver disease (NAFLD), showing great diagnostic efficiency in predicting fibrosis. Since the proposition of the new concept of metabolic associated fatty liver disease (MAFLD), the clinical application values of the above non-invasive scoring systems have not been evaluated till now. The evaluation of the diagnostic performances of the non-invasive scoring systems will provide references for clinicians in the diagnosis of MAFLD. Core tip: The concept of metabolic associated fatty liver disease (MAFLD) is proposed in 2020. Unlike the concept of nonalcoholic fatty liver disease (NAFLD), the MAFLD definition does not require the exclusion of chronic liver disease, but the presence of metabolic associated disease or dysfunction is required. The clinical prediction values and the optimal cutoff values of non-invasive fibrosis scores remain unknown. We read with great interest the recent article “Validation of conventional non-invasive fibrosis scoring systems in patients with metabolic associated fatty liver disease” published by Wu and colleagues. We would like to share our opinions and criticisms about this valuable work.   TO THE EDITOR We read with great interest the recent article “Validation of conventional non-invasive fibrosis scoring systems in patients with metabolic associated fatty liver disease” published by Wu et al. In the retrospective study, the authors stated that they aim to evaluate the diagnosis performances of the four non-invasive scoring systems including aspartate aminotransferase to platelet ratio index (APRI), fibrosis-4 index (FIB-4), body mass index, aspartate aminotransferase/alanine aminotransferase ratio, diabetes score (BARD), and nonalcoholic fatty liver disease fibrosis score (NFS) in patients with MAFLD. We would like to share our opinions and criticisms about this valuable work. The authors evaluated the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and discrimination of the four non-invasive scores in the prediction of advanced fibrosis in patients with MAFLD. The authors compared the clinical characteristics, laboratory variables and non-invasive scores between the patients with advanced fibrosis and no/mild fibrosis. The results showed that the FIB-4 (P<0.001), NFS (P<0.001), APRI (P=0.003) and BARD (P<0.001) scores were all significantly higher in patients with advanced fibrosis compared with patients with no/mild fibrosis. In this study, the comparisons only performed in the univariate analyses. In our opinion, the multivariate analysis should be performed to recognize the independent variables in the prediction of advanced fibrosis, as Nielsen et al did in the study [1]. The authors evaluated the diagnostic efficiency of the prediction scores using the following statistical indexes: sensitivity, specificity, accuracy, PPV, NPV and the area under receiver operating characteristic curve (AUROC). All the above indexes are important statistical variables in the development and validation of prediction models. In our opinion, it will be better if the authors also evaluate the calibration of the prediction scores. In fact, the calibration of the statistical model is a critical component in the evaluation of the diagnostic efficiency [2]. The calibration of the prediction scores can be performed using Hosmer-Lemeshow goodness-of-fit test and the calibration curves. The calibration curves can be easily plotted using the R software. We advise the authors to evaluate calibration of prediction models in the future studies. The calculations of PPV and NPV are very important in the development and validation of prediction models. Unlike the sensitivity and specificity, PPV and NPV cannot be compared directly among different samples except the samples with the same prevalence rate of the disease. It is because that both PPV and NPV can be affected by the prevalence rate of disease [3,4]. The authors compared the PPV and NPV in Table 4 and also stated that “PPV and NPV was better in the HBV-MAFLD group” in the article. In our opinion, the comparisons of PPV and NPV between the HBV-MAFLD group and pure MAFLD group would be valuable only when advanced fibrosis accounts for the same proportion in the two groups. Although the authors stated that the continuous variables are expressed as mean ± SD or median (interquartile range) and were compared using Student’s t test in the case of normally distributed data or Mann-Whitney test in the remaining cases, there are no any continuous variable expressed as median (interquartile range) and all the continuous variables were expressed as mean ± SD in the article. Normally, the authors should test the distribution type of the continuous variables, and then, the normally distributed continuous variables are expressed as mean ± SD, while the non-normally distributed continuous variables are expressed as median (interquartile range). If all the continuous variables are expressed as mean ± SD in the article but the authors did not state all the continuous variables fit the normal distribution, the readers will think if the normal distribution tests have been performed in the study. After all, the situation rarely happens that all the laboratory variables fit the normal distribution in one study. In most studies, the laboratory variables and scores, including ALT, AST, APRI and other variables, do not fit the normal distributed and should be expressed as median (interquartile range) [1, 5-7]. A true normal distribution is rare for any parameter in biomedical research. The European Medicines Agency has issued general guidance that data should be checked for normality of distribution of reported variables and that analysis and presentation of the data should be based on this.[8]. Another possibility is that some continuous variables did not fit the normal distribution but the authors accidentally expressed these variables as mean ± SD. And of course, this situation indeed does not affect the accuracy the study results. And also, we advise the authors should state in the articles if the continuous variables fit normal distribution and if the normal distribution tests have been performed in the future studies. The authors compared the diagnostic ability of the four non-invasive scores. The study demonstrated that APRI and BARD scores do not perform well and FIB and NFS could be more useful. The new thresholds of FIB and NFS proposed by the authors were 1.05 and -2.1, respectively. The two thresholds proposed by the authors in the article were determined based on this sample of the study. The diagnostic efficiency of the thresholds in the prediction of advanced fibrosis should been further evaluated in external validation cohort and/or in prospective validation cohort. And also, if possible, the authors can try to develop the models based on multiple variables including FIB and/or NFS to predict the advanced fibrosis in patients with MAFLD. He et al. proposed that diagnostic model including valuable parameters extracted from more examination tools might bring better results [9]. We believe that the prediction models based on multiple variables including clinical characteristics, radiology examinations and laboratory examinations would exhibit higher sensitivity, higher specificity, higher accuracy, higher PPV, higher NPV, better discrimination and better calibration in the prediction of advanced fibrosis in patients with MAFLD. In general, we are very interested in the article “validation of conventional non-invasive fibrosis scoring systems in patients with metabolic associated fatty liver disease” published by Wu et al. The authors demonstrated the prediction values of APRI, FIB-4, NFS and BADR in a large sample of histology-proven MAFLD. As MAFLD is a new entity, this study will provide important references for clinicians in the prediction of advanced fibrosis in MAFLD patients. The study performed by Wu et al could also provide important references for other research of non-invasive scores and prediction models.   References 1. Nielsen MJ, Leeming DJ, Goodman Z, et al. Comparison of ADAPT, FIB4 and APRI as non-invasive predictors of liver fibrosis and NASH within the CENTAUR Screening Population. J Hepatol. 2021:S0168-8278(21)02011-0. 2. Assel M, Sjoberg D, Elders A, et al. Guidelines for Reporting of Statistics for Clinical Research in Urology. Eur Urol. 2019;75:358-367. 3. Monaghan TF, Rahman SN, Agudelo CW, et al. Foundational Statistical Principles in Medical Research: Sensitivity, Specificity, Positive Predictive Value, and Negative Predictive Value. Medicina (Kaunas). 2021;57:503. 4. Akobeng AK. Understanding diagnostic tests 2: likelihood ratios, pre- and post-test probabilities and their use in clinical practice. Acta Paediatr. 2007;96:487-91. 5. Abdel-Hameed EA, Rouster SD, Kottilil S, Sherman KE. The Enhanced Liver Fibrosis Index Predicts Hepatic Fibrosis Superior to FIB4 and APRI in HIV/HCV Infected Patients. Clin Infect Dis. 2021;73:450-459. 6. Lee J, Vali Y, Boursier J, et al. Prognostic accuracy of FIB-4, NAFLD fibrosis score and APRI for NAFLD-related events: A systematic review. Liver Int. 2021;41:261-270. 7. Amiri M, Murgas S, Stang A, Michel MC. Do overactive bladder symptoms and their treatment-associated changes exhibit a normal distribution? Implications for analysis and reporting. Neurourol Urodyn. 2020;39:754-761. 8. European Medicines Agency. ICH Topic E9. Statistical principles for clinical trials; 1998. https://www.ema.europa.eu/en/ documents/scientific‐guideline/ich‐e‐9‐statistical‐principles‐ clinical‐trials‐step‐5_en.pdf. Accessed December 5, 2019. 9. He Y, Zhu Z, Chen Y, et al. Development and Validation of a Novel Diagnostic Nomogram to Differentiate Between Intestinal Tuberculosis and Crohn's Disease: A 6-year Prospective Multicenter Study. Am J Gastroenterol. 2019;114:490-499.   Conflict-of-interest statement: All other authors have nothing to disclose. Data sharing statement: No additional data are available. Keywords: metabolic associated fatty liver disease; prediction model, calibration; normal distribution

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