Title: Validation of the diagnostic accuracy of the acFibroMASH index for at-risk MASH in
patients with metabolic dysfunction-associated steatotic liver disease
Version: 1 Date: 23 Jan 2025
Reviewer's report:
This biopsy-proven MASH study from a single center in China retrospectively reviewed liver
biopsy specimens and medical records to calculate the acFibroMASH index and validate its
diagnostic accuracy in identifying patients with at-risk MASH in a population with MASLD.
The study also compared the index's performance with other scoring systems. While the research
topic is important, several methodological and interpretative concerns are noted:
1. Liver biopsy evaluation involves inherent subjectivity. Two pathologists assessed histological
features and identified at-risk MASH, but the manuscript does not explain how discrepancies
between their interpretations were resolved. Providing interobserver agreement metrics (e.g.,
Cohen’s kappa) for each histological feature and the overall at-risk MASH diagnosis would
enhance the study's credibility and address potential variability.
2. Some patients classified as MASH had fibrosis stages F0–F1, raising concerns about possible
misclassification due to histological interpretation errors. A sensitivity analysis to evaluate the
impact of such cases on the diagnostic accuracy of the acFibroMASH index is warranted.
3. The study chose low and high cutoffs for the acFibroMASH index based on sensitivity (>90%)
and specificity (>90%). However, for practical clinical application, cutoffs achieving a high
negative predictive value (NPV >90%) to rule out and a high positive predictive value (PPV
>90%) to rule in should be prioritized. This would align better with clinical decision-making
and enhance the model's utility.
4. The diagnostic performance of the acFibroMASH index is reported using this cohort's data.
Validation in populations with varying prevalence rates of at-risk MASH, such as primary care
settings (5%) or diabetes clinics (#20%), should be conducted to assess its broader applicability.
5. The reported prevalence of at-risk MASH (26.1%) reflects the characteristics of a tertiary
care cohort, likely influenced by referral bias. This limits the generalizability of the findings to
community or primary care settings, where the prevalence is likely lower. The authors should
address this limitation explicitly.
6. The manuscript incorrectly references the American Association of Clinical Endocrinology
(AACE) guidelines, suggesting FIB-4 double cutoffs (<1.3 and >2.67) are recommended for at-
risk MASH in the Discussion section. AACE guidelines recommend using FIB-4 followed by
transient elastography to triage patients with clinically significant fibrosis (F2–F4). This section
should be corrected for accuracy.
7. While the study highlights the superior diagnostic accuracy of acFibroMASH and FAST
compared to other models (FIB-4, NFS, APRI), a more comprehensive discussion of the clinical
feasibility, costs, and availability of these models would provide valuable context for their
adoption in different healthcare settings.
8. The potential of acFibroMASH for diagnosing at-risk MASH is noted, but the manuscript
would benefit from a clear statement on how this model could be integrated into current clinical
pathways. Additionally, elaborating on its potential impact on treatment decision-making would
enhance the study's clinical relevance.
Title: Validation of the diagnostic accuracy of the acFibroMASH index for at-risk MASH in
patients with metabolic dysfunction-associated steatotic liver disease
Version: 1 Date: 23 Jan 2025
Reviewer's report:
This biopsy-proven MASH study from a single center in China retrospectively reviewed liver
biopsy specimens and medical records to calculate the acFibroMASH index and validate its
diagnostic accuracy in identifying patients with at-risk MASH in a population with MASLD.
The study also compared the index's performance with other scoring systems. While the research
topic is important, several methodological and interpretative concerns are noted:
1. Liver biopsy evaluation involves inherent subjectivity. Two pathologists assessed histological
features and identified at-risk MASH, but the manuscript does not explain how discrepancies
between their interpretations were resolved. Providing interobserver agreement metrics (e.g.,
Cohen’s kappa) for each histological feature and the overall at-risk MASH diagnosis would
enhance the study's credibility and address potential variability.
2. Some patients classified as MASH had fibrosis stages F0–F1, raising concerns about possible
misclassification due to histological interpretation errors. A sensitivity analysis to evaluate the
impact of such cases on the diagnostic accuracy of the acFibroMASH index is warranted.
3. The study chose low and high cutoffs for the acFibroMASH index based on sensitivity (>90%)
and specificity (>90%). However, for practical clinical application, cutoffs achieving a high
negative predictive value (NPV >90%) to rule out and a high positive predictive value (PPV
>90%) to rule in should be prioritized. This would align better with clinical decision-making
and enhance the model's utility.
4. The diagnostic performance of the acFibroMASH index is reported using this cohort's data.
Validation in populations with varying prevalence rates of at-risk MASH, such as primary care
settings (5%) or diabetes clinics (#20%), should be conducted to assess its broader applicability.
5. The reported prevalence of at-risk MASH (26.1%) reflects the characteristics of a tertiary
care cohort, likely influenced by referral bias. This limits the generalizability of the findings to
community or primary care settings, where the prevalence is likely lower. The authors should
address this limitation explicitly.
6. The manuscript incorrectly references the American Association of Clinical Endocrinology
(AACE) guidelines, suggesting FIB-4 double cutoffs (<1.3 and >2.67) are recommended for at-
risk MASH in the Discussion section. AACE guidelines recommend using FIB-4 followed by
transient elastography to triage patients with clinically significant fibrosis (F2–F4). This section
should be corrected for accuracy.
7. While the study highlights the superior diagnostic accuracy of acFibroMASH and FAST
compared to other models (FIB-4, NFS, APRI), a more comprehensive discussion of the clinical
feasibility, costs, and availability of these models would provide valuable context for their
adoption in different healthcare settings.
8. The potential of acFibroMASH for diagnosing at-risk MASH is noted, but the manuscript
would benefit from a clear statement on how this model could be integrated into current clinical
pathways. Additionally, elaborating on its potential impact on treatment decision-making would
enhance the study's clinical relevance.
Please review this paper.
user
Title: Validation of the diagnostic accuracy of the acFibroMASH index for at-risk MASH in
patients with metabolic dysfunction-associated steatotic liver disease
Version: 1 Date: 23 Jan 2025
Reviewer's report:
This biopsy-proven MASH study from a single center in China retrospectively reviewed liver
biopsy specimens and medical records to calculate the acFibroMASH index and validate its
diagnostic accuracy in identifying patients with at-risk MASH in a population with MASLD.
The study also compared the index's performance with other scoring systems. While the research
topic is important, several methodological and interpretative concerns are noted:
1. Liver biopsy evaluation involves inherent subjectivity. Two pathologists assessed histological
features and identified at-risk MASH, but the manuscript does not explain how discrepancies
between their interpretations were resolved. Providing interobserver agreement metrics (e.g.,
Cohen’s kappa) for each histological feature and the overall at-risk MASH diagnosis would
enhance the study's credibility and address potential variability.
2. Some patients classified as MASH had fibrosis stages F0–F1, raising concerns about possible
misclassification due to histological interpretation errors. A sensitivity analysis to evaluate the
impact of such cases on the diagnostic accuracy of the acFibroMASH index is warranted.
3. The study chose low and high cutoffs for the acFibroMASH index based on sensitivity (>90%)
and specificity (>90%). However, for practical clinical application, cutoffs achieving a high
negative predictive value (NPV >90%) to rule out and a high positive predictive value (PPV
>90%) to rule in should be prioritized. This would align better with clinical decision-making
and enhance the model's utility.
4. The diagnostic performance of the acFibroMASH index is reported using this cohort's data.
Validation in populations with varying prevalence rates of at-risk MASH, such as primary care
settings (5%) or diabetes clinics (#20%), should be conducted to assess its broader applicability.
5. The reported prevalence of at-risk MASH (26.1%) reflects the characteristics of a tertiary
care cohort, likely influenced by referral bias. This limits the generalizability of the findings to
community or primary care settings, where the prevalence is likely lower. The authors should
address this limitation explicitly.
6. The manuscript incorrectly references the American Association of Clinical Endocrinology
(AACE) guidelines, suggesting FIB-4 double cutoffs (<1.3 and >2.67) are recommended for at-
risk MASH in the Discussion section. AACE guidelines recommend using FIB-4 followed by
transient elastography to triage patients with clinically significant fibrosis (F2–F4). This section
should be corrected for accuracy.
7. While the study highlights the superior diagnostic accuracy of acFibroMASH and FAST
compared to other models (FIB-4, NFS, APRI), a more comprehensive discussion of the clinical
feasibility, costs, and availability of these models would provide valuable context for their
adoption in different healthcare settings.
8. The potential of acFibroMASH for diagnosing at-risk MASH is noted, but the manuscript
would benefit from a clear statement on how this model could be integrated into current clinical
pathways. Additionally, elaborating on its potential impact on treatment decision-making would
enhance the study's clinical relevance.
assistant