To the Editor
We read with great interest the study by Alhuneafat et al. examining maternal and fetal outcomes across various hypertensive disorders of pregnancy (HDP) [1]. Using national database, the authors provided valuable insights into the risk stratification of HDP subtypes. Nevertheless, several issues merit further discussion.
First, the potential for residual confounding remains a concern, as administrative datasets such as NIS, however robust, often lack information on important maternal clinical characteristics (e.g., body mass index, smoking, or family history) that are strong predictors of both HDP and adverse outcomes [2].
Second, the study uses diagnostic codes to classify HDP subtypes, but misclassification bias may arise due to variations in physician coding practices or hospital policies [3]. For instance, evolving definitions and diagnostic criteria for preeclampsia may influence trends over time, especially given changes after the ACOG guideline updates [4]. Sensitivity analyses using validated HDP algorithms could enhance the reliability of the findings.
Third, while the authors compared multiple outcome rates across HDP subtypes, they did not formally address the potential inflation of type I error due to multiple comparisons. Advanced statistical approaches, such as false discovery rate adjustments, could further safeguard the interpretation [5].
Lastly, although the study demonstrates an association between specific HDP subtypes and adverse outcomes, caution is warranted when interpreting causality given the cross-sectional nature of the data.
In summary, while this analysis offers an important contribution, future research should aim to incorporate more granular clinical and behavioral data, incorporate validated phenotyping strategies, and adopt statistical safeguards against multiple testing.
CRediT authorship contribution statement
Qiang Ma: Conceptualization, Writing – original draft, Data curation. Jianfen Tang: Supervision, Funding acquisition, Writing – review & editing.
Data availability
None.
Ethical compliance
Not applicable.
Study funding
Jiaxing Science and Technology Bureau (2023AD31099).
Declaration of competing interest
None.
Acknowledgments
None.
Handling Editor: Dr D Levy
References
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Associated Data
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Data Availability Statement
None.