Dear Editor,
We read with great interest the study by Gao and Jia on seasonal et al. [1], admission and long-term mortality in patients with DM and AMI. The authors followed 3838 patients who underwent percutaneous coronary intervention (PCI) for acute myocardial infarction (AMI), and their conclusion that winter admission carries the highest mortality among diabetic patients warrants close scrutiny.
1. Reference group and inferential framework
The analysis fixed diabetic patients admitted in winter as the sole comparator for all eight strata. Consequently, every significant hazard ratio (HR) contrasts non-diabetic patients with diabetic patients, not one season with another. Non-diabetic patients admitted in summer, fall, and winter had near-identical HRs of 0.60 to 0.61, which isolates the effect of diabetes and assigns no independent role to the season. Of particular concern, the model never compared patients with diabetes across seasons and never tested a season-by-diabetes interaction. The crude data also contradict the headline: patients with diabetes admitted in spring recorded all-cause mortality of 13.1%, exceeding the 11.3% in winter [2]. This distinction carries clinical weight because no winter-specific surveillance policy follows these estimates. Furthermore, a median follow-up of 36.7 months with an interquartile range of 36.3 to 37.2 months reveals truncation at a fixed three-year horizon, which reduces the time-to-event analysis to a near-binary endpoint and hides any late divergence in survival rates [3].
2. Selection and confounding structure
The cohort admits only patients with successful PCI and excludes those with incomplete follow-up records. This design removes patients who died before reperfusion or after failed PCI, and those lost during the three years of follow-up, a loss that plausibly differs by.
season and diabetes status. Beyond this, the adjustment set omits variables that sharply separate the two groups. Multivessel disease affected 77–85% of diabetic patients versus 65–72% of non-diabetic patients, and chronic kidney disease (CKD), left main involvement, ST-segment elevation presentation, and discharge therapy stay outside the model. Each of these drives post-infarction mortality independent of season [4].
3. Subgroup interpretation
The authors attributed excess risk to elderly males, yet the estimates rest on confidence intervals with upper limits of 0.96 to 0.97, and no interaction test supports effect modification. The age threshold also shifted between the methods (≥ 65 years) and the results (older than 65 years). Six outcomes across eight strata and multiple subgroup definitions increase the probability of marginal findings.
4. Clinical applicability
The recommendation for intensified glycemic monitoring in colder months lacks data support. Glycated hemoglobin (HbA1c) held steady across seasons at 7.7–7.9%, so glycemic fluctuation does not explain the mortality pattern. Meanwhile, the study reported no antidiabetic therapy, including sodium-glucose cotransporter 2 inhibitors, glucagon-like peptide-1 receptor agonists, or insulin, although the discussion credits these agents with cardiovascular benefit. Taken together, the findings define a diabetes effect, not a winter effect.
Nevertheless, this cohort documented contemporary long-term outcomes after AMI in Chinese patients with diabetes. Within-diabetes seasonal contrasts with formal interaction testing would convert this dataset into a clinically usable tool. Until then, the winter conclusion rests on untested hypotheses.
CRediT authorship contribution statement
V. Manjula: Conceptualization, Data curation, Formal analysis. Rajalaksmi: Writing – review & editing, Writing – original draft, Visualization. Tamanna Koley: Writing – review & editing, Writing – original draft, Visualization. Mohammed Umar: Writing – review & editing, Writing – original draft, Visualization.
Informed consent
Not applicable.
Ethical approval
Not applicable (Commentary on a previously published article).
Declaration of Generative AI and AI-assisted technologies in the writing process
Generative AI tools were used only for language refinement and formatting assistance. All scientific interpretation, critical appraisal, and manuscript preparation were performed independently by the authors.
Funding
None.
Declaration of competing interest
The authors declare that there are no conflicts of interest.
Acknowledgments
None.
Contributor Information
V. Manjula, Email: mvswethamanju@gmil.com.
Rajalaksmi, Email: rajalakshmibaskaran2003@gmail.com.
Tamanna Koley, Email: koleytamanna@gmail.com.
Mohammed Umar, Email: umarrathore0786@gmail.com.
Data availability
No new data were generated or analyzed in this study.
References
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Data Availability Statement
No new data were generated or analyzed in this study.