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We delved into the article titled “Optimizing post-craniotomy recovery: insights from symptom network analysis in primary brain tumor patients” authored by Rongqing and Zikai Zhang, with keen interest [1]. Their use of symptom network analysis highlights key symptom interrelationships, offering a clearer path to better symptom management and early complication detection. Unquestionably, the integration of these insights into ERAS protocols holds great potential for improving patient care and setting new standards for post-craniotomy recovery. Despite this, we have certain reservations that have compelled us to draft this letter.
Firstly, one of the crucial factors that warrants consideration in this study is the role of age in post-craniotomy recovery. As individuals age, their physiological capacity for tissue repair diminishes, making surgical recovery more challenging. Older patients may not only take longer to heal but could also experience more severe symptoms compared to younger individuals [2]. Given the increased vulnerability of this demographic, it is essential to explore age-specific symptom networks to optimize recovery strategies and better manage postoperative complications in older adults.
Secondly, this study provides valuable insights into symptom networks, it lacks a comprehensive discussion on how to manage these symptoms postoperatively. Identifying central and bridge symptoms is critical, but practical recommendations for managing these symptoms are equally important for clinicians [3, 4]. Offering guidance on how to intervene on central and bridge symptoms, such as sadness or loss of appetite, could significantly improve patient outcomes and recovery. Without a clear approach to managing these key symptoms, the clinical utility of the findings remains limited.
Thirdly, the study’s sample size of 211 patients from a single institution, Shanghai Tongji Hospital, is a notable limitation. Expanding the sample size and including a more diverse patient population across multiple centers would improve the generalizability of the findings, making the results more applicable to a broader range of patients and clinical settings [5].
Lastly, the study should consider extending the observation period beyond the initial 24 h post-craniotomy. Capturing symptom evolution over a longer timeframe would offer deeper insights into the dynamics of symptom networks and highlight critical intervention points. Longitudinal analysis could reveal how symptoms develop and interact over time, providing more comprehensive data for managing postoperative care. Additionally, further follow-up assessments could help identify key factors influencing recovery in patients who have undergone surgery [6].
Conclusion
In conclusion, while the study offers valuable insights into symptom networks, addressing these limitations, considering age factors, providing practical management strategies, expanding the sample size, and extending the observation period could significantly enhance its clinical relevance and effectiveness in improving patient care.
Data availability
No datasets were generated or analysed during the current study.
References
Li R, Zhang Z, Zhang X et al (2024) Optimizing post-craniotomy recovery: insights from symptom network analysis in primary brain tumor patients. Neurosurg Rev 47(1):565 Published 2024 Sep 7. https://doi.org/10.1007/s10143-024-02804-3
Stachniak JB, Layon AJ, Day AL, Gallagher TJ (1996) Craniotomy for intracranial aneurysm and subarachnoid hemorrhage. Is course, cost, or outcome affected by age? Stroke 27(2):276–281. https://doi.org/10.1161/01.str.27.2.276
Gruenbaum SE, Guay CS, Gruenbaum BF et al (2021) Perioperative Glycemia Management in patients undergoing craniotomy for Brain Tumor Resection: A Global Survey of neuroanesthesiologists’ perceptions and practices. World Neurosurg 155:e548–e563. https://doi.org/10.1016/j.wneu.2021.08.092
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Nohr EA, Liew Z (2018) How to investigate and adjust for selection bias in cohort studies. Acta Obstet Gynecol Scand 97(4):407–416. https://doi.org/10.1111/aogs.13319
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Taj, I. Letter to Editor: Optimizing post-craniotomy recovery: Insights from symptom network analysis in primary brain tumor patients. Neurosurg Rev 47, 720 (2024). https://doi.org/10.1007/s10143-024-02965-1
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DOI: https://doi.org/10.1007/s10143-024-02965-1