Lack of up-to-date studies and data conversion may lead to biased results: a response to Wang et al

We would like to thank Wang et al. for reading carefully our work and bringing up the comments [1]. Regarding the deadline for literature searches on November 1, 2022, and the potential removal of crucial data, It should be said that the mentioned studies [2, 3], which focused on COVID-19 patients, were identified in our initial search. Nonetheless, we made a deliberate decision to exclude COVID-19 patients from our analysis based on stringent inclusion criteria aimed at maintaining the focus and stability of our review. The reason for this exclusion lies in the unique pathophysiological mechanisms and clinical management associated with COVID-19, which could confound the incidence and outcomes of hospital infections in Extra corporeal membrane oxygenation (ECMO) patients. The objective of our study, focusing on a homogeneous patient population, is to provide clearer insights into the incidence and risk factors for hospital infections in ECMO patients without the confounding variables introduced by COVID-19. However, we believe that our findings remain valuable for understanding the general incidence and risk factors for hospital infections in adult patients undergoing ECMO.

We recognize the importance of applying appropriate transformations to ratio data when conducting meta-analyses, as untransformed raw ratio data may deviate from the assumptions of normal distribution. In our study, we primarily reported the incidence of nosocomial infections as pooled ratios with a 95% confidence interval and utilized the random-effects restricted maximum likelihood Model to account for methodological variations and sample diversity across studies. Given the significant heterogeneity observed, we performed sensitivity analyses to identify influential studies and assess its impact on our pooled estimates. We agree that conducting a sensitivity analysis that compares results from transformed data with untransformed data could enhance the robustness of our findings. While our current approach provides valuable insights, using a logit transformation or Freeman-Tukey double arcsine transformation, as you suggested, stabilizes variance and may potentially yield different results, although these differences are likely to be minimal and would not lead to biased conclusions (Table 1).

Table 1 Pooled incidence nosocomial infection per 1000 ECMO-day based on three methods

Full size table

Furthermore, it is essential to emphasize that the integrity of our results has been strengthened by thorough assessments for heterogeneity, publication bias, and data behavior through various statistical evaluations. As shown in our manuscript [4], we have already conducted extensive methodological reviews, including the use of funnel plots and Egger’s tests for publication bias, which indicate that our findings provide a reliable estimate of incidence across the included studies.

No datasets were generated or analysed during the current study.

ECMO:

Extra corporeal membrane oxygenation

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2. Barbaro RP, MacLaren G, Boonstra PS, Combes A, Agerstrand C, Annich G, et al. Extracorporeal membrane oxygenation for COVID-19: evolving outcomes from the international extracorporeal life support organization registry. Lancet. 2021;398(10307):1230–8.

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4. Ait Hssain A, Vahedian-Azimi A, Ibrahim AS, Hassan IF, Azoulay E, Darmon M. Incidence, risk factors and outcomes of nosocomial infection in adult patients supported by extracorporeal membrane oxygenation: a systematic review and meta-analysis. Crit Care. 2024;28(1):158.

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Thanks to guidance and advice from the “Clinical Research Development Unit" of Baqiyatallah Hospital.

This research did not receive any specific grant from funding agencies in the public, commercial, or not‑for‑profit sectors.

Authors and Affiliations

1. Medical Intensive Care Unit, Hamad General Hospital, Doha, Qatar

   Ali Ait Hssain, Abdulsalam Saif Ibrahim & Ibrahim Fawzy Hassan

2. Department of Medicine, Weill Cornell Medical College, Doha, Qatar

   Ali Ait Hssain, Abdulsalam Saif Ibrahim & Ibrahim Fawzy Hassan

3. College of Health and Life Science, Hamad Bin Khalifa University, Doha, Qatar

   Ali Ait Hssain

4. Nursing Care Research Center, Clinical Sciences Institute, Nursing Faculty, Baqiyatallah University of Medical Sciences, Tehran, Iran

   Amir Vahedian-Azimi

5. Médecine Intensive Et Réanimation, Hôpital Saint-Louis, Assistance Publique-Hôpitaux de Paris, University of Paris, Paris, France

   Elie Azoulay & Michael Darmon

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A. AH, A.VA, M. D, A.SI, I. FH and E.A contributed to manuscript revision, read, and approved the submitted version equally.

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Ait Hssain, A., Vahedian-Azimi, A., Ibrahim, A.S. et al. Lack of up-to-date studies and data conversion may lead to biased results: a response to Wang et al. Crit Care 28, 441 (2024). https://doi.org/10.1186/s13054-024-05223-4

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• Received: 24 October 2024

• Accepted: 19 December 2024

• Published: 31 December 2024

• DOI: https://doi.org/10.1186/s13054-024-05223-4

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