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Assessing the robustness of meta-analyses to variability in the frequency and timing of postoperative delirium assessment – a novel methodological approach

  • Maram Khaled1,2
  • Harsha Shanthanna3,1
  • Lehana Thabane3,1
  • James Paul3,1
  • Anthony Absalom4
  • Sergio Bergese5
  • Federico Bilotta6
  • Douglas Campbell7
  • Matthew Chan8
  • Mark Coburn9
  • David Cowan10
  • Kamal Maheshwari11
9 more authors
  • Edward Mascha12
  • Robert D Sanders13
  • Daniel I Sessler14
  • Tim Short7
  • Jessica Spence3,1,2
  • Alparslan Turan15
  • Dong-Xin Wang16
  • Maria Wittmann17
  • Maura Marcucci18,1,2
18 affiliations
  1. 1Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, Canada.
  2. 2Perioperative and Surgery Research Program, Population Health Research Institute, David Braley Research Institute, Hamilton, Canada.
  3. 3Department of Anesthesia, McMaster University, Hamilton, Canada.
  4. 4Department of Anesthesiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
  5. 5Department of Anesthesiology, Neurological Surgery, and Outcomes and Clinical Research in Perioperative Medicine, Stony Brook University, New York, United States of America.
  6. 6Departments of Anesthesiology, Critical Care, and Pain Medicine, Sapienza University of Rome, Rome, Italy.
  7. 7Department of Anesthesia and Perioperative Medicine, Auckland City Hospital, Auckland, New Zealand.
  8. 8Department of Anaesthesia and Intensive Care, The Chinese University of Hong Kong, Hong Kong Special Administration, Hong Kong, China.
  9. 9Department of Anesthesiology and Intensive Care Medicine, University Hospital Bonn, Bonn, Germany.
  10. 10Department of Medicine, McMaster University, Hamilton, Canada.
  11. 11Department of Anesthesiology, Kaiser South Sacramento, California, United States of America.
  12. 12Department of Quantitative Health Sciences, Cleveland Clinic, Ohio, United States of America.
  13. 13Faculty of Medicine and Health, University of Sydney, Sydney, Australia.
  14. 14Center for Outcomes Research and Departments of Anesthesiology and Biostatistics, U T Health, Texas, United States of America.
  15. 15Center for Outcomes Research and Department of Anesthesiology, Critical Care and Pain Medicine, U T Health, Texas, United States of America.
  16. 16Department of Anesthesiology, Peking University First Hospital, Beijing, China.
  17. 17Clinic and Polyclinic for Anesthesiology and Operative Intensive Care Medicine, University of Bonn, Germany.
  18. 18Clinical Epidemiology and Research Centre (CERC), Department of Biomedical Sciences, Humanitas University and IRCCS Humanitas Research Hospital, Milan, Italy.
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Abstract

Background: Postoperative delirium (POD) is a fluctuating confusional state occurring early after surgery. Given its fluctuating nature, POD detection is influenced by the frequency and timing of screening. Sparse assessments potentially underestimate the true incidence reported in individual studies, subsequently influencing the weight of studies in meta-analyses, and the results and precision of these meta-analyses. Objective: To develop a method to evaluate the robustness of meta-analyses to the variation in the frequency and timing of POD assessment across included studies. Methods: We developed an expert-based scoring system to rate studies according to the confidence that a patient reported not to have POD in the study truly did not, based on the reported timing and frequency of POD assessments. We describe how to use the system to generate hypothetical event rates for studies if the frequency and timing of POD assessment were optimal and how to use these rates in a sensitivity analysis of meta-analyses including these studies. We illustrate the method applying it to two meta-analyses as work examples, one of observational studies and one of randomized controlled trials (RCTs). Results: In our work examples, the expert-based score for the individual studies ranged from 33–77% for the observational and 22–70% for RCTs meta-analysis. Sensitivity analyses showed a change in the pooled effect size, heterogeneity, and precision compared to the original estimates. Conclusion: We present a method to evaluate the robustness of a meta-analysis of studies having POD as an outcome, when there is variation across individual studies in the frequency and timing of POD assessment. The confidence score acts as a preliminary index of the robustness of meta-analyses. Through work examples, we showed that the robustness depends on the characteristics of individual studies included.

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Cite

BibTeXRIS
BibTeX
@article{khaled2026assessing,
  title = {{Assessing the robustness of meta-analyses to variability in the frequency and timing of postoperative delirium assessment – a novel methodological approach}},
  author = {Maram Khaled and Harsha Shanthanna and Lehana Thabane and James Paul and Anthony Absalom and Sergio Bergese and Federico Bilotta and Douglas Campbell and Matthew Chan and Mark Coburn and David Cowan and Kamal Maheshwari and Edward Mascha and Robert D Sanders and Daniel I Sessler and Tim Short and Jessica Spence and Alparslan Turan and Dong-Xin Wang and Maria Wittmann and Maura Marcucci},
  journal = {PLOS One},
  year = {2026},
  month = oct,
  doi = {10.1371/journal.pone.0361124},
  url = {https://doi.org/10.1371/journal.pone.0361124},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Assessing the robustness of meta-analyses to variability in the frequency and timing of postoperative delirium assessment – a novel methodological approach
AU  - Maram Khaled
AU  - Harsha Shanthanna
AU  - Lehana Thabane
AU  - James Paul
AU  - Anthony Absalom
AU  - Sergio Bergese
AU  - Federico Bilotta
AU  - Douglas Campbell
AU  - Matthew Chan
AU  - Mark Coburn
AU  - David Cowan
AU  - Kamal Maheshwari
AU  - Edward Mascha
AU  - Robert D Sanders
AU  - Daniel I Sessler
AU  - Tim Short
AU  - Jessica Spence
AU  - Alparslan Turan
AU  - Dong-Xin Wang
AU  - Maria Wittmann
AU  - Maura Marcucci
PY  - 2026
DA  - 2026/10/22
JO  - PLOS One
DO  - 10.1371/journal.pone.0361124
UR  - https://doi.org/10.1371/journal.pone.0361124
AB  - Background: Postoperative delirium (POD) is a fluctuating confusional state occurring early after surgery. Given its fluctuating nature, POD detection is influenced by the frequency and timing of screening. Sparse assessments potentially underestimate the true incidence reported in individual studies, subsequently influencing the weight of studies in meta-analyses, and the results and precision of these meta-analyses. Objective: To develop a method to evaluate the robustness of meta-analyses to the variation in the frequency and timing of POD assessment across included studies. Methods: We developed an expert-based scoring system to rate studies according to the confidence that a patient reported not to have POD in the study truly did not, based on the reported timing and frequency of POD assessments. We describe how to use the system to generate hypothetical event rates for studies if the frequency and timing of POD assessment were optimal and how to use these rates in a sensitivity analysis of meta-analyses including these studies. We illustrate the method applying it to two meta-analyses as work examples, one of observational studies and one of randomized controlled trials (RCTs). Results: In our work examples, the expert-based score for the individual studies ranged from 33–77% for the observational and 22–70% for RCTs meta-analysis. Sensitivity analyses showed a change in the pooled effect size, heterogeneity, and precision compared to the original estimates. Conclusion: We present a method to evaluate the robustness of a meta-analysis of studies having POD as an outcome, when there is variation across individual studies in the frequency and timing of POD assessment. The confidence score acts as a preliminary index of the robustness of meta-analyses. Through work examples, we showed that the robustness depends on the characteristics of individual studies included.
SN  - 1932-6203
ER  -