(untitled) (Journal of Statistics on Health Decision, 2022)
Introduction: Meta-analysis is the method to pool data from studies that report the same event in a comparable sample and, thus, summarize information from different populations. However, data extraction is limited to the information provided in the included studies. Frequently, individual patient information is not available, hence, real world data access is limited. Individual patient data (IPD) meta-analysis in a research area like health sciences constitutes the ideal method to analyse real world data, assuming a high level of evidence. (1) This method is based on a collaboration network in which variables can be merged among datasets once a single definition is determined reducing methodological heterogeneity. The use of IPD for meta-analysis has advantages, (allows adjustment for confounding factors), and limitations (obtaining individual patient data knowing that data collection and management are often unclear). (1, 2) To tackle high heterogeneity in a study-level meta-analysis, sensitivity analyses are recommended. Leave-one-out analysis is an option that evaluates the cumulative effect of each study by excluding one at a time, portraying its influence in the overall result (3). Considering the lack of long-term data on postoperative acute kidney injury (AKI) in coronary artery bypass grafting surgery (CABG) patients, paired with the expected study heterogeneity, we intend to summarize evidence regarding its effect on early and long-term survival, evaluate heterogeneity and perform sensitivity analysis. Methods: Systematic searches were performed in MEDLINE and ISI Web of Science, restricted by date of publication (January 1960–April 2021). Inclusion criteria comprised observational studies reporting incidence of postoperative AKI in adult patients, submitted to isolated CABG with at least 1-year of follow-up and survival estimates. Analyses were performed using Review Manager 5.4. and R environment. Random effects models were used to compute pooled HR and OR (95% CI), through generic inverse variance method and Mantel-Haenszel method or generalized linear mixed-effects model and logit-transformed proportions of AKI were used for analysis of prevalence data. Heterogeneity was defined using I2 statistics and was considered low (<49%), moderate (50-74%), or high (>75%) (4). Between-study heterogeneity was studied using sensitivity analyses, namely, leave-one-out evaluations (metagen (meta R package)). Early and late survival were evaluated through this sensitivity analysis and were present according to each study impact on both effect size estimates and observed I2. (5) Results: Thirteen retrospective observational studies comprising 63209 patients were included, (N AKI=11366, N non-AKI=51843). AKI incidence ranged 0.6%-54% with a pooled incidence of 16%. Postoperative AKI was associated with higher early mortality (OR (95%CI): 7.59 (3.18-18.15), p<0.01; I2=94%, Chi2=80.14, Tau2=1.06, p<0.01), but also with long-term mortality (HR (95%CI): 2.23 (1.83-2.70), p<0.01), with moderate heterogeneity (I2=74%, Chi2=42.92, Tau2=0.08, p<0.01). In early mortality results, we observed a high effect on the leave-one-out analysis on both effect size (Figure 1) and I2 (Figure 2) by Ivert et al. (6) study which showed a relevant impact on heterogeneity. After removing this study, I2 reduced to 0% and the pooled OR decreased and the 95% CI narrowed, (4.78 (3.74 - 6.09), p<0.001). We observed the same for long-term mortality (Figures 3 and 4). The 2 most relevant studies for heterogeneity were Di Mauro et al. (7) and Lv et al. (8). Although I2 remained higher than 50% (I2=65%), after removing these two studies, the long-term survival pooled HR decreased and the 95% CI also narrowed, (1.98 (1.68-2.35), p<0.001). Discussion and Conclusion: AKI is a frequent complication after isolated CABG surgery, being associated with higher long-term mortality. The high heterogeneity found may be due to the different populations and variables’ definitions. For instance, Ivert et al. (6) focused their analysis on patients who required postoperative dialysis, translating on a lower incidence of AKI, but also higher severity. Di Mauro et al. (9) defined AKI as an increase above 1 mg/dL or a postoperative value > 2 mg/dL according to preoperative renal function status, which differs from the most traditional criteria used by the other studies: increment of at least 0.3 mg/dL. Leave-one-out analysis showed that one must pay attention to real-world data, since different variable definitions, namely: different AKI definitions and the samples included in each study, influence overall measures. Acknowledgements: This study is funded by national funds through FCT - Portuguese Foundation for Science and Technology, under the scope of the Cardiovascular R&D Center – UnIC@RISE [grant numbers UIDB/00051/2020, UIDP/00051/2020]. R. Moreira is supported by FCT - Portuguese Foundation for Science and Technology, FSE – European Social Fund, NORTE 2020 - Programa Operacional Regional do Norte (POR-Norte), (UI/BD/150657/2020). References: Riley RD, Lambert PC, Abo-Zaid G. Meta-analysis of individual participant data: rationale, conduct, and reporting. Bmj. 2010;340. Kanters S, Karim ME, Thorlund K, Anis A, Bansback N. When does the use of individual patient data in network meta-analysis make a difference? A simulation study. BMC medical research methodology. 2021;21(1):1-13. Wallace BC, Schmid CH, Lau J, Trikalinos TA. Meta-Analyst: software for meta-analysis of binary, continuous and diagnostic data. BMC medical research methodology. 2009;9(1):1-12. 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