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Old PRBCs linked to increased mortality across 16 studies
Table 1. Data synthesis method. Demographic variables
Age
BMI
Ejection fraction Outcome variables Mortality
Surgical wound infection Any infection
ical ventilation; SOFA: sepsis-related organ failure assessment score.
sample sizes with older mean ages of transfused PRBC, making them uniquely positioned to identify small adverse effects owing to PRBC units at the end of shelf life.10
One of the key issues with combining data from obser- vational studies is the diverse methods of describing aggre- gate PRBC age, ranging from mean PRBC age to dichotomization at “x” days to maximum PRBC age trans- fused.5 Some paper level meta-analyses have used various adjustments with the aim of unifying aggregate PRBC age measurements and maximizing PRBC age differences between comparison groups.11 However, the temporal effect of storage-induced adverse outcomes has been rela- tively unmapped, and there is no evidence that different measures of aggregate PRBC age are interchangeable in their association with clinical outcomes.12 Moreover, the assumptions used to convert one aggregate measure to another (e.g., median to mean) could potentially lead to statistical inaccuracy. This issue could be abrogated via pooled patient data analysis allowing the use of one aggre- gate PRBC age measure. Pooled patient data analysis also touts improved subgroup analyses and consistency across studies compared to paper level analysis.13
This study analyzed pooled individual patient data (IPD) from 16 observational studies with the aim of quan- tifying the association of PRBC storage duration on mor- tality, nosocomial infection and HLOS. In representing 16 retrospective studies and over 17,000 patients – the study herein is one of the largest pooled patient data analysis completed for the investigation of storage-induced adverse PRBC transfusion outcomes to date.
Methods
Study selection
Institutional ethics approvals were sought from the University of Queensland and The Alfred Hospital prior to initiation of study. Observational studies reporting PRBC storage duration and clinical outcomes such as mortality, infection and HLOS were identified from PubMed and EMBASE using protocols
Duration of MV HLOS
ILOS
MODS
Calculated from date of birth and date of first transfusion
Calculated from height and weight
Classified as good >50%, fair 30-50%, poor <30%
In-hospital mortality used whenever possible; when not reported, the mortality over the shortest duration used (e.g. 30-day mortality over 90-day mortality)
General surgery: abdominal + perineal wound infections, intra-abdominal abscess Cardiothoracic surgery: sternal infection, anastomosis infection
Any infectious outcome recorded including pneumonia, surgical wound infection, bloodstream infection, urinary tract infection
Converted to days when reported as hours
Calculated from hospital admission and discharge date
Calculated from ICU admission and discharge date
Peak SOFA score ≥6 provided that SOFA score<6 prior to intensive care unit admission
BMI: body mass index; HLOS; hospital length of stay; ICU: intensive care medicine; ILOS: intensive care length of stay; MODS: multiple organ dysfunction syndrome; MV: mechan-
haematologica | 2018; 103(9)
described previously.5 Corresponding investigators of each study were contacted to request the underlying patient-level dataset.
Data extraction and synthesis
Demographic, intervention and outcome variables reported in more than three studies were combined into one Microsoft Excel 2016 spreadsheet by author Monica S.Y. Ng. and checked by author Angela S.Y. Ng. Patients who did not receive any PRBC units were excluded from the dataset. Variables not reported in the desired format were calculated from primary data where available. Table 1 demonstrates adjustments made to synthesize the aggregate datasheet.14 When individual PRBC unit ages were available, the aggregate ages of PRBC transfu- sions were expressed as mean age or maximum PRBC age. In so doing, aggregate PRBC ages were expressed in a time-indepen- dent manner for incorporation into logistic models.
Data analysis
A two-stage meta-analysis using IPD was used to account for differences between study cohorts in general analyses. This approach has been shown to increase statistical power and avoid ecological bias when compared with the traditional approach which pools study estimates.15,16 In the first stage, the association between binary outcomes (such as in-hospital mor- tality or nosocomial infection) and PRBC age (expressed as mean PRBC age or maximum PRBC age) were calculated using bino- mial logistic regression for each study with age, sex and PRBC volume as continuous covariates. PRBC age (mean PRBC age, maximum PRBC age), recipient age and PRBC volume were incorporated as continuous variables, while sex was included as a binary variable. Each effect estimate was reported as an odds ratio (OR) with 95% confidence intervals (CI). Regarding HLOS, and due to an excessive number of zeroes, zero-inflated Poisson regression modelling was used to calculate the incidence rate ratio (IRR) for an additional day in hospital as a function of PRBC age (expressed as mean PRBC age or maximum PRBC age) for each paper. In the second stage, random effects models were used to combine the effect estimates for each paper. Funnel plots were generated for each analysis involving more than ten papers to assess for publication bias. This threshold was used as funnel
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