Serum LDH in preeclampsia: is it a useful biomarker? A meta-analysis

Protocol and registration The present meta-analysis will be designed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The study will be based in aggregated data that have been already published in the international literature. Patient consent and institutional review board approval will not be retrieved as they are not required in this type of studies. Types of studies and patients The eligibility criteria for the inclusion of studies will be predetermined. Observational studies that assess differences in serum LDH levels among women with hypertensive disorders of pregnancy (gestational hypertension, preeclampsia, eclampsia, HELLP syndrome) and healthy controls will be considered eligible for inclusion. Small case series (<10 patients), case reports, conference proceedings and animal studies will be excluded from the present systematic review. Studies that involve pregnant women with comorbidities will be excluded from the present systematic review. The control group will consist of healthy pregnant women, without evidence of any gestational complication. Studies will not be excluded based on preeclampsia definition, since no date restrictions are applied. Information sources and search methods We will use the Medline (1966–2021), Scopus (2004–2021), Clinicaltrials.gov (2008–2021), EMBASE (1980-2021), Cochrane Central Register of Controlled Trials CENTRAL (1999-2021) and Google Scholar (2004-2021) databases in our primary search along with the reference lists of electronically retrieved full-text papers. The date of our last search will be set at March 18, 2021. Our search strategy included the text words “lactate dehydrogenase; LDH; preeclampsia; gestational hypertension; HELLP”. Studies will be selected in three consecutive stages. Following deduplication, the titles and abstracts of all electronic articles will be screened independently by three authors (MP, VP and SS) to assess their eligibility. The decision for inclusion of studies in the present meta-analysis will be taken after retrieving and reviewing the full version of articles that will be considered as potentially eligible. Discrepancies that will arise in this latter stage will be resolved by consensus from all authors. Predefined outcomes Outcome measures will be predefined during the design of the present systematic review. Data extraction will be performed using a modified data form that was based in Cochrane`s data collection form for intervention reviews for RCTs and non-RCTs 16. Investigated outcomes will be predetermined based in our previous systematic review that examined the role of serum uric acid as a predictive factor of preeclampsia. Briefly, the main outcome of interest will be the comparison of serum uric acid levels among preeclamptic and healthy pregnant women in all gestational trimesters. The prognostic role of uric acid isplanned to be evaluated by comparing its levels among women with mild and severe preeclampsia, as well as with eclampsia and HELLP (hemolysis, elevated liver enzymes and low platelet count) syndrome. The diagnostic accuracy of serum uric acid in terms of predicting adverse perinatal outcomes (rate of cesarean section, fetal growth restriction, low birth weight, preterm birth, 5’ Apgar score <7, fetal or neonatal death) will be also evaluated. Assessment of risk of bias and quality of evidence The methodological quality of the included studies will be assessed by two independent reviewers (V.P and S.S.) using the Newcastle Ottawa scale. The scale incorporates domains that include assessment of selection of study groups, comparability of groups and methods ascertainment of the outcome of interest. A point is awarded in a 9-point based system. For the purposes of the present systematic review the two points that are awarded for comparability of groups will refer to differences in baseline gestational age at assessment of serum LDH and in maternal age. Quality of evidence will be evaluated under the Grading of Recommendations Assessment, Development and Evaluation (GRADE) framework, ranging from very low to high. More specifically, credibility of evidence will be assessed by taking into account the following domains: study limitations, directness, consistency, precision and publication bias. In particular, study limitations will be evaluated based on risk of bias assessments (NOS score), while directness will be judged using the PICOS (population, intervention, comparison, outcome, study type) approach. To assess consistency and precision, clinically important effects will be defined after determining the 95% CI of individual studies. In this context, consistency will refer to the agreement of 95% confidence and prediction intervals for each outcome in relation to clinically relevant effects, while precision assessment will be made by taking into account whether 95% CI extended into the range of equivalence. Statistical analysis Statistical meta-analysis will be performed with RStudio using the meta and metafor functions (RStudio Team (2015). RStudio: Integrated Development for R. RStudio, Inc., Boston, MA URL http://www.rstudio.com/). Statistical heterogeneity will not be considered during the evaluation of the appropriate model of statistical analysis as the anticipated methodological heterogeneity of included studies is anticipated to not leave space for assumption of comparable effect sizes among studies included in the meta-analysis. Confidence intervals will be set at 95%. We will calculate pooled risk ratios mean differences (MD) and 95% confidence intervals (CI) with the Hartung-Knapp-Sidik-Jonkman instead of the traditional Dersimonian-Laird random effects model analysis (REM). The decision to proceed with this type of analysis was taken after taking into consideration recent reports that support its superiority compared to the Dersimonian-Laird model when comparing studies of varying sample sizes and between-study heterogeneity. Subgroup analysis will be conducted on the basis of pregnancy trimester (1st, 2nd or 3rd), preeclampsia onset (early or late), severity (mild or severe) and complications (eclampsia and HELLP syndrome). Residual heterogeneity will be explored by conducting meta-regression analysis taking into account the following parameters: year of publication, sample size (using a cut-off of 100 patients in at least one arm of the analysis), region (stratified in North America, Europe and other countries), Newcastle-Ottawa Scale score, study design, type of sample and definition of preeclampsia. Meta-regression will not be performed for covariate levels with <3 studies. Publication bias will be assessed by examining the possibility of small-study effects through the visual inspection of funnel plots. The asymmetry of funnel plots will be statistically evaluated using the Egger’s regression and Begg-Mazumdar’s rank correlation tests. Publication bias will be evaluated by examining the potential presence of small-study effects through the visual inspection of funnel plots. Prediction intervals Prediction intervals (PI) wll be calculated as well, using the meta function in RStudio, to evaluate the estimated effect that is expected to be seen by future studies in the field. The estimation of prediction intervals takes into account the inter-study variation of the results and express the existing heterogeneity at the same scale as the examined outcome. Trial sequential analysis To evaluate the information size, we will perform trial sequential analysis (TSA) which permits investigation of the type I error in the aggregated result of meta-analyses performed for primary outcomes that were predefined in the present meta-analysis. A minimum of 3 studies will be considered as appropriate to perform the analysis. Repeated significance testing increases the risk of type I error in meta-analyses and TSA has the ability to re-adjust the desired significance level by using the O` Brien-Flemming a-spending function. Therefore, during TSA sequential interim analyses are performed that permit investigation of the impact of each study in the overall findings of the meta-analysis. The risk for type I errors was set at 5% and for type II errors at 20%. The TSA analysis was performed using the TSA v. 0.9.5.10 Beta software (http://www.ctu.dk/tsa/).

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