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Investigators in the cognitive neurosciences have turned to Big Data to address persistent replication and reliability issues by increasing sample sizes, statistical power, and representativeness of data. While there is tremendous potential to advance science through open data sharing, these efforts unveil a host of new questions about how to integrate data arising from distinct sources and instruments. We focus on the most frequently assessed area of cognition - memory testing - and demonstrate a process for reliable data harmonization across three common measures. We aggregated raw data from 53 studies from around the world which measured at least one of three distinct verbal learning tasks, totaling N = 10,505 healthy and brain-injured individuals. A mega analysis was conducted using empirical bayes harmonization to isolate and remove site effects, followed by linear models which adjusted for common covariates. After corrections, a continuous item response theory (IRT) model estimated each individual subject’s latent verbal learning ability while accounting for item difficulties. Harmonization significantly reduced inter-site variance by 37% while preserving covariate effects. The effects of age, sex, and education on scores were found to be highly consistent across memory tests. IRT methods for equating scores across AVLTs agreed with held-out data of dually-administered tests, and these tools are made available for free online. This work demonstrates that large-scale data sharing and harmonization initiatives can offer opportunities to address reproducibility and integration challenges across the behavioral sciences.
Acute deterioration of liver cirrhosis (e.g., infections, acute‐on‐chronic liver failure [ACLF]) requires an increase in cardiac contractility. The insufficiency to respond to these situations could be deleterious. Left ventricular global longitudinal strain (LV‐GLS) has been shown to reflect left cardiac contractility in cirrhosis better than other parameters and might bear prognostic value. Therefore, this retrospective study investigated the role of LV‐GLS in the outcome after transjugular intrahepatic portosystemic shunt (TIPS) and the development of ACLF. We included 114 patients (48 female patients) from the Noninvasive Evaluation Program for TIPS and Their Follow‐Up Network (NEPTUN) cohort. This number provided sufficient quality and structured follow‐up with the possibility of calculating major scores (Child, Model for End‐Stage Liver Disease [MELD], Chronic Liver Failure Consortium acute decompensation [CLIF‐C AD] scores) and recording of the events (development of decompensation episode and ACLF). We analyzed the association of LV‐GLS with overall mortality and development of ACLF in patients with TIPS. LV‐GLS was independently associated with overall mortality (hazard ratio [HR], 1.123; 95% confidence interval [CI],1.010‐1.250) together with aspartate aminotransferase (HR, 1.009; 95% CI, 1.004‐1.014) and CLIF‐C AD score (HR, 1.080; 95% CI, 1.018‐1.137). Area under the receiver operating characteristic curve (AUROC) analysis for LV‐GLS for overall survival showed higher area under the curve (AUC) than MELD and CLIF‐C AD scores (AUC, 0.688 versus 0.646 and 0.573, respectively). The best AUROC‐determined LV‐GLS cutoff was −16.6% to identify patients with a significantly worse outcome after TIPS at 3 months, 6 months, and overall. LV‐GLS was independently associated with development of ACLF (HR, 1.613; 95% CI, 1.025‐2.540) together with a MELD score above 15 (HR, 2.222; 95% CI, 1.400‐3.528). Conclusion: LV‐GLS is useful for identifying patients at risk of developing ACLF and a worse outcome after TIPS. Although validation is required, this tool might help to stratify risk in patients receiving TIPS.