Abstract
We analyzed data from 13,483 hospitalized patients with acute kidney injury (AKI) from three randomized controlled trials to assess the heterogeneous effects of automated electronic alerts on 14-day mortality. We modeled and predicted individualized alert effects on a subset of the ELAIA-1 patients and validated it internally on ELAIA-1 holdout patients and externally on ELAIA-2 and UPenn trial patients. Patients predicted to benefit from alerts had significantly lower mortality compared to those predicted to be harmed (p-interaction<0.05). In external cohorts, 43 deaths may have been preventable if alerts were restricted to likely beneficiaries. Machine-learning based meta-analysis identified reduced mortality with alerts among patients with higher blood pressures and lower predicted risk, but increased mortality in non-urban and non-teaching hospitals. Provider responses to alerts varied across subgroups. These findings suggest that tailoring alerts to patient phenotypes may improve outcomes and support the need for a prospective trial of individualized alert strategies. TRIAL REGISTRATION: https://clinicaltrials.gov/ct2/show/NCT02753751 and https://clinicaltrials.gov/ct2/show/NCT02771977.