We analyzed data from 13,483 hospitalized patients with acute kidney injury (AKI) from three randomized controlled trials to assess the heterogenous 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.

The authors have declared no competing interest.

This work was partially supported by a grant from the National Institutes of Health (F31 NS115447) to Benjamin Wissel.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

The institutional review board of Duke University Health System gave ethical approval for this work (#00115914), and all trials data utilized were previously approved. All patient data was obtained from other groups, retrospectivley reviewed, and analyzed. No patients were prospectivley assigned nor treatment altered from our findings.

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