Prospective Evaluation of a 90-day Mortality Prediction Model: From Silent Pilots to Real Time Deployment in the EHR*

Prognostication in oncology is increasingly difficult due to the rapid evolution of therapies with significant improvement of survival. Accurate prognostication is essential to provide optimal, value-driven end of life care for cancer patients, and can promote goals of care (GOC) conversations with the potential to minimize chemotherapy or ICU utilization in the last weeks of life, and possibly increase hospice admission and length of stay.[1][1] There are several recent publications on the application of machine learning for prognostication.[2][2],[3][3] We developed a 90-day mortality prediction model trained with data in the Electronic Health Records (EHR). After a non-interventional pilot stage, we deployed the model in February 2021 in the real-time Electronic Health Record Epic infrastructure of our cancer center. Here we present the model and evaluate its overall performance for the first 7.5 months since the go-live and outline our evaluation process for the next stages. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study did not receive any funding. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: City of Hope Institutional Review Board gave approval for this work. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes The data associated with the current study is not publicly available as the data are not legally certified as being deidentified. Summary data may be available by contacting the corresponding author. [1]: #ref-1 [2]: #ref-2 [3]: #ref-3