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Unsupervised Machine Learning for Clustering of Septic Patients to Determine Optimal Treatment

NCT03752489 — Phase 2, Unknown, 51645 participants, Dascena
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RegistryNCT03752489
StatusUnknown
PhasePhase 2
Study typeInterventional; randomized, parallel, masking: triple, diagnostic
Enrollment51645 (Estimated)
SponsorDascena (Industry)
ConditionsSepsis, Severe Sepsis, Septic Shock
InterventionsTreatment-specific InSight, InSight (Diagnostic test)
Datesstart 2022-04-01 (estimated), primary completion 2024-03-31 (estimated), completion 2024-03-31 (estimated), first posted 2018-11-26, last updated 2021-09-23

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-Summary

The focus of this study will be to conduct a prospective, randomized controlled trial (RCT) at Cape Regional Medical Center (CRMC), Oroville Hospital (OH), and UCSF Medical Center (UCSF) in which a fluid treatment-specific algorithm will be applied to EHR data for the detection of severe sepsis. For patients determined to have a high risk of severe sepsis, the algorithm will generate automated voice, telephone notification to nursing staff at CRMC, OH, and UCSF. The algorithm's performance will be measured by analysis of the primary endpoint, reductions in in-hospital mortality.

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+Arms and interventions

ArmTypeInterventionsDescription
Fluid treatment-specific algorithmExperimentalDiagnostic Test: Treatment-specific InSightThe experimental arm will involve patients monitored by the fluid treatment-customized version of InSight.
Standard InSightActive comparatorDiagnostic Test: InSightThe control arm will involve patients monitored with the standard, non-treatment specific version of InSight.
  • Treatment-specific InSight (Diagnostic test): The InSight algorithm which draws information from a patient's electronic health record (EHR) to predict the onset of severe sepsis, and in this study will be customized to differentiate between clusters of patients who respond similarly to fluids treatment according to the nature of their disease progression.
  • InSight (Diagnostic test): The non-customized InSight algorithm which draws information from a patient's electronic health record (EHR) to predict the onset of severe sepsis.

+Outcome measures

Primary outcomes

  • In-hospital SIRS-based mortality [Through study completion, an average of 8 months]: Mortality attributed to patients meeting two or more SIRS criteria at some point during their stay

+Eligibility

Sex: All, ages: 18 Years to any, accepts healthy volunteers

Inclusion Criteria:

* All adults above age 18 who are a member of one of the clinical subpopulations studied in this trial are eligible to participate in the study.

Exclusion Criteria:

* Under age 18

+Publications

  • Calvert J, Mao Q, Hoffman JL, Jay M, Desautels T, Mohamadlou H, Chettipally U, Das R. Using electronic health record collected clinical variables to predict medical intensive care unit mortality. Ann Med Surg (Lond). 2016 Sep 6;11:52-57. doi: 10.1016/j.amsu.2016.09.002. eCollection 2016 Nov. PMID 27699003
  • Shimabukuro DW, Barton CW, Feldman MD, Mataraso SJ, Das R. Effect of a machine learning-based severe sepsis prediction algorithm on patient survival and hospital length of stay: a randomised clinical trial. BMJ Open Respir Res. 2017 Nov 9;4(1):e000234. doi: 10.1136/bmjresp-2017-000234. eCollection 2017. PMID 29435343
  • Mao Q, Jay M, Hoffman JL, Calvert J, Barton C, Shimabukuro D, Shieh L, Chettipally U, Fletcher G, Kerem Y, Zhou Y, Das R. Multicentre validation of a sepsis prediction algorithm using only vital sign data in the emergency department, general ward and ICU. BMJ Open. 2018 Jan 26;8(1):e017833. doi: 10.1136/bmjopen-2017-017833. PMID 29374661

+Investigators

My Notes