Machine Learning Sepsis Alert Notification Using Clinical Data (HindSight P2)

Dascena

Status and phase: Unknown; Phase 2

Conditions: Severe Sepsis; Septic Shock; Sepsis

Treatments: Other: InSight; Other: HindSight

Study type: Interventional

Funder types: Other; Industry; NIH

Identifiers

NCT04005001

19-569185

2R44AA030000-02 (U.S. NIH Grant/Contract)

Details and patient eligibility

About

Machine learning is a powerful method to create clinical decision support (CDS) tools, when training labels reflect the desired alert behavior. In our Phase I work for this project, we developed HindSight, an encoding software that was designed to examine discharged patients' electronic health records (EHRs), identify clinicians' sepsis treatment decisions and patient outcomes, and pass those labeled outcomes and treatment decisions to an online algorithm for retraining of our machine-learning-based CDS tool for real-time sepsis alert notification, InSight. HindSight improved the performance of InSight sepsis alerts in retrospective work. In this study, we propose to assess the clinical utility of HindSight by conducting a multicenter prospective randomized controlled trial (RCT) for more accurate sepsis alerts.

Full description

We will evaluate the performance of HindSight in a randomized controlled trial (RCT). HindSight is a novel encoding software designed to optimize alerts for sepsis alert notification. HindSight identifies clinicians' sepsis-related decisions in the electronic health records of former patients and passes those events to InSight, thus supplying InSight with labeled examples of true positive sepsis cases for retraining. In our retrospective work, we have shown that HindSight enables InSight to adapt to site-specific deviations of real-world clinical deployment by successfully reducing false and irrelevant alarms, without human supervision. The goal of this project is to demonstrate that the retrospective success of HindSight can be successfully translated to live clinical environments. In our Phase I work, HindSight achieved an area under the receiver-operating characteristic (AUROC) of 0.899, 0.831 and 0.877 for clinician sepsis evaluation, treatment, and onset, respectively. By using an online learning algorithm to incorporate HindSight-labeled data into the InSight predictor, we showed that the online-trained InSight can adapt to the HindSight-labeled data and outperform both baseline and periodically re-trained versions of InSight (p < 0.05). In Aim 1, we will prospectively validate HindSight's performance on real-time patient data streams in three diverse hospitals non-interventionally. In Aim 2, we will evaluate the effect of the tool in a prospective, interventional RCT. HindSight will first be evaluated by live deployment at four academic and community hospitals, during which time it will not provide alerts of future sepsis onset. Following any necessary algorithm optimization arising from live hospital validation, we will perform an RCT to evaluate reductions in false alerts from InSight trained on HindSight sepsis labels (experimental arm), compared to InSight trained on gold standard Sepsis-3 labels (control arm).

Enrollment: 37,986 estimated patients

Sex: All

Ages: 18+ years old

Volunteers: Accepts Healthy Volunteers

Inclusion criteria

• During the study period, all patients over the age of 18 presenting to the emergency department or admitted to an inpatient unit at the participating facilities will automatically be enrolled in the study, until the enrollment target for the study is met

Exclusion criteria

• Patients under the age of 18
• Prisoners

Trial design

Primary purpose: Other

Allocation: Randomized

Interventional model: Parallel Assignment

Masking: Triple Blind

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37,986 participants in 2 patient groups

Experimental

Experimental group

Description:

The experimental arm will involve patients monitored by HindSight.

Treatment:

Other: HindSight

Control

Active Comparator group

Description:

The control arm will involve patients monitored by InSight.

Treatment:

Other: InSight