RCT of Sepsis Machine Learning Algorithm

Study Overview
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Brief Summary
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The focus of this study will be to conduct a prospective, multi-center randomized controlled trial (RCT) at Cape Regional Medical Center (CRMC), Oroville Hospital (OH), and UCSF Medical Center (UCSF) in which a machine-learning algorithm will be applied to EHR data for the detection of sepsis. For patients determined to have a high risk of 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, in-hospital SIRS-based mortality.

Detailed Description
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From January 2020 to February 2021, inclusive, investigators will perform a multi-center randomized controlled trial (RCT) at CRMC, OH, and UCSF. All aims of this study have been have been submitted for approval by the Pearl Institutional Review Board with a waiver of informed consent. 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 trial, until the target enrollment for the study is met. Enrollment will entail randomization to either the control or the experimental arms. Patients will be assigned to the experimental group or control group based on a random allocation sequence, generated by a computer program before the start of the trial, using simple randomization, with a 1:1 allocation ratio. This allocation sequence will be concealed to patients, healthcare providers and study investigators. However the trial will have an open-label design, as full blinding is not possible as some group assignments will become naturally revealed upon receipt of telephonic alerts. There will be two arms in the study. The control arm will involve patients with the usual standard of care, and the experimental arm will involve patients monitored by InSight. If the applicable algorithm determines a patient to be at a high risk for sepsis, a telephonic alert will be sent to the charge nurse on duty in the patient's current location. Response to alerts will follow the protocol from our previous sepsis clinical trial. The procedure consists of a nurse conducting a patient bedside evaluation to rule out suspected infection. This includes assessment of patient vital signs, EHR notes, and recent laboratory results. If the nurse suspects sepsis, a physician subsequently assesses the patient and, if appropriate, places an order for administration of the standard sepsis treatment bundle. In the administration of clinical trials, some open-label studies are cluster-randomized while others are randomized at an individual patient level. Cluster randomization is frequently used to minimize contamination between treatment and control groups, because exposure of providers to patients from both arms in an open-label study often invites unintentional behavioral biases. These biases may cause providers to adjust their interventions in the control group to mimic their actions in the experimental group, thereby masking the intervention's effect and skewing the study results towards the null. Although open-label, cluster-randomized trials are effective in minimizing contamination among groups, they have several significant disadvantages, including greater complexity in design and analysis as well as larger patient enrollment requirements to achieve the same statistical power. Because larger sample sizes often necessitate increases in cost, length, or complexity of a trial, current research has indicated that trialists should use individual randomization if possible due to the drawbacks of cluster allocation. Given these considerations, investigators concluded that individual randomization was the best strategy for the trial, as it affords a significant amount of increase in statistical power and allows each patient outcome to be assessed independently of every other patient. To minimize possible bias, investigators also decided to make the automated phone call text identical in both arms. The successful use of patient-level randomization in a previous sepsis clinical trial conducted by investigators gives confidence in this trial design. After the discharge of the last enrolled patient, investigators will evaluate whether the primary endpoint of in-hospital SIRS-based mortality is met. Additional outcome measures of interest for each SIRS-based group will include: time to completion of each element of the Surviving Sepsis Campaign (SSC) bundle; ventilator-free days; ICU days; and 30-day hospital readmission rate. The 1-hour SSC bundle consists of obtaining blood cultures, measuring lactate level, administering broad-spectrum antibiotics, administering 30 mL/kg of crystalloid fluid for hypertension or lactate >4 mmol/L, and applying vasopressors if patient is hypotensive during or after fluid resuscitation. Investigators plan to draw from EHR-based clinical data for primary endpoint analysis, as opposed to claims-based data, due to its ability to provide more objective measurements on patient outcomes. At the conclusion of the study, significant findings will be published as scientific papers.

Official Title
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Randomized Controlled Trial of a Machine Learning Algorithm for Early Sepsis Detection

Conditions
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Sepsis, Severe Sepsis, Septic Shock

Intervention / Treatment
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* Diagnostic Test: InSight


Participation Criteria
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Eligibility Criteria
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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 trial, until the enrollment target for the study is met Exclusion Criteria: Patients under the age of 18

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Diagnostic
Allocation: Randomized
Intervention Model: Parallel Assignment
Masking: Triple


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Experimental: Experimental<br>The experimental arm will involve patients monitored by InSight. | Diagnostic Test: InSight<br>* Clinical decision support (CDS) system for sepsis detection<br>|
| No Intervention: Control<br>The control arm will have no intervention and will involve patients with the usual standard of care. | |

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| In-hospital SIRS-based mortality | Rate of mortality attributed to patients meeting two or more SIRS criteria at some point during their stay | Through study completion, an average of eight months | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Dascena, patient mortality, machine learning, algorithm, diagnostic