Clinical trial NCT03655626: Implementation and Evaluations of Sepsis Watch
Official title: Implementation and Evaluations of Previously Developed Novel Early Warning System to Detect and Treat Sepsis
Study type: INTERVENTIONAL
Overall status: COMPLETED
Phase: NA
Conditions: Sepsis
Severe Sepsis
Septic Shock
Keywords: Machine Learning
Sepsis
Implementation Science
Health Information Technology
Lead sponsor: Duke University
Sponsor class: OTHER
Collaborators: name: Data & Society Research Institute
name: Duke Clinical Research Institute
First posted: 2018-08-31
Enrollment: 32003
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: NA
Intervention model: SINGLE_GROUP
Primary purpose: TREATMENT

Brief summary: The purpose of this study is to study the implementation and impact of an early warning system to detect and treat sepsis in the emergency room. We are observing the implementation of a Sepsis Machine Learning Model on all Adult patients. All data (observations field notes, interview recording \& transcripts, and survey responses) will be stored on HIPAA-compliant Duke servers behind the Duke firewall, and requiring password-protected user authentication to access. The risk to patients is minimal. The two risks to interviewed clinical staff we have identified involve loss of work time and anonymity.

Detailed description: Sepsis represents a significant burden to the healthcare system. National predictions estimate 751,000 cases of severe sepsis per annum which will increase at a rate of 1.5%. Sepsis accounts for \>$23 billion in aggregate hospital costs across all payers and represents nearly 4% of all hospital stays. Six percent of all deaths in the US can be attributed to sepsis. Protocol driven care bundles improve clinical outcomes but require early and accurate detection of sepsis. Unfortunately, identifying sepsis early remains elusive even for experienced clinicians leading to diagnostic uncertainty.

To improve diagnostic consensus, a task force in 2016 agreed upon a new sepsis definition. The task force also included a new risk stratification tool to improve early identification, the quick Sepsis-related Organ Failure Assessment (qSOFA) model, which was more accurate than the older Systemic Inflammatory Response Syndrome (SIRS) in predicting adverse clinical outcomes. However, due to the reliance of end organ dysfunction, the new definition has been criticized for its detection of sepsis late in the clinical course. Clinical decision support tools based on predictive analytics can provide actionable information and improve diagnostic accuracy particularly in sepsis.

Several early warning tools have been described in the published literature based upon predictive analytics and large datasets. One example is the National Early Warning Score (NEWS), which was developed to discriminate patients at risk of cardiac arrest, unplanned intensive care admission, or death. Scores such as NEWS are typically broad in scope and not designed to specifically target sepsis. They are also conceptually simple, as they use only a small number of variables and compare them to normal ranges to generate a composite score. In assigning independent scores to each variable and using only the most recent value, they both ignore complex relationships between the variables and their evolution in time.

In previous work, our group developed a framework to model multivariate time series using multitask Gaussian processes, accounting for the high uncertainty, frequent missing values, and irregular sampling rates typically associated with real clinical data can be read in our prior work. Our machine learning approach is superior to other sepsis detection models that use traditional analytics and machine learning techniques. A custom web application, Sepsis Watch, presents the risk score along with relevant patient information and prompts the user to further evaluate the patient and begin treatment, if appropriate. The Sepsis Watch system is now being implemented by clinical operations at Duke University Hospital.

Our study employs a sequential roll-out study design in the Emergency Department at Duke University Hospital. Our study will involve pods A, B, C, and the Resuscitation Bay. The operational project is not being implemented on the psychiatry wing, fast track, triage or any inpatient encounters. The operational project and thus our study period is based upon a two-phase roll out:

* 1st phase: The predictive model notifies the rapid response team through a dashboard. Nurse notifies team of the risk for sepsis and provides treatment recommendation to primary team and primary team will place orders. Rapid response team nurse documents assessment and actions taken in electronic health record.
* 2nd phase: Improvement and optimization of the workflow integrated in phase 1. One workflow improvement includes the development of an ordering protocol and process whereby the rapid response team can place orders for patients who are deemed appropriate for sepsis treatment. A second workflow improvement includes the development of a clinician feedback and auditing report that would be sent to front-line staff with sepsis bundle compliance performance measures.

In addition to observing patient outcome measures, we propose an additional mixed-methods study component to obtain richer information about the effects of the early warning system on clinicians' situational awareness, decision-making, and workflow. This part of our research will involve (1) gathering data from clinicians through a series of semi-structured interviews, surveys, and observations (2) analysis of this data and identification of relevant patterns and insights. Relevant clinicians include include rapid response team nurses, emergency department (ED) nurses, and ED physicians. These interviews will be conducted in three rounds over the implementation period: before the 1st arm, after the 1st arm, and after the 2nd arm. Electronic surveys will be administered at the end of the 1st arm and the 2nd arm to clinicians. The observations will take place during the 1st and 2nd arms.

The goal of the interviews, surveys, and observations will be to (1) evaluate the effect of the early warning system on the clinicians' situational awareness and decision-making, (2) understand how the early warning system fits into clinician workflow, and, (3) identify opportunities to improve the implementation of the early warning system for future scale-up.

We will be structuring interviews according to the situational awareness model which differentiates between 3 levels of situational awareness: 1) perception of relevant information, 2) comprehension of that information, and 3) anticipation of future events based on that information. Through the interviews, observations, and surveys, we also hope to learn more about clinicians' perceptions of and interactions with the early warning system, and its change on the existing Emergency Department workflow for sepsis diagnosis and management. Data analysis will be conducted with the help of trained qualitative researchers from Data \& Society, a research institute in New York City that is focused on the social and cultural issues arising from data-centric technological development.

Eligibility criteria: Inclusion Criteria:

* Arrival to Duke University Hospital emergency department pods A, B, and C, or resuscitation bay

Exclusion Criteria:

* Under 18 years old at time of emergency department arrival

Primary outcomes:
measure: Rate of Centers for Medicare and Medicaid Services (CMS) bundle completion for patients with sepsis
timeFrame: Within 96 hours of emergency department arrival
Secondary outcomes:
measure: Mean time from ED arrival to sepsis for patients with sepsis
timeFrame: Within 96 hours of emergency department arrival
measure: Average number of patients who develop sepsis per day and month
timeFrame: Within 96 hours of emergency department arrival
measure: Average number of patients who develop sepsis and are not treated per day and month
timeFrame: Within 96 hours of emergency department arrival
measure: Mean ED length of stay for patients with sepsis
timeFrame: Within 96 hours of emergency department arrival
measure: Mean Hospital length of stay for patients with sepsis
timeFrame: Within 30 days of emergency department arrival
measure: Mean Inpatient mortality for patients with sepsis
timeFrame: Within 30 days of emergency department arrival
measure: Mean ICU requirement rate for patients with sepsis
timeFrame: Within 30 days of emergency department arrival
measure: Mean time from sepsis onset to blood culture
timeFrame: Within 96 hours of emergency department arrival
measure: Mean time from sepsis onset to antibiotics
timeFrame: Within 96 hours of emergency department arrival
measure: Mean time from sepsis onset to IV fluids
timeFrame: Within 96 hours of emergency department arrival
measure: Mean time from sepsis onset to lactate
timeFrame: Within 96 hours of emergency department arrival
measure: Mean time from sepsis onset to CMS bundle completion
timeFrame: Within 96 hours of emergency department arrival
measure: Rate of lactate complete for patients with sepsis
timeFrame: Within 96 hours of emergency department arrival
measure: Number of sepsis diagnosis codes across Duke University Hospital patients per month
timeFrame: Within 30 days of emergency department arrival
Other outcomes:
measure: Number of antibiotic orders in Duke University Hospital emergency department per month
timeFrame: Within 96 hours of emergency department arrival
measure: Number of antibiotic days in Duke University Hospital emergency department per month
timeFrame: Within 96 hours of emergency department arrival
measure: Number of blood culture orders in Duke University Hospital emergency department per month
timeFrame: Within 96 hours of emergency department arrival
measure: Number of lactate orders in Duke University Hospital emergency department per month
timeFrame: Within 96 hours of emergency department arrival
measure: Number of IV fluid orders in Duke University Hospital emergency department per month
timeFrame: Within 96 hours of emergency department arrival
measure: Number of vasopressor orders in Duke University Hospital emergency department per month
timeFrame: Within 96 hours of emergency department arrival
measure: Number of vasopressor days in Duke University Hospital emergency department per month
timeFrame: Within 96 hours of emergency department arrival
Locations:
city: Durham
country: United States
facility: Duke University Hospital
state: North Carolina
Study officials:
affiliation: Duke Health
name: Cara O'Brien, MD
role: PRINCIPAL_INVESTIGATOR
affiliation: Duke Institute for Health Innovation
name: Mark Sendak, MD
role: STUDY_DIRECTOR
References: