Cardiopulmonary Arrest, Escalation of Care, Respiratory Arrest, Septic Shock, Severe Sepsis
Conditions
Keywords
clinical deterioration, detection algorithms, automated warning systems
Brief summary
The goal is to develop a two-tiered monitoring system to improve the care of patients at risk for clinical deterioration on general hospital wards (GHWs) at Barnes-Jewish Hospital (BJH). The investigators hypothesize that the use of an automated early warning system (EWS) that identifies patients at risk of clinical deterioration, with notification of nurses on the GHWs when patients are identified, will reduce the risk of ICU transfer or death within 24 hrs of an alert. As a substudy, the investigators will pilot the use of a wireless pulse oximeter to establish feasibility and to develop algorithms for a real-time event detection system (RDS) in these high-risk patients.
Interventions
BEHAVIORALEWS Nursing Alerts
An automated algorithm (EWS) will identify patients at potential risk of clinical deterioration. When a patient satisfies the algorithm, a nurse on the patient's ward will be notified. S/he will assess the patient and institute any interventions that are clinically required.
DEVICEWireless Remote Sensor
A subset of patients will be consented to wear a wireless sensor device which will monitor heart rate and level of oxygen in the blood.
Sponsors
Washington University School of Medicine
Study design
Allocation
RANDOMIZED
Intervention model
CROSSOVER
Primary purpose
PREVENTION
Masking
NONE
Intervention model description
Random allocation of study wards to intervention and control, with washout period and crossover of assignment.
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* All patients age 18 and above, hospitalized in GHWs at Barnes Jewish Hospital.
Exclusion criteria
* Minors, patients younger than 18 years old.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Transfer to ICU or Unexpected Death Within 24 Hrs of Identification by the EWS Algorithm | Within 24 hrs of an EWS alert | The proportion of patients transferred to ICU or death within 24 hrs of identification by the EWS algorithm for intervention and control wards. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Clinical Outcomes and Process Measures | Hospital discharge | length of stay |
Countries
United States
Participant flow
Participants by arm
| Arm | Count |
|---|---|
| Control Patients admitted to 4 GHWs designated as controls. Nurses will not be notified when patients on these GHWs satisfy the EWS algorithm. They will also not wear the wireless sensor devices. | 10,120 |
| Nurse Notification of EWS Alert Patients admitted to 4 GHWs designated as intervention wards at BJH. Nurses will be notified when patients on these wards satisfy the EWS algorithm. Some patients will be asked to wear the wireless remote sensors.
EWS Nursing Alerts: An automated algorithm (EWS) will identify patients at potential risk of clinical deterioration. When a patient satisfies the algorithm, a nurse on the patient's ward will be notified. S/he will assess the patient and institute any interventions that are clinically required.
Wireless Remote Sensor: A subset of patients will be consented to wear a wireless sensor device which will monitor heart rate and level of oxygen in the blood. | 9,911 |
| Total | 20,031 |
Baseline characteristics
| Characteristic | Control | Total | Nurse Notification of EWS Alert |
|---|---|---|---|
| Age, Continuous | 57 years | 57 years | 57 years |
| Race (NIH/OMB) American Indian or Alaska Native | 0 Participants | 0 Participants | 0 Participants |
| Race (NIH/OMB) Asian | 0 Participants | 0 Participants | 0 Participants |
| Race (NIH/OMB) Black or African American | 4864 Participants | 9654 Participants | 4790 Participants |
| Race (NIH/OMB) More than one race | 0 Participants | 0 Participants | 0 Participants |
| Race (NIH/OMB) Native Hawaiian or Other Pacific Islander | 0 Participants | 0 Participants | 0 Participants |
| Race (NIH/OMB) Unknown or Not Reported | 194 Participants | 381 Participants | 187 Participants |
| Race (NIH/OMB) White | 5062 Participants | 9996 Participants | 4934 Participants |
| Region of Enrollment United States | 10120 participants | 20031 participants | 9911 participants |
| Sex: Female, Male Female | 5355 Participants | 10663 Participants | 5308 Participants |
| Sex: Female, Male Male | 4765 Participants | 9368 Participants | 4603 Participants |
Adverse events
| Event type | EG000 affected / at risk | EG001 affected / at risk |
|---|---|---|
| deaths Total, all-cause mortality | — / — | — / — |
| other Total, other adverse events | 0 / 10,120 | 0 / 9,911 |
| serious Total, serious adverse events | 0 / 10,120 | 0 / 9,911 |
Outcome results
Primary
Transfer to ICU or Unexpected Death Within 24 Hrs of Identification by the EWS Algorithm
The proportion of patients transferred to ICU or death within 24 hrs of identification by the EWS algorithm for intervention and control wards.
Time frame: Within 24 hrs of an EWS alert
| Arm | Measure | Value (NUMBER) |
|---|---|---|
| Control | Transfer to ICU or Unexpected Death Within 24 Hrs of Identification by the EWS Algorithm | 426 participants |
| Nurse Notification of EWS Alert | Transfer to ICU or Unexpected Death Within 24 Hrs of Identification by the EWS Algorithm | 444 participants |
Secondary
Clinical Outcomes and Process Measures
length of stay
Time frame: Hospital discharge
| Arm | Measure | Value (MEDIAN) |
|---|---|---|
| Control | Clinical Outcomes and Process Measures | 6.92 days |
| Nurse Notification of EWS Alert | Clinical Outcomes and Process Measures | 7.07 days |