Clinical Trial Details
— Status: Completed
Administrative data
| NCT number |
NCT04062994 |
| Other study ID # |
Anes-Hypotension |
| Secondary ID |
|
| Status |
Completed |
| Phase |
|
| First received |
|
| Last updated |
|
| Start date |
September 28, 2018 |
| Est. completion date |
October 5, 2019 |
Study information
| Verified date |
March 2024 |
| Source |
University of California, Los Angeles |
| Contact |
n/a |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Observational [Patient Registry]
|
Clinical Trial Summary
The purpose of this study is to provide messages to providers if their patient is at high
risk of developing intraoperative hypotension based on past medical history and
co-morbidities preoperatively and minutes of hypotension intraoperatively.
Description:
Preoperative decision support:
Based on the patient's past medical history and comorbidities providers received one of two
messages in the preoperative section of the chart. If the patient had a past medical history
of renal insufficiency, congestive heart failure, or ischemic heart disease the provider
received a message that the patient was at a high risk of developing sequale of hypotension.
If the patient had more than 15 minutes of hypotension (defined as a mean arterial pressure
(MAP)<65mmHg) during a previous anesthetic, then the provider received a message that the
patient was at high risk of developing intraoperative hypotension. Based on the past medical
history of the patient, providers will receive zero, one or both messages.
Intraoperative decision support:
In the vitals signs section of the intraoperative record a line denoting the mean arterial
pressure (MAP) was added. If the patient had more than 10 cumulative minutes of hypotension
(defined as a MAP<65 mmHg) then a yellow alert was displayed in the lower right section of
the screen. If the patient had more than 20 minutes of hypotension, then the alert was
changed to red.
Data Analysis:
All study data were acquired via our previously published Department of Anesthesiology and
Perioperative Medicine at University of California, Los Angeles (UCLA) perioperative data
ware- house (PDW). The PDW is a structured reporting schema that contains all the relevant
clinical data entered into the electronic medical record (EMR) via the use of Clarity, the
relational database created by EPIC for data analytics and reporting. While Clarity contains
raw clinical data, the PDW was designed to organize, filter, and improve data so that it can
be used reliably for creating these types of metrics. Last, the PDW servers interfaced with
other health system resources to allow for automated emailed reports and the generation of
web-based graphical dashboards.
Analysis of Hypotension:
For each case, hypotension was defined as the total number of minutes the patient spent with
a blood pressure below predefined MAP mmHG thresholds (as previously reported Salmasi paper):
60-65, 55-60, 50-55, and <50. In order to determine the effect of the pathway roll out on the
incidence of intraoperative hypotension the investigators plan to carry out several analyses.
1. For each month from 12 months before pathway go-live until 12 months after go-live the
percentage of case that experienced hypotension as defined above in the ranges of 0-10
minutes, 11-20 minutes, and >20 minutes (the cut-points used in the decision support
system) will be computed.
2. In essence patients were assigned to one of four pathways in the CDS program based on
their risk of hypotension or sequalae of hypotension. The investigators will carry out
sub-group analysis on each of the four pathways (hypotension message, high risk of
complications message, both messages, no message) to see if the response of providers
differed based on the message they received.
Analysis of the effect of decision support on postoperative AKI:
In order to determine the effect of the pathway to decrease postoperative AKI (defined by the
KDIGO criteria as a binary instance 0 vs stage 1,2,3) the investigators carried out the
following analysis
Patient characteristics (age, sex, ASA, etc…) and study variables (including hypotension,
AKI) will be summarized using means (SD) and frequencies (%) stratified by pre/post
intervention. The investigators will then create a risk score for postoperative AKI based
upon the risk factors identified by Sun et al. For each patient in the post implementation
group the investigators will identify a patient in the pre-implementation group who has a
similar risk of AKI using propensity score matching in R V3.5.1 (Vienna, AU www.r-project.org
). The investigators will assess the performance of the matching algorithm by exploring
standardized mean difference (SMD) plots before and after matching. If the matching is deemed
to be inadequate (SMD > 0.1 for any matching variable) the investigators will try more
complex models including squared terms, interactions, or widening the caliper width for what
the investigators consider an adequate match (from the standard starting width of 0.2*SD of
the logit of the propensity score). Once the matching has been deemed successful, the
investigators will then assess the effect of the intervention by analyzing the incidence of
hypotension in each month from the 12 months prior to roll until until the 12 months after
roll out after allowing for a 2 month washout period using an interrupted time series
approach as described by Wagner et. Al.
Since the effect of the intervention will likely have a negligible effect on low risk
patients, the investigators will carry out a subgroup analysis on those patients at high risk
of AKI and use methodology similar to those described above.
Finally, if the investigators observe a significant reduction in AKI according to the process
outlined above, the investigators hypothesize that this reduction would be mediated through
hypotension and test this using Baron and Kenny's steps for mediation:
1. First, on the risk matched cohort, the investigators will show that the rate of AKI went
down after implementation (a significant path C)
2. Next, the investigators will show that the intervention is associated with a reduction
in time spent in at least one hypotension category (60-65, 55-60, 50-55, <50) in a
similar manner as above (significant path A)
3. Finally, the investigators will show that the association between the intervention and
AKI goes away (or is reduced) after including hypotension information in the model and
use Sobel's test to obtain a p-value to test for a significant reduction in the
intervention coefficient.
If any of these steps above fail (a,b,c) then the investigators cannot conclude that the
reduction in AKI was mediated exclusively through hypotension.
