A Clinical Decision Support Trial to Reduce Intraoperative Hypotension

Overview

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.

Study Type

  • Study Type: Observational [Patient Registry]
  • Study Design
    • Time Perspective: Prospective
  • Study Primary Completion Date: September 2019

Detailed 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.

Interventions

  • Behavioral: Nudge to reduce intraoperative hypotension
    • Providers will be alerted if their patients are at high risk of hypotension or at increased risk of sequelae from hypotension. Intraoperatively, if patients have more than 10 minutes of hypotension an alert will be displayed on the screen.

Arms, Groups and Cohorts

  • Intervention Group
    • All patients scheduled for surgery at a UCLA site in the one year period after go-live

Clinical Trial Outcome Measures

Primary Measures

  • Acute Kidney Injury (AKI)
    • Time Frame: within 7 days post-surgery
    • Risk adjusted rate of postoperative AKI

Secondary Measures

  • Hypotension
    • Time Frame: 1) For each month from 12 months before pathway go-live until 12 months after go-live.
    • The total number of minutes the patient spent with a blood pressure below predefined MAP mmHG thresholds: 60-65, 55-60, 50-55, and <50.

Participating in This Clinical Trial

Inclusion Criteria

  • All patients undergoing surgery at any of the UCLA operative locations

Exclusion Criteria

  • Patients less than age 18
  • Patients undergoing cardiac surgery or liver transplantation

Gender Eligibility: All

Minimum Age: 18 Years

Maximum Age: N/A

Are Healthy Volunteers Accepted: No

Investigator Details

  • Lead Sponsor
    • University of California, Los Angeles
  • Provider of Information About this Clinical Study
    • Principal Investigator: Ira Hofer, Assistant Clinical Professor – University of California, Los Angeles

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